Mplus ANALYSIS

15

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;
LOGLOW =
min value for logit thresholds;
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