This is a function to create an Mplus model object in R. The object holds all the sections of an Mplus input file, plus some extra R ones. Once created, the model can be run using other functions such as mplusModeler or updated using methods defined for the update function.

mplusObject(
TITLE = NULL,
DATA = NULL,
VARIABLE = NULL,
DEFINE = NULL,
MONTECARLO = NULL,
MODELPOPULATION = NULL,
MODELMISSING = NULL,
ANALYSIS = NULL,
MODEL = NULL,
MODELINDIRECT = NULL,
MODELCONSTRAINT = NULL,
MODELTEST = NULL,
MODELPRIORS = NULL,
OUTPUT = NULL,
SAVEDATA = NULL,
PLOT = NULL,
usevariables = NULL,
rdata = NULL,
autov = TRUE,
imputed = FALSE,
quiet = TRUE,
...
)

## Arguments

TITLE A character string of the title for Mplus. A charater string of the data section for Mplus (note, do not define the filename as this is generated automatically) A character string of the variable section for Mplus (note, do not define the variable names from the dataset as this is generated automatically) A character string of the define section for Mplus (optional) A character string of the montecarlo section for Mplus (optional). If used, autov is defaults to FALSE instead of the usual default, TRUE, but may still be overwritten, if desired. A character string of the MODEL POPULATION section for Mplus (optional). A character string of the MODEL MISSING section for Mplus (optional). A character string of the analysis section for Mplus (optional) A character string of the model section for Mplus (optional, although typically you want to define a model) A character string of the MODEL INDIRECT section for Mplus (optional). A character string of the MODEL CONSTRAINT section for Mplus (optional). A character string of the MODEL TEST section for Mplus (optional). A character string of the MODEL PRIORS section for Mplus (optional). A character string of the output section for Mplus (optional) A character string of the savedata section for Mplus (optional) A character string of the plot section for Mplus (optional) A character vector of the variables from the R dataset to use in the model. An R dataset to be used for the model. A logical (defaults to TRUE) argument indicating whether R should attempt to guess the correct variables to use from the R dataset, if usevariables is left NULL. A logical whether the data are multiply imputed (a list). Defaults to FALSE. optional. If TRUE, show status messages in the console. Arguments passed on to mplusModeler if run > 0.

## Value

A list of class mplusObject with elements

TITLE

The title in Mplus (if defined)

DATA

The data section in Mplus (if defined)

VARIABLE

The variable section in Mplus (if defined)

DEFINE

The define section in Mplus (if defined)

MONTECARLO

The montecarlo section in Mplus (if defined)

MODELPOPULATION

The modelpopulation section in Mplus (if defined)

MODELMISSING

The modelmissing section in Mplus (if defined)

ANALYSIS

The analysis section in Mplus (if defined)

MODEL

The model section in Mplus (if defined)

MODELINDIRECT

The modelindirect section in Mplus (if defined)

MODELCONSTRAINT

The modelconstraint section in Mplus (if defined)

MODELTEST

The modeltest section in Mplus (if defined)

MODELPRIORS

The modelpriors section in Mplus (if defined)

OUTPUT

The output section in Mplus (if defined)

SAVEDATA

The savedata section in Mplus (if defined)

PLOT

The plot section in Mplus (if defined)

results

NULL by default, but can be later updated to include the results from the model run.

usevariables

A character vector of the variables from the R data set to be used.

rdata

The R data set to use for the model.

imputed

A logical whether the data are multiply imputed.

autov

A logical whether the data should have the usevariables detected automatically or not

## Details

Mplus model objects allow a base model to be defined, and then flexibly update the data, change the precise model, etc. If a section does not vary between models, you can leave it the same. For example, suppose you are fitting a number of models, but in all cases, wish to use maximum likelihood estimator, “ANALYSIS: ESTIMATOR = ML;” and would like standardized output, “OUTPUT: STDYX;”. Rather than retype those in every model, they can be defined in one Mplus model object, and then that can simply be updated with different models, leaving the analysis and output sections untouched. This also means that if a reviewer comes back and asks for all analyses to be re-run say using the robust maximum likelihood estimator, all you have to do is change it in the model object once, and re run all your code.

