![[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] -1.09253636 -0.59477108 -1.82467295 -0.76146109 -1.26638399 -1.74993764
#> [7] 0.23754262 -0.96401122 -0.15937951 0.15272558 0.26243114 -0.44729561
#> [13] 1.09370657 0.52372615 0.64018136 -1.30369779 1.09023218 -0.92003661
#> [19] -0.79775352 -1.13683524 -0.61517430 -1.81131837 -0.40654794 -0.79087075
#> [25] -1.06516209 -1.07812202 -0.37872940 0.46956128 -0.41488288 -0.34057775
#> [31] -0.64995571 -2.07427043 -0.88148253 -1.69571573 0.54894135 -1.77697118
#> [37] -1.05490183 -0.77166496 1.35083727 0.04007284 -2.09536772 -2.15183482
#> [43] -2.73054041 -0.44187521 0.59988244 -0.75110851 -0.75363586 -0.39588505
#> [49] -1.28134305 -0.82339506 1.02407345 -0.37973945 0.50199635 -0.26786960
#> [55] -0.61419090 -0.44481948 -0.72046692 1.74732708 -2.64731761 -0.87499439
#> [61] -1.41016639 -3.24946471 -0.25070171 0.69156285 -0.18659647 0.42267016
#> [67] -1.64710769 -0.16873464 -0.39314602 0.24700440 -0.47715149 -0.42750258
#> [73] 0.78746378 -0.95220415 -0.88971763 0.41974047 -0.53030015 -1.26773693
#> [79] -1.46423276 -1.36000524 -2.91948072 -1.25300338 -1.65643582 0.36242701
#> [85] -0.08935474 -1.63425724 -1.87473751 0.03766448 -1.02408344 0.22457904
#> [91] -0.01602413 -1.27084969 0.41931208 -1.91523009 1.88443069 -0.22267726
#> [97] -0.19520865 -1.24102665 -0.04324189 0.33736286
#>
#> $alpha1
#> [1] 1.4096450435 -0.3115053853 1.2149743597 1.7378668789 1.5763554016
#> [6] 1.2738434228 2.0179126804 2.5831307625 2.2564231759 0.0715376280
#> [11] 0.6331097754 1.3144323704 -0.6989955488 2.4298138628 0.8558330309
#> [16] 1.9821378669 0.5068759397 1.1601533719 0.8697467892 1.1665831489
#> [21] 0.3704341311 0.8376450743 -0.0002981322 1.4173430454 1.4801469614
#> [26] 0.0604059127 -0.0614913811 0.3646639658 1.2316932277 0.7326747873
#> [31] -0.1157717458 1.3106721301 2.0385517291 0.7808937325 1.5271009299
#> [36] 1.8218138738 0.2597470213 0.9151590004 -0.2872666143 -0.8145426654
#> [41] 2.3846210442 0.5548584745 1.6587387590 0.8655458197 1.6955605801
#> [46] 0.1421969375 -0.0044842419 0.3068651741 0.8502405242 -0.9578462805
#> [51] -0.7827301836 0.6509079204 0.6155604434 0.4665981237 0.9515239893
#> [56] 1.4748312077 0.9743130217 -2.1999491732 0.8677850656 1.2142304558
#> [61] 1.6228558871 2.3539059919 0.5921200926 -1.4630823167 0.8224175626
#> [66] -1.2619430059 1.0490962906 0.3349931014 2.1386768400 0.3556790114
#> [71] 0.7530140333 0.7368700083 1.6926975255 1.2255273972 1.2962744471
#> [76] -1.2845552597 1.5649994716 0.6037091488 0.6078578764 0.4187874779
#> [81] 2.1351022766 1.4225728963 1.2608167531 -0.2378997409 1.2293356758
#> [86] 0.0325144369 2.8923105286 1.7947882564 -0.0437287083 0.7019259109
#> [91] 0.1476597640 2.1808820866 -0.8763415302 0.6851426112 -0.2377909710
#> [96] 0.6451240081 1.9907134481 0.6580110696 1.8695440935 0.4177421724
#>
#>
#> 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
#>
#>