![[Stable]](figures/lifecycle-stable.svg)
LogisticNormal is the class for the usual logistic regression model with
a bivariate normal prior on the intercept and slope.
Details
The covariate is the natural logarithm of the dose \(x\) divided by the reference dose \(x*\), i.e.: $$logit[p(x)] = alpha0 + alpha1 * log(x/x*),$$ where \(p(x)\) is the probability of observing a DLT for a given dose \(x\). The prior $$(alpha0, alpha1) ~ Normal(mean, cov).$$
Examples
# Define the dose-grid.
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
my_model <- LogisticNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)
my_options <- McmcOptions(burnin = 10, step = 2, samples = 100)
samples <- mcmc(empty_data, my_model, my_options)
samples
#> An object of class "Samples"
#> Slot "data":
#> $alpha0
#> [1] -0.83536949 -1.23919264 -0.57942219 0.85554516 0.72755224 0.09417698
#> [7] -1.97565214 -1.08160438 -1.22358522 0.03028239 -0.21683590 -1.04130078
#> [13] -2.45675322 0.77381916 -1.98165505 -2.61631988 -0.31816934 0.24526347
#> [19] -0.63444272 -1.34438815 -0.78150966 0.91051501 -1.42491119 -1.06504838
#> [25] 0.11596213 -2.24925556 -0.96600475 -0.96543017 0.34206134 -1.32079534
#> [31] -0.12010478 -2.57009856 -1.81641502 -0.90966075 -1.26217369 -0.50648188
#> [37] -0.60070408 0.58468890 0.69694025 -1.25221472 0.24459334 0.69171753
#> [43] -1.23571516 0.68825042 -0.31250679 -0.80726612 0.20678684 -0.57127315
#> [49] 0.89094380 -0.58328442 1.14062355 -2.90505468 -1.85073247 0.98253840
#> [55] -1.45801435 -0.58368640 0.18908819 -1.04597252 -1.09044863 -0.95804559
#> [61] -1.80377773 -0.14782410 -1.77869138 -1.46684103 0.23153796 2.04126183
#> [67] 1.82887083 -0.41568540 -1.05250097 -1.67080595 1.02044352 0.46142009
#> [73] 0.01412235 -0.68601024 -1.16623707 0.10003499 -0.95677934 -2.81139887
#> [79] -3.26171555 -1.50150028 -1.24509379 -1.60804594 -1.36538724 -2.58534945
#> [85] -0.88995768 -1.28669431 -0.71766736 0.46478661 -0.47948668 -3.30881310
#> [91] -1.77826694 -1.24146349 -1.23022997 -0.99485688 -0.50863238 -1.86964010
#> [97] -1.64983940 -0.05468570 -0.20765777 0.57995460
#>
#> $alpha1
#> [1] 0.209483695 0.415735414 2.730993258 1.298488801 0.598419092
#> [6] 0.482521432 3.228555776 -0.173000097 1.052359522 -0.665723486
#> [11] 0.975978102 -0.011675143 2.518848315 -0.333418962 1.896563460
#> [16] 0.868875036 0.217203005 1.040909777 0.823482680 2.451152488
#> [21] 1.448330305 0.270841724 0.277042518 0.432375922 -0.967795591
#> [26] -0.799009330 4.553231873 0.748882180 -0.048003876 2.335115969
#> [31] -1.260108628 2.111874415 0.958620791 1.979482215 0.961765064
#> [36] 2.310567918 2.302011337 -0.242832394 1.528430938 2.935965595
#> [41] 0.478477731 -1.050776283 0.895097728 -0.006769682 0.755797838
#> [46] -1.011581098 0.374539803 1.674016516 0.385565502 0.486995621
#> [51] 0.862725663 2.838930664 0.776018002 0.673637736 2.036399033
#> [56] 2.407770883 -0.252982547 2.471275664 0.879319761 0.538556489
#> [61] 2.523178451 -1.859045284 1.670337831 1.270184291 1.229605584
#> [66] -0.716759560 1.243773466 1.211677208 -0.238539396 2.223796341
#> [71] 0.579060321 0.281731361 0.965633554 0.516678700 2.488023637
#> [76] 0.369026158 0.895200238 1.021745487 0.716373985 1.446059989
#> [81] 1.252789612 1.084468798 0.696258468 1.367009095 0.508643539
#> [86] 0.683795515 1.783253900 0.269158445 -0.429138630 1.564168236
#> [91] 3.100585000 1.748034206 0.513861460 0.301861333 1.785708562
#> [96] 2.925375808 2.919615972 1.252892721 -0.048284961 1.213048628
#>
#>
#> Slot "options":
#> An object of class "McmcOptions"
#> Slot "iterations":
#> [1] 210
#>
#> Slot "burnin":
#> [1] 10
#>
#> Slot "step":
#> [1] 2
#>
#> Slot "rng_kind":
#> [1] NA
#>
#> Slot "rng_seed":
#> [1] NA
#>
#>