Institute for Digital Research and Education. By default, Mplus uses restricted maximum likelihood (MLR), so robust standard errors would be given in the output. The ANALYSIS command block is included so that we can check the data. 2.1 Tools we will use in lab; 3 - Creating an R-Project; 4 - Installing & loading packages. Each command option specification is separated by a semicolon (;). Fret not, Mplus has your back with the DEFINE command. converting the data set to Mplus. All the files for this portion of this seminar can be downloaded here. The difference between this model and the probit model is that we specify that maximum likelihood is to be used as the estimator. This code will appear in the MISSING option of the VARIABLES command of the input file created by stata2pmlus. Here is the Stata command to load and convert the Stata dataset hsb2.dta to Mplus. There are many ways read your data into Mplus: Use Stattransfersoftware (available in BA B-18 on the same machine with Mplus) – seems to work ok, but you still may need additional preparation (be careful with missing and character values). Mplus requires data to be read in from a text file without variable names, with numeric values only, and with missing data coded as a single numeric value, such as -999. Here is a TITLE section for the freely formatted file hsb.dat above: The DATA command is required and contains the location of the data file and information about how it is formatted. We will begin with a probit regression model. A SUMMARY OF THE Mplus LANGUAGE. Other settings for TYPE include TYPE=MIXTURE for categorical latent variable models, and TYPE=TWOLEVEL or TYPE=THREELEVEL for multilevel models. Mplus treats this as a probit model because we declare that honors is a categorical variable. Again, the analysis type = basic statement is included to allow you to run descriptive statistics in order to insure that the data were input correctly. The full set of Mplus commands to read hsb.dat and estimate descriptives are shown below. I have 358 observations on IV and Mediators. Three important keywords (options) are used in the MODEL command to specify relationships among variables: For example, if we wanted to define a latent variable representing academic prowess that is measured by 5 test score variables, we could specify (we would also need to add an ANALYSIS command with TYPE=GENERAL): The MODEL command is technically optional, but almost always specified unless we only want descriptive statistics (ANALYSIS: TYPE=basic;). Although we are using the same predictors in both equations, this is not necessary. In our first example we will use a standardized test, write, as the response variable and Mplus will output all solutions from smallest n to largest n factors extracted. This is a Institute for Digital Research and Education. 2.3. You will also note that the output contains a set of parameter estimates for each equation. The non-bias-corrected bootstrap approach will generally produce preferable confidence limits and standard errors for the indirect effect test (Fritz, Taylor, & MacKinnon, 2012). However, for some models, Mplus drops cases with missing values on any of the predictors. Here is a DATA command for the fixed formatted file fixed.dat above: On the format statement, 3F2.0 indicates that the file begins with three variables each of length two. 1 - Lab outline; 2 - Preparing to work with MplusAutomation. Use the missing option of stata2mplus to specify a missing value code. The code from the input file created appears below. However, in many examples of Mplus code, the Mplus commands and options are in capital letters to This matches what we see in the codebook. Some options for additional output: For example, to request all of the sample statistics available, we can specify this OUTPUT command: If you are a Stata user, a user-written a command, stata2mplus, will values 1, 2 and 3. By default, Mplus will use all of the variables in the data set. Note that for certain models if you specify variables under USEVARIABLES and don’t include them in the model, you will get a warning that the “Variable is uncorrelated with all other variables”. Thus, the estimate for female of 0.214 is for the count equation, and the estimate -4.029 is for the excess zero equation. to read in the data. MODEL FIT INFORMATION . Either a data frame of class ‘mplus.model.coefs’, or in the case of multiple group models, a list of class ‘mplus.model.coefs’, where each element of the list is a data frame of class ‘mplus.model.coefs’, or a named vector of coefficients, if raw=TRUE. Should you use Mplus? If you change a model and want to save a new output file, save the changed input file under a new name or your original output will be over written. The first model in this section is a poisson regression model using awards as the In context, a regression command looks like this: For most of the examples we will be using the hsbdemo.dat dataset. The code from the input file created appears below. For information on interpreting the results of probit models, please visit Annotated Output: Probit Regression. Up near the beginning of the output there is a table that shows the proportion of data present for each of the covariates in the model. For information on interpreting the results of zero-inflated poisson models models, please visit Annotated Output: Zero-inflated Poisson Regression. The TYPE option for the ANALYSIS command is set to “general” by default, which is appropriate for a large variety of models which estimate relationships between observed variables and continuous latent variables (e.g. To change it, you can use the Stata’s cd command. Mplus can also run zero-truncated negative binomial models and negative binomial hurdle models. To obtain standard errors calculated using maximum likelihood, include the analysis: estimator = ml; block. For most free-formatted files, the entirety of the DATA command will be the location of the data file. Files formatted in this way were more commonly encountered in the past. Next, we will take a look at the output file, hsbreg.out. Mplus cannot handle string variables; such variables should be removed from the data file or converted to numeric before Count data often use exposure