version 0.1

DESCRIPTION

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+Package: Bayesthresh
+Version: 0.1
+Date: 2012-08-06
+Title: Bayesian thresholds mixed-effects models for categorial data
+Author: Fabio Mathias Correa <fmcron@gmail.com> and Julio Silvio de
+        Souza Bueno Filho <juliobuenof@gmail.com>
+Maintainer: Fabio Mathias Correa <fmcron@gmail.com>
+Description: This library fits a linear mixed model for ordinal
+        categorical responses using Bayesian inference via Monte Carlo
+        Markov Chains. Default is Nandran & Chen algorithm using
+        Gaussian link function and saving just the summaries of the
+        chains.  Among the options, package allow for two other options
+        of algorithms, for using Student's "t" link function and for
+        saving the full chains.
+Depends: R(>= 2.10.1), lme4 (>= 0.999375-35), MASS, VGAM, mvtnorm,
+        matrixcalc
+License: GPL (>= 2)
+Repository: CRAN
+Date/Publication: 2012-08-08 05:37:26
+Packaged: 2012-08-07 23:21:34 UTC; fmcron

MD5

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+c68ca96c1fb8dc3019fd3f0c1fe817f6 *DESCRIPTION
+637174c4b580e5e2f976596269734abc *NAMESPACE
+4b742abdb54c0f674c2f06b880d63a6f *R/Bayes.factor.R
+ca79ffb9476bc8c8726ec09db03183f7 *R/Bayesthresh.R
+97d72f8e990f3a229d4ddd7408ccb010 *R/coef.Bayesthresh.R
+2433b9d274c1b12429f632c0eee7fa31 *R/compVar.R
+f52b3e962fcf1c240d68304c2b98d08c *R/plot.random.effects.R
+91935a4a020ccf82ac7f1549cb4eb84f *R/random.effects.R
+b51185e0fa4825009b06e9dee61b9358 *R/zzz.R
+bd4a5ef6aad4293f4ff514233e794b7e *data/sensory.rda
+3b4c692402cf955ad72ec62c0a1beb17 *inst/CITATION
+ca591a030c5407c420cdcba3a31c06f2 *man/ACGaussian.Rd
+e02f57831b3a1c651460698503742667 *man/ACt.Rd
+0d794c2437253de1db61ec284b8fb9b1 *man/Bayes.factor.Rd
+5d06b782074a983feb965f728e536ad8 *man/Bayesthresh.Rd
+5f1450d8675c66f5a4d7f56569408e4a *man/MCGaussian.Rd
+3f0ab3eb6a77313f0e041b975e9819ef *man/MCt.Rd
+f1ccbd0f52aa5675a6c605d736f9ab21 *man/NCGaussian.Rd
+8ac8feda967146bacd0a1d19d47d6da8 *man/NCt.Rd
+a699cec2bb477682a005e38e436e0442 *man/coef.Bayesthresh.Rd
+a2ec37d6e8bb71324a4fcfca3409502e *man/compVar.Rd
+28a10a7d8adbd79a4897a3718bec9de5 *man/plot.random.effects.Rd
+c9c19c8d023af5461476ee3fd6352836 *man/predict.Bayesthresh.Rd
+d5d66b115b1b19eb1d2abd95f8ba46e7 *man/random.effects.Rd
+937ef63c15dbe35bdbeb96d83aa639f4 *man/sensory.Rd
+e441f70f7997b49ceba84c8d4bfbeeea *man/summary.Bayesthresh.Rd
+f76f32b3af49874a82e9ef6d4bf995ef *src/acum.c
+af33e99954d91e868233f336cf5b1d7a *src/dist.c
+149e8aa070b60438aa24fc9b0e54c76a *src/lambda.f95
+e28e02b96aeb9e651b03c559caa357b4 *src/partition.f95
+03a51259d331296b00ce9d680dcfeabb *src/vua.f95

NAMESPACE

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+useDynLib(Bayesthresh)
+
+export("Bayesthresh",
+	"Bayes.factor", 
+	"random.effects", 
+	"coef.Bayesthresh", 
+	"summary.Bayesthresh",
+	"ACGaussian",
+	"ACt",
+	"MCGaussian", 
+	"MCt", 
+	"NCGaussian", 
+	"NCt",
+	"predict.Bayesthresh",
+	"plot.random.effects",
+	"compVar")
+
+
+S3method(predict, Bayesthresh)
+S3method(plot, random.effects)
+S3method(coef, Bayesthresh)
+S3method(summary, Bayesthresh)

R/Bayes.factor.R

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+Bayes.factor <- function(model1, model2)
+{
+	if(!inherits(model1, "Bayesthresh"))
+		stop("Model1 does not belong a class Bayesthresh")
+	if(!inherits(model2, "Bayesthresh"))
+		stop("Model2 does not belong a class Bayesthresh")
+	veros1 <- 1/((1/model1$inter$ef.iter)*(as.numeric(model1$lik)))
+	Postmean1 <- veros1[1]
+	veros2 <- 1/((1/model2$inter$ef.iter)*(as.numeric(model2$lik)))
+	Postmean2 <- veros2[1]
+	bf <- Postmean1/Postmean2
+	result <- data.frame(bf)
+	rownames(result) <- c("model1/model2")
+	colnames(result) <- c("Bayes factor")
+	cat("\nBayes factor for comparison two models\n")
+	cat("\n \n")
+	cat("Model 1:", deparse(model1$formula), fill=TRUE )
+	cat("Model 2:", deparse(model2$formula), "\n")
+	cat("\n \n")
+	print(result)
+}
+