![[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] 1.293404985 -0.706650751 1.435640955 -3.314211900 -0.444884496
#> [6] 0.856444476 -1.532102818 -2.183391360 -0.430478928 -1.387657747
#> [11] -1.581073063 -3.015070067 0.001116809 -0.199976073 -0.144609262
#> [16] -1.986475599 0.538266633 -0.125222010 -0.565111328 0.429539188
#> [21] 0.390017500 -0.762552904 -0.089828277 -0.392052287 0.887440720
#> [26] -0.994900797 -1.209532715 -0.786361795 0.240156014 -0.411536192
#> [31] -1.657988845 -2.977173903 -1.468478003 -0.067098620 -2.634200232
#> [36] -1.198810511 1.866525846 -1.930537338 -1.467697819 0.649739574
#> [41] 0.606651256 -1.492502599 -0.587716661 -0.739993796 -2.119967381
#> [46] -1.657461778 -2.561049392 -2.188715467 -1.896974405 -2.209872379
#> [51] -1.607558921 -0.603936351 -1.006739995 -0.448739030 -1.035965699
#> [56] -2.003971071 -1.810622152 -1.802300818 -0.687789381 0.401705404
#> [61] -0.586749652 -0.148764425 -1.735737459 -1.298031203 -0.520228217
#> [66] -1.434119997 -1.398043255 -0.904494146 -0.831237016 -0.092689445
#> [71] -1.348673170 -2.125781378 -3.126642227 -0.505454411 0.160882252
#> [76] 0.218653268 -2.296393737 -1.863799044 -1.960844134 -0.656629485
#> [81] 0.926437321 0.293370776 0.680587439 -1.451417337 0.564641907
#> [86] -0.971287208 -0.685966853 0.044030357 -2.223536588 -1.130594616
#> [91] -1.347428406 -0.180940862 -1.400044113 -1.481984820 -0.707501114
#> [96] -0.456377567 -1.207804767 -0.142879404 -0.642299361 -0.484628045
#>
#> $alpha1
#> [1] -0.42386704 1.08763959 -1.39738330 2.92755811 2.22142301 1.16675204
#> [7] 1.01776946 1.61430140 0.84236294 1.51260205 2.07101767 0.89220590
#> [13] 0.27333930 0.34763931 1.87180549 1.89285167 -0.12693602 0.02580546
#> [19] 1.42439082 0.88093452 2.53796618 0.86282234 -0.13635467 0.88534005
#> [25] 0.76137135 1.21872487 1.74849762 1.72988138 0.26446001 2.16071795
#> [31] 0.89949760 2.62092003 0.97788016 2.21509595 2.21820764 3.11918964
#> [37] 0.12971185 1.88592052 0.61332048 0.29264034 0.98423447 0.76965266
#> [43] 0.47315879 2.75844371 2.41597852 -0.29270874 0.58395234 0.61187774
#> [49] 2.47356065 0.31031407 1.03582655 1.65226814 1.46332824 1.93106268
#> [55] 2.72988565 2.31832248 1.82185287 0.21870881 0.30438548 0.72454538
#> [61] 0.43825854 -0.47494161 1.58367077 1.19278967 0.22914949 1.86699566
#> [67] -0.32656349 -0.92595668 0.66616686 -0.11930123 1.04750830 2.63192464
#> [73] 2.00189452 1.51433225 0.92480168 -0.26836473 0.92406459 1.90651460
#> [79] 2.44055790 0.85563806 1.32379959 -0.06994042 0.21074702 1.90737861
#> [85] -1.46003739 0.58572668 -0.33865509 0.40706869 2.22672178 -0.76215557
#> [91] 1.49433491 0.72372561 1.73252249 1.27850337 0.05335380 1.58571544
#> [97] 2.15371702 1.05419806 0.52026662 0.96762776
#>
#>
#> 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
#>
#>