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A B C D E F G H I L M N P Q R S T V W X misc
| appendbreak | Appends specified break to the data |
| as.mids | Converts an multiply imputed dataset (long format) into a 'mids' object |
| as.mira | Create a 'mira' object from repeated analyses |
| boys | Growth of Dutch boys |
| bwplot | Box-and-whisker plot of observed and imputed data |
| bwplot.mids | Box-and-whisker plot of observed and imputed data |
| cart | Imputation by classification and regression trees |
| cbind.mids | Columnwise combination of a 'mids' object. |
| cc | Complete cases |
| cc-method | Complete cases |
| cci | Complete case indicator |
| cci-method | Complete case indicator |
| ccn | Complete cases n |
| ccn-method | Complete cases n |
| complete | Creates imputed data sets from a 'mids' object |
| densityplot | Density plot of observed and imputed data |
| densityplot.mids | Density plot of observed and imputed data |
| extractBS | Extract broken stick estimates from a 'lmer' object |
| fastpmm | Imputation by fast predictive mean matching |
| fdd | SE Fireworks disaster data |
| fdd.pred | SE Fireworks disaster data |
| fdgs | Fifth Dutch growth study 2009 |
| fico | Fraction of incomplete cases among cases with observed |
| flux | Influx and outflux of multivariate missing data patterns |
| fluxplot | Fluxplot of the missing data pattern |
| getfit | Extracts fit objects from 'mira' object |
| glm.mids | Generalized linear model for 'mids' object |
| hazard | Cumulative hazard rate or Nelson-Aalen estimator |
| ibind | Combine imputations fitted to the same data |
| ic | Incomplete cases |
| ic-method | Incomplete cases |
| ici | Incomplete case indicator |
| ici-method | Incomplete case indicator |
| icn | Incomplete cases n |
| icn-method | Incomplete cases n |
| is.mids | Check for 'mids' object |
| is.mipo | Check for 'mipo' object |
| is.mira | Check for 'mira' object |
| leiden85 | Leiden 85+ study |
| lm.mids | Linear regression for 'mids' object |
| mammalsleep | Mammal sleep data |
| md.pairs | Missing data pattern by variable pairs |
| md.pattern | Missing data pattern |
| mdc | Graphical parameter for missing data plots. |
| mgg | Self-reported and measured BMI |
| mice | Multivariate Imputation by Chained Equations (MICE) |
| mice.impute.2l.norm | Imputation by a two-level normal model |
| mice.impute.2l.pan | Imputation by a two-level normal model using 'pan' |
| mice.impute.2lonly.mean | Imputation of the mean 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.cart | Imputation by classification and regression trees |
| mice.impute.fastpmm | Imputation by fast predictive mean matching |
| 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.norm | Imputation by Bayesian linear regression |
| mice.impute.norm.boot | Imputation by linear regression, bootstrap method |
| mice.impute.norm.nob | Imputation by linear regression (non Bayesian) |
| mice.impute.norm.predict | Imputation by linear regression, prediction method |
| mice.impute.passive | Passive imputation |
| mice.impute.pmm | Imputation by predictive mean matching |
| mice.impute.polr | Imputation by polytomous regression - ordered |
| mice.impute.polyreg | Imputation by polytomous regression - unordered |
| mice.impute.quadratic | Imputation of quadratric terms |
| mice.impute.rf | Imputation by random forests |
| mice.impute.ri | Imputation by the random indicator method for nonignorable data |
| 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') |
| mids-class | Multiply imputed data set ('mids') |
| mids2mplus | Export 'mids' object to Mplus |
| mids2spss | Export 'mids' object to SPSS |
| mipo | Multiply imputed pooled analysis ('mipo') |
| mipo-class | Multiply imputed pooled analysis ('mipo') |
| mira | Multiply imputed repeated analyses ('mira') |
| mira-class | Multiply imputed repeated analyses ('mira') |
| nelsonaalen | Cumulative hazard rate or Nelson-Aalen estimator |
| nhanes | NHANES example - all variables numerical |
| nhanes2 | NHANES example - mixed numerical and discrete variables |
| norm | Imputation by Bayesian linear regression |
| norm.boot | Imputation by linear regression, bootstrap method |
| norm.draw | Draws values of beta and sigma by Bayesian linear regression |
| norm.nob | Imputation by linear regression (non Bayesian) |
| norm.predict | Imputation by linear regression, prediction method |
| pattern | Datasets with various missing data patterns |
| pattern1 | Datasets with various missing data patterns |
| pattern2 | Datasets with various missing data patterns |
| pattern3 | Datasets with various missing data patterns |
| pattern4 | Datasets with various missing data patterns |
| plot.mids | Plot the trace lines of the MICE algorithm |
| pmm | Imputation by predictive mean matching |
| pool | Multiple imputation pooling |
| pool.compare | Compare two nested models fitted to imputed data |
| pool.r.squared | Pooling: R squared |
| pool.scalar | Multiple imputation pooling: univariate version |
| popmis | Hox pupil popularity data with missing popularity scores |
| pops | Project on preterm and small for gestational age infants (POPS) |
| pops.pred | Project on preterm and small for gestational age infants (POPS) |
| potthoffroy | Potthoff-Roy data |
| print.mids | Print a 'mids' object |
| print.mipo | Print a 'mids' object |
| print.mira | Print a 'mids' object |
| quadratic | Imputation of quadratric terms |
| quickpred | Quick selection of predictors from the data |
| rbind.mids | Rowwise combination of a 'mids' object. |
| ri | Imputation by the random indicator method for nonignorable data |
| selfreport | Self-reported and measured BMI |
| sleep | Mammal sleep data |
| squeeze | Squeeze the imputed values to be within specified boundaries. |
| stripplot | Stripplot of observed and imputed data |
| stripplot.mids | Stripplot of observed and imputed data |
| summary.mids | Summary of a 'mira' object |
| summary.mipo | Summary of a 'mira' object |
| summary.mira | Summary of a 'mira' object |
| supports.transparent | Supports semi-transparent foreground colors? |
| tbc | Terneuzen birth cohort |
| tbc.target | Terneuzen birth cohort |
| terneuzen | Terneuzen birth cohort |
| transparent | Supports semi-transparent foreground colors? |
| 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 | Scatterplot of observed and imputed data |
| xyplot.mids | Scatterplot of observed and imputed data |
| .norm.draw | Draws values of beta and sigma by Bayesian linear regression |
| 2l.norm | Imputation by a two-level normal model |
| 2l.pan | Imputation by a two-level normal model using 'pan' |
| 2lonly.mean | Imputation of the mean within the class |
| 2lonly.norm | Imputation at level 2 by Bayesian linear regression |
| 2lonly.pmm | Imputation at level 2 by predictive mean matching |