bootMSD and associated methods.The object class returned by bootMSD and associated
print, summary, and plotting classes.
# S3 method for bootMSD
print(x, ...) # S3 method for bootMSD
plot(x, ...)
# S3 method for bootMSD
barplot(height, ylab="MSD", names.arg=height$labels,
crit.vals=TRUE, lty.crit=c(2,1), col.crit=2, lwd.crit=c(1,2), ylim=NULL, ... )
# S3 method for bootMSD
summary(object, p.adjust="none", ...)
# S3 method for summary.bootMSD
print(x, digits=3, ...,
signif.stars = getOption("show.signif.stars"),
signif.legend=signif.stars)
The print method returns the object, invisibly.
The plot and barplot methods return the values at the midpoint of each bar.
The summary method returns an object of class "summary.bootMSD" which
is a list with members:
Calculated MSD values from msd
character vector of labels for individual data points
Probabilities used for quantiles
matrix of quantiles. Each row corresponds to a probability
in probs and each column to an individual data point.
p-values estimated as the observed proportion of
simulated values exceeding the MSD value calculated by msd.
Character value containing the name of the p-value adjustment method used.
p-values adjusted using the given p-value adjustment method
specified by p.adjust.
Number of bootstrap replicates used.
The sampling method used by the parametric bootstrap.
An R object. For print.bootMSD and plot.bootMSD, an object
of class "bootMSD". For print.summary.bootMSD, an object
of class "summary.bootMSD".
An object of class "bootMSD".
An object of class "MSD".
Multiple correction method for calculated p-values, passed to
p.adjust.
Label for vertical axis, passed to barplot
Labels for individual bars in bar plot, passed to barplot. If names(height)
is NULL, bars are numbered.
If TRUE, individual critical values based on observation-specific
bootstrap quantiles are added to the plot. These are taken from critical.values
in the supplied bootMSD object.
Vectors of line style parameters for plotted critical values, passed to
segments. Recycled to the length of critical.values
in the supplied bootMSD object.
Limits for plot y range, passed to barplot. The default
ensures that the plotted bars and (if crit.vals=TRUE) the critical
values are included in the figure region.
integer; passed to print. The minimum number of
significant digits to be printed in values. Change to NULL for default.
logical; if TRUE, P-values are additionally encoded
visually as ‘significance stars’ in order to help scanning of
long coefficient tables. Defaults to the show.signif.stars
slot of options.
logical; if TRUE, a legend for the ‘significance
stars’ is printed provided signif.stars == TRUE.
Parameters passed to other methods.
S. L. R. Ellison s.ellison@lgcgroup.com
The default plot method is an alias for the barplot method.
For the plot methods, quantiles for each point are taken directly from the quantiles
calulated by bootMSD and retained in the returned object.
For the summary method, p-values are initially calculated as the observed
proportion of simulated values exceeding the MSD value calculated by msd. The
summary method additionally returns p-values after adjustment
for multiple comparisons using the adjustment method specified.
The print method for the summary.bootMSD object prints the summary as a data
frame adjusted with columns for the calculated MSD values, data-specific upper quantiles
(one column for each probability supplied to bootMSD and the p-values
after adjustment for multiple comparisons based on the proportion of simulated values
exceeding the observed MSD. Where that proportion is zero, the summary replaces the
raw zero proportion with 1/B, corrects that proportion using the requested
adjustment method, andreports the p-value as less than ("<") the resulting
adjusted value.
msd, qmsd.
if (FALSE) {
data(Pb)
msd.Pb<-msd(Pb$value, Pb$u) # Uses individual standard uncertainties
set.seed(1023)
boot.Pb <- bootMSD(msd.Pb)
summary(boot.Pb)
# The default summary gives individual observation p-values. To
# avoid over-interpretation for the study as a whole,
# apply a sensible p-value adjustment:
summary(boot.Pb, p.adjust="holm")
plot(boot.Pb, crit=TRUE)
}
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