* Encoding: UTF-8.
* Use Mplus to run a two level model from within SPSS
* By Jamie DeCoster
* This program allows users to identify a path model that
* they want to test on an SPSS data set. The program then
* converts the active data set to Mplus format, writes a program
* that will perform the path analysis in Mplus, then loads the important
* parts of the Mplus output into the SPSS output window.
**** Usage: MplusTwoLevel(inpfile, modellabel, runModel, viewOutput, suppressSPSS,
withinLatent, withinLatentFixed, withinModel, withinMeans, withinVar, withinCovar,
withinCovEndo, withinCovExo, withinIdentifiers, withinMeanIdentifiers, withinSlopes,
betweenLatent, betweenLatentFixed, betweenModel, betweenMeans, betweenVar, betweenCovar,
betweenCovEndo, betweenCovExo,
betweenIdentifiers, betweenMeanIdentifiers,
estimator, starts, useobservations, wald, constraint,
montecarlo, bootstrap, repse,
categorical, censored, count, nominal, groupmean, grandmean,
cluster, complex, weight,
datasetName, datasetMeans, datasetIntercepts, datasetVariances,
datasetResidualV, datasetLabels, processors, waittime)
**** "inpfile" is a string identifying the directory and filename of
* Mplus input file to be created by the program. This filename must end with
* .inp . The data file will automatically be saved to the same directory. This
* argument is required.
**** "modellabel" is a string that indicates what label should be added to the output at the
* top of your model. If this is not specified, the label defaults to "MplusTwoLevel"
**** "runModel" is a boolean argument indicating whether or not you want
* the program to actually run the program it creates based on the model
* you define. You may choose to not run the model when you
* want to use the program to load an existing output file into SPSS.
* By default, the model is run.
**** "viewOutput" is a boolean argument indicating whether or not you want
* the program to read the created output into SPSS. You may choose not
* to read the output into SPSS when you know that it will take a very long
* time to run and you do not want to tie up SPSS while you are waiting
* for Mplus to finish. If you choose not to view the output, then the program
* will also not create a dataset for the coefficients.
* By default, the output is read into SPSS.
**** "suppressSPSS" is a boolean argument indicating whether or not you want
* the program to supress SPSS output while running the model. Typically this
* output is not useful and merely clogs up the output window. If your program
* inconsistently causes SPSS to crash, suppressing the output can sometimes
* help. However, if your model is not running correctly, the SPSS output can
* help you see where the errors are. Setting this argument to True will not
* suppress the Mplus output. By default, the SPSS output is not suppressed.
**** "withinLatent" is a list of lists identifying the relations between observed and
* latent variables for the within model. This argument is optional, and can be omitted
* if your model does not have any latent variables at the within level. When
* creating this argument, you first create a
* list of strings for each latent variable where the first element is the name of
* the latent variable and the remaining elements are the names of the observed
* variables that load on that latent variable. You then combine these individual
* latent variable lists into a larger list identifying the full measurement model.
**** "withinLatentFixed" is a list of lists identifying any values of latent variable links
* that are fixed to constant values. Each entry in this list pairs a within latent
* coefficient with its constant value. The coefficients part must
* specifically match an element of
* the withinLatent statement. To do this, you may need to separate the
* observed values for a single latent variable into different lists. This defaults to None,
* which does not assign any fixed latent coefficients.
**** "withinModel" is a list of lists identifying the equations in the within-cluster part
* of your model. First, you create a set of lists that each have the outcome as
* the first element and then have the predictors as the following elements.
* Then you combine these individual equation lists into a larger list identifying
* the entire within model. All variables included in the within model have to
* have variability within clusters.
**** "withinMeans" is a list of variables indicating which means you want
* estimated in the within model.
**** "withinVar" is a list of strings identifying variables that are to be treated
* as only having within-cluster variability. Note that you can include variables
* in the within model even if you do not include them in this command, in
* which case Mplus will assume that the variable has both within-cluster
* and within-cluster variability. This argument defaults to None, indicating
* that you are not identifying any variables as only having within-cluster
* variability.
**** "withinCovar" is a list of lists identifying within-cluster covariances among
* variables. First, you create a set of lists that identify pairs of variables that
* are allowed to covary.
* Then you combine these lists of pairs into a single, overall list.
* This argument defaults to None, which would indicate that there are
* not explicitly identifying within-cluster covariances among the variables.
* However, your choices for the "withinCovEndo" and the "withinCovExo"
* arguments may allow additional covariances.
**** "withinCovEndo" is a boolean variable that indicates whether you want
* to automatically covary all of the endogenous variables in the within-cluster
* part of the model.
* Endogenous variables are those that are used as an outcome at least
* once in your model. If this variable is set to True, then the program will
* automatically include covariances among all of the endogenous
* variables. If this variable is set to False, then it will not, although
* you can still specify individual covariances between endogenous
* variables using the "withinCovar" argument described above. By default,
* the value of covEndo is False.
**** "withinCovExo" is a boolean variable that indicates whether you want
* to automatically covary all of the exogenous variables in the within-cluster
* part of the model.
* Exogenous variables are those that are only used as predictors and
* never used as outcomes in your model. If this variable is set to True,
* then the program will automatically include covariances among all
* of the exogenous variables. If this variable is set to False, then it
* will not, although you can still specify individual covariances between
* exogenous variables using the "withinCovar" argument described above.
* By default, the value for corrExo is True.
**** "withinIdentifiers" is an optional argument that provides a list of lists pairing
* within-cluster coefficients with identifiers that will be used as part of a
* Wald Z test or a model constraint calculation. The coefficients part must
* specifically match an element of
* the withinModel statement. To do this, you may need to separate the
* predictors for a single outcome into different lists. This defaults to None,
* which does not assign any identifiers.
**** "withinMeanIdentifiers" is an optional argument that provides a list of lists
* pairing means from the within model with identifiers that will be used as
* part of a Wald Z test,
* a Model Constraint calculation, or a model with parameters forced to be equal.
* This defaults to None, which does not assign any identifiers.
**** "withinSlopes" is an optional argument that provides a list of lists pairing
* within-cluster coefficients with identifiers that will be used to test cross-level
* interactions. The coefficients part must specifically match an element of
* the withinModel statement. To do this, you may need to separate the
* predictors for a single outcome into different lists. This defaults to None,
* which does not assign any identifiers. The identifiers created in this statement
* can be used in the betweenLatent, betweenModel, betweenCovar, and
* betweenIdentifiers statements.
**** "betweenLatent" is a list of lists identifying the relations between observed and
* latent variables for the between model. This argument is optional, and can be omitted
* if your model does not have any latent variables at the between level. When
* creating this argument, you first create a
* list of strings for each latent variable where the first element is the name of
* the latent variable and the remaining elements are the names of the observed
* variables that load on that latent variable. You then combine these individual
* latent variable lists into a larger list identifying the full measurement model.
**** "betweenLatentFixed" is a list of lists identifying any values of latent variable links
* that are fixed to constant values. Each entry in this list pairs a within latent
* coefficient with its constant value. The coefficients part must
* specifically match an element of
* the betweenLatent statement. To do this, you may need to separate the
* observed values for a single latent variable into different lists. This defaults to None,
* which does not assign any fixed latent coefficients.
**** "betweenModel" is a list of lists identifying the equations in the between-cluster
* part of your model. First, you create a set of lists that each have the outcome
* as the first element and then have the predictors as the following elements.
* Then you combine these individual equation lists into a larger list identifying
* the entire between model. You can include variables that vary within a
* cluster as elements of the between model. In this case, the between-cluster
* test will specifically identify how between-cluster variability in the predictor
* relates to between-cluster variability in the outcome.
**** "betweenMeans" is a list of variables indicating which means you want
* estimated in the between model.
**** "betweenVar" is a list of strings identifying variables that are to be treated
* as only having between-cluster variability. Note that you can include variables
* in the between model even if you do not include them in this command, in
* which case Mplus will assume that the variable has both within-cluster
* and within-cluster variability. This argument defaults to None, indicating
* that you are not identifying any variables as only having between-cluster
* variability.
**** "betweenCovar" is a list of lists identifying between-cluster covariances among
* variables. First, you create a set of lists that identify pairs of variables that
* are allowed to covary.
* Then you combine these lists of pairs into a single, overall list.
* This argument defaults to None, which would indicate that there are
* not explicitly identifying between-cluster covariances among the variables.
* However, your choices for the "betweenCovEndo" and the "betweenCovExo"
* arguments may allow additional covariances.
**** "betweenCovEndo" is a boolean variable that indicates whether you want
* to automatically covary all of the endogenous variables in the between-cluster
* part of the model.
* Endogenous variables are those that are used as an outcome at least
* once in your model. If this variable is set to True, then the program will
* automatically include covariances among all of the endogenous
* variables. If this variable is set to False, then it will not, although
* you can still specify individual covariances between endogenous
* variables using the "betweenCovar" argument described above. By default,
* the value of covEndo is False.
**** "betweenCovExo" is a boolean variable that indicates whether you want
* to automatically covary all of the exogenous variables in the between-cluster
* part of the model.
* Exogenous variables are those that are only used as predictors and
* never used as outcomes in your model. If this variable is set to True,
* then the program will automatically include covariances among all
* of the exogenous variables. If this variable is set to False, then it
* will not, although you can still specify individual covariances between
* exogenous variables using the "betweenCovar" argument described above.
* By default, the value for corrExo is True.
**** "betweenIdentifiers" is an optional argument provides a list of lists pairing
* between-cluster coefficients with identifiers that will be used as part of a
* Wald Z test. The coefficients part must specifically match one list
* in the betweenModel statement. To do this, you may need to separate the
* predictors for a single outcome into different lists. This defaults to None,
* which does not assign any identifiers.
**** "betweenMeanIdentifiers" is an optional argument that provides a list of lists
* pairing means from the between model with identifiers that will be used as part of a Wald Z test,
* a Model Constraint calculation, or a model with parameters forced to be equal.
* This defaults to None, which does not assign any identifiers.
