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 xlimits of the plot. 
ylim 
The ylimits of the plot. 
xlogscale 
If 
ylogscale 
If 
main 
A main title for the plot, see also 
xlab 
A label for the xaxis, defaults to a description of 
ylab 
A label for the yaxis, defaults to a description of 
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
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