can be used when running the LCVSEM macro from other macros to control the notes generated in the log area

Transcription

1 invoke the LCVSEM macro to conduct structural equation modeling (SEM) analyses setting the macro parameters to appropriate values or using the default values given in the macro declaration SEM models are generated using the proc calis lineqs, std, and cov statements. begnotes set to any value other than Y (meaning N) to shut off printing in the log area of note messages by SAS procedures executed by the macro its value is not error checked so that Y means turn on printing to the log and any other value means turn off that printing can be used when running the LCVSEM macro from other macros to control the notes generated in the log area codeout the name to be assigned to the data set containing a listing of proc calis code for generating the requested model only one-level names are allowed a print out of the contents of this data set is generated when prntcode=y compdf the degrees of freedom to be used to compute a cutoff for a substantial change in LCV scores comptol the tolerance value to be used to compute a cutoff for a substantial change in LCV scores comptst set to Y (or y) to adjust the value of comptol to comptol/0.02 times the log of the ratio of the unnormalized (i.e., not raised to power = the number of measurements) LCV scores exceeds the 95th percentile of the chi-square distribution with DF degree of freedom, i.e, the analogue to the likelihood ratio test applied to LCV scores covarnce Y (or y) means to analyze the covariance matrix rather than to analyze the correlation matrix datain the name of the data set loaded with all input variables for the current invocation of the macro

2 &datain should either be the name of a data set in the default library or a valid two-level SAS data set name ddcovs the specification of inter-disturbance covariances same format and meaning as for ffcovs except that i and j designate indexes for disturbances the first error index i must be less than the second error index j if ddcovs is empty and othddcvs=., then all inter-disturbance covariances are estimated the associated latent factors must be endogeneous, that is, they must have ffpaths leading into them from other latent factors estimated inter-disturbance covariances have names cddi_j where i and j are the indexes of the two covarying disturbances decrdgts the number of digits to round percent decreases in LCV scores in the output dvarnces the specification for the disturbance variances a series of disturbance variance specifications separated by colons (:) of the form i v where i is an index of the disturbance variable (for some endogenous latent factor with the same index) and v is its variance which is either a constant, a name starting with eq, or. to indicate the variance is to be estimated for example, "1 2.0 : 2 1.0" means that the disturbance variances 1 & 2 have variances 2.0 and 1.0, respectively, and all other distrubance variances are determined by the setting of the othdvars macro parameter while "1 eqvd : 2 eqvd" indicates the first two disturbance variances are to have the same estimated value if dvarnces is empty, all disturbance variances are determined by the setting of the othdvars macro parameter constant disturbance variance must be positive the distrubances must correspond to endogeneous latent factors, that is, factors with ffpaths leading into them from other latent factors disturbances have names di with estimated variances named vdi where i is the index of the associated endogenous factor edcovs the specification of error-disturbance covariances between errors and disturbances

3 same format and meaning as for ffcovs except that i designate an index for an error and j designates an index for a disturbance the error index i does not have to be less than the disturbance index j if edcovs is empty, all error-disturbance covariances are determined by the setting of the othedcvs macro parameter the distrubance terms must correspond to endogeneous latent factors, that is, they must have ffpaths leading into them from other latent factor estimated error-disturbance covariances have names cedi_j where i and j are the indexes of the covarying error and disturbance, respectively eecovs the specification of inter-error covariances same format and meaning as for ffcovs except that i and j designate indexes for errors the first error index i must be less than the second error index j if eecovs is empty, all error covariances are determined by the setting of the otheecvs macro parameter estimated inter-error covariances have names ceei_j where i and j are the indexes of the covarying error variables endnotes set to any other value than N (meaning Y) to turn on printing in the log area of note messages by SAS procedures after macro execution stops its value is not error checked so that Y means turn on printing to the log and any other value means turn off that printing can be used when running the LCVSEM macro from other macros to control the notes generated in the log area estmeth the parameter estimation method for proc calis only four methods are currently supported: ML for standard maximum likelihood LSML for least square followed by maximum likelihood FIML for full information ML WLS for weighted least squares default weights are used and so this is equivalent to Browne's asymptotically distribution-free estimation when method=fiml, subjects are allowed to have some missing y variable values (as determined by the yfst, ylst, and yprefix macro parameter settings) but this is only supported by SAS Version 9.3 and later

