heywood=arg (default=“stop”)

Method for handling Heywood cases (negative uniqueness estimates): “stop” (stop and report final results), “last” (stop and report previous iteration results”, “reset” (set negative uniquenesses to zero and continue), “ignore” (ignore and continue).

maxit=integer

Maximum number of iterations.

conv=scalar

Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled estimates. The criterion will be set to the nearest value between 1e-24 and 0.2.

showopts / ‑showopts

[Do / do not] display the starting coefficient values and estimation options in the rotation output.

prompt

Force the dialog to appear from within a program.

p

Print basic estimation results.

n=arg or fsmethod=arg (default=“map”)

Number of factors: “kaiser” (Kaiser-Guttman greater than mean), “mineigen” (Minimum eigenvalue criterion; specified using “eiglimit”), “varfrac” (fraction of variance accounted for; specified using “varlimit”), “map” (Velicer’s Minimum Average Partial method), “bstick” (comparison with broken stick distribution), “parallel” (parallel analysis: number of replications specified using “pnreps”; “pquant” indicates the quantile method value if employed), “scree” (standard error scree method), “bn” (Bai and Ng (2002)), “ah” (Ahn and Horenstein (2013)), integer (user-specified integer value).

eiglimit=number (default=1)

Limit value for retaining factors using the eigenvalue comparison (where “n=mineigen”).

varlimit=number (default=0.5)

Fraction of total variance explained limit for retaining factors using the variance limit criterion (where “n=varlimit”).

porig

Use the unreduced matrix for parallel analysis (the default is to use the reduced matrix).

For parallel analysis only (“n=parallel”).

preps= integer (default=100)

Number of parallel analysis repetitions.

For parallel analysis only (“n=parallel”).

pquant=number

Quantile value for parallel analysis comparison (if not specified, the mean value will be employed).

For parallel analysis only (“n=parallel”).

pseed=positive integer

Seed the random number generator for parallel analysis.

If not specified, EViews will seed the random number generator with a single integer draw from the default global random number generator.

For parallel analysis only (“n=parallel”).

prnd=arg (default=“kn” or method previously set using rndseed)

Type of random number generator for the simulation: improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”) L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”).

For parallel analysis only (“n=parallel”).

mfmethod=arg (default=“user”)

(1) For use with all components retention methods apart from user-specified (“fsmethod=user”).

(2) If setting “mfmethod=user”, you may specify the maximum number of components using “rmax=”.

(3) Schwert’s rule sets the maximum number of components using the rule: let

rmax=arg (default=all)

User-specified maximum number of factors to retain (for use when “mfmethod=user”).

fsic=arg (default=avg)

Factor selection criterion (when “fsmethod=bn”): “icp1” (ICP1), “icp2” (ICP2), “icp3” (ICP3), “pcp1” (PCP1), “pcp2” (PCP1), “pcp3” (ICP3), “avg” (average of all criteria ICP1 through PCP3).

Factor selection criterion (when “fsmethod=ah”): “er” (eigenvalue ratio), “gr” (growth ratio), “avg” (average of eigenvalue ratio and growth ratio).

Factor selection criterion (when “fsmethod=simple”): “min” (minimum of: minimum eigenvalue, cumulative eigenvalue proportion, and maximum number of factors), “max” (maximum of: minimum eigenvalue, cumulative eigenvalue proportion, and maximum number of factors), “avg” (average the optimal number of factors as specified by the min and max rule, then round to the nearest integer).

demeantime

Demeans observations across time prior to component selection procedures, when “n=bn” or “n=ah”.

sdizetime

Standardizes observations across time prior to component selection procedures, when “n=bn” or “n=ah”.

demeancross

Demeans observations across cross-sections prior to component selection procedures, when “n=bn” or “n=ah”.

sdizecross

Standardizes observations across cross-sections prior to component selection procedures, when “n=bn” or “n=ah”.

priors=arg

Method for obtaining initial communalities: “smc” (squared multiple correlations), “max” (maximum absolute correlation”), “pace” (noniterative partitioned covariance estimation), “frac” (fraction of the diagonals of the original matrix; specified using “priorfrac=”), “random” (random fractions of the original diagonals), “user” (user-specified vector; specified using “priorunique”).

priorfrac=number

User-specified common fraction (between 0 and 1) to be used when “priors=frac”.

priorunique=arg

Vector of initial uniqueness estimates to be used when “priors=user”. By default, the values will be taken from the corresponding elements of the coefficient vector C.

cov=arg (default=“cov”)

Covariance calculation method: ordinary (Pearson product moment) covariance (“cov”), ordinary correlation (“corr”), Spearman rank covariance (“rcov”), Spearman rank correlation (“rcorr”), Kendall’s tau-b (“taub”), Kendall’s tau-a (“taua”), uncentered ordinary covariance (“ucov”), uncentered ordinary correlation (“ucorr”).

User-specified covariances are indicated by specifying a sym matrix object in place of a list of series or groups in the command.

wgt=name (optional)

Name of series containing weights.

wgtmethod=arg (default = “sstdev”)

Weighting method (when weights are specified using “weight=”): frequency (“freq”), inverse of variances (“var”), inverse of standard deviation (“stdev”), scaled inverse of variances (“svar”), scaled inverse of standard deviations (“sstdev”).

Only applicable for ordinary (Pearson) calculations. Weights specified by “wgt=” are frequency weights for rank correlation and Kendall’s tau calculations.

pairwise

Compute using pairwise deletion of observations with missing cases (pairwise samples).

df

Compute covariances with a degree-of-freedom correction for the mean (for centered specifications), and any partial conditioning variables.