R/m_estimate.R
m_estimate.RdM-estimation theory provides a framework for asympotic properties of estimators that are solutions to estimating equations. Many R packages implement specific applications of estimating equations. geex aims to be provide a more general framework that any modelling method can use to compute point and variance estimates for parameters that are solutions to estimating equations of the form: $$\sum_i \psi(O_i, \hat{\theta}) = 0$$
a function that takes in group-level data and returns a function that takes parameters as its first argument
a data.frame
an optional character string identifying the grouping variable in data
an optional vector of weights. See details.
a list of arguments passed to the outer (data) function of estFUN. (optional)
a list of arguments passed to the inner (theta) function of estFUN. (optional)
a vector of parameter estimates must be provided if compute_roots = FALSE
whether or not to find the roots of the estimating equations.
Defaults to TRUE.
whether or not to compute the variance-covariance matrix.
Defaults to TRUE.
a function passed to compute_sigma used to compute the
inverse of the "bread" matrix. Defaults to solve.
an optional list of small sample corrections where each
list element is a correct_control object which contains
two elements: correctFUN and correctFUN_options. The function
correction constructs correct_control objects.
See details for more information.
a deriv_control object
a root_control object
a approx_control object
a geex object
The basic idea of geex is for the analyst to provide at least two items:
data
estFUN: (the \(\psi\) function), a function that takes unit-level
data and returns a function in terms of parameters (\(\theta\))
With the estFUN, geex computes the roots of the estimating equations
and/or the empirical sandwich variance estimator.
The root finding algorithm defaults to multiroot to
estimate roots though the solver algorithm can be specified in the rootFUN
argument. Starting values for multiroot are passed via the
root_control argument. See vignette("v03_root_solvers", package = "geex")
for information on customizing the root solver function.
To compute only the covariance matrix, set compute_roots = FALSE and pass
estimates of \(\theta\) via the roots argument.
M-estimation is often used for clustered data, and a variable by which to split
the data.frame into independent units is specified by the units argument.
This argument defaults to NULL, in which case the number of units equals
the number of rows in the data.frame.
For information on the finite-sample corrections, refer to the finite sample
correction API vignette: vignette("v05_finite_sample_corrections", package = "geex")
An estFUN is a function representing \(\psi\). geex works
by breaking \(\psi\) into two parts:
the "outer" part of the estFUN which manipulates data and
outer_args and returns an
"inner" function of theta and inner_args. Internally, this
"inner" function is called psiFUN.
In pseudo-code this looks like:
function(data, <<outer_args>>){
O <- manipulate(data, <<outer_args>>)
function(theta, <<inner_args>>){
map(O, to = theta, and = <<inner_args>>)
}
}See the examples below or the package vignettes to see an estFUN
in action.
Importantly, the data used in an estFUN is *unit* level data,
which may be single rows in a data.frame or block of rows for clustered data.
Additional arguments may be passed to both the inner and outer function of the
estFUN. Elements in an outer_args list are passed to the outer
function; any elements of the inner_args list are passed to the inner
function. For an example, see the finite sample correction vignette [
vignette("v05_finite_sample_corrections", package = "geex")].
To estimate roots of the estimating functions, geex uses the rootSolve
multiroot function by default, which requires starting
values. The root_control argument expects a root_control
object, which the utility function setup_root_control aids in
creating. For example, setup_root_control(start = 4) creates a
root_control setting the starting value to 4. In general,
the dimension of start must the same as theta in the inner
estFUN.
In some situations, use of weights can massively speed computations. Refer
to vignette("v04_weights", package = "geex") for an example.
Stefanski, L. A., & Boos, D. D. (2002). The calculus of M-estimation. The American Statistician, 56(1), 29-38.
# Estimate the mean and variance of Y1 in the geexex dataset
ex_eeFUN <- function(data){
function(theta){
with(data,
c(Y1 - theta[1],
(Y1 - theta[1])^2 - theta[2] ))
}}
m_estimate(
estFUN = ex_eeFUN,
data = geexex,
root_control = setup_root_control(start = c(1,1)))
#> An object of class "geex"
#> Slot "call":
#> m_estimate(estFUN = ex_eeFUN, data = geexex, root_control = setup_root_control(start = c(1,
#> 1)))
#>
#> Slot "basis":
#> An object of class "m_estimation_basis"
#> Slot ".data":
#> Y1 Y2 X1 Y3 W1 Z1 X2
#> 1 3.66830660 2.02817177 4.949316 16.345756 4.823768 8.921782 0
#> 2 10.45245483 1.64329659 7.851962 25.687417 7.884845 13.909474 0
#> 3 3.12341064 2.85262638 4.729075 16.108307 4.709346 9.014695 0
#> 4 8.37150253 2.51336525 2.564395 10.579970 2.786091 6.733378 0
#> 5 -0.83197489 3.01820300 4.782347 16.464013 4.811590 9.290492 0
#> 6 3.39877632 0.97852092 5.335713 18.325769 5.415370 10.322199 0
#> 7 1.89433086 1.43833173 1.386442 5.577536 1.240995 3.497873 0
#> 8 3.52281395 0.98744392 3.453377 13.074664 3.632010 7.894598 0
#> 9 9.96040583 -1.02081430 2.958662 10.050725 2.752347 5.612733 0
#> 10 4.57026477 2.33235027 7.591370 24.414247 7.501404 13.027192 0
#> 11 5.69037402 3.24051157 6.812940 22.528706 6.835412 12.309296 0
#> 12 6.01840507 2.67134960 2.481492 9.540750 2.505561 5.818512 0
#> 13 2.54186468 0.66996589 3.307246 11.720103 3.256837 6.759235 0
#> 14 -0.71686038 1.14941969 2.366527 9.839421 2.551487 6.289631 0
#> 15 3.67609826 0.21116926 6.308752 21.049635 6.339597 11.586507 0
#> 16 5.51354425 3.23152191 2.280638 8.812598 2.273309 5.391641 0
#> 17 9.07247997 1.66560033 2.872154 10.227607 2.774940 5.919377 0
#> 18 3.97770523 1.03267790 4.361465 15.595252 4.489179 9.053054 0
#> 19 3.78983596 2.87937035 3.573053 11.805345 3.344600 6.445765 0
#> 20 11.46076273 1.74642131 5.556376 20.979426 6.133951 12.644862 0
#> 21 1.90514658 0.48212421 7.752991 24.820884 7.643469 13.191397 0
#> 22 6.69600961 1.97611674 6.030068 20.854263 6.221083 11.809162 0
#> 23 2.66421207 2.02665947 4.213262 14.901747 4.278752 8.581854 0
#> 24 6.66014272 2.16368120 2.923132 11.542799 3.116483 7.158102 0
#> 25 -1.18104663 2.41000794 5.156830 16.656110 4.953235 8.920865 0
#> 26 2.92500198 1.37263740 5.519839 18.121067 5.410226 9.841308 0
#> 27 3.88083378 2.63691800 5.477283 17.711627 5.297228 9.495703 0
#> 28 9.02982953 0.79806522 4.055430 14.397234 4.113166 8.314089 0
#> 29 3.12172019 3.34654241 4.319714 13.801412 4.030281 7.321841 0
#> 30 6.19158815 1.40123269 10.283894 33.098758 10.345663 17.672917 0
#> 31 3.32882227 2.44220444 2.557841 9.582409 2.535063 5.745648 0
#> 32 1.59847689 2.61352641 11.152742 37.215603 11.592086 20.486489 0
#> 33 7.75618478 1.70090363 2.538047 9.476212 2.503565 5.669141 0
#> 34 3.15921522 0.39941190 7.939765 25.708101 7.911967 13.798454 0
#> 35 10.39273751 1.66053304 3.629295 12.197870 3.456791 6.753928 0
#> 36 6.77228554 1.41869225 5.644317 18.711156 5.588868 10.244681 0
#> 37 4.39629525 1.60963799 1.385403 6.339116 1.431130 4.261012 0
#> 38 6.82219543 2.84551436 3.651563 13.372011 3.755894 7.894667 0
#> 39 4.83938127 2.68472721 2.075987 9.293362 2.342337 6.179382 0
#> 40 6.82448417 2.23771308 7.947636 26.813109 8.190186 14.891656 0
#> 41 3.36629988 1.28937811 3.893624 13.579242 3.868217 7.738807 0
#> 42 -3.54597542 4.61331896 4.399113 16.600543 4.749914 10.001873 0
#> 43 5.62728767 0.37335265 2.019187 6.280784 1.574993 3.252004 0
#> 44 7.64019560 0.39269371 10.182047 33.169007 10.337763 17.895937 0
#> 45 1.07266235 2.34031745 4.471305 14.891632 4.340734 8.184674 0
#> 46 0.54542518 4.72788771 5.445723 19.659399 5.776280 11.490815 0
#> 47 3.25060929 1.67280996 5.030453 16.727920 4.939593 9.182240 0
#> 48 2.93555501 0.74310325 7.586987 26.080025 7.916753 14.699546 0
#> 49 6.67598396 1.56860189 9.452187 30.400340 9.463132 16.222060 0
#> 50 5.53662175 4.54885325 8.141977 24.547274 7.672313 12.334309 0
#> 51 9.13874582 1.22859200 5.623052 18.422092 5.511286 9.987515 1
#> 52 11.61401290 1.49265765 5.066275 15.460228 4.631626 7.860815 1
#> 53 4.92821273 1.72997742 2.174904 8.703576 2.219620 5.441220 1
#> 54 4.90318672 2.74811656 1.373871 8.019078 1.848237 5.958272 1
#> 55 6.00098760 2.66859381 4.252394 12.485257 3.684413 6.106666 1
#> 56 3.65150186 1.54470134 1.844766 8.514763 2.089882 5.747614 1
#> 57 4.54658518 0.07215478 6.257311 19.373108 5.907605 9.987141 1
#> 58 4.60446834 3.88197707 7.640542 26.746499 8.096760 15.285686 1
#> 59 6.05634729 0.75028887 3.400547 13.582939 3.745871 8.482119 1
#> 60 5.55593474 1.51065503 3.879217 12.798800 3.669504 6.979974 1
#> 61 4.03092200 2.21539129 5.044494 16.871488 4.978996 9.304746 1
#> 62 5.23612553 2.42210867 3.724228 13.103840 3.707017 7.517498 1
#> 63 4.29091253 0.77885172 3.209739 11.250332 3.115018 6.435724 1
#> 64 8.17872107 2.31222782 3.503141 15.091380 4.148630 9.836670 1
#> 65 5.02695115 2.88646213 3.588984 12.896787 3.621443 7.513311 1
#> 66 2.48083883 2.47481069 2.572586 9.004733 2.394330 5.145854 1
#> 67 3.99004087 2.86984135 2.321320 9.601955 2.480819 6.119975 1
#> 68 2.23831135 1.11347620 7.354859 24.266268 7.405282 13.233980 1
#> 69 5.81016858 1.87134447 1.780620 7.271942 1.763140 4.601012 1
#> 70 8.38552575 3.09651049 2.438272 9.222328 2.415150 5.564919 1
#> 71 7.52829625 2.51802955 4.870025 17.058979 4.982251 9.753941 1
#> 72 5.80565410 2.39803318 6.107551 19.258297 5.841462 10.096971 1
#> 73 4.63571743 3.06665941 3.068762 10.043868 2.778158 5.440724 1
#> 74 6.15793650 1.55045992 8.069649 27.857468 8.481779 15.752995 1
#> 75 4.78126024 2.62610198 2.564135 7.630308 2.048611 3.784106 1
#> 76 -3.16739941 1.18116405 6.700594 22.114532 6.703782 12.063641 1
#> 77 6.43347697 1.73648379 5.381833 17.057971 5.109951 8.985221 1
#> 78 3.50959659 2.15457529 12.644899 40.205236 12.712534 21.237888 1
#> 79 10.07323536 2.56844555 2.037142 9.119878 2.289255 6.064165 1
#> 80 13.67440127 -0.66015968 5.883640 17.576515 5.365039 8.751055 1
#> 81 0.04110863 3.13653254 7.093428 24.177106 7.317634 13.536964 1
#> 82 7.35949555 2.42177278 4.873831 16.571498 4.861332 9.260751 1
#> 83 5.49607715 3.35008260 8.291038 25.527766 7.954701 13.091208 1
#> 84 2.90516885 3.10375689 4.051026 12.221867 3.568223 6.145328 1
#> 85 7.48091201 2.64704611 7.689539 25.778200 7.866935 14.243891 1
#> 86 7.83288634 2.17563581 4.933636 16.643004 4.894160 9.242550 1
#> 87 4.62720660 2.65355779 5.774989 19.541334 5.829081 10.878851 1
#> 88 3.81921320 1.93450970 4.483566 16.268060 4.687907 9.542711 1
#> 89 0.65673908 2.64552217 2.739769 11.946482 3.171563 7.836829 1
#> 90 2.50073977 2.36429404 5.286464 17.755621 5.260521 9.825925 1
#> 91 4.06797383 2.84344157 3.701213 12.546517 3.561933 6.994698 1
#> 92 3.99673254 1.32352113 5.795986 20.816259 6.153061 12.122280 1
#> 93 8.81558134 1.60856710 4.883292 15.756919 4.660053 8.431981 1
#> 94 3.93610997 2.40494064 7.172253 22.359187 6.882860 11.600808 1
#> 95 12.58110379 0.89314130 3.340735 11.491910 3.208161 6.480807 1
#> 96 3.28003669 1.61669959 7.262549 26.233329 7.873969 15.339506 1
#> 97 11.30218798 2.29402025 1.940701 6.989609 1.732577 4.078556 1
#> 98 5.64776480 3.79306067 5.958475 20.288944 6.061855 11.351232 1
#> 99 0.65818837 2.81403217 4.432708 14.119440 4.138037 7.470379 1
#> 100 7.30774920 0.67997560 3.283518 10.676520 2.990010 5.751243 1
#> Y4 Y5
#> 1 0.092739260 1
#> 2 1.016727357 1
#> 3 0.493990392 0
#> 4 1.243224329 0
#> 5 0.695205988 1
#> 6 0.952201378 1
#> 7 -0.343146465 0
#> 8 1.159870423 0
#> 9 -0.429393276 0
#> 10 0.499274828 1
#> 11 0.871180147 1
#> 12 0.444423658 0
#> 13 0.229090617 1
#> 14 1.076493168 0
#> 15 0.854254673 1
#> 16 0.298747112 0
#> 17 -0.001638862 0
#> 18 1.047002780 1
#> 19 -0.456508875 1
#> 20 2.965934470 0
#> 21 0.437209150 0
#> 22 1.467067372 0
#> 23 0.783287466 0
#> 24 1.165717760 0
#> 25 -0.198696160 1
#> 26 0.213533342 1
#> 27 -0.072493261 1
#> 28 0.736487513 1
#> 29 -0.625758090 1
#> 30 1.375465405 1
#> 31 0.264670535 0
#> 32 2.972649859 1
#> 33 0.215875121 1
#> 34 0.782782994 1
#> 35 -0.227084853 1
#> 36 0.442637449 1
#> 37 0.421447969 0
#> 38 0.882479555 0
#> 39 1.373000995 1
#> 40 1.864965592 1
#> 41 0.387733146 1
#> 42 1.943114799 1
#> 43 -1.474856978 0
#> 44 1.741072051 1
#> 45 0.024847168 1
#> 46 1.966803213 1
#> 47 0.239605022 0
#> 48 2.177764398 1
#> 49 1.088997768 1
#> 50 -0.964458223 1
#> 51 0.715242972 1
#> 52 -0.631970427 1
#> 53 0.996355205 0
#> 54 2.634852773 1
#> 55 -1.246686055 1
#> 56 1.764940768 0
#> 57 -0.173094497 1
#> 58 3.188926631 1
#> 59 2.321353405 1
#> 60 0.149069864 0
#> 61 0.842453670 1
#> 62 0.903578781 0
#> 63 0.542090297 1
#> 64 3.532272980 0
#> 65 1.088732578 1
#> 66 0.144233610 1
#> 67 1.470126269 0
#> 68 1.537177460 0
#> 69 0.708145014 1
#> 70 0.751337374 0
#> 71 1.535905791 1
#> 72 0.146399418 0
#> 73 -0.255543077 0
#> 74 3.055486628 0
#> 75 -1.205682549 1
#> 76 1.282809142 1
#> 77 0.050654962 1
#> 78 2.135029369 1
#> 79 1.812166070 1
#> 80 -0.886040754 1
#> 81 2.206165066 1
#> 82 1.037387368 1
#> 83 0.083754535 0
#> 84 -0.926108918 0
#> 85 2.078535519 1
#> 86 0.935458616 0
#> 87 1.393866742 0
#> 88 1.865718680 0
#> 89 2.601152645 0
#> 90 1.024876085 1
