Package: miceafter 0.5.0

miceafter: Data and Statistical Analyses after Multiple Imputation

Statistical Analyses and Pooling after Multiple Imputation. A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) <doi:10.1186/1471-2288-7-33>. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>. Sidi (2021) <doi:10.1080/00031305.2021.1898468>. Lott (2018) <doi:10.1080/00031305.2018.1473796>. Grund (2021) <doi:10.31234/osf.io/d459g>.

Authors:Martijn Heymans [cre, aut], Jaap Brand [ctb]

miceafter_0.5.0.tar.gz
miceafter_0.5.0.zip(r-4.7)miceafter_0.5.0.zip(r-4.6)miceafter_0.5.0.zip(r-4.5)
miceafter_0.5.0.tgz(r-4.6-any)miceafter_0.5.0.tgz(r-4.5-any)
miceafter_0.5.0.tar.gz(r-4.7-any)miceafter_0.5.0.tar.gz(r-4.6-any)
miceafter_0.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
miceafter/json (API)
NEWS

# Install 'miceafter' in R:
install.packages('miceafter', repos = c('https://mwheymans.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mwheymans/miceafter/issues

Pkgdown/docs site:https://mwheymans.github.io

Datasets:
  • lbp_orig - Dataset of 159 Low Back Pain Patients with missing values
  • lbpmicox - Survival data of 265 Low Back Pain Patients
  • lbpmilr - Data of 159 Low Back Pain Patients

On CRAN:

Conda:

5.10 score 3 stars 28 scripts 330 downloads 40 exports 132 dependencies

Last updated from:8ec37e9777. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK205
source / vignettesOK261
linux-release-x86_64OK212
macos-release-arm64OK176
macos-oldrel-arm64OK172
windows-develOK165
windows-releaseOK150
windows-oldrelOK140
wasm-releaseOK143

Exports:bf_testcheck_modelcindexclean_Pcor_estcor2fzdf2milistf2chifz2corglm_miinvlogitinvlogit_cilevene_testlist2milistlogit_transmids2milistodds_ratiopool_bftestpool_cindexpool_corpool_D2pool_D4pool_glmpool_levenetestpool_odds_ratiopool_prop_nnapool_prop_waldpool_prop_wilsonpool_propdiff_acpool_propdiff_nwpool_propdiff_waldpool_risk_ratiopool_scalar_RRpool_t_testprop_nnaprop_waldpropdiff_acpropdiff_waldrisk_ratiot_test

Dependencies:abindbackportsbase64encbitbit64bootbroombslibcachemcarcarDatacheckmateclicliprclustercodetoolscolorspacecowplotcpp11crayondata.tableDBIDerivdigestdoBydplyrevaluatefarverfastmapfontawesomeforcatsforeachforecastforeignFormulafracdifffsgenericsggplot2glmnetgluegridExtragtablehavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjomojquerylibjsonliteknitrlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminqamitmlmitoolsmodelrmultcompmvtnormnlmenloptrnnetnumDerivordinalpanpbkrtestpillarpkgconfigpolsplineprettyunitspROCprogresspurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrlangrmarkdownrmsrpartrstudioapiS7sandwichsassscalesshapeSparseMstringistringrsurvivalTH.datatibbletidyrtidyselecttimeDatetinytextzdbucminfurcautf8vctrsviridisLitevroomwithrxfunyamlzoo

mice and miceafter for Regression modelling

Martijn W Heymans

Rendered fromregression_modelling.Rmdusingknitr::rmarkdownon May 13 2026.

Last update: 2021-12-07
Started: 2021-12-07

Pooling C-index of Logistic and Cox Regression Models

Martijn W Heymans

Rendered frompooling_cindex.Rmdusingknitr::rmarkdownon May 13 2026.

Last update: 2021-12-15
Started: 2021-11-24

Pooling Levene's test statistic

Martijn W Heymans

Rendered fromlevene_test.Rmdusingknitr::rmarkdownon May 13 2026.

Last update: 2021-12-15
Started: 2021-11-24

Readme and manuals

Help Manual

Help pageTopics
Calculates the Brown-Forsythe test.bf_test
Calculates the c-index and standard errorcindex
Calculates the correlation coefficientcor_est
Fisher z transformation of correlation coefficientcor2fz
Turns a data frame with multiply imputed data into an object of class 'milist'df2milist
Converts F-values into Chi Square valuesf2chi
Fisher z back transformation of correlation coefficientfz2cor
Direct Pooling and model selection of Linear and Logistic regression models across multiply imputed data.glm_mi
Takes the inverse of a logit transformed valueinvlogit
Takes the inverse of logit transformed parameters and calculates the confidence intervalsinvlogit_ci
Dataset of 159 Low Back Pain Patients with missing valueslbp_orig
Survival data of 265 Low Back Pain Patientslbpmicox
Data of 159 Low Back Pain Patientslbpmilr
Calculates the Levene's testlevene_test
Turns a list object with multiply imputed datasets into an object of class 'milist'.list2milist
Logit transformation of parameter estimateslogit_trans
Turns a 'mice::mids' object into an object of class 'milist' to be further used by 'miceafter::with'mids2milist
Calculates the odds ratio (OR) and standard error.odds_ratio
Calculates the pooled Brown-Forsythe test.pool_bftest
Calculates the pooled C-index and Confidence intervalspool_cindex
Calculates the pooled correlation coefficient and Confidence intervalspool_cor
Combines the Chi Square statistics across Multiply Imputed datasetspool_D2
Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4)pool_D4
Pools and selects Linear and Logistic regression models across multiply imputed data.pool_glm
Calculates the pooled Levene test.pool_levenetest
Calculates the pooled odds ratio (OR) and related confidence interval.pool_odds_ratio
Calculates the pooled proportion and confidence intervals using an approximate Beta distribution.pool_prop_nna
Calculates the pooled proportion and standard error according to Wald across multiply imputed datasets.pool_prop_wald
Calculates the pooled single proportion confidence intervals according to Wilson across multiply imputed datasets.pool_prop_wilson
Calculates the pooled difference between proportions and standard error according to Agresti-Caffo across multiply imputed datasets.pool_propdiff_ac
Calculates the pooled difference between proportions and confidence intervals according to Newcombe-Wilson (NW) across multiply imputed datasets.pool_propdiff_nw
Calculates the pooled difference between proportions and standard error according to Wald across multiply imputed datasets.pool_propdiff_wald
Calculates the pooled risk ratio (RR) and related confidence interval.pool_risk_ratio
Rubin's Rules for scalar estimatespool_scalar_RR
Calculates the pooled t-test and Confidence intervalspool_t_test
Calculates the posterior beta components for a single proportionprop_nna
Calculates a single proportion and related standard error according to Waldprop_wald
Calculates the difference between proportions and standard error according to method Agresti-Caffopropdiff_ac
Calculates the difference between proportions and standard error according to Waldpropdiff_wald
Calculates the risk ratio (RR) and standard error.risk_ratio
Calculates the one, two and paired sample t-testt_test
Evaluate an Expression across a list of multiply imputed datasetswith.milist