version 5.4.6

Author: luca-scr

Diff for: DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mclust
-Version: 5.4.5
-Date: 2019-07-07
+Version: 5.4.6
+Date: 2020-04-09
 Title: Gaussian Mixture Modelling for Model-Based Clustering,
         Classification, and Density Estimation
 Description: Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
@@ -23,14 +23,14 @@ URL: https://mclust-org.github.io/mclust/
 VignetteBuilder: knitr
 Repository: CRAN
 ByteCompile: true
-LazyData: yes
-RoxygenNote: 6.1.1
 NeedsCompilation: yes
-Packaged: 2019-07-07 15:54:41 UTC; luca
+LazyData: yes
+Encoding: UTF-8
+Packaged: 2020-04-09 17:43:44 UTC; luca
 Author: Chris Fraley [aut],
   Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>),
   Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>),
   Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>),
   Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)
 Maintainer: Luca Scrucca <[email protected]>
-Date/Publication: 2019-07-08 18:51:30 UTC
+Date/Publication: 2020-04-11 14:30:08 UTC

Diff for: MD5

@@ -1,47 +1,47 @@
-30dfe17ce10c3d506b6c7813e0949c9b *DESCRIPTION
+0ab7a94956810a24a28f652b84ff52a3 *DESCRIPTION
 6d6add2a10e03c1d9a1675092d5eee19 *NAMESPACE
-9f73c6a77f15384190d683246eefa94e *NEWS.md
-abc4ce05ea40fed290801fb7674bf4ae *R/bootstrap.R
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 7e0361ad8167e56f59687168450e6d22 *R/clustCombi.R
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 b76a3d8060cc93cec384445d98105a21 *R/gmmhd.R
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 ba84097ee321529784cd9c6db5143e77 *R/icl.R
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@@ -52,7 +52,7 @@ eb92ce9bfd46fcf3eb40fb062bd82071 *man/bic.Rd
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@@ -61,17 +61,17 @@ bdb14988bc5193739060f58cca67fd5f *man/clustCombiOptim.Rd
 fd9027722a416f5955196a5cdc985c53 *man/combMat.Rd
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 0ad6fd1b3b38c99e3dbb74292cdea0ae *man/combiTree.Rd
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@@ -93,17 +93,17 @@ cc916d8c4282eb5e20899b25d0b741ea *man/logLik.Mclust.Rd
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@@ -119,26 +119,26 @@ c3796dae5e43a2c26a9ee9b73fcf2276 *man/nMclustParams.Rd
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 9acae98acfaf6b565e9233957a144d22 *man/randomOrthogonalMatrix.Rd
 164e38869f2f45ca98a8ad38912e15bf *man/randomPairs.Rd
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@@ -152,7 +152,7 @@ c1b81d23059192faf2b0fdb40b0bc0d2 *man/wreath.Rd
 2a6f9e9e044a78154d3cfda5936d6f48 *src/Makevars
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-3e070d829bea6cd31852ae8dc6e7f6bc *src/mclustaddson.f
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 7278b7f759063e3034995f25eff22def *vignettes/mclust.Rmd
 b336c79647f3679eee6aff1702d422f5 *vignettes/vignette.css

Diff for: NEWS.md

@@ -1,6 +1,17 @@
+# mclust 5.4.6
+
+- Fixed issues with source Fortran code with gfortran 10 as reported by CRAN.
+- Clean code of `hcCriterion()`.
+- Replaced `CEX` argument in functions with standard base graph `cex` argument.
+- Removed `ylim` argument in function; it can be passed via `...`.
+- MclustDA models use the default SVD transformation of the data for initialisation of the EM algorithm.
+- Added `icl` criterion to object returned by `Mclust()`.
+- Fixed number of pages for the RJ reference.
+- quantileMclust() uses bisection line search method for numerically computing quantiles.
+
 # mclust 5.4.5
 
-* Fixed warnings in Fortran calls raised by CRAN.
+- Fixed warnings in Fortran calls raised by CRAN.
 