#> 91 0.412999035 1
#> 92 2.607900007 0
#> 93 0.195371813 1
#> 94 0.159654048 1
#> 95 0.403777090 0
#> 96 3.771937632 1
#> 97 -0.038425654 1
#> 98 1.609367331 0
#> 99 -0.135412360 1
#> 100 -0.245682938 0
#>
#> Slot ".units":
#> character(0)
#>
#> Slot ".weights":
#> numeric(0)
#>
#> Slot ".psiFUN_list":
#> $`1`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04149af8>
#>
#> $`2`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041489e8>
#>
#> $`3`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0414d858>
#>
#> $`4`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0414c5c0>
#>
#> $`5`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0414f3f8>
#>
#> $`6`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0414e320>
#>
#> $`7`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04151190>
#>
#> $`8`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04154000>
#>
#> $`9`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04152f60>
#>
#> $`10`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04155dd0>
#>
#> $`11`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04154cf8>
#>
#> $`12`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04157b30>
#>
#> $`13`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04156978>
#>
#> $`14`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04159078>
#>
#> $`15`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0415bb30>
#>
#> $`16`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0417c5f8>
#>
#> $`17`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0417edd8>
#>
#> $`18`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041819e0>
#>
#> $`19`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0418eeb8>
#>
#> $`20`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04191b68>
#>
#> $`21`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04190630>
#>
#> $`22`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04193318>
#>
#> $`23`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041960e0>
#>
#> $`24`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04194f60>
#>
#> $`25`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04197cf0>
#>
#> $`26`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04196ba8>
#>
#> $`27`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea04199970>
#>
#> $`28`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041987f0>
#>
#> $`29`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0419b5b8>
#>
#> $`30`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0419a2e8>
#>
#> $`31`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0419d0b0>
#>
#> $`32`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0419fe08>
#>
#> $`33`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea0419ec50>
#>
#> $`34`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a3890>
#>
#> $`35`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a2748>
#>
#> $`36`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a54d8>
#>
#> $`37`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a4390>
#>
#> $`38`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a7190>
#>
#> $`39`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a9f20>
#>
#> $`40`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041a8dd8>
#>
#> $`41`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041abba0>
#>
#> $`42`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041aa908>
#>
#> $`43`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ad628>
#>
#> $`44`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ac3c8>
#>
#> $`45`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041af238>
#>
#> $`46`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b2070>
#>
#> $`47`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b0f98>
#>
#> $`48`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b5d98>
#>
#> $`49`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b4cf8>
#>
#> $`50`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b7b68>
#>
#> $`51`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b6ac8>
#>
#> $`52`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b9778>
#>
#> $`53`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041b8518>
#>
#> $`54`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041bb388>
#>
#> $`55`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ba2b0>
#>
#> $`56`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041bcf60>
#>
#> $`57`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041bfd98>
#>
#> $`58`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041becf8>
#>
#> $`59`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c1970>
#>
#> $`60`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c0898>
#>
#> $`61`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c5708>
#>
#> $`62`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c4668>
#>
#> $`63`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c7350>
#>
#> $`64`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c6278>
#>
#> $`65`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041c90e8>
#>
#> $`66`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041cbf58>
#>
#> $`67`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041cacf8>
#>
#> $`68`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041cdb68>
#>
#> $`69`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ccac8>
#>
#> $`70`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041cf7b0>
#>
#> $`71`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ce6a0>
#>
#> $`72`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d1510>
#>
#> $`73`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d0438>
#>
#> $`74`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d5270>
#>
#> $`75`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d80a8>
#>
#> $`76`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d6e10>
#>
#> $`77`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d9af8>
#>
#> $`78`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041d89e8>
#>
#> $`79`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041db858>
#>
#> $`80`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041da668>
#>
#> $`81`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041dd430>
#>
#> $`82`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041dc2e8>
#>
#> $`83`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041de748>
#>
#> $`84`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e15b8>
#>
#> $`85`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e0278>
#>
#> $`86`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e50e8>
#>
#> $`87`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e7cb8>
#>
#> $`88`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e6b00>
#>
#> $`89`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e9740>
#>
#> $`90`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041e8438>
#>
#> $`91`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041eadd8>
#>
#> $`92`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041eda18>
#>
#> $`93`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ec8d0>
#>
#> $`94`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041ef4a0>
#>
#> $`95`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f20a8>
#>
#> $`96`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f0c50>
#>
#> $`97`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f5858>
#>
#> $`98`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f4710>
#>
#> $`99`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f7318>
#>
#> $`100`
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }
#> <environment: 0x7fea041f9f58>
#>
#>
#> Slot ".GFUN":
#> function (theta)
#> {
#> psii <- lapply(psi_list, function(psi) {
#> do.call(psi, args = append(list(theta = theta), object@.inner_args))
#> })
#> compute_sum_of_list(psii, object@.weights)
#> }
#> <environment: 0x7fea04235740>
#>
#> Slot ".control":
#> An object of class "geex_control"
#> Slot ".approx":
#> An object of class "approx_control"
#> Slot ".FUN":
#> function ()
#> NULL
#> <bytecode: 0x7fea4759ffd0>
#>
#> Slot ".options":
#> list()
#>
#>
#> Slot ".root":
#> An object of class "root_control"
#> Slot ".object_name":
#> [1] "root"
#>
#> Slot ".FUN":
#> function (f, start, maxiter = 100, rtol = 1e-06, atol = 1e-08,
#> ctol = 1e-08, useFortran = TRUE, positive = FALSE, jacfunc = NULL,
#> jactype = "fullint", verbose = FALSE, bandup = 1, banddown = 1,
#> parms = NULL, ...)
#> {
#> initfunc <- NULL
#> if (is.list(f)) {
#> if (!is.null(jacfunc) & "jacfunc" %in% names(f))
#> stop("If 'f' is a list that contains jacfunc, argument 'jacfunc' should be NULL")
#> jacfunc <- f$jacfunc
#> initfunc <- f$initfunc
#> f <- f$func
#> }
#> N <- length(start)
#> if (!is.numeric(start))
#> stop("start conditions should be numeric")
#> if (!is.numeric(maxiter))
#> stop("`maxiter' must be numeric")
#> if (as.integer(maxiter) < 1)
#> stop("maxiter must be >=1")
#> if (!is.numeric(rtol))
#> stop("`rtol' must be numeric")
#> if (!is.numeric(atol))
#> stop("`atol' must be numeric")
#> if (!is.numeric(ctol))
#> stop("`ctol' must be numeric")
#> if (length(atol) > 1 && length(atol) != N)
#> stop("`atol' must either be a scalar, or as long as `start'")
#> if (length(rtol) > 1 && length(rtol) != N)
#> stop("`rtol' must either be a scalar, or as long as `y'")
#> if (length(ctol) > 1)
#> stop("`ctol' must be a scalar")
#> if (useFortran) {
#> if (!is.compiled(f) & is.null(parms)) {
#> Fun1 <- function(time = 0, x, parms = NULL) list(f(x,
#> ...))
#> Fun <- Fun1
#> }
#> else if (!is.compiled(f)) {
#> Fun2 <- function(time = 0, x, parms) list(f(x, parms,
#> ...))
#> Fun <- Fun2
#> }
#> else {
#> Fun <- f
#> f <- function(x, ...) Fun(n = length(start), t = 0,
#> x, f = rep(0, length(start)), 1, 1)$f
#> }
#> JacFunc <- jacfunc
#> if (!is.null(jacfunc))
#> if (!is.compiled(JacFunc) & is.null(parms))
#> JacFunc <- function(time = 0, x, parms = parms) jacfunc(x,
#> ...)
#> else if (!is.compiled(JacFunc))
#> JacFunc <- function(time = 0, x, parms = parms) jacfunc(x,
#> parms, ...)
#> else JacFunc <- jacfunc
#> method <- "stode"
#> if (jactype == "sparse") {
#> method <- "stodes"
#> if (!is.null(jacfunc))
#> stop("jacfunc can not be used when jactype='sparse'")
#> x <- stodes(y = start, time = 0, func = Fun, atol = atol,
#> positive = positive, rtol = rtol, ctol = ctol,
#> maxiter = maxiter, verbose = verbose, parms = parms,
#> initfunc = initfunc)
#> }
#> else x <- steady(y = start, time = 0, func = Fun, atol = atol,
#> positive = positive, rtol = rtol, ctol = ctol, maxiter = maxiter,
#> method = method, jacfunc = JacFunc, jactype = jactype,
#> verbose = verbose, parms = parms, initfunc = initfunc,
#> bandup = bandup, banddown = banddown)
#> precis <- attr(x, "precis")
#> attributes(x) <- NULL
#> x <- unlist(x)
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> i <- length(precis)
#> }
#> else {
#> if (is.compiled(f))
#> stop("cannot combine compiled code with R-implemented solver")
#> precis <- NULL
#> x <- start
#> jacob <- matrix(nrow = N, ncol = N, data = 0)
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> if (length(reffx) != N)
#> stop("'f', function must return as many function values as elements in start")
#> for (i in 1:maxiter) {
#> refx <- x
#> pp <- mean(abs(reffx))
#> precis <- c(precis, pp)
#> ewt <- rtol * abs(x) + atol
#> if (max(abs(reffx/ewt)) < 1)
#> break
#> delt <- perturb(x)
#> for (j in 1:N) {
#> x[j] <- x[j] + delt[j]
#> if (is.null(parms))
#> fx <- f(x, ...)
#> else fx <- f(x, parms, ...)
#> jacob[, j] <- (fx - reffx)/delt[j]
#> x[j] <- refx[j]
#> }
#> relchange <- as.numeric(solve(jacob, -1 * reffx))
#> if (max(abs(relchange)) < ctol)
#> break
#> x <- x + relchange
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> }
#> }
#> names(x) <- names(start)
#> return(list(root = x, f.root = reffx, iter = i, estim.precis = precis[length(precis)]))
#> }
#> <bytecode: 0x7fea475c8ef8>
#> <environment: namespace:rootSolve>
#>
#> Slot ".options":
#> $start
#> [1] 1 1
#>
#>
#>
#> Slot ".deriv":
#> An object of class "deriv_control"
#> Slot ".FUN":
#> function (func, x, method = "Richardson", side = NULL, method.args = list(),
#> ...)
#> UseMethod("jacobian")
#> <bytecode: 0x7fea475bf7f0>
#> <environment: namespace:numDeriv>
#>
#> Slot ".options":
#> $method
#> [1] "Richardson"
#>
#>
#>
#>
#> Slot ".estFUN":
#> function(data){
#> function(theta){
#> with(data,
#> c(Y1 - theta[1],
#> (Y1 - theta[1])^2 - theta[2] ))
#> }}
#> <environment: 0x7fea467e2df8>
#>
#> Slot ".outer_args":
#> list()
#>
#> Slot ".inner_args":
#> list()
#>
#>
#> Slot "rootFUN_results":
#> $root
#> [1] 5.044563 10.041239
#>
#> $f.root
#> [1] -2.131628e-14 -2.238210e-13
#>
#> $iter
#> [1] 4
#>
#> $estim.precis
#> [1] 1.225686e-13
#>
#>
#> Slot "sandwich_components":
#> An object of class "sandwich_components"
#> Slot ".A":
#> [,1] [,2]
#> [1,] 1.000000e+02 0
#> [2,] -1.517693e-10 100
#>
#> Slot ".A_i":
#> $`1`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.752514 1
#>
#> $`2`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 10.81578 1
#>
#> $`3`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.842305 1
#>
#> $`4`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 6.653878 1
#>
#> $`5`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -11.75308 1
#>
#> $`6`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.291574 1
#>
#> $`7`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -6.300465 1
#>
#> $`8`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.043499 1
