int.plot.lm {pda} | R Documentation |
A plot is created showing the requested function of responses
for each level of the x.factor
at each level of the
trace.factor
.
## S3 method for class 'lm': ci.plot(object, ..., bar.plot="ci") ## S3 method for class 'lme': ci.plot(object, data, ..., bar.plot="ci") ## S3 method for class 'lmer': ci.plot(object, data, ..., bar.plot="ci") ## S3 method for class 'lm': int.plot(object, data, factors, lsm, ci, offset, bar.plot="lsd", ylim, type, width, sort.mean=FALSE, edge, xlab, ylab, level, rdf, fine, xpos, ypos, cex, white, panelf = NULL,...) ## S3 method for class 'lme': int.plot(object, data, ...) ## S3 method for class 'lmer': int.plot(object, data, ...)
object |
fitted model object (optional unless response is omitted). |
data |
data frame in which to interpret the variables named
in the fit object (taken from object if omitted). |
factors |
Character string of length 2 with names of
x.factor and trace.factor as found in object
and data (default is first 2 factors in object ). |
lsm |
Least squares means for plotting (default is object from model). |
sort.mean |
should x.factor levels be sorted by mean value
(default=FALSE). |
ylim,type,xlab,ylab |
plot parameters |
ci,offset,width,edge,xpos,ypos,level,rdf,fine,cex |
optional
parameters for bar-drawing routines lsd.bar and
se.bar . There are some parameters, such as width and
edge which can change to plotting area. |
bar.plot |
"lsd" (default for int.plot ), "ci" (default for
ci.plot ), "test", "ellipse" or "none". The "test" is a
sqrt(2) contraction of confidence interval for approximate graphical
comparisons of means. |
white |
white plot background if TRUE (default) |
panelf |
optional extra panel function for lattice plot |
... |
optional parameters for plotting routines |
list containing sd, rdf, level and sample size, and either lsd (lsd.plot) or width (ci.plot).
int.plot
,
xyplot
, margin.plot.lm
,
lsd.bar
, ci.width
,
pvalue.ellipse
.
data(ToothGrowth) ToothGrowth$dose = ordered(ToothGrowth$dose) tooth.fit = aov(len~dose*supp, ToothGrowth) lsd.plot( tooth.fit, ToothGrowth, factors = c("supp","dose")) cat.lsm = lsmean(tooth.fit) lsd.plot(tooth.fit, ToothGrowth, factors = c("supp","dose"), lsm = cat.lsm)