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_pkgdown.yaml
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title: mice
url: http://stefvanbuuren.github.io/mice
template:
params:
bootswatch: flatly
docsearch:
api_key: '491b6c2ead7f15b8c26c513b923dd6a0'
index_name: 'mice'
reference:
- title: "Missing data exploration"
desc: >
Functions to count and explore the structure of the missing data.
contents:
- md.pattern
- md.pairs
- cc
- cci
- ic
- ici
- ncc
- nic
- nimp
- fico
- flux
- fluxplot
- title: "Main imputation functions"
desc: >
The main workflow functions will multiply-impute the data,
apply the complete-data model to each data set, and pool
the results to get to the final inference. There are two
main functions for imputing the data.
contents:
- mice
- mice.mids
- parlmice
- title: "Elementary imputation functions"
desc: >
Elementary imputation functions are work horses that create
the actual imputations. The funcions are called through the
`method` argument of `mice` function.
Each function imputes one or more columns in the data. There
are also `mice.impute.xxx` functions outside the `mice`
package.
contents:
- starts_with("mice.impute")
- title: "Imputation model helpers"
desc: >
Specification of the imputation models can be made more
convenient using the following set of helpers.
contents:
- quickpred
- squeeze
- make.blocks
- make.blots
- make.formulas
- make.method
- make.post
- make.predictorMatrix
- make.visitSequence
- make.where
- construct.blocks
- name.blocks
- name.formulas
- title: "Plots comparing observed to imputed/amputed data"
desc: >
These plots contrast the observed data with the
imputed/amputed data, usually with a blue/red distinction.
contents:
- bwplot.mids
- densityplot.mids
- plot.mids
- stripplot.mids
- xyplot.mids
- title: "Repeated analyses and combining analytic estimates"
desc: >
Multiple imputation create m > 1 completed data sets, fit
the model of interest to each of these, and combine the
analytic estimates to a final result. The following functions
assits in executing these steps:
contents:
- with.mids
- pool
- pool.r.squared
- pool.scalar
- nelsonaalen
- pool.compare
- anova.mira
- fix.coef
- D1
- D2
- D3
- title: "Data manipulation"
desc: >
The multiply-imputed data can be combined in various ways,
and exported into other formats.
contents:
- complete
- as.mids
- as.mira
- as.mitml.result
- cbind.mids
- rbind.mids
- ibind
- mids2mplus
- mids2spss
- title: "Class descriptions"
desc: >
The data created at the various analytic phases are stored
as list objects of a specific class. The most important classes
and class-test functions are:
contents:
- mids-class
- mira-class
- is.mids
- is.mipo
- is.mira
- is.mitml.result
- title: "Extraction functions"
desc: >
Helpers to extract and print information from objects of
specific classes.
contents:
- getfit
- getqbar
- print.mids
- print.mira
- print.mice.anova
- print.mice.anova.summary
- summary.mira
- summary.mids
- summary.mice.anova
- title: "Low-level imputation functions"
desc: >
Several functions are dedicated to common low-level operations
to generate the imputations:
contents:
- estimice
- norm.draw
- .norm.draw
- .pmm.match
- title: "Multivariate amputation"
desc: >
Amputation is the inverse of imputation, starting with a
complete dataset, and creating missing data pattern according
to the posited missing data mechanism. Amputation is
useful for simulation studies.
contents:
- ampute
- bwplot.mads
- xyplot.mads
- is.mads
- mads-class
- print.mads
- summary.mads
- title: "Datasets"
desc: "Built-in datasets"
contents:
- boys
- brandsma
- employee
- fdd
- fdgs
- leiden85
- mammalsleep
- nhanes
- nhanes2
- pattern
- popmis
- pops
- potthoffroy
- selfreport
- tbc
- walking
- windspeed
- title: "Miscellaneous functions"
desc: "Miscellaneous functions"
contents:
- appendbreak
- extractBS
- glm.mids
- lm.mids
- mdc
- mice.theme
- supports.transparent
- version
navbar:
type: inverse