est_flexmirt: Unidimensional Item Calibration via flexMIRT

In irt: Item Response Theory and Computerized Adaptive Testing Functions

Unidimensional Item Calibration via flexMIRT

Description

est_flexmirt runs flexMIRT in batch mode. This function requires flexMIRT program already installed on the Windows machine. Visit <https://vpgcentral.com/software/flexmirt/> for more details about the software. Even though flexMIRT can run various models, only a selected set of unidimensional models can be fitted using est_flexmirt function.

Usage

est_flexmirt(
  x = NULL,
  model = NULL,
  target_dir = getwd(),
  analysis_name = "flexMIRT_calibration",
  item_ids = NULL,
  D = 1,
  max_em_cycles = c(500, 100),
  quadrature = c(49, 6),
  em_tol = c(1e-04, 1e-09),
  prior = NULL,
  gof = "Basic",
  examinee_id_var = NULL,
  group_var = NULL,
  scoring_method = NULL,
  additional_options = NULL,
  additional_constraints = NULL,
  flexmirt_exe = NULL,
  overwrite = FALSE,
  show_output_on_console = TRUE
)

Arguments

x	A matrix/data.frame/Response_set object including examinee item responses. In it's bare form, it can be a matrix of item responses, where ideally the column names are the item IDs and row names are the examinee IDs (though neither are necessary).
model	The psychometric model(s) of items. The user can provide an input in the following three ways: (a) A vector of length one which represents the model of each item. (b) A vector which has the same length as the number of items that will be calibrated that specifies the model of each item. (c) NULL, the default value, where the program will check the number of categories of each item. Items with two or fewer categories will be calibrated using "3PL" (three-parameter logistic IRT model), items with more than two categories will be calibrated using "GRM" (Graded Response Model). "1PL" One-parameter logistic model. "2PL" Two-parameter logistic model. "3PL" Three-parameter logistic model. "GRM" Graded Response Model "GPCM" Generalized Partial Credit Model
target_dir	The directory/folder where the flexMIRT syntax data files and output will be saved. The default value is the current working directory, i.e. get_wd().
analysis_name	This will be the file names of the data, flexMIRT syntax file and output files. The default value is "flexMIRT_calibration".
item_ids	A vector of column names or numbers of the x that represents the responses. The default value is NULL where all of the columns in the data are assumed to be a response matrix (unless specified by group_var or examinee_id_var arguments).
D	Scaling constant. Default value is 1. If it is not equal to 1, a new line added to constraints to multiply the slope parameter with the D value specified.
max_em_cycles	A numeric vector of length two specifying the maximum number of iterations allowed in E- and M-steps. The default value is c(500, 100) where there will be maximum 500 iterations allowed in E-steps and 100 iterations M-steps,
quadrature	A numeric vector of length two specifying the number of quadrature points and the maximum theta value. The default value is c(49, 6) where there will be 49 rectangular quadrature points over -6 and +6,
em_tol	A numeric vector of length two specifying the convergence criteria for E- and M-steps. The default value is c(1e-4, 1e-9) where convergence criteria for E-steps is 0.0001 and the convergence criteria for M-step is 1e-9.
prior	A data frame that specifies the priors for the estimated item parameters. There are two possible options. Option 1: The same priors will be imposed on all items. The data.frame should have four columns: "par" The parameter on which prior will be imposed. It can take the following values: "intercept" for location parameters (usually for item difficulty parameters, "slope" for item discrimination parameters, "guessing" for lower asymptote parameter of 3PL. "dist" The distribution of the prior. It can take the following values: "normal" for normal distribution, "lognormal" for log-normal distribution, and "beta" for Beta distribution. "v1" A number for the first parameter of the selected distribution. For normal and log-normal distributions this is the mean of the distribution. For Beta distribution, this is the alpha-1 value. So, if the a Beta distribution with alpha = 10 desired, the value of 'v1' should be 11. "v2" A number for the second parameter of the selected distribution. For normal and log-normal distributions this is the standard deviation of the distribution. For Beta distribution, this is the beta-1 value. So, if the a Beta distribution with beta = 3 desired, the value of 'v2' should be 4. Here is an example: prior <- data.frame(par = c("intercept", "slope", "guessing"), dist = c("normal", "lognormal", "beta"), v1 = c(0, 0, 1), v2 = c(1, 0.5, 3)) Option 2: Different priors will be assigned to individual items. The data.frame should have five columns. In addition to four columns described above, a column specifying item's ID should be added. The column name should be "item_id" and it's values should correspond to the item ID's specified in the response data. Here is an example: prior <- data.frame( item_id = c("Item_1", "Item_1", "Item_3", "Item_3", "Item_10"), par = c("intercept", "slope", "intercept", "slope", "slope"), dist = c("normal", "lognormal", "normal", "lognormal", "lognormal"), v1 = c(0, 0, 0, 0, 0), v2 = c(1, 0.5, 2, 0.6, 0.7))