4 for versions earlier than 9.3, the request for method=fiml will generate an error in the log for the other methods, there must be either no missing y variable values for all subjects or the missing y variable values must be imputed or deleted as determined by the setting of the setmssng macro parameter estout the name to be assigned to the data set containing parameter estimates generated through the proc calis outest option using all the data in the datain data set only one-level names are allowed evarnces the specification for the error variances a series of error variance specifications separated by colons (:) of the form i v where i is an index of an error variable (for some y variable as determined by the yfst, ylst, and yprefix macro parameter settings) and v is its variance which is either a constant, a name starting with eq, or. to indicate the variance is to be estimated for example, "1 2.0 : 2 1.0" means that the error variances 1 & 2 have variances 2.0 and 1.0, respectively, and all other error variances are determined by the setting of the othevars macro parameter while "1 eqve : 2 eqve" indicates that the first two error variances are to have the same estimated value if evarnces is empty, all error variances are determined by the setting of the othevars macro parameter constant error variances must be positive errors have names ei with estimated variances named vei where i is the index of the associated y variable factnams a list of names for the factors must be the same number of names as the setting of the nfactors macro parameter each must be a valid SAS variable name and different from the others indexes for these factors are determined by their order in the setting of the factnams macro parameter for example, when factnams=factora factorb factorc then the index for factora is 1, for factorb is 2, and for factorc is 3

5 these indexes are used if other macro parameters like loadyonf and ffpaths to indicate which of the factnams factors are being referenced when factnams has an empty setting, the factor names are f1, f2,, f&nfactors fdcovs the specification of factor-disturbance covariances between exogenous latent factors and disturbances for endogeous latent factors same format and meaning as for ffcovs except that i designate an index for an exogenous latent factor and j designates an index for a disturbance the factor index i does not have to be less than the disturbance index j if fdcovs is empty, all factor-disturbance covariances are determined by the setting of the othfdcvs macro parameter estimated factor-disturbance covariances have names cfdi_j where i and j are the indexes of the covarying factor and disturbance, respectively fecovs the specification of factor-error covariances between exogenous latent factors and errors same format and meaning as for ffcovs except that i designate an index for an exogenous latent factor and j designates an index for an error the factor index i does not have to be less than the error index j if fecovs is empty, all factor-error covariances are determined by the setting of the othfecvs macro parameter estimated factor-error covariances have names cfei_j where i and j are the indexes of the covarying factor and error, respectively ffcovs the specification of inter-factor covariances for pairs of exogenous latent factors a series of covariance specifications separated by colons (:) of the form i j c where i and j are indexes of the exogenous latent factors and c is their covariance which is either a constant or. to indicate the corresponding covariance is to be estimated for example, "1 2. : " means that factor 1 & 2 have estimated covariance, factors 2 and 3 have covariance 1.0, and all other pairs of covariances are determined by the setting of the othffcvs macro parameter the first factor index i must be less than the second factor index j estimated inter-factor covariances have names cffi_j where i and j are the indexes of the covarying factors

6 ffpaths the specification of inter-factor directional paths a series of path specifications separated by colons (:) of the form i j c where i and j are indexes of the factors and c is their path coefficient which is either a constant, a. to indicate the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from factor 1 to factor 2 has an estimated coefficient, and the path from factor 2 to factor 3 has coefficient 1.0, and there are no other paths between factors names starting in eqffp are recommended for setting ffpaths coefficients to have equal estimated values an empty ffpaths specification means there no inter-factor paths in the model any latent factor listed second in an ffpaths specification is an endogenous variable any latent factor not listed second in any ffpaths, xfpaths, or yfpaths specification is an exogenous variable all endogenous latent factors have associated disturbance terms with variances controlled through the dvarnces and othdvars parameters estimated inter-factor directional paths have names ffpi_j where i and j are the indexes of the two associated factors with the path starting at factor i and ending at factor j fitout the name to be assigned to the data set containing fit statistics generated by proc calis using all the data in the datain data set only one-level names are allowed foldcnt indicates the number of folds to use in LCV computations all measurements for y variables (as determined by the yfst, ylst, and yprefix macro parameter settings) for a subject are assigned to the same fold it is recommended that the same initseed value be used for all models for the same data so that LCV scores for those models are comparable foldplot Y (or y) means to generate normal and standardized residuals plots for each fold separately as well as for all folds combined, which will only be produced when ranlysis=y and foldwise=y

