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View Code? Open in Web Editor NEWMultidimensional item response theory
Home Page: https://philchalmers.github.io/mirt/
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installation trouble
I'm getting errors trying to install the latest version. Here's my R code and output (with some formatting to enhance readability):
'> library('devtools')
'> install_github('mirt', 'philchalmers', quick = TRUE)
Installing github repo(s) mirt/master from philchalmers
Installing mirt.zip from https://github.com/philchalmers/mirt/archive/master.zip
Installing mirt
"C:/PROGRA1/R/R-2151.3/bin/x64/R" --vanilla CMD build
"C:\Users\USERNAME\AppData\Local\Temp\RtmpI1eE0M\mirt-master" --no-manual
--no-resave-data --no-vignettes
- checking for file 'C:\Users\USERNAME\AppData\Local\Temp\RtmpI1eE0M\mirt-master/DESCRIPTION' ... OK
- preparing 'mirt':
- checking DESCRIPTION meta-information ... OK
- cleaning src
- checking for LF line-endings in source and make files
- checking for empty or unneeded directories
- looking to see if a 'data/datalist' file should be added
- building 'mirt_0.6.1.tar.gz'
"C:/PROGRA
1/R/R-2151.3/bin/x64/R" --vanilla CMD INSTALL
"C:\Users\USERNAME\AppData\Local\Temp\RtmpI1eE0M/mirt_0.6.1.tar.gz"
--library="C:/Program Files/R/R-2.15.3/library" --with-keep.source --no-docs
--no-multiarch --no-demo
- installing source package 'mirt' ...
- libs
g++ -m64 -I"C:/PROGRA
1/R/R-2151.3/include" -DNDEBUG -I"C:/Program Files/R/R->2.15.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c Estep.cpp -o Estep.o
g++ -m64 -I"C:/PROGRA1/R/R-2151.3/include" -DNDEBUG -I"C:/Program Files/R/R-2.15.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c Misc.cpp -o Misc.o
gcc -m64 -I"C:/PROGRA1/R/R-2151.3/include" -DNDEBUG -I"C:/Program Files/R/R-2.15.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -std=gnu99 -mtune=core2 -c dgroup.c -o dgroup.o
g++ -m64 -I"C:/PROGRA1/R/R-2151.3/include" -DNDEBUG -I"C:/Program Files/R/R-2.15.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c dpars.cpp -o dpars.o
g++ -m64 -I"C:/PROGRA1/R/R-2151.3/include" -DNDEBUG -I"C:/Program Files/R/R-2.15.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c traceLinePts.cpp -o traceLinePts.o
g++ -m64 -shared -s -static-libgcc -o mirt.dll tmp.def Estep.o Misc.o dgroup.o dpars.o traceLinePts.o C:/Program Files/R/R-2.15.3/library/Rcpp/lib/x64/libRcpp.a -Ld:/RCompile/CRANpkg/extralibs64/local/lib/x64 -Ld:/RCompile/CRANpkg/extralibs64/local/lib -LC:/PROGRA1/R/R-2151.3/bin/x64 -lR
g++.exe: error: C:/Program: No such file or directory
g++.exe: error: Files/R/R-2.15.3/library/Rcpp/lib/x64/libRcpp.a: No such file or directory
ERROR: compilation failed for package 'mirt'
- removing 'C:/Program Files/R/R-2.15.3/library/mirt'
- restoring previous 'C:/Program Files/R/R-2.15.3/library/mirt'
Error: Command failed (1)
The problem appears to be with the file names on my system, specifically the space in "C:/Program Files" (i.e., between "Program" and "Files". That piece repeated is:
g++.exe: error: C:/Program: No such file or directory
g++.exe: error: Files/R/R-2.15.3/library/Rcpp/lib/x64/libRcpp.a: No such file or directory
ERROR: compilation failed for package 'mirt'
Can you help?
I'm using R version 2.15.3 (2013-03-01) on a laptop running Windows 7 x64 Image v2. I installed Rtools30.exe yesterday (from http://cran.r-project.org/bin/windows/Rtools/).
Thanks -
Kevin Stanford
Cincinnati, Ohio USA
Relative efficiency plot
Construct a relative efficiency plot which will work for MultipleGroupClass and accross different nested model type (e.g., 1PL vs 2PL). RE = I(\theta,x) / I(\theta, y), with baseline at 1
mirt and confmirt with model parameter > 1
I just installed mirt_0.4.1-2 and tried some of the examples from the reference manual. For exploratory IRT the mirt and confmirt methods produce the following error if the model parameter is greater than 1:
Error in get(get(name, envir = exports, inherits = FALSE), envir = ns) :
internal error -3 in R_decompress1
Calls: mirt ... :: -> getExportedValue -> getInternalExportName -> get
In addition: Warning message:
In get(get(name, envir = exports, inherits = FALSE), envir = ns) :
restarting interrupted promise evaluation
Error in get(get(name, envir = exports, inherits = FALSE), envir = ns) :
internal error -3 in R_decompress1
Calls: mirt ... :: -> getExportedValue -> getInternalExportName -> get
I got this error with every sample data set i tried, for example if i call
pmod2 <- mirt(Science, 2)
(page 30 of the reference)
PS: Don't know whether this related, but during installation i got the following warnings
...
** inst
** byte-compile and prepare package for lazy loading
in method for 'itemplot.internal' with signature 'object="ExploratoryClass"': no definition for class "ExploratoryClass"
in method for 'itemplot.internal' with signature 'object="ConfirmatoryClass"': no definition for class "ConfirmatoryClass"
in method for 'itemplot.internal' with signature 'object="MultipleGroupClass"': no definition for class "MultipleGroupClass"
in method for 'fscores.internal' with signature '"ExploratoryClass"': no definition for class "ExploratoryClass"
in method for 'fscores.internal' with signature '"ConfirmatoryClass"': no definition for class "ConfirmatoryClass"
in method for 'fscores.internal' with signature '"MultipleGroupClass"': no definition for class "MultipleGroupClass"
Creating a generic function for 'residuals' from package 'stats' in package 'mirt'
...
Feature: artibrary vector in fscores()
Requested by Adilson dos Anjos: allow users to input individual response vectors into the function that were not in the original dataset.
Covariance Estimation Issue
Hello Phil,
I recently downloaded your latest version of “mirt” on GitHub (version 1.10.1), and I am running into a discrepancy when using this package on my PC versus on linux. I’m fitting between-item MIRT 2PL/GPCM models for my data and am specifying to freely estimate the covariances between the dimensions using the “COV” option in the “mirt.model” syntax (e.g., COV=F1F2F3). However, when I run on linux, all between-dimension covariances are always outputted as “0.25”, whereas when I run on my PC, the covariances are freely estimated as I specified them to be. I’m including a simple example below (modified version of one of your examples in the R mirt documentation). Do you know what could be causing this discrepancy? I tried updating my version on R to the most recent version (3.2.1) on linux and then installing “mirt” from GitHub, but that did not resolve the problem. Thank you very much for your help.
Sincerely,
Katherine
Example
library(mirt)
set.seed(52486)
simulate data
a <- matrix(c(
1.5,NA,NA,
0.5,NA,NA,
1.0,NA,NA,
NA,1.0,NA,
NA,1.5,NA,
NA,NA,0.5,
NA,NA,1.0,
NA,NA,1.0),ncol=3,byrow=TRUE)
d <- matrix(c(
-1.0,
-1.5,
1.5,
0.0,
3.0,
2.5,
2.0,
1.0),ncol=1,byrow=TRUE)
sigma <- diag(3)
sigma[upper.tri(sigma)] <- sigma[lower.tri(sigma)] <- .8
items <- rep('dich',8)
dataset <- simdata(a,d,2000,items,sigma)
analyses
model.1 <- mirt.model('
F1 = 1-3
F2 = 4-5
F3 = 6-8
COV = F1F2F3')
mod1 <- mirt(dataset, model.1, method = 'MHRM')
coef(mod1,simplify=TRUE)$cov
Results from my PC
coef(mod1,simplify=TRUE)$cov
F1 F2 F3
F1 1.000 NA NA
F2 0.637 1.000 NA
F3 0.686 0.657 1mod1
Call:
mirt(data = dataset, model = model.1, method = "MHRM")
Full-information item factor analysis with 3 factor(s).
Converged within 0.001 tolerance after 197 MHRM iterations.
mirt version: 1.10.1
M-step optimizer: NR
Log-likelihood = -7691.483, SE = 0.021
AIC = 15420.97; AICc = 15421.35
BIC = 15527.38; SABIC = 15467.02
G2 (236) = 207.6, p = 0.9087
RMSEA = 0, CFI = 1, TLI = 1.073>
Results from linux
coef(mod1,simplify=TRUE)$cov
F1 F2 F3
F1 1.00 NA NA
F2 0.25 1.00 NA
F3 0.25 0.25 1mod1
Call:
mirt(data = dataset, model = model.1, method = "MHRM")
Full-information item factor analysis with 3 factor(s).
Converged within 0.001 tolerance after 285 MHRM iterations.
mirt version: 1.10.1
M-step optimizer: NR
Log-likelihood = -7736.41, SE = 0.021
AIC = 15510.82; AICc = 15511.2
BIC = 15617.24; SABIC = 15556.87
G2 (236) = 297.28, p = 0.0042
One column 'covdata' argument to mixedmirt()
When 'covdata' argument to mixedmirt() is data frame with only one column, error occurs. It seems that such a data frame is converted to vector while mixedmirt() is making some operations on it. Simple workaround is to include more columns in the data frame provided as 'covdata' even when only one of them is used. Nevertheless it would be nice if also one-column data frame can be provided as 'covdata'.
