removed library call to MASS

DESCRIPTION

@@ -1,7 +1,6 @@
 Package: logitnorm
 Title: Functions for the Logitnormal Distribution
-Version: 0.8.35
-Date: 2017-12-08
+Version: 0.8.36
 Author: Thomas Wutzler
 Maintainer: Thomas Wutzler <[email protected]>
 Description: Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

cranComments.md → cran-comments.md

@@ -1,6 +1,9 @@
 ## Note
 In response to email from Kurt Hornik Dez, 2nd 2017:
-Avoided writing file reports during testing and installation, 
+These seem to have undeclared package dependencies in their unit test
+code (R files in inst/unitTests), see below.
+
+We removed the library call to MASS
 
 ## Test environments
 * local R 3.4.3 on Mint17 64bit

inst/unitTests/runitLogitnorm.R

@@ -1,149 +1,156 @@
-.setUp <-function () {
-	#library(MASS)
-	.setUpDf <- within( list(),{
-			x <- seq(0,1,length.out=41)[-c(1,41)]; #x[1] = x[1] + .Machine$double.eps; x[length(x)] <- x[length(x)]- .Machine$double.eps
-			lx <- logit(x)			
-		})
-	attach(.setUpDf)
-}
-
-.tearDown <- function () {
-	#detach(.setUpDf)
-	detach()
-}
-
-
-
-test.inverseSame <- function(){
-	xnorm <- logit(x)
-	xinv <- invlogit(xnorm)
-	checkEquals(x, xinv)
-}
-
-test.plogitnorm <- function(){
-	px <- plogitnorm(x)	#percentiles
-	checkEquals( pnorm(lx), px)
-	px2 <- plogitnorm(x,mu=2,sigma=1)
-	checkEquals( pnorm(lx,mean=2,sd=1), px2)
-	#plot( px ~ x)
-	#plot( px ~ logit(x))
-	#lines( pnorm( logit(x)) ~ logit(x) )
-}
-
-test.twCoefLogitnorm <- function(){
-	theta <- twCoefLogitnorm(0.7,0.9,perc=0.999)
-	px <- plogitnorm(x,mu=theta[1],sigma=theta[2])	#percentiles function
-	dx <- dlogitnorm(x,mu=theta[1],sigma=theta[2])	#density function
-	#plot(px~x); abline(v=c(0.7,0.9)); abline(h=c(0.5,0.975))
-	#plot(dx~x); abline(v=c(0.7,0.9))
-	# upper percentile at 0.9
-	checkEquals(which.min(abs(px-0.999)), which(x==0.9) )
-	checkEquals(which.min(abs(px-0.5)), which.min(abs(x-0.7)) )
-	# mode at 0.7
-	#checkEquals(which(abs(x-0.7)<.Machine$double.eps), which.max(dx) )
-}
-
-test.twCoefLogitnormN <- function(){
-	quant=c(0.7,0.8,0.9)
-	perc=c(0.5,0.75,0.975)
-	(theta <- twCoefLogitnormN( quant=quant, perc=perc ))
-	#px <- plogitnorm(x,mu=theta[1],sigma=theta[2])	#percentiles function
-	#dx <- dlogitnorm(x,mu=theta[1],sigma=theta[2])	#density function
-	#plot(px~x); abline(v=quant,col="gray"); abline(h=perc,col="gray")
-}
-
-
-test.twCoefLogitnormMLE <- function(){
-	theta <- twCoefLogitnormMLE(0.7,0.9,perc=0.975)
-	px <- plogitnorm(x,mu=theta[1],sigma=theta[2])	#percentiles function
-	dx <- dlogitnorm(x,mu=theta[1],sigma=theta[2])	#density function
-	#plot(px~x); abline(v=c(0.7,0.9)); abline(h=c(0.5,0.975))
-	#plot(dx~x); abline(v=c(0.7,0.9))
-	# upper percentile at 0.9
-	checkEquals(which.min(abs(px-0.975)), which(x==0.9) )