#>
#> $`9`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 9.831685 1
#>
#> $`10`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.9485972 1
#>
#> $`11`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.291621 1
#>
#> $`12`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.947683 1
#>
#> $`13`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -5.005397 1
#>
#> $`14`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -11.52285 1
#>
#> $`15`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -2.73693 1
#>
#> $`16`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] 0.9379618 1
#>
#> $`17`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 8.055833 1
#>
#> $`18`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.133716 1
#>
#> $`19`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.509455 1
#>
#> $`20`
#> [,1] [,2]
#> [1,] 1.0000 0
#> [2,] 12.8324 1
#>
#> $`21`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -6.278834 1
#>
#> $`22`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.302893 1
#>
#> $`23`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -4.760703 1
#>
#> $`24`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.231159 1
#>
#> $`25`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -12.45122 1
#>
#> $`26`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -4.239123 1
#>
#> $`27`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.327459 1
#>
#> $`28`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 7.970532 1
#>
#> $`29`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.845686 1
#>
#> $`30`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 2.29405 1
#>
#> $`31`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.431482 1
#>
#> $`32`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -6.892173 1
#>
#> $`33`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 5.423243 1
#>
#> $`34`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.770696 1
#>
#> $`35`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 10.69635 1
#>
#> $`36`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.455444 1
#>
#> $`37`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -1.296536 1
#>
#> $`38`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.555264 1
#>
#> $`39`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.4103641 1
#>
#> $`40`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.559842 1
#>
#> $`41`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.356527 1
#>
#> $`42`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -17.18108 1
#>
#> $`43`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.165449 1
#>
#> $`44`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 5.191265 1
#>
#> $`45`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -7.943802 1
#>
#> $`46`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -8.998276 1
#>
#> $`47`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.587908 1
#>
#> $`48`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -4.218017 1
#>
#> $`49`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 3.262841 1
#>
#> $`50`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] 0.9841168 1
#>
#> $`51`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 8.188365 1
#>
#> $`52`
#> [,1] [,2]
#> [1,] 1.0000 0
#> [2,] 13.1389 1
#>
#> $`53`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.2327012 1
#>
#> $`54`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.2827533 1
#>
#> $`55`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.912849 1
#>
#> $`56`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.786123 1
#>
#> $`57`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.9959563 1
#>
#> $`58`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -0.88019 1
#>
#> $`59`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 2.023568 1
#>
#> $`60`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.022743 1
#>
#> $`61`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.027283 1
#>
#> $`62`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] 0.3831244 1
#>
#> $`63`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -1.507302 1
#>
#> $`64`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 6.268315 1
#>
#> $`65`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.0352244 1
#>
#> $`66`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -5.127449 1
#>
#> $`67`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.109045 1
#>
#> $`68`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -5.612504 1
#>
#> $`69`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 1.53121 1
#>
#> $`70`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 6.681925 1
#>
#> $`71`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 4.967466 1
#>
#> $`72`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.522182 1
#>
#> $`73`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.8176918 1
#>
#> $`74`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 2.226746 1
#>
#> $`75`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.5266062 1
#>
#> $`76`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -16.42393 1
#>
#> $`77`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 2.777827 1
#>
#> $`78`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.069934 1
#>
#> $`79`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 10.05734 1
#>
#> $`80`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 17.25968 1
#>
#> $`81`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -10.00691 1
#>
#> $`82`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 4.629864 1
#>
#> $`83`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] 0.9030276 1
#>
#> $`84`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -4.278789 1
#>
#> $`85`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 4.872697 1
#>
#> $`86`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 5.576646 1
#>
#> $`87`
#> [,1] [,2]
#> [1,] 1.0000000 0
#> [2,] -0.8347135 1
#>
#> $`88`
#> [,1] [,2]
#> [1,] 1.0000 0
#> [2,] -2.4507 1
#>
#> $`89`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -8.775649 1
#>
#> $`90`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -5.087647 1
#>
#> $`91`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -1.953179 1
#>
#> $`92`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.095662 1
#>
#> $`93`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 7.542036 1
#>
#> $`94`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -2.216907 1
#>
#> $`95`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 15.07308 1
#>
#> $`96`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] -3.529053 1
#>
#> $`97`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] 12.51525 1
#>
#> $`98`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 1.206403 1
#>
#> $`99`
#> [,1] [,2]
#> [1,] 1.00000 0
#> [2,] -8.77275 1
#>
#> $`100`
#> [,1] [,2]
#> [1,] 1.000000 0
#> [2,] 4.526372 1
#>
#>
#> Slot ".B":
#> [,1] [,2]
#> [1,] 1004.1239 366.7969
#> [2,] 366.7969 24921.9638
#>
#> Slot ".B_i":
#> $`1`
#> [,1] [,2]
#> [1,] 1.894083 11.21258
#> [2,] 11.212579 66.37615
#>
#> $`2`
#> [,1] [,2]
#> [1,] 29.24529 103.8534
#> [2,] 103.85343 368.7956
#>
#> $`3`
#> [,1] [,2]
#> [1,] 3.690828 12.20011
#> [2,] 12.200110 40.32772
#>
#> $`4`
#> [,1] [,2]
#> [1,] 11.068524 3.417716
#> [2,] 3.417716 1.055315
#>
#> $`5`
#> [,1] [,2]
#> [1,] 34.5337 -143.9309
#> [2,] -143.9309 599.8807
#>
#> $`6`
#> [,1] [,2]
#> [1,] 2.708615 12.06794
#> [2,] 12.067937 53.76737
#>
#> $`7`
#> [,1] [,2]
#> [1,] 9.9239647 0.36944079
#> [2,] 0.3694408 0.01375322
#>
#> $`8`
#> [,1] [,2]
#> [1,] 2.315721 11.75630
#> [2,] 11.756302 59.68362
#>
#> $`9`
#> [,1] [,2]
#> [1,] 24.16551 69.43268
#> [2,] 69.43268 199.49496
#>
#> $`10`
#> [,1] [,2]
#> [1,] 0.2249591 4.655848
#> [2,] 4.6558475 96.359348
#>
#> $`11`
#> [,1] [,2]
#> [1,] 0.4170714 -6.21539
#> [2,] -6.2153901 92.62460
#>
#> $`12`
#> [,1] [,2]
#> [1,] 0.9483677 -8.855017
#> [2,] -8.8550173 82.680306
#>
#> $`13`
#> [,1] [,2]
#> [1,] 6.263501 9.454541
#> [2,] 9.454541 14.271306
#>
#> $`14`
#> [,1] [,2]
#> [1,] 33.1940 -133.3929
#> [2,] -133.3929 536.0505
#>
#> $`15`
#> [,1] [,2]
#> [1,] 1.872697 11.17836
#> [2,] 11.178365 66.72508
#>
#> $`16`
#> [,1] [,2]
#> [1,] 0.2199431 -4.60600
#> [2,] -4.6060002 96.45785
#>
#> $`17`
#> [,1] [,2]
#> [1,] 16.22411 24.90410
#> [2,] 24.90410 38.22792
#>
#> $`18`
#> [,1] [,2]
#> [1,] 1.138186 9.498294
#> [2,] 9.498294 79.264346
#>
#> $`19`
#> [,1] [,2]
#> [1,] 1.574341 10.62365
#> [2,] 10.623649 71.68836
#>
#> $`20`
#> [,1] [,2]
#> [1,] 41.16761 199.7130
#> [2,] 199.71303 968.8513
#>
#> $`21`
#> [,1] [,2]
#> [1,] 9.8559376 0.58173782
#> [2,] 0.5817378 0.03433655
#>
#> $`22`
#> [,1] [,2]
#> [1,] 2.727275 -12.07862
#> [2,] -12.078619 53.49407
#>
#> $`23`
#> [,1] [,2]
#> [1,] 5.666072 10.41443
#> [2,] 10.414434 19.14208
#>
#> $`24`
#> [,1] [,2]
#> [1,] 2.610097 -12.00560
#> [2,] -12.005600 55.22187
#>
#> $`25`
#> [,1] [,2]
#> [1,] 38.75822 -178.7807
#> [2,] -178.78072 824.6650
#>
#> $`26`
#> [,1] [,2]
#> [1,] 4.49254 11.76081
#> [2,] 11.76081 30.78805
#>
#> $`27`
#> [,1] [,2]
#> [1,] 1.354266 10.10929
#> [2,] 10.109287 75.46349
#>
#> $`28`
#> [,1] [,2]
#> [1,] 15.88235 23.27837
#> [2,] 23.27837 34.11854
#>
#> $`29`
#> [,1] [,2]
#> [1,] 3.697326 12.19835
#> [2,] 12.198350 40.24523
#>
#> $`30`
#> [,1] [,2]
#> [1,] 1.315666 -10.00845
#> [2,] -10.008449 76.13562
#>
#> $`31`
#> [,1] [,2]
#> [1,] 2.943767 12.17742
#> [2,] 12.177423 50.37410
#>
#> $`32`
#> [,1] [,2]
#> [1,] 11.875512 -6.321063
#> [2,] -6.321063 3.364557
#>
#> $`33`
#> [,1] [,2]
#> [1,] 7.352891 -7.289782
#> [2,] -7.289782 7.227215
#>
#> $`34`
#> [,1] [,2]
#> [1,] 3.554538 12.22969
#> [2,] 12.229690 42.07729
#>
#> $`35`
#> [,1] [,2]
#> [1,] 28.60297 99.27135
#> [2,] 99.27135 344.53775
#>
#> $`36`
#> [,1] [,2]
#> [1,] 2.985024 -12.19118
#> [2,] -12.191179 49.79017
#>
#> $`37`
#> [,1] [,2]
#> [1,] 0.4202515 6.236979
#> [2,] 6.2369792 92.563397
#>
#> $`38`
#> [,1] [,2]
#> [1,] 3.159976 -12.23235
#> [2,] -12.232354 47.35178
#>
#> $`39`
#> [,1] [,2]
#> [1,] 0.04209968 2.051644
#> [2,] 2.05164409 99.982784
#>
#> $`40`
#> [,1] [,2]
#> [1,] 3.168118 -12.23361
#> [2,] -12.233611 47.23979
#>
#> $`41`
#> [,1] [,2]
#> [1,] 2.816568 12.12490
#> [2,] 12.124901 52.19587
#>
#> $`42`
#> [,1] [,2]
#> [1,] 73.79736 -547.6994
#> [2,] -547.69940 4064.8425
#>
#> $`43`
#> [,1] [,2]
#> [1,] 0.3395676 -5.65340
#> [2,] -5.6533998 94.12242
#>
#> $`44`
#> [,1] [,2]
#> [1,] 6.737307 -8.575793
#> [2,] -8.575793 10.915967
#>
#> $`45`
#> [,1] [,2]
#> [1,] 15.77600 -22.77789
#> [2,] -22.77789 32.88746
#>
#> $`46`
#> [,1] [,2]
#> [1,] 20.24224 -45.89573
#> [2,] -45.89573 104.06051
#>
#> $`47`
#> [,1] [,2]
#> [1,] 3.218271 12.24009
#> [2,] 12.240091 46.55289
#>
#> $`48`
#> [,1] [,2]
#> [1,] 4.447916 11.79636
#> [2,] 11.796364 31.28526
#>
#> $`49`
#> [,1] [,2]
#> [1,] 2.661533 -12.03940
#> [2,] -12.039404 54.46006
#>
#> $`50`
#> [,1] [,2]
#> [1,] 0.2421215 -4.821738
#> [2,] -4.8217380 96.022702
#>
#> $`51`
#> [,1] [,2]
#> [1,] 16.76233 27.51737
#> [2,] 27.51737 45.17307
#>
#> $`52`
#> [,1] [,2]
#> [1,] 43.15767 217.5567
#> [2,] 217.55671 1096.6978
#>
#> $`53`
#> [,1] [,2]
#> [1,] 0.01353747 1.166729
#> [2,] 1.16672924 100.554795
#>
#> $`54`
#> [,1] [,2]
#> [1,] 0.01998735 1.416771
#> [2,] 1.41677076 100.425482
#>
#> $`55`
#> [,1] [,2]
#> [1,] 0.9147474 -8.728798
#> [2,] -8.7287978 83.292847
#>
#> $`56`
#> [,1] [,2]
#> [1,] 1.94062 11.28466
#> [2,] 11.28466 65.62002
#>
#> $`57`
#> [,1] [,2]
#> [1,] 0.2479823 4.876828
#> [2,] 4.8768280 95.907875
#>
#> $`58`
#> [,1] [,2]
#> [1,] 0.1936836 4.33386
#> [2,] 4.3338599 96.97434
#>
#> $`59`
#> [,1] [,2]
#> [1,] 1.023707 -9.123794
#> [2,] -9.123794 81.315885
#>
#> $`60`
#> [,1] [,2]
#> [1,] 0.2615007 -5.001078
#> [2,] -5.0010784 95.643278
#>
#> $`61`
#> [,1] [,2]
#> [1,] 1.027469 9.13673
#> [2,] 9.136730 81.24805
#>
#> $`62`
#> [,1] [,2]
#> [1,] 0.03669607 -1.916492
#> [2,] -1.91649203 100.090877
#>
#> $`63`
#> [,1] [,2]
#> [1,] 0.5679896 7.139522
#> [2,] 7.1395221 89.742452
#>
#> $`64`
#> [,1] [,2]
#> [1,] 9.8229447 -0.68416849
#> [2,] -0.6841685 0.04765236
#>
#> $`65`
#> [,1] [,2]
#> [1,] 0.0003101896 0.1768428
#> [2,] 0.1768428421 100.8202487
#>
#> $`66`
#> [,1] [,2]
#> [1,] 6.572683 8.892421
#> [2,] 8.892421 12.030877
#>
#> $`67`
#> [,1] [,2]
#> [1,] 1.112018 9.416064
#> [2,] 9.416064 79.730991
#>
#> $`68`
#> [,1] [,2]
#> [1,] 7.875050 6.078871
#> [2,] 6.078871 4.692373
#>
#> $`69`
#> [,1] [,2]
#> [1,] 0.5861514 -7.238864
#> [2,] -7.2388645 89.398679
#>
#> $`70`
#> [,1] [,2]
#> [1,] 11.16203 3.744520
#> [2,] 3.74452 1.256172
#>
#> $`71`
#> [,1] [,2]
#> [1,] 6.168929 -9.617783
#> [2,] -9.617783 14.994783
#>
#> $`72`
#> [,1] [,2]
#> [1,] 0.5792591 -7.201425
#> [2,] -7.2014253 89.529060
#>
#> $`73`
#> [,1] [,2]
#> [1,] 0.167155 4.036979
#> [2,] 4.036979 97.497532
#>
#> $`74`
#> [,1] [,2]
#> [1,] 1.239600 -9.799509
#> [2,] -9.799509 77.468851
#>
#> $`75`
#> [,1] [,2]
#> [1,] 0.06932852 2.625635
#> [2,] 2.62563494 99.438996
#>
#> $`76`
#> [,1] [,2]
#> [1,] 67.43633 -471.3264
#> [2,] -471.32637 3294.1968
#>
#> $`77`
#> [,1] [,2]
#> [1,] 1.929081 -11.26709
#> [2,] -11.267086 65.80710
#>
#> $`78`
#> [,1] [,2]
#> [1,] 2.356123 11.79640
#> [2,] 11.796397 59.06101
#>
#> $`79`
#> [,1] [,2]
#> [1,] 25.28754 76.66866
#> [2,] 76.66866 232.44977
#>
#> $`80`
#> [,1] [,2]
#> [1,] 74.4741 556.0452
#> [2,] 556.0452 4151.5939
#>
#> $`81`
#> [,1] [,2]
#> [1,] 25.03456 -75.0184
#> [2,] -75.01840 224.7997
#>
#> $`82`
#> [,1] [,2]
#> [1,] 5.358911 -10.83927