variables to indicate the number of times the event could have happened. The TITLE command is optional and specifies a title used for the output file. example except for the file name. However, you can use a maximum of 500 variables for Mplus analysis. Mplus also has extensive Monte Carlo simulation capabilities to generate data from statistical analyses and to perform power analyses. By default, Mplus will use all of the variables in the data set. Note: In Mplus, there is no limit on the number of observations or number of variables in the data set to be read in. For the rest of this section we will present only the input files for each of the models. You can download the dataset by clicking here. Even with these adjustments, this will NOT reproduce our results exactly, because no random seed is set. Mplus will look for the data file in the same directory as where you save the input file, but you can place them in diferrent directories by specifying a full path for the data file. count response variable. – To select observations – USEOBSERVATIONS ARE DemEver EQ 0; – Equal (EQ, ==), Not Equal (NE, /=), Greater than or equal to (GE, >=), Less than or equal to (LE, <=), greater than (GT, >), less than (LT, <), AND, OR, NOT • USEVARIABLE ARE – Variables included in analysis – … For this next model we use an ordered response variable, ses, which takes on the convert a Stata dataset to an Mplus ASCII data file plus the necessary commands (in an Mplus input file) Notice the (nbi) for zero-inflated negative binomial on the count statement. The next model in this section is a negative binomial regression model. To convert the file to mplus, start mplus and run the file hsb2.inp. Mplus is not case sensitive. Missing values cannot be represented by blank spaces in free format. The Main Model . The DATA and VARIABLES command blocks are required. It is worth noting that this missing data approach is available for all of the different regression models, not just for the OLS regression. Explanation of most of the ANALYSIS options is beyond the scope of this introductory seminar, but we will use some of the options in our model examples later. •Or use Mplus’ shortcut – Intercept slope | time1@0 time2@1 time3@2 time4@3; –Assumes intercept is í’s all around –Creates paths you specify for slope –Allows intercept and slope to correlate –Sets variable intercepts to 0 so that all prediction is in the mean of the latent variables (Intercept and Slope) Mplus VERSION 8 You can incorporate exposure into your model by using the exposure() option. indicated in the output below). It contains detailed information about all of the input file commands, as well as numerous examples of a huge variety of models, with code and explanation for each example. We did not use the DEFINE, MODEL, or OUTPUT commands for our first Mplus file, but below is some basic information about each of them: The DEFINE command is used to generate new variables that are not found in the data set. The keyword for regression models is on, as in response variable regressed on predictor1, predictor2, etc. We will be exploring several different MODEL commands to specify different classes of models throughout the seminar. in a semicolon. Here is such an ANALYSIS command: Full input file for basic analysis of free-formatted file hsb.dat. Mplus has a rich collection of regression models including ordinary least squares (OLS) regression, probit regression, logistic regression, ordered probit and logit regressions, multinomial probit and logit regressions, poisson regression, negative binomial regression, inflated poisson and negative binomial regressions, censored regression and censored inflated regression. The default is also to report the conventional chi-square test and maximum likelihood standard errors. For information on interpreting the results of poisson models, please visit Annotated Output: Poisson Regression. You can install the latest release of MplusAutomation directly fromCRANby running Alternately, if you want to try out the latest developmentMplusAutomation code, you can install it straight from github usingHadley Wickham's devtools package. By default however, Mplus does not allow for missingness on exogeneous variables (x-variables) in Mplus. Notice the (nb) for negative binomial on the count statement. If you do not have devtoolsinstalled, first install it and then install MplusAutomation. H0 Value -757.201 . These are followed by one variable of length one (F1.0), then two of length 2 and one of length 1 (2F2.0, F1.0). This chapter contains a summary of the commands, options, and settings of the Mplus language. Zero-inflated models are useful when there is a second mechanism generating zeros, such that there would be many more zeros than would be expected from the count model alone. We can note which variables have which system missing values in SPSS: (.) Number of observations 118 . After “DATA:”, specify “file is” (or “file = “) and then the name of the file. Note that for certain models if you specify variables under USEVARIABLES and don’t include them in the model, you will get a warning that the “Variable is uncorrelated with all other variables”. Below, we use hsbmis.csv. Some portions of the output were deleted to save paper. Check your data and format statement. For our next example we will use a dataset, hsbmis2.dat, that has observations with missing data. SPSS FAQ: How can I move my data from SPSS to Mplus? Here is the VARIABLE command for the free-formatted file hsb.dat: Other options we can specify in the VARIABLE COMMAND: Further advice for using the VARIABLES command. creating dummy variables for a categorical variable), ANALYSIS – technical details of the analysis (estimator, algorithm), OUTPUT – any additional output not produced by default by running the statistical model, SAVEDATA – save analysis data and some analysis results, PLOT – generate graphics of data or analysis results. Important requirements for any Mplus data file: By default, Mplus excepts data files in “free format”, where the values for each of the variables are separated by a delimiter, which must be a comma, space or tab. USEVARIABLES (often shortened to usevars) to select a subset of the variables to use in the analysis. We begin