# itemfit: Parameter estimation of a test In LatentREGpp: Item Response Theory Implemented in R and Cpp

## Description

Estimates the test parameters according to the Multidimensional Item Response Theory

## Usage

 1 2 3 4 itemfit(data, dim, model = "2PL", EMepsilon = 1e-04, clusters = NULL, quad_tech = NULL, quad_points = NULL, individual_weights = as.numeric(c()), initial_values = NULL, SD = FALSE, verbose = TRUE, save_time = TRUE) 

## Arguments

 data The matrix containing the answers of tested individuals dim The dimensionality of the test model "1PL", "2PL" or "3PL" EMepsilon Convergence value to determine the accuracy of the test clusters A vector with cluster per dimension quad_tech A string with technique. "Gaussian" for Gaussian quadrature or "QMCEM" for Quasi-Monte Carlo quadrature quad_points Amount of quadrature points. If quadratura_technique is "Gaussian". It can be NULL individual_weights A vector with Weights of the quadrature points. initial_values A matrix with initial values for estimation process. Be sure about dimension, model and consistency with data. SD calculate for standar desviation for items verbose True for get information about estimation process in runtime. False in otherwise. save_time True for save estimation time. False otherwise.

## Models

LatentREGpp has different models to fit likelihood value according parameters to estimate.

3PL

General. Probability is given by

P_{ij} = c_j + \frac{1 - c_j}{1 + exp(-η_{ij})}

Where i references individual and j references the item; c is a value for guessing parameter between 0 and 1. i index is referenced by number of examinees or individuals and j index is referenced by items in test. η is

η_{ij} = \strong{\emph{a}}^{t}_jθ_i+d_j

In unidimensional an a is scalar, in multidimensional an a is vector. For 1PL model a has value 1

2PL

c = 0

1PL

c = 0 a vector has a value 1 for each element

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ## Not run: #Example 1 dir = normalizePath(system.file(package="LatentREGpp"),winslash = "/") folder = "/dataset/1D/dicho/" file = "1000x50-1.csv" data_dir = paste(c(dir, folder, file), collapse = "") data = read.table(file = data_dir, sep = ";") est <- itemfit(data = data, dim = 1) #Example 2 #Dichotomous and multidimensional data dir = normalizePath(system.file(package="LatentREGpp"),winslash = "/") folder = "/dataset/3D/dicho/" file = "1000x55-1.csv" data_dir = paste(c(dir, folder, file), collapse = "") data = read.table(file = data_dir, sep = ";") clust <- c(20,20,15) st <- itemfit(data = data, model = "2PL",dim = 3, EMepsilon = 1e-03, clusters = clust, quad_tech = "Gaussian") ## End(Not run) 

LatentREGpp documentation built on April 14, 2017, 11:55 a.m.