search for: gpcm

Dear useRs, does any of you have suggestions on how to conduct a proper DIF analysis starting from a model of class gpcm (from the wonderful package ltm by prof. Rizopoulos)? difR will handle only dichotomous items, and I have a mix of dicho- and polytomous ones (that's why I chose the partial credit model). I also found the package lordif, but I'm not really sure if that's what I need. I basically w...

...Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and plotting are available. New features include: * function gpcm() (along with supporting methods, for the generic functions, anova, plot, margins, factor.scores, summary, vcov) has been added for fitting the Generalized Partial Credit Model. The 'constraint' argument of gpcm() can be used to specify constrained versions of the model. Options are:...

...Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and plotting are available. New features include: * function gpcm() (along with supporting methods, for the generic functions, anova, plot, margins, factor.scores, summary, vcov) has been added for fitting the Generalized Partial Credit Model. The 'constraint' argument of gpcm() can be used to specify constrained versions of the model. Options are:...

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

The function factor.scores is used with package ltm and others to estimate IRT type scores for various models. It inherits objects of class grm, gpcm and a few others. What I would like to do is to use the factor.scores function, but feed it my own item parameters (from a bifactor model where the 2PL parameters are adjusted for the bifactor structure). Does anybody have an idea of how this might be done? I can, of course, create a list, matrix...

...j} = 0 or 1) and p(x) is Muraki's generalized partial credit model when items are scored polytomously (x_{ij} = 0 \ldots J). Now, I wrote the following two functions to maximize the likelihood. The first one is for the 3PL and works when all items are scored dichotmously. The second is for the GPCM and gives the MLE when all items are scored polytomously. In the code below, this requires that I first have in hand estimates of the item parameters so that the maximization is only w.r.t. theta. # x = response pattern # b = vector of location parameters # a = vector of discrimination parameter...

...flush(stderr()); flush(stdout()) > > ### Name: choose.M > ### Title: Constant Tuning > ### Aliases: choose.M > ### Keywords: misc > > ### ** Examples > > params <- list("3pl" = list(a = c(1,1), b = c(0, 1), c = c(0,0)), + "gpcm" = list(a = c(1,1), d = list(item1 = c(0 ,1,2,3,4), item2 = c(0,.5,1, 1.5)))) > choose.M(x = c(0,1,2,2), theta = -5, params = params, ind.dichot=c(1,2)) Error in posterior(x, theta, params, ind.dichot, ...) : couldn't find function "seq_along" Execution halted [[alte...

...m information and test information functions) 3) Various "Classical" statistics for item analysis 4) Coefficient alpha 5) Score conversion tables 6) Classification consistency analysis 7) Procedures for linking scales using mean equating Future development will include * 2PL, 3PL, and GPCM also using JML * Stocking-Lord procedure * Various methods for assessing DIF, including MH * Bayesian scoring methods (e.g., EAP) The JML procedure is not the world's best technology as MML is commonly preferred. However, that is already implemented in the ltm package and these procedures are...

...ion 'plink'. Parameters should be formatted as either an object of class 'irt.pars' with multiple groups, a set of 'irt.pars' objects, or a set of 'sep.pars' objects. Once in this format, response probabilities can be computed using the functions 'drm', 'gpcm', 'grm', 'mcm', or 'nrm' or linking constants can be computed using 'plink'. The functions 'as.irt.pars', 'sep.pars', and 'combine.pars' can be used to create the 'irt.pars' and 'sep.pars' objects. 'summary' can be...

...ion 'plink'. Parameters should be formatted as either an object of class 'irt.pars' with multiple groups, a set of 'irt.pars' objects, or a set of 'sep.pars' objects. Once in this format, response probabilities can be computed using the functions 'drm', 'gpcm', 'grm', 'mcm', or 'nrm' or linking constants can be computed using 'plink'. The functions 'as.irt.pars', 'sep.pars', and 'combine.pars' can be used to create the 'irt.pars' and 'sep.pars' objects. 'summary' can be...

...all linked tests. Once this object has been created, the function 'plink' is used to estimate the linking constants and rescale item and/or ability parameters. In addition to the estimation of linking constants, response probabilities can be computed using the functions 'drm', 'gpcm', 'grm', 'mcm', or 'nrm' (for both unidimensional and multidimensional models). I am currently working on a step-by-step walk through of the package, but for now the current documentation contains extensive details and examples. The best documentation to start with is h...

...all linked tests. Once this object has been created, the function 'plink' is used to estimate the linking constants and rescale item and/or ability parameters. In addition to the estimation of linking constants, response probabilities can be computed using the functions 'drm', 'gpcm', 'grm', 'mcm', or 'nrm' (for both unidimensional and multidimensional models). I am currently working on a step-by-step walk through of the package, but for now the current documentation contains extensive details and examples. The best documentation to start with is h...