fit.gv.lm: Linear regression of observed pixel values

In ClairBee/IO.Pixels: Functions for analysis of distribution of dead pixels (part of Inside Out project).

Description

Support function: perform linear regression of specified variables

Usage

fit.gv.lm(im, terms = "g ~ b * w", midline = 1024.5, res.only = T)

Arguments

im	Acquisition array on which regression is to be performed. Must contain black, white and grey images.
terms	Text string providing formula to be fitted. Default is "g ~ b * w", predicting grey value based on black and white.
res.only	Boolean: return only a matrix of residuals (T) or (F) a list containing the fitted data, residuals and original data, along with the RMSE and adjusted RMSE.

Value

If res.only == T, returns a matrix of residuals. If F, returns a list containing a data.frame and the RMSE of the fitted model.

ClairBee/IO.Pixels documentation built on May 6, 2019, 11:50 a.m.