![[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] -0.45259863 0.78458933 -2.25020606 -2.00854855 -2.12681215 -2.92081818
#> [7] 0.04766727 -1.57286988 -0.78746123 -2.54447547 -0.17303336 -2.10244874
#> [13] -1.55492391 -0.36725079 0.89659379 -0.14139626 -1.06758766 -1.06490916
#> [19] 0.15801843 -1.63082927 0.05635640 -3.70732386 0.74417196 0.75235421
#> [25] -0.93421832 0.48760361 -1.46012568 -1.43693931 -2.31927276 0.13797908
#> [31] -0.98883850 -1.25989156 -0.35877968 -1.67151839 -0.10588302 -1.32682490
#> [37] -1.38667141 -1.76796268 -2.32085805 -0.81885135 -2.37483121 -0.56751701
#> [43] -0.34913855 -1.45016073 -1.03586209 -2.90809711 -0.10195593 -0.48909511
#> [49] -1.38312772 -0.43139577 -2.11924962 0.03175558 0.02992716 0.15586514
#> [55] -2.13120736 -1.44580347 -1.16899554 -0.25381565 -1.93070281 -0.90978440
#> [61] -1.92634916 -0.71837169 1.04071425 -1.71023808 -0.94540742 -0.81197276
#> [67] 0.16304686 -0.72000208 -0.08509905 -0.59761939 -2.09157328 -0.37500866
#> [73] 0.39716028 -0.60896448 -1.53888006 1.39035123 1.21966101 -2.48714090
#> [79] -1.76099818 -1.52019421 -0.62059812 0.20003207 -1.52474501 -0.81320150
#> [85] 0.28477761 -1.54422362 -0.47370123 -2.27950850 1.60205254 -1.73053944
#> [91] 0.13742094 -0.77527417 -0.83884445 -1.04431649 -0.57746206 -0.64644032
#> [97] -0.74706921 -0.56099759 -1.82311972 1.03028100
#>
#> $alpha1
#> [1] 0.249178742 0.012466017 1.658467865 0.603495781 2.200183097
#> [6] 2.109863737 1.090421049 0.250775682 1.777997683 0.190372642
#> [11] 0.584432078 0.839217796 1.733692438 1.049634802 -0.154957799
#> [16] 1.029528294 2.357134793 1.266003078 0.999715051 3.190148070
#> [21] -0.113400232 1.859574358 -0.820114267 0.341059837 1.366528360
#> [26] 1.246836779 1.403497302 0.218887504 2.355380677 0.134901171
#> [31] 1.300013730 2.388088814 2.142780523 -0.780748528 0.014219873
#> [36] 2.009011640 0.502597474 0.296581160 1.221556012 0.647850597
#> [41] 1.527146011 0.675149536 1.446621475 1.402441330 0.276211775
#> [46] 1.402638787 1.111500365 0.215736899 2.251389946 0.850328371
#> [51] 2.443343681 0.224788878 0.255271999 1.016444069 2.265679245
#> [56] 1.690055185 0.831068832 1.748913951 1.467404183 1.168143588
#> [61] 2.534978635 2.023431400 -0.079582236 2.243242087 -0.487813932
#> [66] 0.837098381 1.638218983 -0.460137545 1.461950353 0.509439650
#> [71] 1.135079925 -0.342749264 1.064531088 -0.544034329 1.039163989
#> [76] -0.297837413 -1.136493443 3.444623515 0.593572071 1.207516952
#> [81] 1.679434461 0.360533970 4.131983164 0.780422167 -0.148655830
#> [86] -0.007621493 0.614101853 2.437243924 -0.278823907 1.142373577
#> [91] -0.001800539 0.628452815 0.259206883 -0.619936666 1.938293933
#> [96] 1.241954556 0.797068067 1.873642381 1.815394048 -2.027044031
#>
#>
#> 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
#>
#>