gof	A string specifying the extent of Goodness-of-fit indices that will be calculated and reported. The available options are "Basic" (the default value), "Extended" and "Complete".
examinee_id_var	If examinee IDs are saved in one of the columns of argument x, this will be the name of that column. The default value is NULL. When the value is NULL, the program will check the row names of the data.frame or matrix. If the row names are not NULL, the program will use these values as examinee IDs.
group_var	The column name or number that contains group membership information if multi-group calibration is desired. Ideally, it grouping variable is represented by single digit integers. If other type of data provided, an integer value will automatically assigned to the variables. The default value is NULL, where no multi-group analysis will be performed.
scoring_method	A string value representing the method of scoring. The currently available options are: "EAP" and "MAP".
additional_options	A vector of strings that will be added to the syntax. For example, when scoring_method = "ML", the minimum and maximum values of theta estimates needs to be specified: c("MinMLscore = -5;", "MaxMLscore = 5;"). Or, when estimating "3PL" parameters "NormalMetric3PL = Yes;" can be added to get a normal metric scale. See flexMIRT manual for other options. The default value is NULL, where no additional options will be added to the syntax. Note that following additional options will be added by other arguments of this function, so you don't need to add them in this argument separately: "Quadrature =", "MaxE =", "MaxM =", "Etol =", "Mtol =", "Score ="
additional_constraints	The The default value is NULL, where no additional constraints will be added to the syntax except the constraints that will be added for models such as "Rasch" or "1PL". Examples of additional constraints can be: Guessing parameter. For example, if the items are multiple choice and there are four possible choices, the prior distribution of the pseudo-guessing parameter can be set to Beta(1, 3), where parameters alpha = 2 (2 - 1 = 1) and beta = 4 (4-1 = 3). This will correspond to prior sample size of 4 and prior mode of (1 / (3+1) = 0.25). You can add the following line to the additional_constraints (see Example 2 below.): Prior (Item_1-Item_35), Guessing : Beta(1.0,3.0); The distribution can be different, for example, for Normal distribution provide mean and standard deviation, for Log-normal distribution provide mean and standard deviation in logarithmic scale. For example, in the code below, a normal prior with mean -1.09 and standard deviation 0.5 is imposed on the logit of the guessing parameters: Prior (Item_1-Item_35), Guessing : Normal(-1.09,0.5); Slope parameters. The following argument will set the item slopes for Item_1 to Item_10 equal: Fix (Item_1-Item_10), Slope; Similarly a log-normal prior can be imposed on slope parameters: Prior (Item_1-Item_35), Slope : logNormal(1, 1.6487);
flexmirt_exe	This is the executable file to run flexMIRT syntax. On most Windows computers this is the path where "WinFlexMIRT.exe" can be found. For example: "C:\Program Files\flexMIRT3.5.2\WinFlexMIRT.exe". By default the value is NULL, the function will search the relevant locations for "WinFlexMIRT.exe".
overwrite	If TRUE and there is already a BILOG-MG data file in the target path with the same name, the file will be overwritten.
show_output_on_console	logical (not NA), indicates whether to capture the output of the command and show it on the R console. The default value is TRUE.

Value

A list containing the calibration results

"ip"

An Itempool-class object holding the item parameters. Please check whether model converged (using ...$converged) before interpreting/using ip.

"score"

A data frame object that holds examinee IDs, ability estimates and standard error of ability estimates.

"syntax"

The syntax file.

"converged"

A logical value indicating whether a model has been converged or not. This value is TRUE only when both converged_first_order and converged_second_order are TRUE.

"converged_first_order"

A logical value indicating whether first-order test indicates convergence.

"converged_second_order"

A logical value indicating whether second-order test indicates convergence.

