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Welcome to IRTest!

Please feel free to create an issue for bug reports or potential improvements.

IRTest is a useful tool for $\mathcal{\color{red}{IRT}}$ (item response theory) parameter $\mathcal{\color{red}{est}}\textrm{imation}$, especially when the violation of normality assumption on latent distribution is suspected.
IRTest deals with uni-dimensional latent variable.
For missing values, IRTest adopts full information maximum likelihood (FIML) approach.
In IRTest, including the conventional usage of Gaussian distribution, several methods are available for estimation of latent distribution:
- empirical histogram method,
- two-component Gaussian mixture distribution,
- Davidian curve,
- kernel density estimation,
- log-linear smoothing.

Installation

The CRAN version of IRTest can be installed on R-console with:

install.packages("IRTest")

For the development version, it can be installed on R-console with:

devtools::install_github("SeewooLi/IRTest")

Functions

Followings are the functions of IRTest.

IRTest_Dich is the estimation function when items are dichotomously scored.
IRTest_Poly is the estimation function when items are polytomously scored.
IRTest_Cont is the estimation function when items are continuously scored.
IRTest_Mix is the estimation function for a mixed-format test, a test comprising both dichotomous item(s) and polytomous item(s).
factor_score estimates factor scores of examinees.
coef_se returns standard errors of item parameter estimates.
best_model selects the best model using an evaluation criterion.
item_fit tests the statistical fit of all items individually.
inform_f_item calculates the information value(s) of an item.
inform_f_test calculates the information value(s) of a test.
plot_item draws item response function(s) of an item.
reliability calculates marginal reliability coefficient of IRT.
latent_distribution returns evaluated PDF value(s) of an estimated latent distribution.
DataGeneration generates several objects that can be useful for computer simulation studies. Among these are simulated item parameters, ability parameters and the corresponding item-response data.
dist2 is a probability density function of two-component Gaussian mixture distribution.
original_par_2GM converts re-parameterized parameters of two-component Gaussian mixture distribution into original parameters.
cat_clps recommends category collapsing based on item parameters (or, equivalently, item response functions).
recategorize implements the category collapsing.
For S3 methods, anova, coef, logLik, plot, print, and summary are available.

Example

A simple simulation study for a 2PL model can be done in following manners:

library(IRTest)

Data generation

An artificial data of 1000 examinees and 20 items.

Alldata <- DataGeneration(seed = 123456789,
                          model_D = 2,
                          N=1000,
                          nitem_D = 10,
                          latent_dist = "2NM",
                          m=0, # mean of the latent distribution
                          s=1, # s.d. of the latent distribution
                          d = 1.664,
                          sd_ratio = 2,
                          prob = 0.3)

data <- Alldata$data_D
item <- Alldata$item_D
theta <- Alldata$theta
colnames(data) <- paste0("item",1:10)

Analysis

For an illustrative purpose, the two-component Gaussian mixture distribution (2NM) method is used for the estimation of latent distribution.

Mod1 <- 
  IRTest_Dich(
    data = data,
    latent_dist = "2NM"
    )

Summary of the result

summary(Mod1)
#> Convergence:  
#> Successfully converged below the threshold of 1e-04 on 52nd iterations. 
#> 
#> Model Fit:  
#>  log-likeli   -4786.734 
#>    deviance   9573.469 
#>         AIC   9619.469 
#>         BIC   9732.347 
#>          HQ   9662.37 
#> 
#> The Number of Parameters:  
#>        item   20 
#>        dist   3 
#>       total   23 
#> 
#> The Number of Items:  10 
#> 
#> The Estimated Latent Distribution:  
#> method - 2NM 
#> ----------------------------------------
#>                                           
#>                                           
#>                                           
#>                       . @ @ .             
#>           .         . @ @ @ @ .           
#>         @ @ @ . . . @ @ @ @ @ @           
#>       @ @ @ @ @ @ @ @ @ @ @ @ @ @         
#>     . @ @ @ @ @ @ @ @ @ @ @ @ @ @ @       
#>     @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ .     
#>   @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @   
#> +---------+---------+---------+---------+
#> -2        -1        0         1         2

Parameter estimation results

colnames(item) <- c("a", "b", "c")

knitr::kables(
  list(
    ### True item parameters 
    knitr::kable(item, format='simple', caption = "True item parameters", digits = 2)%>%
  kableExtra::kable_styling(font_size = 4),

    ### Estimated item parameters
    knitr::kable(coef(Mod1), format='simple', caption = "Estimated item parameters", digits = 2)%>%
  kableExtra::kable_styling(font_size = 4)
  )
)

a	b	c
2.25	0.09	0
1.42	0.16	0
2.11	-1.57	0
1.94	-1.15	0
1.41	-1.89	0
2.43	0.42	0
2.41	-1.57	0
2.08	-0.47	0
1.32	-0.50	0
1.17	0.33	0

