irt_me help file

Title     irt_me –  Calculates marginal effects for the latent variable (Theta) after IRT                models General syntax     irt_me [varlist] , [options] Overview     irt_me calculates marginal effects for the latent variable (theta) after an IRT     model. The latent variable is the independent variable; the variables specifed in the     varlist are the observed items which are the dependent variables in an IRT model. If     no varlist is specified, irt_me calculates marginal effects for theta across all of     the observed items.     irt_me supports all models that can be estimated using the Stata irt command and also     models for continuous and count items (regress, poisson, and nbreg). A mix of     different item types is also allowed (e.g., a mix of binary and ordinal items).         +———————————+     —-+ Required Option for gsem models +———————————————-     latent(string)                   is required if gsem was used to fit the model of interest. This is the                   name of the latent variable you wish to obtain marginal effects for. The                   latent( ) option should not be used if the model was fit with the irt                   command as irt automatically names the latent variable Theta.         +———+     —-+ Options +———————————————————————-     model(string)                   specifies the name of saved model estimates to use.  See estimates store                   for saving model estimates. By default, irt_me will use the IRT/GSEM                   estimates in memory. If the relevant model estimates are not in memory,                   you must specify their name.     decimals(#)   changes the number of decimal places reported for the statistics. The                   default is 3. Any integer between 1 - 8 is allowed.     start(#)      specifies the starting value for the prediction used in the calculation                   of the marginal effect. The default is -0.5 for a default marginal effect                   estimate of a centered +1 unit change.     end(#)        specifies the ending value for the prediction used in the calculation of                   the marginal effect. The default is 0.5 for a default marginal effect                   estimate of a centered +1 unit change.     range         calculates marginal effects across the trimmed range of the predicted                   values of the latent variable theta. Predictions are made at the 1st                   percentile of theta (start) and at the 99th percentile of theta (end)     title(string)                   changes the title of the table of results.  A default title is                   automatically included.     help          prints footnotes below the table describing what the columns in the table                   represent. Examples     webuse masc1     irt 2pl q1 q2 q3 q4 q5     irt_me, help     gsem (Theta -> q1 q2 q3 q4 q5, logit), var(Theta@1)     irt_me, latent(Theta) Authorship     irt_me is written by Trenton D Mize (Department of Sociology & Advanced     Methodologies, Purdue University).  Questions can be sent to tmize@purdue.edu