cc,data.frame-method

cc,matrix-method

cc,mids-method

An <code>R</code> object. Currently supported are methods for the
following classes: <code>mids</code>, <code>mira</code>, <code>mipo</code>, <code>data.frame</code>
and <code>matrix</code>. In addition, <code>x</code> can be a vector of any kind.

A logical flag for matrices and arrays. If <code>drop=TRUE</code> the
result is coerced to the lowest possible dimension.

drop


Extracting complete cases is also known as 'listwise deletion' or
'complete case analyses'. <code>cc(x)</code> is equivalent to
<code>na.omit(x)</code>. Missing values in <code>x</code> are coded as <code>NA</code>.
The companion function for selecting the incomplete cases is <code>ic()</code>.


univar

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.

cc: Complete cases

Description

Usage

Arguments

Value

See Also

Examples