quantile {fitdistrplus} | R Documentation |
Quantile estimation from a fitted distribution
Description
Quantile estimation from a fitted distribution, optionally with confidence intervals calculated from the bootstrap result.
Usage
## S3 method for class 'fitdist'
quantile(x, probs = seq(0.1, 0.9, by=0.1), ...)
## S3 method for class 'fitdistcens'
quantile(x, probs = seq(0.1, 0.9, by=0.1), ...)
## S3 method for class 'bootdist'
quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = "two.sided",
CI.level = 0.95, ...)
## S3 method for class 'bootdistcens'
quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = "two.sided",
CI.level = 0.95, ...)
## S3 method for class 'quantile.fitdist'
print(x, ...)
## S3 method for class 'quantile.fitdistcens'
print(x, ...)
## S3 method for class 'quantile.bootdist'
print(x, ...)
## S3 method for class 'quantile.bootdistcens'
print(x, ...)
Arguments
x |
An object of class |
probs |
A numeric vector of probabilities with values in [0, 1] at which quantiles must be calculated. |
CI.type |
Type of confidence intervals : either |
CI.level |
The confidence level. |
... |
Further arguments to be passed to generic functions. |
Details
Quantiles of the parametric distribution are calculated for
each probability specified in probs
, using the estimated parameters.
When used with an object of class "bootdist"
or "bootdistcens"
, percentile
confidence intervals and medians etimates
are also calculated from the bootstrap result.
If CI.type
is two.sided
,
the CI.level
two-sided confidence intervals of quantiles are calculated.
If CI.type
is less
or greater
,
the CI.level
one-sided confidence intervals of quantiles are calculated.
The print functions show the estimated quantiles with percentile confidence intervals
and median estimates when a bootstrap resampling has been done previously,
and the number of bootstrap iterations
for which the estimation converges if it is inferior to the whole number of bootstrap iterations.
Value
quantile
returns a list with 2 components (the first two described below) when called with an object
of class "fitdist"
or "fitdistcens"
and 8 components (described below)
when called with an object of class
"bootdist"
or "bootdistcens"
:
quantiles |
a dataframe containing the estimated quantiles for each probability value specified in
the argument |
probs |
the numeric vector of probabilities at which quantiles are calculated. |
bootquant |
A data frame containing the bootstraped values for each quantile
(many rows, as specified in the call to |
quantCI |
If |
quantmedian |
Median of bootstrap estimates (per probability). |
CI.type |
Type of confidence interval: either |
CI.level |
The confidence level. |
nbboot |
The number of samples drawn by bootstrap. |
nbconverg |
The number of iterations for which the optimization algorithm converges. |
Author(s)
Marie-Laure Delignette-Muller and Christophe Dutang.
References
Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04.
See Also
fitdist
, bootdist
, fitdistcens
, bootdistcens
and CIcdfplot
.
Examples
# (1) Fit of a normal distribution on acute toxicity log-transformed 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, followed with calculations of their
# confidence intervals with various definitions, from a small number of bootstrap
# iterations to satisfy CRAN running times constraint.
# For practical applications, we recommend to use at least niter=501 or niter=1001.
#
data(endosulfan)
ATV <- subset(endosulfan, group == "NonArthroInvert")$ATV
log10ATV <- log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
fln <- fitdist(log10ATV, "norm")
quantile(fln, probs = c(0.05, 0.1, 0.2))
bln <- bootdist(fln, bootmethod="param", niter=101)
quantile(bln, probs = c(0.05, 0.1, 0.2))
quantile(bln, probs = c(0.05, 0.1, 0.2), CI.type = "greater")
quantile(bln, probs = c(0.05, 0.1, 0.2), CI.level = 0.9)
# (2) Draw of 95 percent confidence intervals on quantiles of the
# previously fitted distribution
#
cdfcomp(fln)
q1 <- quantile(bln, probs = seq(0,1,length=101))
points(q1$quantCI[1,],q1$probs,type="l")
points(q1$quantCI[2,],q1$probs,type="l")
# (2b) Draw of 95 percent confidence intervals on quantiles of the
# previously fitted distribution
# using the NEW function CIcdfplot
#
CIcdfplot(bln, CI.output = "quantile", CI.fill = "pink")
# (3) Fit of a distribution on acute salinity log-transformed tolerance
# for riverine macro-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, followed with calculations of
# their confidence intervals with various definitions.
# from a small number of bootstrap iterations to satisfy CRAN running times constraint.
# For practical applications, we recommend to use at least niter=501 or niter=1001.
#
data(salinity)
log10LC50 <-log10(salinity)
flncens <- fitdistcens(log10LC50,"norm")
quantile(flncens, probs = c(0.05, 0.1, 0.2))
blncens <- bootdistcens(flncens, niter = 101)
quantile(blncens, probs = c(0.05, 0.1, 0.2))
quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.type = "greater")
quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.level = 0.9)