#> [2,] -10.839271 21.92419
#>
#> $`83`
#> [,1] [,2]
#> [1,] 0.2038647 -4.44171
#> [2,] -4.4417103 96.77393
#>
#> $`84`
#> [,1] [,2]
#> [1,] 4.577009 11.69014
#> [2,] 11.690144 29.85781
#>
#> $`85`
#> [,1] [,2]
#> [1,] 5.935795 -10.00229
#> [2,] -10.002293 16.85467
#>
#> $`86`
#> [,1] [,2]
#> [1,] 7.774745 -6.319717
#> [2,] -6.319717 5.136994
#>
#> $`87`
#> [,1] [,2]
#> [1,] 0.1741867 4.118081
#> [2,] 4.1180809 97.358719
#>
#> $`88`
#> [,1] [,2]
#> [1,] 1.501483 10.46419
#> [2,] 10.464191 72.92743
#>
#> $`89`
#> [,1] [,2]
#> [1,] 19.2530 -40.41960
#> [2,] -40.4196 84.85658
#>
#> $`90`
#> [,1] [,2]
#> [1,] 6.471038 9.08196
#> [2,] 9.081960 12.74633
#>
#> $`91`
#> [,1] [,2]
#> [1,] 0.9537271 8.874769
#> [2,] 8.8747688 82.582870
#>
#> $`92`
#> [,1] [,2]
#> [1,] 1.097949 9.371054
#> [2,] 9.371054 79.982427
#>
#> $`93`
#> [,1] [,2]
#> [1,] 14.22058 15.76036
#> [2,] 15.76036 17.46687
#>
#> $`94`
#> [,1] [,2]
#> [1,] 1.228669 9.768323
#> [2,] 9.768323 77.661390
#>
#> $`95`
#> [,1] [,2]
#> [1,] 56.79944 352.3951
#> [2,] 352.39509 2186.3295
#>
#> $`96`
#> [,1] [,2]
#> [1,] 3.113554 12.22408
#> [2,] 12.224084 47.99281
#>
#> $`97`
#> [,1] [,2]
#> [1,] 39.15787 182.2009
#> [2,] 182.20092 847.7780
#>
#> $`98`
#> [,1] [,2]
#> [1,] 0.363852 -5.837414
#> [2,] -5.837414 93.651817
#>
#> $`99`
#> [,1] [,2]
#> [1,] 19.24029 -40.35047
#> [2,] -40.35047 84.62246
#>
#> $`100`
#> [,1] [,2]
#> [1,] 5.12201 -11.13313
#> [2,] -11.13313 24.19881
#>
#>
#> Slot ".ee_i":
#> $`1`
#> [1] -1.376257 -8.147156
#>
#> $`2`
#> [1] 5.407891 19.204051
#>
#> $`3`
#> [1] -1.921153 -6.350411
#>
#> $`4`
#> [1] 3.326939 1.027285
#>
#> $`5`
#> [1] -5.876538 24.492463
#>
#> $`6`
#> [1] -1.645787 -7.332624
#>
#> $`7`
#> [1] -3.1502325 -0.1172741
#>
#> $`8`
#> [1] -1.521749 -7.725518
#>
#> $`9`
#> [1] 4.915842 14.124269
#>
#> $`10`
#> [1] -0.4742986 -9.8162797
#>
#> $`11`
#> [1] 0.6458107 -9.6241674
#>
#> $`12`
#> [1] 0.9738417 -9.0928712
#>
#> $`13`
#> [1] -2.502699 -3.777738
#>
#> $`14`
#> [1] -5.761424 23.152765
#>
#> $`15`
#> [1] -1.368465 -8.168542
#>
#> $`16`
#> [1] 0.4689809 -9.8212958
#>
#> $`17`
#> [1] 4.027917 6.182873
#>
#> $`18`
#> [1] -1.066858 -8.903053
#>
#> $`19`
#> [1] -1.254727 -8.466898
#>
#> $`20`
#> [1] 6.416199 31.126376
#>
#> $`21`
#> [1] -3.1394168 -0.1853012
#>
#> $`22`
#> [1] 1.651446 -7.313964
#>
#> $`23`
#> [1] -2.380351 -4.375167
#>
#> $`24`
#> [1] 1.615579 -7.431142
#>
#> $`25`
#> [1] -6.22561 28.71698
#>
#> $`26`
#> [1] -2.119561 -5.548698
#>
#> $`27`
#> [1] -1.163730 -8.686972
#>
#> $`28`
#> [1] 3.985266 5.841108
#>
#> $`29`
#> [1] -1.922843 -6.343913
#>
#> $`30`
#> [1] 1.147025 -8.725573
#>
#> $`31`
#> [1] -1.715741 -7.097471
#>
#> $`32`
#> [1] -3.446086 1.834273
#>
#> $`33`
#> [1] 2.711621 -2.688348
#>
#> $`34`
#> [1] -1.885348 -6.486701
#>
#> $`35`
#> [1] 5.348174 18.561728
#>
#> $`36`
#> [1] 1.727722 -7.056215
#>
#> $`37`
#> [1] -0.6482681 -9.6209873
#>
#> $`38`
#> [1] 1.777632 -6.881263
#>
#> $`39`
#> [1] -0.2051821 -9.9991392
#>
#> $`40`
#> [1] 1.779921 -6.873121
#>
#> $`41`
#> [1] -1.678263 -7.224671
#>
#> $`42`
#> [1] -8.590539 63.756117
#>
#> $`43`
#> [1] 0.5827243 -9.7016712
#>
#> $`44`
#> [1] 2.595632 -3.303932
#>
#> $`45`
#> [1] -3.971901 5.734759
#>
#> $`46`
#> [1] -4.499138 10.201005
#>
#> $`47`
#> [1] -1.793954 -6.822968
#>
#> $`48`
#> [1] -2.109008 -5.593323
#>
#> $`49`
#> [1] 1.631421 -7.379706
#>
#> $`50`
#> [1] 0.4920584 -9.7991174
#>
#> $`51`
#> [1] 4.094182 6.721091
#>
#> $`52`
#> [1] 6.56945 33.11643
#>
#> $`53`
#> [1] -0.1163506 -10.0277014
#>
#> $`54`
#> [1] -0.1413766 -10.0212515
#>
#> $`55`
#> [1] 0.9564243 -9.1264915
#>
#> $`56`
#> [1] -1.393061 -8.100619
#>
#> $`57`
#> [1] -0.4979782 -9.7932566
#>
#> $`58`
#> [1] -0.440095 -9.847555
#>
#> $`59`
#> [1] 1.011784 -9.017532
#>
#> $`60`
#> [1] 0.5113714 -9.7797382
#>
#> $`61`
#> [1] -1.013641 -9.013770
#>
#> $`62`
#> [1] 0.1915622 -10.0045428
#>
#> $`63`
#> [1] -0.7536508 -9.4732493
#>
#> $`64`
#> [1] 3.1341577 -0.2182942
#>
#> $`65`
#> [1] -0.0176122 -10.0409287
#>
#> $`66`
#> [1] -2.563725 -3.468555
#>
#> $`67`
#> [1] -1.054522 -8.929221
#>
#> $`68`
#> [1] -2.806252 -2.166189
#>
#> $`69`
#> [1] 0.7656052 -9.4550875
#>
#> $`70`
#> [1] 3.340962 1.120791
#>
#> $`71`
#> [1] 2.483733 -3.872310
#>
#> $`72`
#> [1] 0.7610908 -9.4619797
#>
#> $`73`
#> [1] -0.4088459 -9.8740839
#>
#> $`74`
#> [1] 1.113373 -8.801639
#>
#> $`75`
#> [1] -0.2633031 -9.9719103
#>
#> $`76`
#> [1] -8.211963 57.395094
#>
#> $`77`
#> [1] 1.388914 -8.112158
#>
#> $`78`
#> [1] -1.534967 -7.685116
#>
#> $`79`
#> [1] 5.028672 15.246303
#>
#> $`80`
#> [1] 8.629838 64.432864
#>
#> $`81`
#> [1] -5.003455 14.993320
#>
#> $`82`
#> [1] 2.314932 -4.682328
#>
#> $`83`
#> [1] 0.4515138 -9.8373741
#>
#> $`84`
#> [1] -2.139394 -5.464230
#>
#> $`85`
#> [1] 2.436349 -4.105444
#>
#> $`86`
#> [1] 2.788323 -2.266494
#>
#> $`87`
#> [1] -0.4173568 -9.8670522
#>
#> $`88`
#> [1] -1.225350 -8.539756
#>
#> $`89`
#> [1] -4.387824 9.211763
#>
#> $`90`
#> [1] -2.543824 -3.570200
#>
#> $`91`
#> [1] -0.9765895 -9.0875118
#>
#> $`92`
#> [1] -1.047831 -8.943289
#>
#> $`93`
#> [1] 3.771018 4.179338
#>
#> $`94`
#> [1] -1.108453 -8.812570
#>
#> $`95`
#> [1] 7.53654 46.75820
#>
#> $`96`
#> [1] -1.764527 -6.927685
#>
#> $`97`
#> [1] 6.257625 29.116627
#>
#> $`98`
#> [1] 0.6032015 -9.6773869
#>
#> $`99`
#> [1] -4.386375 9.199047
#>
#> $`100`
#> [1] 2.263186 -4.919229
#>
#>
#>
#> Slot "GFUN":
#> function ()
#> NULL
#> <bytecode: 0x7fea4756b2b0>
#>
#> Slot "corrections":
#> list()
#>
#> Slot "estimates":
#> [1] 5.044563 10.041239
#>
#> Slot "vcov":
#> [,1] [,2]
#> [1,] 0.10041239 0.03667969
#> [2,] 0.03667969 2.49219638
#>
# compare to the mean() and variance() functions
mean(geexex$Y1)
#> [1] 5.044563
n <- nrow(geexex)
var(geexex$Y1) * (n - 1)/n
#> [1] 10.04124
# A simple linear model for regressing X1 and X2 on Y4
lm_eefun <- function(data){
X <- cbind(1, data$X1, data$X2)
Y <- data$Y4
function(theta){
t(X) %*% (Y - X %*% theta)
}
}
m_estimate(
estFUN = lm_eefun,
data = geexex,
root_control = setup_root_control(start = c(0, 0, 0)))
#> An object of class "geex"
#> Slot "call":
#> m_estimate(estFUN = lm_eefun, data = geexex, root_control = setup_root_control(start = c(0,
#> 0, 0)))
#>
#> Slot "basis":
#> An object of class "m_estimation_basis"
#> Slot ".data":
#> Y1 Y2 X1 Y3 W1 Z1 X2
#> 1 3.66830660 2.02817177 4.949316 16.345756 4.823768 8.921782 0
#> 2 10.45245483 1.64329659 7.851962 25.687417 7.884845 13.909474 0
#> 3 3.12341064 2.85262638 4.729075 16.108307 4.709346 9.014695 0
#> 4 8.37150253 2.51336525 2.564395 10.579970 2.786091 6.733378 0
#> 5 -0.83197489 3.01820300 4.782347 16.464013 4.811590 9.290492 0
#> 6 3.39877632 0.97852092 5.335713 18.325769 5.415370 10.322199 0
#> 7 1.89433086 1.43833173 1.386442 5.577536 1.240995 3.497873 0
#> 8 3.52281395 0.98744392 3.453377 13.074664 3.632010 7.894598 0
#> 9 9.96040583 -1.02081430 2.958662 10.050725 2.752347 5.612733 0
#> 10 4.57026477 2.33235027 7.591370 24.414247 7.501404 13.027192 0
#> 11 5.69037402 3.24051157 6.812940 22.528706 6.835412 12.309296 0
#> 12 6.01840507 2.67134960 2.481492 9.540750 2.505561 5.818512 0
#> 13 2.54186468 0.66996589 3.307246 11.720103 3.256837 6.759235 0
#> 14 -0.71686038 1.14941969 2.366527 9.839421 2.551487 6.289631 0
#> 15 3.67609826 0.21116926 6.308752 21.049635 6.339597 11.586507 0
#> 16 5.51354425 3.23152191 2.280638 8.812598 2.273309 5.391641 0
#> 17 9.07247997 1.66560033 2.872154 10.227607 2.774940 5.919377 0
#> 18 3.97770523 1.03267790 4.361465 15.595252 4.489179 9.053054 0
#> 19 3.78983596 2.87937035 3.573053 11.805345 3.344600 6.445765 0
#> 20 11.46076273 1.74642131 5.556376 20.979426 6.133951 12.644862 0
#> 21 1.90514658 0.48212421 7.752991 24.820884 7.643469 13.191397 0
#> 22 6.69600961 1.97611674 6.030068 20.854263 6.221083 11.809162 0
#> 23 2.66421207 2.02665947 4.213262 14.901747 4.278752 8.581854 0
#> 24 6.66014272 2.16368120 2.923132 11.542799 3.116483 7.158102 0
#> 25 -1.18104663 2.41000794 5.156830 16.656110 4.953235 8.920865 0
#> 26 2.92500198 1.37263740 5.519839 18.121067 5.410226 9.841308 0
#> 27 3.88083378 2.63691800 5.477283 17.711627 5.297228 9.495703 0
#> 28 9.02982953 0.79806522 4.055430 14.397234 4.113166 8.314089 0
#> 29 3.12172019 3.34654241 4.319714 13.801412 4.030281 7.321841 0
#> 30 6.19158815 1.40123269 10.283894 33.098758 10.345663 17.672917 0
#> 31 3.32882227 2.44220444 2.557841 9.582409 2.535063 5.745648 0
#> 32 1.59847689 2.61352641 11.152742 37.215603 11.592086 20.486489 0
#> 33 7.75618478 1.70090363 2.538047 9.476212 2.503565 5.669141 0
#> 34 3.15921522 0.39941190 7.939765 25.708101 7.911967 13.798454 0
#> 35 10.39273751 1.66053304 3.629295 12.197870 3.456791 6.753928 0
#> 36 6.77228554 1.41869225 5.644317 18.711156 5.588868 10.244681 0
#> 37 4.39629525 1.60963799 1.385403 6.339116 1.431130 4.261012 0
#> 38 6.82219543 2.84551436 3.651563 13.372011 3.755894 7.894667 0
#> 39 4.83938127 2.68472721 2.075987 9.293362 2.342337 6.179382 0
#> 40 6.82448417 2.23771308 7.947636 26.813109 8.190186 14.891656 0
#> 41 3.36629988 1.28937811 3.893624 13.579242 3.868217 7.738807 0
#> 42 -3.54597542 4.61331896 4.399113 16.600543 4.749914 10.001873 0
#> 43 5.62728767 0.37335265 2.019187 6.280784 1.574993 3.252004 0
#> 44 7.64019560 0.39269371 10.182047 33.169007 10.337763 17.895937 0
#> 45 1.07266235 2.34031745 4.471305 14.891632 4.340734 8.184674 0
#> 46 0.54542518 4.72788771 5.445723 19.659399 5.776280 11.490815 0
#> 47 3.25060929 1.67280996 5.030453 16.727920 4.939593 9.182240 0
#> 48 2.93555501 0.74310325 7.586987 26.080025 7.916753 14.699546 0
#> 49 6.67598396 1.56860189 9.452187 30.400340 9.463132 16.222060 0
#> 50 5.53662175 4.54885325 8.141977 24.547274 7.672313 12.334309 0
#> 51 9.13874582 1.22859200 5.623052 18.422092 5.511286 9.987515 1
#> 52 11.61401290 1.49265765 5.066275 15.460228 4.631626 7.860815 1
#> 53 4.92821273 1.72997742 2.174904 8.703576 2.219620 5.441220 1
#> 54 4.90318672 2.74811656 1.373871 8.019078 1.848237 5.958272 1
#> 55 6.00098760 2.66859381 4.252394 12.485257 3.684413 6.106666 1
#> 56 3.65150186 1.54470134 1.844766 8.514763 2.089882 5.747614 1
#> 57 4.54658518 0.07215478 6.257311 19.373108 5.907605 9.987141 1
#> 58 4.60446834 3.88197707 7.640542 26.746499 8.096760 15.285686 1
#> 59 6.05634729 0.75028887 3.400547 13.582939 3.745871 8.482119 1
#> 60 5.55593474 1.51065503 3.879217 12.798800 3.669504 6.979974 1
#> 61 4.03092200 2.21539129 5.044494 16.871488 4.978996 9.304746 1
#> 62 5.23612553 2.42210867 3.724228 13.103840 3.707017 7.517498 1
#> 63 4.29091253 0.77885172 3.209739 11.250332 3.115018 6.435724 1
#> 64 8.17872107 2.31222782 3.503141 15.091380 4.148630 9.836670 1
#> 65 5.02695115 2.88646213 3.588984 12.896787 3.621443 7.513311 1
#> 66 2.48083883 2.47481069 2.572586 9.004733 2.394330 5.145854 1
#> 67 3.99004087 2.86984135 2.321320 9.601955 2.480819 6.119975 1
#> 68 2.23831135 1.11347620 7.354859 24.266268 7.405282 13.233980 1
#> 69 5.81016858 1.87134447 1.780620 7.271942 1.763140 4.601012 1
#> 70 8.38552575 3.09651049 2.438272 9.222328 2.415150 5.564919 1
#> 71 7.52829625 2.51802955 4.870025 17.058979 4.982251 9.753941 1
#> 72 5.80565410 2.39803318 6.107551 19.258297 5.841462 10.096971 1
#> 73 4.63571743 3.06665941 3.068762 10.043868 2.778158 5.440724 1
#> 74 6.15793650 1.55045992 8.069649 27.857468 8.481779 15.752995 1
#> 75 4.78126024 2.62610198 2.564135 7.630308 2.048611 3.784106 1
#> 76 -3.16739941 1.18116405 6.700594 22.114532 6.703782 12.063641 1
#> 77 6.43347697 1.73648379 5.381833 17.057971 5.109951 8.985221 1
#> 78 3.50959659 2.15457529 12.644899 40.205236 12.712534 21.237888 1
#> 79 10.07323536 2.56844555 2.037142 9.119878 2.289255 6.064165 1
#> 80 13.67440127 -0.66015968 5.883640 17.576515 5.365039 8.751055 1
#> 81 0.04110863 3.13653254 7.093428 24.177106 7.317634 13.536964 1
#> 82 7.35949555 2.42177278 4.873831 16.571498 4.861332 9.260751 1
#> 83 5.49607715 3.35008260 8.291038 25.527766 7.954701 13.091208 1
#> 84 2.90516885 3.10375689 4.051026 12.221867 3.568223 6.145328 1
#> 85 7.48091201 2.64704611 7.689539 25.778200 7.866935 14.243891 1
#> 86 7.83288634 2.17563581 4.933636 16.643004 4.894160 9.242550 1
#> 87 4.62720660 2.65355779 5.774989 19.541334 5.829081 10.878851 1
#> 88 3.81921320 1.93450970 4.483566 16.268060 4.687907 9.542711 1
#> 89 0.65673908 2.64552217 2.739769 11.946482 3.171563 7.836829 1
#> 90 2.50073977 2.36429404 5.286464 17.755621 5.260521 9.825925 1
#> 91 4.06797383 2.84344157 3.701213 12.546517 3.561933 6.994698 1
#> 92 3.99673254 1.32352113 5.795986 20.816259 6.153061 12.122280 1
#> 93 8.81558134 1.60856710 4.883292 15.756919 4.660053 8.431981 1
#> 94 3.93610997 2.40494064 7.172253 22.359187 6.882860 11.600808 1
#> 95 12.58110379 0.89314130 3.340735 11.491910 3.208161 6.480807 1
#> 96 3.28003669 1.61669959 7.262549 26.233329 7.873969 15.339506 1
#> 97 11.30218798 2.29402025 1.940701 6.989609 1.732577 4.078556 1
#> 98 5.64776480 3.79306067 5.958475 20.288944 6.061855 11.351232 1
#> 99 0.65818837 2.81403217 4.432708 14.119440 4.138037 7.470379 1
#> 100 7.30774920 0.67997560 3.283518 10.676520 2.990010 5.751243 1
#> Y4 Y5
#> 1 0.092739260 1
#> 2 1.016727357 1
#> 3 0.493990392 0
#> 4 1.243224329 0
#> 5 0.695205988 1
#> 6 0.952201378 1
#> 7 -0.343146465 0
#> 8 1.159870423 0
#> 9 -0.429393276 0
#> 10 0.499274828 1
#> 11 0.871180147 1
#> 12 0.444423658 0
#> 13 0.229090617 1
#> 14 1.076493168 0
#> 15 0.854254673 1
#> 16 0.298747112 0
#> 17 -0.001638862 0
#> 18 1.047002780 1
#> 19 -0.456508875 1
#> 20 2.965934470 0
#> 21 0.437209150 0
#> 22 1.467067372 0
#> 23 0.783287466 0