Parallel processing for MH sampler
Parallel processing option for draw.thetas(), should work better now that the draws are done
mainly in R. May be especially useful in multilevel models
Request: Specify lbound and ubound in mirt
It would be very convenient to be able to specify upper and lower bounds for parameters when calling a model using mirt or other functions, similar to how priors can be specified. At present, the three-step process to extract pars='values', change the bounds, and pass the table back to the model can be rather cumbersome for coding.
Thanks!
Error in fscores() for the MultipleGroupClass
When using "scores.only=T" in "fscores()" for MultipleGroupClass the returned estimates are not sorted in the same way as rows in data provided to the "multipleGroup()" function.
Please compare lines 247 and 250 of the "R/fscores.internal.R".
model1 = mirt(myData, 1)
model2 = multipleGroup(myData, 1, groups, invariance=c('slopes', 'intercepts'))
scores1 = fscores(model1, full.scores=T)
scores2 = fscores(model2, full.scores=T)
scoresOnly1 = fscores(model1, full.scores=T, scores.only=T)
scoresOnly2 = fscores(model2, full.scores=T, scores.only=T)
plot(scores1[, 'F1'], scores2[, 'F1'])
plot(scoresOnly1, scoresOnly2)
leads to plots below:
Fit statistics in multigroup models with missing data
When I try to compute fit statistics for multigroup model with missing data I encounter an error:
Error in mod2values(x) :
cannot get a slot ("est") from an object of type "S4"
The problem does not occur in multigroup models without missing data nor in in single-group models with missing data, but coincidence of this two features (multigroup and missing data) causes problems.
See:
# code from examples in help for itemfit()
set.seed(1234)
a <- matrix(rlnorm(20, meanlog=0, sdlog = .1),ncol=1)
d <- matrix(rnorm(20),ncol=1)
items <- rep('dich', 20)
data <- simdata(a,d, 2000, items)
# further part of the example in which missing data is added
data[sample(1:prod(dim(data)), 500)] <- NA
raschfit <- mirt(data, 1, itemtype='Rasch')
Theta <- fscores(raschfit, method = 'ML', full.scores=TRUE)
itemfit(raschfit, impute = 10, Theta=Theta)
# new code - multigroup Rasch model (with full measurement invariance)
raschGrFit = multipleGroup(data, 1,
group = c(rep("gr1", 1000), rep("gr2", 1000)),
invariance = c("free_means", "free_var",
"slopes", "intercepts"),
itemtype = "Rasch")
# trying to compute itemfit (causes an error...)
ThetaGr <- fscores(raschGrFit, method = 'ML', full.scores=TRUE)
itemfit(raschGrFit, impute = 10, Theta=ThetaGr)multipleGroup function relative issues
(forwarding from https://groups.google.com/d/msg/mirt-package/JWkCc23LYa4/i9P9aIJP7foJ)
Here is my raw data and code.
https://www.dropbox.com/s/ugym9z4pa25ob8u/entrepreneur%20%28debug%29.tar.gz?dl=0
You may execute line number 1 to 55 and 74 to 148 to reproduce error messages.
Seongho Bae
Using Custom items
Hi, long time no see, actually I've working on the experimental Itemtype, but I forgot to fork so,
here I am.
Actually, I succeeded using createItem but I am having some trouble with using PRIOR or
setting STARTING POINTS, especially when categories are collapsed...
Here's my code and the funny thing is sometimes estimates are quite accurate even when
n.subj=100, n.item=10, but at other times, it's awful. and I don't know why,
it happens even when I delete all variables with rm(list=ls())
Here's code =====
Actually I have troublesome data stored in test_egrm.RData
but with n.subj=100 and n.item=10, categories are quite often collapsed... so
PS) and in my model, second factor is called xi, and it's moderating first factor so,
I don't think it can be called factor and when I do summary the model,
the result doesn't make sense, so...
create response with n.subj, n.item, ncat, and 1-dim latent var and xi, both from N(0,1)
createR=function(n.subj, n.item, ncat, Theta) {
#
a = runif(n.item, 1, 2)
d = matrix(runif(n.item*(ncat-1), -3, 3), ncol=ncat-1)
d = t(apply(d, 1, sort))
#P2.egrm for creating response data R
P2.egrm <- function(par, Theta, ncat) {
th1 = Theta[,1]; xi1 = Theta[,2];
a = par[1]
d = par[2:ncat]
d.mean=mean(d);
D.star = matrix(exp(Theta[,2]), nrow=nrow(Theta), ncol=ncat-1) *
matrix((d - d.mean) + d.mean, nrow=nrow(Theta), ncol=ncat-1, byrow=T)
TH1 = matrix(th1, nrow=nrow(Theta), ncol=ncat-1)
A = matrix(a, nrow=nrow(Theta), ncol=ncat-1)
P = 1/(1+exp(-1*(A*(TH1-D.star))))
return(P)
}
# P.egrm for creating Items
P.egrm <- function(par, Theta, ncat=4) {
th1 = Theta[,1]; xi1 = Theta[,2];
a = par[1]
d = par[2:ncat]
d.mean=mean(d);
D.star = matrix(exp(Theta[,2]), nrow=nrow(Theta), ncol=ncat-1) *
matrix((d - d.mean) + d.mean, nrow=nrow(Theta), ncol=ncat-1, byrow=T)
TH1 = matrix(th1, nrow=nrow(Theta), ncol=ncat-1)
A = matrix(a, nrow=nrow(Theta), ncol=ncat-1)
P = 1/(1+exp(-1*(A*(TH1-D.star))))
P.star=cbind(1, P)-cbind(P, 0)
# Is this correct or justifiable?
P.star <- ifelse(P.star < 1e-20, 1e-20, P.star)
P.star <- ifelse(P.star > (1 - 1e-20), (1 - 1e-20), P.star)
#
return(P.star)
}
R = matrix(0, nrow=n.subj, ncol=n.item)
R2 = matrix(0, nrow=n.subj, ncol=n.item)
for (i.item in 1:n.item) {
u <- runif(n.subj, 0, 1)
U <- matrix(u, ncol=ncat, nrow=n.subj)
P <- cbind(1, P2.egrm(c(a[i.item], d[i.item,]), Theta, ncat))
R[, i.item]=apply(U-P <= 0 , 1, function(x) max(which(x)))
R2[, i.item]=apply(U-P <=0, 1, sum) # in this case, all categories should be number 1,2,3,4,...
}
return(R)
}
Data(R) Generation
n.subj = 100; n.item = 10; ncat=4; # we need this for following scripts
Theta = matrix(rnorm(n.subj*2), ncol=2)
R <- createR(n.subj = n.subj, n.item = n.item, ncat=ncat, Theta=Theta)
save(R, file= "test_egrm.RData")
load("test_egrm.RData")
USING customItems EsTIMATE egrm model
name <- "c.egrm"
par <- c(a=1, d1=-1, d2=0, d3=1)
est <- c(T, T, T, T)
P.egrm for creating Items
P.egrm <- function(par, Theta, ncat) {
th1 = Theta[,1]; xi1 = Theta[,2];
a = par[1]
d = par[2:ncat]
d.mean=mean(d);
D.star = matrix(exp(Theta[,2]), nrow=nrow(Theta), ncol=ncat-1) *
matrix((d - d.mean) + d.mean, nrow=nrow(Theta), ncol=ncat-1, byrow=T)
TH1 = matrix(th1, nrow=nrow(Theta), ncol=ncat-1)
A = matrix(a, nrow=nrow(Theta), ncol=ncat-1)
P = 1/(1+exp(-1_(A_(TH1-D.star))))
P.star=cbind(1, P)-cbind(P, 0)
# Is this correct or justifiable?
P.star <- ifelse(P.star < 1e-20, 1e-20, P.star)
P.star <- ifelse(P.star > (1 - 1e-20), (1 - 1e-20), P.star)
#
return(P.star)
}
item.egrm <- createItem(name, par=par, est=est, P=P.egrm)
mirt(R, 2, rep('egrm', n.item), customItems=list(egrm=item.egrm), pars='values')
system.time(mod1 <- mirt(R, 2, rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm)))
Are the estimates accurate?
cor(fscores(mod1, full.scores=T)[,1], Theta[,1])
cor(fscores(mod1, full.scores=T)[,2], Theta[,2])
Estimating with PRIORS
s.mirt.model1 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (1-10, d3, norm, 0, 1)"
s.mirt.model2 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a1, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (1-10, d3, norm, 0, 1)"
s.mirt.model3 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a1, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (2-10, d3, norm, 0, 1)"
s.mirt.model4 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a1, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1)"
system.time(mod1.prior <- mirt(R, mirt.model(s.mirt.model1), rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm)))
Works fine, but item1 has parameter "d3"
coef(mod1.prior)
Are the estimates accurate?
cor(fscores(mod1.prior, full.scores=T)[,1], Theta[,1])
cor(fscores(mod1.prior, full.scores=T)[,2], Theta[,2])
And estimates are poor
s.mirt.model11 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (1-10, d3, norm, 0, 1)
"
system.time(mod21.prior <- mirt(R, mirt.model(s.mirt.model11), rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm)))
cor(fscores(mod21.prior, full.scores=T)[,1], Theta[,1])
cor(fscores(mod21.prior, full.scores=T)[,2], Theta[,2])
Estimates distorted?