7 foldwise Y (or y) means to compute standardized residuals for all y variables for subjects within each of the folds separately has no effect when ranlysis=n when ranlysis=y and foldwise=y, a variable named SEM_fold is incldued in the &residout data set containing fold indexes for all observations when ranlysis=y and foldwise=n, standardized residuals for all y variable measurements for all subjects will be computed in conjunction for very large numbers of subjects, foldwise=n might require more memory than is available or take substantially longer to compute fullcov Y (or y) means to compute standardized residuals when foldwise=y using the covariance matrix estimated with all the data even though the y variable means are estimated for each fold separately fvarnces the specification for the latent factor variances a series of latent factor variance specifications separated by colons (:) of the form i v where i is an index of a latent factor and v is its variance which is either a constant, a name starting with eq, or. to indicate the variance is to be estimated for example, "1 2.0 : 2 1.0" means that the factors 1 & 2 have variances 2.0 and 1.0, respectively, and all other factor variances are determined by the setting of the othfvars macro parameter while "1 eqvf : 2 eqvf" indicates that the first two latent factors are to have the same estimated value if fvarnces is empty, all latent factor variances are determined by the setting of the othfvars macro parameter constant latent factor variances must be positive latent factors have names either determined by the setting of the factnams macro parameter or are set to fi with estimated variances named vfi where i is the index of the associated factor variances should only be generated for exogeneous latent factors, that is, factors without any ffpaths leading into them from other latent factors when a variance for a latent factor is estimated, there should be one y variable loading on that factor with loading equal to 1 to avoid indeterminacy fypaths the specification of latent factor to y variable directional paths

8 a series of path specifications separated by colons (:) of the form i j c where i is an index of a factor, j is an iddex of a y variable, and c is their path coefficient which is either a constant, a. to indicate the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from factor 1 to y variable 2 has an estimated coefficient, the path from factor 2 to y variable 3 has coefficient 1.0, and there are no other paths from factors to y variables except as determined by the setting of the loadyonf macro names starting in eqfyp are recommended for setting fypaths coefficients to have equal estimated values an empty fypaths specification means there no factor to y variable paths in the model except as determined by the setting of the loadyonf macro estimated factor to y variable directional paths have names fypi_j where i is the index of the factor the path starts at and j is the index of the y variable the path ends at note that paths from latent factors to y variables can be specified using either the fypaths macro parameter or the loadyonf macro parameter loadings of latent factors for a y variable must all be specified through the loadyonf macro parameter or all through the yfpaths macro parameter y variables used in any fypaths must not be listed in the setting of the yexclude macro parameter it can be simpler in some cases to use yfpaths than loadyonf initseed for example if there are 8 factors, the first y variable can be assigned a unit loading on the last factor and 0 loadings on the other factors using the simpler "fypaths=8 1 1" (assuming no paths from factors 1-7 to y variable 1 are also included in fypaths) rather than the more complicated "loadyonf=1 < " the initial seed for fold specification it should be an integer > 2 and < 2**31-1 it needs to remain unchanged so that all LCV scores are comparable, but may be changed in order to generate a repeated LCV analysis linesrch denotes the type of line search to be used when the method specified in the setting of the optmeth macro parameter involves a line search must either be empty, meaning to use the proc calis default line search, or be a value from 1-8 with the same meaning as for the proc calis lmethod