"convergence_details"

A more detailed information about convergence. This element has two values, "First-order test" and "Second-order test". Use this information to further judge the convergence. From flexMIRT user manual (p.11): "the reported first-order test examines if the gradient has vanished sufficiently for the solution to be a stationary point. The second-order test tests if the information matrix is positive definite, a prerequisite for the solution to be a possible maximum. For the second-order test, reporting that the solution is a possible maximum simply means that the program reached a statistically desirable solution. The other possible message that may be printed for the outcome of the second-order test is “Solution is not a maximum; caution is advised.” If a warning message is received for either the first- or second-order test, all parameter estimates should be taken a provisional and should not be used as final estimates, for future scoring, etc. but, rather, should be used to diagnose possible issues with the model/items."

"gof"

The goodness-of-fit statistics.

"input"

A list object that stores the arguments that are passed to the function.

Author(s)

Emre Gonulates

Examples

## Not run: 

#############################################
############## Example 1 - 2PL ##############
#############################################
# IRT Two-parameter Logistic Model Calibration

# Create responses to be used in flexMIRT estimation
true_theta <- rnorm(1000)
true_ip <- generate_ip(n = 30, model = "2PL")
resp <- sim_resp(true_ip, true_theta)
# The following line will run flexMIRT, estimate 2PL model and put the
# analysis results in the target directory:
fm_calib <- est_flexmirt(x = resp, model = "2PL",
                         target_dir = "C:/Temp/Analysis", overwrite = TRUE)
# Check whether the calibration converged
fm_calib$converged
fm_calib$convergence_details

# Get the estimated item pool
fm_calib$ip

# See the BILOG-MG syntax
cat(fm_calib$syntax, sep = "\n")

# Get goodness-of-fit statistics and marginal reliability:
fm_calib$gof

# Get estimated scores
head(fm_calib$score)

# Compare true and estimated abilities
plot(true_theta, fm_calib$score$theta, xlab = "True Theta",
     ylab = "Estimated theta")
abline(a = 0, b = 1, col = "red", lty = 2)

# Compare true item parameters
plot(true_ip$a, fm_calib$ip$a, xlab = "True 'a'", ylab = "Estimated 'a'")
abline(a = 0, b = 1, col = "red", lty = 2)

plot(true_ip$b, fm_calib$ip$b, xlab = "True 'b'", ylab = "Estimated 'b'")
abline(a = 0, b = 1, col = "red", lty = 2)

mean(fm_calib$score$theta)

#############################################
############## Example 2 - 3PL ##############
#############################################
# IRT Three-parameter Logistic Model Calibration with D = 1.7

# Create responses to be used in flexMIRT estimation
true_theta <- rnorm(5000)
true_ip <- generate_ip(n = 35, model = "3PL", D = 1)
resp <- sim_resp(true_ip, true_theta)

# The following line will run 3PL calibration via flexMIRT:
fm_calib <- est_flexmirt(
  x = resp,
  model = "3PL",
  max_em_cycles = c(1000, 200),
  prior = data.frame(par = c("intercept", "slope", "guessing"),
                     dist = c("normal", "lognormal", "beta"),
                     v1 = c(0, 0, 1),
                     v2 = c(1, 0.5, 3)),
  target_dir = "C:/Temp/Analysis",
  overwrite = TRUE)

# Check whether the calibration converged
fm_calib$converged

fm_calib$convergence_details

# Get the estimated item pool
fm_calib$ip

# Get goodness-of-fit statistics and marginal reliability:
fm_calib$gof

# Get estimated scores
head(fm_calib$score)

# Compare true and estimated abilities
plot(true_theta, fm_calib$score$theta, xlab = "True Theta",
     ylab = "Estimated theta")
abline(a = 0, b = 1, col = "red", lty = 2)

# Compare true item parameters
plot(true_ip$a, fm_calib$ip$a, xlab = "True 'a'", ylab = "Estimated 'a'")
abline(a = 0, b = 1, col = "red", lty = 2)

plot(true_ip$b, fm_calib$ip$b, xlab = "True 'b'", ylab = "Estimated 'b'")
abline(a = 0, b = 1, col = "red", lty = 2)

mean(fm_calib$score$theta)


## End(Not run) # end dontrun

irt documentation built on Nov. 10, 2022, 5:50 p.m.