lm.mids

a formula object, with the response on the left of a ~ operator, and the 
    terms, separated by + operators, on the right. See the documentation
    of <code><a href="/link/lm?package=mice&version=2.14" rd-options="" data-mini-rdoc="mice::lm">lm</a></code> and <code><a href="/link/formula?package=mice&version=2.14" rd-options="" data-mini-rdoc="mice::formula">formula</a><</code>

formula

An object of type 'mids', which stands for 'multiply imputed data set', typically
    created by a call to function <code>mice()</code>.

data

Additional parameters passed to <code><a href="/link/lm?package=mice&version=2.14" rd-options="" data-mini-rdoc="mice::lm">lm</a></code>

Applies <code>lm()</code> to  multiply imputed data set

multivariate

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.

lm.mids: Linear Regression on Multiply Imputed Data

Description

Usage

Arguments

Value

Details

References

See Also

Examples