This function simulates one or more pseudo-random datasets from a specified Bradley-Terry model. Counts are simulated from independent binomial distributions, with the binomial probabilities and totals specified through the function arguments.
simulate_BT(pi, N, nsim = 1, seed = NULL, result_class = c("sparseMatrix", "btdata")) # S3 method for btfit simulate(object, nsim = 1, seed = NULL, result_class = c("sparseMatrix", "btdata"), ...)
| pi | a numeric vector, with all values finite and positive. The vector of item strengths in the Bradley-Terry model. |
|---|---|
| N | a symmetric, numeric matrix with dimensions the same as
|
| nsim | a scalar integer, the number of datasets to be generated. |
| seed | an object specifying if and how the random number generator
should be initialized (‘seeded’).
For details see |
| result_class | a character vector specifying whether the generated datasets should be of class "sparseMatrix" or of class "btdata". If not specified, the first match among those alternatives is used. |
| object | An object of class "btfit", typically the result of |
| ... | Other arguments |
a list of length nsim of simulated datasets.
If result_class = "sparseMatrix", the datasets are sparse matrices
with the same dimensions as N. If result_class = "btdata" then
the datasets are "btdata" objects. See btdata
set.seed(1) n <- 6 N <- matrix(rpois(n ^ 2, lambda = 1), n, n) N <- N + t(N) ; diag(N) <- 0 p <- exp(rnorm(n)/4) names(p) <- rownames(N) <- colnames(N) <- letters[1:6] simulate_BT(p, N, seed = 6)#> $sim_1 #> 6 x 6 sparse Matrix of class "dgCMatrix" #> a b c d e f #> a . 2 2 2 . 2 #> b 2 . 2 2 0 0 #> c 0 0 . 2 1 3 #> d 1 0 2 . 0 . #> e . 1 0 2 . 2 #> f 1 1 2 . 0 . #> #> attr(,"seed") #> [1] 6 #> attr(,"seed")attr(,"kind") #> attr(,"seed")attr(,"kind")[[1]] #> [1] "Mersenne-Twister" #> #> attr(,"seed")attr(,"kind")[[2]] #> [1] "Inversion" #>citations_btdata <- btdata(BradleyTerryScalable::citations) fit1 <- btfit(citations_btdata, 1) simulate(fit1, nsim = 2, seed = 1)#> $sim_1 #> 4 x 4 sparse Matrix of class "dgCMatrix" #> citing #> cited JRSS-B Biometrika JASA Comm Statist #> JRSS-B . 286 316 285 #> Biometrika 219 . 495 725 #> JASA 151 323 . 817 #> Comm Statist 8 38 64 . #> #> $sim_2 #> 4 x 4 sparse Matrix of class "dgCMatrix" #> citing #> cited JRSS-B Biometrika JASA Comm Statist #> JRSS-B . 291 315 273 #> Biometrika 214 . 511 723 #> JASA 152 307 . 811 #> Comm Statist 20 40 70 . #> #> attr(,"seed") #> [1] 1 #> attr(,"seed")attr(,"kind") #> attr(,"seed")attr(,"kind")[[1]] #> [1] "Mersenne-Twister" #> #> attr(,"seed")attr(,"kind")[[2]] #> [1] "Inversion" #>toy_df_4col <- codes_to_counts(BradleyTerryScalable::toy_data, c("W1", "W2", "D")) toy_btdata <- btdata(toy_df_4col) fit2 <- btfit(toy_btdata, 1, subset = function(x) "Amy" %in% x) fit2_sim <- simulate(fit2, nsim = 3, result_class = "btdata") fit2_sim$sim_1#> $wins #> 4 x 4 sparse Matrix of class "dgCMatrix" #> player2 #> player1 Cyd Amy Ben Dan #> Cyd . 1 . 1 #> Amy 1 . 1 0 #> Ben . 0 . 1 #> Dan 0 2 1 . #> #> $components #> $components$`1` #> [1] "Cyd" "Amy" "Ben" "Dan" #> #> #> attr(,"class") #> [1] "btdata" "list"purrr::map(fit2_sim, "wins")#> $sim_1 #> 4 x 4 sparse Matrix of class "dgCMatrix" #> player2 #> player1 Cyd Amy Ben Dan #> Cyd . 1 . 1 #> Amy 1 . 1 0 #> Ben . 0 . 1 #> Dan 0 2 1 . #> #> $sim_2 #> 4 x 4 sparse Matrix of class "dgCMatrix" #> player2 #> player1 Cyd Amy Ben Dan #> Cyd . 2 . 1 #> Amy 0 . 1 2 #> Ben . 0 . 1 #> Dan 0 0 1 . #> #> $sim_3 #> 4 x 4 sparse Matrix of class "dgCMatrix" #> player2 #> player1 Cyd Amy Ben Dan #> Cyd . 1 . 1 #> Amy 1 . 0 2 #> Ben . 1 . 1 #> Dan 0 0 1 . #>