tfiplot(x, tfi.formula = formula(x), method, clims = T,
sig = 0.1, ...)
aov object, or more typically, a
fac.aov object created
from fitting an aov model to a fractional factorial design.
clims = TRUE (see below). Setting
method="mse" uses root
mean squared error, "pse" uses a pseudo standard error estimate.
"tse"
uses a 60% trimmed standard error of the effects.
"ase" uses an
adaptive standard error estimate. The default is
"pse" unless
"mse" is available.
sig level of significance.
clims=TRUE, 100(1-sig)% confidence limits are shown.
plot.
Creates two and three way interaction plots. Each two way interaction plot consists of the predicted means of the response at all combinations of the two experimental factors involved. The levels of the first factor are shown on the x-axis. The response means are connected by different patterned lines for each level of the second factor. The three way interaction plots are similar, except that each two way interaction plot is at different levels of the third factor.
tfiplot.aov
computes and plots predicted means via
proj. In many
cases this will mean refitting the model to obtain the
qr component.
Haaland, P. D. (1989), Experimental Design in Biotechnology, New York: Marcel Dekker.
buffer.fac <- fac.aov(buffer.df)
tfiplot(buffer.fac, ~pH:thimer+pH:gent) # two sets of interactions
tfiplot(buffer.fac, ~thimer*pH+pH:gent) # same as above: '*' and ':'
# are equivalent in this function
tfiplot(buffer.fac, ~pH:thimer+pH:gent, sig=.05) # change sig level
tfiplot(buffer.fac, ~pH:thimer, sig=.05, method="ase") # one interaction
tfiplot(buffer.fac, ~pH:thimer, clims=F) # do not plot confidence limits