![[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] -1.504515829 -1.240117636 -0.922305305 2.345597016 0.849757732
#> [6] -1.853759583 -0.175636056 -1.111392152 -2.827078985 0.295324331
#> [11] -2.119873431 -0.198570934 -0.380429647 -0.114640886 0.237907115
#> [16] -0.337349944 -1.552462347 -0.275798031 -0.759169805 0.997108456
#> [21] -0.924009706 -0.669599386 -2.054686866 0.011012049 -1.247346651
#> [26] -1.179110183 -2.121437033 -0.923972987 -1.145858905 -1.563890897
#> [31] -1.229759813 -1.464545110 1.317985134 -1.350145404 1.362672066
#> [36] -0.580728992 -0.572511674 -1.440834602 -1.231733676 -1.119935656
#> [41] -0.666335899 -0.705352001 1.013134261 -1.532218118 -0.129445894
#> [46] -0.625382411 -0.266313363 -0.865825601 -1.440150876 -2.077904661
#> [51] -2.086080017 0.115041154 -0.114994982 1.093500170 0.150079166
#> [56] -0.514334787 -2.108762033 0.430339282 -0.517221083 -1.034061902
#> [61] -1.226673676 -0.001541701 -1.067388941 -0.651657646 -1.402990533
#> [66] -0.225899242 0.042712998 1.079705218 -0.854163673 -1.398299863
#> [71] 0.464226977 -1.912033978 -0.481251214 -2.075946624 -0.819212971
#> [76] -0.305572321 0.496684485 -2.069002223 -0.359129020 -2.052027106
#> [81] -0.779783107 -1.296295543 -0.769150306 -0.163779132 -0.241527825
#> [86] 0.273167861 -0.518678456 0.371983546 -0.563219307 -0.625173975
#> [91] -0.013910660 -0.415630076 -1.830272753 -0.837231692 -0.944239681
#> [96] -1.002709201 0.573991444 -0.314568673 0.776375222 -0.086650829
#>
#> $alpha1
#> [1] 0.78567279 0.93456687 0.28392370 -1.45327431 1.60682075 1.52007986
#> [7] -1.12181091 1.18388887 2.18483698 1.08010327 1.49179759 2.26166211
#> [13] -0.27276536 0.61258951 1.54471678 0.54056142 1.05392901 -1.04479894
#> [19] 0.02838910 -0.41871733 1.45150779 1.05760036 1.05142435 0.89566070
#> [25] 0.98849292 1.42653702 1.75929066 1.13232385 1.71400633 2.09580272
#> [31] -0.32570360 1.30258015 0.31315281 2.06234754 -0.43389193 1.35755738
#> [37] -0.31755621 1.95170074 0.52035680 0.12611108 1.89017298 -0.11598377
#> [43] -0.10861372 -0.21614941 0.52610135 0.78089618 0.78943184 1.27234095
#> [49] 1.07062428 1.27946756 1.88942740 0.44878809 -0.10490495 -1.16594864
#> [55] -0.08843268 1.43617081 2.89077167 0.16537986 0.51401006 0.60396013
#> [61] 1.39735101 1.13245230 1.53326814 1.61451331 0.37708943 0.32796669
#> [67] 0.30797245 -2.15705229 0.20380581 2.29007699 0.58269619 2.98625913
#> [73] 1.70953981 0.97760395 1.27731039 0.48506981 0.29798077 0.96652579
#> [79] 1.27721440 0.98987410 0.91197928 1.33665958 -0.77160163 1.04745416
#> [85] -0.06476333 0.44262963 -1.18241752 1.65008002 0.71101803 1.82946338
#> [91] 0.30894468 0.26385527 -0.13591625 0.97276561 1.59180505 2.63960464
#> [97] 1.11001803 1.15675446 0.51731864 0.33940800
#>
#>
#> 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
#>
#>