# Check for mira object

### Description

Check for `mira`

object

### Usage

1 | ```
is.mira(x)
``` |

### Arguments

`x` |
An object |

### Value

A logical indicating whether `x`

is an object of class `mira`

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- 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
- boys: Growth of Dutch boys
- bwplot.mids: Box-and-whisker plot of observed and imputed data
- cbind.mids: Columnwise combination of a 'mids' object.
- cci-methods: Complete case indicator
- cc-methods: Complete cases
- ccn-methods: Complete cases n
- complete: Creates imputed data sets from a 'mids' object
- densityplot.mids: Density plot of observed and imputed data
- 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
- 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
- ibind: Combine imputations fitted to the same data
- ici-methods: Incomplete case indicator
- ic-methods: Incomplete cases
- icn-methods: Incomplete cases n
- ifdo: Conditional imputation helper
- 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
- long2mids: Conversion of a imputed data set (long form) to a 'mids'...
- 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: Multivariate Imputation by Chained Equations (MICE)
- 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.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...
- 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-class: Multiply imputed pooled analysis ('mipo')
- 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.draw: Draws values of beta and sigma by Bayesian linear regression
- pattern: Datasets with various missing data patterns
- plot.mids: Plot the trace lines of the MICE algorithm
- 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...
- potthoffroy: Potthoff-Roy data
- print: Print a 'mids' object
- quickpred: Quick selection of predictors from the data
- rbind.mids: Rowwise combination of a 'mids' object.
- 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.mids: Scatterplot of observed and imputed data