mirt | Multidimensional item response theory | Analytics library
kandi X-RAY | mirt Summary
kandi X-RAY | mirt Summary
Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item functioning and modeling item and person covariates.
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Trending Discussions on mirt
QUESTION
library(mirt) #this contains a dataset called deAyala.
library(psych) #this contains the alpha() function.
alpha(deAyala)
ANSWER
Answered 2021-Jun-05 at 09:41You can get rid of the warning bit by wrapping it with suppressWarnings() but the first bit of the message looks like just a print statement in the alpha() function. This will work though
QUESTION
There is a function in psych package called 'alpha' which gives out various statistics. I want a specific column from the output so I use a code. This code works perfectly in console but it doesn't work when I try to use it in shiny.
...ANSWER
Answered 2021-Jun-05 at 12:38You can use renderTable or renderText to display the output instead of renderUI.
QUESTION
I have a column called 'KEY' which stores the correct response for each MCQ question. I also have the data set which contains scored responses such as A,B,C,D. This(notice how it has some responses which are NA or --) is how my data looks like. This is how my Key looks like.
I am using this function to score the data:-
...ANSWER
Answered 2021-May-28 at 10:40You have NA values as strings ('NA') and not actual NA's. It should work once you convert it to actual NA values.
QUESTION
I'm trying to increase font size in mirt plots, however, so far I'm able to increase size of ticks only:
ANSWER
Answered 2021-Jan-30 at 13:52You can set parameters globally with trellis.par.set or pass to the individual plot using the par.settings parameter. trellis.par.get() can be used to get a list of the names of the objects that can be updated.
So for example the following can be used to update specific parameters within a plot
QUESTION
I recently upgraded Mirth to version 3.8.1. Since the upgrade I have been unable to log in to the Web dashboard nor the Connect Administrator. I have tried my previous credentials for the previous ver and the default admin/admin.
I was wondering has anyone else experienced similar issues?
Additional: When I try to connect using Mirth Connect Server:
ERROR 2020-03-10 13:47:08,925 [Main Server Thread] com.mirth.connect.server.Mirt h: http.port port is already in use: 8082 ERROR 2020-03-10 13:47:08,925 [Main Server Thread] com.mirth.connect.server.Mirt h: https.port port is already in use: 8443
But looking at port 8443 it appears to be listening: C:\windows\system32>netstat -a | findstr 8443 TCP 0.0.0.0:8443 LAB003708:0 LISTENING TCP [::]:8443 LAB003708:0 LISTENING
I have checked the
mirth-cli-config
file and the username and password is correct.
...ANSWER
Answered 2020-Mar-12 at 16:11After allot of troubleshooting it appeared that although the Mirth 3.8.1 installer detected the previous version it had not removed the Mirth Connect Server ver 3.7, so when I launched Mirth 3.8.1 there appeared to be two Mirth connections to same port and there was a conflict.
Running a combination of manually deleting and using the MS uninstall program fro Mirth 3.7, then reinstalling Mirth 3.8.1 worked.
Hopefully this might be useful for someone with same issue.
QUESTION
I am looking to combine all three" test information function" lines (one for each model) into one and the same graph. I have a data set of category 1-5 Likert responses in 400 rows in sets of 8 columns (one for each item). I have ran three IRT models on these sets using mirt package in R, and produced test info plots. I would like to combine IRT test info plots from three different (graded response) models, three lines, in one and the same grid.
...ANSWER
Answered 2020-Jan-13 at 16:40Your plots from the mirt package are a lattice object, so you can try using latticeExtra, since you did not provide your dataset, I provide an example code below using the example dataset in the package:
QUESTION
I'm building a shiny app and I'm noticing that when I submit the code to console everything loads correctly and runs as expected; however when I render the app with the Run App button I'm getting errors.
Specifically, when I use the Run App button I get the following error in the application:'Error: cannot open the connection.' Additionally, I'm getting this error in the console: 'Error: cannot open the connection,' while the console reads: 'Warning in gzfile(file, "rb") :cannot open compressed file 'DATA//grm_mod.rds', probable reason 'No such file or directory''
The application is straightforward: A user uploads a data file, while on the back end an R model object is loaded, scores are estimated from the model, and results display in a table that the user can download.
What is the likely cause of this error? Note, the likely source of the error is under the code comment "Conversion steps" in the server logic.
Thank you.
...ANSWER
Answered 2019-May-10 at 17:14The filepath is relative to the Shiny App, not your working directory, so when you use runApp() and call readRDS('DATA//grm_mod.rds') it expects a directory DATA that is a subdirectory of the directory in which the .R file that contains your app is stored. If you move DATA to the same directory as app.r it should work.
QUESTION
---
title: "Untitled"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
```{r,message=FALSE}
library(mirt)
set.seed(12345)
a <- matrix(abs(rnorm(15,1,.3)), ncol=1)
d <- matrix(rnorm(15,0,.7),ncol=1)
itemtype <- rep('2PL', 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 <- 'F1 = 1-15'
mod_configural <- multipleGroup(dat, models, group = group)
coef(mod_configural )
```
ANSWER
Answered 2019-Apr-18 at 08:20The solution is very simple and has nothing to do with RMD.
QUESTION
#!/usr/bin/Rscript
install.packages("Deriv")
install.packages("vegan")
packageurl <- "https://cran.r-project.org/src/contrib/Archive/mirt/mirt_1.27.1.tar.gz"
install.packages(packageurl, repos=NULL, type="source")
ANSWER
Answered 2019-Mar-14 at 07:28Is this what you are looking for?
QUESTION
So I have some data that I fit a IRT graded response model to, using this code:
...ANSWER
Answered 2018-Nov-16 at 21:22probtrace(extract.item(results.grm, 1), c(-1)) should work - also, might be helpful to look at https://github.com/philchalmers/mirt/issues/21
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