Developed by Stef van Buuren, Karin Groothuis-Oudshoorn.
NEWS.md
model0 <- lm(Volume ~ Girth + Height, data = trees) formula(model0) #> Volume ~ Girth + Height -#> <environment: 0x564341528388> +#> <environment: 0x55dacd4d50e0> coef(model0) #> (Intercept) Girth Height #> -57.9876589 4.7081605 0.3392512 @@ -110,7 +110,7 @@ Examplesmodel1 <- fix.coef(model0) formula(model1) #> Volume ~ 1 -#> <environment: 0x564341528388> +#> <environment: 0x55dacd4d50e0> coef(model1) #> (Intercept) #> 1.17136e-14 @@ -157,11 +157,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/flux.html b/reference/flux.html index afe81006a..118b4a22b 100644 --- a/reference/flux.html +++ b/reference/flux.html @@ -1,7 +1,7 @@ Influx and outflux of multivariate missing data patterns — flux • miceInflux and outflux of multivariate missing data patterns — flux • miceFluxplot of the missing data pattern — fluxplot • miceFluxplot of the missing data pattern — fluxplot • miceWrapper function that runs MICE in parallel — futuremice • micemice - 3.16.10 + 3.16.11 @@ -205,11 +205,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/getfit.html b/reference/getfit.html index eefb5a44b..b05ef6115 100644 --- a/reference/getfit.html +++ b/reference/getfit.html @@ -3,7 +3,7 @@ results, or optionally, one of these fitted objects. The function looks for a list element called analyses, and return this component as a list with mira class. If element analyses is not found in x, then -it returns x as a mira object.">Extract list of fitted models — getfit • miceExtract list of fitted models — getfit • micemice - 3.16.10 + 3.16.11 @@ -128,11 +128,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/getqbar.html b/reference/getqbar.html index 9a07900f3..b5aaa8b08 100644 --- a/reference/getqbar.html +++ b/reference/getqbar.html @@ -1,5 +1,5 @@ -Extract estimate from mipo object — getqbar • miceExtract estimate from mipo object — getqbar • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -73,11 +73,11 @@ Arguments - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/glance.mipo.html b/reference/glance.mipo.html index ba18e5e9d..fb7373bfc 100644 --- a/reference/glance.mipo.html +++ b/reference/glance.mipo.html @@ -1,5 +1,5 @@ -Glance method to extract information from a `mipo` object — glance.mipo • miceGlance method to extract information from a `mipo` object — glance.mipo • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -89,11 +89,11 @@ Note diff --git a/reference/glm.mids.html b/reference/glm.mids.html index 7069af2aa..3ca6c7264 100644 --- a/reference/glm.mids.html +++ b/reference/glm.mids.html @@ -1,5 +1,5 @@ -Generalized linear model for mids object — glm.mids • miceGeneralized linear model for mids object — glm.mids • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -232,11 +232,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/ibind.html b/reference/ibind.html index 363fffd70..a51a0d3f1 100644 --- a/reference/ibind.html +++ b/reference/ibind.html @@ -3,7 +3,7 @@ single mids object, with the objective of increasing the number of imputed data sets. If the number of imputations in x and y are m(x) and m(y), then the combined object will have -m(x)+m(y) imputations.">Enlarge number of imputations by combining mids objects — ibind • miceEnlarge number of imputations by combining mids objects — ibind • micemice - 3.16.10 + 3.16.11 @@ -129,11 +129,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/ic.html b/reference/ic.html index 2f6c370d1..7d5f43df2 100644 --- a/reference/ic.html +++ b/reference/ic.html @@ -1,6 +1,6 @@ Select incomplete cases — ic • miceSelect incomplete cases — ic • miceIncomplete case indicator — ici • miceIncomplete case indicator — ici • miceConditional imputation helper — ifdo • miceConditional imputation helper — ifdo • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -87,11 +87,11 @@ Author< diff --git a/reference/index.html b/reference/index.html index d9d2c5e9b..cc4f9d2cb 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,5 +1,5 @@ -Function reference • miceFunction reference • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -53,7 +53,7 @@ Missing data exploration - Functions to count and explore the structure of the missing data. + Functions to count and explore the structure of the missing data. @@ -129,7 +129,7 @@ Missing data exploration Main imputation functions - The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are: + The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are: @@ -160,7 +160,7 @@ Main imputation functions Elementary imputation functions - The elementary imputation function is the workhorse that creates the actual imputations. Elementary functions 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. + The elementary imputation function is the workhorse that creates the actual imputations. Elementary functions 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. @@ -336,7 +336,7 @@ Elementary imputation functions Imputation model helpers - Specification of the imputation models can be made more convenient using the following set of helpers. + Specification of the imputation models can be made more convenient using the following set of helpers. @@ -412,7 +412,7 @@ Imputation model helpers Plots comparing observed to imputed/amputed data - These plots contrast the observed data with the imputed/amputed data, usually with a blue/red distinction. + These plots contrast the observed data with the imputed/amputed data, usually with a blue/red distinction. @@ -448,7 +448,7 @@ Plots comparing observ Repeated analyses and combining analytic estimates - Multiple imputation creates m > 1 completed data sets, fits the model of interest to each of these, and combines the analytic estimates. The following functions assist in executing the analysis and pooling steps: + Multiple imputation creates m > 1 completed data sets, fits the model of interest to each of these, and combines the analytic estimates. The following functions assist in executing the analysis and pooling steps: @@ -519,7 +519,7 @@ Repeated analyses an Data manipulation - The multiply-imputed data can be combined in various ways, and exported into other formats. + The multiply-imputed data can be combined in various ways, and exported into other formats. @@ -575,7 +575,7 @@ Data manipulation Class descriptions - 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: + 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: @@ -616,7 +616,7 @@ Class descriptions Extraction functions - Helpers to extract and print information from objects of specific classes. + Helpers to extract and print information from objects of specific classes. @@ -662,7 +662,7 @@ Extraction functions Low-level imputation functions - Several functions are dedicated to common low-level operations to generate the imputations: + Several functions are dedicated to common low-level operations to generate the imputations: @@ -688,7 +688,7 @@ Low-level imputation functions Multivariate amputation - 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. + 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. @@ -734,7 +734,7 @@ Multivariate amputation Datasets - Built-in datasets + Built-in datasets @@ -845,7 +845,7 @@ Datasets Miscellaneous functions - Miscellaneous functions + Miscellaneous functions @@ -904,11 +904,11 @@ Miscellaneous functions diff --git a/reference/is.mads.html b/reference/is.mads.html index d451fcab3..bd5bd7628 100644 --- a/reference/is.mads.html +++ b/reference/is.mads.html @@ -1,5 +1,5 @@ -Check for mads object — is.mads • miceCheck for mads object — is.mads • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -81,11 +81,11 @@ Value diff --git a/reference/is.mids.html b/reference/is.mids.html index dc911ed1d..e6d171158 100644 --- a/reference/is.mids.html +++ b/reference/is.mids.html @@ -1,5 +1,5 @@ -Check for mids object — is.mids • miceCheck for mids object — is.mids • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -81,11 +81,11 @@ Value diff --git a/reference/is.mipo.html b/reference/is.mipo.html index 3f598c498..e153e9b76 100644 --- a/reference/is.mipo.html +++ b/reference/is.mipo.html @@ -1,5 +1,5 @@ -Check for mipo object — is.mipo • miceCheck for mipo object — is.mipo • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -81,11 +81,11 @@ Value diff --git a/reference/is.mira.html b/reference/is.mira.html index 9f9ede6f2..e34b076a7 100644 --- a/reference/is.mira.html +++ b/reference/is.mira.html @@ -1,5 +1,5 @@ -Check for mira object — is.mira • miceCheck for mira object — is.mira • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -81,11 +81,11 @@ Value diff --git a/reference/is.mitml.result.html b/reference/is.mitml.result.html index f31af3d3a..a40ae4195 100644 --- a/reference/is.mitml.result.html +++ b/reference/is.mitml.result.html @@ -1,5 +1,5 @@ -Check for mitml.result object — is.mitml.result • miceCheck for mitml.result object — is.mitml.result • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -81,11 +81,11 @@ Value diff --git a/reference/leiden85.html b/reference/leiden85.html index 735d99e0c..6ebf8f336 100644 --- a/reference/leiden85.html +++ b/reference/leiden85.html @@ -1,5 +1,5 @@ -Leiden 85+ study — leiden85 • miceLeiden 85+ study — leiden85 • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -94,11 +94,11 @@ Details diff --git a/reference/lm.mids.html b/reference/lm.mids.html index c41c01fc8..c0eacbba5 100644 --- a/reference/lm.mids.html +++ b/reference/lm.mids.html @@ -1,5 +1,5 @@ -Linear regression for mids object — lm.mids • miceLinear regression for mids object — lm.mids • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -209,11 +209,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mads-class.html b/reference/mads-class.html index 10f7d9778..ee55770fc 100644 --- a/reference/mads-class.html +++ b/reference/mads-class.html @@ -2,7 +2,7 @@ Multivariate amputed data set (mads) — mads-class • miceMultivariate amputed data set (mads) — mads-class • miceCreates a blocks argument — make.blocks • miceCreates a blocks argument — make.blocks • miceCreates a blots argument — make.blots • micemice - 3.16.10 + 3.16.11 @@ -120,11 +120,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/make.formulas.html b/reference/make.formulas.html index 7df317b2e..722e79354 100644 --- a/reference/make.formulas.html +++ b/reference/make.formulas.html @@ -2,7 +2,7 @@ Creates a formulas argument — make.formulas • miceCreates a formulas argument — make.formulas • miceCreates a method argument — make.method • miceCreates a method argument — make.method • miceCreates a post argument — make.post • miceCreates a post argument — make.post • miceCreates a predictorMatrix argument — make.predictorMatrix • micemice - 3.16.10 + 3.16.11 @@ -121,11 +121,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/make.visitSequence.html b/reference/make.visitSequence.html index 2b91e3ea8..782650576 100644 --- a/reference/make.visitSequence.html +++ b/reference/make.visitSequence.html @@ -1,7 +1,7 @@ Creates a visitSequence argument — make.visitSequence • miceCreates a visitSequence argument — make.visitSequence • miceCreates a where argument — make.where • miceCreates a where argument — make.where • miceMammal sleep data — mammalsleep • miceMammal sleep data — mammalsleep • miceFind index of matched donor units — matchindex • miceFind index of matched donor units — matchindex • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -159,11 +159,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/md.pairs.html b/reference/md.pairs.html index 83c09eca8..16239f758 100644 --- a/reference/md.pairs.html +++ b/reference/md.pairs.html @@ -1,5 +1,5 @@ -Missing data pattern by variable pairs — md.pairs • miceMissing data pattern by variable pairs — md.pairs • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -159,11 +159,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/md.pattern.html b/reference/md.pattern.html index be4dd28c6..159819aef 100644 --- a/reference/md.pattern.html +++ b/reference/md.pattern.html @@ -1,5 +1,5 @@ -Missing data pattern — md.pattern • miceMissing data pattern — md.pattern • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -134,11 +134,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mdc.html b/reference/mdc.html index 11fe7cb66..e95cf1ca3 100644 --- a/reference/mdc.html +++ b/reference/mdc.html @@ -2,7 +2,7 @@ Graphical parameter for missing data plots — mdc • miceGraphical parameter for missing data plots — mdc • micemice: Multivariate Imputation by Chained Equations — mice • micemice - 3.16.10 + 3.16.11 @@ -402,9 +402,10 @@ Methodologymice software was published in the Journal of Statistical Software (Van Buuren and Groothuis-Oudshoorn, 2011). doi:10.18637/jss.v045.i03 - -The first application of the method +The mice software was published in the +Journal of Statistical Software (Van Buuren and Groothuis-Oudshoorn, 2011). +doi:10.18637/jss.v045.i03 +. The first application of the method concerned missing blood pressure data (Van Buuren et. al., 1999). The term Fully Conditional Specification was introduced in 2006 to describe a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Van Buuren et. al., 2006). Further details on mixes of variables and applications can be found in the book Flexible Imputation of Missing Data. Second Edition. @@ -438,7 +439,7 @@ Referencesdoi:10.18637/jss.v045.i03 Van Buuren, S. (2018). -Flexible Imputation of Missing Data. Second Edition. +Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL. Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of @@ -447,8 +448,8 @@ ReferencesExamples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.2l.bin.html b/reference/mice.impute.2l.bin.html index 5b591401d..d0decfd37 100644 --- a/reference/mice.impute.2l.bin.html +++ b/reference/mice.impute.2l.bin.html @@ -1,6 +1,6 @@ Imputation by a two-level logistic model using glmer — mice.impute.2l.bin • miceImputation by a two-level logistic model using glmer — mice.impute.2l.bin • miceImputation by a two-level normal model using lmer — mice.impute.2l.lmer • miceImputation by a two-level normal model using lmer — mice.impute.2l.lmer • miceImputation by a two-level normal model — mice.impute.2l.norm • miceImputation by a two-level normal model — mice.impute.2l.norm • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -157,11 +157,11 @@ Author< diff --git a/reference/mice.impute.2l.pan.html b/reference/mice.impute.2l.pan.html index 980586c95..4a5bb61cb 100644 --- a/reference/mice.impute.2l.pan.html +++ b/reference/mice.impute.2l.pan.html @@ -3,7 +3,7 @@ homogeneous within group variances. Aggregated group effects (i.e. group means) can be automatically created and included as predictors in the two-level regression (see argument type). This function needs the -pan package.">Imputation by a two-level normal model using pan — mice.impute.2l.pan • miceImputation by a two-level normal model using pan — mice.impute.2l.pan • micemice - 3.16.10 + 3.16.11 @@ -271,11 +271,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.2lonly.mean.html b/reference/mice.impute.2lonly.mean.html index ba016f07a..c02b5079f 100644 --- a/reference/mice.impute.2lonly.mean.html +++ b/reference/mice.impute.2lonly.mean.html @@ -2,7 +2,7 @@ Imputation of most likely value within the class — mice.impute.2lonly.mean • miceImputation of most likely value within the class — mice.impute.2lonly.mean • miceImputation at level 2 by Bayesian linear regression — mice.impute.2lonly.norm • miceImputation at level 2 by Bayesian linear regression — mice.impute.2lonly.norm • miceImputation at level 2 by predictive mean matching — mice.impute.2lonly.pmm • miceImputation at level 2 by predictive mean matching — mice.impute.2lonly.pmm • miceImputation by classification and regression trees — mice.impute.cart • miceImputation by classification and regression trees — mice.impute.cart • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -201,11 +201,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.jomoImpute.html b/reference/mice.impute.jomoImpute.html index c0173bdb0..bb66151f5 100644 --- a/reference/mice.impute.jomoImpute.html +++ b/reference/mice.impute.jomoImpute.html @@ -6,7 +6,7 @@ multiple imputation of multilevel data https://CRAN.R-project.org/package=jomo. Imputations can be generated using type or formula, -which offer different options for model specification.">Multivariate multilevel imputation using jomo — mice.impute.jomoImpute • miceMultivariate multilevel imputation using jomo — mice.impute.jomoImpute • micemice - 3.16.10 + 3.16.11 @@ -188,11 +188,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.lasso.logreg.html b/reference/mice.impute.lasso.logreg.html index 1a41d91e6..05519d3da 100644 --- a/reference/mice.impute.lasso.logreg.html +++ b/reference/mice.impute.lasso.logreg.html @@ -1,5 +1,5 @@ -Imputation by direct use of lasso logistic regression — mice.impute.lasso.logreg • miceImputation by direct use of lasso logistic regression — mice.impute.lasso.logreg • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -162,11 +162,11 @@ Author< diff --git a/reference/mice.impute.lasso.norm.html b/reference/mice.impute.lasso.norm.html index ac9c23c9a..468d486f7 100644 --- a/reference/mice.impute.lasso.norm.html +++ b/reference/mice.impute.lasso.norm.html @@ -1,5 +1,5 @@ -Imputation by direct use of lasso linear regression — mice.impute.lasso.norm • miceImputation by direct use of lasso linear regression — mice.impute.lasso.norm • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -162,11 +162,11 @@ Author< diff --git a/reference/mice.impute.lasso.select.logreg.html b/reference/mice.impute.lasso.select.logreg.html index fccaf433c..d406a767a 100644 --- a/reference/mice.impute.lasso.select.logreg.html +++ b/reference/mice.impute.lasso.select.logreg.html @@ -1,6 +1,6 @@ Imputation by indirect use of lasso logistic regression — mice.impute.lasso.select.logreg • miceImputation by indirect use of lasso logistic regression — mice.impute.lasso.select.logreg • miceImputation by indirect use of lasso linear regression — mice.impute.lasso.select.norm • miceImputation by indirect use of lasso linear regression — mice.impute.lasso.select.norm • miceImputation by linear discriminant analysis — mice.impute.lda • miceImputation by linear discriminant analysis — mice.impute.lda • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -168,11 +168,11 @@ Author< diff --git a/reference/mice.impute.logreg.boot.html b/reference/mice.impute.logreg.boot.html index cd5f02ebf..39832cacf 100644 --- a/reference/mice.impute.logreg.boot.html +++ b/reference/mice.impute.logreg.boot.html @@ -2,7 +2,7 @@ Imputation by logistic regression using the bootstrap — mice.impute.logreg.boot • miceImputation by logistic regression using the bootstrap — mice.impute.logreg.boot • miceImputation by logistic regression — mice.impute.logreg • miceImputation by logistic regression — mice.impute.logreg • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -162,11 +162,11 @@ Author< diff --git a/reference/mice.impute.mean.html b/reference/mice.impute.mean.html index 5fda621b7..6aad7b223 100644 --- a/reference/mice.impute.mean.html +++ b/reference/mice.impute.mean.html @@ -1,5 +1,5 @@ -Imputation by the mean — mice.impute.mean • miceImputation by the mean — mice.impute.mean • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -147,11 +147,11 @@ See also diff --git a/reference/mice.impute.midastouch.html b/reference/mice.impute.midastouch.html index 85c2f1d81..b3404e892 100644 --- a/reference/mice.impute.midastouch.html +++ b/reference/mice.impute.midastouch.html @@ -1,5 +1,5 @@ -Imputation by predictive mean matching with distance aided donor selection — mice.impute.midastouch • miceImputation by predictive mean matching with distance aided donor selection — mice.impute.midastouch • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -345,11 +345,11 @@ Examples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.mnar.html b/reference/mice.impute.mnar.html index 69a100095..b37873daf 100644 --- a/reference/mice.impute.mnar.html +++ b/reference/mice.impute.mnar.html @@ -2,7 +2,7 @@ Imputation under MNAR mechanism by NARFCS — mice.impute.mnar.logreg • miceImputation under MNAR mechanism by NARFCS — mice.impute.mnar.logreg • miceImputation by multivariate predictive mean matching — mice.impute.mpmm • miceImputation by multivariate predictive mean matching — mice.impute.mpmm • miceImputation by linear regression, bootstrap method — mice.impute.norm.boot • miceImputation by linear regression, bootstrap method — mice.impute.norm.boot • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -144,11 +144,11 @@ Author< diff --git a/reference/mice.impute.norm.html b/reference/mice.impute.norm.html index 40a3d7944..5f7ecc6a3 100644 --- a/reference/mice.impute.norm.html +++ b/reference/mice.impute.norm.html @@ -1,6 +1,6 @@ Imputation by Bayesian linear regression — mice.impute.norm • miceImputation by Bayesian linear regression — mice.impute.norm • miceImputation by linear regression without parameter uncertainty — mice.impute.norm.nob • miceImputation by linear regression without parameter uncertainty — mice.impute.norm.nob • miceImputation by linear regression through prediction — mice.impute.norm.predict • miceImputation by linear regression through prediction — mice.impute.norm.predict • miceImpute multilevel missing data using pan — mice.impute.panImpute • micemice
Functions to count and explore the structure of the missing data.
The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are:
The elementary imputation function is the workhorse that creates the actual imputations. Elementary functions 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.
method
mice
mice.impute.xxx
Specification of the imputation models can be made more convenient using the following set of helpers.
