read.mplus {ThurMod}

Reads results from Mplus output file.

Description

This function reads and returns results from an Mplus output file.

Usage

read.mplus(blocks, itf, model, output_path, convergence = TRUE,
  fit.stat = TRUE, loading = TRUE, cor = TRUE, intercept = TRUE,
  threshold = TRUE, resvar = TRUE, standardized = FALSE)

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.

output_path

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

convergence

Logical. Should a message for convergence be returned? Defaults to 'TRUE'.

fit.stat

Logical. Should fit statistics be returned? Defaults to 'TRUE'.

loading

Logical. Should loading estimates be returned? Defaults to 'TRUE'.

cor

Logical. Should latent correlation estimates be returned? Defaults to 'TRUE'.

intercept

Logical. Should intercepts be returned? Does only work for ‘model = ’lmean''. Defaults to 'TRUE'.

threshold

Logical. Should thresholds be returned? Does only work for ‘model = ’uc'‘ or '’irt''. Defaults to 'TRUE'.

resvar

Logical. Should residual variances be returned? Defaults to 'TRUE'.

standardized

Logical. Should standardized values be returned? Defaults to 'FALSE'.

Value

Returns a list containing the specified results, after model analysis, by reading the results from the 'output_path'.

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'))


# After estimation
read.mplus(blocks,itf,'irt',output_path = paste0(tempdir(),'/','myFC_model.out'))

## End(Not run)