9 option loadyonf a sequence of terms in two parts with terms separated by a colon (:) and parts separated by a less than (<) where the first part is a set of integers indicating a set of y variables (as determined by the yfst, ylst, and yprefix macro parameter settings) and the second part is a set of loadings for those y variables on the factors (with. meaning the loading is to be estimated and names starting with eq meaning use that name for all estimated loadings for associated factors) names starting in eqlyf are recommended for setting loadyonf loadings to have equal estimated values for example, "1 2 4 <. 0 : 3 5 < 0." means that y variables with indexes 1, 2, and 4 load only on the first factor while y variables with indexes 3 and 5 load only on the second factor with all such loadings to be estimated and "1 2 4 < eqlyf1 0 : 3 5 < 0 eqlyf2" means that y variables with indexes 1, 2, and 4 load only on the first factor with the same estimated loadings while y variables with indexes 3 and 5 load only on the second factor with the same estimated loadings different from the common estimated loadings for variables 1, 2,and 4 use "1 2 4 < eqlyf 0 : 3 5 < 0 eqlyf" to get the same estimated constant loadings but note that this is not the same as " < eqlyf 0" since this puts the same constant loading on factor 1 for all y variables any y variables not listed in any of the terms and not assigned any paths through the fypaths macro parameter will have zero loadings on all factors a y variable can be listed in at most one loadyonf term each term must have loadings for no more than the number of factors with these loadings associated with factors in increasing order and if there are less loadings than factors then the loadings on the remaining factors are set to zero at least one y variable must have a nonzero loading on each factor estimated loadings have names lyfi_j where i is the index of the y variable and j is the index of the factor that the y variable loads on note that loadings of latent factors on y variables can be specified using either the loadyonf macro parameter or the fypaths macro parameter maxiters loadings of latent factors for a y variable must all be specified through the loadyonf macro parameter or all through the yfpaths macro parameter the maximum number of iterations for parameter estimation computations maxfuncs the maximum number of function calls for parameter estimation computations

10 modindxs Y (or y) requests that modification indexes be generated in the proc calis output for the full data requires procmod=y nfactors set to the number of latent factors (or latent variables) nocheck Y (or y) means do not error check any of the macro parameter settings can be used when calling LCVSEM from another macro to reduce the computation time assuming the parameter settings for all LCVSEM calls have been verified to always be correct nodiag noint nolog Y (or y) means proc calis models are to be generated with the NODIAG option, that is, with the diagonal elements of the covariance matrix excluded note that, LCVSEM can be used to generate exploratory factor analysis (EFA) models with all y variables loading on all factors while one would expect that nodiag=y is need for these models to be equivalent to models generated by proc factor, that is not the case nodiag=n is required for such equivalence Y (or y) means proc calis models are to be generated with the NOINT option, that is, without adusting the correlation or covariance matrix for a constant term used when structural equations are to include intercepts Y (or y) means to turn off writing to the log useful for invoking LCVSEM from other macros noprint Y (or y) means to turn off printing of all output error messages if any will still be printed useful for invoking LCVSEM from other macros

11 nscrevar the name of the variable to be loaded with the normal scores for the standardized (or scaled) residuals when ranlysis=y optmeth the type of optimization method to be used must either be empty, meaning to use the proc calis default optimization method, or one of the following alternatives with meaning the same as for the proc calis omethod option CONGRA, DBLDOG, LEVMAR, NEWRAP, NRRIDG, QUANEW, TRUREG note that the proc calis default optimization method changes with the number of observations and the alternative default optimization methods changed from SAS Version 9.2 to Version 9.3 note also that synonyms for the above methods supported by proc calis are not supported by LCVSEM othddcvs sets the value for all inter-disturbance covariances other than those set through ddcovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names starting with with eqcdd to constrain inter-disturbance covariances to be equal to each other covariances are only generated for pairs of endogeneous latent factors, that is, factors with ffpaths leading into them from other latent factors othdvars sets the value for all disturbance variances other than those set through dvarnces possible values are. meaning estimate all these variances, any positive number to set their common value to, or a name starting with eq meaning that all these variances have the same value which is to be estimated it is recommended to use names eqvd to constrain disturbance variances to be equal to each other if dvarnces is empty and othdvars=., then all disturbance variances are estimated variances are only generated for endogeneous latent factors, that is, factors with ffpaths leading into them from other latent factors othedcvs

