Man pages for mice
Multivariate Imputation by Chained Equations
| ampute | Generate missing data for simulation purposes |
| ampute.continuous | Multivariate amputation based on continuous probability... |
| ampute.default.freq | Default 'freq' in 'ampute' |
| ampute.default.odds | Default 'odds' in 'ampute()' |
| ampute.default.patterns | Default 'patterns' in 'ampute' |
| ampute.default.type | Default 'type' in 'ampute()' |
| ampute.default.weights | Default 'weights' in 'ampute' |
| ampute.discrete | Multivariate amputation based on discrete probability... |
| ampute.mcar | Multivariate amputation under a MCAR mechanism |
| anova | Compare several nested models |
| appendbreak | Appends specified break to the data |
| as.mids | Converts an imputed dataset (long format) into a 'mids'... |
| as.mira | Create a 'mira' object from repeated analyses |
| as.mitml.result | Converts into a 'mitml.result' object |
| boys | Growth of Dutch boys |
| brandsma | Brandsma school data used Snijders and Bosker (2012) |
| bwplot.mads | Box-and-whisker plot of amputed and non-amputed data |
| bwplot.mids | Box-and-whisker plot of observed and imputed data |
| cbind | Combine R objects by rows and columns |
| cc | Select complete cases |
| cci | Complete case indicator |
| complete.mids | Extracts the completed data from a 'mids' object |
| construct.blocks | Construct blocks from 'formulas' and 'predictorMatrix' |
| convergence | Computes convergence diagnostics for a 'mids' object |
| D1 | Compare two nested models using D1-statistic |
| D2 | Compare two nested models using D2-statistic |
| D3 | Compare two nested models using D3-statistic |
| densityplot.mids | Density plot of observed and imputed data |
| employee | Employee selection data |
| estimice | Computes least squares parameters |
| extend.formula | Extends a formula with predictors |
| extend.formulas | Extends formula's with predictor matrix settings |
| extractBS | Extract broken stick estimates from a 'lmer' object |
| fdd | SE Fireworks disaster data |
| fdgs | Fifth Dutch growth study 2009 |
| fico | Fraction of incomplete cases among cases with observed |
| filter.mids | Subset rows of a 'mids' object |
| fix.coef | Fix coefficients and update model |
| flux | Influx and outflux of multivariate missing data patterns |
| fluxplot | Fluxplot of the missing data pattern |
| futuremice | Wrapper function that runs MICE in parallel |
| getfit | Extract list of fitted models |
| getqbar | Extract estimate from 'mipo' object |
| glance.mipo | Glance method to extract information from a 'mipo' object |
| glm.mids | Generalized linear model for 'mids' object |
| ibind | Enlarge number of imputations by combining 'mids' objects |
| ic | Select incomplete cases |
| ici | Incomplete case indicator |
| ifdo | Conditional imputation helper |
| is.mads | Check for 'mads' object |
| is.mids | Check for 'mids' object |
| is.mipo | Check for 'mipo' object |
| is.mira | Check for 'mira' object |
| is.mitml.result | Check for 'mitml.result' object |
| leiden85 | Leiden 85+ study |
| lm.mids | Linear regression for 'mids' object |
| mads | Multivariate amputed data set ('mads') |
| make.blocks | Creates a 'blocks' argument |
| make.blots | Creates a 'blots' argument |
| make.calltype | Create calltype of the imputation model |
| make.formulas | Creates a 'formulas' argument |
| make.method | Creates a 'method' argument |
| make.post | Creates a 'post' argument |
| make.predictorMatrix | Creates a 'predictorMatrix' argument |
| make.visitSequence | Creates a 'visitSequence' argument |
| make.where | Creates a 'where' argument |
| mammalsleep | Mammal sleep data |
| matchindex | Find index of matched donor units |
| mcar | Jamshidian and Jalal's Non-Parametric MCAR Test |
| mdc | Graphical parameter for missing data plots |
| md.pairs | Missing data pattern by variable pairs |
| md.pattern | Missing data pattern |
| mice | 'mice': Multivariate Imputation by Chained Equations |
| mice.impute.2l.bin | Imputation by a two-level logistic model using 'glmer' |
| mice.impute.2l.lmer | Imputation by a two-level normal model using 'lmer' |
| mice.impute.2l.norm | Imputation by a two-level normal model |
| mice.impute.2lonly.mean | Imputation of most likely value within the class |
| mice.impute.2lonly.norm | Imputation at level 2 by Bayesian linear regression |
| mice.impute.2lonly.pmm | Imputation at level 2 by predictive mean matching |
