assessGrouping {nearBynding} | R Documentation

## assessGrouping

### Description

Assess grouping of samples assigned to the same category relative to random.

### Usage

    assessGrouping(
      distances,
      annotations,
      measurement = "mean",
      output = "KS.pvalue",
      ctrl_iterations = 10000
    )

### Arguments

`distances` | Data frame object with at least three columns where the first three columns are sample 1 name, sample 2 name, and the distance between them.
`annotations` | Data frame object with at least two columns where the first two columns are sample name and the category of the sample for grouping. Sample names must match sample 1 and sample 2 names in distances data frame.
`measurement` | The measurement for comparison between cases and controls and statistical analysis ("mean", "max", or "min). Default "mean"
`output` | A string denoting what information will be returned: either a list of test and control measurement distances ("measurements"), the p-value of the Kolmogorov-Smirnov test comparing test and control distributions ("KS.pvalue"), or a ggplot object plotting the test and control distributions ("plot"). Default "KS.pvalue"
`ctrl_iterations` | The number of iterations to test for the control distribution; an integer. Default 10000.

### Value

`output = "KS.pvalue"` | the p-value of the Kolmogorov-Smirnov test comparing test and control distributions
`output = "plot"` | a ggplot object plotting the test and control distributions
`output = "measurements"` | a list or test and control measurement distances

### Examples

    ## create random distance data frame
    dist<-expand.grid(letters, letters)
    dist$distance<-rnorm(nrow(dist))
    annot<-data.frame(sample<-letters, category<- rep(1:13, 2))
    ## get KS p-value
    assessGrouping(dist, annot)
    ## get plot of test vs control distributions
    assessGrouping(dist, annot,
                   output = "plot")

[Package nearBynding version 1.2.0 Index]