findmodel: Create a model-type vector template
In manuelreif/PP: Person Parameter Estimation
Description
Usage
Arguments
Details
Author(s)
See Also
Examples
Description
This is a small helper function which creates a vector template quick and easily for the PPall() function. Modify this template as you like.
Usage
1
findmodel(thres)
Arguments
thres
A numeric matrix which contains the threshold parameter for each item. NA is allowed - in fact expected!
Details
This function tries to guess the model which was applied to each item by using the matrix of threshold parameters. It only discriminates between GPCM and 4-PL model, and returns a character vector of length equal to the number of items, that contains "GPCM" or "4PL" entries depending on the structure of the thres matrix.
Author(s)
Manuel Reif
See Also
PPall
Examples
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################# GPCM and 4PL mixed #########################################
# some threshold parameters
THRES <- matrix(c(-2,-1.23,1.11,3.48,1
,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2)
# slopes
sl <- c(0.5,1,1.5,1.1,1,0.98)
THRESx <- THRES
THRESx[2,1:3] <- NA
# for the 4PL item the estimated parameters are submitted,
# for the GPCM items the lower asymptote = 0
# and the upper asymptote = 1.
la <- c(0.02,0.1,0,0,0,0)
ua <- c(0.97,0.91,1,1,1,1)
awmatrix <- matrix(c(1,0,1,0,1,1,1,0,0,1
,2,0,0,0,0,0,0,0,0,1
,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5)
# create model2est
# this function tries to help finding the appropriate
# model by inspecting the THRESx.
model2est <- findmodel(THRESx)
# MLE
respmixed_mle <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua,type = "mle",
model2est=model2est)
# WLE
respmixed_wle <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua,type = "wle",
model2est=model2est)
# MAP estimation
respmixed_map <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "map",
model2est=model2est)
# EAP estimation
respmixed_eap <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "eap",
model2est=model2est)
# Robust estimation
respmixed_rob <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "robust",
model2est=model2est)
# summary to summarize the results
summary(respmixed_mle)
summary(respmixed_wle)
summary(respmixed_map)
summary(respmixed_eap)
summary(respmixed_rob)
manuelreif/PP documentation built on May 31, 2021, 11:23 p.m.
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Description Usage Arguments Details Author(s) See Also Examples
Description
This is a small helper function which creates a vector template quick and easily for the PPall() function. Modify this template as you like.
Usage
1 | findmodel(thres)
|
Arguments
thres |
A numeric matrix which contains the threshold parameter for each item. NA is allowed - in fact expected! |
Details
This function tries to guess the model which was applied to each item by using the matrix of threshold parameters. It only discriminates between GPCM and 4-PL model, and returns a character vector of length equal to the number of items, that contains "GPCM" or "4PL" entries depending on the structure of the thres matrix.
Author(s)
Manuel Reif
See Also
PPall
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ################# GPCM and 4PL mixed #########################################
# some threshold parameters
THRES <- matrix(c(-2,-1.23,1.11,3.48,1
,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2)
# slopes
sl <- c(0.5,1,1.5,1.1,1,0.98)
THRESx <- THRES
THRESx[2,1:3] <- NA
# for the 4PL item the estimated parameters are submitted,
# for the GPCM items the lower asymptote = 0
# and the upper asymptote = 1.
la <- c(0.02,0.1,0,0,0,0)
ua <- c(0.97,0.91,1,1,1,1)
awmatrix <- matrix(c(1,0,1,0,1,1,1,0,0,1
,2,0,0,0,0,0,0,0,0,1
,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5)
# create model2est
# this function tries to help finding the appropriate
# model by inspecting the THRESx.
model2est <- findmodel(THRESx)
# MLE
respmixed_mle <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua,type = "mle",
model2est=model2est)
# WLE
respmixed_wle <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua,type = "wle",
model2est=model2est)
# MAP estimation
respmixed_map <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "map",
model2est=model2est)
# EAP estimation
respmixed_eap <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "eap",
model2est=model2est)
# Robust estimation
respmixed_rob <- PPall(respm = awmatrix,thres = THRESx,
slopes = sl,lowerA = la, upperA=ua, type = "robust",
model2est=model2est)
# summary to summarize the results
summary(respmixed_mle)
summary(respmixed_wle)
summary(respmixed_map)
summary(respmixed_eap)
summary(respmixed_rob)
|
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