-	# mode at 0.7
-	checkEquals(which.min(abs(x-0.7)), which.max(dx) )
-}
-
-
-test.twCoefLogitnormE <- function(){
-	theta <- twCoefLogitnormE(0.7,0.9)
-	px <- plogitnorm(x,mu=theta[1],sigma=theta[2])	#percentiles function
-	dx <- dlogitnorm(x,mu=theta[1],sigma=theta[2])	#density function
-	#plot(px~x); abline(v=c(0.7,0.9)); abline(h=c(0.5,0.975))
-	#plot(dx~x); abline(v=c(0.7,0.9))
-	# upper percentile at 0.9
-	checkEquals(which.min(abs(px-0.975)), which(x==0.9) )
-	# mean at 0.7
-	checkEqualsNumeric( momentsLogitnorm(mu=theta[1],sigma=theta[2])["mean"], 0.7, tolerance=1e-3)
-	z <- rlogitnorm(1e5, mu=theta[1],sigma=theta[2])
-	checkEqualsNumeric(0.7, mean(z), tolerance=5e-3 )
-}
-
-.tmp.f <- function(){
-	px <- plogitnorm(x)	#percentiles
-	plot( px ~ x )	
-	plot( qlogitnorm(px) ~ x ) 	#one to one line
-	plot( dlogitnorm(x,mu=0.9) ~ x, type="l" )
-	abline( v=qlogitnorm(c(0.025,0.5,0.975), mu=0.9))
-}
-
-.tmp.f <- function(){
-	library(MASS)
-	?fitdistr	#not implemented
-	quant = c(0.6,0.9)
-	perc = c(0.5,0.975)
-	theta0=c(mu=0,sigma=1)
-	method="BFGS"
-	#mtrace(ofLogitnorm)
-	#popt <- as.list(tmp$par)
-	popt <- as.list(coefLogitnorm(quant))
-	popt2 <- as.list(coefLogitnorm(quant, perc=c(0.5,0.9995)))
-	ofLogitnorm(popt,quant,perc)
-
-	plot( dlogitnorm(x,mu=popt$mu,sigma=popt$sigma) ~ x, type="l" )
-	abline( v=qlogitnorm(c(0.025,0.5,0.975),mu=popt$mu,sigma=popt$sigma))
-
-	lines( dlogitnorm(x,mu=popt2$mu,sigma=popt2$sigma) ~ x, type="l", col="maroon" )
-	abline( v=qlogitnorm(c(0.5,0.9995),mu=popt2$mu,sigma=popt2$sigma), col="maroon")
-
-	popt <- as.list(coefLogitnorm(c(0.9, 0.9995), perc=c(0.5,0.9995)))
-	plot( dlogitnorm(x,mu=popt$mu,sigma=popt$sigma) ~ x, type="l" )
-	abline( v=qlogitnorm(c(0.025,0.5,0.975),mu=popt$mu,sigma=popt$sigma))
-
-}
-
-.tmp.f <- function(){
-	#visualize the objective functions surface
-	quant = c(0.6,0.9)
-	perc = c(0.5,0.975)
-	perc = c(0.5,0.9995)
-	perc=c(0.5,upperBoundProb)
-	quant = parms.var[varDist=="logitnorm",c("qMedian","qUpper")]
-	quant = parms.var["epsF",c("qMedian","qUpper")]
-	quant = parms.var["epsG",c("qMedian","qUpper")]
-	quant = parms.var["epsP",c("qMedian","qUpper")]
-
-
-	tmp.n <- 80
-	tmp.mu <- seq(0,1,length.out=tmp.n)
-	tmp.sigma <- seq(0.01,2.5,length.out=tmp.n)
-	tmp <- as.matrix(expand.grid( mu=tmp.mu, sigma=tmp.sigma))
-	tmp.of <- apply(tmp,1,ofLogitnorm, quant=quant,perc=perc )
-	tmp.ofm <- matrix(tmp.of, nrow=tmp.n )
-	image(tmp.mu, tmp.sigma, tmp.ofm)
-	image(tmp.mu, tmp.sigma, exp(-0.5*tmp.ofm))	#very flat
-	image(tmp.mu, tmp.sigma, exp(-0.5*1/(1/800)*tmp.ofm))  #distort by decreasing Temp *1/T
-	tmp.o <- coefLogitnorm(quant=quant, perc=perc, returnDetails=TRUE)
-	tmp.o
-	popt <- as.list(tmp.o$par)
-	points( popt$mu, popt$sigma)
-
-	windows()
-	unlist(popt)
