R Under development (unstable) (2024-07-10 r86888 ucrt) -- "Unsuffered Consequences"
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> library(fitdistrplus)
Loading required package: MASS
Loading required package: survival
> 
> # ?ppcomp
> 
> visualize <- FALSE # TRUE for manual tests with visualization of results
> nsample <- 1000
> nsample <- 10
> 
> # (1) Plot various distributions fitted to serving size data
> #
> data(groundbeef)
> serving <- groundbeef$serving
> fitW <- fitdist(serving, "weibull")
> fitln <- fitdist(serving, "lnorm")
> fitg <- fitdist(serving, "gamma")
> 
> # sanity checks
> try(ppcomp("list(fitW, fitln, fitg)"), silent = TRUE)
> try(ppcomp(list(fitW, fitln, fitg, a = 1)), silent = TRUE)
> 
> # real call
> res <- ppcomp(list(fitW, fitln, fitg))
> str(res)
List of 2
 $ obs          : num [1:254] 0.00197 0.00591 0.00984 0.01378 0.01772 ...
 $ probabilities: num [1:254, 1:3] 0.00966 0.01309 0.03048 0.0432 0.0432 ...
> 
> ppcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
+        main="ground beef fits", xlab="Theo.",
+        ylab="serving sizes (g)", xlim = c(0, 1/2))
> 
> ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+        main="ground beef fits", xlab="Theo.",
+        ylab="serving sizes (g)", xlogscale=TRUE, line01=FALSE)
Warning message:
In ppcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal",  :
  As a P-P plot should use the same scale on x and y axes, 
            both or none of the axes should be put in a logarithmic scale.
> 
> ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+        main="ground beef fits", xlab="Theo.",
+        ylab="serving sizes (g)", ylogscale=TRUE, line01=FALSE)
Warning message:
In ppcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal",  :
  As a P-P plot should use the same scale on x and y axes, 
            both or none of the axes should be put in a logarithmic scale.
> 
> ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+        main="ground beef fits", ylim=c(1e-3, 1), xlim=c(1e-3, 1),
+        fitpch=c("+", "-", "."))
> 
> if (requireNamespace ("ggplot2", quietly = TRUE)) {
+   ppcomp(list(fitW, fitln, fitg), plotstyle = "ggplot")
+ }
> 
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+          main="ground beef fits", xlab="Theo.",
+          ylab="serving sizes (g)", xlim = c(0, 1/2), plotstyle = "ggplot")
+   
+   ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+          main="ground beef fits", xlab="Theo.",
+          ylab="serving sizes (g)", xlogscale=TRUE, line01=FALSE, plotstyle = "ggplot")
+   
+   ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+          main="ground beef fits", xlab="Theo.",
+          ylab="serving sizes (g)", ylogscale=TRUE, line01=FALSE, plotstyle = "ggplot")
+   
+   ppcomp(list(fitW, fitln, fitg), legendtext=c("Weibull","lognormal","gamma"),
+          main="ground beef fits", ylim=c(1e-3, 1), xlim=c(1e-3, 1),
+          fitpch=c("+", "-", "."), plotstyle = "ggplot")
+ }
> 
> 
> 
> # (2) Plot lognormal distributions fitted by 
> # maximum goodness-of-fit estimation
> # using various distances (data plotted in log scale)
> #
> data(endosulfan)
> ATV <-subset(endosulfan, group == "NonArthroInvert")$ATV
> flnMGEKS <- fitdist(ATV,"lnorm",method="mge",gof="KS")
> flnMGEAD <- fitdist(ATV,"lnorm",method="mge",gof="AD")
> flnMGEADL <- fitdist(ATV,"lnorm",method="mge",gof="ADL")
> flnMGEAD2L <- fitdist(ATV,"lnorm",method="mge",gof="AD2L")
> llfit <- list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L)
> 
> ppcomp(llfit, main="fits of a lognormal dist. using various GOF dist.")
> 
> ppcomp(llfit, xlegend="topleft",
+        xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+        legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"))
Warning message:
In ppcomp(llfit, xlegend = "topleft", xlogscale = TRUE, main = "fits of a lognormal dist. using various GOF dist.",  :
  As a P-P plot should use the same scale on x and y axes, 
            both or none of the axes should be put in a logarithmic scale.
> 
> ppcomp(llfit, xlegend="topleft",
+        xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+        legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), 
+        fitcol=c("black", "darkgreen", "yellowgreen", "yellow2"))
Warning message:
In ppcomp(llfit, xlegend = "topleft", xlogscale = TRUE, main = "fits of a lognormal dist. using various GOF dist.",  :
  As a P-P plot should use the same scale on x and y axes, 
            both or none of the axes should be put in a logarithmic scale.