12 sets the value for all error-disturbance covariances other than those set through edcovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names starting with eqced to constrain errordisturbance covariances to be equal to each other covariances are only generated for disturbances corresponding to endogeneous latent factors, that is, factors with ffpaths leading into them from other latent factors otheecvs sets the value for all inter-error covariances other than those set through eecovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names eqcee to constrain inter-error covariances to be equal to each other othevars sets the value for all error variances other than those set through evarnces possible values are. meaning estimate all these variances, any number to set their common value to, or a name starting with eq meaning that all these variances have the same value which is to be estimated it is recommended to use names starting with eqve to constrain error variances to be equal to each other if evarnces is empty and othevars=., then all error variances are estimated othfdcvs sets the value for all factor-disturbance covariances other than those set through fdcovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names starting with eq to constrain any types of parameters to be equal, names starting with eqc to constrain any types of covariances to be equal to each other and names starting with eqcfd to constrain factor-disturbance covariances to be equal to each other covariances are only generated for exogeneous latent factors and for disturbances for endogeneous latent factors

13 othfecvs sets the value for all factor-error covariances other than those set through fecovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names starting with eqcfe to constrain factorerror covariances to be equal to each other covariances are only generated for exogeneous latent factors othffcvs sets the value for all inter-factor covariances other than those set through ffcovs possible values are. meaning estimate all these covariances, any number to set their common value to, or a name starting with eq meaning that all these covariances have the same value which is to be estimated it is recommended to use names starting with eqcff to constrain interfactor covariances to be equal to each other covariances are only generated for pairs of exogeneous factors, which is the same as all factor pairs when the setting of the ffpaths macro parameter is empty othfvars sets the value for all latent factor variances other than those set through fvarnces possible values are. meaning estimate all these variances, any positive number to set their common value to, or a name starting with eq meaning that all these variances have the same value which is to be estimated it is recommended to use names eqvf to constrain latent factor variances to be equal to each other if fvarnces is empty and othfvars=., then all latent factor variances are estimated variances are only generated for exogeneous latent factors, that is, factors without any ffpaths leading into them from other latent factors when a variance for a latent factor is estimated, there must be one y variable loading on that factor with loading equal to 1 to avoid indeterminacy partlvrs the list of names of the variables to partial out of the correlation/ covariance matrix

14 both proc calis and proc corr invocations are changed to include partial statements has no effect on the setmssng imputation alternatives predvar the name of the variable to contain the mean values for the y variables the values take into account any partialled variables specified in the partlvrs macro parameter setting prntcode Y (or y) means to print out the generated proc calis code used in the computations the code is generated in the &codeout data set in all cases procmod Y (or y) means to generate an equivalent model for the full data set using SAS proc calis and print out its results as long as noprint=n ranlysis Y (or y) means to compute standardized (or scaled) residuals and when noprint=n to also generate residual analysis output including a variety of plots note that these residuals are computed for the observed measurements of the y variables (as deterimied by the settings of the yfst, ylst, and yprefix macro parameters) and not for the unstructured correlations/ cvariances as are computed by proc calis these residuals are standardized (or scaled) using an approach analogous to that used by proc mixed so that they may be treated are independent residout the name of the data set to be loaded with the residuals when ranlysis=y rprttime set to Y to request that clock time be printed out for the current execution of the macro the elapsed clock time is generated in the &timeout data set in all cases rnddigts the number of decimal digits to round imputed y variable values when round=y and setmssng has settings CORRYMEAN, PERSMEAN, or YMEAN

15 round Y (or y) means to round all imputed missing values only affects the CORRYMEAN, PERSMEAN, and YMEAN settings for the setmssng macro parameter scorefmt a SAS w.d format for printing of LCV scores in macro output with w the width in numbers of characters and d the number of decimal digits (with d w-2 so that there is room for the decimal point and one digit before it) scoreout the name of the data set containing LCV scores for the requested model and for the associated unstructured covariance model only one-level names are allowed only generated when xvalid=y SEM_errcode a global macro variable generated by LCVSEM to indicate whether or not it has completed its execution without error or not the possible generated settings are (all in all lower case letters) completed LCVSEM completed execution normally without any errors occurring computational error a computational error occurred while computing the LCV score input error an input error was identified so that execution completed normally but the LCV score was not generated proc calis problem a problem occurred in a call to proc calis with reason displayed in the log when nolog=n started LCVSEM started execution but an error occurred that interrupted execution so that it did not complete normally LCVSEM also generates a variety of other macro variables during its execution but these are all local macro variables and so settings of macro variables/parameters in macros calling LCVSEM are not changed by LCVSEM even if the names are the same for both macros setmssng indicates how to handle any missing y variable measurements note that only the internal SAS missing value corresponding to a period (.) is treated as missing, special codes indicating issues like not

