![[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] 0.51240626 0.04522624 -1.58634982 -1.08628357 -0.98422252 -0.88787463
#> [7] -0.55647852 -0.28325887 -2.41442998 -1.60701311 -1.17969030 0.34175941
#> [13] -2.41356307 -0.81815491 -1.52017735 -2.67502456 -1.63977679 -1.01322783
#> [19] -0.33154471 0.43638398 0.76894160 -0.92198393 -1.85933844 -1.29210043
#> [25] -0.57687554 -0.21347387 -0.44512752 -0.62825388 -0.13269731 -0.48409597
#> [31] 0.89706099 0.60513316 -0.97205235 -0.96389657 0.36128803 -1.05909291
#> [37] -2.59176392 -0.64107941 -1.85547056 0.20120883 1.89196987 -3.14324346
#> [43] 0.67835819 -0.36146876 -0.12075227 -0.98450831 0.60250180 -0.33290108
#> [49] 0.31129479 -0.07802432 -1.72582763 -2.26378096 -0.92627371 -0.94172618
#> [55] -1.44039896 -0.92241376 1.76637429 -1.10460214 -1.68593012 0.77791840
#> [61] -1.89673641 0.36235708 -1.09376778 0.10594080 -1.11006917 -1.10322704
#> [67] -0.03138457 -1.97054974 -0.56309840 -1.04624601 -0.80398716 -1.41132036
#> [73] 0.03468466 -2.38571188 -1.30721743 -1.40097516 -1.76047944 -0.94238650
#> [79] 0.87814649 0.05434349 -2.67584217 -0.91335419 0.71823756 -1.32884822
#> [85] 0.03263311 -0.73955102 -1.72273386 0.20565635 0.02940077 -1.59632295
#> [91] -2.69625554 -1.31121836 -0.62850195 -1.34799274 -0.33084905 -1.13332674
#> [97] -0.40379741 -2.14331418 -0.55641970 -0.75020250
#>
#> $alpha1
#> [1] 0.91090261 -0.26205494 0.83092116 0.58364903 1.14782308 0.91546881
#> [7] -0.56030839 1.70206028 2.79846910 1.67295811 1.37184726 0.93359474
#> [13] 2.12605724 -0.35057496 2.70175211 1.66298217 0.78511892 2.05571344
#> [19] 1.88733594 0.66271904 -0.65008943 0.69009033 -0.20855518 2.59459143
#> [25] -0.13594146 1.27962590 0.21005815 -0.45562800 0.14904588 1.50543680
#> [31] 0.72189128 -0.35509813 0.38291332 1.45034314 0.09201677 0.68198804
#> [37] 2.34058396 -0.42367763 1.63263320 -0.81245311 -0.58630720 0.66680452
#> [43] 0.77457202 -0.14880885 0.78316258 1.47253081 -0.52388252 -0.29038593
#> [49] 0.66486952 0.80292716 1.96344930 1.95947829 1.89776936 1.26143324
#> [55] 3.60984141 0.32264490 -0.66439556 1.14871361 0.04923490 0.16451394
#> [61] -0.18854069 0.45230733 0.42940462 1.33984564 0.65273831 0.86521231
#> [67] 0.30173080 2.20946159 1.11819583 0.19911863 1.39221036 1.34426930
#> [73] 0.01969738 1.72594245 0.98508706 3.37436489 1.23679970 0.50568648
#> [79] -0.64929032 1.14945934 1.43555265 2.02249462 0.60094127 2.09481528
#> [85] -1.24730971 0.89084592 0.14047789 -0.65726229 -0.78267120 0.98237793
#> [91] 2.35876593 2.10695308 0.24447389 1.72988111 1.56652542 0.08957273
#> [97] -0.18265489 0.93844653 1.59215715 2.33375757
#>
#>
#> 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
#>
#>