# Separate Item Parameters This function splits the item parameters in the specified object into discrimination/slope parameters, difficulty/step/threshold/category parameters, and lower asymptote/category probability parameters. methods ```r sep.pars(x, cat, poly.mod, dimensions = 1, location = FALSE, loc.out = FALSE, ...) ## S4 method for signature 'numeric' sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...) ## S4 method for signature 'matrix' sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...) ## S4 method for signature 'data.frame' sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...) ## S4 method for signature 'irt.pars' sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...) ## S4 method for signature 'list' sep.pars(x, cat, poly.mod, dimensions, location, loc.out, ...) ``` ## Arguments - `x`: Object containing item parameters. For details on the formatting of parameters for specific item response models see the corresponding methods (i.e., `drm`, `gpcm`, `grm`, `mcm`, and `nrm` ). See the `Methods` section for `as.irt.pars` for details on how to format the item parameters when combining parameters from multiple models. - `cat`: vector identifying the number of response categories for each item. If multiple-choice model items are included, `cat` for these items should equal the number of response categories plus one (the additional category is for 'do not know') - `poly.mod`: object of class `poly.mod` identifying the items associated with each IRT model - `dimensions`: number of modeled dimensions - `location`: if `TRUE`, the step parameters are deviations from a location parameter - `loc.out`: if `TRUE`, the step/threshold parameters will be reformated to be deviations from a location parameter - `...`: further arguments passed to or from other methods ## Returns Returns an object of class `sep.pars` ## Author(s) Jonathan P. Weeks weeksjp@gmail.com ## Examples ```r ###### Unidimensional Examples ###### # Create object for three dichotomous (1PL) items with difficulties -1, 0, 1 x <- sep.pars(c(-1,0,1)) # Create object for three dichotomous (3PL) items and two polytomous # (gpcm) items without a location parameter (the parameters are # formatted as a matrix) dichot <- matrix(c(1.2, .8, .9, 2.3, -1.1, -.2, .24, .19, .13),3,3) poly <- matrix(c(.64, -1.8, -.73, .45, NA, .88, .06, 1.4, 1.9, 2.6), 2,5,byrow=TRUE) pars <- rbind(cbind(dichot,matrix(NA,3,2)),poly) cat <- c(2,2,2,4,5) pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5)) x <- sep.pars(pars, cat, pm) summary(x) # Create object for three dichotomous (3PL) items and two polytomous # (gpcm) items without a location parameter (the parameters are # included in a list) a <- c(1.2, .8, .9, .64, .88) b <- matrix(c( 2.3, rep(NA,3), -1.1, rep(NA,3), -.2, rep(NA,3), -1.8, -.73, .45, NA, .06, 1.4, 1.9, 2.6),5,4,byrow=TRUE) c <- c(1.4, 1.9, 2.6, NA, NA) pars <- list(a,b,c) cat <- c(2,2,2,4,5) pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5)) x <- sep.pars(pars, cat, pm) summary(x) # Create object for three dichotomous (3PL) items, four polytomous # items, two gpcm items and two nrm items. Include a location parameter # for the gpcm items. Maintain the location parameter in the output. a <- matrix(c( 1.2, rep(NA,4), .8, rep(NA,4), .9, rep(NA,4), .64, rep(NA,4), .88, rep(NA,4), .905, .522, -.469, -.959, NA, .828, .375, -.357, -.079, -.817),7,5,byrow=TRUE) b <- matrix(c( 2.3, rep(NA,4), -1.1, rep(NA,4), -.2, rep(NA,4), -.69, -1.11, -.04, 1.14, NA, 1.49, -1.43, -.09, .41, 1.11, .126, -.206, -.257, .336, NA, .565, .865, -1.186, -1.199, .993),7,5,byrow=TRUE) c <- c(.14, .19, .26, rep(NA,4)) pars <- list(a,b,c) cat <- c(2,2,2,4,5,4,5) pm <- as.poly.mod(7, c("drm","gpcm","nrm"), list(1:3,4:5,6:7)) x <- sep.pars(pars, cat, pm, location=TRUE, loc.out=TRUE) summary(x, descrip=TRUE) # Create irt.pars object with two groups then run sep.pars pm <- as.poly.mod(36) x <- as.irt.pars(KB04$pars, KB04$common, cat=list(rep(2,36),rep(2,36)), list(pm,pm), grp.names=c("form.x","form.y")) out <- sep.pars(x) summary(out, descrip=TRUE) ###### Multidimensional Examples ###### # Create object for three dichotomous (M1PL) items for two dimensions # with parameters related to item difficulties of -1, 0, 1 x <- sep.pars(c(-1,0,1), dimensions=2) # Create object for three dichotomous (M3PL) items and two polytomous # (MGPCM) items without a location parameter for four dimensions # (the parameters are included in a list) a <- matrix(c(0.5038, 2.1910, 1.1317, 0.2493, 2.9831, 0.4811, 0.3566, 0.4306, 0.2397, 0.2663, 1.5588, 0.5295, 0.2020, 0.2410, 1.2061, 0.5552, 0.2054, 0.6302, 0.3152, 0.2037),5,4,byrow=TRUE) b <- matrix(c(0.5240, rep(NA,3), -1.8841, rep(NA,3), 0.2570, rep(NA,3), -1.4207, 0.3041, -0.5450, NA, -2.1720, 0.0954, 0.6531, 0.9114),5,4,byrow=TRUE) c <- c(0.1022, 0.3528, 0.2498, NA, NA) pars <- list(a,b,c) cat <- c(2,2,2,4,5) pm <- as.poly.mod(5, c("drm","gpcm"), list(1:3,4:5)) x <- sep.pars(pars, cat, pm, dimensions=4) summary(x, descrip=TRUE) ```

sep.pars-methods function