![[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] -0.98628982 1.31159844 -2.00031700 -0.74907179 -0.37960724 -3.01623453
#> [7] -2.03187625 0.40614848 -0.84735181 -0.84795984 -1.91962695 -1.94150066
#> [13] -2.66177740 -0.69162855 -1.05224745 -0.10626818 -0.92227779 -1.56183596
#> [19] -1.18043433 -0.46058073 -2.21619291 0.76160154 -0.26559128 -0.08340375
#> [25] -1.00334479 -0.76939944 -0.82573678 -2.82147015 -1.42378814 -2.05772792
#> [31] 0.41042825 -0.49861421 -0.53433867 -1.40164492 -1.02231988 -1.45082831
#> [37] -1.78168494 -1.35749518 -0.85135143 -0.80742340 -2.56406959 -0.51610768
#> [43] -1.31980008 -0.69369421 0.11833316 -0.65073540 -2.23389637 -0.36892466
#> [49] -0.13393407 -0.07389120 0.03980892 1.04557208 -1.09464774 -0.76034329
#> [55] -0.43374946 -0.96327643 -1.44163890 -2.05428319 -0.72995441 -2.71387851
#> [61] -0.29070896 -0.03912142 0.84685646 -0.25465408 -0.66435976 0.26919980
#> [67] -1.14997047 -1.37134173 -0.28406990 1.11161390 -2.08872109 -1.92703180
#> [73] -1.29030337 0.40987583 0.48993603 0.45871946 -1.40296966 -3.40132187
#> [79] 0.02128583 -0.51545785 -1.16228501 -1.59135895 -1.02328640 -1.59944315
#> [85] -0.35555736 0.06035698 -1.83369469 -1.05884565 0.02753722 0.39607407
#> [91] -1.48927380 -1.37576533 -1.13823114 -1.14742620 -1.58677485 0.60075494
#> [97] -0.08465653 -1.48187786 -1.07853654 -0.56820465
#>
#> $alpha1
#> [1] -0.82133449 -0.54112967 0.34221318 0.59824616 2.11305294 2.13024710
#> [7] 0.16662125 1.97447940 0.79848971 0.49555350 1.02095827 0.96352992
#> [13] 1.76186230 1.52050892 0.85985246 -0.71754449 0.65571913 0.73334731
#> [19] 1.87106967 0.94046208 1.43165960 -0.80430163 0.01736601 -0.11829274
#> [25] 0.69133683 0.86274235 1.39891805 2.82902240 2.75015027 1.50914245
#> [31] -0.15370141 1.56230562 0.06438363 2.19665635 0.70243169 2.53138933
#> [37] 0.14489325 1.26425802 0.88019017 1.57870037 3.52281189 0.75025828
#> [43] 0.10417406 2.88360907 0.67064257 0.03852237 2.08449703 -0.80880681
#> [49] 2.70426181 0.60718300 1.32051779 1.71981751 1.37782567 -0.01203686
#> [55] 1.86404547 0.74354208 1.10672199 2.28779021 0.42610735 2.63858350
#> [61] 0.39743386 0.32431900 0.16339115 2.22345556 0.17093147 0.01210716
#> [67] 1.58836120 0.05623011 0.59750623 0.50448991 1.86536615 1.19410214
#> [73] 1.06246799 1.67355675 -0.48876119 -0.28558106 0.93138470 3.59708844
#> [79] 1.84499848 1.48430647 0.82070075 1.29193244 -0.42876471 2.37629160
#> [85] 0.25107739 1.24642566 -0.37248335 1.03877080 -0.60778037 0.67063028
#> [91] 0.10495363 0.96838465 2.68808249 0.94235359 3.36587790 -0.02467303
#> [97] -0.29228916 1.80335334 1.40846286 -0.16950679
#>
#>
#> 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
#>
#>