plotPercentileSeries {cNORM} | R Documentation

## Generates a series of plots with number curves by percentile for different models

### Description

This functions makes use of 'plotPercentiles' to generate a series of plots with different number of predictors. It draws on the information provided by the model object to determine the bounds of the modeling (age and standard score range). It can be used as an additional model check to determine the best fitting model. Please have a look at the ' plotPercentiles' function for further information.

### Usage

    plotPercentileSeries(
      data,
      model,
      start = 1,
      end = NULL,
      group = NULL,
      percentiles = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
      type = 7,
      filename = NULL
    )

### Arguments

`data` | The raw data including the percentiles and norm scores or a cnorm object
`model` | The model from the bestModel function (optional)
`start` | Number of predictors to start with
`end` | Number of predictors to end with
`group` | The name of the grouping variable; the distinct groups are automatically determined
`percentiles` | Vector with percentile scores, ranging from 0 to 1 (exclusive)
`type` | The type parameter of the quantile function to estimate the percentiles of the raw data (default 7)
`filename` | Prefix of the filename. If specified, the plots are saves as png files in the directory of the workspace, instead of displaying them

### Value

the complete list of plots

### See Also

plotPercentiles

Other plot: `plot.cnorm()`, `plotDensity()`, `plotDerivative()`, `plotNormCurves()`, `plotNorm()`, `plotPercentiles()`, `plotRaw()`, `plotSubset()`

### Examples

    # Load example data set, compute model and plot results
    result <- cnorm(raw = elfe$raw, group = elfe$group)
    plotPercentileSeries(result, start=1, end=5, group="group")

[Package cNORM version 2.0.3 Index]