meanDifferences {benchdamic} | R Documentation

## meanDifferences

### Description

Compute the differences between the estimated and the observed continuity corrected logarithms of the average count values (MD), and between the estimated average probability to observe a zero and the the observed zero rate (ZPD).

### Usage

    meanDifferences(estimated, observed)

### Arguments

`estimated` | a two column data.frame, output of `fitNB`, `fitZINB`, `fitDM`, `fitZIG`, or `fitHURDLE` functions. More in general, a data frame containing the continuity corrected logarithm for the average of the fitted values for each row of a matrix of counts in the `Y` column, and the estimated probability to observe a zero in the `Y0` column.
`observed` | a two column data.frame, output of `prepareObserved` function. More in general, a data frame containing the continuity corrected logarithm for the average of the observed values for each row of a matrix of counts in the `Y` column, and the estimated proportion of zeroes in the `Y0` column.

### Value

a `data.frame` containing the differences between the estimated and the observed continuity corrected logarithms of the average count values in the `MD` column, and between the estimated average probability to observe a zero and the the observed zero rate in the `ZPD` column.

### See Also

`prepareObserved`.

### Examples

    # Randomly generate the observed and estimated data.frames
    observed <- data.frame(Y = rpois(10, 5), Y0 = runif(10, 0, 1))
    estimated <- data.frame(Y = rpois(10, 5), Y0 = runif(10, 0, 1))

    # Compute the mean differences between estimated and observed data.frames
    meanDifferences(estimated, observed)

[Package benchdamic version 1.0.0 Index]