![[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] -8.981381e-02 -4.980076e-01 -1.409527e+00 -2.578294e+00 -2.351110e+00
#> [6] -6.183267e-01 -1.672454e+00 5.476478e-01 -4.381458e-01 -3.132636e-01
#> [11] 6.845051e-01 -2.800253e+00 3.602947e-01 -1.513970e-01 -2.016704e+00
#> [16] -6.048468e-02 -1.268629e+00 -1.149232e+00 -2.550779e+00 -1.839635e+00
#> [21] -1.295015e+00 -1.493028e+00 -2.562569e+00 4.500619e-01 -2.342576e+00
#> [26] 5.223945e-01 -1.501877e+00 2.386207e-01 -2.041239e+00 -1.022720e+00
#> [31] -8.232285e-01 1.115811e+00 -3.851624e+00 -9.400995e-05 -3.633510e-01
#> [36] 1.200753e+00 6.380239e-01 3.641610e-01 -1.522947e+00 -9.202687e-01
#> [41] -1.326283e+00 -3.336404e-01 -4.220710e+00 -2.227417e+00 -1.892591e+00
#> [46] 4.307372e-01 -1.158765e+00 -1.539250e+00 -1.560239e+00 -1.452555e+00
#> [51] 7.777713e-01 -1.090625e+00 -2.200735e+00 -2.506771e+00 1.405008e-01
#> [56] -4.073842e-01 -2.102113e+00 -2.687454e+00 -1.879116e+00 -5.699346e-01
#> [61] -1.127144e+00 -2.500415e+00 -1.619589e+00 -4.584195e-02 9.766160e-01
#> [66] -6.330056e-01 -4.307436e-01 -1.467695e+00 -4.048267e-01 -1.522041e+00
#> [71] -1.922118e+00 7.124175e-01 -4.704217e-01 -2.246491e+00 -2.559189e+00
#> [76] -6.200446e-01 -8.198456e-01 -2.188716e-01 -1.167597e+00 -1.208344e+00
#> [81] -9.229780e-01 -2.497374e-01 -1.522768e+00 2.247337e-01 1.679438e+00
#> [86] 3.436265e-01 9.368353e-02 -2.452906e+00 1.821536e-01 -6.220450e-01
#> [91] -2.210738e+00 1.282203e-01 -5.053239e-01 -1.356469e+00 -3.092771e-02
#> [96] -5.839456e-01 -1.944858e+00 -7.267802e-01 -1.795845e+00 -7.032269e-01
#>
#> $alpha1
#> [1] 0.2425399 0.8327681 1.1312562 3.1675134 1.8900495 1.1836805
#> [7] 2.1615909 -1.1543454 1.2611240 0.5692559 -1.0566265 1.4077780
#> [13] 0.5798423 0.6498462 2.1156964 -0.1937975 1.0892070 0.9530183
#> [19] 2.9718905 1.9750239 0.8566104 1.7231860 1.6111177 0.6561863
#> [25] 1.0434993 -1.0996811 2.2940992 0.5290545 1.0015056 1.8501093
#> [31] 1.3716321 -1.1012398 2.9900390 -0.2886969 1.9355722 -1.9175719
#> [37] 0.7882932 1.0610569 1.3624898 1.6354919 0.7418671 -0.4891231
#> [43] 3.7784893 0.4932685 0.6432517 -0.1519720 0.2867294 0.8752883
#> [49] 1.4243589 0.4992674 -1.8212931 1.8121280 0.2649196 1.6654123
#> [55] 1.0548402 0.8723112 1.6172721 2.4428387 2.7619623 1.4570338
#> [61] 1.5677850 1.4893635 0.7061828 0.7130753 -0.3204491 1.6018398
#> [67] 1.5129865 1.2995436 0.7507325 0.7163406 2.5515228 -0.4172362
#> [73] 0.3096576 2.1018490 1.2624254 0.5352602 0.4709719 1.1344253
#> [79] 1.5875529 1.3065206 0.6950016 0.3498329 0.4625664 1.0849135
#> [85] -0.2446769 1.0155103 0.5066958 2.0875097 0.5240144 1.9660618
#> [91] 2.4286354 -0.4688659 0.7234600 1.3982826 0.4604734 0.1204930
#> [97] -0.4587477 0.7156587 2.0941232 1.3338890
#>
#>
#> 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
#>
#>