Description Usage Arguments Methods (by class) See Also Examples
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, and levene = TRUE
then the Pvalue 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 
x 
A linear model formula. Alternatively, a fitted lm object from a linear model. 
... 
Optional arguments 
data 
A data frame in which to evaluate the formula. 
xlab 
a title for the x axis: see 
ylab 
a title for the y axis: see 
col 
a color for the lowess smoother line. 
smoother 
if TRUE then a smoothed lowess line will be added to the plot 
twosd 
if 
levene 
if 
formula
: Testing for equality of variance plot
lm
: Testing for equality of variance plot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  # 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 twoway model without interaction
data(soyabean.df)
soya.fit=lm(yield ~ planttime + cultivar, data = soyabean.df)
eovcheck(soya.fit)
# A twoway 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)

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