16 applicable, don't know, or refused must be recoded to the SAS missing value before running LCVSEM possible values are an empty setting meaning do not adjust missing data only allowed when method=fiml CORRELATE means compute the correlation matrix and pass it to proc calis to compute the model CORRYMEAN means to replace all missing y variable values by the mean of the non-missing values for the associated y variable corrected by the values of the non-missing y variable values for each person (for details, see Huismann, M. (2000). Imputation of missing item responses: Some simple techniques, Quality & Quantity, 34, ) appropriate when the y variables are items of a survey instrument as when using LCVSEM to conduct a confirmatory factor analysis DELETE means to delete all observations with any missing y variable values YMEAN means to replace all missing y variable values by the mean of the non-missing values for the associated y variable PERSMEAN means to replace all missing y variable values by the mean of the non-missing values for the associated person (or subject or observation) note that this uses all the y variables and does not take into account factors that y variables load on as would be the case if missing values were computed for separate scales appropriate when the y variables are items of a survey instrument as when using LCVSEM to conduct a confirmatory factor analysis a numeric value to be assigned to all missing y variable values y variables specified in yreverse are reverse coded prior to computing these means, the mean values are then unreverse coded to impute the missing values for the reverse coded y variables since they are used in the analysis without being reverse coded the setting of the partlvrs macro parameter has no effect on any of the above imputation alternatives stdrsvar the name of the variable to contain the standardized (or scaled) residuals subsetvr name of the subsetting variable (default value is the empty specification)

17 when subsetvr has a non-empty value, the analysis will use only those observations in the datain data set for which the subsetting variable has nonmissing and nonzero values these observations are allocated to the same folds as they are in analyses of the full data and so LCV scores are different than they would be if only these observations are in the datain data set an empty setting means use all the observations a data set can be partitioned into disjoint subsets, an LCV score computed for each subset using subsetvr set to the indicator variable for that subset, and the subset LCV scores combined to obtain a LCV score for the full data set that is comparable to LCV scores generated directly for the full data set timeout the name of the data set containing the elapsed clock time in seconds for the current execution of the genreg macro only one-level names are allowed this data set is always generated but its contents are only printed if rprttime=y update denotes the type of update approach to be used when the optimization method specified in the setting of the optmeth macro parameter involves an update must either be empty, meaning to use the proc calis default update approach, or one of the following: weightvr when optmeth=congra, must be one of CD, FR, PB, or PR with the same meaning as for the proc calis update option when optmeth=dbldog, must be one of DBFGS or DDFP with the same meaning as for the proc calis update option when optmeth=quanew, must be one of BFGS, DBFGS, DDFP, or DFP with the same meaning as for the proc calis update option has no effect for other settings of the optmeth macro parameter the name of the variable in the datain data set with weights for the observations should have all positive values weights are not supported by proc calis with estmeth=fiml

18 xfpaths the specification of x variable to factor directional paths a series of path specifications separated by colons (:) of the form i j c where i is the index of the x variable the path starts at, j is the index of the factor the path ends at, and c is their path coefficient which is either a constant, a. to indicate the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from x variable 1 to factor 2 has an estimated coefficient, and the path from x variable 2 to factor 3 has coefficient 1.0, and there are no other paths between x and factors variables names starting in eqxfp are recommended for setting xfpaths coefficients to have equal estimated values an empty xfpaths specification means there are no x variable to factor paths in the model estimated x variable to factor directional paths have names xfpi_j where i and j are the indexes of the x variable and factor, respectively, with the path starting at x variable i and ending at factor j xvalid xvars Y (or y) means turn on LCV computations xvalid=n can be used to generate a CFA model with less computation time a list of names of variables in the datain data set to be used to predict selected factors and/or y variables (i.e, the manifest variables to be treated as measured without error) use the xfpaths and xypaths to indicate which factors and y variables, respectively, each xvars variable is related to each xvars variable should be in at least one such path paths from factors to x variables or from y variables to x variables are not supported all xvars variables must be numeric without missing values and are treated as measured without error when a predictor is to affect the means all the y variables, including it in the xvars list would require one xypaths specification for each y variable so, if the predictor is not needed for any xfpaths specifications, the predictor can be included in the partlvrs list instead without the need for extra xypaths specifications xypaths

