• using R version 2.13.0 Under development (unstable) (2011-02-22 r54548)
• using platform: i386-apple-darwin9.8.0 (32-bit)
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• this is package ‘mixAK’ version ‘1.3’
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  sub-directories of 1Mb or more:
  data 3.4Mb
  doc 11.4Mb
  libs 5.1Mb
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• loading checks for arch ‘i386’
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• checking for unstated dependencies in R code ... OK
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• checking sizes of PDF files under inst/doc ... OK
• checking examples ...
  ** running examples for arch ‘i386’ ... OK
  ** running examples for arch ‘ppc’ ... ERROR
  Running examples in ‘mixAK-Ex.R’ failed
  The error most likely occurred in:
  
  > ### Name: MVNmixture
  > ### Title: Mixture of (multivariate) normal distributions
  > ### Aliases: MVNmixture dMVNmixture dMVNmixture2 rMVNmixture rMVNmixture2
  > ### Keywords: distribution multivariate
  >
  > ### ** Examples
  >
  > set.seed(1977)
  >
  > ##### Univariate normal mixture
  > ##### =========================
  > mu <- c(-1, 1)
  > Sigma <- c(0.25^2, 0.4^2)
  > Q <- 1/Sigma
  > w <- c(0.3, 0.7)
  >
  > xx <- seq(-2, 2.5, length=100)
  > yyA <- dMVNmixture(xx, weight=w, mean=mu, Sigma=Sigma)
  > yyB <- dMVNmixture(xx, weight=w, mean=mu, Q=Q)
  > yyC <- dMVNmixture2(xx, weight=w, mean=mu, Sigma=Sigma)
  > yyD <- dMVNmixture2(xx, weight=w, mean=mu, Q=Q)
  >
  > xxSample <- rMVNmixture(1000, weight=w, mean=mu, Sigma=Sigma)
  > xxSample2 <- rMVNmixture2(1000, weight=w, mean=mu, Sigma=Sigma)
  >
  > sum(abs(xxSample2$dens - dMVNmixture(xxSample2$x, weight=w, mean=mu, Sigma=Sigma)) > 1e-15)
  [1] 0
  > xxSample2 <- xxSample2$x
  >
  > par(mfrow=c(2, 2), bty="n")
  > plot(xx, yyA, type="l", col="red", xlab="x", ylab="f(x)")
  > points(xx, yyB, col="darkblue")
  > hist(xxSample, col="lightblue", prob=TRUE, xlab="x", xlim=range(xx), ylim=c(0, max(yyA)), main="Sampled values")
  > lines(xx, yyA, col="red")
  > plot(xx, yyC, type="l", col="red", xlab="x", ylab="f(x)")
  > points(xx, yyD, col="darkblue")
  > hist(xxSample2, col="sandybrown", prob=TRUE, xlab="x", xlim=range(xx), ylim=c(0, max(yyA)), main="Sampled values")
  > lines(xx, yyC, col="red")
  >
  >
  > ##### Bivariate normal mixture
  > ##### ========================
  > ### Choice 1
  > sd11 <- sd12 <- 1.1
  > sd21 <- 0.5
  > sd22 <- 1.5
  > rho2 <- 0.7
  > Xlim <- c(-3, 4)
  > Ylim <- c(-6, 4)
  >
  > ### Choice 2
  > sd11 <- sd12 <- 0.3
  > sd21 <- 0.5
  > sd22 <- 0.3
  > rho2 <- 0.8
  > Xlim <- c(-3, 2.5)
  > Ylim <- c(-2.5, 2.5)
  >
  > mu <- matrix(c(1,1, -1,-1), nrow=2, byrow=TRUE)
  > Sigma <- list(diag(c(sd11^2, sd12^2)), matrix(c(sd21^2, rho2*sd21*sd22, rho2*sd21*sd22, sd22^2), nrow=2))
  > Q <- list(chol2inv(chol(Sigma[[1]])), chol2inv(chol(Sigma[[2]])))
  > w <- c(0.3, 0.7)
  >
  > xx1 <- seq(mu[2,1]-3*sd21, mu[1,1]+3*sd11, length=100)
  > xx2 <- seq(mu[2,2]-3*sd22, mu[1,2]+3*sd12, length=90)
  > XX <- cbind(rep(xx1, length(xx2)), rep(xx2, each=length(xx1)))
  > yyA <- matrix(dMVNmixture(XX, weight=w, mean=mu, Sigma=Sigma), nrow=length(xx1), ncol=length(xx2))
  > yyB <- matrix(dMVNmixture(XX, weight=w, mean=mu, Q=Q), nrow=length(xx1), ncol=length(xx2))
  > yyC <- matrix(dMVNmixture2(XX, weight=w, mean=mu, Sigma=Sigma), nrow=length(xx1), ncol=length(xx2))
  > yyD <- matrix(dMVNmixture2(XX, weight=w, mean=mu, Q=Q), nrow=length(xx1), ncol=length(xx2))
  >
  > xxSample <- rMVNmixture(1000, weight=w, mean=mu, Sigma=Sigma)
  
  *** caught segfault ***
• elapsed time (check, wall clock): 204:53