# weitrix_calplot: Weight calibration plots, optionally versus a covariate In weitrix: Tools for matrices with precision weights, test and explore weighted or sparse data

## 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

 ```1 2 3 4 5 6 7 8``` ```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`.

A ggplot2 plot.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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)) ```

weitrix documentation built on Nov. 8, 2020, 8:10 p.m.