![[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] -0.735623187 0.783761480 -0.909300394 -0.397288435 -0.469660950
#> [6] -0.085499525 -1.571314268 -0.218611097 -0.583551218 -0.969386836
#> [11] -0.193127977 1.497875178 -0.011616638 -0.147940529 -0.673185183
#> [16] -1.261240079 -2.502411514 -1.907327500 -0.781472625 0.717954402
#> [21] -2.200824700 -3.380234822 -2.677933842 1.113980527 -1.973549139
#> [26] -1.466773007 -1.668613570 -1.501954891 -1.207980617 -0.122612958
#> [31] -0.748140683 -2.896310373 0.029977159 -0.756435595 -1.214591080
#> [36] -1.616575072 -1.386856902 0.017164538 0.681833726 0.678712925
#> [41] -2.755701913 -1.153222340 -0.203240100 -0.670306680 -1.205568060
#> [46] -1.633999297 -1.492755370 -2.718044235 -0.608063634 -0.369644221
#> [51] -1.116799861 -0.818013910 -0.141131871 -0.490545820 2.081435801
#> [56] 0.965460774 0.004815959 -1.970927111 -1.491539588 -1.299406027
#> [61] -1.066019638 -1.509732294 -1.915016852 0.069759805 -1.713913348
#> [66] -2.584089599 -1.151129756 -0.897080404 0.341218892 -2.265239712
#> [71] 0.100556647 -0.613567684 -1.788103444 -1.100380785 1.155202042
#> [76] 0.043635595 -1.615748187 -0.232697319 -1.819662340 0.884341000
#> [81] -1.998396689 -2.586100447 0.293203538 -1.528283985 0.716788673
#> [86] 0.434798381 -1.125029913 -0.746454594 -1.386386725 -1.357124488
#> [91] -0.226578864 -0.138127792 0.064245673 -0.854233058 -0.287527906
#> [96] 0.582889463 -0.467752770 -1.534618380 -1.963097533 0.249853404
#>
#> $alpha1
#> [1] 1.21071138 -1.36055853 2.44406704 -0.51800995 -0.43520940 -0.60912451
#> [7] 0.86917066 1.19297599 0.41921542 3.12153306 0.48804229 -0.23228884
#> [13] -0.05469132 1.10570254 0.80147808 1.38676212 1.71172326 2.88093227
#> [19] 1.75917311 0.69412798 2.91677243 0.89450029 1.87237283 -1.51637135
#> [25] 0.95484828 1.48703918 1.03005959 1.42083932 -0.65742138 0.76522930
#> [31] 2.28352129 1.85464428 1.18390009 1.58976460 2.27483423 1.13237110
#> [37] 0.90938700 -0.30797374 -0.36062897 1.83167603 2.12113770 1.23302038
#> [43] 0.99912120 -0.45713883 0.62682827 0.90456186 0.07934561 3.85926629
#> [49] 1.40619098 1.44444085 1.45461164 1.39009000 1.31000876 0.08472004
#> [55] 0.85807189 -0.24289759 0.94533819 1.33018190 -0.61547230 0.77931994
#> [61] 1.92497521 1.98878871 1.22454276 0.81814389 1.90118762 2.59378047
#> [67] 1.41859624 -0.90162770 0.20688676 1.80079582 -0.31235708 -0.19180465
#> [73] 1.03391446 -0.10860207 -0.18913159 2.41226488 2.21804193 2.60675527
#> [79] 0.81467780 0.45232670 0.91434862 2.85842328 -0.26453871 1.45927282
#> [85] 1.59107456 -0.94631693 -0.03362782 2.92264624 0.62687149 -0.30767497
#> [91] 0.62065438 0.56090165 1.75881014 1.37584551 2.42432238 -0.25806744
#> [97] 2.03721597 1.58264774 0.07294693 -0.05636140
#>
#>
#> 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
#>
#>