44 lines
1.4 KiB
R
44 lines
1.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/pcm.R
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\name{pcm}
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\alias{pcm}
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\title{Compute Partial Credit Model (PCM) for polytomous and dichotomous items}
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\usage{
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pcm(
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df = NULL,
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items = NULL,
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grp = NULL,
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dif.items = NULL,
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type.dif = NULL,
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weights = NULL,
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verbose = T,
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fit = "ucminf",
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method.theta = "eap"
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)
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}
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\arguments{
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\item{df}{data.frame containing the data}
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\item{items}{vector containing the names of columns where item responses are stored in df}
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\item{grp}{string containing the name of the column where an optional group membership variable is stored in df}
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\item{dif.items}{vector containing the list of indexes in "items" corresponding to dif items}
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\item{type.dif}{vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF}
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\item{weights}{string containing the name of the column where an optional variable containing weights is stored in df}
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\item{verbose}{set to TRUE to print a detailed output, FALSE otherwise}
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\item{fit}{string determining the optimization algorithm. Values "ucminf" or "nlminb" ar recommended}
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\item{method.theta}{string determining the estimation method for individual latent variable values. Either "eap", "mle" or "wle"}
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}
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\value{
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A data.frame containing various model outputs
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}
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\description{
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This function computes a frequentist PCM, potentially accounting for DIF on specified items
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}
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