Variables Missing Pattern
For a set of variables X_{1}
X_{2},…, X_{i}, this function is to
report on their joint missing pattern.
The information on joint missing pattern is helpful in building regression models. For example, to build a model as: Y=β_{0}+ β_{0}X_{1}+ β_{0}X_{2}+ β_{0}X_{3}+ β_{0}X_{4}+ β_{0}X_{5}
Observation
(record) with any missing of X_{1} X_{5} will be excluded. If one X (e.g. X_{5}) was removed from
the model, how many more observations will be save dependents on the missing
pattern.
A sample screen shot of input window:
Sample output and explanation:
Frequency of missing for each
variable
Variable 
#
Nonmissing 
#
Missing 
SBP 
795

37

DBP 
795

37

SNP1 
817

15

SNP2 
814

18

BMI 
795

37

Frequency of joint missing pattern for selected variables
SBP 
DBP

SNP1

SNP2

BMI

Frequency

0 
0

0

1

0

2

0 
0

1

1

0

35

1 
1

0

0

1

1

1 
1

0

1

1

12

1 
1

1

0

1

17

1 
1

1

1

1

765

Created on 7/10/2011 with Empower(R) (www.empowerstats.com)
utilizing R
The first table lists the number of missing for each
individual variable. The table 2 shows
the missing pattern and number of observations with such joint missing pattern.
The “0” represents the variable is missing, “1” represents the variable is nonmissing.
E.g. 35 records had SBP, DBP and BMI missing and SNP1, SNP2 nonmissing. 765
records had complete information (nonmissing) for all the 5 variables.