![[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] -0.462055221 -0.309653268 -0.803543293 0.371682848 -0.529811301
#> [6] -1.416830279 -0.634107144 -1.953299222 0.010775761 -0.285276489
#> [11] -2.138613048 0.642277636 1.622743949 -3.022148629 -0.612930397
#> [16] -0.722004989 -2.382290773 -0.318771770 -1.211046044 -1.843257196
#> [21] -1.287030193 -0.641439240 -1.114420482 -0.846545110 -0.335114284
#> [26] -2.339594369 -2.224777010 -1.566816341 -0.791797270 0.040077820
#> [31] -2.517809803 0.101186815 -0.381848632 -1.485190423 0.139427073
#> [36] 0.097653669 -1.303770943 -0.324990079 0.228814386 0.158430591
#> [41] -1.566969062 -2.376140651 -1.949670006 -1.754041005 0.902180455
#> [46] -2.067402483 -2.987973880 -2.371987603 -0.862506359 -0.924819742
#> [51] 0.771482004 -0.737266608 -2.924026571 -0.640261118 -1.311804640
#> [56] -0.497234815 -0.410577740 -0.614903308 -1.794745174 -0.002459648
#> [61] 1.367650836 -1.698855747 -1.705491329 -0.869892546 -2.247728131
#> [66] -0.550984053 -2.186038462 -2.206114417 -0.891360969 -0.555518628
#> [71] -1.888729859 -0.876430375 -2.555075212 -2.732562322 -1.632542482
#> [76] -1.922442771 -1.233838038 0.630086608 -0.169110034 -0.585939532
#> [81] -1.089516076 -0.710983617 -1.525675651 -0.667508703 -2.576314053
#> [86] -0.342575382 -1.820076103 -1.609176857 -0.355811002 -0.282605047
#> [91] -0.314991708 -0.321869525 -0.757372061 0.783884741 -0.855446270
#> [96] -1.330680155 0.047174382 -1.231734777 -1.568990025 -0.879043277
#>
#> $alpha1
#> [1] 1.04052877 -0.07250274 0.28272235 -0.10407449 1.28845047 -0.58514450
#> [7] 0.11497982 0.92211741 0.34399300 1.65865775 0.18097073 -0.55025432
#> [13] -1.08177676 2.52025815 2.11859486 1.36703676 3.74165385 0.22919595
#> [19] 2.41851238 1.21958723 -0.12654275 0.80189293 0.07660077 0.60592231
#> [25] 1.20075375 2.30882064 1.38668337 1.05833029 1.84208921 2.61309149
#> [31] 1.35987289 0.36211980 1.08367769 0.83249539 -0.22208569 0.26352922
#> [37] -0.51699888 0.31585251 1.44291690 0.27164848 0.79471411 0.91677464
#> [43] 2.00229641 1.56636686 -0.46590892 0.58935242 1.92207494 2.65563141
#> [49] 0.17096463 -0.20494427 -1.32000360 0.38623124 0.94982711 1.16812483
#> [55] 0.42443272 2.44096484 1.26731442 1.69227854 4.02219715 0.37500353
#> [61] -0.52742556 2.15951520 0.30739126 0.73257601 1.59682704 2.04765715
#> [67] 2.16069063 2.89531947 1.82155028 0.94263925 2.81833678 0.25691453
#> [73] 2.05201918 0.52593040 1.82161369 0.50013795 1.58800658 -0.01865203
#> [79] 1.87740169 1.48432393 1.15651310 -0.26459592 0.65481978 0.77619749
#> [85] 1.99515055 0.63410493 1.09595040 1.10407758 1.66303525 -0.51722451
#> [91] 1.68227532 0.68159589 -0.34465365 -0.84564044 0.98089851 3.02859935
#> [97] 1.41458670 0.61933616 1.52508412 1.76351987
#>
#>
#> 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
#>
#>