-	plot( dlogitnorm(x,mu=popt$mu,sigma=popt$sigma) ~ x, type="l" )
-	abline( v=qlogitnorm(perc,mu=popt$mu,sigma=popt$sigma))
-	lines( dlogitnorm(x,mu=popt$mu,sigma=1) ~ x, type="l", col="maroon" )
-	abline( v=qlogitnorm(c(0.025,0.5,0.975),mu=popt$mu,sigma=1), col="maroon")
-}
-
+.setUp <- function() {
+	#library(MASS)
+	.setUpDf <- within( list(),{
+			x <- seq(0,1,length.out = 41)[-c(1,41)]; 
+			#x[1] = x[1] + .Machine$double.eps; 
+			#x[length(x)] <- x[length(x)]- .Machine$double.eps
+			lx <- logit(x)			
+		})
+	attach(.setUpDf)
+}
+
+.tearDown <- function() {
+	#detach(.setUpDf)
+	detach()
+}
+
+
+
+test.inverseSame <- function(){
+	xnorm <- logit(x)
+	xinv <- invlogit(xnorm)
+	checkEquals(x, xinv)
+}
+
+test.plogitnorm <- function(){
+	px <- plogitnorm(x)	#percentiles
+	checkEquals( pnorm(lx), px)
+	px2 <- plogitnorm(x,mu = 2,sigma = 1)
+	checkEquals( pnorm(lx,mean = 2,sd = 1), px2)
+	#plot( px ~ x)
+	#plot( px ~ logit(x))
+	#lines( pnorm( logit(x)) ~ logit(x) )
+}
+
+test.twCoefLogitnorm <- function(){
+	theta <- twCoefLogitnorm(0.7,0.9,perc = 0.999)
+	px <- plogitnorm(x,mu = theta[1],sigma = theta[2])	#percentiles function
+	dx <- dlogitnorm(x,mu = theta[1],sigma = theta[2])	#density function
+	#plot(px~x); abline(v = c(0.7,0.9)); abline(h = c(0.5,0.975))
+	#plot(dx~x); abline(v = c(0.7,0.9))
+	# upper percentile at 0.9
+	checkEquals(which.min(abs(px - 0.999)), which(x == 0.9) )
+	checkEquals(which.min(abs(px - 0.5)), which.min(abs(x - 0.7)) )
+	# mode at 0.7
+	#checkEquals(which(abs(x-0.7)<.Machine$double.eps), which.max(dx) )
+}
+
+test.twCoefLogitnormN <- function(){
+	quant = c(0.7,0.8,0.9)
+	perc = c(0.5,0.75,0.975)
+	(theta <- twCoefLogitnormN( quant = quant, perc = perc ))
+	#px <- plogitnorm(x,mu = theta[1],sigma = theta[2])	#percentiles function
+	#dx <- dlogitnorm(x,mu = theta[1],sigma = theta[2])	#density function
+	#plot(px~x); abline(v = quant,col = "gray"); abline(h = perc,col = "gray")
+}
+
+
+test.twCoefLogitnormMLE <- function(){
+	theta <- twCoefLogitnormMLE(0.7,0.9,perc = 0.975)
+	px <- plogitnorm(x,mu = theta[1],sigma = theta[2])	#percentiles function
+	dx <- dlogitnorm(x,mu = theta[1],sigma = theta[2])	#density function
+	#plot(px~x); abline(v = c(0.7,0.9)); abline(h = c(0.5,0.975))
+	#plot(dx~x); abline(v = c(0.7,0.9))
+	# upper percentile at 0.9
+	checkEquals(which.min(abs(px - 0.975)), which(x == 0.9) )
+	# mode at 0.7
+	checkEquals(which.min(abs(x - 0.7)), which.max(dx) )
+}
+
+
+test.twCoefLogitnormE <- function(){
+	theta <- twCoefLogitnormE(0.7,0.9)
+	px <- plogitnorm(x,mu = theta[1],sigma = theta[2])	#percentiles function
+	dx <- dlogitnorm(x,mu = theta[1],sigma = theta[2])	#density function
+	#plot(px~x); abline(v = c(0.7,0.9)); abline(h = c(0.5,0.975))
+	#plot(dx~x); abline(v = c(0.7,0.9))
+	# upper percentile at 0.9
