READ PAD MISSING COLUMNS (SET)
Name:
READ PAD MISSING COLUMNS (SET)
Type:
Purpose:
If the number of variables specified on a READ command is
greater than the number of values read from a data record,
specify whether these extra values will be set to a
specified "missing value" or this data record will be
considered an error.
Description:
By default, Dataplot considers a data record that returns
fewer values than the number of variables specified on the
READ command an error. This results in not only that
particular data record being in error, but the entire READ
is invalid.
In some cases, it may be more convenient to assign a
missing value to any variables that did not have a
corresponding data item (e.g., if 8 variables were specified
on the READ command and 5 items were detected on the data
record, the data values would assigned to the first five
variables on the READ and a missing value would be assigned
to the remaining three variables.
The default missing value is zero. You can specify the
value to use for missing values with the command
SET READ MISSING VALUE <value>
This command is intended for the case when you expect
data columns of equal length and you simply want to specify
whether detecting an unequal column length will be treated
as an error or not. If you know your data columns are of
unequal length, you can either use a comma delimited file
or use the variable form of the COLUMN LIMITS command. This
is described in the Note: section below. In these cases,
Dataplot will automatically handle missing data regardless
of what the SET READ PAD MISSING COLUMNS specifies.
Syntax:
SET READ PAD MISSING COLUMNS <ON/OFF>
where ON specifies that missing columns will be padded with
a missing data value and OFF specifies that missing
columns will be treated as an error.
Examples:
SET READ PAD MISSING COLUMN ON
SET READ PAD MISSING COLUMN OFF
Note:
If you know that your data contains columns of unequal
length, there are several alternatives (in these cases,
the SET READ PAD MISSING COLUMN ON command does not need
to be entered).
- Pick some value to represent a missing value and fill
in missing data points with that value. After reading
the data, you can use a RETAIN command to remove them.
For example, if you use -99 to signify a missing value,
you can enter
Alternatively, you can use a SUBSET clause on subsequent
plot and analysis commands.
This is only a viable alternative if you have relatively
few missing values in your file.
- Use the variable form of the COLUMN LIMITS command. This
command allows you to specify the start and stop columns
of each data field. By default, when a blank field is
encountered, it is set to the missing value. This value
can be set with the command
SET READ MISSING VALUE <value>
This option depends on having consistent columns for
each of the data fields.
- If your data has both columns of unequal length and
inconsistent columns for given data fields, an alternative
is to use a comma delimited data file. That is, separate
data values with a comma. If there is no data data between
successive commas, this is treated as a missing value. The
default is to assign a value of zero. Alternatively, you
can use the SET READ MISSING VALUE command described above.
You can specify a delimiter other than a comma with the
command
SET READ DELIMITER <character>
In order to place data in the appropriate columns if we have
data columns of unequal length, we either need to have
consistent columns (we can use COLUMN LIMITS) or comma
separated (or some other non-space delimiter) data. Otherwise
Dataplot will not know how to assign the data values to the
correct variable and it will simply assign data values to
variables in the order the variables are found on the READ
command.
Default:
OFF (an insufficient number of data values on a data record
is treated as an error).
Synonyms:
Related Commands:
Applications:
Implementation Date:
Program:
SET READ MISSING VALUE -99
SET READ PAD MISSING COLUMN ON
READ DUMMY.DAT Y X
RETAIN Y X SUBSET Y > -99 SUBSET X > -99
PLOT Y X
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Date created: 12/05/2005
Last updated: 11/04/2015
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