# Estimate an item response model with Stan ```r irt_stan(data_list, model = "", ...) ``` ## Arguments - `data_list`: A Stan data list created with `irt_data`. - `model`: The file name for one of the provided .stan files, or alternatively, a user-created .stan file that accepts `data_list` as input data. The ".stan" file extension may be omitted. Defaults to either "rasch_latent_reg.stan" or "pcm_latent_reg.stan". - `...`: Additional options passed to `stan`. The usual choices are `iter` for the number of iterations and `chains` for the number of chains. ## Returns A `stanfit-class` object. ## Details The following table lists the models inlcuded in `edstan` along with the associated **.stan** files. The file names are given as the `model` argument. ||| |:--|:--| |Model|File| |Rasch|**rasch_latent_reg.stan**| |Partial credit|**pcm_latent_reg.stan**| |Rating Scale|**rsm_latent_reg.stan**| |Two-parameter logistic|**2pl_latent_reg.stan**| |Generalized partial credit|**gpcm_latent_reg.stan**| |Generalized rating Scale|**grsm_latent_reg.stan**| Three simplified models are also available: **rasch_simple.stan**, **pcm_simple.stan**, **rsm_simple.stan**. These are (respectively) the Rasch, partial credit, and rating scale models omitting the latent regression. There is no reason to use these instead of the models listed above, given that the above models allow for rather than require the inclusion of covariates for a latent regression. Instead, the purpose of the simplified models is to provide a straightforward starting point researchers who wish to craft their own Stan models. ## Examples ```r # List the Stan models included in edstan folder <- system.file("extdata", package = "edstan") dir(folder, "\\.stan$") # List the contents of one of the .stan files rasch_file <- system.file("extdata/rasch_latent_reg.stan", package = "edstan") cat(readLines(rasch_file), sep = "\n") ## Not run: # Fit the Rasch and 2PL models on wide-form data with a latent regression spelling_list <- irt_data(response_matrix = spelling[, 2:5], covariates = spelling[, "male", drop = FALSE], formula = ~ 1 + male) rasch_fit <- irt_stan(spelling_list, iter = 300, chains = 4) print_irt_stan(rasch_fit, spelling_list) twopl_fit <- irt_stan(spelling_list, model = "2pl_latent_reg.stan", iter = 300, chains = 4) print_irt_stan(twopl_fit, spelling_list) # Fit the rating scale and partial credit models without a latent regression agg_list_1 <- irt_data(y = aggression$poly, ii = labelled_integer(aggression$description), jj = aggression$person) fit_rsm <- irt_stan(agg_list_1, model = "rsm_latent_reg.stan", iter = 300, chains = 4) print_irt_stan(fit_rsm, agg_list_1) fit_pcm <- irt_stan(agg_list_1, model = "pcm_latent_reg.stan", iter = 300, chains = 4) print_irt_stan(fit_pcm, agg_list_1) # Fit the generalized rating scale and partial credit models including # a latent regression agg_list_2 <- irt_data(y = aggression$poly, ii = labelled_integer(aggression$description), jj = aggression$person, covariates = aggression[, c("male", "anger")], formula = ~ 1 + male*anger) fit_grsm <- irt_stan(agg_list_2, model = "grsm_latent_reg.stan", iter = 300, chains = 4) print_irt_stan(fit_grsm, agg_list_2) fit_gpcm <- irt_stan(agg_list_2, model = "gpcm_latent_reg.stan", iter = 300, chains = 4) print_irt_stan(fit_grsm, agg_list_2) ## End(Not run) ``` ## See Also See `stan`, for which this function is a wrapper, for additional options. See `irt_data` and `labelled_integer` for functions that facilitate creating a suitable `data_list`. See `print_irt_stan` and `print.stanfit` for ways of getting tables summarizing parameter posteriors.

irt_stan function