**** "estimator" is a string specifying the estimation method to be used.
* Valid values are ML, MLM, MLMV, MLR, MLF, MUML, WLS, WLSM,
* WLSMV, ULS, ULSMV, GLS, and BAYES. If this argument is omitted,
* the Mplus default will be used, which depends on the data and model
* types you are using (most commonly MLR).
**** "starts" is number specifying the number of random starts to be used.
* Omitting this value has Mplus use the default number of random starts,
* which depends on the exact analysis you are doing.
**** "useobservations" is a string specifying a selection
* criteriion that must be met for observations to be included in the
* analysis. This is an optional argument that defaults to None, indicating
* that all observations are to be included in the analysis.
**** "wald" is an optional argument that identifies a list of constraints that
* will be tested using a Wald Z test. The constraints will be definted using the
* identifiers specified in the "identifiers" argument. This can be used
* to create an omnibus test that several coefficients are equal to zero, or it
* can be used to test the equivalence of different coefficients. This argument
* defaults to None, which would indicate that you do not want to perform
* a Wald Z test.
**** "constraint" is an optional argument that identifies a string
* to be included in the Model Constraint section, allowing you to estimate
* linear combinations of means and coefficients from your model.
**** "montecarlo" is an optional argument that allows you to specify Monte
* Carlo integration. If you omit this argument, Mplus will not use Monte Carlo
* integration. If you want to use Monte Carlo integration, you set this argument
* to a number that is the number of integration points you want to use. The
* default used by Mplus is 2000.
**** "bootstrap" is an optional argument that allows you to request bootstrap
* confidence intervals. If you want to obtain bootstrap CIs, you set this
* argument equal to the number of bootstrap samples you want to use. This
* number should be at least 1000, but can go notably higher. Researchers
* typically use 5000, but it's not unheard of to use 20000 or more.
* NOTE: Bootstrapping is not currently implemented for two-level models.
* The authors have indicated that they will be implementing it soon,
* but right now models with bootstrapping will not work.
**** "repse" is an optional argument that allows you to identify the resampling
* method used to create replicate weights. Valid options are bootstrap,
* jackknife, jackknife1, jackknife2, brr, and fay(#)
**** "categorical" is an optional argument that identifies a list of variables
* that should be treated as categorical by Mplus. Note that what Mplus
* calls categorical is typically called "ordinal" in other places. Use the
* "nominal" command described below for true categorical variables.
**** "censored" is an optional argument that identifies a list of variables
* that should be treated as censored by Mplus.
**** "count" is an optional argument that identifies a list of variables
* that should be treated as count variables
* (i.e., for Poisson regression) by Mplus.
**** "nominal" is an optional argument that identifies a list of variables
* that should be treated as nominal variables by Mplus.
**** "groupmean" is an optional argument that identifies a list of variables
* that should be group mean centered.
**** "grandmean" is an optional argument that identifies a list of variables
* that should be grand mean centered.
**** "cluster" is a string that identifies the primary cluster variable.
* This is required, because we cannot run a two-level model without
* a cluster variable.
**** "complex" is a string that identifies a second cluster variable that
* shoule be used to adjust the standard error of the coeffficients.
* This defaults to None, meaning that there is not a second cluster variable.
**** "weight" is an optional argument that identifies a sample weight.
* This defaults to None, which would indicate that there all observations
* are given equal weight. Note that not all estimators can make use of
* weights. MLR is typically a good option.
**** "datasetName" is an optional argument that identifies the name of
* an SPSS dataset that should be used to record the coefficients.
**** "datasetMeans" is an optional argument that determines whether
* the means are included in the coefficient dataset. This is False
* by default.
**** "datasetIntercepts" is an optional argument that determines whether
* the model intercepts are included in the coefficient dataset. This is False
* by default.
**** "datasetVariances" is an optional argument that determines whether
* the variances are included in the coefficient dataset. This is False
* by default.
**** "datasetResidualV" is an optional argument that determines whether
* the residual variances are included in the coefficient dataset. This is False
* by default.
**** "datasetLabels" is an optional argument that identifies a list of
* labels that would be applied to the datasets. This can be useful if
* you are appending the results from multiple analyses to the same dataset.
**** "miThreshold" is an optional argument that identifies the
* minimum chi-square change required for a modificiation index
* to be reported. Omitting this argument uses a default of 10.
**** "processors" is an optional argument that specifies how many logical processors
* Mplus should use when running the analysis. You should not specify more
* processors than are available in your machine. If this argument is omitted, Mplus will
* use 1 processor.
**** "waittime" is an optional argument that specifies how many seconds
* the program should wait after running the Mplus program before it
* tries to read the output file. This defaults to 5. You should be sure that
* you leave enough time for Mplus to finish the analyses before trying
* to import them into SPSS
* Example:
MplusTwoLevel(inpfile = "C:/users/jamie/workspace/spssmplus/path.inp",
runModel = True,
viewOutput = True,
suppressSPSS = False,
withinLatent = [ ["CHSES", "chincome_mean", "chfrl_mean", "chmomed_mean"] ],
withinModel = [ ["CO", "CHSES", "att_ch", "yrs_tch"],
["ES", "CHSES", "att_ch", "yrs_tch"],
["IS", "CHSES", "att_ch", "yrs_tch"],
["CO", "satis"],
["ES", "satis"],
["IS", "satis"] ],
withinCovar = [ ["CO","ES"], ["CO", "IS"] ],
withinCovEndo = False,
withinCovExo = True,
withinIdentifiers = None,
withinSlopes = [ [ ["CO", "satis"], "StoCO"],
[ ["ES", "satis"], "StoES"],
[ ["IS", "satis"], "StoIS"] ],
betweenLatent = [ ["CHSES", "chincome_mean", "chfrl_mean", "chmomed_mean"] ],
betweenModel = [ ["CO", "CHSES", "att_ch", "yrs_tch", "schoolsize"],
["CO", "Tx"],
["ES", "CHSES", "att_ch", "yrs_tch", "schoolsize"],
["ES", "Tx"],
["IS", "CHSES", "att_ch", "yrs_tch", "schoolsize"]
["IS", "Tx"],
["StoCO", "schoolsize"],
["StoES", "schoolsize"],
["StoIS", "schoolsize"] ],
betweenCovar = [ ["CO","ES"], ["CO", "IS"],
["StoCO", "StoES"], ["StoCO", "StoIS"], ["StoES", "StoIS"] ],
betweenCovEndo = False,
betweenCovExo = True,
betweenIdentifiers = [ [ ["CO", "Educ"], "b1"],
[ ["ES", "Educ"], "b2"],
[ ["IS", "Educ"], "b3"] ],
wald = [ "b1 = 0", "b2 = 0", "b3 = 0" ],
useobservations = "p2cond==1",
categorical = ["att_ch", "yrs_tch"],
censored = None,
count = None,
nominal = ["Tx"],
groupmean = ["CO", "CHSES", "att_ch", "yrs_tch"],
grandmean = ["schoolsize"],
cluster = "school",
weight = "demoweight",
datasetName = "CLASS",
datasetMeans = True,
datasetIntercepts = True,
datasetVariances = True,
datasetResidualV = True,
datasetLabels = ["2009 cohort"],
miThreshold = 4,
waittime = 10)
* This would test a model where three measures assessing classroom
* interactions (CO, ES, and IS) are predicted by within-school (i.e., classroom)
* and between-school predictors (defined by the cluster variable school).
* The toggles are set so that the program will run the model in Mplus
* and read the output into SPSS. The SPSS output is not suppressed.
* A single latent variable (CHSES) is created to represent child socio-
* economic status, based on observed variables assessing income
* (chincome_mean), free/reduced lunch status (chfrl_mean), and
* mother education (chmomed_mean). Other within-school predictors
* are teacher attitudes toward childen (att_ch), teacher satisfaction (satis), and
* teacher experience (yrs_tch). The exogenous variables (CHSES, att_ch, and
* yrs_tch) are allowed to freely covary in the within model. The endogenous
* variables (CO, ES, and IS) are not automatically allowed to covary in the
* within model, although two specific covariances are allowed (CO with ES
* and CO with IS). The slopes of teacher satisfaction with the three outcomes
* at the within level identified so that they can be used in the between model
* to test cross-level interactions. Between-school predictors are the number of
* students in the school (schoolsize) and treatment condition (Tx). School size is
* also used to predict the relations of satisfaction with the three outcomes.
* The exogenous variables (CHSES, att_ch, and yrs_tch) are allowed
* to freely covary in the between model. The endogenous variables (CO, ES,
* and IS) are not automatically allowed to covary in the between model, although
* two specific covariances among the outcomes are allowed (CO with ES and
CO with IS). The three slopes for satisfaction are all allowed to freely covary.
* Identifiers are created representing the treatment effects on the three
* outcomes at A Wald test is created testing whether this collect of effects is
* significant. The analysis will only include observations where the value of
* pcond is 1. att_ch and trs_tch are treated as a categorical variables, whereas
* Tx is treated as a nominal variable. The four within variables will be
* group mean centered, while schoolsize will be grand mean centered.
* The analysis weights the observations using the values in the variable
* "demoweight." The regression coefficients will be recorded in the
* SPSS dataset "CLASS". The dataset will additionally contain estimates
* of the means, model intercepts, variances, and residual variances.
* This dataset will have a label variable, which will
* have the value of "2009 cohort" for all results from this analysis.
