A class for fitted dynamic group IRT models

dgirt returns a fitted model object of class dgirt_fit, which inherits from dgo_fit.

Details

dgo 0.2.8 deprecated the dgirtfit class and replaced it with the dgirt_fit class.

Slots

dgirt_in

dgirtin-class data used to fit the model.

See also

dgmrp_fit dgo_fit

Examples

data(toy_dgirtfit)
# summarize the fitted results
summary(toy_dgirtfit, pars = 'xi')
#> Error in ss[["summary"]]: subscript out of bounds

# get posterior means with a convenience function
get_posterior_mean(toy_dgirtfit, pars = 'theta_bar')
#>         param state race3 year        mean
#>  1: theta_bar    SC black 2009  2.38534059
#>  2: theta_bar    SC black 2010  1.29432287
#>  3: theta_bar    SC other 2009 -1.45219991
#>  4: theta_bar    SC other 2010  0.06047909
#>  5: theta_bar    SC white 2009 -1.76593109
#>  6: theta_bar    SC white 2010 -1.50148111
#>  7: theta_bar    VA black 2009  2.18758597
#>  8: theta_bar    VA black 2010  1.58467566
#>  9: theta_bar    VA other 2009 -1.07409462
#> 10: theta_bar    VA other 2010  0.35206957
#> 11: theta_bar    VA white 2009 -1.01025758
#> 12: theta_bar    VA white 2010 -0.85962410

# generally apply functions to posterior samples after warmup; n.b.
# `as.array` is iterations x chains x parameters so `MARGIN = 3` applies
# `FUN` over iterations and chains
apply(as.array(toy_dgirtfit, pars = 'xi'), 3, mean)
#>     xi[1]     xi[2] 
#> 1.6310294 0.1488338 

# access the posterior samples
head(as.data.frame(toy_dgirtfit, pars = 'theta_bar'))
#>        param state race3 year iteration     value
#> 1: theta_bar    SC black 2009         1 3.5166090
#> 2: theta_bar    SC black 2009         2 1.5096366
#> 3: theta_bar    SC black 2009         3 1.7412044
#> 4: theta_bar    SC black 2009         4 1.8779541
#> 5: theta_bar    SC black 2009         5 1.6961017
#> 6: theta_bar    SC black 2009         6 0.9672285