Is it because of d3 parameter of item1?
s.mirt.model12 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (2-10, d3, norm, 0, 1)"
system.time(mod22.prior <- mirt(R, mirt.model(s.mirt.model12), rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm)))
cor(fscores(mod22.prior, full.scores=T)[,1], Theta[,1])
cor(fscores(mod22.prior, full.scores=T)[,2], Theta[,2])
Or maybe Starint point?
s.mirt.model13 <- "F1 = 1-10
F2 = 1-10
PRIOR = (1-10, a, lnorm, 0, 1), (1-10, d1, norm, 0, 1), (1-10, d2, norm, 0, 1), (1-10, d3, norm, 0, 1)
START = (1-10, a, 1.0), (1-10, d1, -1.0), (1-10, d2, 0), (1-10, d3, 1.0)"
mirt(R, mirt.model(s.mirt.model13), rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm), pars='values')
temp <- mirt(R, mirt.model(s.mirt.model13), rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm), technical=list(NCYCLES=1))
coef(temp)
If you look at the result of coef(temp), START doesn't seem to work.
THE LAST RESORT
parprior=list()
parnum=1:40
itemtype=rep("norm", 40); par1=rep(0, 40); par2=rep(1, 40);
itemtype[seq(1,37,4)]="lnorm";
for (i.parnum in parnum) {
parprior=c(parprior, list(c(i.parnum, itemtype[i.parnum],par1[i.parnum],par2[i.parnum])))
}
system.time(mod23.prior <- mirt(R, 2, rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm), parprior=parprior))
mirt(R, 2, rep('c.egrm', n.item), customItems=list(c.egrm=item.egrm), pars="values")
Are the estimates accurate?
cor(fscores(mod23.prior, full.scores=T)[,1], Theta[,1])
cor(fscores(mod23.prior, full.scores=T)[,2], Theta[,2])
Not so good, .16, .10
maybe parameter d3 of item1 should be deleted?
Quite a long scrips, anyway, to summarize,
I think when there's PRIOR for parameters that are useless from collapsing,
i think i should be deleted,
and START = doesn't seem to work when using customItems,
anyway I already created built-in itemtype but since I didn't fork,
I don't know how I can show it to you,
maybe pull and fork and moving the files...
hope you can give some advice on how to managing with RStudio and github and fork and branching?
Cheers
Warning in mac during install
Hello, Phil.
I was found a warning message in mac during install the dev version. Cheers!
Best regards,
Seongho Bae
> try(library('devtools'), silent = T)
> try(install_github('philchalmers/mirt'), silent = T)
Downloading github repo philchalmers/mirt@master
Installing mirt
'/Library/Frameworks/R.framework/Resources/bin/R' --vanilla CMD INSTALL \
'/private/var/folders/sw/y1nzt3cj2dv0qbp6_tmd8v7w0000gn/T/RtmpNAeypC/devtools3e7722b5a11b/philchalmers-mirt-3141f85' \
--library='/Library/Frameworks/R.framework/Versions/3.2/Resources/library' --install-tests
* installing *source* package ‘mirt’ ...
** libs
clang++ -I/Library/Frameworks/R.framework/Resources/include -DNDEBUG -I/usr/local/include -I/usr/local/include/freetype2 -I/opt/X11/include -DPLATFORM_PKGTYPE='"mac.binary.mavericks"' -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/RcppArmadillo/include" -fPIC -Wall -mtune=core2 -g -O2 -c Estep.cpp -o Estep.o
clang++ -I/Library/Frameworks/R.framework/Resources/include -DNDEBUG -I/usr/local/include -I/usr/local/include/freetype2 -I/opt/X11/include -DPLATFORM_PKGTYPE='"mac.binary.mavericks"' -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/RcppArmadillo/include" -fPIC -Wall -mtune=core2 -g -O2 -c Misc.cpp -o Misc.o
clang++ -I/Library/Frameworks/R.framework/Resources/include -DNDEBUG -I/usr/local/include -I/usr/local/include/freetype2 -I/opt/X11/include -DPLATFORM_PKGTYPE='"mac.binary.mavericks"' -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/RcppArmadillo/include" -fPIC -Wall -mtune=core2 -g -O2 -c dpars.cpp -o dpars.o
dpars.cpp:183:15: warning: unused variable 'npars2' [-Wunused-variable]
const int npars2 = nfact + nfact * (nfact + 1) / 2;
^
1 warning generated.
clang++ -I/Library/Frameworks/R.framework/Resources/include -DNDEBUG -I/usr/local/include -I/usr/local/include/freetype2 -I/opt/X11/include -DPLATFORM_PKGTYPE='"mac.binary.mavericks"' -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/3.2/Resources/library/RcppArmadillo/include" -fPIC -Wall -mtune=core2 -g -O2 -c traceLinePts.cpp -o traceLinePts.o
clang++ -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o mirt.so Estep.o Misc.o dpars.o traceLinePts.o -framework Accelerate -L/usr/local/lib/gcc/x86_64-apple-darwin13.0.0/4.8.2 -lgfortran -lquadmath -lm -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/3.2/Resources/library/mirt/libs
** R
** data
*** moving datasets to lazyload DB
** inst
** tests
** byte-compile and prepare package for lazy loading
Creating a generic function for ‘print’ from package ‘base’ in package ‘mirt’
Creating a generic function for ‘anova’ from package ‘stats’ in package ‘mirt’
Creating a generic function for ‘residuals’ from package ‘stats’ in package ‘mirt’
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (mirt)
> sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.4 (Yosemite)
locale:
[1] ko_KR.UTF-8/ko_KR.UTF-8/ko_KR.UTF-8/C/ko_KR.UTF-8/ko_KR.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] devtools_1.7.0
loaded via a namespace (and not attached):
[1] httr_0.6.1 magrittr_1.5 tools_3.2.0 RCurl_1.95-4.6 stringi_0.4-1 knitr_1.10 stringr_1.0.0 bitops_1.0-6
> WLE and BME factor scores
Add weighted factor score estimates for ML and MAP method (Warm, 1989).
Warning: M-step optimimizer converged immediately.
Hello,
I am having this warning and don't know how to fix it...
Iteration: 1, Log-Lik: -3129.128, Max-Change: 0.00000
Warning: M-step optimimizer converged immediately. Solution is either at the ML or
starting values are causing issues and should be adjusted.
I tried different starting values but got the same result.
Here's my code
fit <- mirt(R, 1, itemtype="grsm", TOL=1e-14)
pars <- mirt(R, 1, itemtype="grsm", TOL=1e-14, pars='values')
pars$value[1:21] = 1
fit <- mirt(R, 1, itemtype="grsm", TOL=1e-14, pars=pars)
and Here's data for reproducing the result.
R <-
structure(c(4, 4, 5, 4, 4, 5, 5, 5, 3, 5, 4, 4, 5, 4, 3, 4, 4,
4, 4, 3, 5, 5, 4, 5, 3, 5, 4, 3, 5, 4, 5, 4, 5, 5, 5, 5, 4, 4,
5, 5, 4, 5, 4, 3, 5, 4, 4, 5, 4, 4, 5, 5, 4, 3, 4, 4, 5, 4, 4,
5, 5, 4, 5, 4, 5, 5, 4, 4, 4, 4, 5, 5, 3, 5, 4, 5, 5, 5, 5, 4,
4, NA, 4, 5, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 5,
5, 5, 5, 4, 3, 5, 5, 3, 3, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 4, 5,
5, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 4, 5, 4, 5, 5, 4, 2, 5, 5, 5,
5, NA, 4, 4, 5, 3, 5, 4, 5, 5, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4,
5, 5, 4, 5, 3, 4, 5, 4, 4, 5, 4, 5, 5, 4, 5, 3, 4, 3, 5, 4, 5,
4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 4, 5, 4, 4, 4, 5, 4, 4,
5, 4, 5, 5, 4, 5, 5, 4, 4, 3, 5, 4, 5, 4, 4, 5, 4, 5, 4, 4, 4,
5, 4, 4, 4, 5, 5, 4, 5, 5, 5, 4, 5, 4, 5, 4, 5, 5, 5, 4, 5, 5,
4, 3, 4, 4, 4, 3, 4, 4, 5, 4, 3, 5, 3, 5, 5, 3, 5, 5, 5, 5, 4,
3, 4, 4, 4, 3, 3, 4, 4, 5, 5, 5, 4, 4, 4, 4, 5, 5, 5, 5, 1, 5,
5, 4, 4, 4, 4, 4, 5, 5, 5, 3, NA, 4, 5, 4, 4, 5, 5, 3, 3, 5,
4, 2, 5, 4, 3, 4, 3, 4, 5, 2, 5, 5, 5, 5, 4, 5, 4, 4, 3, 4, 4,
4, 4, 3, 5, 5, 3, 4, 4, 5, 4, 5, 4, 3, 5, 4, 4, 5, 4, 4, 5, 5,
4, 2, 4, 3, 5, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 3, 4, 4, 5, 4, 5,