#> 24 1.165717760 0
#> 25 -0.198696160 1
#> 26 0.213533342 1
#> 27 -0.072493261 1
#> 28 0.736487513 1
#> 29 -0.625758090 1
#> 30 1.375465405 1
#> 31 0.264670535 0
#> 32 2.972649859 1
#> 33 0.215875121 1
#> 34 0.782782994 1
#> 35 -0.227084853 1
#> 36 0.442637449 1
#> 37 0.421447969 0
#> 38 0.882479555 0
#> 39 1.373000995 1
#> 40 1.864965592 1
#> 41 0.387733146 1
#> 42 1.943114799 1
#> 43 -1.474856978 0
#> 44 1.741072051 1
#> 45 0.024847168 1
#> 46 1.966803213 1
#> 47 0.239605022 0
#> 48 2.177764398 1
#> 49 1.088997768 1
#> 50 -0.964458223 1
#> 51 0.715242972 1
#> 52 -0.631970427 1
#> 53 0.996355205 0
#> 54 2.634852773 1
#> 55 -1.246686055 1
#> 56 1.764940768 0
#> 57 -0.173094497 1
#> 58 3.188926631 1
#> 59 2.321353405 1
#> 60 0.149069864 0
#> 61 0.842453670 1
#> 62 0.903578781 0
#> 63 0.542090297 1
#> 64 3.532272980 0
#> 65 1.088732578 1
#> 66 0.144233610 1
#> 67 1.470126269 0
#> 68 1.537177460 0
#> 69 0.708145014 1
#> 70 0.751337374 0
#> 71 1.535905791 1
#> 72 0.146399418 0
#> 73 -0.255543077 0
#> 74 3.055486628 0
#> 75 -1.205682549 1
#> 76 1.282809142 1
#> 77 0.050654962 1
#> 78 2.135029369 1
#> 79 1.812166070 1
#> 80 -0.886040754 1
#> 81 2.206165066 1
#> 82 1.037387368 1
#> 83 0.083754535 0
#> 84 -0.926108918 0
#> 85 2.078535519 1
#> 86 0.935458616 0
#> 87 1.393866742 0
#> 88 1.865718680 0
#> 89 2.601152645 0
#> 90 1.024876085 1
#> 91 0.412999035 1
#> 92 2.607900007 0
#> 93 0.195371813 1
#> 94 0.159654048 1
#> 95 0.403777090 0
#> 96 3.771937632 1
#> 97 -0.038425654 1
#> 98 1.609367331 0
#> 99 -0.135412360 1
#> 100 -0.245682938 0
#>
#> Slot ".units":
#> character(0)
#>
#> Slot ".weights":
#> numeric(0)
#>
#> Slot ".psiFUN_list":
#> $`1`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04c85f58>
#>
#> $`2`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04c96070>
#>
#> $`3`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04ca0b00>
#>
#> $`4`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04ca2208>
#>
#> $`5`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04cb7f90>
#>
#> $`6`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04cc30e8>
#>
#> $`7`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04cc4400>
#>
#> $`8`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04cc9d98>
#>
#> $`9`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04ccdbd8>
#>
#> $`10`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04cd0f98>
#>
#> $`11`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d14898>
#>
#> $`12`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d22ac8>
#>
#> $`13`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d2c358>
#>
#> $`14`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d35e78>
#>
#> $`15`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d37cf0>
#>
#> $`16`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d398c8>
#>
#> $`17`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d45430>
#>
#> $`18`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d46e10>
#>
#> $`19`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d48da0>
#>
#> $`20`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d4aba8>
#>
#> $`21`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d4c780>
#>
#> $`22`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d56860>
#>
#> $`23`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d5ceb8>
#>
#> $`24`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d5eac8>
#>
#> $`25`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d609b0>
#>
#> $`26`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d622b0>
#>
#> $`27`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d7be78>
#>
#> $`28`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d7dd60>
#>
#> $`29`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d7fcb8>
#>
#> $`30`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d81a50>
#>
#> $`31`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d83858>
#>
#> $`32`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d85430>
#>
#> $`33`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d893c0>
#>
#> $`34`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d8b2e0>
#>
#> $`35`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d8d200>
#>
#> $`36`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04d91e78>
#>
#> $`37`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e044a8>
#>
#> $`38`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e78630>
#>
#> $`39`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e7d858>
#>
#> $`40`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e7f510>
#>
#> $`41`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e81238>
#>
#> $`42`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e87fc8>
#>
#> $`43`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e89a50>
#>
#> $`44`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e8b9e0>
#>
#> $`45`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e8d970>
#>
#> $`46`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e8f890>
#>
#> $`47`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e91820>
#>
#> $`48`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e93740>
#>
#> $`49`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04e975f0>
#>
#> $`50`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f09e40>
#>
#> $`51`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f0bdd0>
#>
#> $`52`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f0fd60>
#>
#> $`53`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f11cb8>
#>
#> $`54`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f13c48>
#>
#> $`55`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f159e0>
#>
#> $`56`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f17970>
#>
#> $`57`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f198c8>
#>
#> $`58`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f1b858>
#>
#> $`59`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f1f7e8>
#>
#> $`60`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f21778>
#>
#> $`61`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f235f0>
#>
#> $`62`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f27510>
#>
#> $`63`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f29468>
#>
#> $`64`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f2b3f8>
#>
#> $`65`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f2d190>
#>
#> $`66`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f2f0b0>
#>
#> $`67`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f32fd0>
#>
#> $`68`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f34e48>
#>
#> $`69`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f36d30>
#>
#> $`70`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f38c18>
#>
#> $`71`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f3ab38>
#>
#> $`72`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f3ef28>
#>
#> $`73`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f40e48>
#>
#> $`74`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f42d30>
#>
#> $`75`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f44c88>
#>
#> $`76`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f48b70>
#>
#> $`77`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f4aac8>
#>
#> $`78`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f4c978>
#>
#> $`79`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f4e898>
#>
#> $`80`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f507b8>
#>
#> $`81`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f52710>
#>
#> $`82`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f57f58>
#>
#> $`83`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f59dd0>
#>
#> $`84`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f5ba50>
#>
#> $`85`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f5d938>
#>
#> $`86`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f5f078>
#>
#> $`87`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f60f98>
#>
#> $`88`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f64ef0>
#>
#> $`89`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f66e10>
#>
#> $`90`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f68d68>
#>
#> $`91`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f6ac88>
#>
#> $`92`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f6cbe0>
#>
#> $`93`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f6eb00>
#>
#> $`94`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f75ac0>
#>
#> $`95`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f7b900>
#>
#> $`96`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f7d858>
#>
#> $`97`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f7f708>
#>
#> $`98`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f81430>
#>
#> $`99`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f85350>
#>
#> $`100`
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> <environment: 0x7fea04f87270>
#>
#>
#> Slot ".GFUN":
#> function (theta)
#> {
#> psii <- lapply(psi_list, function(psi) {
#> do.call(psi, args = append(list(theta = theta), object@.inner_args))
#> })
#> compute_sum_of_list(psii, object@.weights)
#> }
#> <environment: 0x7fea04faf858>
#>
#> Slot ".control":
#> An object of class "geex_control"
#> Slot ".approx":
#> An object of class "approx_control"
#> Slot ".FUN":
#> function ()
#> NULL
#> <bytecode: 0x7fea4759ffd0>
#>
#> Slot ".options":
#> list()
#>
#>
#> Slot ".root":
#> An object of class "root_control"
#> Slot ".object_name":
#> [1] "root"
#>
#> Slot ".FUN":
#> function (f, start, maxiter = 100, rtol = 1e-06, atol = 1e-08,
#> ctol = 1e-08, useFortran = TRUE, positive = FALSE, jacfunc = NULL,
#> jactype = "fullint", verbose = FALSE, bandup = 1, banddown = 1,
#> parms = NULL, ...)
#> {
#> initfunc <- NULL
#> if (is.list(f)) {
#> if (!is.null(jacfunc) & "jacfunc" %in% names(f))
#> stop("If 'f' is a list that contains jacfunc, argument 'jacfunc' should be NULL")
#> jacfunc <- f$jacfunc
#> initfunc <- f$initfunc
#> f <- f$func
#> }
#> N <- length(start)
#> if (!is.numeric(start))
#> stop("start conditions should be numeric")
#> if (!is.numeric(maxiter))
#> stop("`maxiter' must be numeric")
#> if (as.integer(maxiter) < 1)
#> stop("maxiter must be >=1")
#> if (!is.numeric(rtol))
#> stop("`rtol' must be numeric")
#> if (!is.numeric(atol))
#> stop("`atol' must be numeric")
#> if (!is.numeric(ctol))
#> stop("`ctol' must be numeric")
#> if (length(atol) > 1 && length(atol) != N)
#> stop("`atol' must either be a scalar, or as long as `start'")
#> if (length(rtol) > 1 && length(rtol) != N)
#> stop("`rtol' must either be a scalar, or as long as `y'")
#> if (length(ctol) > 1)
#> stop("`ctol' must be a scalar")
#> if (useFortran) {
#> if (!is.compiled(f) & is.null(parms)) {
#> Fun1 <- function(time = 0, x, parms = NULL) list(f(x,
#> ...))
#> Fun <- Fun1
#> }
#> else if (!is.compiled(f)) {
#> Fun2 <- function(time = 0, x, parms) list(f(x, parms,
#> ...))
#> Fun <- Fun2
#> }
#> else {
#> Fun <- f
#> f <- function(x, ...) Fun(n = length(start), t = 0,
#> x, f = rep(0, length(start)), 1, 1)$f
#> }
#> JacFunc <- jacfunc
#> if (!is.null(jacfunc))
#> if (!is.compiled(JacFunc) & is.null(parms))
#> JacFunc <- function(time = 0, x, parms = parms) jacfunc(x,
#> ...)
#> else if (!is.compiled(JacFunc))
#> JacFunc <- function(time = 0, x, parms = parms) jacfunc(x,
#> parms, ...)
#> else JacFunc <- jacfunc
#> method <- "stode"
#> if (jactype == "sparse") {
#> method <- "stodes"
#> if (!is.null(jacfunc))
#> stop("jacfunc can not be used when jactype='sparse'")
#> x <- stodes(y = start, time = 0, func = Fun, atol = atol,
#> positive = positive, rtol = rtol, ctol = ctol,
#> maxiter = maxiter, verbose = verbose, parms = parms,
#> initfunc = initfunc)
#> }
#> else x <- steady(y = start, time = 0, func = Fun, atol = atol,
#> positive = positive, rtol = rtol, ctol = ctol, maxiter = maxiter,
#> method = method, jacfunc = JacFunc, jactype = jactype,
#> verbose = verbose, parms = parms, initfunc = initfunc,
#> bandup = bandup, banddown = banddown)
#> precis <- attr(x, "precis")
#> attributes(x) <- NULL
#> x <- unlist(x)
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> i <- length(precis)
#> }
#> else {
#> if (is.compiled(f))
#> stop("cannot combine compiled code with R-implemented solver")
#> precis <- NULL
#> x <- start
#> jacob <- matrix(nrow = N, ncol = N, data = 0)
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> if (length(reffx) != N)
#> stop("'f', function must return as many function values as elements in start")
#> for (i in 1:maxiter) {
#> refx <- x
#> pp <- mean(abs(reffx))
#> precis <- c(precis, pp)
#> ewt <- rtol * abs(x) + atol
#> if (max(abs(reffx/ewt)) < 1)
#> break
#> delt <- perturb(x)
#> for (j in 1:N) {
#> x[j] <- x[j] + delt[j]
#> if (is.null(parms))
#> fx <- f(x, ...)
#> else fx <- f(x, parms, ...)
#> jacob[, j] <- (fx - reffx)/delt[j]
#> x[j] <- refx[j]
#> }
#> relchange <- as.numeric(solve(jacob, -1 * reffx))
#> if (max(abs(relchange)) < ctol)
#> break
#> x <- x + relchange
#> if (is.null(parms))
#> reffx <- f(x, ...)
#> else reffx <- f(x, parms, ...)
#> }
#> }
#> names(x) <- names(start)
#> return(list(root = x, f.root = reffx, iter = i, estim.precis = precis[length(precis)]))
#> }
#> <bytecode: 0x7fea475c8ef8>
#> <environment: namespace:rootSolve>
#>
#> Slot ".options":
#> $start
#> [1] 0 0 0
#>
#>
#>
#> Slot ".deriv":
#> An object of class "deriv_control"
#> Slot ".FUN":
#> function (func, x, method = "Richardson", side = NULL, method.args = list(),
#> ...)
