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[Stable]

LogisticNormal is the class for the usual logistic regression model with a bivariate normal prior on the intercept and slope.

Usage

LogisticNormal(mean, cov, ref_dose = 1)

.DefaultLogisticNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

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 $$(alpha0, alpha1) ~ Normal(mean, cov).$$

Note

Typically, end users will not use the .DefaultLogisticNormal() function.

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] -0.724261515 -2.068807238 -0.116184894 -0.463547542 -1.408496701
#>   [6] -3.226525679 -0.240526328 -1.801558739 -1.981044994 -0.709098794
#>  [11] -0.561993262 -1.850363628 -1.023864413 -0.509148864 -0.828319450
#>  [16] -0.951446437 -1.009433791 -0.297329783 -2.286939190 -0.002537672
#>  [21] -0.650961936 -0.740379802  0.182255049 -0.564341160 -0.271065329
#>  [26] -0.670303968  0.325679721 -2.108703425 -0.035031792 -0.056225702
#>  [31] -0.830162358 -2.295499736 -0.357691676 -1.473961852 -0.079489129
#>  [36] -1.244716526 -0.888136009 -1.970326735 -0.730823187 -0.477871290
#>  [41]  0.513582912 -0.689418855 -0.073740295 -0.939938230 -1.291268286
#>  [46] -2.127622164 -1.626638880  0.600476918 -0.245769178 -1.499616905
#>  [51] -1.994920172  0.404564604 -0.387984434 -2.398667470 -0.448523575
#>  [56] -0.374451409 -0.005265208 -0.037812628 -0.211691277 -0.047621673
#>  [61] -1.396992055 -0.093742497 -2.558137689  0.054362342 -1.365084756
#>  [66] -2.549806756 -1.483248592 -0.773814167 -0.983948049  0.446165580
#>  [71] -0.820119446 -0.236995109 -0.848461968 -0.798150903 -1.266452416
#>  [76] -1.388736419 -0.667263143 -1.979985251 -0.581460813 -0.020356233
#>  [81] -2.575241509 -0.838493160 -2.224529407 -0.378933224  0.472532462
#>  [86]  0.283951675 -1.371180955  1.211707552 -1.392739236  0.146329651
#>  [91] -1.153419056 -1.720736583 -0.233609573 -1.153144511  0.681647997
#>  [96]  1.248542352 -2.029108292 -1.473729784 -1.950952773 -2.829265417
#> 
#> $alpha1
#>   [1]  2.3067547498  1.6330953917  1.0922057378  1.9816621519  2.1436840811
#>   [6]  3.3173513150  0.8671235517 -0.5982061373  2.4017019230  1.6700612559
#>  [11]  1.5920609763  1.1777661551  2.2531522608  0.8799735180  2.3733653657
#>  [16]  3.3330821399  0.7140066102  1.5743262055  2.2606677432  0.0846862773
#>  [21]  0.8201796382  2.4716165434  0.1335636729  0.8185274728  0.7352455981
#>  [26]  1.1411702062  0.4181017749  3.3914280269  0.6461590810  0.8377603669
#>  [31]  2.0428645932  1.9479446550  0.5084461609  0.9702748503  0.7159659322
#>  [36]  2.1272428371  1.0484543107  2.2017528009  0.3265638846  1.1216967137
#>  [41]  1.0762155024 -0.1572173858  0.1418561854 -0.0218230584  0.4434704909
#>  [46]  1.1379093469  0.2939405412  0.1608609151  1.9283091175  1.3735615938
#>  [51]  1.1969074787 -1.1307978813 -1.1750129020  1.9117079404  0.5495993959
#>  [56]  0.7283040678  0.4829393041  0.6016480955  0.1530204146 -0.0008545159
#>  [61]  0.5573304990  0.7471612802  1.0640264167 -0.5199115996  0.1922805266
#>  [66]  2.8358289020  1.5383630634 -0.5044412072  0.6576748518 -0.1569110757
#>  [71] -0.2980389418  1.0310991813  1.1273156919  2.4583076976  2.2747603284
#>  [76]  1.0535267830  2.1440425165  2.6644960796  2.3863246067 -0.1122383724
#>  [81]  3.2990868286  0.5058768381  2.1429613225 -0.5160181543  0.4630778766
#>  [86] -0.8965726890  1.1173819506  0.3207835379  0.8634751578  1.5783173670
#>  [91]  1.4722922960  2.3711792304  1.6434117293  1.7422731423  0.7646038356
#>  [96] -0.0370878836  1.5511771073  1.1286212287  2.4018855342  0.8040435093
#> 
#> 
#> 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
#> 
#>