5, 4, 5, 5, 4, 4, 4, 3, NA, 3, 4, 4, 5, 5, 5, 4, 2, 5, 5, 5,
4, 4, 4, 4, 3, 5, 5, 5, 3, 5, 4, 2, 4, 5, 4, 3, 4, 5, 5, 5, 3,
4, 5, 4, 3, 5, 5, 5, 4, 4, 3, 4, 3, 5, 4, 5, 5, 5, 4, 5, 5, 5,
4, 5, 4, 3, 5, 4, 5, 4, 4, 5, 4, 4, 3, 5, 4, 5, 4, 4, 5, 4, 5,
4, 4, 5, 4, 3, 3, 4, 4, 4, 5, 3, 4, 4, 3, 2, 4, 5, 5, 4, 5, 5,
NA, 3, 3, 5, 4, 5, 4, 4, 5, 5, 5, 5, 5, 5, 2, 3, 4, 4, 4, 4,
4, 5, 4, 3, 5, 4, 4, 4, 5, 5, 5, 4, 5, NA, 2, 4, 3, 4, 4, 4,
4, 2, 5, 4, 4, 3, 4, 4, 5, 3, 4, 3, 4, 5, 4, 3, 5, 4, 3, 5, 4,
4, 4, 4, 5, 4, 4, 3, 5, 4, 4, 5, 4, 4, 3, 3, 4, 5, 5, 3, 4, NA,
5, 3, 4, 5, 3, 4, 5, 3, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 5, 4, 4,
4, 2, 5, 5, 4, 3, 2, 5, 5, 4, 4, 5, 4, 4, 5, 5, 4, 5, NA, 4,
3, 4, 4, 5, 5, 5, 3, 5, 4, 4, 5, 4, 3, 4, 2, 4, 4, 2, 5, 5, 4,
5, 4, 5, 4, 4, 5, 5, 4, 4, 4, 3, 5, 5, 4, 4, 3, 5, 4, 5, 4, 4,
5, 4, 4, 5, 4, 3, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 5, 4, 4,
5, 4, 4, 4, 4, 3, 4, 5, 5, 4, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4,
5, 5, 5, 3, 4, 5, 5, 5, 4, 4, 3, 4, 5, 5, 5, 3, 5, 4, 3, 4, 5,
3, 3, 4, 5, 5, 5, 4, 3, 5, 4, 4, 5, 5, 5, 4, 4, 4, 4, 3, 4, 4,
5, 5, 5, 4, 5, 5, 3, 5, 5, 4, 2, 4, 5, 5, 4, 4, 5, 4, 4, 3, 5,
4, 5, 4, 4, 5, 4, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5, 3, 4, 4, 3,
4, 5, 5, 5, 5, 4, 5, 2, 3, 5, 5, 4, 5, 4, 4, 3, 5, 5, 4, 5, 5,
4, 4, 5, 4, 4, 4, 5, 5, 4, 3, 5, 4, 4, 4, 4, 5, 4, 4, 5, 4, 3,
4, 3, 4, 4, 5, 4, 3, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 4, 5, NA,
4, 5, 4, 3, 5, 4, 4, 5, 5, 5, 4, 4, 3, 5, 4, 4, 5, 4, 4, 3, 3,
4, 4, 5, 3, 4, 3, 5, 5, 4, 5, 5, 4, 4, 3, 3, 4, 4, 5, 4, 4, 4,
4, 4, 4, 5, 3, 4, 5, 4, 5, 4, 5, 4, 3, 5, 5, 5, 4, 4, 4, 4, 5,
5, 5, 5, NA, 4, 3, 4, 3, 5, 5, 5, 3, 5, 4, 4, 5, 4, NA, 4, 2,
4, 4, 2, 5, 5, 4, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 5, 5, 3, 4,
3, 5, 5, 5, 4, 3, 5, 4, 4, 5, 4, 2, 4, 4, 4, 4, 4, 4, 5, 4, 4,
4, 5, 5, 4, 4, 4, 5, 5, 4, 3, 4, 4, 5, 5, 5, 4, 5, 4, 5, 5, 4,
5, NA, 5, 4, 4, 4, 5, 4, 4, 3, 4, 5, 5, 5, 4, 4, 4, 4, 4, 5,
5, 2, 4, 5, 3, 5, 5, 3, 3, 4, 4, 5, 5, 4, 3, 5, 4, 4, 5, 4, 5,
4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 5, 4, 3, 5, 4, 3, 4, 5, 5,
2, 5, 5, 4, 4, 3, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 3, 4,
5, 5, 5, 3, 3, 4, 3, 4, 4, 4, 5, 5, 5, 5, 4, 3, 3, 5, 4, 5, 4,
4, 4, 5, 4, 4, 5, 5, 3, 3, NA, 4, 4, 4, 4, 5, 4, 3, 5, 4, 4,
5, 4, 5, 5, 4, 5, NA, 3, 5, 3, 4, 4, 4, 4, 4, 5, 5, 5, 4, 5,
4, 5, 3, 5, 4, 4, 5, 4, 2, 5, 4, 3, 4, 4, 4, 5, 5, 5, 4, 4, 3,
5, 2, NA, 4, 4, 5, 3, 2, 4, 5, 5, 3, 4, 4, 5, 5, 3, 5, 5, 4,
4, 2, 3, 4, 5, 5, 4, 4, 4, 4, 4, 5, 5, 3, 4, 5, 4, 5, 4, 5, 4,
4, 5, 5, 5, 4, 4, 4, 4, 5, 5, 5, 4), .Dim = c(298L, 4L), .Dimnames = list(
c("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", "58", "59", "60", "61",
"62", "63", "64", "65", "66", "67", "68", "69", "70", "71",
"72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91",
"92", "93", "94", "95", "96", "97", "98", "99", "100", "101",
"102", "103", "104", "105", "106", "107", "108", "109", "110",
"111", "112", "113", "114", "115", "116", "117", "118", "119",
"120", "121", "122", "123", "124", "125", "126", "127", "128",
"129", "130", "131", "132", "133", "134", "135", "136", "137",
"138", "139", "140", "141", "142", "143", "144", "145", "146",
"147", "148", "149", "150", "151", "152", "153", "154", "155",
"156", "157", "158", "159", "160", "161", "162", "163", "164",
"165", "166", "167", "168", "169", "170", "171", "172", "173",
"174", "175", "176", "177", "178", "179", "180", "181", "182",
"183", "184", "185", "186", "187", "188", "189", "190", "191",
"192", "193", "194", "195", "196", "197", "198", "199", "200",
"201", "202", "203", "204", "205", "206", "207", "208", "209",
"210", "211", "212", "213", "214", "215", "216", "217", "218",
"219", "220", "221", "222", "223", "224", "225", "226", "227",
"228", "229", "230", "231", "232", "233", "234", "235", "236",
"237", "238", "239", "240", "241", "242", "243", "244", "245",
"246", "247", "248", "249", "250", "251", "252", "253", "254",
"255", "256", "257", "258", "259", "260", "261", "262", "263",
"264", "265", "266", "267", "268", "269", "270", "271", "272",
"273", "274", "275", "276", "277", "278", "279", "280", "281",
"282", "283", "284", "285", "286", "287", "288", "289", "290",
"291", "292", "293", "294", "295", "296", "297", "298"),
c("1", "2", "3", "4")))
If I fit other models(graded, gpcm, Rasch), it works fine.
Here's the result from other models...
logLik AIC AICc BIC SABIC DIC
graded -1002.291 2038.581 2040.767 2101.432 2047.519 2038.581
grsm -3129.128 6274.257 6274.755 6303.833 6278.462 6274.257
gpcm -1010.193 2054.387 2056.572 2117.237 2063.324 2054.387
Rasch -1025.173 2078.346 2079.830 2130.106 2085.706 2078.346
Do FIX and START work for experimental item?
Since I have to use experimental items.. I pushed my modified package on my site...
Here's code to replicate the problem..
rm(list=ls())
R <-
structure(c(1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 3, 1, 2, 1, 2, 1, 1,
2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1,
1, 1, 2, 2, 3, 3, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 2, 1, 2, 4,
1, 2, 2, 2, 1, 1, 4, 1, 1, 2, 1, 1, 3, 2, 1, 4, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 3,
2, 4, 1, 3, 2, 2, 1, 1, 1, 3, 3, 2, 3, 2, 3, 3, 2, 1, 2, 3, 3,
3, 1, 1, 1, 3, 3, 2, 2, 3, 1, 3, 3, 1, 2, 2, 3, 1, 3, 2, 2, 3,
3, 1, 2, 3, 1, 3, 3, 2, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 2, 2,
1, 4, 1, 3, 3, 1, 3, 3, 2, 3, 4, 1, 3, 3, 3, 2, 2, 3, 3, 3, 2,
2, 3, 1, 2, 1, 1, 1, 2, 2, 3, 3, 3, 1, 3, 2, 3, 2, 1, 4, 2, 2,
2, 1, 1, 1, 4, 1, 4, 4, 2, 3, 3, 2, 3, 2, 4, 3, 4, 4, 1, 1, 4,
4, 3, 2, 3, 1, 3, 1, 1, 2, 2, 2, 1, 3, 2, 2, 2, 4, 1, 4, 1, 1,
4, 3, 1, 3, 3, 4, 3, 2, 1, 2, 4, 3, 4, 4, 2, 1, 1, 4, 1, 3, 3,
1, 1, 3, 2, 4, 4, 1, 3, 3, 3, 2, 4, 3, 4, 1, 2, 2, 2, 1, 1, 1,
1, 1, 2, 2, 1, 2, 3, 1, 1, 2, 3, 2, 1, 3, 1, 2, 1, 1, 1, 1, 4,
1, 4, 1, 2, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 1,
3, 1, 1, 2, 3, 1, 1, 2, 2, 3, 3, 4, 1, 2, 1, 1, 1, 1, 1, 4, 1,
3, 3, 2, 1, 2, 1, 3, 4, 3, 1, 3, 1, 4, 1, 3, 1, 1, 1, 3, 2, 1,
2, 1, 2, 2, 1, 2, 3, 1, 3, 1, 2, 2, 1, 1, 3, 1, 1, 1, 2, 2, 2,
1, 2, 1, 3, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 4, 4, 1, 4, 2, 2,
1, 1, 1, 2, 1, 1, 4, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 1, 1, 2, 3,
3, 1, 2, 1, 3, 2, 4, 1, 2, 1, 1, 3, 1, 1, 4, 1, 3, 3, 2, 1, 2,
1, 3, 4, 1, 1, 1, 1, 2, 1, 1, 3, 1, 1, 3, 2, 1, 1, 1, 4, 4, 2,
2, 4, 1, 3, 3, 2, 2, 2, 1, 3, 1, 1, 1, 2, 2, 1, 1, 2, 1, 3, 2
), .Dim = c(100L, 5L))
ncat = function(R) {
stopifnot(is.matrix(R))
max(apply(R, 2, function(x) length(unique(x[!is.na(x)]))))
}
n.item <- ncol(R); ncat <- ncat(R)
# Do START and FIXED work?
model <- paste("F1 = 1-",n.item,
"\nCONSTRAIN = (1-",n.item,",d1)",
paste(",(1-",n.item,",d",2:(ncat-1),")",sep="", collapse=""),
"\nSTART = (1, c, 0.0)",
"\nFIXED = (1, c)",
sep="")
fit.grsm3_ <- mirt(R, mirt.model(model), itemtype=rep("grsm3", n.item))
coef2mat = function(cf) {
stopifnot(is.list(cf))
do.call(rbind, cf[1:(length(cf)-1)])
}
coef2mat(coef(fit.grsm3_))
Check if the c for first item is 0...
