The OUTSTAT= data set is similar to the TYPE=CORR data set that the CORR procedure produces. The following table relates the TYPE= value for the OUTSTAT= data set to the options that are specified in the PROC HPPRINCOMP statement:
Note that the default (neither the COV nor NOINT option) produces a TYPE=CORR data set.
The new data set contains the following variables:
the BY variables, if any
two new variables, _TYPE_
and _NAME_
, both character variables
the variables that are analyzed (that is, those in the VAR statement); or, if there is no VAR statement, all numeric variables not listed in any other statement; or, if there is a PARTIAL statement, the residual variables as described in the section "OUT= Data Set"
Each observation in the new data set contains some type of statistic, as indicated by the _TYPE_
variable. The values of the _TYPE_
variable are as follows:
Contents
mean of each variable. If you specify the PARTIAL statement, this observation is omitted.
standard deviations. If you specify the COV option, this observation is omitted, so the SCORE procedure does not standardize the variables before computing scores. If you use the PARTIAL statement, the standard deviation of a variable is computed as its root mean squared error as predicted from the PARTIAL variables.
uncorrected standard deviations. When you specify the NOINT option in the PROC HPPRINCOMP statement, the OUTSTAT= data set contains standard deviations not corrected for the mean. However, if you also specify the COV option in the PROC HPPRINCOMP statement, this observation is omitted.
number of observations on which the analysis is based. This value is the same for each variable. If you specify the PARTIAL statement and the value of the VARDEF= option is DF or unspecified, then the number of observations is decremented by the degrees of freedom for the PARTIAL variables.
the sum of the weights of the observations. This value is the same for each variable. If you specify the PARTIAL statement
and VARDEF=WDF, then the sum of the weights is decremented by the degrees of freedom for the PARTIAL variables. This observation
is output only if the value is different from that in the observation for which _TYPE_
=‘N’.
correlations between each variable and the variable specified by the _NAME_
variable. The number of observations for which _TYPE_
=‘CORR’ is equal to the number of variables being analyzed. If you specify the COV option, no _TYPE_
=‘CORR’ observations are produced. If you use the PARTIAL statement, the partial correlations, not the raw correlations, are
output.
uncorrected correlation matrix. When you specify the NOINT option without the COV option in the PROC HPPRINCOMP statement, the OUTSTAT= data set contains a matrix of correlations not corrected for the means. However, if you also specify the COV option in the PROC HPPRINCOMP statement, this observation is omitted.
covariances between each variable and the variable specified by the _NAME_
variable. _TYPE_
=‘COV’ observations are produced only if you specify the COV option. If you use the PARTIAL statement, the partial covariances,
not the raw covariances, are output.
uncorrected covariance matrix. When you specify the NOINT and COV options in the PROC HPPRINCOMP statement, the OUTSTAT= data set contains a matrix of covariances not corrected for the means.
eigenvalues. If the N= option requests less than the maximum number of principal components, only the specified number of eigenvalues are produced, with missing values filling out the observation.
eigenvectors. The _NAME_
variable contains the name of the corresponding principal component as constructed from the PREFIX= option. The number of
observations for which _TYPE_
=‘SCORE’ equals the number of principal components computed. The eigenvectors have unit length unless you specify the STD
option, in which case the unit-length eigenvectors are divided by the square roots of the eigenvalues to produce scores that
have unit standard deviations.
To obtain the principal component scores, if the COV option is not specified, these coefficients should be multiplied by the
standardized data. For the COV option, these coefficients should be multiplied by the centered data. To center and standardize
the data, you should use means that are obtained from the observation for which _TYPE_
=’MEAN’ and standard deviations that are obtained from the observation for which _TYPE_
=’STD’.
scoring coefficients to be applied without subtracting the mean from the raw variables. Observations for which _TYPE_
=‘USCORE’ are produced when you specify the NOINT option in the PROC HPPRINCOMP statement.
To obtain the principal component scores, these coefficients should be multiplied by the data that are standardized by the
uncorrected standard deviations obtained from the observation for which _TYPE_
=’USTD’.
R squares for each VAR variable as predicted by the PARTIAL variables.
regression coefficients for each VAR variable as predicted by the PARTIAL variables. This observation is produced only if you specify the COV option.
standardized regression coefficients for each VAR variable as predicted by the PARTIAL variables. If you specify the COV option, this observation is omitted.
You can use the data set in the SCORE procedure to compute principal component scores, or you can use it as input to the FACTOR procedure and specify METHOD=SCORE to rotate the components. If you use the PARTIAL statement, the scoring coefficients should be applied to the residuals, not to the original variables.