![[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] 0.83114984 -2.39193020 -1.43862158 -0.56768529 0.12791161 -0.38929961
#> [7] 0.59319654 -0.29541847 -1.74878970 -0.95124209 -0.93142309 -0.25285855
#> [13] 0.62346402 0.13187426 -0.64564354 -1.54087116 0.37940256 -2.40833342
#> [19] -0.33092483 1.75282776 1.36024821 -1.57003291 -2.10348693 -1.59002374
#> [25] -1.42138363 0.18394835 -1.47937590 -0.07542564 -0.46981504 0.10592122
#> [31] -0.89704538 -0.86869021 -0.42001103 -2.96585219 -1.58963306 -1.20429162
#> [37] -0.63860084 -0.88114634 -0.20809013 -2.74363610 -2.25585562 -0.17377528
#> [43] -0.34928321 -0.78250368 -1.00174068 -2.39319058 -0.07257599 -0.91073210
#> [49] -0.41189806 0.63418670 -1.80437392 -0.98901638 -0.36419029 -0.92945751
#> [55] -0.73299996 0.24771667 -0.49514165 -0.45733481 -0.79075362 -0.36524845
#> [61] -2.14990395 -0.34597179 -0.29843606 -0.58752626 -2.59686254 -0.03165751
#> [67] -1.80939844 -0.59577467 -0.62666954 -0.56568460 -0.69564637 0.68707668
#> [73] -0.23670025 -1.29916471 -1.02750103 0.37280427 -1.16985426 -0.63327507
#> [79] -1.14697003 -0.40262103 0.13025368 -1.37388497 -0.16085129 0.67777436
#> [85] -3.31044434 -2.00423634 -1.57870934 -0.95044633 1.07681726 -1.81599947
#> [91] -1.56559194 -0.21172757 -0.65852862 -0.14870920 -0.32884058 -0.23226911
#> [97] -0.17070160 -0.91278856 -0.74205966 -2.53075437
#>
#> $alpha1
#> [1] 0.12679427 1.65126356 0.90992535 0.72732767 -0.31459019 0.53892615
#> [7] -0.13916768 1.39464828 1.67590183 1.54934864 0.65053429 1.00136763
#> [13] 0.47976439 0.70216822 2.07030734 2.47009383 0.47184828 2.07471157
#> [19] 0.83057687 -0.35567163 -0.22659425 2.00505309 0.63487132 2.23303444
#> [25] 1.01880267 -0.57166659 2.24374729 0.34629352 0.39071249 1.04081652
#> [31] 1.49154010 -0.29925024 1.46393623 2.30926619 1.42757539 0.22084030
#> [37] 1.89460368 -0.01850355 1.26742618 2.45296517 0.35775456 -0.15037559
#> [43] 0.63023733 1.41207155 0.87739152 1.83416901 0.67509205 0.86985004
#> [49] 0.52885987 -0.50935544 2.16187021 0.27263594 1.35443925 1.47301113
#> [55] 2.22560312 1.28696157 3.14244888 1.48079589 0.72072026 0.92368241
#> [61] 1.01854774 1.83946817 1.99526460 1.50870186 1.41748507 1.06904825
#> [67] 1.12425183 0.76999056 1.98355563 1.78924115 -0.33876633 0.82331493
#> [73] -0.63647954 2.13382851 1.38592719 0.07876736 0.16738096 1.07110620
#> [79] 1.71986213 0.48257436 0.59975994 1.62486552 2.04118058 0.53498293
#> [85] 1.89499278 1.90660501 0.59055142 1.93202033 -1.02757035 1.31436747
#> [91] 1.45929907 -0.60236825 -0.35961366 1.62630633 1.42172430 0.08260956
#> [97] -0.61868072 1.59068652 1.13441616 1.38263046
#>
#>
#> 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
#>
#>