Ability estimation
From Okan Bulut:
"I'm wondering if I can use "fscores" function to estimate abilities by providing item parameters and response data. I don't want to estimate item parameters. I will use real item parameters and simulated response data to estimate abilities. I know that "fscores" function uses mirt class objects. Can I manually create input for this function? Also, I have another question. Does EAP and MAP take the correlations among dimensions into account when estimating abilities?"
fscores() 2-dim inputs fail
Hello Phil,
When working with uni-dimensional model if I was giving response pattern for few of the items and make rest in the data as NA I was still getting the fscores() result.
When I tried this with 2 dimensional but it is throwing error.
I can give an example to demonstrate what is going wrong
data(LSAT7)
data_LSAT7=expand.table(LSAT7)
modL <- mirt(data_LSAT7, 1)
> fscores(modL, response.pattern = c(1,1,1,1,NA), method="EAP")
Item.1 Item.2 Item.3 Item.4 Item.5 F1 SE_F1
[1,] 1 1 1 1 NA 0.683704 0.8074962
> fscores(modL, response.pattern = c(1,NA,NA,NA,NA), method="EAP")
Item.1 Item.2 Item.3 Item.4 Item.5 F1 SE_F1
[1,] 1 NA NA NA NA 0.1491945 0.9488469As one can see, as long as I am providing at least one response I am getting the score.
Whereas, when I try to do the same thing for multidimensional models, it doesn't work, throws an error.
for 2 dimensional:
modL2_noRot = mirt(data_LSAT7, 2, rotate = "none")
fscores(modL2, response.pattern = c(1,1,1,1,NA), method="ML")
Error in `rownames<-`(`*tmp*`, value = c("Item.1", "Item.2", "Item.3", :
length of 'dimnames' [1] not equal to array extentsame happens for 3 dimensional models
modL3_noRot = mirt(data_LSAT7, 3, rotate = "none")
fscores(modL3_noRot, response.pattern = c(1,1,1,1,NA), method="ML")
Error in `rownames<-`(`*tmp*`, value = c("Item.1", "Item.2", "Item.3", :
length of 'dimnames' [1] not equal to array extentIs there any way to fix this error?
Thanking you,
Irshad
Valid group names in multipleGroup()
The problem was on my side, sorry for bothering you.
mirt bugs
Hello there
Been using the MIRT package (version 0.4.2). It’s been really useful and seems to work very well but I have spotted a couple of bugs (I think) that I thought I ought to report in case you’re interested.
I think the “read.mirt” functions divides the item difficulty parameters by “D” when it doesn’t need to.
Under the intial item parametrization (as written in the 1-4PL section on page 29 of the manual) D is already factored out of the location parameters and so the bit in the function read.mirt that divides by D
“
abc[2] <- -abc[2]/(abc[1] * D)
”
isn’t necessary.
Not sure the item information function works quite as it should. Fitted a unidimensional model with some 3PL items and found that some of the item information functions generated by “extract.item” and “iteminfo” looked very odd. Specifically they didn’t peak at the item location and in fact seemed to get larger and larger with increasing abilities. Happy to send over my data and code if you’re interested in investigating this.
Best wishes
Tom
Tom Benton
Principal Research Officer
Assessment Research and Development Division
Cambridge Assessment
1 Regent Street, Cambridge CB1 2EU
Telephone: +44 (0) 1223 558706
www.cambridgeassessment.org.uk
Cambridge Assessment is the brand name of the University of Cambridge Local Examinations Syndicate, a department of the University of Cambridge. Cambridge Assessment is a not-for-profit organisation.__
fscores(): response pattern containing NA causes: Error in tabdata[i, ] : subscript out of bounds
Could you please let me know if I am doing something wrong. Thanks in advance.
Executing following:
data <- expand.table(LSAT7)
fscores(mod, response.vector = c(0,NA,0,0,0))causes an error:
Error in tabdata[i, ] : subscript out of boundsp.s. I tried fscore() with different method parameters and all of them produced the same error; with the exception of EAPsum (although it is probably not using the response parameter).
LSAT7 does not contain NA responses; but the same error occurs with the data that does; e.g.:
fscores(mod, method='MAP') # ability estimates
Method: MAP
Empirical Reliability:
F1
0.0193
V1 V2 V3 Freq F1 SE_F1
[1,] 0 0 0 222 -1.396151e+00 0.7691358
[2,] 0 0 1 268 -1.090677e+00 0.7731981
[3,] 0 0 NA 219 -1.215985e+00 0.7853614
[4,] 0 1 0 263 -9.076819e-01 0.7803819
[5,] 0 1 1 378 -5.867751e-01 0.8001421
[6,] 0 1 NA 379 -6.998354e-01 0.8076445
[7,] 0 NA 0 80 -1.091278e+00 0.8120889
[8,] 0 NA 1 176 -7.463413e-01 0.8269984
[9,] 0 NA NA 928 -8.744946e-01 0.8376759
...
Warning messages in Windows during install but maybe harmful
Hello, Phil.
I'm reporting warning messages in Windows during install but maybe harmful. Cheers!
Best regards,
Seongho Bae
> try(library('devtools'), silent = T)
> try(install_github('philchalmers/mirt'), silent = T)
Downloading github repo philchalmers/mirt@master
Installing mirt
"C:/PROGRA~1/RRO/R-32~1.0/bin/x64/R" --vanilla CMD INSTALL \
"C:/Users/Seongho/AppData/Local/Temp/Rtmpoxp5M1/devtoolsd2546ca626ca/philchalmers-mirt-dbe2a10" \
--library="C:/Users/Seongho/Documents/R/win-library/3.2" --install-tests
* installing *source* package 'mirt' ...
** libs
g++ -m64 -I"C:/PROGRA~1/RRO/R-32~1.0/include" -DNDEBUG -I"C:/Users/Seongho/Documents/R/win-library/3.2/Rcpp/include" -I"C:/Users/Seongho/Documents/R/win-library/3.2/RcppArmadillo/include" -I"c:/applications/extsoft/include" -O2 -Wall -mtune=core2 -c Estep.cpp -o Estep.o
g++ -m64 -I"C:/PROGRA~1/RRO/R-32~1.0/include" -DNDEBUG -I"C:/Users/Seongho/Documents/R/win-library/3.2/Rcpp/include" -I"C:/Users/Seongho/Documents/R/win-library/3.2/RcppArmadillo/include" -I"c:/applications/extsoft/include" -O2 -Wall -mtune=core2 -c Misc.cpp -o Misc.o
g++ -m64 -I"C:/PROGRA~1/RRO/R-32~1.0/include" -DNDEBUG -I"C:/Users/Seongho/Documents/R/win-library/3.2/Rcpp/include" -I"C:/Users/Seongho/Documents/R/win-library/3.2/RcppArmadillo/include" -I"c:/applications/extsoft/include" -O2 -Wall -mtune=core2 -c dpars.cpp -o dpars.o
dpars.cpp: In function 'void _dgroup(std::vector<double, std::allocator<double> >&, Rcpp::NumericMatrix&, const NumericMatrix&, const mat&, const mat&, const vec&, const vec&, const bool&)':
dpars.cpp:271:34: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
dpars.cpp:272:38: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
dpars.cpp: In function 'void _dgroup_pre(std::vector<double, std::allocator<double> >&, Rcpp::NumericMatrix&, Rcpp::S4&, const NumericMatrix&, const bool&)':
dpars.cpp:296:38: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
g++ -m64 -I"C:/PROGRA~1/RRO/R-32~1.0/include" -DNDEBUG -I"C:/Users/Seongho/Documents/R/win-library/3.2/Rcpp/include" -I"C:/Users/Seongho/Documents/R/win-library/3.2/RcppArmadillo/include" -I"c:/applications/extsoft/include" -O2 -Wall -mtune=core2 -c traceLinePts.cpp -o traceLinePts.o
g++ -m64 -shared -s -static-libgcc -o mirt.dll tmp.def Estep.o Misc.o dpars.o traceLinePts.o -LC:/PROGRA~1/RRO/R-32~1.0/bin/x64 -lRlapack -LC:/PROGRA~1/RRO/R-32~1.0/bin/x64 -lRblas -lgfortran -Lc:/applications/extsoft/lib/x64 -Lc:/applications/extsoft/lib -LC:/PROGRA~1/RRO/R-32~1.0/bin/x64 -lR
installing to C:/Users/Seongho/Documents/R/win-library/3.2/mirt/libs/x64
** R
** data
*** moving datasets to lazyload DB
** inst
** tests
** byte-compile and prepare package for lazy loading
Creating a generic function for 'print' from package 'base' in package 'mirt'
Creating a generic function for 'anova' from package 'stats' in package 'mirt'
Creating a generic function for 'residuals' from package 'stats' in package 'mirt'
** help
*** installing help indices
converting help for package 'mirt'
finding HTML links ... done
Bock1997 html
DIF html
DTF html
DiscreteClass-class html
LSAT6 html
LSAT7 html
M2 html
MDIFF html
MDISC html
MixedClass-class html
MultipleGroupClass-class html
PLCI.mirt html
SAT12 html
Science html
SingleGroupClass-class html
anova-method html
averageMI html
bfactor html
boot.mirt html
coef-method html
createItem html
deAyala html
expand.table html
expected.item html
expected.test html
extract.group html
extract.item html
fixef html
fscores html
imputeMissing html
itemGAM html
itemfit html
iteminfo html
itemplot html
key2binary html
marginal_rxx html
mdirt html
mirt-package html
mirt html
mirt.model html
mirtCluster html
mixedmirt html
mod2values html
multipleGroup html
personfit html
plot-method html
print-method html
probtrace html
randef html
residuals-method html
show-method html
simdata html
summary-method html
testinfo html
wald html
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (mirt)
> sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
locale:
[1] LC_COLLATE=Korean_Korea.949 LC_CTYPE=Korean_Korea.949 LC_MONETARY=Korean_Korea.949
[4] LC_NUMERIC=C LC_TIME=Korean_Korea.949
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] devtools_1.7.0
loaded via a namespace (and not attached):
[1] httr_0.6.1 magrittr_1.5 tools_3.2.0 RCurl_1.95-4.6 stringi_0.4-1 knitr_1.10
[7] stringr_1.0.0 bitops_1.0-6
> Q matrix specification
Feature request: Allow for 'Q-matrix' specification of loadings in confmirt().
