eovcheck {s20x} | R Documentation |
Plots the residuals versus the fitted (or predicted) values from a linear model. A horizontal line is drawn at y = 0, reflecting the fact that we expect the residuals to have a mean of zero. An optional lowess line is drawn if smoother is set to TRUE. This can be useful in determining whether a trend still exists in the residuals. An optional pair of lines is drawn at +/- 2 times the standard deviation of the residuals - which is estimated from the Residual Mean Sqare (Within group mean square = WGMS). This can be useful in highlighting potential outliers. If the model has one or two factors and no continous variables, i.e. if it is a oneway or twoway ANOVA model then the P-value from Levene's test for equality variance is displayed in the top left hand corner,as long as the number of observations per group exceeds two.
eovcheck(object, ...) ## S3 method for class 'formula': eovcheck (object, data = NULL, xlab = NULL, col = NULL ,smoother = FALSE, twosd = FALSE, ...) ## S3 method for class 'lm': eovcheck (object, smoother = FALSE, twosd = FALSE, ...)
object |
A linear model formula. Alternatively, a fitted lm object from a linear model. |
data |
A data frame in which to evaluate the formula. |
xlab |
a title for the x axis: see title . |
col |
a color for the lowess smoother line. |
smoother |
if TRUE then a smoothed lowess line will be added to the plot |
twosd |
if TRUE then horizontal dotted lines will be drawn at +/-2sd |
... |
Optional arguments |
"levene.test"
# one way ANOVA - oysters data(oysters.df) oyster.fit<-lm(Oysters~Site, data = oysters.df) eovcheck(oyster.fit) # Same model as the previous example, but using eovcheck.formula data(oysters.df) eovcheck(Oysters~Site, data = oysters.df) # A two-way model without interaction data(soyabean.df) soya.fit<-lm(yield~planttime+cultivar, data = soyabean.df) eovcheck(soya.fit) # A two-way model with interaction data(arousal.df) arousal.fit<-lm(arousal~gender*picture, data = arousal.df) eovcheck(arousal.fit) # A regression model data(peru.df) peru.fit<-lm(BP~height+weight+age+years, data = peru.df) eovcheck(peru.fit) # A time series model data(airpass.df) t<-1:144 month<-factor(rep(1:12,12)) airpass.df<-data.frame(passengers = airpass.df$passengers, t = t, month = month) airpass.fit<-lm(log(passengers)[-1]~t[-1]+month[-1]+log(passengers)[-144], data = airpass.df) eovcheck(airpass.fit)