plot.manylm  R Documentation 
Four plots (selectable by which
) are currently available: a plot
of residuals against fitted values, a Normal QQ plot,
a ScaleLocation plot of √{ residuals } against fitted values,
a plot of Cook's distances versus row labels.
By default, all of them are provided.
The function is not yet available for manyglm object
## S3 method for class 'manylm' plot( x, res.type="pearson", which=1:4, caption=c("Residuals vs Fitted", "Normal QQ", "ScaleLocation", "Cook's distance"), overlay=TRUE, n.vars=Inf, var.subset=NULL, sub.caption=NULL, studentized= TRUE, ...) ## S3 method for class 'manyglm' plot( x, res.type="pit.norm", which=1, caption=c("Residuals vs Fitted", "Normal QQ", "ScaleLocation", "Cook's distance"), overlay=TRUE, n.vars=Inf, var.subset=NULL, sub.caption=NULL, ...)
x 

res.type 
type of residuals to plot. By default, 
which 
if a subset of the plots is required, specify a subset of
the numbers 
caption 
captions to appear above the plots 
overlay 
logical, whether or not the different variables should be overlaid on a single plot. 
n.vars 
the number of variables to include in the plot. 
var.subset 
the variables to include in the plot. 
sub.caption 
common title—above figures if there are multiple;
used as 
... 
other parameters to be passed through to plotting functions. 
studentized 
logical indicating whether studentized or standardized residuals should be used for plot 2 and 3. 
plot.manylm
is used to check the linear model assumptions that are made
when fitting a model via manylm
. Similarly, plot.manyglm
checks
the generalised linear model assumptions made when using manyglm
.
As in Wang et al (2012), you should check the residual vs fits plot for no pattern
(hence no suggestion of failure of key linearity and meanvariance assumptions).
For manylm fits of small datasets, it is desirable that residuals on the normal QQ plot be close
to a straight line, although in practice the most important thing is to make
sure there are no big outliers and no suggestion of strong skew in the data.
The recommended res.type
option for manyglm calls, "pitnorm", uses randomised quantile or "DunnSmyth"
residuals (Dunn & Smyth 1996). Note that for discrete data, these residuals
involve random number generation, and will not return identical results on replicate runs  so it is recommended
that you plot your data a few times to check if any pattern shows up consistently across replicate plots.
The other main residual option is "pearson", Pearson residuals. Note that all res.type options
are equivalent for manylm.
Some technical details on usage of this function:
sub.caption
 by default the function call  is shown as
a subtitle (under the xaxis title) on each plot when plots are on
separate pages, or as a subtitle in the outer margin (if any) when
there are multiple plots per page.
The ‘ScaleLocation’ plot, also called ‘SpreadLocation’ or ‘SL’ plot, takes the square root of the absolute residuals in order to diminish skewness (√{ E } is much less skewed than  E  for Gaussian zeromean E).
If studentized=FALSE
the ‘SL’, the QQ, and the ResidualLeverage
plot, use standardized residuals which have identical variance
(under the hypothesis) otherwise studentized residuals are used.
Unlike other plotting functions plot.manylm
and plot.manyglm
respectively do not have a subset argument, the subset needs to be specified
in the manylm
or respectively manyglm
function.
For all arguments that are formally located after the position of ...
,
positional matching does not work.
For restrictions on filename
see R's help on eps/pdf/jpeg.
Note that keep.window
will be ignored if write.plot
is
not show
.
Ulrike Naumann and David Warton <David.Warton@unsw.edu.au>.
Dunn, P.K., & Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236244.
Wang Y., Naumann U., Wright S.T. & Warton D.I. (2012). mvabund  an R package for modelbased analysis of multivariate abundance data. Methods in Ecology and Evolution 3, 471474.
manylm
require(graphics) data(spider) spiddat < mvabund(spider$abund) ## plot the diagnostics for the linear fit of the spider data spidlm < manylm(spiddat~., data=spider$x) plot(spidlm,which=1:2,col.main="red",cex=3,overlay=FALSE) plot(spidlm,which=1:4,col.main="red",cex=3,overlay=TRUE) ## plot the diagnostics for Poisson and negative binomial regression of the spider data glmP.spid < manyglm(spiddat~., family="poisson", data=spider$x) plot(glmP.spid, which=1) #note the marked fanshape on the plot glmNB.spid < manyglm(spiddat~., data=spider$x, family="negative.binomial") plot(glmNB.spid, which=1) #no fanshape plot(glmNB.spid, which=1) #note the residuals change on replotting, but no consistent trend
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