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 |