 # mclust 5.4.4
 

Diff for: R/bootstrap.R

@@ -13,22 +13,39 @@ mclustBootstrapLRT <- function(data, modelName = NULL,
   if(is.null(modelName))
     stop("A 'modelName' must be provided. Please see help(mclustModelNames) which describes the available models.")
   modelName <- modelName[1]
-  if(is.null(maxG)) G <- seq.int(1, 9)
-  else { maxG <- as.numeric(maxG); G <- seq.int(1, maxG+1) }
-  Bic <- mclustBIC(data, G = G, modelNames = modelName, 
+  if(is.null(maxG))
+  { 
+    G <- seq.int(1, 9)
+  } else 
+  { 
+    maxG <- as.numeric(maxG)
+    G <- seq.int(1, maxG+1) 
+  }
+  BIC <- mclustBIC(data, G = G, modelNames = modelName, 
                    warn = FALSE, verbose = FALSE, ...)
-  if(!(modelName %in% attr(Bic, "modelNames")))
+  
+  if(!(modelName %in% attr(BIC, "modelNames")))
     stop("'modelName' not compatibile with data. Please see help(mclustModelNames) which describes the available models.")
-  if(all(is.na(Bic)))
+  
+  # select only sequential models that can be fit
+  bic <- BIC[, attr(BIC, "modelNames") == modelName]
+  G <- G[!is.na(BIC)]
+  if(length(G) == 0)
     stop(paste("no model", modelName, "can be fitted."))
-  # select only models that can be fit
-  G <- which(!is.na(Bic[, attr(Bic, "modelNames") == modelName]))
+  if(all(G == 1))
+  {
+    warning("only 1-component model could be fitted. No LRT is performed!")
+    return()
+  }
+  if(sum(is.na(bic)) > 0)
+    warning("some model(s) could not be fitted!")
 
   if(verbose) 
-    { cat("bootstrapping LRTS ...\n")
-      flush.console()
-      pbar <- txtProgressBar(min = 0, max = (max(G)-1)*nboot, style = 3)
-      on.exit(close(pbar))
+  { 
+    flush.console()
+    cat("bootstrapping LRTS ...\n")
+    pbar <- txtProgressBar(min = 0, max = (max(G)-1)*nboot, style = 3)
+    on.exit(close(pbar))
   }
 
   obsLRTS <- p.value <- vector("numeric", length = max(G)-1)
@@ -37,9 +54,9 @@ mclustBootstrapLRT <- function(data, modelName = NULL,
   while(g < (max(G)-1) & continue)
   { g <- g + 1
     # fit model under H0
-    Mod0 <- summary(Bic, data, G = g, modelNames = modelName)
+    Mod0 <- summary(BIC, data, G = g, modelNames = modelName)
     # fit model under H1
-    Mod1 <- summary(Bic, data, G = g+1, modelNames = modelName)
+    Mod1 <- summary(BIC, data, G = g+1, modelNames = modelName)
     # observed LRTS
     obsLRTS[g] <- 2*(Mod1$loglik - Mod0$loglik)
     # bootstrap

Diff for: R/densityMclust.R

@@ -460,6 +460,7 @@ cdfMclust <- function(object, data, ngrid = 100, ...)
   return(out)
 }
 
+# old version using spline approx
 quantileMclust <- function(object, p, ...)
 {
 # Calculate the quantile of a univariate mixture corresponding to cdf equal to p 
@@ -470,13 +471,48 @@ quantileMclust <- function(object, p, ...)
 
   if(!any(class(object) == "densityMclust"))
     { stop("first argument must be an object of class 'densityMclust'") }
-  
   eval.points <- extendrange(object$data, f = 1)
   eval.points <- seq(eval.points[1], eval.points[2], length.out = 10000) 
   cdf <- cdfMclust(object, data = eval.points)
-  q <- spline(cdf$y, cdf$x, method = "fmm", xmin = 0, xmax = 1, xout = p)$y
+  dup <- duplicated(cdf$y)
+  q <- spline(cdf$y[!dup], cdf$x[!dup], method = "fmm", 
+              xmin = 0, xmax = 1, xout = p)$y
   q[ p < 0 | p > 1] <- NaN
   q[ p == 0 ] <- -Inf
   q[ p == 1 ] <- Inf
   return(q)  
-}
+}
+
+quantileMclust <- function(object, p, ...)
+{
+# Calculate the quantile of a univariate mixture corresponding to cdf
+# equal to p using bisection line search method.
+#
+# Arguments:
+# object = a 'densityMclust' object
+# p = vector of probabilities (0 <= p <= 1)
+
+  stopifnot(inherits(object, "densityMclust"))
+  if(object$d != 1)
+    { stop("quantile function only available for 1-dimensional data") }
+  p <- as.vector(p)
+  m <- object$parameters$mean
+  s <- sqrt(object$parameters$variance$sigmasq)
+  if(object$modelName == "E") s <- rep(s, object$G)
+  r <- matrix(as.double(NA), nrow = length(p), ncol = object$G)
+  for(g in 1:object$G)
+  {
+    r[,g] <- qnorm(p, mean = m[g], sd = s[g])
+  }
+  if(object$G == 1) return(as.vector(r))
+  q <- rep(as.double(NA), length(p))
+  for(i in 1:length(p))
+  {
+    F <- function(x) cdfMclust(object, x)$y - p[i]
+    q[i] <- uniroot(F, interval = range(r[i,]), tol = sqrt(.Machine$double.eps))$root
+  }
+  q[ p < 0 | p > 1] <- NaN
+  q[ p == 0 ] <- -Inf
+  q[ p == 1 ] <- Inf
+  return(q)  
+}