> 
> ppcomp(llfit, ynoise=FALSE,	xlogscale=TRUE, ylogscale=TRUE, xlim=c(1e-3,1), ylim=c(1e-3,1))
> 
> ppcomp(flnMGEKS)
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   ppcomp(llfit, main="fits of a lognormal dist. using various GOF dist.", plotstyle = "ggplot")
+   
+   ppcomp(llfit, xlegend="topleft",
+          xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+          legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), plotstyle = "ggplot")
+   
+   ppcomp(llfit, xlegend="topleft",
+          xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+          legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), 
+          fitcol=c("black", "darkgreen", "yellowgreen", "yellow2"), plotstyle = "ggplot")
+   
+   ppcomp(llfit, ynoise=FALSE,	xlogscale=TRUE, ylogscale=TRUE, xlim=c(1e-3,1), ylim=c(1e-3,1), plotstyle = "ggplot")
+   
+   ppcomp(flnMGEKS, plotstyle = "ggplot")
+ }
> 
> 
> 
> # (3) Plot lognormal distributions fitted by 
> # maximum goodness-of-fit estimation
> # using various distances (data plotted in log scale)
> #
> if (visualize)
+ {
+   x1 <- c(6.4,13.3,4.1,1.3,14.1,10.6,9.9,9.6,15.3,22.1,13.4,13.2,8.4,6.3,8.9,5.2,10.9,14.4)
+   n1 <- length(x1)
+   
+   dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b))
+   pgumbel <- function(q,a,b) exp(-exp((a-q)/b))
+   
+   f1 <- mledist(x1, "norm")
+   f2 <- mledist(x1, "gumbel", start = list(a = 10, b = 5))
+   f3 <- mledist(x1, "exp")
+   
+   xx1 <- pnorm(sort(x1), f1$estimate[1], f1$estimate[2])
+   xx2 <- pgumbel(sort(x1), f2$estimate[1], f2$estimate[2])
+   xx3 <- pexp(sort(x1), f3$estimate[1])
+   xlim <- c(xx1, xx2, xx3)
+   xlim <- range(xlim[which(is.finite(xlim))])
+   
+   # graph 1
+   plot(xx1, 1:n1/n1, col = "red", xlim = xlim)
+   points(xx2, 1:n1/n1, col = "green")
+   points(xx3, 1:n1/n1, col = "blue")
+   legend("bottomright", pch = 1, leg = c("Normal","Gumbel","Exp"), col = c("red","green","blue"))
+   
+   # graph 2
+   f1 <- fitdist(x1,"norm")
+   f2 <- fitdist(x1,"gumbel",start=list(a=10,b=5))
+   f3 <- fitdist(x1, "exp")
+   ppcomp(list(f1, f2, f3), fitcol=c("red","green","blue"), ynoise = FALSE, legendtext = c("Normal","Gumbel","Exp"))
+   
+   # graph 3
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     ppcomp(list(f1, f2, f3), fitcol=c("red","green","blue"), ynoise = FALSE, legendtext = c("Normal","Gumbel","Exp"), plotstyle = "gg")
+   }
+ }
> 
> 
> # (4) normal mixture
> #
> 
> #mixture of two normal distributions
> #density
> dnorm2 <- function(x, poid, m1, s1, m2, s2)
+   poid*dnorm(x, m1, s1) + (1-poid)*dnorm(x, m2, s2)
> #numerical approximate quantile function
> qnorm2 <- function(p, poid, m1, s1, m2, s2)
+ {
+   L2 <- function(x, prob)
+     (prob - pnorm2(x, poid, m1, s1, m2, s2))^2	
+   sapply(p, function(pr) optimize(L2, c(-1000, 1000), prob=pr)$minimum)
+ }	
> #distribution function		
> pnorm2 <- function(q, poid, m1, s1, m2, s2)
+   poid*pnorm(q, m1, s1) + (1-poid)*pnorm(q, m2, s2)		
> 
> 
> #basic normal distribution
> set.seed(1234)
> x2 <- c(rnorm(nsample, 5),  rnorm(nsample, 10))
> #MLE fit
> fit1 <- fitdist(x2, "norm2", "mle", start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0))
Warning messages:
1: In cov2cor(varcovar) :
  diag(V) had non-positive or NA entries; the non-finite result may be dubious
2: In sqrt(diag(varcovar)) : NaNs produced
> fit2 <- fitdist(x2, "norm2", "qme", probs=c(1/6, 1/4, 1/3, 1/2, 2/3), 
+                 start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0), upper=c(1/2, Inf, Inf, Inf, Inf))
> fit3 <- fitdist(x2, "norm2", "mge", gof="AD", 
+                 start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0), upper=c(1/2, Inf, Inf, Inf, Inf))
> 
> ppcomp(list(fit1, fit2, fit3), fitpch=rep(".", 3), fitcol=c("green", "red", "blue"))
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   ppcomp(list(fit1, fit2, fit3), fitpch=rep(".", 3), fitcol=c("green", "red", "blue"), plotstyle = "gg")
+ }
> 
> # (5) large data
> #
> if (visualize)
+ {
+   n <- 2e4
+   x <- rlnorm(n)
+   f1 <- fitdist(x, "lnorm")
+   f2 <- fitdist(x, "exp")
+   
+   ppcomp(list(f1, f2), fitpch=3)
+   ppcomp(list(f1, f2), fitlwd=2)
+   
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     ppcomp(list(f1, f2), fitpch=3, plotstyle = "ggplot")
+     ppcomp(list(f1, f2), fitlwd=2, plotstyle = "ggplot")
+   }
+   
+ }
> 
> 
> # (6) test legend labels
> #
> if (visualize)
+ {
+   serving <- groundbeef$serving
+   fitW <- fitdist(serving,"weibull")
+   fitW2 <- fitdist(serving,"weibull", method="qme", probs=c(1/3,2/3))
+   fitW3 <- fitdist(serving,"weibull", method="qme", probs=c(1/2,2/3))
+   fitln <- fitdist(serving,"lnorm")
+   fitg <- fitdist(serving,"gamma")
+   
+   ppcomp(list(fitW, fitln, fitg)) #distrib
+   ppcomp(list(fitW, fitW2, fitln, fitg)) #distrib+method
+   ppcomp(list(fitW, fitW2, fitW3, fitln, fitg)) #distrib+method+num
+   if (requireNamespace ("ggplot2", quietly = TRUE))
+     ppcomp(list(fitW, fitW2, fitW3, fitln, fitg), plotstyle = "ggplot") #distrib+method+num
+   
+ }
> 
> 
> 
> proc.time()
   user  system elapsed 
   2.89    0.51    3.37