* All modification indices that are greater than 4 will be reported in
* the output. The program will wait 10 seconds after starting to
* run the Mplus program before it tries to read the results back into SPSS.
set printback = off.
begin program python3.
import spss, spssaux, os, sys, time, re, tempfile, SpssClient
from subprocess import Popen, PIPE
def _titleToPane():
"""See titleToPane(). This function does the actual job"""
outputDoc = SpssClient.GetDesignatedOutputDoc()
outputItemList = outputDoc.GetOutputItems()
textFormat = SpssClient.DocExportFormat.SpssFormatText
filename = tempfile.mktemp() + ".txt"
for index in range(outputItemList.Size()):
outputItem = outputItemList.GetItemAt(index)
if outputItem.GetDescription() == "Page Title":
outputItem.ExportToDocument(filename, textFormat)
with open(filename, 'r', encoding='utf-8') as f:
outputItem.SetDescription(f.read().rstrip())
os.remove(filename)
return outputDoc
def titleToPane(spv=None):
"""Copy the contents of the TITLE command of the designated output document
to the left output viewer pane"""
try:
outputDoc = None
SpssClient.StartClient()
if spv:
SpssClient.OpenOutputDoc(spv)
outputDoc = _titleToPane()
if spv and outputDoc:
outputDoc.SaveAs(spv)
except Exception as e:
print("Error filling TITLE in Output Viewer [{}]".format(e))
finally:
SpssClient.StopClient()
def MplusSplit(splitstring, linelength):
returnstring = ""
curline = splitstring
while len(curline) > linelength:
splitloc = linelength
while curline[splitloc] == " " or curline[splitloc - 1] == " ":
splitloc -= 1
returnstring += curline[:splitloc] + "\n"
curline = curline[splitloc:]
returnstring += curline
return returnstring
def SPSSspaceSplit(splitstring, linelength):
stringwords = splitstring.split()
returnstring = "'"
curline = ""
for word in stringwords:
if len(word) > linelength:
break
if len(word) + len(curline) < linelength - 1:
curline += word + " "
else:
returnstring += curline + "' +\n'"
curline = word + " "
returnstring += curline[:-1] + "'"
return returnstring
def numericMissing(definition):
for varnum in range(spss.GetVariableCount()):
if spss.GetVariableType(varnum) == 0:
# for numeric variables
submitstring = """
missing values %s (%s).""" % (spss.GetVariableName(varnum), definition)
spss.Submit(submitstring)
def exportMplus(filepath):
######
# Get list of current variables in SPSS data set
######
SPSSvarlist = []
for varnum in range(spss.GetVariableCount()):
SPSSvarlist.append(spss.GetVariableName(varnum))
##########
# Replace non-alphanumeric characters with _ in the variable names
##########
nonalphanumeric = [".", "@", "#", "$"]
for t in range(spss.GetVariableCount()):
oldname = spss.GetVariableName(t)
newname = ""
for i in range(len(oldname)):
if oldname[i] in nonalphanumeric:
newname = newname + "_"
else:
newname = newname + oldname[i]
for i in range(t):
compname = spss.GetVariableName(i)
if newname.lower() == compname.lower():
newname = "var" + "%05d" % (t + 1)
if oldname != newname:
submitstring = "rename variables (%s = %s)." % (oldname, newname)
spss.Submit(submitstring)
#########
# Rename variables with names > 8 characters
#########
for t in range(spss.GetVariableCount()):
if len(spss.GetVariableName(t)) > 8:
name = spss.GetVariableName(t)[0:8]
for i in range(spss.GetVariableCount()):
compname = spss.GetVariableName(i)
if name.lower() == compname.lower():
name = "var" + "%05d" % (t + 1)
submitstring = "rename variables (%s = %s)." % (spss.GetVariableName(t), name)
spss.Submit(submitstring)
# Obtain lists of variables in the dataset
varlist = []
numericlist = []
stringlist = []
for t in range(spss.GetVariableCount()):
varlist.append(spss.GetVariableName(t))
if spss.GetVariableType(t) == 0:
numericlist.append(spss.GetVariableName(t))
else:
stringlist.append(spss.GetVariableName(t))
###########
# Automatically recode string variables into numeric variables
###########
# First renaming string variables so the new numeric vars can take the
# original variable names
submitstring = "rename variables"
for var in stringlist:
submitstring = submitstring + "\n " + var + "=" + var + "_str"
submitstring = submitstring + "."
spss.Submit(submitstring)
# Recoding variables
if len(stringlist) > 0:
submitstring = "AUTORECODE VARIABLES="
for var in stringlist:
submitstring = submitstring + "\n " + var + "_str"
submitstring = submitstring + "\n /into"
for var in stringlist:
submitstring = submitstring + "\n " + var
submitstring = submitstring + """
/BLANK=MISSING
/PRINT."""
spss.Submit(submitstring)
# Dropping string variables
submitstring = "delete variables"
for var in stringlist:
submitstring = submitstring + "\n " + var + "_str"
submitstring = submitstring + "."
spss.Submit(submitstring)
# Set all missing values to be -999
submitstring = "RECODE "
for var in varlist:
submitstring = submitstring + " " + var + "\n"
submitstring = submitstring + """ (MISSING=-999).
EXECUTE."""
spss.Submit(submitstring)
numericMissing("-999")
########
# Convert date and time variables to numeric
########
# SPSS actually stores dates as the number of seconds that have elapsed since October 14, 1582.
# This syntax takes variables with a date type and puts them in their natural numeric form
submitstring = """numeric ddate7663804 (f11.0).
alter type ddate7663804 (date11).
ALTER TYPE ALL (DATE = F11.0).
alter type ddate7663804 (adate11).
ALTER TYPE ALL (ADATE = F11.0).
alter type ddate7663804 (time11).
ALTER TYPE ALL (TIME = F11.0).
delete variables ddate7663804."""
spss.Submit(submitstring)
######
# Obtain list of transformed variables
######
submitstring = """MATCH FILES /FILE=*
/keep="""
for var in varlist:
submitstring = submitstring + "\n " + var
submitstring = submitstring + """.
EXECUTE."""
spss.Submit(submitstring)
MplusVarlist = []
for varnum in range(spss.GetVariableCount()):
MplusVarlist.append(spss.GetVariableName(varnum))
############
# Create data file
############
# Break filename over multiple lines
splitfilepath = SPSSspaceSplit(filepath, 60)
# Save data as a tab-delimited text file
submitstring = """SAVE TRANSLATE OUTFILE=
%s
/TYPE=TAB
/MAP
/REPLACE
/CELLS=VALUES
/keep""" % (splitfilepath)
for var in varlist:
submitstring = submitstring + "\n " + var
submitstring = submitstring + "."
spss.Submit(submitstring)
##############
# Rename variables back to original values
##############
submitstring = "rename variables"
for s, m in zip(SPSSvarlist, MplusVarlist):
submitstring += "\n(" + m + "=" + s + ")"
submitstring += "."
spss.Submit(submitstring)
return MplusVarlist
class MplusTLprogram:
def __init__(self):
self.title = "TITLE:\n"
self.data = "DATA:\n"
self.variable = "VARIABLE:\n"
self.define = "DEFINE:\n"
self.analysis = "ANALYSIS:\n"
self.model = "MODEL:\n"
self.constraint = "MODEL CONSTRAINT:\n"
self.output = "OUTPUT:\n"
self.savedata = "SAVEDATA:\n"
self.plot = "PLOT:\n"
self.montecarlo = "MONTECARLO:\n"
def setTitle(self, titleText):
self.title += titleText
def setData(self, filename):
self.data += "File is\n"
splitName = MplusSplit(filename, 75)
self.data += "'" + splitName + "';"
def setVariableTL(self, fullList, withinLatent, withinModel, withinVar, slopeVars,
betweenLatent, betweenModel, betweenVar, useobservations,
categorical, censored, count, nominal, cluster, complex, weight):
self.variable += "Names are\n"
for var in fullList:
self.variable += var + "\n"
self.variable += ";\n\n"
# Determine usevariables
useList = []
latentName = []
if withinLatent is not None:
for equation in withinLatent:
latentName.append(equation[0])
for var in equation[1:]:
if var not in useList:
useList.append(var)
if betweenLatent is not None:
for equation in betweenLatent:
latentName.append(equation[0])
for var in equation[1:]:
if var not in useList and var not in slopeVars:
useList.append(var)
if withinModel is not None:
for equation in withinModel:
for var in equation:
if var not in useList and var not in latentName:
useList.append(var)
if betweenModel is not None:
for equation in betweenModel:
for var in equation:
if var not in useList and var not in latentName and var not in slopeVars:
useList.append(var)
self.variable += "Usevariables are\n"
for var in useList:
self.variable += var + "\n"
# Other variable additions
if useobservations is not None:
self.variable += ";\n\nuseobservations are " + useobservations
self.variable += ";\n\ncluster is"
if complex is not None:
self.variable += " " + complex
self.variable += " " + cluster
if weight is not None:
self.variable += ";\n\nweight is " + weight
vartypeList = [categorical, censored, count, nominal, withinVar, betweenVar]
varnameList = ["categorical", "censored", "count", "nominal", "within", "between"]
for t in range(len(vartypeList)):
if vartypeList[t]:
self.variable += ";\n\n{0} = ".format(varnameList[t])
for var in vartypeList[t]:
self.variable += var + "\n"
self.variable += ";\n\nMISSING ARE ALL (-999);"
def setDefine(self, MplusGroupmean, MplusGrandmean):
if not MplusGroupmean and not MplusGrandmean:
self.define += ""
else:
self.define += "CENTER"
if MplusGroupmean:
for var in MplusGroupmean:
self.define += " " + var
self.define += " (GROUPMEAN)"
if MplusGrandmean:
for var in MplusGrandmean:
self.define += " " + var
self.define += " (GRANDMEAN)"
self.define += ";"
def setAnalysis(self, cluster, complex, MplusWithinSlopes, estimator, starts, weight,
mc, boot, repse, processors):
self.analysis += "type = twolevel"
if complex is not None:
self.analysis += " complex"
if MplusWithinSlopes is not None:
self.analysis += " random"
self.analysis += ";"
if estimator is not None:
self.analysis += "\nestimator = {0};".format(estimator)
if starts is not None:
self.analysis += "\nstarts = {0};".format(starts)
if mc is not None:
self.analysis += "\nintegration = montecarlo({0});".format(mc)
if boot is not None:
self.analysis += "\nbootstrap = {0};".format(boot)
if repse is not None:
self.analysis += "\nrepse = {0};".format(repse)
if processors is not None:
