"mids", which stands
for 'multiply imputed data set'. Multivariate missing data
are imputed under Fully Conditional Specification (FCS).mice(data, m = 5,
imputationMethod = vector("character",length=ncol(data)),
predictorMatrix = (1 - diag(1, ncol(data))),
visitSequence = (1:ncol(data))[apply(is.na(data),2,any)],
defaultImputationMethod=c("pmm","logreg","polyreg"),
maxit = 5,
diagnostics = TRUE,
printFlag = TRUE,
seed = NA)NA.ncol(data),
specifying the elementary imputation method to be used
for each column in data. If specified as a single
string, the same method will be used foncol(data) containing 0/1 data specifying
the set of predictors to be used for each target column. Rows correspond
to target variables (i.e. variables to be imputed), in the sequence as
they appear in data. A value TRUE, diagnostic
information will be appended to the value of the function. If
FALSE, only the imputed data are saved. The default is TRUE.set.seed()
for offsetting the random number generator. Default is to leave the random number generator alone."mids" (multiply imputed data set) with componentsncol(data) containing the number of missing observations
per columnncol(data) components with the generated multiple imputations.
Each part of the list is a nmis[j] by m matrix of imputed values for
variable data[,j]. The component equals NULL for columns without
missing data.ncol(data) specifying the elementary
imputation method per columnncol(data) containing 0/1 data specifying
the predictor setlength(visitSequence)
by maxit matrix containing the mean of the generated multiple
imputations. The array can be used for monitoring convergence.
Note that observed data are not present in this mean.chainMean, containing the covariances
of the imputed values.pad.
Built-in elementary imputation methods are:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
These corresponding functions are coded in the mice library under
names
paste("mice.impute.",method,sep=""), where method
is a string with the name of the elementary imputation method name,
for example method="norm". For the j'th column, the mice
function constructs the function
name
paste("mice.impute.",imputationMethod[j],sep="") and calls the
function with this name that occurs on top of the search path.
The mechanism allows uses to write customized imputation function,
mice.impute.myfunc. To call it for all columns specify
imputationMethod="myfunc".
To call it only for, say, column 2 specify
imputationMethod=c("norm","myfunc","logreg",...).
Passive imputation:
mice supports a special built-in method, called passive imputation. This
method can be used to ensure that a data transform always depends on the
most recently generated imputations.
In some cases, an imputation model may need transformed data in addition
to the original data (e.g. log, quadratic, recodes, interaction, sum scores,
and so on).
Passive imputation maintains consistency among different transformations of the same data.
Passive imputation is invoked if ~ is specified as the first
character of the string that specifies the elementary method.
mice interprets the entire string, including the ~ character,
as the formula argument
in a call to model.frame(formula, data[!r[,j],]). This provides a simple
mechanism for specifying determinstic dependencies among the
columns. For example, suppose that the missing entries in
variables data$height and data$weight are imputed. The
body mass index (BMI) can be calculated within mice by
specifying the string "~I(weight/height^2)" as the elementary
imputation method for the target column data$bmi.
Note that the ~ mechanism works only on those entries which have
missing values in the target column. You should make sure that the
combined observed and imputed parts of the target column make sense. An
easy way to create consistency is by coding all entries in the target as
NA, but for large data sets, this could be inefficient.
Note that you may also need to adapt the default predictorMatrix to
evade linear dependencies among the predictors that could cause errors
like Error in solve.default() or Error: system is exactly singular.
Though not strictly needed, it is often
useful to specify visitSequence such that the column that is imputed by
the ~ mechanism is visited each time after one of its predictors was
visited. In that way, deterministic relation between columns will always
be synchronized.
Side effects:
Some elementary imputation method require access to the nnet or MASS
libraries of Venables & Ripley. Where needed, these libraries will be
attached.complete, mids, lm.mids, set.seeddata(nhanes)
# do default multiple imputation on a numeric matrix
imp <- mice(nhanes)
imp
# list the actual imputations for BMI
imp$imputations$bmi
# first completed data matrix
complete(imp)
# repeated linear regression on imputed data
lm.mids(chl~age+bmi+hyp, imp)
# imputation on mixed data with a different method per column
data(nhanes2)
mice(nhanes2,im=c("sample","pmm","logreg","norm"))Run the code above in your browser using DataLab