![[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] -0.276158077 -2.248508969 -0.803546405 -0.917853342 -1.303271497
#> [6] -3.236243518 -1.954313721 -1.194123563 1.737052437 0.982293257
#> [11] 0.421391690 -1.136327617 -1.696884379 0.181226383 -1.441199868
#> [16] 0.289752478 -0.581090743 -1.292171326 -0.709224581 0.172762767
#> [21] -0.707163926 0.417529992 -0.872192515 -0.335649693 1.727501968
#> [26] -0.938157445 -1.338725240 -2.395959920 0.033478270 -0.086623854
#> [31] -2.007837967 -0.663066857 -0.933127652 1.653853460 -1.141480352
#> [36] -0.109977537 -1.072634951 -3.457624198 -1.624622859 -1.671974237
#> [41] -1.667874186 -1.273300123 -0.103221866 1.190657630 0.482936086
#> [46] 0.626259012 -0.473490819 -2.190476402 0.873277952 -2.688200940
#> [51] -0.003025586 0.860397693 -0.335211849 -0.504623477 0.561239502
#> [56] -0.636896909 0.094923850 -0.146721707 -0.217433745 -1.587305120
#> [61] -0.619009317 -2.125855239 -0.532016043 -1.727326143 -0.982096753
#> [66] -1.932115745 -0.223663547 -1.377617537 -2.179497347 -0.972171014
#> [71] -2.705132160 1.010465388 -0.905532889 0.890960920 -1.069172349
#> [76] 0.233639468 -2.174795554 -1.791051211 -1.955788691 -0.368321248
#> [81] -0.057803805 -1.371037427 -0.095722385 -1.698981691 -1.303515843
#> [86] 0.095668331 -0.926934898 -0.163520525 0.273121028 -2.743318587
#> [91] -1.816293344 0.917327592 -1.404089324 -0.692985065 -2.136418022
#> [96] 0.565119874 0.545319729 -1.198402869 0.647888689 -1.165731676
#>
#> $alpha1
#> [1] 0.635506789 2.144001402 0.986402715 1.352065726 1.000749148
#> [6] 2.645070199 2.646525372 1.156651226 0.278151486 -0.141931300
#> [11] 1.971721419 0.012468361 1.491846697 -0.298173153 0.897726527
#> [16] 0.571489630 1.728433976 1.906025193 3.086219061 1.499812302
#> [21] 0.614563595 1.227553796 0.252749393 0.971144607 -0.358806779
#> [26] 0.261209395 1.088938757 2.318829359 0.149887651 2.094820341
#> [31] 2.406407489 1.854897651 0.325498575 0.191630303 0.775532433
#> [36] 0.058924413 0.733654536 2.751551848 2.295180460 0.045382512
#> [41] 0.242028884 -0.212401237 1.426717082 0.024639543 0.623459498
#> [46] 1.046977854 1.303150307 1.501094567 0.843362259 1.036768466
#> [51] -0.004986161 -0.148010676 0.989677035 0.759858882 1.346034350
#> [56] 0.921702184 1.185039021 0.433717382 0.436700563 0.961862736
#> [61] 1.982592921 1.626890784 1.632797562 1.230247226 0.531847041
#> [66] 1.764062149 0.574281765 0.755666851 0.508125306 2.007318939
#> [71] 2.437617946 -0.614271103 0.036862845 1.720310962 0.789230604
#> [76] 1.118913583 2.215875156 0.499537440 0.686934656 2.082877333
#> [81] -0.378877255 3.449433799 -1.069109587 0.961421902 2.120940953
#> [86] 0.617586226 1.198776048 0.076788817 1.894746422 3.311830134
#> [91] 1.873078181 0.081762548 1.055105567 1.209089603 1.963408266
#> [96] 1.712874441 -1.424081186 0.411609674 -0.146441457 0.699675663
#>
#>
#> 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
#>
#>