“Mplus ANALYSIS”的版本间的差异
来自OBHRM百科
Lichaoping（讨论 | 贡献） |
Lichaoping（讨论 | 贡献） |
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ANALYSIS命令语法 | ANALYSIS命令语法 | ||
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2017年2月27日 (一) 14:16的版本
ANALYSIS命令语法
| ANALYSIS: | ||
| TYPE = | GENERAL; | GENERAL |
| BASIC; | ||
| RANDOM; | ||
| COMPLEX; | ||
| MIXTURE;
BASIC; RANDOM; COMPLEX; |
||
| TWOLEVEL;
BASIC; RANDOM; MIXTURE; COMPLEX; |
||
| THREELEVEL;
BASIC; RANDOM; COMPLEX; |
||
| CROSSCLASSIFIED;
RANDOM; |
||
| EFA # #;
BASIC; MIXTURE; COMPLEX; TWOLEVEL; EFA # # UW* # # UB*; EFA # # UW # # UB; |
||
| ESTIMATOR = | ML; | depends on |
| MLM; | analysis type | |
| MLMV; | ||
| MLR; | ||
| MLF; | ||
| MUML; | ||
| WLS; | ||
| WLSM; | ||
| WLSMV; | ||
| ULS; | ||
| ULSMV; | ||
| GLS; | ||
| BAYES; | ||
| MODEL = | CONFIGURAL; | |
| METRIC; | ||
| SCALAR; | ||
| NOMEANSTRUCTURE; | means | |
| NOCOVARIANCES; | covariances | |
| ALLFREE; | equal | |
| ALIGNMENT = | FIXED; | last class |
| CONFIGURAL | ||
| FIXED (reference class CONFIGURAL); | ||
| FIXED (reference class BSEM); | ||
| FREE; | last class | |
| CONFIGURAL | ||
| FREE (reference class CONFIGURAL); | ||
| FREE (reference class BSEM); | ||
| DISTRIBUTION = | NORMAL; | NORMAL |
| SKEWNORMAL; | ||
| TDISTRIBUTION; | ||
| SKEWT; | ||
| PARAMETERIZATION = | DELTA; | DELTA |
| THETA; | ||
| LOGIT; | LOGIT | |
| LOGLINEAR; | ||
| PROBABILITY;
RESCOVARIANCES; |
RESCOV | |
| LINK = | LOGIT; | LOGIT |
| PROBIT; | ||
| ROTATION = | GEOMIN; | GEOMIN (OBLIQUE value) |
| GEOMIN (OBLIQUE value); | ||
| GEOMIN (ORTHOGONAL value); | ||
| QUARTIMIN; | OBLIQUE | |
| CF-VARIMAX; | OBLIQUE | |
| CF-VARIMAX (OBLIQUE); | ||
| CF-VARIMAX (ORTHOGONAL); | ||
| CF-QUARTIMAX; | OBLIQUE | |
| CF- QUARTIMAX (OBLIQUE); | ||
| CF- QUARTIMAX (ORTHOGONAL); | ||
| CF-EQUAMAX; | OBLIQUE | |
| CF- EQUAMAX (OBLIQUE); | ||
| CF- EQUAMAX (ORTHOGONAL); | ||
| CF-PARSIMAX; | OBLIQUE | |
| CF- PARSIMAX (OBLIQUE); | ||
| CF- PARSIMAX (ORTHOGONAL); | ||
| CF-FACPARSIM; | OBLIQUE | |
| CF- FACPARSIM (OBLIQUE); | ||
| CF- FACPARSIM (ORTHOGONAL); | ||
| CRAWFER; | OBLIQUE 1/p | |
| CRAWFER (OBLIQUE value); | ||
| CRAWFER (ORTHOGONAL value); | ||
| OBLIMIN; | OBLIQUE 0 | |
| OBLIMIN (OBLIQUE value); | ||
| OBLIMIN (ORTHOGONAL value); | ||
| VARIMAX; | ||
| PROMAX; | ||
| TARGET; | ||
| BI-GEOMIN; | OBLIQUE | |
| BI-GEOMIN (OBLIQUE); | ||
| BI-GEOMIN (ORTHOGONAL); | ||
| BI-CF-QUARTIMAX; | OBLIQUE | |
| BI-CF-QUARTIMAX (OBLIQUE); | ||
| BI-CF-QUARTIMAX (ORTHOGONAL); | ||
| ROWSTANDARDIZATION = | CORRELATION; | CORRELATION |
| KAISER; | ||
| COVARIANCE; | ||
| PARALLEL = | number; | 0 |
| REPSE = | BOOTSTRAP;
JACKKNIFE; JACKKNIFE1; JACKKNIFE2; BRR; FAY (#); |
.3 |
| BASEHAZARD = | ON;
OFF; ON (EQUAL); ON (UNEQUAL); OFF (EQUAL); OFF (UNEQUAL); |
depends on
analysis type EQUAL EQUAL |
| CHOLESKY = | ON;
OFF; |
depends on
analysis type |
| ALGORITHM = | EM; | depends on |
| EMA; | analysis type | |
| FS;
ODLL; INTEGRATION; |
||
| INTEGRATION = | number of integration points;
STANDARD (number of integration points) ; GAUSSHERMITE (number of integration points) ; MONTECARLO (number of integration points); |
STANDARD
depends on analysis type 15 depends on analysis type |
| MCSEED = | random seed for Monte Carlo integration; | 0 |
| ADAPTIVE = | ON;
OFF; |
ON |
| INFORMATION = | OBSERVED; | depends on |
| EXPECTED; | analysis type | |
| COMBINATION; | ||
| BOOTSTRAP = | number of bootstrap draws;
number of bootstrap draws (STANDARD); number of bootstrap draws (RESIDUAL): |
STANDARD |
| LRTBOOTSTRAP = | number of bootstrap draws for TECH14; | depends on
analysis type |
| STARTS = | number of initial stage starts and number of final stage optimizations; | depends on
analysis type |
| STITERATIONS = | number of initial stage iterations; | 10 |
| STCONVERGENCE = | initial stage convergence criterion; | 1 |
| STSCALE = | random start scale; | 5 |
| STSEED = | random seed for generating random starts; | 0 |
| OPTSEED = | random seed for analysis; | |
| K-1STARTS = | number of initial stage starts and number of final stage optimizations for the k-1 class model for TECH14; | 20 4 |
| LRTSTARTS = | number of initial stage starts and number of final stage optimizations for TECH14; | 0 0 40 8 |