#> UseMethod("jacobian")
#> <bytecode: 0x7fea475bf7f0>
#> <environment: namespace:numDeriv>
#>
#> Slot ".options":
#> $method
#> [1] "Richardson"
#>
#>
#>
#>
#> Slot ".estFUN":
#> function(data){
#> X <- cbind(1, data$X1, data$X2)
#> Y <- data$Y4
#> function(theta){
#> t(X) %*% (Y - X %*% theta)
#> }
#> }
#> <environment: 0x7fea467e2df8>
#>
#> Slot ".outer_args":
#> list()
#>
#> Slot ".inner_args":
#> list()
#>
#>
#> Slot "rootFUN_results":
#> $root
#> [1] -0.04061272 0.14435320 0.35436823
#>
#> $f.root
#> [,1]
#> [1,] 4.174439e-14
#> [2,] 8.970602e-14
#> [3,] 2.575717e-14
#>
#> $iter
#> [1] 3
#>
#> $estim.precis
#> [1] 5.240253e-14
#>
#>
#> Slot "sandwich_components":
#> An object of class "sandwich_components"
#> Slot ".A":
#> [,1] [,2] [,3]
#> [1,] 100.000 487.7690 50.0000
#> [2,] 487.769 2902.9210 237.3681
#> [3,] 50.000 237.3681 50.0000
#>
#> Slot ".A_i":
#> $`1`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.949316 0
#> [2,] 4.949316 24.495730 0
#> [3,] 0.000000 0.000000 0
#>
#> $`2`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.851962 0
#> [2,] 7.851962 61.653306 0
#> [3,] 0.000000 0.000000 0
#>
#> $`3`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.729075 0
#> [2,] 4.729075 22.364149 0
#> [3,] 0.000000 0.000000 0
#>
#> $`4`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.564395 0
#> [2,] 2.564395 6.576123 0
#> [3,] 0.000000 0.000000 0
#>
#> $`5`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.782347 0
#> [2,] 4.782347 22.870844 0
#> [3,] 0.000000 0.000000 0
#>
#> $`6`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.335713 0
#> [2,] 5.335713 28.469832 0
#> [3,] 0.000000 0.000000 0
#>
#> $`7`
#> [,1] [,2] [,3]
#> [1,] 1.000000 1.386442 0
#> [2,] 1.386442 1.922222 0
#> [3,] 0.000000 0.000000 0
#>
#> $`8`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.453377 0
#> [2,] 3.453377 11.925814 0
#> [3,] 0.000000 0.000000 0
#>
#> $`9`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.958662 0
#> [2,] 2.958662 8.753678 0
#> [3,] 0.000000 0.000000 0
#>
#> $`10`
#> [,1] [,2] [,3]
#> [1,] 1.00000 7.59137 0
#> [2,] 7.59137 57.62889 0
#> [3,] 0.00000 0.00000 0
#>
#> $`11`
#> [,1] [,2] [,3]
#> [1,] 1.00000 6.81294 0
#> [2,] 6.81294 46.41615 0
#> [3,] 0.00000 0.00000 0
#>
#> $`12`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.481492 0
#> [2,] 2.481492 6.157802 0
#> [3,] 0.000000 0.000000 0
#>
#> $`13`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.307246 0
#> [2,] 3.307246 10.937874 0
#> [3,] 0.000000 0.000000 0
#>
#> $`14`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.366527 0
#> [2,] 2.366527 5.600449 0
#> [3,] 0.000000 0.000000 0
#>
#> $`15`
#> [,1] [,2] [,3]
#> [1,] 1.000000 6.308752 0
#> [2,] 6.308752 39.800348 0
#> [3,] 0.000000 0.000000 0
#>
#> $`16`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.280638 0
#> [2,] 2.280638 5.201311 0
#> [3,] 0.000000 0.000000 0
#>
#> $`17`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.872154 0
#> [2,] 2.872154 8.249268 0
#> [3,] 0.000000 0.000000 0
#>
#> $`18`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.361465 0
#> [2,] 4.361465 19.022379 0
#> [3,] 0.000000 0.000000 0
#>
#> $`19`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.573053 0
#> [2,] 3.573053 12.766708 0
#> [3,] 0.000000 0.000000 0
#>
#> $`20`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.556376 0
#> [2,] 5.556376 30.873320 0
#> [3,] 0.000000 0.000000 0
#>
#> $`21`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.752991 0
#> [2,] 7.752991 60.108874 0
#> [3,] 0.000000 0.000000 0
#>
#> $`22`
#> [,1] [,2] [,3]
#> [1,] 1.000000 6.030068 0
#> [2,] 6.030068 36.361715 0
#> [3,] 0.000000 0.000000 0
#>
#> $`23`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.213262 0
#> [2,] 4.213262 17.751575 0
#> [3,] 0.000000 0.000000 0
#>
#> $`24`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.923132 0
#> [2,] 2.923132 8.544699 0
#> [3,] 0.000000 0.000000 0
#>
#> $`25`
#> [,1] [,2] [,3]
#> [1,] 1.00000 5.15683 0
#> [2,] 5.15683 26.59289 0
#> [3,] 0.00000 0.00000 0
#>
#> $`26`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.519839 0
#> [2,] 5.519839 30.468624 0
#> [3,] 0.000000 0.000000 0
#>
#> $`27`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.477283 0
#> [2,] 5.477283 30.000628 0
#> [3,] 0.000000 0.000000 0
#>
#> $`28`
#> [,1] [,2] [,3]
#> [1,] 1.00000 4.05543 0
#> [2,] 4.05543 16.44651 0
#> [3,] 0.00000 0.00000 0
#>
#> $`29`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.319714 0
#> [2,] 4.319714 18.659927 0
#> [3,] 0.000000 0.000000 0
#>
#> $`30`
#> [,1] [,2] [,3]
#> [1,] 1.00000 10.28389 0
#> [2,] 10.28389 105.75848 0
#> [3,] 0.00000 0.00000 0
#>
#> $`31`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.557841 0
#> [2,] 2.557841 6.542550 0
#> [3,] 0.000000 0.000000 0
#>
#> $`32`
#> [,1] [,2] [,3]
#> [1,] 1.00000 11.15274 0
#> [2,] 11.15274 124.38366 0
#> [3,] 0.00000 0.00000 0
#>
#> $`33`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.538047 0
#> [2,] 2.538047 6.441684 0
#> [3,] 0.000000 0.000000 0
#>
#> $`34`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.939765 0
#> [2,] 7.939765 63.039867 0
#> [3,] 0.000000 0.000000 0
#>
#> $`35`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.629295 0
#> [2,] 3.629295 13.171780 0
#> [3,] 0.000000 0.000000 0
#>
#> $`36`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.644317 0
#> [2,] 5.644317 31.858312 0
#> [3,] 0.000000 0.000000 0
#>
#> $`37`
#> [,1] [,2] [,3]
#> [1,] 1.000000 1.385403 0
#> [2,] 1.385403 1.919341 0
#> [3,] 0.000000 0.000000 0
#>
#> $`38`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.651563 0
#> [2,] 3.651563 13.333910 0
#> [3,] 0.000000 0.000000 0
#>
#> $`39`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.075987 0
#> [2,] 2.075987 4.309721 0
#> [3,] 0.000000 0.000000 0
#>
#> $`40`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.947636 0
#> [2,] 7.947636 63.164914 0
#> [3,] 0.000000 0.000000 0
#>
#> $`41`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.893624 0
#> [2,] 3.893624 15.160307 0
#> [3,] 0.000000 0.000000 0
#>
#> $`42`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.399113 0
#> [2,] 4.399113 19.352196 0
#> [3,] 0.000000 0.000000 0
#>
#> $`43`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.019187 0
#> [2,] 2.019187 4.077114 0
#> [3,] 0.000000 0.000000 0
#>
#> $`44`
#> [,1] [,2] [,3]
#> [1,] 1.00000 10.18205 0
#> [2,] 10.18205 103.67407 0
#> [3,] 0.00000 0.00000 0
#>
#> $`45`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.471305 0
#> [2,] 4.471305 19.992570 0
#> [3,] 0.000000 0.000000 0
#>
#> $`46`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.445723 0
#> [2,] 5.445723 29.655894 0
#> [3,] 0.000000 0.000000 0
#>
#> $`47`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.030453 0
#> [2,] 5.030453 25.305462 0
#> [3,] 0.000000 0.000000 0
#>
#> $`48`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.586987 0
#> [2,] 7.586987 57.562365 0
#> [3,] 0.000000 0.000000 0
#>
#> $`49`
#> [,1] [,2] [,3]
#> [1,] 1.000000 9.452187 0
#> [2,] 9.452187 89.343839 0
#> [3,] 0.000000 0.000000 0
#>
#> $`50`
#> [,1] [,2] [,3]
#> [1,] 1.000000 8.141977 0
#> [2,] 8.141977 66.291783 0
#> [3,] 0.000000 0.000000 0
#>
#> $`51`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.623052 1.000000
#> [2,] 5.623052 31.618709 5.623052
#> [3,] 1.000000 5.623052 1.000000
#>
#> $`52`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.066275 1.000000
#> [2,] 5.066275 25.667144 5.066275
#> [3,] 1.000000 5.066275 1.000000
#>
#> $`53`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.174904 1.000000
#> [2,] 2.174904 4.730206 2.174904
#> [3,] 1.000000 2.174904 1.000000
#>
#> $`54`
#> [,1] [,2] [,3]
#> [1,] 1.000000 1.373871 1.000000
#> [2,] 1.373871 1.887521 1.373871
#> [3,] 1.000000 1.373871 1.000000
#>
#> $`55`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.252394 1.000000
#> [2,] 4.252394 18.082857 4.252394
#> [3,] 1.000000 4.252394 1.000000
#>
#> $`56`
#> [,1] [,2] [,3]
#> [1,] 1.000000 1.844766 1.000000
#> [2,] 1.844766 3.403163 1.844766
#> [3,] 1.000000 1.844766 1.000000
#>
#> $`57`
#> [,1] [,2] [,3]
#> [1,] 1.000000 6.257311 1.000000
#> [2,] 6.257311 39.153943 6.257311
#> [3,] 1.000000 6.257311 1.000000
#>
#> $`58`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.640542 1.000000
#> [2,] 7.640542 58.377883 7.640542
#> [3,] 1.000000 7.640542 1.000000
#>
#> $`59`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.400547 1.000000
#> [2,] 3.400547 11.563718 3.400547
#> [3,] 1.000000 3.400547 1.000000
#>
#> $`60`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.879217 1.000000
#> [2,] 3.879217 15.048328 3.879217
#> [3,] 1.000000 3.879217 1.000000
#>
#> $`61`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.044494 1.000000
#> [2,] 5.044494 25.446924 5.044494
#> [3,] 1.000000 5.044494 1.000000
#>
#> $`62`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.724228 1.000000
#> [2,] 3.724228 13.869875 3.724228
#> [3,] 1.000000 3.724228 1.000000
#>
#> $`63`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.209739 1.000000
#> [2,] 3.209739 10.302421 3.209739
#> [3,] 1.000000 3.209739 1.000000
#>
#> $`64`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.503141 1.000000
#> [2,] 3.503141 12.271994 3.503141
#> [3,] 1.000000 3.503141 1.000000
#>
#> $`65`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.588984 1.000000
#> [2,] 3.588984 12.880809 3.588984
#> [3,] 1.000000 3.588984 1.000000
#>
#> $`66`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.572586 1.000000
#> [2,] 2.572586 6.618198 2.572586
#> [3,] 1.000000 2.572586 1.000000
#>
#> $`67`
#> [,1] [,2] [,3]
#> [1,] 1.00000 2.321320 1.00000
#> [2,] 2.32132 5.388528 2.32132
#> [3,] 1.00000 2.321320 1.00000
#>
#> $`68`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.354859 1.000000
#> [2,] 7.354859 54.093946 7.354859
#> [3,] 1.000000 7.354859 1.000000
#>
#> $`69`
#> [,1] [,2] [,3]
#> [1,] 1.00000 1.780620 1.00000
#> [2,] 1.78062 3.170606 1.78062
#> [3,] 1.00000 1.780620 1.00000
#>
#> $`70`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.438272 1.000000
#> [2,] 2.438272 5.945173 2.438272
#> [3,] 1.000000 2.438272 1.000000
#>
#> $`71`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.870025 1.000000
#> [2,] 4.870025 23.717145 4.870025
#> [3,] 1.000000 4.870025 1.000000
#>
#> $`72`
#> [,1] [,2] [,3]
#> [1,] 1.000000 6.107551 1.000000
#> [2,] 6.107551 37.302178 6.107551
#> [3,] 1.000000 6.107551 1.000000
#>
#> $`73`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.068762 1.000000
#> [2,] 3.068762 9.417303 3.068762
#> [3,] 1.000000 3.068762 1.000000
#>
#> $`74`
#> [,1] [,2] [,3]
#> [1,] 1.000000 8.069649 1.000000
#> [2,] 8.069649 65.119232 8.069649
#> [3,] 1.000000 8.069649 1.000000
#>
#> $`75`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.564135 1.000000
#> [2,] 2.564135 6.574787 2.564135
#> [3,] 1.000000 2.564135 1.000000
#>
#> $`76`
#> [,1] [,2] [,3]
#> [1,] 1.000000 6.700594 1.000000
#> [2,] 6.700594 44.897963 6.700594
#> [3,] 1.000000 6.700594 1.000000
#>
#> $`77`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.381833 1.000000
#> [2,] 5.381833 28.964128 5.381833
#> [3,] 1.000000 5.381833 1.000000
#>
#> $`78`
#> [,1] [,2] [,3]
#> [1,] 1.0000 12.6449 1.0000
#> [2,] 12.6449 159.8935 12.6449
#> [3,] 1.0000 12.6449 1.0000
#>
#> $`79`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.037142 1.000000
#> [2,] 2.037142 4.149947 2.037142
#> [3,] 1.000000 2.037142 1.000000
#>
#> $`80`
#> [,1] [,2] [,3]
#> [1,] 1.00000 5.88364 1.00000
#> [2,] 5.88364 34.61722 5.88364
#> [3,] 1.00000 5.88364 1.00000
#>
#> $`81`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.093428 1.000000
#> [2,] 7.093428 50.316720 7.093428
#> [3,] 1.000000 7.093428 1.000000
#>
#> $`82`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.873831 1.000000
#> [2,] 4.873831 23.754232 4.873831
#> [3,] 1.000000 4.873831 1.000000
#>
#> $`83`
#> [,1] [,2] [,3]
#> [1,] 1.000000 8.291038 1.000000
#> [2,] 8.291038 68.741319 8.291038
#> [3,] 1.000000 8.291038 1.000000
#>
#> $`84`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.051026 1.000000
#> [2,] 4.051026 16.410813 4.051026
#> [3,] 1.000000 4.051026 1.000000
#>
#> $`85`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.689539 1.000000
