pareto(x, method=<<see below>>, sig=.05, xmax=<<see below>>, prt.values=T, shade=F, ...)
fac.aov
object, typically created by
fac.aov
.
all lines are drawn;
"pse"
if
df.residual = 0
, otherwise, if
"mse"
is
available, both
"mse"
and
"pse"
are used.
xlim
.
If
fac.aov
or
update.fac.aov
are used to create an unsaturated
model, then the Pareto plot will show the
seffects
(from the
saturated model) rather than the
feffects
. The significance
line for the
pse
method will also be based on the
seffects
.
Haaland, P. D. (1989),
Experimental Design in Biotechnology,
New York: Marcel Dekker.
Haaland, P. D. and M. A. O'Connell (1994), Inference for effect
saturated fractional factorials, to appear in Technometrics.
buffer.fac <- fac.aov(buffer.df) pareto(buffer.fac) # use all defaults pareto(buffer.fac, sig=.1) # use .1 significance level to plot lines pareto(buffer.fac, method="none") # do not plot significance lines pareto(buffer.fac, "all") # plot all possible significance lines pareto(buffer.fac, "ase") # plot only "ase" significance line # show what happens with an unsaturated model buffer.fac1 <- update(buffer.fac,~pH*thimer+pH*gent) pareto(buffer.fac1) buffer.fac2 <- fac.aov(rate~pH*thimer+pH*gent,buffer.df) pareto(buffer.fac2)