These plots contrast the observed data with the imputed/amputed data, usually with a blue/red distinction.
Multiple imputation creates m > 1 completed data sets, fits the model of interest to each of these, and combines the analytic estimates. The following functions assist in executing the analysis and pooling steps:
The multiply-imputed data can be combined in various ways, and exported into other formats.
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:
Helpers to extract and print information from objects of specific classes.
Several functions are dedicated to common low-level operations to generate the imputations:
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.
Built-in datasets
Miscellaneous functions
The mice software was published in the +Journal of Statistical Software (Van Buuren and Groothuis-Oudshoorn, 2011). +doi:10.18637/jss.v045.i03 +. The first application of the method concerned missing blood pressure data (Van Buuren et. al., 1999). The term Fully Conditional Specification was introduced in 2006 to describe a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Van Buuren et. al., 2006). Further details on mixes of variables and applications can be found in the book Flexible Imputation of Missing Data. Second Edition. @@ -438,7 +439,7 @@
Van Buuren, S. (2018). -Flexible Imputation of Missing Data. Second Edition. +Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of @@ -447,8 +448,8 @@ ReferencesExamples - Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. + Developed by Stef van Buuren, Karin Groothuis-Oudshoorn. diff --git a/reference/mice.impute.2l.bin.html b/reference/mice.impute.2l.bin.html index 5b591401d..d0decfd37 100644 --- a/reference/mice.impute.2l.bin.html +++ b/reference/mice.impute.2l.bin.html @@ -1,6 +1,6 @@ Imputation by a two-level logistic model using glmer — mice.impute.2l.bin • miceImputation by a two-level logistic model using glmer — mice.impute.2l.bin • miceImputation by a two-level normal model using lmer — mice.impute.2l.lmer • miceImputation by a two-level normal model using lmer — mice.impute.2l.lmer • miceImputation by a two-level normal model — mice.impute.2l.norm • miceImputation by a two-level normal model — mice.impute.2l.norm • mice @@ -10,7 +10,7 @@ mice - 3.16.10 + 3.16.11 @@ -157,11 +157,11 @@ Author< diff --git a/reference/mice.impute.2l.pan.html b/reference/mice.impute.2l.pan.html index 980586c95..4a5bb61cb 100644 --- a/reference/mice.impute.2l.pan.html +++ b/reference/mice.impute.2l.pan.html @@ -3,7 +3,7 @@ homogeneous within group variances. Aggregated group effects (i.e. group means) can be automatically created and included as predictors in the two-level regression (see argument type). This function needs the -pan package.">Imputation by a two-level normal model using pan — mice.impute.2l.pan • miceImputation by a two-level normal model using pan — mice.impute.2l.pan • micemice
Calculate \(\dot\beta = \hat\beta + \dot\sigma\dot z_1 V^{1/2}\).
Calculate \(\dot\eta(i,j)=|X_{{obs},[i]|}\hat\beta-X_{{mis},[j]}\dot\beta\) with \(i=1,\dots,n_1\) and \(j=1,\dots,n_0\).
Construct \(n_0\) sets \(Z_j\), each containing \(d\) candidate donors, from Y_obs such that \(\sum_d\dot\eta(i,j)\) is minimum for all \(j=1,\dots,n_0\). Break ties randomly.
Construct \(n_0\) sets \(Z_j\), each containing \(d\) +candidate donors, from \(y_{obs}\) such that \(\sum_d\dot\eta(i,j)\) is +minimum for all \(j=1,\dots,n_0\). Break ties randomly.
Draw one donor \(i_j\) from \(Z_j\) randomly for \(j=1,\dots,n_0\).
Calculate imputations \(\dot y_j = y_{i_j}\) for \(j=1,\dots,n_0\).
The name predictive mean matching was proposed by Little (1988).
version() -#> [1] "mice 3.16.10 2024-04-17 /home/runner/work/_temp/Library" +#> [1] "mice 3.16.11 2024-05-04 /home/runner/work/_temp/Library" version("base") -#> [1] "base 4.3.3 /opt/R/4.3.3/lib/R/library" +#> [1] "base 4.4.0 /opt/R/4.4.0/lib/R/library"