![[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] 0.406104198 0.732515263 1.097123919 -0.383898820 -1.189107304
#> [6] 0.591995617 -0.265435328 0.648585505 -1.121026001 1.532607126
#> [11] -0.084981322 -2.298299692 -0.902681809 -2.591620308 -3.719050579
#> [16] -0.724114462 -0.841962691 -1.649367272 0.997023331 1.263043415
#> [21] -0.574627336 -1.206864876 -0.349072575 -0.696964324 1.044847558
#> [26] -1.100518801 0.514290427 -0.933883675 -1.565528255 -2.023245937
#> [31] -1.039454854 -0.988174911 -0.253465400 -1.089776476 -1.471952210
#> [36] -0.737585898 -1.028530069 -0.469983037 -0.001092752 0.686136213
#> [41] -0.740051500 -3.045960719 -0.486850811 -0.505964930 -2.036683053
#> [46] -1.722963765 -0.884179975 -1.021072088 -2.384539554 -1.139855056
#> [51] -2.179717869 -0.304431093 -0.739666526 -1.702908543 0.152230762
#> [56] -1.116851906 -1.297483417 -0.940353388 -1.320901208 -1.253373738
#> [61] -0.916510429 -0.723718625 -0.039420831 0.038662007 1.044179410
#> [66] -0.366465313 -0.860392348 -1.182965122 -1.273167705 0.677844681
#> [71] -3.463876055 -0.372840372 -0.320749446 0.129452636 0.675847791
#> [76] -1.675715638 -0.501076143 0.114831950 0.492109528 -0.285325462
#> [81] 0.251561211 0.611435319 0.296438748 -0.246927923 -1.036127238
#> [86] -1.869681008 -2.165609271 -0.854484672 -2.662183109 -1.132798248
#> [91] -1.043034890 1.459625161 -1.571768097 -1.294869055 -0.982150317
#> [96] -0.444754549 -0.996320147 -0.795358182 -0.585987177 -0.858874811
#>
#> $alpha1
#> [1] 0.29655312 -0.83520224 1.17147418 0.36279689 1.17644080 -0.02725459
#> [7] -0.04733869 0.54586358 1.48357275 -1.07077087 1.27019293 2.56287745
#> [13] 1.11241584 0.76706813 0.40521619 0.96394229 0.76917258 1.76944204
#> [19] 1.32563601 -0.21400357 -0.60931063 1.31386312 0.23413655 0.60879322
#> [25] 0.06848188 0.50006310 0.94675755 -0.15213709 1.82954976 0.30621656
#> [31] 3.08026250 0.50315354 1.17243108 0.94869555 0.01660033 0.88542173
#> [37] 0.20302384 1.15334492 0.65058158 0.39260808 1.04850803 1.41670756
#> [43] 0.02105337 0.87468226 2.61196636 1.37148207 0.80042941 2.69321100
#> [49] 3.25339674 1.57004001 2.23880775 -0.23544201 0.48034507 2.97783401
#> [55] 0.88352440 2.47649594 1.19919145 2.99833338 1.79402432 1.45884977
#> [61] 0.58929005 1.25785865 0.62037789 -0.93335162 0.30949436 1.82067931
#> [67] 0.78294612 1.81860445 1.66436683 -0.45620833 4.15182112 1.79469983
#> [73] 0.44432811 -0.52369833 0.91039091 1.12188835 1.34820651 1.66305622
#> [79] 0.41277080 0.48511386 0.34431235 -1.15157443 1.10685097 1.43200378
#> [85] 0.03088063 1.73176540 1.97924602 1.62980369 0.89626455 0.70846183
#> [91] 1.08013999 1.15583542 2.15132696 1.53187726 0.73196455 0.55889071
#> [97] -0.38526753 -1.05768424 0.65253986 1.80313692
#>
#>
#> 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
#>
#>