CIcdfplot  R Documentation 
cdfband
plots the empirical cumulative distribution function with the bootstraped pointwise confidence intervals on probabilities of on quantiles.
CIcdfplot(b, CI.output, CI.type = "two.sided", CI.level = 0.95, CI.col = "red",
CI.lty = 2, CI.fill = NULL, CI.only = FALSE, xlim, ylim, xlogscale = FALSE,
ylogscale = FALSE, main, xlab, ylab, datapch, datacol, fitlty, fitcol, fitlwd,
horizontals = TRUE, verticals = FALSE, do.points = TRUE, use.ppoints = TRUE,
a.ppoints = 0.5, name.points = NULL, lines01 = FALSE, plotstyle = "graphics", ...)
b 
One 
CI.output 
The quantity on which (bootstraped) bootstraped confidence intervals are computed:
either 
CI.type 
Type of confidence intervals : either 
CI.level 
The confidence level. 
CI.col 
the color of the confidence intervals. 
CI.lty 
the line type of the confidence intervals. 
CI.fill 
a color to fill the confidence area. Default is 
CI.only 
A logical whether to plot empirical and fitted distribution functions
or only the confidence intervals. Default to 
xlim 
The 
ylim 
The 
xlogscale 
If 
ylogscale 
If 
main 
A main title for the plot, see also 
xlab 
A label for the 
ylab 
A label for the 
datapch 
An integer specifying a symbol to be used in plotting data points,
see also 
datacol 
A specification of the color to be used in plotting data points. 
fitcol 
A (vector of) color(s) to plot fitted distributions. If there are fewer colors than fits they are recycled in the standard fashion. 
fitlty 
A (vector of) line type(s) to plot fitted distributions/densities.
If there are fewer values than fits they are recycled in the standard fashion.
See also 
fitlwd 
A (vector of) line size(s) to plot fitted distributions/densities.
If there are fewer values than fits they are recycled in the standard fashion.
See also 
horizontals 
If 
verticals 
If 
do.points 
logical; if 
use.ppoints 
If 
a.ppoints 
If 
name.points 
Label vector for points if they are drawn i.e. if do.points = TRUE (only for non censored data). 
lines01 
A logical to plot two horizontal lines at 
plotstyle 

... 
Further graphical arguments passed to 
CIcdfplot
provides a plot of the empirical distribution using
cdfcomp
or cdfcompcens
,
with bootstraped pointwise confidence intervals on probabilities (y values)
or on quantiles (x values).
Each interval is computed by evaluating the quantity of interest (probability
associated to an x value or quantile associated to an y value) using all the
bootstraped values of parameters to get a bootstraped sample
of the quantity of interest and then by calculating percentiles on this sample to get a
confidence interval (classically 2.5 and 97.5 percentiles for a 95 percent
confidence level).
If CI.fill != NULL
, then the whole confidence area is filled by the color CI.fill
thanks to the function polygon
, otherwise only borders are drawn thanks to the function
matline
. Further graphical arguments can be passed to these functions using
the three dots arguments ...
.
Christophe Dutang and MarieLaure DelignetteMuller.
DelignetteMuller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 134.
See also cdfcomp
, cdfcompcens
,
bootdist
and quantile
.
# We choose a low number of bootstrap replicates in order to satisfy CRAN running times
# constraint.
# For practical applications, we recommend to use at least niter=501 or niter=1001.
if (requireNamespace ("ggplot2", quietly = TRUE)) {ggplotEx < TRUE}
# (1) Fit of an exponential distribution
#
set.seed(123)
s1 < rexp(50, 1)
f1 < fitdist(s1, "exp")
b1 < bootdist(f1, niter= 11) #voluntarily low to decrease computation time
# plot 95 percent bilateral confidence intervals on y values (probabilities)
CIcdfplot(b1, CI.level= 95/100, CI.output = "probability")
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "probability", plotstyle = "ggplot")
# plot of the previous intervals as a band
CIcdfplot(b1, CI.level= 95/100, CI.output = "probability",
CI.fill = "pink", CI.col = "red")
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "probability",
CI.fill = "pink", CI.col = "red", plotstyle = "ggplot")
# plot of the previous intervals as a band without empirical and fitted dist. functions
CIcdfplot(b1, CI.level= 95/100, CI.output = "probability", CI.only = TRUE,
CI.fill = "pink", CI.col = "red")
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "probability", CI.only = TRUE,
CI.fill = "pink", CI.col = "red", plotstyle = "ggplot")
# same plot without contours
CIcdfplot(b1, CI.level= 95/100, CI.output = "probability", CI.only = TRUE,
CI.fill = "pink", CI.col = "pink")
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "probability", CI.only = TRUE,
CI.fill = "pink", CI.col = "pink", plotstyle = "ggplot")
# plot 95 percent bilateral confidence intervals on x values (quantiles)
CIcdfplot(b1, CI.level= 95/100, CI.output = "quantile")
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "quantile", plotstyle = "ggplot")
# plot 95 percent unilateral confidence intervals on quantiles
CIcdfplot(b1, CI.level = 95/100, CI.output = "quant", CI.type = "less",
CI.fill = "grey80", CI.col = "black", CI.lty = 1)
if (ggplotEx) CIcdfplot(b1, CI.level = 95/100, CI.output = "quant", CI.type = "less",
CI.fill = "grey80", CI.col = "black", CI.lty = 1, plotstyle = "ggplot")
CIcdfplot(b1, CI.level= 95/100, CI.output = "quant", CI.type = "greater",
CI.fill = "grey80", CI.col = "black", CI.lty = 1)
if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = "quant", CI.type = "greater",
CI.fill = "grey80", CI.col = "black", CI.lty = 1, plotstyle = "ggplot")
# (2) Fit of a normal distribution on acute toxicity logtransformed values of
# endosulfan for nonarthropod invertebrates, using maximum likelihood estimation
# to estimate what is called a species sensitivity distribution
# (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile
# values of the fitted distribution, which are called the 5, 10, 20 percent hazardous
# concentrations (HC5, HC10, HC20) in ecotoxicology, with their
# confidence intervals, from a small number of bootstrap
# iterations to satisfy CRAN running times constraint and plot of the band
# representing pointwise confidence intervals on any quantiles (any HCx values)
# For practical applications, we recommend to use at least niter=501 or niter=1001.
#
data(endosulfan)
log10ATV < log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
namesATV < subset(endosulfan, group == "NonArthroInvert")$taxa
fln < fitdist(log10ATV, "norm")
bln < bootdist(fln, bootmethod ="param", niter=101)
quantile(bln, probs = c(0.05, 0.1, 0.2))
CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlim = c(0,5), name.points=namesATV)
if (ggplotEx) CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlim = c(0,5), name.points=namesATV, plotstyle = "ggplot")
# (3) Same type of example as example (2) from ecotoxicology
# with censored data
#
data(salinity)
log10LC50 <log10(salinity)
fln < fitdistcens(log10LC50,"norm")
bln < bootdistcens(fln, niter=101)
(HC5ln < quantile(bln,probs = 0.05))
CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlab = "log10(LC50)",xlim=c(0.5,2),lines01 = TRUE)
if (ggplotEx) CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlab = "log10(LC50)",xlim=c(0.5,2),lines01 = TRUE, plotstyle = "ggplot")
# zoom around the HC5
CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1))
abline(h = 0.05, lty = 2) # line corresponding to a CDF of 5 percent
if (ggplotEx) CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1),
plotstyle = "ggplot") +
ggplot2::geom_hline(yintercept = 0.05, lty = 2) # line corresponding to a CDF of 5 percent
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.