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This module will explore missing data in Stata, focusing on numeric missing data. It will describe how to indicate missing data in your raw data files, as well as how missing data are handled in Stata logical commands and assignment statements.
The missing data patterns are listed in descending frequency, here the most common missing data pattern is complete data (“+++++”). The table also shows the number of missing values in that pattern (_mv), and the number of cases with that missing data pattern (_freq).
10 Ιουλ 2014 · 4. I'd add mdesc command to proposed solutions. According to description mdesc: Produces a table with the number of missing values, total number of cases, and percent missing for each variable in varlist. mdesc works with both numeric and character variables.
These functions return the indicated count of missing or nonmissing values. colmissing(X) returns the count of missing values of each column of X, rowmissing(X) returns the count of missing values of each row, and missing(X) returns the overall count.
This entry provides a quick reference for Stata’s missing values. Remarks and examples. stata.com. .a, .b, .c, : : : , .z, which are called the extended missing va. Numeric missing values are represented by large positive values. The ordering is. all nonmissing numbers < . < .a < .b < < .z. Thus the expression. age > 60.
8 Μαΐ 2019 · Lets look at some useful tips for handling missing values in Stata, as those can cause a lot of problems if not taken care of properly when working with any dataset. Identify: By using the missing() function to identify missing values in variables we count the number of missing values in a variable.
Stata provides 27 different missing values, namely, ., .a, .b, :::, .z. The first of those, ., is often called system missing. The remaining missing values are called extended missings. The nonmissing and missing values are ordered nonmissing < . < .a < .b < < .z. Thus reported in the column
