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 In A MCAR Manner |

anova | Compare several nested models |

appendbreak | Appends specified break to the data |

as.mids | Converts an multiply imputed dataset (long format) into a... |

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 |

cbind.mids | Combine 'mids' objects by columns |

cc | Select complete cases |

cci | Complete case indicator |

complete | Extracts the completed data from a 'mids' object |

construct.blocks | Construct blocks from 'formulas' and 'predictorMatrix' |

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 |

fix.coef | Fix coefficients and update model |

flux | Influx and outflux of multivariate missing data patterns |

fluxplot | Fluxplot of the missing data pattern |

getfit | Extract list of fitted model |

getqbar | Extract estimate from '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-class | Multivariate Amputed Data Set ('mads') |

make.blocks | Creates a 'blocks' argument |

make.blots | Creates a 'blots' argument |

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 |

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 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.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.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.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 |

mids2mplus | Export 'mids' object to Mplus |

mids2spss | Export 'mids' object to SPSS |

mids-class | Multiply imputed data set ('mids') |

mipo | 'mipo': Multiple imputation pooled object |

mira-class | Multiply imputed repeated analyses ('mira') |

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 |

plot.mids | Plot the trace lines of the MICE algorithm |

pmm.match | Finds an imputed value from matches in the predictive metric... |

pool | Combine estimates by Rubin's rules |

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... |

potthoffroy | Potthoff-Roy data |

Print a 'mids' object | |

print.mads | Print a 'mads' object |

quickpred | Quick selection of predictors from the data |

rbind.mids | Combine 'mids' objects by rows |

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 |

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 |

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