fit.mplus {ThurMod}

Performs Mplus estimation of the given model.

Description

This function writes the Mplus syntax given the specifications of a Thurstonian forced choice design. Additionally it runs the code (given Mplus is installed) and returns the results.

Usage

fit.mplus(blocks, itf, model, input_path, output_path = NULL,
  data_path = "myDataFile.dat", fscore_path = "myFactorScores.dat",
  title = "myFC_model", ID = FALSE, byblock = TRUE,
  estimator = "ULSMV", data_full = FALSE, standardized = TRUE,
  rename_list = NULL, ...)

Arguments

blocks

A matrix defining the blocks of the model. The number of rows must be the number of blocks, each row represents a block and contains the item numbers. The number of columns present the number of items per block.

itf

A vector defining the items-to-factor relation. For example 'c(1,1,1,2,2,2)' defines six items, the first three correspond to factor 1, the second three correspond to factor 2.

model

A descriptor for the model. Can be one of ''lmean'', ''uc'‘, '’irt'‘ or '’simple2'‘, '’simple3'‘ or '’simple5''. The Number behind the ''simple'' statement defines the Thurstone case.

input_path

Path to save the Mplus input file. Defaults to ''myFC_model.inp''.

output_path

Path to the Mplus output file. Defaults to ''myFC_model.out''.

data_path

Path of the data file for Mplus. Defaults to ''myDataFile.dat''.

fscore_path

Path to save the file of factor scores. Defaults to ''myFactorScores.dat''.

title

Title of the Mplus model. Defaults to ''myFC_model''.

ID

Logical. Should a ID variable be included? The ID must be the first variable in the data set. Defaults to 'FALSE'.

byblock

Logical. Should the order in Mplus variable statement be the same as in the blocks. Defaults to 'TRUE'.

estimator

Which estimator should be used? All Estimators that are available in Mplus can be used. Defaults to ''ULSMV''.

data_full

Logical. Are the data considered to be from a full design? Defaults to 'FALSE'.

standardized

Logical. Should standardized values be computed? Defaults to 'TRUE'.

rename_list

A list with two vectors to rename the objects in the syntax. Vector one is the original names, vector two the new names. Defaults to 'NULL'.

...

Further arguments passed to function 'read.mplus'.

Details

The syntax currently is able to perform model analysis for the latent utility models (''simple'‘ and '’lmean''; Maydeu-Olivares & Böckenholt, 2005) the unconstrained factor model (''uc''; Maydeu-Olivares & Böckenholt, 2005) and the IRT model(''irt''; Maydeu-Olivares & Brown, 2010). Additionally, all model types can be performed with all types of forced choice designs (full, block, partially linked block, linked block). For an overview and review see Jansen and Schulze (2023a,2023b).

The function writes and saves the Mplus input files, keeps the output files and reads the results specified for the function 'read.mplus'.

The standard naming procedure ixiy, for the comparison of items x and y, can be changed by specifying the 'rename_list' argument. The first vector of the schould be the vector of original names, for example ‘c(’i1i2','i1i3','i2i3','Trait1','Trait2','Trait3')' the second vector should contain the new names, for example ‘c(’A01E12','A01C13','E01C23','Agree','Extra','Consc')'.

Value

Returns a list containing the specified results, after model analysis.

References

Maydeu-Olivares, A., & Böckenholt, U. (2005). Structural equation modeling of paired-comparison and ranking data. Psychological Methods, 10(3), 285-304. doi:10.1037/1082-989X.10.3.285

Maydeu-Olivares, A., & Brown, A. (2010). Item response modeling of paired comparison and ranking data. Multivariate Behavioural Research, 45(6), 935-974. doi:10.1080/00273171.2010.531231

Jansen, M. T., & Schulze, R. (2023a). Linear factor analytic Thurstonian forced-choice models: Current status and issues. Educational and Psychological Measurement.

Jansen, M. T., & Schulze, R. (2023b, in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.

Examples

# read and save data set FC
data(FC)
write.table(FC,paste0(tempdir(),'/','my_data.dat'),quote=FALSE, sep=" ",
col.names = FALSE, row.names = FALSE)

# set seed and define blocks
set.seed(1)
blocks <- matrix(sample(1:15,15), ncol = 3)

# define the item-to-factor relation
itf <- rep(1:3,5)

# perform analysis 
## Not run: 
fit.mplus(blocksort(blocks),itf,'irt',data_path = 'mydata.dat', data_full = TRUE,
input_path = paste0(tempdir(),'/','myFC_model'))

## End(Not run)