#> [2,] 7.689539 59.129016 7.689539
#> [3,] 1.000000 7.689539 1.000000
#>
#> $`86`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.933636 1.000000
#> [2,] 4.933636 24.340769 4.933636
#> [3,] 1.000000 4.933636 1.000000
#>
#> $`87`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.774989 1.000000
#> [2,] 5.774989 33.350494 5.774989
#> [3,] 1.000000 5.774989 1.000000
#>
#> $`88`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.483566 1.000000
#> [2,] 4.483566 20.102364 4.483566
#> [3,] 1.000000 4.483566 1.000000
#>
#> $`89`
#> [,1] [,2] [,3]
#> [1,] 1.000000 2.739769 1.000000
#> [2,] 2.739769 7.506334 2.739769
#> [3,] 1.000000 2.739769 1.000000
#>
#> $`90`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.286464 1.000000
#> [2,] 5.286464 27.946699 5.286464
#> [3,] 1.000000 5.286464 1.000000
#>
#> $`91`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.701213 1.000000
#> [2,] 3.701213 13.698980 3.701213
#> [3,] 1.000000 3.701213 1.000000
#>
#> $`92`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.795986 1.000000
#> [2,] 5.795986 33.593451 5.795986
#> [3,] 1.000000 5.795986 1.000000
#>
#> $`93`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.883292 1.000000
#> [2,] 4.883292 23.846542 4.883292
#> [3,] 1.000000 4.883292 1.000000
#>
#> $`94`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.172253 1.000000
#> [2,] 7.172253 51.441211 7.172253
#> [3,] 1.000000 7.172253 1.000000
#>
#> $`95`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.340735 1.000000
#> [2,] 3.340735 11.160513 3.340735
#> [3,] 1.000000 3.340735 1.000000
#>
#> $`96`
#> [,1] [,2] [,3]
#> [1,] 1.000000 7.262549 1.000000
#> [2,] 7.262549 52.744614 7.262549
#> [3,] 1.000000 7.262549 1.000000
#>
#> $`97`
#> [,1] [,2] [,3]
#> [1,] 1.000000 1.940701 1.000000
#> [2,] 1.940701 3.766322 1.940701
#> [3,] 1.000000 1.940701 1.000000
#>
#> $`98`
#> [,1] [,2] [,3]
#> [1,] 1.000000 5.958475 1.000000
#> [2,] 5.958475 35.503422 5.958475
#> [3,] 1.000000 5.958475 1.000000
#>
#> $`99`
#> [,1] [,2] [,3]
#> [1,] 1.000000 4.432708 1.000000
#> [2,] 4.432708 19.648898 4.432708
#> [3,] 1.000000 4.432708 1.000000
#>
#> $`100`
#> [,1] [,2] [,3]
#> [1,] 1.000000 3.283518 1.000000
#> [2,] 3.283518 10.781492 3.283518
#> [3,] 1.000000 3.283518 1.000000
#>
#>
#> Slot ".B":
#> [,1] [,2] [,3]
#> [1,] 101.49842 498.0078 69.60688
#> [2,] 498.00784 2943.3859 325.89676
#> [3,] 69.60688 325.8968 69.60688
#>
#> Slot ".B_i":
#> $`1`
#> [,1] [,2] [,3]
#> [1,] 0.3376745 1.671258 0
#> [2,] 1.6712577 8.271583 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`2`
#> [,1] [,2] [,3]
#> [1,] 0.005793609 0.0454912 0
#> [2,] 0.045491199 0.3571952 0
#> [3,] 0.000000000 0.0000000 0
#>
#> $`3`
#> [,1] [,2] [,3]
#> [1,] 0.02191998 0.1036612 0
#> [2,] 0.10366122 0.4902217 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`4`
#> [,1] [,2] [,3]
#> [1,] 0.8347717 2.140684 0
#> [2,] 2.1406844 5.489561 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`5`
#> [,1] [,2] [,3]
#> [1,] 0.002067665 0.009888292 0
#> [2,] 0.009888292 0.047289245 0
#> [3,] 0.000000000 0.000000000 0
#>
#> $`6`
#> [,1] [,2] [,3]
#> [1,] 0.04954491 0.2643574 0
#> [2,] 0.26435742 1.4105353 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`7`
#> [,1] [,2] [,3]
#> [1,] 0.2526782 0.3503238 0
#> [2,] 0.3503238 0.4857037 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`8`
#> [,1] [,2] [,3]
#> [1,] 0.4927719 1.701727 0
#> [2,] 1.7017270 5.876705 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`9`
#> [,1] [,2] [,3]
#> [1,] 0.6656484 1.969428 0
#> [2,] 1.9694284 5.826872 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`10`
#> [,1] [,2] [,3]
#> [1,] 0.3090815 2.346352 0
#> [2,] 2.3463516 17.812022 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`11`
#> [,1] [,2] [,3]
#> [1,] 0.005137569 0.03500195 0
#> [2,] 0.035001949 0.23846618 0
#> [3,] 0.000000000 0.00000000 0
#>
#> $`12`
#> [,1] [,2] [,3]
#> [1,] 0.0160846 0.03991380 0
#> [2,] 0.0399138 0.09904578 0
#> [3,] 0.0000000 0.00000000 0
#>
#> $`13`
#> [,1] [,2] [,3]
#> [1,] 0.04314268 0.1426834 0
#> [2,] 0.14268345 0.4718892 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`14`
#> [,1] [,2] [,3]
#> [1,] 0.601385 1.423194 0
#> [2,] 1.423194 3.368026 0
#> [3,] 0.000000 0.000000 0
#>
#> $`15`
#> [,1] [,2] [,3]
#> [1,] 0.0002503076 0.001579129 0
#> [2,] 0.0015791286 0.009962330 0
#> [3,] 0.0000000000 0.000000000 0
#>
#> $`16`
#> [,1] [,2] [,3]
#> [1,] 0.0001028684 0.0002346056 0
#> [2,] 0.0002346056 0.0005350506 0
#> [3,] 0.0000000000 0.0000000000 0
#>
#> $`17`
#> [,1] [,2] [,3]
#> [1,] 0.1410985 0.4052565 0
#> [2,] 0.4052565 1.1639589 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`18`
#> [,1] [,2] [,3]
#> [1,] 0.2097860 0.9149744 0
#> [2,] 0.9149744 3.9906291 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`19`
#> [,1] [,2] [,3]
#> [1,] 0.8680235 3.101494 0
#> [2,] 3.1014942 11.081803 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`20`
#> [,1] [,2] [,3]
#> [1,] 4.859672 27.00217 0
#> [2,] 27.002169 150.03422 0
#> [3,] 0.000000 0.00000 0
#>
#> $`21`
#> [,1] [,2] [,3]
#> [1,] 0.4113263 3.189009 0
#> [2,] 3.1890091 24.724360 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`22`
#> [,1] [,2] [,3]
#> [1,] 0.406050 2.448509 0
#> [2,] 2.448509 14.764675 0
#> [3,] 0.000000 0.000000 0
#>
#> $`23`
#> [,1] [,2] [,3]
#> [1,] 0.04652751 0.1960326 0
#> [2,] 0.19603258 0.8259366 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`24`
#> [,1] [,2] [,3]
#> [1,] 0.6152317 1.798403 0
#> [2,] 1.7984033 5.256969 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`25`
#> [,1] [,2] [,3]
#> [1,] 0.8144851 4.200161 0
#> [2,] 4.2001611 21.659516 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`26`
#> [,1] [,2] [,3]
#> [1,] 0.2944803 1.625484 0
#> [2,] 1.6254838 8.972409 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`27`
#> [,1] [,2] [,3]
#> [1,] 0.6765784 3.705811 0
#> [2,] 3.7058113 20.297777 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`28`
#> [,1] [,2] [,3]
#> [1,] 0.03674351 0.1490107 0
#> [2,] 0.14901071 0.6043025 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`29`
#> [,1] [,2] [,3]
#> [1,] 1.460980 6.311013 0
#> [2,] 6.311013 27.261771 0
#> [3,] 0.000000 0.000000 0
#>
#> $`30`
#> [,1] [,2] [,3]
#> [1,] 0.004683337 0.04816295 0
#> [2,] 0.048162946 0.49530264 0
#> [3,] 0.000000000 0.00000000 0
#>
#> $`31`
#> [,1] [,2] [,3]
#> [1,] 0.00408951 0.01046032 0
#> [2,] 0.01046032 0.02675582 0
#> [3,] 0.00000000 0.00000000 0
#>
#> $`32`
#> [,1] [,2] [,3]
#> [1,] 1.969331 21.96344 0
#> [2,] 21.963440 244.95259 0
#> [3,] 0.000000 0.00000 0
#>
#> $`33`
#> [,1] [,2] [,3]
#> [1,] 0.01207524 0.03064754 0
#> [2,] 0.03064754 0.07778491 0
#> [3,] 0.00000000 0.00000000 0
#>
#> $`34`
#> [,1] [,2] [,3]
#> [1,] 0.1041577 0.8269879 0
#> [2,] 0.8269879 6.5660893 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`35`
#> [,1] [,2] [,3]
#> [1,] 0.504629 1.831447 0
#> [2,] 1.831447 6.646862 0
#> [3,] 0.000000 0.000000 0
#>
#> $`36`
#> [,1] [,2] [,3]
#> [1,] 0.1099088 0.6203603 0
#> [2,] 0.6203603 3.5015103 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`37`
#> [,1] [,2] [,3]
#> [1,] 0.06868244 0.09515285 0
#> [2,] 0.09515285 0.13182503 0
#> [3,] 0.00000000 0.00000000 0
#>
#> $`38`
#> [,1] [,2] [,3]
#> [1,] 0.1567982 0.5725584 0
#> [2,] 0.5725584 2.0907330 0
#> [3,] 0.0000000 0.0000000 0
#>
#> $`39`
#> [,1] [,2] [,3]
#> [1,] 1.240859 2.576006 0
#> [2,] 2.576006 5.347755 0
#> [3,] 0.000000 0.000000 0
#>
#> $`40`
#> [,1] [,2] [,3]
#> [1,] 0.5750366 4.570181 0
#> [2,] 4.5701811 36.322135 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`41`
#> [,1] [,2] [,3]
#> [1,] 0.01787868 0.06961287 0
#> [2,] 0.06961287 0.27104634 0
#> [3,] 0.00000000 0.00000000 0
#>
#> $`42`
#> [,1] [,2] [,3]
#> [1,] 1.818996 8.001968 0
#> [2,] 8.001968 35.201560 0
#> [3,] 0.000000 0.000000 0
#>
#> $`43`
#> [,1] [,2] [,3]
#> [1,] 2.978111 6.013361 0
#> [2,] 6.013361 12.142097 0
#> [3,] 0.000000 0.000000 0
#>
#> $`44`
#> [,1] [,2] [,3]
#> [1,] 0.09726523 0.9903592 0
#> [2,] 0.99035916 10.0838831 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`45`
#> [,1] [,2] [,3]
#> [1,] 0.3363853 1.504081 0
#> [2,] 1.5040813 6.725207 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`46`
#> [,1] [,2] [,3]
#> [1,] 1.491594 8.122809 0
#> [2,] 8.122809 44.234564 0
#> [3,] 0.000000 0.000000 0
#>
#> $`47`
#> [,1] [,2] [,3]
#> [1,] 0.1988663 1.000388 0
#> [2,] 1.0003879 5.032405 0
#> [3,] 0.0000000 0.000000 0
#>
#> $`48`
#> [,1] [,2] [,3]
#> [1,] 1.261514 9.571088 0
#> [2,] 9.571088 72.615719 0
#> [3,] 0.000000 0.000000 0
#>
#> $`49`
#> [,1] [,2] [,3]
#> [1,] 0.05515122 0.5212996 0
#> [2,] 0.52129963 4.9274216 0
#> [3,] 0.00000000 0.0000000 0
#>
#> $`50`
#> [,1] [,2] [,3]
#> [1,] 4.406497 35.8776 0
#> [2,] 35.877599 292.1146 0
#> [3,] 0.000000 0.0000 0
#>
#> $`51`
#> [,1] [,2] [,3]
#> [1,] 0.1682788 0.9462405 0.1682788
#> [2,] 0.9462405 5.3207592 0.9462405
#> [3,] 0.1682788 0.9462405 0.1682788
#>
#> $`52`
#> [,1] [,2] [,3]
#> [1,] 2.812527 14.24903 2.812527
#> [2,] 14.249035 72.18953 14.249035
#> [3,] 2.812527 14.24903 2.812527
#>
#> $`53`
#> [,1] [,2] [,3]
#> [1,] 0.1358994 0.2955682 0.1358994
#> [2,] 0.2955682 0.6428323 0.2955682
#> [3,] 0.1358994 0.2955682 0.1358994
#>
#> $`54`
#> [,1] [,2] [,3]
#> [1,] 4.506172 6.190898 4.506172
#> [2,] 6.190898 8.505494 6.190898
#> [3,] 4.506172 6.190898 4.506172
#>
#> $`55`
#> [,1] [,2] [,3]
#> [1,] 4.72753 20.10332 4.72753
#> [2,] 20.10332 85.48724 20.10332
#> [3,] 4.72753 20.10332 4.72753
#>
#> $`56`
#> [,1] [,2] [,3]
#> [1,] 1.403958 2.589974 1.403958
#> [2,] 2.589974 4.777898 2.589974
#> [3,] 1.403958 2.589974 1.403958
#>
#> $`57`
#> [,1] [,2] [,3]
#> [1,] 1.932414 12.09172 1.932414
#> [2,] 12.091715 75.66162 12.091715
#> [3,] 1.932414 12.09172 1.932414
#>
#> $`58`
#> [,1] [,2] [,3]
#> [1,] 3.140815 23.99753 3.140815
#> [2,] 23.997528 183.35412 23.997528
#> [3,] 3.140815 23.99753 3.140815
#>
#> $`59`
#> [,1] [,2] [,3]
#> [1,] 2.300434 7.822733 2.300434
#> [2,] 7.822733 26.601568 7.822733
#> [3,] 2.300434 7.822733 2.300434
#>
#> $`60`
#> [,1] [,2] [,3]
#> [1,] 0.5251366 2.037119 0.5251366
#> [2,] 2.0371190 7.902427 2.0371190
#> [3,] 0.5251366 2.037119 0.5251366
#>
#> $`61`
#> [,1] [,2] [,3]
#> [1,] 0.03979656 0.2007535 0.03979656
#> [2,] 0.20075354 1.0127001 0.20075354
#> [3,] 0.03979656 0.2007535 0.03979656
#>
#> $`62`
#> [,1] [,2] [,3]
#> [1,] 0.002726827 0.01015533 0.002726827
#> [2,] 0.010155326 0.03782075 0.010155326
#> [3,] 0.002726827 0.01015533 0.002726827
#>
#> $`63`
#> [,1] [,2] [,3]
#> [1,] 0.05522558 0.1772597 0.05522558
#> [2,] 0.17725968 0.5689572 0.17725968
#> [3,] 0.05522558 0.1772597 0.05522558
#>
#> $`64`
#> [,1] [,2] [,3]
#> [1,] 7.359435 25.78114 7.359435
#> [2,] 25.781136 90.31494 25.781136
#> [3,] 7.359435 25.78114 7.359435
#>
#> $`65`
#> [,1] [,2] [,3]
#> [1,] 0.06599539 0.2368564 0.06599539
#> [2,] 0.23685644 0.8500741 0.23685644
#> [3,] 0.06599539 0.2368564 0.06599539
#>
#> $`66`
#> [,1] [,2] [,3]
#> [1,] 0.2925543 0.7526211 0.2925543
#> [2,] 0.7526211 1.9361825 0.7526211
#> [3,] 0.2925543 0.7526211 0.2925543
#>
#> $`67`
#> [,1] [,2] [,3]
#> [1,] 0.6745021 1.565735 0.6745021
#> [2,] 1.5657353 3.634573 1.5657353
#> [3,] 0.6745021 1.565735 0.6745021
#>
#> $`68`
#> [,1] [,2] [,3]
#> [1,] 0.02615483 0.1923651 0.02615483
#> [2,] 0.19236510 1.4148181 0.19236510
#> [3,] 0.02615483 0.1923651 0.02615483
#>
#> $`69`
#> [,1] [,2] [,3]
#> [1,] 0.0188654 0.03359210 0.0188654
#> [2,] 0.0335921 0.05981475 0.0335921
#> [3,] 0.0188654 0.03359210 0.0188654
#>
#> $`70`
#> [,1] [,2] [,3]
#> [1,] 0.007328974 0.01787004 0.007328974
#> [2,] 0.017870037 0.04357202 0.017870037
#> [3,] 0.007328974 0.01787004 0.007328974
#>
#> $`71`
#> [,1] [,2] [,3]
#> [1,] 0.2695132 1.312536 0.2695132
#> [2,] 1.3125358 6.392083 1.3125358
#> [3,] 0.2695132 1.312536 0.2695132
#>
#> $`72`
#> [,1] [,2] [,3]
#> [1,] 1.100402 6.720763 1.100402
#> [2,] 6.720763 41.047402 6.720763
#> [3,] 1.100402 6.720763 1.100402
#>
#> $`73`
#> [,1] [,2] [,3]
#> [1,] 1.024719 3.144621 1.024719
#> [2,] 3.144621 9.650093 3.144621
#> [3,] 1.024719 3.144621 1.024719
#>
#> $`74`
#> [,1] [,2] [,3]
#> [1,] 2.486461 20.06486 2.486461
#> [2,] 20.064864 161.91640 20.064864
#> [3,] 2.486461 20.06486 2.486461