self.analysis += "\nprocessors = {0};".format(processors)
def setModel(self, MplusWithinLatent, MplusWithinLatentFixed, MplusWithinModel,
MplusWithinMeans, MplusWithinCovar, MplusWithinIdentifiers, MplusWithinMeanIdentifiers,
withinEndo, withinExo, MplusWithinSlopes,
MplusBetweenLatent, MplusBetweenLatentFixed, MplusBetweenModel, MplusBetweenMeans,
MplusBetweenCovar, MplusBetweenIdentifiers, MplusBetweenMeanIdentifiers,
betweenEndo, betweenExo, wald):
def modelCode(label, latent, latentFixed, model, means, covar, identifiers, meanIdentifiers,
cEndo, cExo, slopes, slopeList):
code = "%{0}%\n".format(label)
# Latent variable definitions
if latent is not None:
for equation in latent:
curline = equation[0] + " by"
for var in equation[1:]:
if len(curline) + len(var) < 75:
curline += " " + var
else:
code += curline + "\n"
curline = var
if latentFixed is not None:
for t in latentFixed:
if equation == t[0]:
curline += "@" + str(t[1])
code += curline + ";\n\n"
# Regression equations
if model is not None:
for equation in model:
curline = ""
if slopes is not None:
for s in slopes:
if equation == s[0]:
curline += s[1] + " | "
curline += equation[0] + " on"
for var in equation[1:]:
if len(curline) + len(var) < 75:
curline += " " + var
else:
code += curline + "\n"
curline = var
if identifiers is not None:
for id in identifiers:
if equation == id[0]:
curline += " (" + id[1] + ")"
code += curline + ";\n"
# Means
if means is not None:
for m in means:
curline = "[" + m + "]"
if meanIdentifiers is not None:
for id in meanIdentifiers:
if m == id[0]:
curline += " (" + id[1] + ")"
code += curline + ";\n"
# Getting lists of endogenous and exogenous variables
endo = []
for equation in model:
if equation[0] not in slopeList:
endo.append(equation[0])
endo = list(set(endo))
exo = []
for equation in model:
for var in equation:
if var not in endo and var not in exo and var not in slopeList:
exo.append(var)
# Add defined covariances
if covar is not None:
for t in range(len(covar)):
code += "\n" + covar[t][0] + " with "
code += covar[t][1] + ";"
code += "\n"
# Covariances for all exogenous variables
if cExo and model is not None:
# Estimate variances for exogenous variables so that they
# will be included in FIML
# Cannot include predictors that are also included as random slopes
if exo:
for var in exo:
code += "\n" + var + ";"
code += "\n"
for t in range(len(exo) - 1):
code += "\n"
curline = exo[t] + " with"
for var in exo[t + 1:]:
if len(curline) + len(var) < 75:
curline = curline + " " + var
else:
code += curline + "\n"
curline = var
code += curline + ";"
# Covariances for all endogenous variables
if cEndo and model is not None:
if endo:
code += "\n"
for t in range(len(endo) - 1):
code += "\n"
curline = endo[t] + " with"
for var in endo[t + 1:]:
if len(curline) + len(var) < 75:
curline = curline + " " + var
else:
code += curline + "\n"
curline = var
code += curline + ";"
code += "\n\n"
return code
# List of variables involved in random slopes
slopeList = []
if MplusWithinSlopes is not None:
for slope in MplusWithinSlopes:
for var in slope[0]:
slopeList.append(var)
# Within Model
withinCode = modelCode("WITHIN", MplusWithinLatent, MplusWithinLatentFixed,
MplusWithinModel, MplusWithinMeans, MplusWithinCovar, MplusWithinIdentifiers,
MplusWithinMeanIdentifiers, withinEndo, withinExo, MplusWithinSlopes, slopeList)
self.model += withinCode
# Between Model
betweenCode = modelCode("BETWEEN", MplusBetweenLatent, MplusBetweenLatentFixed,
MplusBetweenModel, MplusBetweenMeans, MplusBetweenCovar, MplusBetweenIdentifiers,
MplusBetweenMeanIdentifiers, betweenEndo, betweenExo, None, slopeList)
self.model += betweenCode
# Wald test
if wald is not None:
self.model += "\n\nMODEL TEST:"
for line in wald:
self.model += "\n" + line + ";"
def setConstraint(self, constraintText):
if constraintText is not None:
self.constraint += "\n" + constraintText
def setOutput(self, MplusComplex, miThreshold, boot):
if MplusComplex is None:
self.output += "stdyx;"
if boot is not None:
self.output += "\ncinterval(bcbootstrap);"
self.output += "\nmodindices({0});".format(miThreshold)
def write(self, filename):
# Write input file
sectionList = [self.title, self.data, self.variable, self.define,
self.analysis, self.model, self.constraint, self.output, self.savedata,
self.plot, self.montecarlo]
with open(filename, "w") as outfile:
for sec in sectionList:
if sec[-2:] != ":\n":
outfile.write(sec)
outfile.write("\n\n")
def batchfile(directory, filestem):
# Write batch file
with open(os.path.join(directory, filestem + ".bat"), "w") as batchFile:
batchFile.write("cd " + directory + "\n")
batchFile.write("call mplus \"" + filestem + ".inp" + "\"\n")
# Run batch file
p = Popen([os.path.join(directory, filestem + ".bat")], cwd=directory)
def removeBlanks(processString):
if processString is None:
return None
else:
for t in range(len(processString), 0, -1):
if processString[t - 1] != "\n":
return processString[0:t]
def getCoefficients(outputBlock):
coefficients = []
if outputBlock is not None:
outputBlock2 = outputBlock.replace("\r", "")
outputBlock2 = outputBlock2.replace("*********", "-999")
blockList = outputBlock2.split("\n")
for t in range(len(blockList)):
values1 = blockList[t].split(" ")
values2 = []
for i in values1:
if i != "":
values2.append(i)
if len(values2) > 1:
if values2[1] == "ON":
outcome = values2[0]
if len(values2) > 2 and values2[0] != "Estimate":
line = [outcome]
line.extend(values2[0:1])
for j in values2[1:]:
if j != "*":
line.append(float(j))
coefficients.append(line)
return coefficients
def getStats(outputBlock, startList, stopList):
stats = []
startRead = 0
if outputBlock is not None:
outputBlock2 = outputBlock.replace("\r", "")
blockList = outputBlock2.split("\n")
for t in range(len(blockList)):
values1 = blockList[t].split(" ")
values2 = []
for i in values1:
if i != "":
values2.append(i)
if len(values2) > 0:
if values2[0] in stopList:
break
if startRead == 1:
line = [values2[0]]
for j in values2[1:]:
line.append(float(j))
stats.append(line)
if values2[0] in startList:
startRead = 1
return stats
class MplusTLoutput:
def __init__(self, modellabel, filename, Mplus, SPSS, slopes, complex, estimator, starts):
self.label = modellabel
with open(filename, "rb") as infile:
fileText = infile.read().decode('utf-8')
outputList = fileText.split("\n")
if estimator == "BAYES":
self.header = """ Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Sig"""
else:
self.header = """ Two-Tailed
Estimate S.E. Est./S.E. P-Value"""
self.summary = None
self.warnings = None
self.fit = None
self.wmeasurement = None
self.wcoefficients = None
self.wcovariances = None
self.wdescriptives = None
self.bmeasurement = None
self.bcoefficients = None
self.bcovariances = None
self.bdescriptives = None
self.newParam = None
self.Zwmeasurement = None
self.Zwcoefficients = None
self.Zwcovariances = None
self.Zwdescriptives = None
self.Zbmeasurement = None
self.Zbcoefficients = None
self.Zbcovariances = None
self.Zbdescriptives = None
self.wr2 = None
self.br2 = None
self.wmi = None
self.bmi = None
# Summary
for t in range(len(outputList)):
if "SUMMARY OF ANALYSIS" in outputList[t]:
start = t
if "Number of continuous latent variables" in outputList[t]:
end = t
self.summary = "\n".join(outputList[start:end + 1])
# Warnings
for t in range(len(outputList)):
if "Covariance Coverage" in outputList[t]:
covcov = t
blank = 0
for t in range(covcov, len(outputList)):
if len(outputList[t]) < 2:
blank = 1
if blank == 1 and len(outputList[t]) > 1:
start = t
break
for t in range(start, len(outputList)):
if "MODEL FIT INFORMATION" in outputList[t] or "MODEL RESULTS" in outputList[t]:
end = t
break
self.warnings = "\n".join(outputList[start:end])
self.warnings = removeBlanks(self.warnings)
if "MODEL ESTIMATION TERMINATED NORMALLY" in self.warnings:
# Fit statistics
start = end
for t in range(start, len(outputList)):
if "MODEL RESULTS" in outputList[t]:
end = t
break
self.fit = "\n".join(outputList[start:end])
self.fit = removeBlanks(self.fit)
# Within Unstandardized measurement model
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bBY\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]) or re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.wmeasurement = "\n".join(outputList[start:end])
self.wmeasurement = removeBlanks(self.wmeasurement)
# Within Unstandardized coefficients
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]) or re.search(r"\bSTANDARDIZED\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.wcoefficients = "\n".join(outputList[start:end])
self.wcoefficients = removeBlanks(self.wcoefficients)
# Within Unstandardized covariances
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]) or re.search(r"\bSTANDARDIZED\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.wcovariances = "\n".join(outputList[start:end])
self.wcovariances = removeBlanks(self.wcovariances)
# Within Unstandardized Descriptives
start = end
for t in range(start, len(outputList)):
if "STANDARDIZED MODEL RESULTS" in outputList[t] or "MODEL COMMAND" in outputList[t] or "New/Additional Parameters" in outputList[t] or "Between Level" in outputList[t]:
end = t
break
self.wdescriptives = "\n".join(outputList[start:end])
self.wdescriptives = removeBlanks(self.wdescriptives)
# Between Unstandardized measurement model
start = end
secexists = 0
for t in range(start, len(outputList)):
if "QUALITY OF NUMERICAL RESULTS" in outputList[t]:
break
if re.search(r"\bBY\b", outputList[t]):
start = t
secexists = 1
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]) or re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.bmeasurement = "\n".join(outputList[start:end])
self.bmeasurement = removeBlanks(self.bmeasurement)
# Between Unstandardized coefficients
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bSTANDARDIZED\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.bcoefficients = "\n".join(outputList[start:end])
