eovcheck {s20x} | R Documentation |
Testing for equality of variance plot
Description
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 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.
Usage
eovcheck(x, ...)
## S3 method for class 'formula'
eovcheck(
x,
data = NULL,
xlab = "Fitted values",
ylab = "Residuals",
col = NULL,
smoother = FALSE,
twosd = FALSE,
levene = FALSE,
...
)
## S3 method for class 'lm'
eovcheck(x, smoother = FALSE, twosd = FALSE, levene = FALSE, ...)
Arguments
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 |
Methods (by class)
-
eovcheck(formula)
: Testing for equality of variance plot -
eovcheck(lm)
: Testing for equality of variance plot
See Also
Examples
# 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)