Nothing
library(fitdistrplus)
# (1) Plot of an empirical censored distribution (censored data) as a CDF
# using the default Turnbull method
#
data(smokedfish)
plotdistcens(smokedfish)
d1 <- as.data.frame(log10(smokedfish))
plotdistcens(d1)
# (2) Add the CDF of a normal distribution and QQ and PP plots
#
plotdistcens(smokedfish,"lnorm", para=list(meanlog=-3.6,sdlog=3.5))
plotdistcens(d1,"norm", para=list(mean=-1.6,sd=1.5))
# (3) Various plots of the same empirical distribution
#
# default Wang plot
plotdistcens(d1, NPMLE = TRUE, NPMLE.method = "Wang")
plotdistcens(d1, NPMLE = TRUE, NPMLE.method = "Wang", lwd = 3, main = "Wang ECDF plot")
# Turnbull plot
plotdistcens(d1, NPMLE = TRUE, NPMLE.method = "Turnbull", col = "red",
main = "Turnbull ECDF plot")
plotdistcens(d1,Turnbull = TRUE) # deprecated way to do it
# Turnbull plot with confidence intervals
plotdistcens(d1,NPMLE = TRUE, NPMLE.method = "Turnbull", Turnbull.confint = TRUE)
plotdistcens(d1,Turnbull = TRUE,Turnbull.confint = TRUE) # deprecated way to do it
# with intervals and points
plotdistcens(d1,NPMLE = FALSE)
plotdistcens(d1,NPMLE = FALSE, col = "red", lwd = 2)
plotdistcens(d1,rightNA=3, NPMLE = FALSE)
plotdistcens(d1,rightNA=3, Turnbull = FALSE) # deprecated way to do it
# with intervals and points
# defining a minimum value for left censored values
plotdistcens(d1,leftNA=-3, NPMLE = FALSE)
# (4) Goodness-of-fit plots for the same dataset after logarithmic transformation
# with a lognormal distribution, successively using the three proposed methods
#
d3 <- smokedfish
plotdistcens(d3,"lnorm",para=list(meanlog=-3.6,sdlog=3.5), main = "Wang plot")
plotdistcens(d3,"lnorm",para=list(meanlog=-3.6,sdlog=3.5),
NPMLE.method = "Turnbull", main = "Turnbull plot")
plotdistcens(d3,"lnorm",para=list(meanlog=-3.6,sdlog=3.5),
NPMLE = FALSE, leftNA=0, main = "Plot of ordered intervals")
# Test with the salinity data set
#
data(salinity)
log10LC50 <-log10(salinity)
plotdistcens(log10LC50)
plotdistcens(log10LC50, NPMLE.method = "Turnbull")
plotdistcens(log10LC50, NPMLE = FALSE)
fn <- fitdistcens(log10LC50,"norm")
fl <- fitdistcens(log10LC50,"logis")
plot(fn)
plot(fl)
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