mplusModeler

## Author

Joshua F. Wiley <jwiley.psych@gmail.com>

## Examples


example1 <- mplusObject(MODEL = "mpg ON wt;",
usevariables = c("mpg", "hp"), rdata = mtcars)
str(example1)
#> List of 21
#>  $TITLE : NULL #>$ DATA           : NULL
#>  $VARIABLE : NULL #>$ DEFINE         : NULL
#>  $MONTECARLO : NULL #>$ MODELPOPULATION: NULL
#>  $MODELMISSING : NULL #>$ ANALYSIS       : NULL
#>  $MODEL : chr "mpg ON wt;" #>$ MODELINDIRECT  : NULL
#>  $MODELCONSTRAINT: NULL #>$ MODELTEST      : NULL
#>  $MODELPRIORS : NULL #>$ OUTPUT         : NULL
#>  $SAVEDATA : NULL #>$ PLOT           : NULL
#>  $results : NULL #>$ usevariables   : chr [1:2] "mpg" "hp"
#>  $rdata :'data.frame': 32 obs. of 11 variables: #> ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#>   ..$cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ... #> ..$ disp: num [1:32] 160 160 108 258 360 ...
#>   ..$hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ... #> ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#>   ..$wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ... #> ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
#>   ..$vs : num [1:32] 0 0 1 1 0 1 0 1 1 1 ... #> ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
#>   ..$gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ... #> ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
#>  $imputed : logi FALSE #>$ quiet          : logi TRUE
#>  - attr(*, "class")= chr [1:2] "mplusObject" "list"rm(example1)

# R figures out the variables automagically, with a message
example2 <- mplusObject(MODEL = "mpg ON wt;",
rdata = mtcars, autov = TRUE)
str(example2)
#> List of 21
#>  $TITLE : NULL #>$ DATA           : NULL
#>  $VARIABLE : NULL #>$ DEFINE         : NULL
#>  $MONTECARLO : NULL #>$ MODELPOPULATION: NULL
#>  $MODELMISSING : NULL #>$ ANALYSIS       : NULL
#>  $MODEL : chr "mpg ON wt;" #>$ MODELINDIRECT  : NULL
#>  $MODELCONSTRAINT: NULL #>$ MODELTEST      : NULL
#>  $MODELPRIORS : NULL #>$ OUTPUT         : NULL
#>  $SAVEDATA : NULL #>$ PLOT           : NULL
#>  $results : NULL #>$ usevariables   : chr [1:2] "mpg" "wt"
#>  $rdata :'data.frame': 32 obs. of 11 variables: #> ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#>   ..$cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ... #> ..$ disp: num [1:32] 160 160 108 258 360 ...
#>   ..$hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ... #> ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#>   ..$wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ... #> ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
#>   ..$vs : num [1:32] 0 0 1 1 0 1 0 1 1 1 ... #> ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
#>   ..$gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ... #> ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
#>  $imputed : logi FALSE #>$ quiet          : logi TRUE
#>  - attr(*, "class")= chr [1:2] "mplusObject" "list"rm(example2)

# R can also try to figure out a list of variables when
# variable names are hyphenated first-last variable, all variables
# between the first and last one will be included
example3 <- mplusObject(MODEL = "mpg ON wt-vs;",
rdata = mtcars, autov = TRUE)
str(example3)
#> List of 21
#>  $TITLE : NULL #>$ DATA           : NULL
#>  $VARIABLE : NULL #>$ DEFINE         : NULL
#>  $MONTECARLO : NULL #>$ MODELPOPULATION: NULL
#>  $MODELMISSING : NULL #>$ ANALYSIS       : NULL
#>  $MODEL : chr "mpg ON wt-vs;" #>$ MODELINDIRECT  : NULL
#>  $MODELCONSTRAINT: NULL #>$ MODELTEST      : NULL
#>  $MODELPRIORS : NULL #>$ OUTPUT         : NULL
#>  $SAVEDATA : NULL #>$ PLOT           : NULL
#>  $results : NULL #>$ usevariables   : chr [1:4] "mpg" "wt" "qsec" "vs"
#>  $rdata :'data.frame': 32 obs. of 11 variables: #> ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#>   ..$cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ... #> ..$ disp: num [1:32] 160 160 108 258 360 ...
#>   ..$hp : num [1:32] 110 110 93 110 175 105 245 62 95 123 ... #> ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#>   ..$wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ... #> ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
#>   ..$vs : num [1:32] 0 0 1 1 0 1 0 1 1 1 ... #> ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
#>   ..$gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ... #> ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
#>  $imputed : logi FALSE #>$ quiet          : logi TRUE
#>  - attr(*, "class")= chr [1:2] "mplusObject" "list"rm(example3)

# R warns if the first 8 characters of a (used) variable name are not unique
# as they will be indistinguishable in the Mplus output
example4 <- mplusObject(MODEL = "basename_01 ON basename_02;",
rdata = data.frame(basename_01 = 1:5, basename_02 = 5:1),
autov = TRUE)
#> The following variables are not unique in the first 8 characters:
#>  basename_02rm(example4)