#>
#> $`75`
#> [,1] [,2] [,3]
#> [1,] 3.570509 9.155267 3.570509
#> [2,] 9.155267 23.475337 9.155267
#> [3,] 3.570509 9.155267 3.570509
#>
#> $`76`
#> [,1] [,2] [,3]
#> [1,] 3.245084e-06 2.174399e-05 3.245084e-06
#> [2,] 2.174399e-05 1.456977e-04 2.174399e-05
#> [3,] 3.245084e-06 2.174399e-05 3.245084e-06
#>
#> $`77`
#> [,1] [,2] [,3]
#> [1,] 1.081570 5.820827 1.081570
#> [2,] 5.820827 31.326720 5.820827
#> [3,] 1.081570 5.820827 1.081570
#>
#> $`78`
#> [,1] [,2] [,3]
#> [1,] 1.646536e-05 0.0002082028 1.646536e-05
#> [2,] 2.082028e-04 0.0026327036 2.082028e-04
#> [3,] 1.646536e-05 0.0002082028 1.646536e-05
#>
#> $`79`
#> [,1] [,2] [,3]
#> [1,] 1.450441 2.954754 1.450441
#> [2,] 2.954754 6.019254 2.954754
#> [3,] 1.450441 2.954754 1.450441
#>
#> $`80`
#> [,1] [,2] [,3]
#> [1,] 4.198887 24.70474 4.198887
#> [2,] 24.704738 145.35378 24.704738
#> [3,] 4.198887 24.70474 4.198887
#>
#> $`81`
#> [,1] [,2] [,3]
#> [1,] 0.7542063 5.349908 0.7542063
#> [2,] 5.3499082 37.949188 5.3499082
#> [3,] 0.7542063 5.349908 0.7542063
#>
#> $`82`
#> [,1] [,2] [,3]
#> [1,] 0.0004031544 0.001964907 0.0004031544
#> [2,] 0.0019649066 0.009576623 0.0019649066
#> [3,] 0.0004031544 0.001964907 0.0004031544
#>
#> $`83`
#> [,1] [,2] [,3]
#> [1,] 2.035869 16.87947 2.035869
#> [2,] 16.879471 139.94834 16.879471
#> [3,] 2.035869 16.87947 2.035869
#>
#> $`84`
#> [,1] [,2] [,3]
#> [1,] 3.329322 13.48717 3.329322
#> [2,] 13.487171 54.63688 13.487171
#> [3,] 3.329322 13.48717 3.329322
#>
#> $`85`
#> [,1] [,2] [,3]
#> [1,] 0.4287243 3.296692 0.4287243
#> [2,] 3.2966921 25.350044 3.2966921
#> [3,] 0.4287243 3.296692 0.4287243
#>
#> $`86`
#> [,1] [,2] [,3]
#> [1,] 0.008187193 0.04039263 0.008187193
#> [2,] 0.040392634 0.19928257 0.040392634
#> [3,] 0.008187193 0.04039263 0.008187193
#>
#> $`87`
#> [,1] [,2] [,3]
#> [1,] 0.0607490 0.3508248 0.0607490
#> [2,] 0.3508248 2.0260093 0.3508248
#> [3,] 0.0607490 0.3508248 0.0607490
#>
#> $`88`
#> [,1] [,2] [,3]
#> [1,] 0.8185654 3.670092 0.8185654
#> [2,] 3.6700922 16.455101 3.6700922
#> [3,] 0.8185654 3.670092 0.8185654
#>
#> $`89`
#> [,1] [,2] [,3]
#> [1,] 3.579296 9.806444 3.579296
#> [2,] 9.806444 26.867390 9.806444
#> [3,] 3.579296 9.806444 3.579296
#>
#> $`90`
#> [,1] [,2] [,3]
#> [1,] 0.002703729 0.01429316 0.002703729
#> [2,] 0.014293163 0.07556029 0.014293163
#> [3,] 0.002703729 0.01429316 0.002703729
#>
#> $`91`
#> [,1] [,2] [,3]
#> [1,] 0.1892585 0.7004859 0.1892585
#> [2,] 0.7004859 2.5926477 0.7004859
#> [3,] 0.1892585 0.7004859 0.1892585
#>
#> $`92`
#> [,1] [,2] [,3]
#> [1,] 2.124235 12.31203 2.124235
#> [2,] 12.312033 71.36037 12.312033
#> [3,] 2.124235 12.31203 2.124235
#>
#> $`93`
#> [,1] [,2] [,3]
#> [1,] 0.6778271 3.310028 0.6778271
#> [2,] 3.3100277 16.163832 3.3100277
#> [3,] 0.6778271 3.310028 0.6778271
#>
#> $`94`
#> [,1] [,2] [,3]
#> [1,] 1.414765 10.14706 1.414765
#> [2,] 10.147055 72.77725 10.147055
#> [3,] 1.414765 10.14706 1.414765
#>
#> $`95`
#> [,1] [,2] [,3]
#> [1,] 0.1538399 0.5139383 0.1538399
#> [2,] 0.5139383 1.7169320 0.5139383
#> [3,] 0.1538399 0.5139383 0.1538399
#>
#> $`96`
#> [,1] [,2] [,3]
#> [1,] 5.807184 42.17496 5.807184
#> [2,] 42.174957 306.29768 42.174957
#> [3,] 5.807184 42.17496 5.807184
#>
#> $`97`
#> [,1] [,2] [,3]
#> [1,] 0.3998382 0.7759666 0.3998382
#> [2,] 0.7759666 1.5059195 0.7759666
#> [3,] 0.3998382 0.7759666 0.3998382
#>
#> $`98`
#> [,1] [,2] [,3]
#> [1,] 0.1896488 1.130018 0.1896488
#> [2,] 1.1300179 6.733183 1.1300179
#> [3,] 0.1896488 1.130018 0.1896488
#>
#> $`99`
#> [,1] [,2] [,3]
#> [1,] 1.186016 5.257261 1.186016
#> [2,] 5.257261 23.303900 5.257261
#> [3,] 1.186016 5.257261 1.186016
#>
#> $`100`
#> [,1] [,2] [,3]
#> [1,] 1.067967 3.506689 1.067967
#> [2,] 3.506689 11.514276 3.506689
#> [3,] 1.067967 3.506689 1.067967
#>
#>
#> Slot ".ee_i":
#> $`1`
#> [,1]
#> [1,] -0.5810976
#> [2,] -2.8760359
#> [3,] 0.0000000
#>
#> $`2`
#> [,1]
#> [1,] -0.07611576
#> [2,] -0.59765806
#> [3,] 0.00000000
#>
#> $`3`
#> [,1]
#> [1,] -0.1480540
#> [2,] -0.7001583
#> [3,] 0.0000000
#>
#> $`4`
#> [,1]
#> [1,] 0.9136584
#> [2,] 2.3429812
#> [3,] 0.0000000
#>
#> $`5`
#> [,1]
#> [1,] 0.04547158
#> [2,] 0.21746090
#> [3,] 0.00000000
#>
#> $`6`
#> [,1]
#> [1,] 0.2225869
#> [2,] 1.1876596
#> [3,] 0.0000000
#>
#> $`7`
#> [,1]
#> [1,] -0.5026711
#> [2,] -0.6969244
#> [3,] 0.0000000
#>
#> $`8`
#> [,1]
#> [1,] 0.7019771
#> [2,] 2.4241917
#> [3,] 0.0000000
#>
#> $`9`
#> [,1]
#> [1,] -0.8158728
#> [2,] -2.4138915
#> [3,] 0.0000000
#>
#> $`10`
#> [,1]
#> [1,] -0.555951
#> [2,] -4.220429
#> [3,] 0.000000
#>
#> $`11`
#> [,1]
#> [1,] -0.07167684
#> [2,] -0.48832999
#> [3,] 0.00000000
#>
#> $`12`
#> [,1]
#> [1,] 0.1268251
#> [2,] 0.3147154
#> [3,] 0.0000000
#>
#> $`13`
#> [,1]
#> [1,] -0.2077082
#> [2,] -0.6869419
#> [3,] 0.0000000
#>
#> $`14`
#> [,1]
#> [1,] 0.7754902
#> [2,] 1.8352182
#> [3,] 0.0000000
#>
#> $`15`
#> [,1]
#> [1,] -0.01582111
#> [2,] -0.09981147
#> [3,] 0.00000000
#>
#> $`16`
#> [,1]
#> [1,] 0.01014241
#> [2,] 0.02313116
#> [3,] 0.00000000
#>
#> $`17`
#> [,1]
#> [1,] -0.3756307
#> [2,] -1.0788693
#> [3,] 0.0000000
#>
#> $`18`
#> [,1]
#> [1,] 0.458024
#> [2,] 1.997656
#> [3,] 0.000000
#>
#> $`19`
#> [,1]
#> [1,] -0.9316778
#> [2,] -3.3289343
#> [3,] 0.0000000
#>
#> $`20`
#> [,1]
#> [1,] 2.204466
#> [2,] 12.248846
#> [3,] 0.000000
#>
#> $`21`
#> [,1]
#> [1,] -0.6413472
#> [2,] -4.9723596
#> [3,] 0.0000000
#>
#> $`22`
#> [,1]
#> [1,] 0.6372205
#> [2,] 3.8424829
#> [3,] 0.0000000
#>
#> $`23`
#> [,1]
#> [1,] 0.2157024
#> [2,] 0.9088105
#> [3,] 0.0000000
#>
#> $`24`
#> [,1]
#> [1,] 0.7843671
#> [2,] 2.2928082
#> [3,] 0.0000000
#>
#> $`25`
#> [,1]
#> [1,] -0.9024883
#> [2,] -4.6539785
#> [3,] 0.0000000
#>
#> $`26`
#> [,1]
#> [1,] -0.5426604
#> [2,] -2.9953980
#> [3,] 0.0000000
#>
#> $`27`
#> [,1]
#> [1,] -0.8225439
#> [2,] -4.5053054
#> [3,] 0.0000000
#>
#> $`28`
#> [,1]
#> [1,] 0.191686
#> [2,] 0.777369
#> [3,] 0.000000
#>
#> $`29`
#> [,1]
#> [1,] -1.208710
#> [2,] -5.221281
#> [3,] 0.000000
#>
#> $`30`
#> [,1]
#> [1,] -0.06843491
#> [2,] -0.70377740
#> [3,] 0.00000000
#>
#> $`31`
#> [,1]
#> [1,] -0.06394928
#> [2,] -0.16357208
#> [3,] 0.00000000
#>
#> $`32`
#> [,1]
#> [1,] 1.403329
#> [2,] 15.650961
#> [3,] 0.000000
#>
#> $`33`
#> [,1]
#> [1,] -0.1098874
#> [2,] -0.2788995
#> [3,] 0.0000000
#>
#> $`34`
#> [,1]
#> [1,] -0.3227348
#> [2,] -2.5624382
#> [3,] 0.0000000
#>
#> $`35`
#> [,1]
#> [1,] -0.7103724
#> [2,] -2.5781509
#> [3,] 0.0000000
#>
#> $`36`
#> [,1]
#> [1,] -0.331525
#> [2,] -1.871232
#> [3,] 0.000000
#>
#> $`37`
#> [,1]
#> [1,] 0.2620734
#> [2,] 0.3630772
#> [3,] 0.0000000
#>
#> $`38`
#> [,1]
#> [1,] 0.3959775
#> [2,] 1.4459367
#> [3,] 0.0000000
#>
#> $`39`
#> [,1]
#> [1,] 1.113938
#> [2,] 2.312521
#> [3,] 0.000000
#>
#> $`40`
#> [,1]
#> [1,] 0.7583116
#> [2,] 6.0267848
#> [3,] 0.0000000
#>
#> $`41`
#> [,1]
#> [1,] -0.1337112
#> [2,] -0.5206211
#> [3,] 0.0000000
#>
#> $`42`
#> [,1]
#> [1,] 1.348701
#> [2,] 5.933090
#> [3,] 0.000000
#>
#> $`43`
#> [,1]
#> [1,] -1.725720
#> [2,] -3.484551
#> [3,] 0.000000
#>
#> $`44`
#> [,1]
#> [1,] 0.3118737
#> [2,] 3.1755130
#> [3,] 0.0000000
#>
#> $`45`
#> [,1]
#> [1,] -0.5799873
#> [2,] -2.5933004
#> [3,] 0.0000000
#>
#> $`46`
#> [,1]
#> [1,] 1.221308
#> [2,] 6.650907
#> [3,] 0.000000
#>
#> $`47`
#> [,1]
#> [1,] -0.4459443
#> [2,] -2.2433022
#> [3,] 0.0000000
#>
#> $`48`
#> [,1]
#> [1,] 1.123171
#> [2,] 8.521486
#> [3,] 0.000000
#>
#> $`49`
#> [,1]
#> [1,] -0.234843
#> [2,] -2.219780
#> [3,] 0.000000
#>
#> $`50`
#> [,1]
#> [1,] -2.099166
#> [2,] -17.091360
#> [3,] 0.000000
#>
#> $`51`
#> [,1]
#> [1,] -0.410218
#> [2,] -2.306677
#> [3,] -0.410218
#>
#> $`52`
#> [,1]
#> [1,] -1.677059
#> [2,] -8.496442
#> [3,] -1.677059
#>
#> $`53`
#> [,1]
#> [1,] 0.3686454
#> [2,] 0.8017682
#> [3,] 0.3686454
#>
#> $`54`
#> [,1]
#> [1,] 2.122775
#> [2,] 2.916418
#> [3,] 2.122775
#>
#> $`55`
#> [,1]
#> [1,] -2.174288
#> [2,] -9.245931
#> [3,] -2.174288
#>
#> $`56`
#> [,1]
#> [1,] 1.184887
#> [2,] 2.185840
#> [3,] 1.184887
#>
#> $`57`
#> [,1]
#> [1,] -1.390113
#> [2,] -8.698369
#> [3,] -1.390113
#>
#> $`58`
#> [,1]
#> [1,] 1.772234
#> [2,] 13.540832
#> [3,] 1.772234
#>
#> $`59`
#> [,1]
#> [1,] 1.516718
#> [2,] 5.157671
#> [3,] 1.516718
#>
#> $`60`
#> [,1]
#> [1,] -0.7246631
#> [2,] -2.8111257
#> [3,] -0.7246631
#>
#> $`61`
#> [,1]
#> [1,] -0.1994908
#> [2,] -1.0063300
#> [3,] -0.1994908
#>
#> $`62`
#> [,1]
#> [1,] 0.05221903
#> [2,] 0.19447557
#> [3,] 0.05221903
#>
#> $`63`
#> [,1]
#> [1,] -0.2350012
#> [2,] -0.7542925
#> [3,] -0.2350012
#>
#> $`64`
#> [,1]
#> [1,] 2.712828
#> [2,] 9.503417
#> [3,] 2.712828
#>
#> $`65`
#> [,1]
#> [1,] 0.2568957
#> [2,] 0.9219946
#> [3,] 0.2568957
#>
#> $`66`
#> [,1]
#> [1,] -0.5408829
#> [2,] -1.3914677
#> [3,] -0.5408829
#>
#> $`67`
#> [,1]
#> [1,] 0.8212808
#> [2,] 1.9064557
#> [3,] 0.8212808
#>
#> $`68`
#> [,1]
#> [1,] 0.1617246
#> [2,] 1.1894613
#> [3,] 0.1617246
#>
#> $`69`
#> [,1]
#> [1,] 0.1373514
#> [2,] 0.2445705
#> [3,] 0.1373514
#>
#> $`70`
#> [,1]
#> [1,] 0.08560943
#> [2,] 0.20873912
#> [3,] 0.08560943
#>
#> $`71`
#> [,1]
#> [1,] 0.5191466
#> [2,] 2.5282568
#> [3,] 0.5191466
#>
#> $`72`
#> [,1]
#> [1,] -1.049001
#> [2,] -6.406825
#> [3,] -1.049001
#>
#> $`73`
#> [,1]
#> [1,] -1.012284
#> [2,] -3.106460
#> [3,] -1.012284
#>
#> $`74`
#> [,1]
#> [1,] 1.576851
#> [2,] 12.724638
#> [3,] 1.576851
#>
#> $`75`
#> [,1]
#> [1,] -1.889579
#> [2,] -4.845135
#> [3,] -1.889579
#>
#> $`76`
#> [,1]
#> [1,] 0.001801412
#> [2,] 0.012070529
#> [3,] 0.001801412
#>
#> $`77`
#> [,1]
#> [1,] -1.039985
#> [2,] -5.597028
#> [3,] -1.039985
#>
#> $`78`
#> [,1]
#> [1,] -0.004057753
#> [2,] -0.051309878
#> [3,] -0.004057753
#>
#> $`79`
#> [,1]
#> [1,] 1.204343
#> [2,] 2.453417
#> [3,] 1.204343
#>
#> $`80`
#> [,1]
#> [1,] -2.049119
#> [2,] -12.056276
#> [3,] -2.049119
#>
#> $`81`
#> [,1]
#> [1,] 0.8684505
#> [2,] 6.1602912
#> [3,] 0.8684505
#>
#> $`82`
#> [,1]
#> [1,] 0.02007871
#> [2,] 0.09786022
#> [3,] 0.02007871
#>
#> $`83`
#> [,1]
#> [1,] -1.426839
#> [2,] -11.829976
#> [3,] -1.426839
#>
#> $`84`
#> [,1]
#> [1,] -1.824643
#> [2,] -7.391677
#> [3,] -1.824643
#>
#> $`85`
#> [,1]
#> [1,] 0.6547704
#> [2,] 5.0348827
#> [3,] 0.6547704
#>
#> $`86`
#> [,1]
#> [1,] -0.09048311
#> [2,] -0.44641077
#> [3,] -0.09048311
#>
#> $`87`
#> [,1]
#> [1,] 0.2464731
#> [2,] 1.4233795
#> [3,] 0.2464731
#>
#> $`88`
#> [,1]
#> [1,] 0.9047461
#> [2,] 4.0564887
#> [3,] 0.9047461
#>
#> $`89`
#> [,1]
#> [1,] 1.891903
#> [2,] 5.183376
#> [3,] 1.891903
#>
#> $`90`
#> [,1]
#> [1,] -0.05199739
#> [2,] -0.27488232
#> [3,] -0.05199739
#>
#> $`91`
#> [,1]
#> [1,] -0.4350385
#> [2,] -1.6101701
#> [3,] -0.4350385
#>
#> $`92`
#> [,1]
#> [1,] 1.457475
#> [2,] 8.447507
#> [3,] 1.457475
#>
#> $`93`
#> [,1]
#> [1,] -0.8233025
#> [2,] -4.0204269
#> [3,] -0.8233025
#>
#> $`94`
#> [,1]
#> [1,] -1.189439
#> [2,] -8.530958
#> [3,] -1.189439
#>
#> $`95`
#> [,1]
#> [1,] -0.3922243
#> [2,] -1.3103175
#> [3,] -0.3922243
#>
#> $`96`
#> [,1]
#> [1,] 2.40981
#> [2,] 17.50136
#> [3,] 2.40981
#>
#> $`97`
#> [,1]
#> [1,] -0.6323276
#> [2,] -1.2271591
#> [3,] -0.6323276
#>
#> $`98`
#> [,1]
#> [1,] 0.4354869
#> [2,] 2.5948378
#> [3,] 0.4354869
#>
#> $`99`
#> [,1]
#> [1,] -1.089043
#> [2,] -4.827411
#> [3,] -1.089043
#>
#> $`100`
#> [,1]
#> [1,] -1.033425
#> [2,] -3.393269
#> [3,] -1.033425
#>
#>
#>
#> Slot "GFUN":
#> function ()
#> NULL
#> <bytecode: 0x7fea4756b2b0>
#>
#> Slot "corrections":
#> list()
#>
#> Slot "estimates":
#> [1] -0.04061272 0.14435320 0.35436823
#>
#> Slot "vcov":
#> [,1] [,2] [,3]
#> [1,] 0.053446050 -0.0085998270 -0.0117461490
#> [2,] -0.008599827 0.0018120535 -0.0001770491
#> [3,] -0.011746149 -0.0001770491 0.0403839561
#>
# Compare to lm() results
summary(lm(Y4 ~ X1 + X2, data = geexex))
#>
#> Call:
#> lm(formula = Y4 ~ X1 + X2, data = geexex)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.17429 -0.59391 -0.05797 0.64161 2.71283
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.04061 0.26683 -0.152 0.87934
#> X1 0.14435 0.04477 3.224 0.00172 **
#> X2 0.35437 0.20492 1.729 0.08693 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.023 on 97 degrees of freedom
#> Multiple R-squared: 0.1165, Adjusted R-squared: 0.0983
#> F-statistic: 6.396 on 2 and 97 DF, p-value: 0.002458
#>