self.bcoefficients = removeBlanks(self.bcoefficients)
# Between Unstandardized covariances
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bSTANDARDIZED\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.bcovariances = "\n".join(outputList[start:end])
self.bcovariances = removeBlanks(self.bcovariances)
# Between Unstandardized Descriptives
start = end
for t in range(start, len(outputList)):
if "STANDARDIZED MODEL RESULTS" in outputList[t] or "MODEL COMMAND" in outputList[t] or "STANDARDIZED" in outputList[t] or "New/Additional Parameters" in outputList[t] or "QUALITY OF NUMERICAL RESULTS" in outputList[t]:
end = t
break
self.bdescriptives = "\n".join(outputList[start:end])
self.bdescriptives = removeBlanks(self.bdescriptives)
# New/additional parameters
start = end
if "New/Additional Parameters" in outputList[start]:
for t in range(start, len(outputList)):
if "STANDARDIZED MODEL RESULTS" in outputList[t] or "STANDARDIZED" in outputList[t] or "MODEL COMMAND" in outputList[t] or "QUALITY OF NUMERICAL RESULTS" in outputList[t]:
end = t
break
self.newParam = "\n".join(outputList[start:end])
self.newParam = removeBlanks(self.newParam)
noStand = 0
for t in range(len(outputList)):
if "STANDARDIZED (STD, STDY, STDYX) options are not available" in outputList[t] or "Request for STANDARDIZED (STD, STDY, STDYX) is ignored" in outputList[t]:
noStand = 1
break
if "SUMMARY OF ANALYSIS" in outputList[t]:
break
if complex is not None:
noStand = 1
if noStand == 0:
# Within standardized measurement model
if "MODEL ESTIMATION TERMINATED NORMALLY" in self.warnings:
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bBY\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]) or re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zwmeasurement = "\n".join(outputList[start:end])
self.Zwmeasurement = removeBlanks(self.Zwmeasurement)
# Within standardized coefficients
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zwcoefficients = "\n".join(outputList[start:end])
self.Zwcoefficients = removeBlanks(self.Zwcoefficients)
# Within standardized covariances
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bBetween Level\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zwcovariances = "\n".join(outputList[start:end])
self.Zwcovariances = removeBlanks(self.Zwcovariances)
# Within Unstandardized Descriptives
start = end
for t in range(start, len(outputList)):
if "STANDARDIZED MODEL RESULTS" in outputList[t] or "MODEL COMMAND" in outputList[t] or "Between Level" in outputList[t]:
end = t
break
self.Zwdescriptives = "\n".join(outputList[start:end])
self.Zwdescriptives = removeBlanks(self.Zwdescriptives)
# Between standardized measurement model
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bBY\b", outputList[t]):
start = t
secexists = 1
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]) or re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zbmeasurement = "\n".join(outputList[start:end])
self.Zbmeasurement = removeBlanks(self.Zbmeasurement)
# Between standardized coefficients
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bON\b", outputList[t]):
start = t
secexists = 1
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]) or re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zbcoefficients = "\n".join(outputList[start:end])
self.Zbcoefficients = removeBlanks(self.Zbcoefficients)
# Between standardized covariances
start = end
secexists = 0
for t in range(start, len(outputList)):
if re.search(r"\bWITH\b", outputList[t]):
start = t
secexists = 1
break
if re.search(r"\bR-SQUARE\b", outputList[t]):
break
if secexists == 1:
for t in range(start, len(outputList)):
if re.search(r"\bMeans\b", outputList[t]) or re.search(r"\bVariances\b", outputList[t]):
end = t
break
self.Zbcovariances = "\n".join(outputList[start:end])
self.Zbcovariances = removeBlanks(self.Zbcovariances)
# Between standardized Descriptives
start = end
for t in range(start, len(outputList)):
if "STANDARDIZED MODEL RESULTS" in outputList[t] or "MODEL COMMAND" in outputList[t] or "R-SQUARE" in outputList[t]:
end = t
break
self.Zbdescriptives = "\n".join(outputList[start:end])
self.Zbdescriptives = removeBlanks(self.Zbdescriptives)
# Within R squares
start = end
for t in range(start, len(outputList)):
if "Between Level" in outputList[t]:
end = t
break
self.wr2 = "\n".join(outputList[start:end])
self.wr2 = removeBlanks(self.wr2)
# Between R squares
start = end
for t in range(start, len(outputList)):
if "QUALITY OF NUMERICAL RESULTS" in outputList[t] or "MODEL MODIFICATION INDICES" in outputList[t]:
end = t
break
self.br2 = "\n".join(outputList[start:end])
self.br2 = removeBlanks(self.br2)
# Within Modification indices
for t in range(end, len(outputList)):
stest = 0
if "MODEL MODIFICATION INDICES" in outputList[t]:
start = t
stest = 1
break
if stest == 1:
for t in range(start, len(outputList)):
if "Between Level" in outputList[t]:
end = t - 1
break
self.wmi = "\n".join(outputList[start:end])
self.wmi = removeBlanks(self.wmi)
# Between level modification indices
start = end
if stest == 1:
for t in range(start, len(outputList)):
if "Beginning Time" in outputList[t] or "TECHNICAL" in outputList[t]:
end = t - 1
break
self.bmi = "\n".join(outputList[start:end])
self.bmi = removeBlanks(self.bmi)
# Replacing variable names
# In the Coefficients section, initially room for 17
# A) Increasing overall width from 61 to 75 = gain of 14
# In the Modification indices section,
# there is initially room for 2 vars X 10 characters
# A) Increasing overall width from 67 to 77 = gain of 5 for each var
# B) Drop STD EPC = gain of 6 for each var
# C) Change "StdYX E.P.C." to "StdYX EPC" = gain of 2 for each var
# Making all variables length of 23
# Variables
Mplus.extend(slopes)
SPSS.extend(slopes)
for var1, var2 in zip(Mplus, SPSS):
var1 += " " * (8 - len(var1))
var1 = " " + var1 + " "
if len(var2) < 23:
var2 += " " * (23 - len(var2))
else:
var2 = var2[:23]
var2 = " " + var2 + " "
if self.wmeasurement is not None:
self.wmeasurement = self.wmeasurement.replace(var1.upper(), var2)
if self.wcoefficients is not None:
self.wcoefficients = self.wcoefficients.replace(var1.upper(), var2)
if self.wcovariances is not None:
self.wcovariances = self.wcovariances.replace(var1.upper(), var2)
if self.wdescriptives is not None:
self.wdescriptives = self.wdescriptives.replace(var1.upper(), var2)
if self.Zwmeasurement is not None:
self.Zwmeasurement = self.Zwmeasurement.replace(var1.upper(), var2)
if self.Zwcoefficients is not None:
self.Zwcoefficients = self.Zwcoefficients.replace(var1.upper(), var2)
if self.Zwcovariances is not None:
self.Zwcovariances = self.Zwcovariances.replace(var1.upper(), var2)
if self.Zwdescriptives is not None:
self.Zwdescriptives = self.Zwdescriptives.replace(var1.upper(), var2)
if self.bmeasurement is not None:
self.bmeasurement = self.bmeasurement.replace(var1.upper(), var2)
if self.bcoefficients is not None:
self.bcoefficients = self.bcoefficients.replace(var1.upper(), var2)
if self.bcovariances is not None:
self.bcovariances = self.bcovariances.replace(var1.upper(), var2)
if self.bdescriptives is not None:
self.bdescriptives = self.bdescriptives.replace(var1.upper(), var2)
if self.Zbmeasurement is not None:
self.Zbmeasurement = self.Zbmeasurement.replace(var1.upper(), var2)
if self.Zbcoefficients is not None:
self.Zbcoefficients = self.Zbcoefficients.replace(var1.upper(), var2)
if self.Zbcovariances is not None:
self.Zbcovariances = self.Zbcovariances.replace(var1.upper(), var2)
if self.Zbdescriptives is not None:
self.Zbdescriptives = self.Zbdescriptives.replace(var1.upper(), var2)
# Within MI section
if self.wmi is not None and not ("THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT" in self.warnings):
for var1, var2 in zip(Mplus, SPSS):
if len(var2) > 23:
var2 = var2[:23]
else:
var1 = var1 + " "
var2 = var2 + " "
self.wmi = self.wmi.replace(var1.upper(), var2)
self.wmi = self.wmi.replace("""M.I. E.P.C. Std E.P.C. StdYX E.P.C.""", """ MI EPC StdYX EPC""")
newMI = []
miLines = self.wmi.split("\n")
for line in miLines:
if " ON " in line or " BY " in line or " WITH " in line:
miWords = line.split()
newLine = miWords[0] + " " * (23 - len(miWords[0]))
newLine += " " + miWords[1] + " " * (5 - len(miWords[1]))
newLine += miWords[2] + " " * (23 - len(miWords[2]))
newLine += " " * (8 - len(miWords[3])) + miWords[3] + " "
newLine += " " * (8 - len(miWords[4])) + miWords[4] + " "
newLine += " " * (8 - len(miWords[6])) + miWords[6] + " "
newMI.append(newLine)
else:
newMI.append(line)
self.wmi = "\n".join(newMI)
# Between MI section
if self.bmi is not None and not ("THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT" in self.warnings):
for var1, var2 in zip(Mplus, SPSS):
if len(var2) > 23:
var2 = var2[:23]
else:
var1 = var1 + " "
var2 = var2 + " "
self.bmi = self.bmi.replace(var1.upper(), var2)
self.bmi = self.bmi.replace("""M.I. E.P.C. Std E.P.C. StdYX E.P.C.""", """ MI EPC StdYX EPC""")
newMI = []
miLines = self.bmi.split("\n")
for line in miLines:
if (" ON " in line or " BY " in line or " WITH " in line) and ("/" not in line):
miWords = line.split()
newLine = miWords[0] + " " * (23 - len(miWords[0]))
newLine += " " + miWords[1] + " " * (5 - len(miWords[1]))
newLine += miWords[2] + " " * (23 - len(miWords[2]))
newLine += " " * (8 - len(miWords[3])) + miWords[3] + " "
newLine += " " * (8 - len(miWords[4])) + miWords[4] + " "
newLine += " " * (8 - len(miWords[6])) + miWords[6] + " "
newMI.append(newLine)
else:
newMI.append(line)
self.bmi = "\n".join(newMI)
if self.newParam is not None:
newNP = ["New/Additional Parameters"]
npLines = self.newParam.split("\n")
for line in npLines[1:]:
if len(line) > 1:
firstWord = line.split()[0]
line = line.replace(firstWord, firstWord + " " * 15)
newNP.append(line)
self.newParam = "\n".join(newNP)
# Print function
def toSPSSoutput(self):
spss.Submit("title '" + self.label + "'.")
