plotCovariate {nullranges} | R Documentation

## Covariate plotting for Matched objects

### Description

This function plots the distributions of a covariate from each matched set of a Matched object.

### Usage

    plotCovariate(
      x,
      covar = NULL,
      sets = c("focal", "matched", "pool", "unmatched"),
      type = NULL,
      log = NULL,
      ...
    )

    ## S4 method for signature
    ## 'Matched,
    ##   character_OR_missing,
    ##   character_OR_missing,
    ##   character_OR_missing,
    ##   character_OR_missing'
    plotCovariate(x, covar, sets, type, log, thresh = 12)

### Arguments

`x` | Matched object
`covar` | Character naming the covariate to plot. If multiple are provided, only the first one is used.
`sets` | Character vector describing which matched set(s) to include in the plot. Options are 'focal', 'matched', 'pool', or 'unmatched'. Multiple options are accepted.
`type` | Character naming the plot type. Available options are one of either 'ridges', 'jitter', 'lines', or 'bars'. Note that for large datasets, use of 'jitter' is discouraged because the large density of points can stall the R-graphics device.
`log` | Character vector describing which axis or axes to apply log-transformation. Available options are 'x' and/or 'y'.
`...` | Additional arguments.
`thresh` | Integer describing the number of unique values required to classify a numeric variable as discrete (and convert it to a factor). If the number of unique values exceeds `thresh` then the variable is considered continuous.

### Details

By default, `plotCovariate` will sense the class of covariate and make a plot best suited to that data type. For example, if the covariate class is categorical in nature then the `type` argument defaults to 'bars'. `type` is set to 'lines' for continuous covariates. These settings can also be overwritten manually.

### Value

Returns a plot of a covariate's distribution among matched sets.

### See Also

`plotPropensity()` to plot propensity scores.

### Examples

    ## Matched example dataset
    set.seed(123)
    mdf <- makeExampleMatchedDataSet(matched = TRUE)

    ## Visualize covariates
    plotCovariate(mdf)
    plotCovariate(mdf, covar = 'feature3')
    plotCovariate(mdf,
                  covar = 'feature2',
                  sets = c('focal', 'matched', 'pool'))
    plotCovariate(mdf,
                  covar = 'feature2',
                  sets = c('focal', 'matched', 'pool'),
                  type = 'ridges')
    plotCovariate(mdf,
                  covar = 'feature2',
                  sets = c('focal', 'matched', 'pool'),
                  type = 'jitter')

[Package nullranges version 1.0.1 Index]