SE and GPCM
Hi Phil,
i tried to post this to the google-group but somehow my posts doesnt get posted :(
i have some troubles estimating SE in a GPCM where estimation of the information matrix fails. i have no idea, i don't think the model is somewhat uncommon and i use a pars-table. can you have a look at this?
thanks a lot, felix
Limited information stats
Add limited-information model fit statistics like M2
dev installation issue, failing on tex dependency
Running R 2.15.2 on Mac OS X 10.7.5, encountered the following error while trying to install development version:
install_github('mirt','philchalmers')
Installing github repo(s) mirt/master from philchalmers
Installing mirt.zip from https://api.github.com/repos/philchalmers/mirt/zipball/master
Installing mirt
/Library/Frameworks/R.framework/Resources/bin/R --vanilla CMD build '/private/var/folders/pn/2z45qwvs7257v8v31w62vq7r0000gn/T/RtmpvuaSlu/philchalmers-mirt-ce9cfae' --no-manual
--no-resave-data
- checking for file '/private/var/folders/pn/2z45qwvs7257v8v31w62vq7r0000gn/T/RtmpvuaSlu/philchalmers-mirt-ce9cfae/DESCRIPTION' ... OK
- preparing 'mirt':
- checking DESCRIPTION meta-information ... OK
- cleaning src
- installing the package to re-build vignettes
- creating vignettes ... ERROR
Error in texi2dvi(file = file, pdf = TRUE, clean = clean, quiet = quiet, :
Running 'texi2dvi' on 'mirt-presentation-2012.tex' failed.
Calls: -> texi2pdf -> texi2dvi
Execution halted
Error: Command failed (1)
Error in itemfit()
itemfit() returns "Fehler in itemfit(fit.mirt) : Ersetzung hat Länge 0", i.e. "Error in itemfit(fit.mirt) : Replacement has length 0".
LD and Cramer's V
From Paula Elosua:
"First at all, let me congratulate you for your excellent work doing mirt.
I’m professor of psychometrics in Spain, and I’m interested in the statistics that mirt uses for assessing LD. You mention Chen and Thissen, and Cramer’s V…but could you please give me more precise references about those, - theoretical and practical??
Thanks is advance"
itemtype = 'partial credit'
From D. Alian:
I want to fit a 3-Faktor partial credit model using "confmirt". What "itemtype" is appropriate for pcm? Is it "Rasch". When I use "Rasch" with my data I am returned an error message even if I fix factor number to 1.
u2.confmirt<-confmirt(data,1,itemtype="Rasch")
error in LoadPars(itemtype = itemtype, itemloc = itemloc, lambdas = lambdas, :
Attribut 'names' [4] are not the same length as vector [3] (...my translation...)Does that mean, that I did some wrong specification?
coef(..., IRTpars = TRUE) doesn't work in multipleGroups
Hi Phil,
apparently coef(..., IRTpars = TRUE) doesn't work in multipleGroups:
set.seed(12345)
a <- matrix(abs(rnorm(15,1,.3)), ncol=1)
d <- matrix(rnorm(15,0,.7),ncol=1)
d <- cbind(d, d-1, d-2)
itemtype <- rep('graded', nrow(a))
N <- 1000
dataset1 <- simdata(a, d, N, itemtype)
dataset2 <- simdata(a, d, N, itemtype, mu = .1, sigma = matrix(1.5))
dat <- rbind(dataset1, dataset2)
group <- c(rep('D1', N), rep('D2', N))
model <- mirt.model('F1 = 1-15')
mod_configural <- multipleGroup(dat, model, group = group)
coef(mod_configural, IRTpars = FALSE)
coef(mod_configural, IRTpars = TRUE)
Best wishes
RMSEA for missing data
From Wen-Ta Tseng, PhD:
"I've got a question: Why couldn't I obtain G*2 value and RMSEA value as in your MIRT package, although I used a no-missing data set?
Your answer and guidance will be highly appreciated! Below is the outcome message from R 2.15.1
> SCORE=read.csv(file='D:\\Attitude.csv', header = TRUE, sep = ",", quote="")
> (mod1 <- mirt(SCORE, 1))
Call:
mirt(data = SCORE, nfact = 1)
Full-information factor analysis with 1 factor
Converged in 11 iterations using 40 quadrature points.
Log-likelihood = -14045.37
AIC = 28234.74
BIC = 28578.64
G^2 = NA, df = 725, p = NA, RMSEA = NABest,"
Processing of plausible.draws in imputeMissing()
Hi, Phil.
I want to impute values in variables with multiple imputations.
So I tried to use score <- fscores(mod1, method = 'MAP', plausible.draws = 100, MI = 100) and imputeMissing(mod1, score), but I failed.
What happened to me?
Best regards,
Seongho Bae
> dat <- expand.table(LSAT7)
> (original <- mirt(dat, 1))
Iteration: 28, Log-Lik: -2658.805, Max-Change: 0.00010
Call:
mirt(data = dat, model = 1)
Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 28 EM iterations.
mirt version: 1.10.2
M-step optimizer: BFGS
EM acceleration: Ramsay
Number of rectangular quadrature: 61
Log-likelihood = -2658.805
AIC = 5337.61; AICc = 5337.833
BIC = 5386.688; SABIC = 5354.927
G2 (21) = 31.7, p = 0.0628
RMSEA = 0.023, CFI = 0.939, TLI = 0.924
> NAperson <- sample(1:nrow(dat), 20, replace = TRUE)
> NAitem <- sample(1:ncol(dat), 20, replace = TRUE)
> for(i in 1:20)
+ dat[NAperson[i], NAitem[i]] <- NA
> (mod <- mirt(dat, 1))
Iteration: 33, Log-Lik: -2648.603, Max-Change: 0.00010
Call:
mirt(data = dat, model = 1)
Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 33 EM iterations.
