Extracts the completed data from a mids object
Takes an object of class mids, fills in the missing data, and returns
the completed data in a specified format.
## S3 method for class 'mids' complete(data, action = 1L, include = FALSE, mild = FALSE, ...)
data |
An object of class |
action |
A numeric vector or a keyword. Numeric
values between 1 and |
include |
A logical to indicate whether the original data with the missing values should be included. |
mild |
A logical indicating whether the return value should
always be an object of class |
... |
Additional arguments. Not used. |
The argument action can be length-1 character, which is
matched to one of the following keywords:
"all"produces a mild object of imputed data sets. When
include = TRUE, then the original data are appended as the first list
element;
"long" produces a data set where imputed data sets
are stacked vertically. The columns are added: 1) .imp, integer,
referring the imputation number, and 2) .id, character, the row
names of data$data;
"stacked" same as "long" but without the two
additional columns;
"broad"produces a data set with where imputed data sets are stacked horizontally. Columns are ordered as in the original data. The imputation number is appended to each column name;
"repeated" same as "broad", but with
columns in a different order.
Complete data set with missing values replaced by imputations.
A data.frame, or a list of data frames of class mild.
Technical note: mice 3.7.5 renamed the complete() function
to complete.mids() and exported it as an S3 method of the
generic tidyr::complete(). Name clashes between
mice::complete() and tidyr::complete() should no
longer occur.
# obtain first imputed data set sum(is.na(nhanes2)) imp <- mice(nhanes2, print = FALSE, maxit = 1) dat <- complete(imp) sum(is.na(dat)) # obtain stacked third and fifth imputation dat <- complete(imp, c(3, 5)) # obtain all datasets, with additional identifiers head(complete(imp, "long")) # same, but now as list, mild object dslist <- complete(imp, "all") length(dslist) # same, but also include the original data dslist <- complete(imp, "all", include = TRUE) length(dslist) # select original + 3 + 5, store as mild dslist <- complete(imp, c(0, 3, 5), mild = TRUE) names(dslist)
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