19 the specification of x variable to y variable directional paths a series of path specifications separated by colons (:) of the form i j c where i is the index of the x variables the path starts at, j is the index of the y variable the path ends at, and c is their path coefficient which is either a constant, a. to indicate the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from x variable 1 to y variable 2 has an estimated coefficient, and the path from x variable 2 to y variable 3 has coefficient 1.0, and there are no other paths between x and y variables names starting in eqxyp are recommended for setting xypaths coefficients to have equal estimated values an empty xypaths specification means there no x variable to y variable paths in the model estimated x variable to y variable directional paths have names xypi_j where i and j are the indexes of the x and y variables, respectively, with the path starting at x variable i and ending at y variable j yexclude a list of y variables whose loadyonf loadings are to be reset to zero for all factors when an y variable is excluded, it is set equal to its associated error variable which in turn has an associated variance this is equivalent to not using an error variable and modeling the variance of the y variable directly a y variable with index listed in the setting of the yexclude macro parameter may be generated loadings through the loadyonf macro parameter but these have no effect on the model a y variable with index listed in the setting of the yexclude macro parameter should not be listed in any fypaths speicifcations yfpaths the specification of y variable to factor directional paths a series of path specifications separated by colons (:) of the form i j c where i is the index of the y variable the path starts at, j is the index of the factor the path ends at, and c is their path coefficient, which is either a constant, a. to indicate that the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from y variable 1 to factor 2 has an estimated coefficient, and the path from y variable 2 to factor 3 has coefficient 1.0, and there are no other paths from y vartiable to factors

20 yfst names starting in eqyfp are recommended for setting yfpaths coefficients to have equal estimated values an empty yfpaths specification means there no y variable to factor paths in the model any latent factor listed second in a yfpaths specification is an endogenous variable all endogenous latent factors have associated disturbance terms with variances controlled through the dvarnces and othdvars parameters estimated y variable to factor directional paths have names yfpi_j where i is the index of the y variable the path starts at and j is the index of the factor the path ends at the beginning index for y variables (i.e., the manifest variables to be treated as measured with error) the y variables are numbered from &yfst to &ylst and incremented by 1 yhival the highest possible y variable value only needed if yreverse has a nonenpty setting yindxvar the name of the variable to be loaded in the residout data set with the indexes for the y variables (as determined by the yfst, ylst, and yprefix macro parameter settings) when ranlysis=y yloval ylst the lowest possible y variable value only needed if yreverse has a nonenpty setting the ending index for y variables (i.e., the manifest variables to be treated as measured with error) the y variables are numbered from &yfst to &ylst and incremented by 1 yprefix the prefix for the names of the y variables (i.e., the manifest variables to be treated as measured with error) all these variables must have names that start with &yprefix followed by an index between &yfst and &ylst

21 yreverse the list of indexes separate by blanks for the y variables to be reverse coded this only affects setmssng=persmean and setmssng=corrymean and is appropriate when the y variables are items of a survey instrument as when using LCVSEM to conduct a confirmatory factor analysis y variables are not reverse coded for proc calis computations just for imputation purposes yxclddep Y (or y) means that error terms for excluded y variables are to be treated as covarying with estimated covariances rather than as independent only error terms for y variables with indexes in the yexclude macro parameter setting are treated as covarying error terms with indexes not in the yexclude macro parameter setting that load on no factors have covariances with other error terms determined by the setting of the eecovs and otheecvs macro parameters yypaths the specification of inter-y-variable directional paths a series of path specifications separated by colons (:) of the form i j c where i and j are indexes of the y variables and c is their path coefficient which is either a constant, a. to indicate the corresponding coefficient is to be estimated, or a name starting with eq to constrain path coefficients to be equal to other model parameters for example, "1 2. : " means that the path from y variable 1 to y variable 2 has an estimated coefficient, and the path from y variable 2 to y variable 3 has coefficient 1.0, and there are no other paths between y variables names starting in eqyyp are recommended for setting yypaths coefficients to have equal estimated values an empty yypaths specification means there no inter-y-variable paths in the model estimated inter-y-variable directional paths have names yypi_j where i and j are the indexes of the two associated y variables with the path starting at y variable i and ending at y variable j