![[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.
Arguments
- mean
(
numeric)
the prior mean vector.- cov
(
matrix)
the prior covariance matrix. The precision matrixprecis internally calculated as an inverse ofcov.- ref_dose
(
number)
the reference dose \(x*\) (strictly positive number).
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] -0.352284523 0.415250546 0.136617271 -0.915800348 -0.956140969
#> [6] -1.540418459 -1.285320433 -2.814325136 -1.496469415 -1.798868829
#> [11] -2.089338299 0.229596003 -1.714441377 -0.701358352 -1.692901801
#> [16] -0.807645567 -1.892518686 -1.228836075 0.309122813 -0.666667003
#> [21] -2.762690756 -2.684513533 -2.128729847 -3.264671662 -0.596711286
#> [26] 1.837632346 0.892497172 0.861430035 -0.457767271 -2.284906558
#> [31] -1.019079928 -1.509552543 -1.456622704 -1.008569509 0.674609645
#> [36] -0.534690526 -0.890899635 -2.292266693 -1.197535535 -0.852435389
#> [41] -2.464283431 -1.062254968 -2.134125703 0.336744954 -1.605069838
#> [46] 0.080323412 -1.930921601 1.610862862 -0.969200803 -2.682920325
#> [51] -0.820194493 0.728173599 -1.513048855 -0.332817827 -1.464981496
#> [56] -1.025416127 -0.549320154 -1.376887185 -2.642330491 -1.366118721
#> [61] -0.633616246 -2.116721136 -1.666967673 -1.507223378 -1.449562668
#> [66] -0.407292571 -0.502859873 -1.362180188 0.606095384 0.368747143
#> [71] -0.553848292 0.036548279 -1.854035772 -2.542642276 -0.334132510
#> [76] -0.011929349 -1.565621026 -1.625801973 0.650488025 -2.544360566
#> [81] -0.779759786 -0.264910496 -1.344362669 -1.719950056 0.739570682
#> [86] 0.963061391 -0.270914638 -0.732041482 -2.376616383 -0.425247697
#> [91] -0.216212947 -2.420244778 -2.303296494 -1.844348959 -1.020397966
#> [96] -1.762553523 -0.134098379 1.013594961 -1.431302773 0.007853832
#>
#> $alpha1
#> [1] 0.54472507 2.38887312 -1.06519858 1.86898530 0.73114002 3.20003656
#> [7] 0.92144629 1.93252123 1.46744081 2.15623986 1.87182496 0.39835688
#> [13] 1.63813613 1.51015066 1.32631212 0.41178161 2.61943886 -0.46310071
#> [19] -0.85145776 0.48787069 3.14054757 2.27826808 3.77078124 3.85203947
#> [25] -0.65962987 -0.16174630 1.19594776 1.49728016 1.83093724 1.82415190
#> [31] 2.08299076 0.97492060 0.82449138 0.83274439 -0.23071605 0.47912136
#> [37] 1.60720763 1.24826227 0.93382229 -0.72222362 1.05070066 3.71185575
#> [43] 1.43077102 0.07812144 1.45827787 1.21304990 0.39789489 -1.21983582
#> [49] 0.01595346 1.93595450 0.95409211 -0.79264459 1.17465364 0.98044336
#> [55] 2.01894252 2.44374141 1.04684582 1.53789934 1.56495120 0.69736444
#> [61] 0.83687502 1.15925190 1.43917073 2.20371933 2.61206902 0.39803895
#> [67] -0.54803748 1.46271632 1.70306476 0.48620314 1.49719364 1.71539091
#> [73] 0.65248721 2.52677133 1.94787293 0.22084220 1.30927020 2.31290893
#> [79] 0.38559691 3.88570626 0.36527281 0.67692775 2.12591246 2.02792703
#> [85] 1.14438826 0.44710818 0.04727742 2.47148711 1.34925833 1.54926159
#> [91] 0.90665050 0.33456291 1.76984145 0.49793820 1.60172068 2.32919679
#> [97] -0.24619694 -1.34103784 0.17490191 0.58215937
#>
#>
#> 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
#>
#>