mirt version: 1.10.2
M-step optimizer: BFGS
EM acceleration: Ramsay
Number of rectangular quadrature: 61
Log-likelihood = -2648.603
AIC = 5317.206; AICc = 5317.429
BIC = 5366.284; SABIC = 5334.523
> scores <- fscores(mod, method = 'MAP', full.scores = TRUE, plausible.draws = 100, MI = 100)
> TESTfulldata <- imputeMissing(mod, scores)
> (TESTfullmod <- mirt(TESTfulldata, 1))
Error: data argument is required
>
> require(psych)
> describe(data.frame(TESTfulldata))
vars n mean sd median trimmed mad min max range skew kurtosis se
Item.1 1 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2 2 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3 3 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4 4 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5 5 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.1 6 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.1 7 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.1 8 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.1 9 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.1 10 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.2 11 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.2 12 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.2 13 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.2 14 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.2 15 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.3 16 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.3 17 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.3 18 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.3 19 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.3 20 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.4 21 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.4 22 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.4 23 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.4 24 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.4 25 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.5 26 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.5 27 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.5 28 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.5 29 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.5 30 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.6 31 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.6 32 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.6 33 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.6 34 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.6 35 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.7 36 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.7 37 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.7 38 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.7 39 1000 0.61 0.49 1 0.63 0 0 1 1 -0.43 -1.81 0.02
Item.5.7 40 1000 0.84 0.36 1 0.93 0 0 1 1 -1.88 1.55 0.01
Item.1.8 41 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.8 42 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.8 43 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.8 44 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.8 45 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.9 46 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.9 47 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.9 48 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.9 49 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.9 50 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.10 51 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.10 52 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.10 53 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.10 54 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.10 55 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.11 56 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.11 57 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.11 58 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.11 59 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.11 60 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.12 61 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.12 62 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.12 63 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.12 64 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.12 65 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.13 66 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.13 67 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.13 68 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.13 69 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.13 70 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.14 71 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.14 72 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.14 73 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.14 74 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.14 75 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.15 76 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.15 77 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.15 78 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.15 79 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.15 80 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.16 81 1000 0.83 0.38 1 0.91 0 0 1 1 -1.72 0.95 0.01
Item.2.16 82 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.16 83 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.16 84 1000 0.61 0.49 1 0.63 0 0 1 1 -0.43 -1.81 0.02
Item.5.16 85 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.17 86 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.17 87 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.17 88 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.17 89 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.17 90 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.18 91 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.18 92 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.18 93 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.18 94 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.18 95 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.19 96 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.19 97 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.19 98 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.19 99 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.19 100 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.20 101 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.20 102 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.20 103 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.20 104 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.20 105 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.21 106 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.21 107 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.21 108 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.21 109 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.21 110 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.22 111 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.22 112 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.22 113 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.22 114 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.22 115 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.23 116 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.23 117 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.23 118 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.23 119 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.23 120 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.24 121 1000 0.83 0.38 1 0.91 0 0 1 1 -1.72 0.95 0.01
Item.2.24 122 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.24 123 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.24 124 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.24 125 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.25 126 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.25 127 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.25 128 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
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Item.5.73 370 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
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Item.5.79 400 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.80 401 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
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Item.3.80 403 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
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Item.5.80 405 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
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Item.5.81 410 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.82 411 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.82 412 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.82 413 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.82 414 1000 0.61 0.49 1 0.63 0 0 1 1 -0.43 -1.81 0.02
Item.5.82 415 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.83 416 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.83 417 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.83 418 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.83 419 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.83 420 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.84 421 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
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Item.4.84 424 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.84 425 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.85 426 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
Item.2.85 427 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.85 428 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
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Item.5.85 430 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
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Item.5.86 435 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.87 436 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
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Item.4.88 444 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.88 445 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.89 446 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.89 447 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.89 448 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.89 449 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.89 450 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.90 451 1000 0.83 0.38 1 0.91 0 0 1 1 -1.72 0.95 0.01
Item.2.90 452 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.90 453 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.90 454 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.90 455 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.91 456 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.91 457 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.91 458 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.91 459 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.91 460 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.92 461 1000 0.83 0.38 1 0.91 0 0 1 1 -1.72 0.95 0.01
Item.2.92 462 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.92 463 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.32 0.01
Item.4.92 464 1000 0.61 0.49 1 0.63 0 0 1 1 -0.43 -1.81 0.02
Item.5.92 465 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.93 466 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.93 467 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
Item.3.93 468 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.93 469 1000 0.61 0.49 1 0.63 0 0 1 1 -0.44 -1.81 0.02
Item.5.93 470 1000 0.84 0.36 1 0.93 0 0 1 1 -1.89 1.59 0.01
Item.1.94 471 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.94 472 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.94 473 1000 0.77 0.42 1 0.84 0 0 1 1 -1.29 -0.34 0.01
Item.4.94 474 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.94 475 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
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Item.4.95 479 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
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Item.2.96 482 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.56 0.02
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Item.3.97 488 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.97 489 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.97 490 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.98 491 1000 0.83 0.38 1 0.91 0 0 1 1 -1.73 0.98 0.01
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Item.3.98 493 1000 0.77 0.42 1 0.84 0 0 1 1 -1.28 -0.36 0.01
Item.4.98 494 1000 0.61 0.49 1 0.64 0 0 1 1 -0.45 -1.80 0.02
Item.5.98 495 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
Item.1.99 496 1000 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.01 0.01
Item.2.99 497 1000 0.66 0.47 1 0.70 0 0 1 1 -0.67 -1.55 0.01
Item.3.99 498 1000 0.77 0.42 1 0.84 0 0 1 1 -1.27 -0.38 0.01
Item.4.99 499 1000 0.61 0.49 1 0.64 0 0 1 1 -0.44 -1.81 0.02
Item.5.99 500 1000 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.63 0.01
>
> sessionInfo()
R version 3.1.2 (2014-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=ko_KR.UTF-8 LC_NUMERIC=C LC_TIME=ko_KR.UTF-8
[4] LC_COLLATE=ko_KR.UTF-8 LC_MONETARY=ko_KR.UTF-8 LC_MESSAGES=ko_KR.UTF-8
[7] LC_PAPER=ko_KR.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=ko_KR.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] psych_1.5.6 semTools_0.4-9 lavaan_0.5-18 GPArotation_2014.11-1
[5] TAM_1.9-0 CDM_4.2-12 mvtnorm_1.0-2 car_2.0-25
[9] rsm_2.7-2 pracma_1.8.6 psychometric_2.2 rmeta_2.16
[13] metafor_1.9-7 Matrix_1.1-5 meta_4.3-0 lsr_0.5
[17] multilevel_2.5 MASS_7.3-37 nlme_3.1-121 plyr_1.8.3
[21] latticeExtra_0.6-26 RColorBrewer_1.1-2 bfa_0.3.1 gWidgets_0.0-54
[25] RGtk2_2.20.31 rgenoud_5.7-12 rrcovNA_0.4-7 rrcov_1.3-8
[29] robustbase_0.92-3 SQUAREM_2014.8-1 stringr_1.0.0 RCurl_1.95-4.6
[33] bitops_1.0-6 mirt_1.10.2 lattice_0.20-29
loaded via a namespace (and not attached):
[1] DEoptimR_1.0-2 Rcpp_0.11.6 RcppArmadillo_0.5.200.1.0 SparseM_1.6
[5] WrightMap_1.1 cluster_2.0.1 coda_0.17-1 expm_0.99-1.1
[9] lme4_1.1-7 magrittr_1.5 mgcv_1.8-4 minqa_1.2.4
[13] mnormt_1.5-3 msm_1.5 nloptr_1.0.4 nnet_7.3-9
[17] norm_1.0-9.5 pbivnorm_0.6.0 pbkrtest_0.4-2 pcaPP_1.9-60
[21] polycor_0.7-8 quadprog_1.5-5 quantreg_5.11 sfsmisc_1.0-27
[25] splines_3.1.2 stringi_0.4-1 survival_2.37-7 tensor_1.5
[29] tools_3.1.2
>
> fscores() on new response patterns
From Adilson dos Angos:
"I was using the fscores function and I have a question: is it possible to
estimate the ability for new response patterns?"
multimirt
Multilevel structure function using lme4 or nlme via multimirt extension
Suggested stats to include
Some statistics that users have contacted me about that should be developed in the package. Feel free to add more by commenting.
- EAP sum score conversions (Thissen et al., 1995)
- M*2 global fit statistic, which Cai and Hansen (2012) show is superior to the M2 statistic currently available in the fitIndices() function, in terms of providing better control over the type I error rate, greater power, and being more computationally efficient.
- The S-X^2 statistic for item fit, given it seems to outperform many other item fit indices (Kang & Chen, 2008; Orlando & Thissen, 2000, 2003).
- Fit statistics based on the information matrix in Ranger and Kuhn (2012)
bug in read.mirt
hi phil,
i have problems using read.mirt() with a multiple groups model:
set.seed(12345)
a <- matrix(abs(rnorm(15,1,.3)), ncol=1)
d <- matrix(rnorm(15,0,.7),ncol=1)
itemtype <- rep('dich', nrow(a))
N <- 1000
dataset1 <- simdata(a, d, N, itemtype)
dataset2 <- simdata(a, d, N, itemtype, mu = .1, sigma = matrix(1.5))
dat <- rbind(dataset1, dataset2)
group <- c(rep('D1', N), rep('D2', N))
models <- mirt.model('F1 = 1-15')
mod_configural <- multipleGroup(dat, models, group = group) #completely separate analyses
read.mirt(mod_configural)
gives.
Fehler in read.mirt(mod_configural) :
vector: kann keinen Vektor vom Mode '2' erzeugen.
cannot make vector with mode = 2.
best, felix
Error in checkAtAssignment("NULL", "nfact", "integer") : ‘nfact’ is not a slot in class “NULL”
p.s. Thanks so much for the great package (and nice documentation).
Would very much appreciate help with the following.
I get an error:
Error in checkAtAssignment("NULL", "nfact", "integer") :
‘nfact’ is not a slot in class “NULL”When executing:
dat = structure(list(V1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), V2 = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), V3 = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), V4 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), V5 = c(1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0L, 0L,
0L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("V1", "V2",
"V3", "V4", "V5"), row.names = c(NA, 50L), class = "data.frame")
library(mirt)
mod1 <- mirt(dat, 1)Tests
Mirt is a first R package for IRT capable of dealing with serious-size data sets.
But to use it not only for experiments but also in "production environments" it should be not only fast but also reliable.
To achieve reliability, a good set of tests and testing data should be prepared. Such tests are useful not only for finding existing bugs, but also for regression tests (making sure, that after making changes previous functionalities still work as they worked before).
I hope I will be able to prepare some high-level tests (tests that call only functions available to the user) this spring and I hope this issue will help me to remember about that :)
Can I increase MHRM iterations in boot.mirt?
Hello, Phil.
Can I increase MHRM iterations in boot.mirt?
I tried to use technical arguments in boot.mirt function, but It did not works.
Additionally, I want to know group:items reflects group+items+group:items within random argument in mixedmirt. I can not find any description on manual.
entrepreneur.mixed <- mixedmirt(data = trainingset, covdata = person.info, model = entrepreneur.cfa.syntax, random = list(~1+coworker + employees + age + operation_date|industry+items+industry:items), itemtype = 'gpcm', technical = list(NCYCLES = 1e+5))
entrepreneur.mixed.boot <- boot.mirt(entrepreneur.mixed, R = 1000)
MHRM terminated after 2000 iterations.
