I second Dennis' question. While indeed "MINITAB recognizes the missing
values", what it does with them depends on the procedure being used:
e.g., for CORRelation it uses all cases for which each pair of variables
is complete ("pairwise deletion of missing data"), and therefore, for a
data set like yours, the numbers of cases (as well as the particular set
of cases) used for each correlation coefficient are possibly different;
whereas for REGRession, where any of the variables named on the REGRession
command is missing, the case is deleted ("listwise deletion"). Whether it
is even useful to construct a subset of the data for which all variables
are non-missing depends on how badly infected the variables are with
missing data, and on whether the missing data occur in (useful?) patterns.
If you have about 10% missing in each column, unsystematically spread
through the set of columns, you could end up with a subset containing zero
cases.
To answer your question however, on the (possibly unjustified)
assumption that it's a useful thing to do:
COPY c1-c35 to c41-c75; # Always retain the original data
OMIT c1 = '*';
OMIT c2 = '*';
. . . ;
OMIT c35 = '*'.
There is probably a limit on the number of subcommands that MINITAB
can handle (or on the number of OMIT subcommands that COPY can handle),
but I don't know offhand what it is. (It is also imaginable that the
OMIT subcommand permits naming more than one column, which would greatly
simplify things, but I am inclined to suspect not.) If 35 subcommands
are too many, proceed in batches of, say, 10 (or whatever):
copy c1-c35 to c41-c75, omitting '*" in c1-c10;
then copy c41-c75 to c81-c115, omitting '*' in c51-c60;
then copy c81-c115 back to c41-c75, omitting '*' in c101-c110;
then copy c41-c75 to c81-c115, omitting '*' in c71-c75.
Finally, to check that no missing values have been retained, count the
number of missing values in that set of columns:
NMISS c81
NMISS c82
. . .
NMISS c115
To avoid having to inspect the result for each column, store the NMISSes
in 35 constants:
NMISS c81 k1
NMISS c82 k2
. . .
NMISS c115 k35
copy them into an unused column somewhere (e.g., c116):
COPY k1-k35 c116
and verify that they're all zero by
SSQ c116
which will return "0" iff all values in the colunmn are 0.
An easier way of verifying that there are no missng values in c81-c115
is to call for the INFO window (or give the INFO command:
INFO c81-c115 )
which will report, inter alia, the number of missing values in each
column. (I prefer the command in this situation, to avoid being
confused by information about columns not relevant to the question.)
On Fri, 28 Sep 2001, John Spitzer wrote:
> I have a dataset which has about 35 column. Many of the cells have
> missing values. Since MINITAB recognizes the missing values, I can
> perform the statistical work I need to do and don't need to worry
> about the missing values.
Perhaps you "don't need to", but you probably should.
> However, I would like to be able to obtain the subset of observations
> which MINITAB used for its calculations.
As remarked above, this subset may vary from one pair of columns
to another, or from one list of columns to another, depending on the
calculations being performed. Yes, you definitely should worry about
the missing values.
> I would like to be able to create a worksheet with only the rows from
> my dataset which do NOT contain any missing values.
Which may or may not correspond to any particular subset of the
data that MINITAB defined for its work.
< snip, hypothetical example >
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Donald F. Burrill [EMAIL PROTECTED]
184 Nashua Road, Bedford, NH 03110 603-471-7128
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