spss.Submit("title 'SUMMARY'.")
print(self.summary)
spss.Submit("title 'WARNINGS'.")
print(self.warnings)
if "MODEL ESTIMATION TERMINATED NORMALLY" in self.warnings:
spss.Submit("title 'FIT STATISTICS'.")
print(self.fit)
# Unstandardized Within
if self.wmeasurement is not None:
spss.Submit("title 'UNSTANDARDIZED WITHIN MEASUREMENT MODEL'.")
print("Unstandardized Within")
print(self.header)
print(self.wmeasurement)
if self.wcoefficients is not None:
spss.Submit("title 'UNSTANDARDIZED WITHIN COEFFICIENTS'.")
print("Unstandardized Within")
print(self.header)
print(self.wcoefficients)
if self.wcovariances is not None:
spss.Submit("title 'UNSTANDARDIZED WITHIN COVARIANCES'.")
print("Unstandardized Within")
print(self.header)
print(self.wcovariances)
spss.Submit("title 'UNSTANDARDIZED WITHIN DESCRIPTIVES'.")
print("Unstandardized Within")
print(self.header)
print(self.wdescriptives)
# Unstandardized Between
if self.bmeasurement is not None:
spss.Submit("title 'UNSTANDARDIZED BETWEEN MEASUREMENT MODEL'.")
print("Unstandardized Between")
print(self.header)
print(self.bmeasurement)
if self.bcoefficients is not None:
spss.Submit("title 'UNSTANDARDIZED BETWEEN COEFFICIENTS'.")
print("Unstandardized Between")
print(self.header)
print(self.bcoefficients)
if self.bcovariances is not None:
spss.Submit("title 'UNSTANDARDIZED BETWEEN COVARIANCES'.")
print("Unstandardized Between")
print(self.header)
print(self.bcovariances)
spss.Submit("title 'UNSTANDARDIZED BETWEEN DESCRIPTIVES'.")
print("Unstandardized Between")
print(self.header)
print(self.bdescriptives)
if self.newParam is not None:
spss.Submit("title 'NEW/ADDITIONAL PARAMETERS'.")
print(self.header)
print(self.newParam)
# Standardized Within
if self.Zwmeasurement is not None:
spss.Submit("title 'STANDARDIZED WITHIN MEASUREMENT MODEL'.")
print("Standardized Within")
print(self.header)
print(self.Zwmeasurement)
if self.Zwcoefficients is not None:
spss.Submit("title 'STANDARDIZED WITHIN COEFFICIENTS'.")
print("Standardized Within")
print(self.header)
print(self.Zwcoefficients)
if self.Zwcovariances is not None:
spss.Submit("title 'STANDARDIZED WITHIN COVARIANCES'.")
print("Standardized Within")
print(self.header)
print(self.Zwcovariances)
if self.Zwdescriptives is not None:
spss.Submit("title 'STANDARDIZED WITHIN DESCRIPTIVES'.")
print("Standardized Within")
print(self.header)
print(self.Zwdescriptives)
# Standardized Between
if self.Zbmeasurement is not None:
spss.Submit("title 'STANDARDIZED BETWEEN MEASUREMENT MODEL'.")
print("Standardized Between")
print(self.header)
print(self.Zbmeasurement)
if self.Zbcoefficients is not None:
spss.Submit("title 'STANDARDIZED BETWEEN COEFFICIENTS'.")
print("Standardized Between")
print(self.header)
print(self.Zbcoefficients)
if self.Zbcovariances is not None:
spss.Submit("title 'STANDARDIZED BETWEEN COVARIANCES'.")
print("Standardized Between")
print(self.header)
print(self.Zbcovariances)
if self.Zbdescriptives is not None:
spss.Submit("title 'STANDARDIZED BETWEEN DESCRIPTIVES'.")
print("Standardized Between")
print(self.header)
print(self.Zbdescriptives)
if self.wr2 is not None:
spss.Submit("title 'WITHIN R-SQUARES'.")
print(self.wr2)
if self.br2 is not None:
spss.Submit("title 'BETWEEN R-SQUARES'.")
print(self.br2)
if self.wmi is not None:
spss.Submit("title 'WITHIN MODIFICATION INDICES'.")
print(self.wmi)
if self.bmi is not None:
spss.Submit("title 'BETWEEN MODIFICATION INDICES'.")
print(self.bmi)
# Save coefficients to dataset
def toSPSSdata(self, datasetName, datasetMeans, datasetIntercepts, datasetVariances, datasetResidualV, estimator, labelList=[]):
# Determine active data set so we can return to it when finished
activeName = spss.ActiveDataset()
# Set up data set if it doesn't already exist
tag, err = spssaux.createXmlOutput('Dataset Display', omsid='Dataset Display', subtype='Datasets')
datasetList = spssaux.getValuesFromXmlWorkspace(tag, 'Datasets')
if datasetName not in datasetList:
spss.StartDataStep()
datasetObj = spss.Dataset(name=None)
dsetname = datasetObj.name
datasetObj.varlist.append("wbLevel", 50)
datasetObj.varlist.append("Outcome", 50)
datasetObj.varlist.append("Predictor", 50)
if estimator == "BAYES":
datasetObj.varlist.append("b_Coefficient", 0)
datasetObj.varlist.append("b_PostSD", 0)
datasetObj.varlist.append("b_p", 0)
datasetObj.varlist.append("b_lower", 0)
datasetObj.varlist.append("b_upper", 0)
datasetObj.varlist.append("beta_Coefficient", 0)
datasetObj.varlist.append("beta_PostSD", 0)
datasetObj.varlist.append("beta_p", 0)
datasetObj.varlist.append("beta_lower", 0)
datasetObj.varlist.append("beta_upper", 0)
else:
datasetObj.varlist.append("b_Coefficient", 0)
datasetObj.varlist.append("b_SE", 0)
datasetObj.varlist.append("b_Ratio", 0)
datasetObj.varlist.append("b_p", 0)
datasetObj.varlist.append("beta_Coefficient", 0)
datasetObj.varlist.append("beta_SE", 0)
datasetObj.varlist.append("beta_Ratio", 0)
datasetObj.varlist.append("beta_p", 0)
spss.EndDataStep()
submitstring = """dataset activate {0}.
dataset name {1}.""".format(dsetname, datasetName)
spss.Submit(submitstring)
spss.StartDataStep()
datasetObj = spss.Dataset(name=datasetName)
spss.SetActive(datasetObj)
# Label variables
variableList = []
for t in range(spss.GetVariableCount()):
variableList.append(spss.GetVariableName(t))
for t in range(len(labelList)):
if "label{0}".format(str(t)) not in variableList:
datasetObj.varlist.append("label{0}".format(str(t)), 50)
spss.EndDataStep()
# Set variables to f8.3
if estimator == "BAYES":
submitstring = "alter type b_Coefficient to beta_upper (f8.3)."
else:
submitstring = "alter type b_Coefficient to beta_p (f8.3)."
spss.Submit(submitstring)
# Get coefficients
lList = ["WITHIN", "BETWEEN"]
cList = [getCoefficients(self.wcoefficients), getCoefficients(self.bcoefficients)]
if self.Zwcoefficients is not None and self.Zbcoefficients is not None:
zList = [getCoefficients(self.Zwcoefficients), getCoefficients(self.Zbcoefficients)]
else:
zList = [None, None]
# Determine values for dataset
dataValues = []
for l, c, z in zip(lList, cList, zList):
for t in range(len(c)):
rowList = [l]
rowList.extend(c[t])
if z is None:
rowList.extend([None] * 4)
else:
rowList.extend(z[t][2:])
rowList.extend(labelList)
dataValues.append(rowList)
# Dataset Means
if datasetMeans:
lList = ["WITHIN", "BETWEEN"]
start = ["Means"]
stop = ["Intercepts", "Variances", "Residual"]
cList = [getStats(self.wdescriptives, start, stop), getStats(self.bdescriptives, start, stop)]
for l, c in zip(lList, cList):
for t in range(len(c)):
rowList = c[t]
rowList.insert(0, l)
rowList.insert(2, "Mean")
rowList.extend([None] * 4)
rowList.extend(labelList)
dataValues.append(rowList)
# Dataset Intercepts
if datasetIntercepts:
lList = ["WITHIN", "BETWEEN"]
start = ["Intercepts"]
stop = ["Variances", "Residual"]
cList = [getStats(self.wdescriptives, start, stop), getStats(self.bdescriptives, start, stop)]
for l, c in zip(lList, cList):
for t in range(len(c)):
rowList = c[t]
rowList.insert(0, l)
rowList.insert(2, "Intercept")
rowList.extend([None] * 4)
rowList.extend(labelList)
dataValues.append(rowList)
# Dataset Variances
if datasetVariances:
lList = ["WITHIN", "BETWEEN"]
start = ["Variances"]
stop = ["Residual"]
cList = [getStats(self.wdescriptives, start, stop), getStats(self.bdescriptives, start, stop)]
for l, c in zip(lList, cList):
for t in range(len(c)):
rowList = c[t]
rowList.insert(0, l)
rowList.insert(2, "Variance")
rowList.extend([None] * 4)
rowList.extend(labelList)
dataValues.append(rowList)
# Dataset Residual Variances
if datasetResidualV:
lList = ["WITHIN", "BETWEEN"]
start = ["Residual"]
stop = ["XXX"]
cList = [getStats(self.wdescriptives, start, stop), getStats(self.bdescriptives, start, stop)]
for l, c in zip(lList, cList):
for t in range(len(c)):
rowList = c[t]
rowList.insert(0, l)
rowList.insert(2, "ResidualVariance")
rowList.extend([None] * 4)
rowList.extend(labelList)
dataValues.append(rowList)
# Put values in dataset
spss.StartDataStep()
datasetObj = spss.Dataset(name=datasetName)
for t in dataValues:
datasetObj.cases.append(t)
spss.EndDataStep()
# Return to original data set
spss.StartDataStep()
datasetObj = spss.Dataset(name=activeName)
spss.SetActive(datasetObj)
spss.EndDataStep()
def MplusTwoLevel(inpfile, modellabel="MplusTwoLevel",
runModel=True, viewOutput=True, suppressSPSS=False,
withinLatent=None, withinLatentFixed=None, withinModel=None, withinMeans=None,
withinVar=None, withinCovar=None, withinCovEndo=False, withinCovExo=True,
withinIdentifiers=None, withinMeanIdentifiers=None, withinSlopes=None,
betweenLatent=None, betweenLatentFixed=None, betweenModel=None,
betweenMeans=None, betweenVar=None, betweenCovar=None,
betweenCovEndo=False, betweenCovExo=True,
betweenIdentifiers=None, betweenMeanIdentifiers=None,
estimator=None, starts=None, useobservations=None,
wald=None, constraint=None,
montecarlo=None, bootstrap=None, repse=None,
categorical=None, censored=None, count=None, nominal=None,
groupmean=None, grandmean=None,
cluster=None, complex=None, weight=None,
datasetName=None, datasetMeans=False, datasetIntercepts=False,
datasetVariances=False, datasetResidualV=False,
datasetLabels=[], miThreshold=10, processors=None, waittime=5):
spss.Submit("display scratch.")