+	checkEquals(which.min(abs(px - 0.975)), which(x == 0.9) )
+	# mean at 0.7
+	checkEqualsNumeric( momentsLogitnorm(
+	  mu = theta[1],sigma = theta[2])["mean"], 0.7, tolerance = 1e-3)
+	z <- rlogitnorm(1e5, mu = theta[1],sigma = theta[2])
+	checkEqualsNumeric(0.7, mean(z), tolerance = 5e-3 )
+}
+
+.tmp.f <- function(){
+	px <- plogitnorm(x)	#percentiles
+	plot( px ~ x )	
+	plot( qlogitnorm(px) ~ x ) 	#one to one line
+	plot( dlogitnorm(x,mu = 0.9) ~ x, type = "l" )
+	abline( v = qlogitnorm(c(0.025,0.5,0.975), mu = 0.9))
+}
+
+.tmp.f <- function(){
+	#library(MASS)
+	#?fitdistr	#not implemented
+	quant = c(0.6,0.9)
+	perc = c(0.5,0.975)
+	theta0 = c(mu = 0,sigma = 1)
+	method = "BFGS"
+	#mtrace(ofLogitnorm)
+	#popt <- as.list(tmp$par)
+	popt <- as.list(coefLogitnorm(quant))
+	popt2 <- as.list(coefLogitnorm(quant, perc = c(0.5,0.9995)))
+	ofLogitnorm(popt,quant,perc)
+
+	plot( dlogitnorm(x,mu = popt$mu,sigma = popt$sigma) ~ x, type = "l" )
+	abline( v = qlogitnorm(c(0.025,0.5,0.975),mu = popt$mu,sigma = popt$sigma))
+
+	lines( dlogitnorm(
+	  x,mu = popt2$mu,sigma = popt2$sigma) ~ x, type = "l", col = "maroon" )
+	abline( v = qlogitnorm(
+	  c(0.5,0.9995),mu = popt2$mu,sigma = popt2$sigma), col = "maroon")
+
+	popt <- as.list(coefLogitnorm(c(0.9, 0.9995), perc = c(0.5,0.9995)))
+	plot( dlogitnorm(x,mu = popt$mu,sigma = popt$sigma) ~ x, type = "l" )
+	abline( v = qlogitnorm(c(0.025,0.5,0.975),mu = popt$mu,sigma = popt$sigma))
+
+}
+
+.tmp.f <- function(){
+	#visualize the objective functions surface
+	quant = c(0.6,0.9)
+	perc = c(0.5,0.975)
+	perc = c(0.5,0.9995)
+	perc = c(0.5,upperBoundProb)
+	quant = parms.var[varDist == "logitnorm",c("qMedian","qUpper")]
+	quant = parms.var["epsF",c("qMedian","qUpper")]
+	quant = parms.var["epsG",c("qMedian","qUpper")]
+	quant = parms.var["epsP",c("qMedian","qUpper")]
+
+
+	tmp.n <- 80
+	tmp.mu <- seq(0,1,length.out = tmp.n)
+	tmp.sigma <- seq(0.01,2.5,length.out = tmp.n)
+	tmp <- as.matrix(expand.grid( mu = tmp.mu, sigma = tmp.sigma))
+	tmp.of <- apply(tmp,1,ofLogitnorm, quant = quant,perc = perc )
+	tmp.ofm <- matrix(tmp.of, nrow = tmp.n )
+	image(tmp.mu, tmp.sigma, tmp.ofm)
+	image(tmp.mu, tmp.sigma, exp(-0.5*tmp.ofm))	#very flat
+	#distort by decreasing Temp *1/T
+	image(tmp.mu, tmp.sigma, exp(-0.5*1/(1/800)*tmp.ofm))  
+	tmp.o <- coefLogitnorm(quant = quant, perc = perc, returnDetails = TRUE)
+	tmp.o
+	popt <- as.list(tmp.o$par)
+	points( popt$mu, popt$sigma)
+
+	windows()
+	unlist(popt)
+	plot( dlogitnorm(x,mu = popt$mu,sigma = popt$sigma) ~ x, type = "l" )
+	abline( v = qlogitnorm(perc,mu = popt$mu,sigma = popt$sigma))
+	lines( dlogitnorm(x,mu = popt$mu,sigma = 1) ~ x, type = "l", col = "maroon" )
+	abline( v = qlogitnorm(
+	  c(0.025,0.5,0.975),mu = popt$mu,sigma = 1), col = "maroon")
+}
+