Stata 15 help for missing

Title

[U] 12.2.1 Missing values

Description

Stata has 27 numeric missing values:

., the default, which is called the "system missing value" or sysmiss

and

.a, .b, .c, ..., .z, which are called the "extended missing values".

Numeric missing values are represented by large positive values. The ordering is

all nonmissing numbers < . < .a < .b < ... < .z

Thus, the expression age > 60 is true if variable age is greater than 60 or missing.

To exclude missing values, ask whether the value is less than ".". For instance,

. list if age > 60 & age < .

To specify missing values, ask whether the value is greater than or equal to ".". For instance,

. list if age >=.

Stata has one string missing value, which is denoted by "" (blank).

Remarks

More details concerning missing values and their treatment in Stata are provided under the following headings:

Overview Expressions Operators Functions Matrices Useful commands Value labels Estimation commands Technical note: checking if a value is missing

Overview

1. Stata supports different types of numeric missing values that can be used to specify different reasons that a value is unknown. The most frequently used missing value ., referred to as sysmiss, is nearly always generated by Stata when it cannot assign a specific value. The 26 extended missing values .a, .b, ..., .z are available to users requiring more elaborate tracking of missing values.

Empty strings are treated as missing values of type string.

2. Numeric missing values are represented by large positive values. This means that an expression such as income > 100 evaluates to true for missing values of the variable income, as well as to those that are greater than 100. Also, the simple expression if varname evaluates to true for all nonzero values of varname, including missing values.

3. The ordering of missing values is

all nonmissing numbers < . < .a < .b < ... < .z

4. Most Stata statistical commands deal with missing values by disregarding observations with one or more missing values (called "listwise deletion" or "complete cases only").

Expressions

Expressions occur in many places in Stata (see [P] syntax and exp). For example,

. generate newvarname = exp

evaluates the expression exp for each observation of the variable newvarname. Observations of newvarname are set to missing if exp evaluates to missing.

Expressions are also used to restrict a command's operation to a subset of the observations. For instance,

. summarize varname if exp

summarizes varname by using all observations for which exp evaluates to true (not zero), including observations that are missing.

Operators

The relational operators (see operators) interpret missing values as large positive numbers (see above). All the following thus evaluate to true

73 < . . == . .a == .a .a != . .a < .b .a <= .b

whereas all the following evaluate to false

73 >= . . == .a . > .a

The numerical operators (+ etc) return missing if any of their arguments are missing.

Functions

Stata has a few special functions for dealing with missing values:

missing() returns 1 (meaning true) if any of its arguments, numeric or string, evaluates to missing and 0 (meaning false) otherwise.

mi() is a shorthand for missing().

matmissing(K) returns 1 (meaning true) if any elements of the matrix K are missing and 0 (meaning false) otherwise.

Some Stata functions interpret . in a special way. For instance, the function inrange(x,a,b) returns 1 if x belongs in the interval [a,b]. This function interprets a==. as -infinity and b==. as +infinity. These special interpretations are discussed in functions.

Other Stata functions return missing (.) if one or more of the arguments are missing or invalid.

Matrices

Matrices may contain all types of missing values. The matrix operators (see matrix operators)

- negate ' transpose

\ row join , column join + add - subtract * multiply (including multiply by scalar) / division by scalar # Kronecker product

generate missing values elementwise.

In the matrix product C=A*B, C[i,j] is missing if row i of A or column j of B contain a missing value.

Matrix division by scalar C=A/b is not allowed if the scalar b is a missing value. Otherwise, missing values in matrix A generate missing values in C elementwise.

Like the list command, the matrix list command has a nodotz option to display extended missing value .z as a blank string rather than as ".z".

Useful commands

------------------------------------------------------------------------- mvencode changes missing values into numeric values mvdecode changes numeric values into missing values codebook provides extensive information about variables, including the occurrence of simple and extended missing values misstable tabulates missing values egen, rownonmiss() number of valid observations in a varlist egen, rowmiss() number of missing values in a varlist recode recodes a variable, optionally into a new variable, with special facilities to recode missing values. mi multiple imputation of missing values xtdescribe describes participation patterns in panel data -------------------------------------------------------------------------

Value labels

It is possible to define value labels for the extended missing values .a to .z, but not for sysmiss .. These value labels show up in the same way as value labels for nonmissing values. See [D] label.

Estimation commands

Most Stata commands ignore observations that are missing in one or more of the variables referred to in the command. For instance, the regression command regress disregards all observations that have a missing value for the dependent variable or missing values for any of the independent variables. This method is known as "listwise deletion", "complete cases only", etc. It is statistically appropriate only if the missing values are "at random". In an if or weight expression to a command, the expressions will be evaluated, and the missing values will be processed using the operators and function() logic.

Stata commands that can treat multiple observations as being related to one observational unit (for example, observations from a panel in xt models, episodes in st models) ignore specific observations from the "group", namely, those that have missing values.

Technical note: checking if a value is missing

You might think you can test whether an expression or variable exp is missing with the expression exp==.. Remember, however, that Stata has 27 different missing values (., a, b, ..., z).

exp==. means that the expression exp equals a specific missing value, namely, sysmiss .. exp==. returns false if exp equals one of the extended missing-value types such as .a or .z. To test whether exp is missing, that is, equals either . or one of the extended missing values, one should use the expression

exp >= . or missing(exp)

which can be abbreviated to

mi(exp)

To test whether exp is missing, use one of the following forms:

exp < . !missing(exp) !mi(exp)

An advantage of the last two forms is that the missing functions missing() and mi() allow multiple (numeric or string) arguments to test whether any of the argument is missing.


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