What is analysis weight in multiple imputation?
Analysis Weight. Analysis (regression or sampling) weights are incorporated in summaries of missing values and in fitting imputation models. Cases with a negative or zero analysis weight are excluded.
How does regression imputation work?
With regression imputation the information of other variables is used to predict the missing values in a variable by using a regression model. Commonly, first the regression model is estimated in the observed data and subsequently using the regression weights the missing values are predicted and replaced.
How are missing values replaced in multiple imputation?
In multiple imputation each missing value is replaced (imputed) multiple times through a specified algorithm, that uses the observed data of every unit to find a plausible value for the missing cell. Every time a missing value is replaced through an estimated value, some uncertainty/randomness is introduced.
When to use an imputation in a data set?
So imputation is useful if we know (or suspect) that the data are MNAR. Under MAR, there are generally no benefits to impute the outcome, and for a low number of imputations the results may even be somewhat more variable because of simulation error. There is an important exception to this.
Which is an example of multiple imputation in R?
A typical example would be, that people refuse to answer questions on their income if it exceeds a certain amount. Or if you ask for the number of sex partners a person had and people with high numbers don´t answer it. In this case your data is not missing at random.
Why is listwise deletion problematic in multiple imputation?
The problem is, that if you run a correlation of variable a and variable b, and a correlation of variable a and variable c, your results can be based on a different amount of cases (N). Listwise and pairwise deletion are problematic in multiple ways: both reduce your samplesize and your statistical power decreases.