MVA
MVA
is available in the Missing Values Analysis
option.
MVA
(Missing Value Analysis) describes the missing value patterns in a data
file (data matrix). It can estimate the means, the covariance matrix, and the correlation
matrix by using listwise, pairwise, regression, and EM estimation methods. Missing values
themselves can be estimated (imputed), and you can then save the new data file.
MVA VARIABLES= {varlist}
{ALL }
[/CATEGORICAL=varlist]
[/MAXCAT={25**}]
{n }
[/ID=varname]
Description:
[/NOUNIVARIATE]
[/TTEST [PERCENT={5}] [{T }] [{DF } [{PROB }] [{COUNTS }] [{MEANS }]]
{n} {NOT} {NODF} {NOPROB}] {NOCOUNTS} {NOMEANS}
[/CROSSTAB [PERCENT={5}]]
{n}
[/MISMATCH [PERCENT={5}] [NOSORT]]
{n}
[/DPATTERN [SORT=varname[({ASCENDING })] [varname ... ]]
{DESCENDING}
[DESCRIBE=varlist]]
[/MPATTERN [NOSORT] [DESCRIBE=varlist]]
[/TPATTERN [NOSORT] [DESCRIBE=varlist] [PERCENT={1}]]
{n}
Estimation:
[/LISTWISE]
[/PAIRWISE]
[/EM [predicted_varlist] [WITH predictor_varlist]
[([TOLERANCE={0.001} ]
{value}
[CONVERGENCE={0.0001}]
{value }
[ITERATIONS={25} ]
{n }
[TDF=n ]
[LAMBDA=a ]
[PROPORTION=b ]
[OUTFILE='file' ])]
[/REGRESSION [predicted_varlist] [WITH predictor_varlist]
[([TOLERANCE={0.001} ]
{n }
[FLIMIT={4.0} ]
{N }
[NPREDICTORS=number_of_predictor_variables]
[ADDTYPE={RESIDUAL*} ]
{NORMAL }
{T[({5}) }
{n}
{NONE }
[OUTFILE='file' ])]]
*If the number of complete cases is less than half the number of
cases, the default ADDTYPE
specification is NORMAL
.
**Default if the subcommand is omitted.
This command reads the active dataset and causes execution of any pending commands. See the topic Command Order for more information.
Syntax for
the MVA
command can be generated from the Missing Value Analysis dialog.
Examples
MVA VARIABLES=populatn density urban religion lifeexpf region
/CATEGORICAL=region
/ID=country
/MPATTERN DESCRIBE=region religion.
MVA VARIABLES=all
/EM males msport WITH males msport gradrate facratio.