True item parameters

	a	b
item1	2.15	0.12
item2	1.43	0.06
item3	2.05	-1.45
item4	2.07	-1.03
item5	1.26	-1.97
item6	2.24	0.38
item7	2.21	-1.68
item8	2.08	-0.45
item9	1.31	-0.49
item10	1.06	0.41

Estimated item parameters



### Plotting
fscores <- factor_score(Mod1, ability_method = "WLE")

par(mfrow=c(1,3))
plot(item[,1], Mod1$par_est[,1], xlab = "true", ylab = "estimated", main = "item discrimination parameters")
abline(a=0,b=1)
plot(item[,2], Mod1$par_est[,2], xlab = "true", ylab = "estimated", main = "item difficulty parameters")
abline(a=0,b=1)
plot(theta, fscores$theta, xlab = "true", ylab = "estimated", main = "ability parameters")
abline(a=0,b=1)

The result of latent distribution estimation

plot(Mod1, mapping = aes(colour="Estimated"), linewidth = 1) +
  stat_function(
    fun = dist2,
    args = list(prob = .3, d = 1.664, sd_ratio = 2),
    mapping = aes(colour = "True"),
    linewidth = 1) +
  lims(y = c(0, .75)) + 
  labs(title="The estimated latent density using '2NM'", colour= "Type")+
  theme_bw()

Posterior distributions for the examinees

Each examinee’s posterior distribution is calculated in the E-step of EM algorithm. Posterior distributions can be found in Mod1$Pk.

set.seed(1)
selected_examinees <- sample(1:1000,6)
post_sample <- 
  data.frame(
    X = rep(seq(-6,6, length.out=121),6), 
    prior = rep(Mod1$Ak/(Mod1$quad[2]-Mod1$quad[1]), 6),
    posterior = 10*c(t(Mod1$Pk[selected_examinees,])), 
    ID = rep(paste("examinee", selected_examinees), each=121)
    )

ggplot(data=post_sample, mapping=aes(x=X))+
  geom_line(mapping=aes(y=posterior, group=ID, color='Posterior'))+
  geom_line(mapping=aes(y=prior, group=ID, color='Prior'))+
  labs(title="Posterior densities for selected examinees", x=expression(theta), y='density')+
  facet_wrap(~ID, ncol=2)+
  theme_bw()

Item fit

item_fit(Mod1)
#>            stat df p.value
#> item1  21.05639  5  0.0008
#> item2  39.02560  5  0.0000
#> item3  18.38326  5  0.0025
#> item4  26.05405  5  0.0001
#> item5  14.32893  5  0.0136
#> item6  38.58140  5  0.0000
#> item7  25.55899  5  0.0001
#> item8  14.43694  5  0.0131
#> item9  18.29131  5  0.0026
#> item10 65.25700  5  0.0000

Item response function

p1 <- plot_item(Mod1,1)
p2 <- plot_item(Mod1,4)
p3 <- plot_item(Mod1,8)
p4 <- plot_item(Mod1,10)
grid.arrange(p1, p2, p3, p4, ncol=2, nrow=2)

Reliability

reliability(Mod1)
#> $summed.score.scale
#> $summed.score.scale$test
#> test reliability 
#>        0.8133725 
#> 
#> $summed.score.scale$item
#>     item1     item2     item3     item4     item5     item6     item7     item8 
#> 0.4586843 0.3014154 0.3020563 0.3805659 0.1425990 0.4534580 0.2688948 0.4475414 
#>     item9    item10 
#> 0.2661783 0.1963062 
#> 
#> 
#> $theta.scale
#> test reliability 
#>        0.7457047

Test information function

ggplot()+
  stat_function(
    fun = inform_f_test,
    args = list(Mod1)
  )+ 
  stat_function(
    fun=inform_f_item,
    args = list(Mod1, 1),
    mapping = aes(color="Item 1")
  )+
  stat_function(
    fun=inform_f_item,
    args = list(Mod1, 2),
    mapping = aes(color="Item 2")
  )+
  stat_function(
    fun=inform_f_item,
    args = list(Mod1, 3),
    mapping = aes(color="Item 3")
  )+
  stat_function(
    fun=inform_f_item,
    args = list(Mod1, 4),
    mapping = aes(color="Item 4")
  )+
  stat_function(
    fun=inform_f_item,
    args = list(Mod1, 5),
    mapping = aes(color="Item 5")
  )+
  lims(x=c(-6,6))+
  labs(title="Test information function", x=expression(theta), y='information')+
  theme_bw()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.