Error in draw.thetas(theta0 = gtheta0[[g]], pars = pars[[g]], fulldata = Data$fulldata[[g]], :
NAs are not allowed in subscripted assignments
additional information: warnings:
In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :
NaN was generated
Error in draw.thetas(theta0 = gtheta0[[g]], pars = pars[[g]], fulldata = Data$fulldata[[g]], :
NAs are not allowed in subscripted assignmentsabout grsm parametrization
Hello,
I am trying grsm model, and I think it should be made clear of the parametrization.
first, for the object from mirt( , itemtype="grsm")
coef(obj, IRTpars=T) doesn't work.
When I saw the result from coef() and coef( , IRTpars=T) were the same,
I thought the model is parameterized in IRT way.
But as I go trying several times, it found it wasn't. So for anyone I might get confused as I did...
Of course transforming the coefficient is not so hard,
Anyway, do you know any reference about "rating scale graded response model"?
Mostly people say grsm as Generalized Rating Scaling Model which is nested model of GPCM,
I tried searching and googling with no result so far...
Crossvalidation
Hi,
I've been trying to cross-validate an estimated model. I guess the respective functionality is implemented? However, I seem to stumble into an error:
# example data
dat <- expand.table(deAyala)
# generate two subsamples
set.seed(1529)
sel <- sample(nrow(dat), 10000, replace=F)
d1 <- dat[sel,] # calibaration sample
d2 <- dat[-sel,] # validation sample
# estimate model based on subsample d1
fit <- mirt(d1, model= mirt.model('G = 1-5'), itemtype="2PL")
# estimate thetas for subsample d2
fs <- fscores(fit, full.scores=T, scores.only=T, response.pattern=d2)
Note that the argument scores.only is ignored when specifying a response.pattern.
The following works fine:
# item fit for subsample 1 (calibration sample)
itemfit(fit)
residuals(fit, type="Q3")
However, this won't work:
# item fit for subsample 2 (validation sample)
itemfit(fit, Theta=fs[,"F1"])
residuals(fit, type="Q3", Theta=fs[,"F1"])
Actually residuals produecs a series of warnings. Did I misunderstand the function or is there a glitch?
Best, Timo
Cannot run polytomous data on mirt 1.6.1
I can not run polytomous data on mirt 1.6.1 even using the graded response model option.
So, I tried to install dev version on github, I still cannot run.
Wired Model Fit Indices in M2() function.
Hello Phil,
I've got weird model fit indices in M2() function with 1.10 releases when try to calculate confirmatory models.
In case of 1.9;
But in case of 1.10;
M2 df p RMSEA RMSEA_5 RMSEA_95 TLI CFI SRMSR
stats 20722.69 1059 0 0.2677532 0.2640676 0.2704207 -1.205233 0 0.190344And, my syntax input style was like this;
entrepreneur.cfa.syntax <- mirt.model('
readiness = 1-5
C = 6-11
O = 12-15
Grit_consistency = 16-19
Grit_perservance = 20-25
EA = 26-41
performance = 42-48
CMV = 1-48
COV = readiness*C*O*Grit_consistency*Grit_perservance*EA*performance
')Was my syntax wrong? Any changes 1.9 to 1.10 for the confirmatory model syntax?
Best regards,
Seongho Bae
Error messages occured during install what using a github repo.
please check this error messages.
session_info()
Session info--------------------------------------------------------------------------------------------
setting value
version R version 3.1.2 (2014-10-31)
system x86_64, darwin13.4.0
ui RStudio (0.98.1102)
language (EN)
collate ko_KR.UTF-8
tz Asia/Seoul
Packages------------------------------------------------------------------------------------------------
package * version date source
bfa * 0.3.1 2014-02-11 CRAN (R 3.1.0)
bitops 1.0.6 2013-08-17 CRAN (R 3.1.0)
car * 2.0.22 2014-11-18 CRAN (R 3.1.2)
CDM * 4.0 2014-11-22 CRAN (R 3.1.2)
coda 0.16.1 2012-11-06 CRAN (R 3.1.0)
DEoptimR 1.0.2 2014-10-19 CRAN (R 3.1.1)
devtools * 1.6.1 2014-10-07 CRAN (R 3.1.1)
evaluate 0.5.5 2014-04-29 CRAN (R 3.1.0)
foreign * 0.8.61 2014-03-28 CRAN (R 3.1.0)
formatR 1.0 2014-08-25 CRAN (R 3.1.1)
GPArotation * 2014.11.1 2014-11-25 CRAN (R 3.1.2)
httr 0.5 2014-09-02 CRAN (R 3.1.1)
knitr 1.8 2014-11-11 CRAN (R 3.1.2)
lattice * 0.20.29 2014-04-04 CRAN (R 3.1.2)
latticeExtra * 0.6.26 2013-08-15 CRAN (R 3.1.0)
lavaan * 0.5.18.788 2015-03-01 local
lsr * 0.3.2 2014-01-31 CRAN (R 3.1.0)
MASS * 7.3.35 2014-09-30 CRAN (R 3.1.2)
Matrix 1.1.4 2014-06-15 CRAN (R 3.1.2)
meta * 4.0.1 2014-11-19 CRAN (R 3.1.2)
metafor * 1.9.5 2014-11-24 CRAN (R 3.1.2)
mirt 1.8.5 2015-03-11 Github (9f2eddb)
mnormt 1.5.1 2014-06-30 CRAN (R 3.1.1)
multilevel * 2.5 2013-04-10 CRAN (R 3.1.0)
mvtnorm * 1.0.1 2014-11-13 CRAN (R 3.1.2)
nlme * 3.1.118 2014-10-07 CRAN (R 3.1.2)
nnet 7.3.8 2014-03-28 CRAN (R 3.1.2)
norm 1.0.9.5 2013-02-28 CRAN (R 3.1.0)
pbivnorm 0.5.1 2012-10-31 CRAN (R 3.1.0)
pcaPP * 1.9.60 2014-10-22 CRAN (R 3.1.2)
plyr * 1.8.1 2014-02-26 CRAN (R 3.1.0)
polycor 0.7.8 2010-04-03 CRAN (R 3.1.0)
pracma * 1.7.9 2014-11-15 CRAN (R 3.1.2)
psych * 1.4.8.11 2014-08-12 CRAN (R 3.1.1)
psychometric * 2.2 2010-08-08 CRAN (R 3.1.0)
quadprog 1.5.5 2013-04-17 CRAN (R 3.1.0)
RColorBrewer * 1.0.5 2011-06-17 CRAN (R 3.1.0)
Rcpp 0.11.3 2014-09-29 CRAN (R 3.1.1)
RcppArmadillo 0.4.550.1.0 2014-11-28 CRAN (R 3.1.2)
RCurl 1.95.4.4 2014-11-29 CRAN (R 3.1.2)
rmeta * 2.16 2012-10-29 CRAN (R 3.1.0)
robustbase * 0.92.2 2014-11-22 CRAN (R 3.1.2)
rrcov * 1.3.4 2013-08-26 CRAN (R 3.1.0)
rrcovNA * 0.4.4 2013-08-29 CRAN (R 3.1.0)
rsm * 2.7 2014-10-02 CRAN (R 3.1.1)
rstudioapi 0.1 2014-03-27 CRAN (R 3.1.0)
semTools * 0.4.6 2014-10-03 CRAN (R 3.1.1)
sfsmisc 1.0.26 2014-06-16 CRAN (R 3.1.0)
stringr 0.6.2 2012-12-06 CRAN (R 3.1.0)
TAM * 1.2 2014-11-22 CRAN (R 3.1.2)
tensor 1.5 2012-05-05 CRAN (R 3.1.0)
Seongho Bae
SE for IRTpars = TRUE
Thank you for the excellent resource with the mirt package.
When outputting coef(data, IRTpars=TRUE, printSE=TRUE) no standard errors are printed. If I change the IRTpars=TRUE to FALSE the SE are printed.
My model has SE = TRUE
Thank You
'data inputations'
Hello Phil,
I found a word 'data inputations' in M2(). How about change to 'data imputations' from 'data inputations'?
Best regards,
Seongho Bae
> findM2(mod5, Theta = mod5_theta)
quadpts: 15000 / iteration: 1
Error: Fit statistics cannot be computed when there are missing data. Pass a suitable
impute argument to compute statistics following multiple
data inputations IRT parameters
Hi Phil,
I've been using the MIRT package and it's really a tremendous resource - thanks for all the work you put into it. It does appear, however, that the "IRTparm=TRUE" does not return transformed item parameter estimates.
I'm not sure if I'm supposed to provide reproducible code or what the proper protocol here is.
Thanks.
Charlie
Survey weights
Feature Request: Allow survey weights to be included in estimation.
parameter data.frame depends on order
dear phil,
when one changes the parameters of a mirt model in the parameter-data.frame, mirt throws an error when the order of the parameters is changed (happened to me after merging another column with known parameters).
pars = mirt(Science, 1, pars="values")
pars = pars[sample(18),]
mirt(Science, 1, pars = pars)
i guess, mirt could sort the parameter-df in order to prevent this problem.
best, felix
obtaining IRF value for a specific theta (ability)
Could you please let me know if there is a way to obtain IRF value for a specific theta (ability)
itemplot(mod, 1) provides a nice IRF plot.
However, if possible, I would like to obtain a numeric value of P(\theta) for a specific theta; something like:
> irf(mod, item = 1, theta = 0.2)
0.81
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