| RSTARTS = | number of random starts for the rotation algorithm and number of factor solutions printed for exploratory factor analysis; | depends on
analysis type |
| ASTARTS = | number of random starts for the alignment
optimization; |
30 |
| H1STARTS = | Number of initial stage starts and number of final stage optimizations for the H1 model; | 0 0 |
| DIFFTEST = | file name; | |
| MULTIPLIER = | file name; | |
| COVERAGE = | minimum covariance coverage with missing data; | .10 |
| ADDFREQUENCY = | value divided by sample size to add to cells with zero frequency; | .5 |
| ITERATIONS = | maximum number of iterations for the Quasi-Newton algorithm for continuous outcomes; | 1000 |
| SDITERATIONS = | maximum number of steepest descent iterations for the Quasi-Newton algorithm for continuous outcomes; | 20 |
| H1ITERATIONS = | maximum number of iterations for unrestricted model with missing data; | 2000 |
| MITERATIONS = | number of iterations for the EM algorithm; | 500 |
| MCITERATIONS = | number of iterations for the M step of the EM algorithm for categorical latent variables; | 1 |
| MUITERATIONS = | number of iterations for the M step of the EM algorithm for censored, categorical, and count outcomes; | 1 |
| RITERATIONS = | maximum number of iterations in the rotation algorithm for exploratory factor analysis; | 10000 |
| AITERATIONS = | maximum number of iterations in the | 5000 |
| alignment optimization; | ||
| CONVERGENCE = | convergence criterion for the Quasi-Newton algorithm for continuous outcomes; | depends on
analysis type |
| H1CONVERGENCE = | convergence criterion for unrestricted model with missing data; | .0001 |
| LOGCRITERION = | likelihood convergence criterion for the EM algorithm; | depends on
analysis type |
| RLOGCRITERION = | relative likelihood convergence criterion for the EM algorithm; | depends on
analysis type |
| MCONVERGENCE = | convergence criterion for the EM algorithm; | depends on
analysis type |
| MCCONVERGENCE = | convergence criterion for the M step of the EM algorithm for categorical latent variables; | .000001 |
| MUCONVERGENCE = | convergence criterion for the M step of the EM algorithm for censored, categorical, and count outcomes; | .000001 |
| RCONVERGENCE = | convergence criterion for the rotation algorithm for exploratory factor analysis; | .00001 |
| ACONVERGENCE = | convergence criterion for the derivatives of
the alignment optimization;. |
.001 |
| MIXC = | ITERATIONS; | ITERATIONS |
| CONVERGENCE; | ||
| M step iteration termination based on number of iterations or convergence for categorical latent variables; | ||
| MIXU = | ITERATIONS; | ITERATIONS |
| CONVERGENCE; | ||
| M step iteration termination based on number of iterations or convergence for censored, categorical, and count outcomes; | ||
| LOGHIGH = | max value for logit thresholds; | +15 |
| LOGLOW = | min value for logit thresholds; | - 15 |
| UCELLSIZE = | minimum expected cell size; | .01 |
| VARIANCE = | minimum variance value; | .0001 |
| SIMPLICITY = | SQRT; | SQRT |
| FOURTHRT; | ||
| TOLERANCE = | simplicity tolerance value; | .0001 |
| METRIC= | REFGROUP; | REFGROUP |
| PRODUCT; | ||
| MATRIX = | COVARIANCE; | COVARIANCE |
| CORRELATION; | ||
| POINT = | MEDIAN;
MEAN; MODE; |
MEDIAN |
| CHAINS = | number of MCMC chains; | 2 |
| BSEED = | seed for MCMC random number generation; | 0 |
| STVALUES = | UNPERTURBED;
PERTURBED; ML; |
UNPERTURBED |
| MEDIATOR = | LATENT;
OBSERVED; |
depends on
analysis type |
| ALGORITHM = | GIBBS;
GIBBS (PX1); GIBBS (PX2); GIBBS (PX3); GIBBS (RW); MH; |
GIBBS (PX1) |
| BCONVERGENCE = | MCMC convergence criterion using Gelman-Rubin PSR; | .05 |
| BITERATIONS = | maximum and minimum number of iterations for each MCMC chain when Gelman-Rubin PSR is used; | 50000 0 |
| FBITERATIONS = | fixed number of iterations for each MCMC chain when Gelman-Rubin PSR is not used; | |
| THIN = | k where every k-th MCMC iteration is saved; | 1 |
| MDITERATIONS = | maximum number of iterations used to compute the Bayes multivariate mode; | 10000 |
| KOLMOGOROV = | number of draws from the MCMC chains; | 100 |
| PRIOR = | number of draws from the prior distribution; | 1000 |
| INTERACTIVE = | file name; | |
| PROCESSORS = | # of processors # of threads; | 1 1 |
