![[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] -1.61539880 -1.83337323 0.34788707 -1.48386852 -0.98308182 -0.68362138
#> [7] 1.15719309 -1.87632706 -1.94156002 -0.75905850 -0.56366834 -0.48692204
#> [13] -1.27651475 1.03949883 -0.49150359 -2.48262068 -0.13855160 -1.26927102
#> [19] -1.03157179 1.66630356 -0.58265950 -0.86321279 -0.99556030 -1.28817246
#> [25] -0.50941739 -3.55585733 0.89552078 -0.74037534 -0.10173649 -2.02786410
#> [31] -0.68157742 -1.73869454 1.21101593 0.39586144 -2.40066231 -1.37903061
#> [37] 0.50615762 -0.81505868 -1.65109275 -0.16651020 -1.34851935 -1.74778189
#> [43] -0.65299458 -1.81125429 0.44407824 -0.59118037 -0.80960372 -1.99164236
#> [49] -0.60046698 -0.56764065 -0.27570862 -1.35833015 -2.61717723 -1.82518120
#> [55] -1.08482608 -0.96360152 -1.82501376 0.64450866 -1.18106870 -1.58370538
#> [61] 0.13696575 -0.34618910 -1.46666859 -1.98040472 -0.91923370 -0.08138232
#> [67] 0.23514500 -1.20617000 -1.43597632 -0.14236744 -1.25569537 -0.22675278
#> [73] -0.35636357 -2.42235508 -0.85477029 -1.42304071 0.11332503 -0.32595282
#> [79] -0.28167500 -1.08816456 -1.00280691 -2.62330214 -1.46434834 -1.76603251
#> [85] -0.58968108 -1.28199551 0.54640810 -1.49187116 2.57533580 -0.40830315
#> [91] -1.83778903 0.06938926 -0.28458947 -3.32883085 -0.80628076 -1.84430689
#> [97] -2.54129778 -1.56180993 -0.65640160 -1.01174056
#>
#> $alpha1
#> [1] 0.55550805 1.70803509 0.71434607 0.61550325 0.36489578 1.77619596
#> [7] 0.61485305 3.23601168 1.85182646 0.23134109 1.63568001 1.39289866
#> [13] 0.09311571 -0.78823332 -0.51381260 1.84314483 2.27658484 0.95229681
#> [19] 0.19329808 -0.62214148 0.89030671 0.98740689 -0.40506069 0.93622640
#> [25] 1.78323416 2.13142764 -1.86822163 0.86838156 0.72893937 2.76789165
#> [31] 0.59948469 3.11928782 0.43058441 2.16774408 1.75928354 0.12833633
#> [37] 0.88286580 0.85240638 0.89068321 0.07144288 0.92735335 0.52995409
#> [43] 1.34088924 1.76841896 -0.25756194 -0.47260067 1.37359098 0.93031881
#> [49] 1.34101481 0.38207982 0.34991732 1.65293076 2.16250546 0.93478970
#> [55] -0.05716090 1.23083520 1.55118922 0.40860203 1.84802824 0.96852078
#> [61] -0.45951988 1.18485694 0.86029954 2.84697796 0.46788876 0.91823037
#> [67] 0.11741544 1.90038776 0.76188683 2.43666384 1.88062960 1.30122722
#> [73] 0.98051727 1.90947932 1.81399587 0.42344258 1.39150145 1.15967318
#> [79] 0.16864025 -0.01056444 0.71172135 0.68318300 1.39424186 0.41188149
#> [85] 0.87967264 1.96204725 0.81847628 2.42840313 -1.37110373 1.51065641
#> [91] 1.71584114 1.80430857 -0.33556830 2.39948768 0.77671288 2.50430747
#> [97] 1.70877363 0.84435894 1.36590346 2.87074026
#>
#>
#> 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
#>
#>