Output Data Set
The PRINQUAL procedure produces an output data set by default.
By specifying the OUT=, APPROXIMATIONS, SCORES, REPLACE, and CORRELATIONS
options in the PROC PRINQUAL statement, you can name this
data set and control, to some extent, the contents of it.
Structure and Content
The output data set can have 16 different forms,
depending on the specified combinations of the REPLACE,
SCORES, APPROXIMATIONS, and CORRELATIONS options.
You can specify any combination of these options.
To illustrate, assume that the data matrix consists of N
observations and m variables, and n components are computed.
Then, define the following:
- D
- the N ×m matrix of original data with variable names that
correspond to the names of the variables in the input data set.
However, when you use the OPSCORE transformation on character variables,
those variables are replaced by numeric variables that contain category numbers
- T
- the N ×m matrix of transformed data with variable names
constructed from the value of the TPREFIX= option (if you do not
specify the REPLACE option) and the names of the variables in
the input data set
- S
- the N ×n matrix of component scores with variable names
constructed from the value of the PREFIX= option and integers
- A
- the N ×m matrix of data approximations with
variable names constructed from the value of the APREFIX=
option and the names of the variables in the input data set
- RTD
- the m ×m matrix of correlations between the transformed
variables and the original variables with variable names that
correspond to the names of the variables in the input data set.
When missing values exist, casewise deletion
is used to compute the correlations.
- RTT
- the m ×m matrix of correlations among the transformed
variables with the variable names constructed from the
value of the TPREFIX= option (if you do not specify the REPLACE
option) and the names of the variables in the input data set
- RTS
- the m ×n matrix of correlations between the
transformed variables and the principal component scores
(component structure matrix) with variable names constructed
from the value of the PREFIX= option and integers
- RTA
- the m ×m matrix of correlations between the
transformed variables and the variable approximations with
variable names constructed from the value of the APREFIX=
option and the names of the variables in the input data set
To create a data set WORK.A that contains all information,
specify the following options in the PROC PRINQUAL statement
proc prinqual scores approximations correlations out=a;
and also use a TRANSFORM statement appropriate for your data.
Then the WORK.A data set contains
To eliminate the bottom partitions that contain the correlations
and component structure, do not specify the CORRELATIONS option.
For example, use the following PROC PRINQUAL
statement with an appropriate TRANSFORM statement.
proc prinqual scores approximations out=a;
Then the WORK.A data set contains
D T S A
If you use the following PROC PRINQUAL statement
(with an appropriate TRANSFORM statement)
proc prinqual out=a;
this creates a data set WORK.A of the form
D T
To output transformed data and component scores only,
specify the following options in the PROC PRINQUAL statement:
proc prinqual replace scores out=a;
Then the WORK.A data set contains
T S
_TYPE_ and _NAME_ Variables
In addition to the preceding information, the output data set
contains two character variables, the variable _TYPE_ (length
8) and the variable _NAME_ (length 32).
The _TYPE_ variable has the value 'SCORE' if the observation contains
variables, transformed variables, components, or data
approximations; the _TYPE_ variable has the value 'CORR' if the
observation contains correlations or component structure.
By default, the _NAME_ variable has values 'ROW1',
'ROW2', and so on,
for the observations with _TYPE_='SCORE'.
If you use an ID statement, the variable _NAME_ contains
the formatted ID variable for SCORES observations.
The values of the variable _NAME_ for
observations with _TYPE_='CORR'
are the names of the transformed variables.
Certain procedures, such as PROC PRINCOMP, which can use
the PROC PRINQUAL output data set, issue a warning
that the PROC PRINQUAL data set contains _NAME_
and _TYPE_ variables but is not a TYPE=CORR data set.
You can ignore this warning.
Variable Names
The TPREFIX=, APREFIX=, and PREFIX= options specify prefixes
for the transformed and approximation variable names
and for principal component score variables, respectively.
PROC PRINQUAL constructs transformed and
approximation variable names from a prefix and the
first characters of the original variable name.
The number of characters in the prefix plus the number
of characters in the original variable name (including
the final digits, if any) required to uniquely
designate the new variables should not exceed 32.
For example, if the APREFIX= parameter that you specify is one
character, PROC PRINQUAL adds the first 31 characters of the
original variable name; if your prefix is four characters, only
the first 28 characters of the original variable name are added.
Effect of the TSTANDARD= and COVARIANCE Options
The values in the output data set are affected
by the TSTANDARD= and COVARIANCE options.
If you specify TSTANDARD=NOMISS, the NOMISS
standardization is
performed on the transformed data after the iterations have
been completed, but before the output data set is created.
The new means and variances are used
in creating the output data set.
Then, if you do not specify the COVARIANCE option, the data
are transformed to mean zero and variance one.
The principal component scores and data
approximations are computed from the resulting matrix.
The data are then linearly transformed to have the
mean and variance specified by the TSTANDARD= option.
The data approximations are transformed so that
the means within each pair of a transformed
variable and its approximation are the same.
The ratio of the variance of a variable approximation to
the variance of the corresponding transformed variable
equals the proportion of the variance of the variable
that is accounted for by the components model.
If you specify the COVARIANCE option and do not specify
TSTANDARD=Z, you can input the transformed data to
PROC PRINCOMP, again specifying the COVARIANCE option, to perform
a components analysis of the results of PROC PRINQUAL.
Similarly, if you do not specify the COVARIANCE option with
PROC PRINQUAL and you input the transformed data to PROC
PRINCOMP without the COVARIANCE option, you receive the same report.
However, some combinations of PROC PRINQUAL options, such as
COVARIANCE and TSTANDARD=Z, while valid, produce approximations
and scores that cannot be reproduced by PROC PRINCOMP.
The component scores in the output data set are computed
from the correlations among the transformed variables,
or from the covariances if you specified the COVARIANCE option.
The component scores are computed after the
TSTANDARD=NOMISS transformation, if specified.
The means of the component scores in
the output data set are always zero.
The variances equal the corresponding eigenvalues, unless
you specify the STANDARD option; then the variances are set to one.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