# Redirect output
if suppressSPSS:
submitstring = """OMS /SELECT ALL EXCEPT = [WARNINGS]
/DESTINATION VIEWER = NO
/TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Find directory and filename
for t in range(len(inpfile)):
if inpfile[-t] == "/":
break
outdir = inpfile[:-t+1]
fname, fext = os.path.splitext(inpfile[-(t-1):])
# Obtain list of variables in data set
SPSSvariables = []
SPSSvariablesCaps = []
for varnum in range(spss.GetVariableCount()):
SPSSvariables.append(spss.GetVariableName(varnum))
SPSSvariablesCaps.append(spss.GetVariableName(varnum).upper())
# Obtain lists of latent variables
withinLatentVars = []
if withinLatent is not None:
for t in withinLatent:
withinLatentVars.append(t[0].upper())
betweenLatentVars = []
if betweenLatent is not None:
for t in betweenLatent:
betweenLatentVars.append(t[0].upper())
# Obtain list of slope identifiers
slopeVars = []
if withinSlopes is not None:
for t in withinSlopes:
slopeVars.append(t[1].upper())
# Restore output
if suppressSPSS:
submitstring = """OMSEND TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Check for errors
error = 0
if fext.upper() != ".INP":
print("Error: Input file specification does not end with .inp")
error = 1
if not os.path.exists(outdir):
print("Error: Output directory does not exist")
error = 1
if cluster is None:
print("Error: No cluster variable identified")
error = 1
if estimator is not None:
estimator = estimator.upper()
if estimator not in ["ML", "MLM", "MLMV", "MLR", "MLF", "MUML", "WLS", "WLSM", "WLSMV", "ULS", "ULSMV", "GLS", "BAYES"]:
print("Error: Estimator not valid")
error = 1
variableError = 0
for var in withinLatentVars:
if var in SPSSvariablesCaps:
variableError = 1
break
if variableError == 1:
print("Error: Within latent variable name overlaps with existing variable name")
error = 1
variableError = 0
for var in betweenLatentVars:
if var in SPSSvariablesCaps:
variableError = 1
break
if variableError == 1:
print("Error: Between latent variable name overlaps with existing variable name")
error = 1
if withinLatent is not None:
variableError = 0
for equation in withinLatent:
for var in equation[1:]:
if var.upper() not in SPSSvariablesCaps:
variableError = 1
break
if variableError == 1:
print("Error: Variable listed in within latent variable definition not in current data set")
error = 1
if betweenLatent is not None:
variableError = 0
for equation in betweenLatent:
for var in equation[1:]:
if var.upper() not in SPSSvariablesCaps and var.upper() not in slopeVars:
variableError = 1
if variableError == 1:
print("Error: Variable listed in between latent variable definition not in current data set")
error = 1
if withinLatent is not None and betweenLatent is not None:
variableError = 0
for w in withinLatentVars:
for b in betweenLatentVars:
if w == b:
variableError = 1
if variableError == 1:
print("Error: Same name used for within and between latent variable definitions")
variableError = 0
if withinModel is not None:
for equation in withinModel:
for var in equation:
if var.upper() not in SPSSvariablesCaps and var.upper() not in withinLatentVars:
variableError = 1
print("Missing " + var)
if variableError == 1:
print("Error: Variable listed in within model not in current data set")
error = 1
if betweenModel is not None:
variableError = 0
for equation in betweenModel:
for var in equation:
if var.upper() not in SPSSvariablesCaps and var.upper() not in slopeVars and var.upper() not in betweenLatentVars and var.upper() not in withinLatentVars:
variableError = 1
print("Missing " + var)
if variableError == 1:
print("Error: Variable listed in between model not in current data set")
error = 1
if error == 0:
# Redirect output
if suppressSPSS:
submitstring = """OMS /SELECT ALL EXCEPT = [WARNINGS]
/DESTINATION VIEWER = NO
/TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Export data
dataname = outdir + fname + ".dat"
MplusVariables = exportMplus(dataname)
# Define within latent variables using Mplus variables
if withinLatent is None:
MplusWithinLatent = None
else:
MplusWithinLatent = []
for t in withinLatent:
MplusWithinLatent.append([i.upper() for i in t])
for t in range(len(MplusWithinLatent)):
for i in range(len(MplusWithinLatent[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusWithinLatent[t][i] == s:
MplusWithinLatent[t][i] = m
# Convert withinLatentFixed to Mplus
if withinLatentFixed is None:
MplusWithinLatentFixed = None
else:
MplusWithinLatentFixed = []
fixedEquations = []
for t in withinLatentFixed:
j = []
for i in t[0]: # t[0] is the equation, t[1] is the fixed value
j.append(i.upper())
fixedEquations.append(j) # appending the upper-case version of the equation
for t in range(len(fixedEquations)):
for i in range(len(fixedEquations[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if fixedEquations[t][i] == s:
fixedEquations[t][i] = m
MplusWithinLatentFixed.append([fixedEquations[t], withinLatentFixed[t][1]])
# Define between latent variables using Mplus variables
if betweenLatent is None:
MplusBetweenLatent = None
else:
MplusBetweenLatent = []
for t in betweenLatent:
MplusBetweenLatent.append([i.upper() for i in t])
for t in range(len(MplusBetweenLatent)):
for i in range(len(MplusBetweenLatent[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusBetweenLatent[t][i] == s:
MplusBetweenLatent[t][i] = m
# Convert betweenLatentFixed to Mplus
if betweenLatentFixed is None:
MplusBetweenLatentFixed = None
else:
MplusBetweenLatentFixed = []
fixedEquations = []
for t in betweenLatentFixed:
j = []
for i in t[0]: # t[0] is the equation, t[1] is the fixed value
j.append(i.upper())
fixedEquations.append(j) # appending the upper-case version of the equation
for t in range(len(fixedEquations)):
for i in range(len(fixedEquations[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if fixedEquations[t][i] == s:
fixedEquations[t][i] = m
MplusBetweenLatentFixed.append([fixedEquations[t], betweenLatentFixed[t][1]])
# Define withinModel using Mplus variables
if withinModel is None:
MplusWithinModel = None
else:
MplusWithinModel = []
for t in withinModel:
MplusWithinModel.append([i.upper() for i in t])
for t in range(len(MplusWithinModel)):
for i in range(len(MplusWithinModel[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusWithinModel[t][i] == s:
MplusWithinModel[t][i] = m
# Convert variables in betweenCovariance list to Mplus
if withinCovar is None:
MplusWithinCovar = None
else:
MplusWithinCovar = []
for t in withinCovar:
MplusWithinCovar.append([i.upper() for i in t])
for t in range(len(MplusWithinCovar)):
for i in range(2):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusWithinCovar[t][i] == s:
MplusWithinCovar[t][i] = m
# Convert withinIdentifiers to Mplus
if withinIdentifiers is None:
MplusWithinIdentifiers = None
else:
MplusWithinIdentifiers = []
idEquations = []
for t in withinIdentifiers:
j = []
for i in t[0]:
j.append(i.upper())
idEquations.append(j)
for t in range(len(idEquations)):
for i in range(len(idEquations[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if idEquations[t][i] == s:
idEquations[t][i] = m
MplusWithinIdentifiers.append([idEquations[t], withinIdentifiers[t][1]])
# Convert within mean identifiers to Mplus
if withinMeanIdentifiers is None:
MplusWithinMeanIdentifiers = None
else:
MplusWithinMeanIdentifiers = []
idMeans = []
for t in withinMeanIdentifiers:
idMeans.append(t[0].upper())
for t in range(len(idMeans)):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if idMeans[t] == s:
idMeans[t] = m
MplusWithinMeanIdentifiers.append([idMeans[t], withinMeanIdentifiers[t][1]])
# Convert withinSlopes to Mplus
if withinSlopes is None:
MplusWithinSlopes = None
else:
MplusWithinSlopes = []
idEquations = []
for t in withinSlopes:
j = []
for i in t[0]:
j.append(i.upper())
idEquations.append(j)
for t in range(len(idEquations)):
for i in range(len(idEquations[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if idEquations[t][i] == s:
idEquations[t][i] = m
MplusWithinSlopes.append([idEquations[t], withinSlopes[t][1]])
# Define betweenModel using Mplus variables
if betweenModel is None:
MplusBetweenModel = None
else:
MplusBetweenModel = []
for t in betweenModel:
MplusBetweenModel.append([i.upper() for i in t])
for t in range(len(MplusBetweenModel)):
for i in range(len(MplusBetweenModel[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusBetweenModel[t][i] == s:
MplusBetweenModel[t][i] = m
# Convert variables in betweenCovariance list to Mplus
if betweenCovar is None:
