plot.mids

A formula that specifies which variables, stream and iterations are plotted.
If omitted, all streams, variables and iterations are plotted.

The trellis theme to applied to the graphs. The default is <code>mice.theme()</code>.

theme

A vector of length 2 given the number of columns and rows in the plot.
The default is <code>c(2, 3)</code>.

layout

Parameter <code>type</code> of <code><a rd-options="" href="/link/panel.xyplot?package=mice&version=2.25" data-mini-rdoc="mice::panel.xyplot">panel.xyplot</a></code>.

type

Parameter <code>col</code> of <code><a rd-options="" href="/link/panel.xyplot?package=mice&version=2.25" data-mini-rdoc="mice::panel.xyplot">panel.xyplot</a></code>.

Parameter <code>lty</code> of <code><a rd-options="" href="/link/panel.xyplot?package=mice&version=2.25" data-mini-rdoc="mice::panel.xyplot">panel.xyplot</a></code>.

Extra arguments for <code><a rd-options="" href="/link/xyplot?package=mice&version=2.25" data-mini-rdoc="mice::xyplot">xyplot</a></code>.


Trace line plots, also called stream plots or history plots, portray the value of an estimate
against the iteration number. The estimate can be anything that you can calculate, but
typically are chosen as parameter of scientific interest. The <code>plot</code> method for
a <code>mids</code> object plots the mean and standard deviation of the imputed (not observed)
values against the iteration number for each of the $m$ replications. By default,
the function plot the development of the mean and standard deviation for each incomplete
variable. On convergence, the streams should intermingle and be free of any trend.


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.

plot.mids: Plot the trace lines of the MICE algorithm

Description

Usage

Arguments

Value

See Also