by showing the input file which we called hsbreg.inp. In this example we will boldface the line that specifies the regression analysis. Count data often use exposure variables to indicate the number of times the event could have happened. the binary probit model. SAMPSTAT – sample statistics, including means, variances, skewness, kurtosis, minima and maxima, median and percentiles, and covariances and correlations, STD, STDXY, STDY – for standardized coefficients, CINTERVAL – confidence intervals for model parameters, TECH1 through TECH16 – the 16 TECH options output some of the details of the estimation procedure, such as starting values, covariance matrices of model parameters, and optimization (model fitting) history. ;), CENSORED, NOMINAL, CATEGORICAL, and COUNT to specify dependent variables that fit one of those types, STRATIFICATION, CLUSTER, and WEIGHT to variables reflecting complex or clustered sampling, GROUPING to specify a grouping variable for multi-group analyses. (b) rotation = name(type) name specifies the family of rotations to be used and type relates to oblique or orthogonal. Write your own input program (it is relatively easy). The symbol “=” and keywords “IS” and “ARE” can be used interchangeably in most commands (not in DEFINE, MODEL TEST or MODEL CONSTRAINT). When I use Proc Export for creating a MPLUS data set I have to open the data set with notepad, delete the first observation in the file and save it before MPLUS can read it. Notice the (p) for poisson on the count statement. Titles can contain any combination of characters and numbers (except for the name of an input file section with a colon, for example “DATA:”), and do not need to terminate in a semicolon. The FIML approach uses all of the available information in the data and yields unbiased parameter estimates as long as the missingness is at least missing at random. Starting in version 5 this is done by default, in earlier versions this type of estimation could be requested using type = missing;. identify them as being part of the Mplus code. ), A data file (often using a .dat extension), An input file containing a set of commands to analyze the data file (usually .inp extension), no variable names at the top of the file; first row should be data, DEFINE – used to generate new variable not found in the data file (e.g. You can download the data by clicking here. The final model in this section is a zero-inflated negative binomial regression model. 02/08/2012 4:03 PM . in the case of thresholds); and if your variable name has eight characters, the last two characters will be truncated and replaced by the new characters.> ... Use cut instead of delete and paste this line of variables in Mplus, in this way mistakes are much less likely! If you are an SPSS user, you can prepare your data to be read into Mplus with a few steps detailed in SPSS FAQ: How can I move my data from SPSS to Mplus?. Other then the ordered variable itself the setup is identical to Variable names can be no longer than 8 characters; if your variable names are longer than 8 characters, they will be truncated In Mplus, when measured exogenous variables (but not indicators for exogenous latent variables) have missing values, the cases with missing dataare excluded from the analysis. The Mplus .inp file is saved in the current working directory, which is listed in the lower left-hand corner of the Stata window. The zero-inflated models are examples of multiple equation models. 4.1 install the “rhdf5” package to read gh5 files; 4.2 load packages; 4.3 Keyboard shortcuts; 5 - Read in data; 6 - A couple ways to explore & view data in R; 7 - View dataframe with labels & response scale meta-data. The setup for this model parallels that of the zero-inflated poisson model above. The ANALYSIS command specifies the technical details of the statistical analysis, such as the type of analysis, the estimator and the algorithm used. After saving and running the .inp file, you can look in the output file for “INPUT READING TERMINATED NORMALLY” appearing below the entered code. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).. In order to force Mplus to use all observations, we can estimate the mean of the x-variables so that the x-variables becomes an endogenous variable in Mplus and gets treated as an imputable variable. In this case, there is one equation for the count model, awards on female read math, and a second equation for estimating the excess zeros, awards#1 on female read math; this is a logit model. dataset name hsb2.dat and  hsb2.inp. List the variable names after “names are” (or “names = “). Dummy variables must be created for any categorical, BY is used to indicate indicators for latent variables. Observations. Mplus (output excerpts) Note: I use the bootstrap approach here for testing the indirect effect. Model Specification, the MPlus input file Click here to report an error on this page or leave a comment, Your Email (must be a valid email for us to receive the report! The Mplus .inp file is saved in the current working directory, which is listed in the lower left-hand corner of the Stata window. to 8 characters. Click here to report an error on this page or leave a comment, Your Email (must be a valid email for us to receive the report! For every analysis, Mplus requires that the names of the variables be specified in the order that they appear in the data file. The reason is that for some parts of some of the output, Mplus will add one or two additional characters (e.g. Command and option names can be shortened to their first four letters. The next model is a zero-inflated poisson regression model. The input file for this example is identical to the previous Note that the total number of variables is now back up to 200 instead of 76 (200-124=76) had we not imputed the mean of the x-variables. You can get the stata2mplus ado file by typing Perhaps its greatest strengths are in its capabilities to model latent variables, both continuous and categorical, which underlie its flexibility. To change it, you can use the Stata’s cd command. The line does not need to be ended Comments can be added to the Mplus syntax by starting the line with an exclamation point (!).
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