![[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] -0.67429202 0.44816607 -1.87688588 0.53365723 -1.10760394 0.23034328
#> [7] -0.46836205 0.12892867 -0.46010207 -0.20674150 -0.90435068 -1.93960044
#> [13] -1.90402539 -0.88751181 -0.77897500 -2.14867243 -0.95732434 0.97631730
#> [19] -0.56079240 0.98586899 -0.27027762 0.40613133 -2.40196473 -0.28269983
#> [25] -1.87715898 -2.03044512 0.61475051 1.37168738 -1.05295658 -0.32328500
#> [31] -1.08074985 -1.16444088 -1.88302106 -0.97020612 -0.55026796 -0.54414857
#> [37] -0.35397842 -1.36692328 -0.33240507 0.01654378 -1.57636317 -1.85685687
#> [43] -0.56174714 -0.94214216 -1.69473827 -0.60009728 -0.82406381 0.40972636
#> [49] -1.16514605 -0.59564016 0.86411630 -2.62304268 0.42327812 1.05884811
#> [55] 0.11660892 -1.25590501 -0.11531111 -0.11029681 -0.09584960 -1.56498420
#> [61] 0.41400837 -2.74551314 -2.18947751 0.25949790 -0.44610781 -2.00751551
#> [67] -1.97651529 -1.48984763 -0.79192668 -0.44972240 -0.46285710 -2.03253929
#> [73] 0.16672170 0.15932802 -0.22208391 0.33161398 -0.48189120 -0.75321929
#> [79] -1.37517430 0.48096018 -0.88121837 -1.84680028 -1.50577709 0.28425540
#> [85] -1.60638672 -1.71492168 -1.69937538 -0.10665115 -1.24867212 -1.02549128
#> [91] -0.08025514 0.93811404 -1.16579210 0.57978598 -0.75874506 -1.24444801
#> [97] -0.22032262 -0.51090077 -0.25274099 -1.24938305
#>
#> $alpha1
#> [1] 0.94904571 0.89475790 0.75242566 0.05918137 1.74200377 1.59556642
#> [7] 1.43403712 -0.45571877 0.98343337 -0.10839615 0.78342529 1.69109778
#> [13] -0.37119805 0.61908337 1.91839518 2.52591476 1.96130823 -0.16515170
#> [19] 1.17752920 0.20905539 0.88295457 0.03395067 1.72194737 0.23013313
#> [25] 0.27995783 1.87138933 -0.41232167 0.75453028 1.04249185 0.90421774
#> [31] -2.10004876 1.40224226 0.73612781 0.99974222 -0.56816644 0.27030424
#> [37] 0.06934331 1.45534198 1.60820258 0.42664796 2.31103867 2.69774526
#> [43] 0.61323154 1.79483310 2.29372537 0.54287641 1.01421685 0.87463624
#> [49] 1.90948079 1.16972193 -0.25488029 2.59437756 -0.40219699 -1.92233484
#> [55] -0.81870246 0.18269909 -0.65191919 -0.70545219 1.73288832 3.71381121
#> [61] 0.09491452 2.25363311 2.30318428 0.53519992 0.06596655 2.31313656
#> [67] 2.55218740 2.22351738 0.32226808 1.50476666 1.88961236 1.26290004
#> [73] 0.74280823 -0.14428765 -0.32833060 0.83079523 0.86570295 1.06196654
#> [79] 0.17343813 0.75982994 0.15176532 1.55175155 0.94024873 1.48196121
#> [85] 1.57462851 0.70267291 2.17337919 0.41308997 1.24585442 1.89891370
#> [91] 0.54225644 0.29890759 2.94380924 1.58278464 0.26930821 -0.47892052
#> [97] 0.74020144 1.40642681 1.85449988 -0.76707927
#>
#>
#> 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
#>
#>