weitrix_calplot {weitrix} | R Documentation

## Weight calibration plots, optionally versus a covariate

### Description

Various plots based on weighted squared residuals of each element in the weitrix. `weight*residual^2` is the Pearson residual for a gamma GLM plus one, as used by `weitrix_calibrate_all`.

### Usage

    weitrix_calplot(
      weitrix,
      design = ~1,
      covar,
      cat,
      funnel = FALSE,
      guides = TRUE
    )

### Arguments

`weitrix` | A weitrix object, or an object that can be converted to a weitrix with `as_weitrix`.
`design` | A formula in terms of `colData(weitrix` or a design matrix, which will be fitted to the weitrix on each row. Can also be a Components object.
`covar` | Optional. A covariate. Specify as you would with `ggplot2::aes`. Can be a matrix of the same size as `weitrix`.
`cat` | Optional. A categorical variable to break down the data by. Specify as you would with `ggplot2::aes`.
`funnel` | Flag. Produce a funnel plot? Note: `covar` can not be used for funnel plots.
`guides` | Show blue guide lines.

### Details

This function is not memory efficient. It is suitable for typical bulk data, but generally not not for single-cell.

Defaults to a boxplot of sqrt(weight) weighted residuals. Blue guide bars are shown for the expected quartiles, these will ideally line up with the boxplot.

If `cat` is given, it will be used to break the elements down into categories.

If `covar` is given, sqrt(weight) weighted residuals are plotted versus the covariate, with red trend lines for the mean and for the mean +/- one standard deviation. If the weitrix is calibrated, the trend lines should be horizontal lines with y intercept close to -1, 0 and 1. Blue guide lines are shown for this ideal outcome.

Any of the variables available with `weitrix_calibrate_all` can be used for `covar` or `cat`.

### Value

A ggplot2 plot.

### Examples

    weitrix_calplot(simwei, ~1)
    weitrix_calplot(simwei, ~1, covar=mu)
    weitrix_calplot(simwei, ~1, cat=col)

    # weitrix_calplot should generally be used after calibration
    cal <- weitrix_calibrate_all(simwei, ~1, ~col+log(weight))
    weitrix_calplot(cal, ~1, cat=col)

    # You can use a matrix of the same size as the weitrix as a covariate.
    # It will often be useful to assess vs the original weighting.
    weitrix_calplot(cal, ~1, covar=weitrix_weights(simwei))

[Package weitrix version 1.6.0 Index]