# modcheck: Model checking plots In s20x: Functions for University of Auckland Course STATS 201/208 Data Analysis

## Description

Plots four model checking plots: an pred-res plot (residuals against predicted values), a Normal Quantile-Quantile (Q-Q) plot, a histogram of the residuals with a normal distribution super-imposed and a Cook's Distance plot.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```modcheck(x, ...) ## S3 method for class 'lm' modcheck( x, plotOrder = 1:4, args = list(eovcheck = list(smoother = FALSE, twosd = FALSE, levene = FALSE, ...), normcheck = list(xlab = c("Theoretical Quantiles", ""), ylab = c("Sample Quantiles", ""), main = c("", ""), col = "light blue", bootstrap = FALSE, B = 5, bpch = 3, bcol = "lightgrey", shapiro.wilk = FALSE, whichPlot = 1:2, usePar = TRUE, ...), cooks20x = list(main = "Cook's Distance plot", xlab = "observation number", ylab = "Cook's distance", line = c(0.5, 0.1, 2), cex.labels = 1, axisOpts = list(xAxis = TRUE), ...)), parVals = list(mfrow = c(2, 2), xaxs = "r", yaxs = "r", pty = "s", mai = c(0.2, 0.2, 0.05, 0.05)), ... ) ```

## Arguments

 `x` a vector of observations, or the residuals from fitting a linear model. Alternatively, a fitted `lm` object. If `x` is a single vector, then the implicit assumption is that the mean (or null) model is being fitted, i.e. `lm(x ~ 1)` and that the data are best summarised by the sample mean. `plotOrder` the order of the plots. 1: pred-res plot, 2: normal Q-Q plot, 3: histogram, 4: Cooks's Distance plot. `args` a list containing three additional lists `eovcheckArgs`, `normcheckArgs` and `cooksArgs`. The elements of these lists are the optional arguments of `eovcheck`, `normcheck` and `cooks20x`, and are explained in more detail in those functions. Most users will never use these arguments, but they provide super-flexibility in terms of what is displayed. `parVals` the values that are set via `par` for this plot. These are `mfrow`, `xaxs`, `yaxs`, `pty`, and `mai`. Most users will never use these arguments, but they provide super-flexibility in terms of what is displayed. `...` additional paramaters. Included for future flexibility, but unsure how this might be used currently.

## Methods (by class)

• `lm`: Model checking plots

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# An exponential growth curve e = rnorm(100, 0, 0.1) x = rnorm(100) y = exp(5 + 3 * x + e) fit = lm(y ~ x, data = data.frame(x, y)) modcheck(fit) # An exponential growth curve with the correct transformation fit = lm(log(y) ~ x, data = data.frame(x, y)) modcheck(fit) # Peruvian Indians data data(peru.df) modcheck(lm(BP ~ weight, data = peru.df)) ```

s20x documentation built on Aug. 23, 2021, 9:15 a.m.