MplusBetweenCovar = None
else:
MplusBetweenCovar = []
for t in betweenCovar:
MplusBetweenCovar.append([i.upper() for i in t])
for t in range(len(MplusBetweenCovar)):
for i in range(2):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if MplusBetweenCovar[t][i] == s:
MplusBetweenCovar[t][i] = m
# Convert betweenIdentifiers to Mplus
if betweenIdentifiers is None:
MplusBetweenIdentifiers = None
else:
MplusBetweenIdentifiers = []
idEquations = []
for t in betweenIdentifiers:
j = []
for i in t[0]:
j.append(i.upper())
idEquations.append(j)
for t in range(len(idEquations)):
for i in range(len(idEquations[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if idEquations[t][i] == s:
idEquations[t][i] = m
MplusBetweenIdentifiers.append([idEquations[t], betweenIdentifiers[t][1]])
# Convert between mean identifiers to Mplus
if betweenMeanIdentifiers is None:
MplusBetweenMeanIdentifiers = None
else:
MplusBetweenMeanIdentifiers = []
idMeans = []
for t in betweenMeanIdentifiers:
idMeans.append(t[0].upper())
for t in range(len(idMeans)):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if idMeans[t] == s:
idMeans[t] = m
MplusBetweenMeanIdentifiers.append([idMeans[t], betweenMeanIdentifiers[t][1]])
# Convert useobservations to Mplus
if useobservations is None:
MplusUseobservations = None
else:
MplusUseobservations = useobservations
for s, m in zip(SPSSvariablesCaps, MplusVariables):
z = re.compile(s, re.IGNORECASE)
MplusUseobservations = z.sub(m, MplusUseobservations)
# Convert cluster variable to Mplus
if cluster is None:
MplusCluster = None
else:
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if cluster.upper() == s:
MplusCluster = m
# Convert complex variable to Mplus
if complex is None:
MplusComplex = None
else:
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if complex.upper() == s:
MplusComplex = m
# Convert variable list arguments to Mplus
lvarList = [categorical, censored, count, nominal,
groupmean, grandmean, withinVar, withinMeans, betweenVar, betweenMeans]
MplusCategorical = []
MplusCensored = []
MplusCount = []
MplusNominal = []
MplusGroupmean = []
MplusGrandmean = []
MplusWithinVar = []
MplusWithinMeans = []
MplusBetweenVar = []
MplusBetweenMeans = []
lvarMplusList = [MplusCategorical, MplusCensored,
MplusCount, MplusNominal, MplusGroupmean, MplusGrandmean,
MplusWithinVar, MplusWithinMeans, MplusBetweenVar, MplusBetweenMeans]
for t in range(len(lvarList)):
if lvarList[t] is None:
lvarMplusList[t] = None
else:
for i in lvarList[t]:
lvarMplusList[t].append(i.upper())
for i in range(len(lvarMplusList[t])):
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if lvarMplusList[t][i] == s:
lvarMplusList[t][i] = m
# Convert weight variable to Mplus
if weight is None:
MplusWeight = None
else:
for s, m in zip(SPSSvariablesCaps, MplusVariables):
if weight.upper() == s:
MplusWeight = m
# Create input program
pathProgram = MplusTLprogram()
pathProgram.setTitle("Created by MplusPathAnalysis")
pathProgram.setData(dataname)
pathProgram.setVariableTL(MplusVariables, MplusWithinLatent,
MplusWithinModel, MplusWithinVar, slopeVars,
MplusBetweenLatent, MplusBetweenModel,
MplusBetweenVar, MplusUseobservations, MplusCategorical, MplusCensored,
MplusCount, MplusNominal, MplusCluster, MplusComplex, MplusWeight)
pathProgram.setDefine(MplusGroupmean, MplusGrandmean)
pathProgram.setAnalysis(MplusCluster, MplusComplex, MplusWithinSlopes, estimator,
starts, MplusWeight, montecarlo, bootstrap, repse, processors)
pathProgram.setModel(MplusWithinLatent, MplusWithinLatentFixed, MplusWithinModel,
MplusWithinMeans, MplusWithinCovar, MplusWithinIdentifiers, MplusWithinMeanIdentifiers,
withinCovEndo, withinCovExo, MplusWithinSlopes,
MplusBetweenLatent, MplusBetweenLatentFixed, MplusBetweenModel, MplusBetweenMeans,
MplusBetweenCovar, MplusBetweenIdentifiers, MplusBetweenMeanIdentifiers,
betweenCovEndo, betweenCovExo, wald)
pathProgram.setConstraint(constraint)
pathProgram.setOutput(MplusComplex, miThreshold, bootstrap)
pathProgram.write(outdir + fname + ".inp")
# Add latent variables to SPSSvariables lists
if withinLatent is not None:
for equation in withinLatent:
if equation[0].upper() not in SPSSvariablesCaps:
SPSSvariables.append(equation[0])
SPSSvariablesCaps.append(equation[0].upper())
if betweenLatent is not None:
for equation in betweenLatent:
if equation[0].upper() not in SPSSvariablesCaps:
SPSSvariables.append(equation[0])
SPSSvariablesCaps.append(equation[0].upper())
# Add latent variables to MplusVariable list
if withinLatent is not None:
for equation in withinLatent:
if equation[0].upper() not in MplusVariables:
MplusVariables.append(equation[0].upper())
if betweenLatent is not None:
for equation in betweenLatent:
if equation[0].upper() not in MplusVariables:
MplusVariables.append(equation[0].upper())
# Run input program
if runModel:
batchfile(outdir, fname)
time.sleep(waittime)
# Restore output
if suppressSPSS:
submitstring = """OMSEND TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Parse output
if viewOutput:
pathOutput = MplusTLoutput(modellabel, outdir + fname + ".out",
MplusVariables, SPSSvariables, slopeVars, complex, estimator, starts)
pathOutput.toSPSSoutput()
# Redirect output
if suppressSPSS:
submitstring = """OMS /SELECT ALL EXCEPT = [WARNINGS]
/DESTINATION VIEWER = NO
/TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Create coefficient dataset
if datasetName is not None and "MODEL ESTIMATION TERMINATED NORMALLY" in pathOutput.warnings:
pathOutput.toSPSSdata(datasetName, datasetMeans,
datasetIntercepts, datasetVariances, datasetResidualV, estimator,
datasetLabels)
# Restore output
if suppressSPSS:
submitstring = """OMSEND TAG = 'NoJunk'."""
spss.Submit(submitstring)
# Replace titles
titleToPane()
end program python3.
set printback = on.
************
* Version History
************
* 2014-08-19 Created based on MplusPathAnalysis 2014-08-19.sps
* 2014-08-21 Created .inp file
* 2014-08-24 Separated between and within latent definitions
* 2014-08-25 Read output file
* 2014-08-26 Fixed latent names
Added level variable to dataset
* 2014-08-28 Removed problematic reference to auxiliary variables
* 2014-09-02 Renamed function
Fixed error when no latent variables
* 2014-09-05 Added runModel and viewOutput arguments
* 2015-01-19 Suppressed output
* 2015-05-02 Added the ability to examine random slopes
Added toggle to suppress output
* 2015-05-18 Corrected extraction of output file
* 2018-03-04 When a variable is missing from the data set, that variable is printed
Replaced nonalphanumerics before checking for duplicate variable names
* 2018-03-05 Added complex option
Required the identification of a cluster variable
* 2018-04-12 Added groupmean and grandmean arguments
* 2018-04-18 Fixed error when replacing variable names
* 2018-04-18a Added titleToPane
* 2018-04-18b Added miThreshold
Fixed coefficient dataset generation when no standardized output
* 2018-04-19 Added datasetIntercepts
* 2018-04-19a Added datasetResidualV
* 2018-04-26 Skip standardized results if complex != None
* 2018-04-28 Added datasetVariances
* 2018-04-28a Consolidated getIntercepts, getVariances, and
getResidualV into getStats
* 2018-04-28b Added datasetMeans
* 2018-04-28c Corrected error where means are reported with
intercepts
* 2018-10-02 Still writes .inp file when runModel = False
* 2021-04-05 Added MLR command
* 2021-04-05a Added constraint option
* 2021-04-05b Added mean and meanidentifier commands
* 2021-04-08 Added withinLatentFixed and betweenLatentFixed
* 2021-04-08a Made sure each latent variable is only added to variable lists once
* 2021-04-09 Allowed within latent variables to be used at the between level
* 2021-04-09a Added label to output.
* 2021-05-07 Added estimator command/dropped MLR
* 2021-05-08 Added montecarlo, bootstrap, repse
* 2021-05-09 Corrected section breaks when there are no regression coefs
* 2021-05-09a Changed dataset values for Bayes estimation
* 2021-05-10 Revised title for new/additional parameters
* 2022-02-13 Added processors argument
* 2022-04-04 Added starts argument
* 2022-04-19 Fixed output parsing when there are no predictors
* 2023-07-23 Fixed between r2 end point
* 2023-08-29 Does not try to save coefficients if the model does not converge
* 2023-08-30 Set coefficient elements to -999 when undefined
* 2024-01-03 Renamed twoLevel function so it doesn't conflict with MPA
* 2024-05-28 Converted to Python 3
* 2024-07-22 Corrected syntax error
COMMENT BOOKMARK;LINE_NUM=383;ID=1.