| mice.impute.2l.pan | Imputation by a two-level normal model using 'pan' |
| mice.impute.cart | Imputation by classification and regression trees |
| mice.impute.jomoImpute | Multivariate multilevel imputation using 'jomo' |
| mice.impute.lasso.logreg | Imputation by direct use of lasso logistic regression |
| mice.impute.lasso.norm | Imputation by direct use of lasso linear regression |
| mice.impute.lasso.select.logreg | Imputation by indirect use of lasso logistic regression |
| mice.impute.lasso.select.norm | Imputation by indirect use of lasso linear regression |
| mice.impute.lda | Imputation by linear discriminant analysis |
| mice.impute.logreg | Imputation by logistic regression |
| mice.impute.logreg.boot | Imputation by logistic regression using the bootstrap |
| mice.impute.mean | Imputation by the mean |
| mice.impute.midastouch | Imputation by predictive mean matching with distance aided... |
| mice.impute.mnar | Imputation under MNAR mechanism by NARFCS |
| mice.impute.mpmm | Imputation by multivariate predictive mean matching |
| mice.impute.norm | Imputation by Bayesian linear regression |
| mice.impute.norm.boot | Imputation by linear regression, bootstrap method |
| mice.impute.norm.nob | Imputation by linear regression without parameter uncertainty |
| mice.impute.norm.predict | Imputation by linear regression through prediction |
| mice.impute.panImpute | Impute multilevel missing data using 'pan' |
| mice.impute.passive | Passive imputation |
| mice.impute.pmm | Imputation by predictive mean matching |
| mice.impute.polr | Imputation of ordered data by polytomous regression |
| mice.impute.polyreg | Imputation of unordered data by polytomous regression |
| mice.impute.quadratic | Imputation of quadratic terms |
| mice.impute.rf | Imputation by random forests |
| mice.impute.ri | Imputation by the random indicator method for nonignorable... |
| mice.impute.sample | Imputation by simple random sampling |
| mice.mids | Multivariate Imputation by Chained Equations (Iteration Step) |
| mice.theme | Set the theme for the plotting Trellis functions |
| mids | Multiply imputed data set ('mids') |
| mids2mplus | Export 'mids' object to Mplus |
| mids2spss | Export 'mids' object to SPSS |
| mipo | 'mipo': Multiple imputation pooled object |
| mira | Create an object of class "mira" |
| mnar_demo_data | MNAR demo data |
| name.blocks | Name imputation blocks |
| name.formulas | Name formula list elements |
| ncc | Number of complete cases |
| nelsonaalen | Cumulative hazard rate or Nelson-Aalen estimator |
| nhanes | NHANES example - all variables numerical |
| nhanes2 | NHANES example - mixed numerical and discrete variables |
| nic | Number of incomplete cases |
| nimp | Number of imputations per block |
| norm.draw | Draws values of beta and sigma by Bayesian linear regression |
| parlmice | Wrapper function that runs MICE in parallel |
| pattern | Datasets with various missing data patterns |
| pmm.match | Finds an imputed value from matches in the predictive metric... |
| pool | Combine estimates by pooling rules |
| pool.compare | Compare two nested models fitted to imputed data |
| pool.r.squared | Pools R^2 of m models fitted to multiply-imputed data |
| pool.scalar | Multiple imputation pooling: univariate version |
| pool.table | Combines estimates from a tidy table |
| popmis | Hox pupil popularity data with missing popularity scores |
| pops | Project on preterm and small for gestational age infants... |
| potthoffroy | Potthoff-Roy data |
| Print a 'mira' object | |
| quickpred | Quick selection of predictors from the data |
| reexports | Objects exported from other packages |
| selfreport | Self-reported and measured BMI |
| squeeze | Squeeze the imputed values to be within specified boundaries. |
| stripplot.mids | Stripplot of observed and imputed data |
| summary | Summary of a 'mira' object |
| supports.transparent | Supports semi-transparent foreground colors? |
| tbc | Terneuzen birth cohort |
| tidy.mipo | Tidy method to extract results from a 'mipo' object |
| toenail | Toenail data |
| toenail2 | Toenail data |
| version | Echoes the package version number |
| walking | Walking disability data |
| windspeed | Subset of Irish wind speed data |
| with.mids | Evaluate an expression in multiple imputed datasets |
| xyplot.mads | Scatterplot of amputed and non-amputed data against weighted... |
| xyplot.mids | Scatterplot of observed and imputed data |
