version 0.2

ChangeLog

@@ -0,0 +1,4 @@
+2012-10-01 Fabio Mathias Correa  <fmcorrea@uesc.br>
+
+	* Changes in initial parameters for the MCMC process
+	* Change in the summary function Bayes.factor

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Bayesthresh
-Version: 0.1.1
-Date: 2012-08-08
+Version: 0.2
+Date: 2012-10-01
 Title: Bayesian thresholds mixed-effects models for categorical data
 Author: Fabio Mathias Correa <fmcron@gmail.com> and Julio Silvio de
         Souza Bueno Filho <juliobuenof@gmail.com>
@@ -16,5 +16,5 @@ Depends: R(>= 2.10.1), lme4 (>= 0.999375-35), MASS, VGAM, mvtnorm,
         matrixcalc
 License: GPL (>= 2)
 Repository: CRAN
-Date/Publication: 2012-08-16 07:09:31
-Packaged: 2012-08-08 08:04:18 UTC; fmcron
+Date/Publication: 2012-10-01 18:58:13
+Packaged: 2012-10-01 17:21:53 UTC; fmc

MD5

@@ -1,7 +1,8 @@
-e33a45ee693436d529c3b1a99f3afb23 *DESCRIPTION
+344da2fdd6d9bbbe824e4dfe83fadf00 *ChangeLog
+887f45889eda6062beec80f929899592 *DESCRIPTION
 637174c4b580e5e2f976596269734abc *NAMESPACE
-4b742abdb54c0f674c2f06b880d63a6f *R/Bayes.factor.R
-ca79ffb9476bc8c8726ec09db03183f7 *R/Bayesthresh.R
+61e49276d3548f6b4199d30cb27630d4 *R/Bayes.factor.R
+a1a2b02ccadaff165b99099ca6b0752a *R/Bayesthresh.R
 97d72f8e990f3a229d4ddd7408ccb010 *R/coef.Bayesthresh.R
 2433b9d274c1b12429f632c0eee7fa31 *R/compVar.R
 f52b3e962fcf1c240d68304c2b98d08c *R/plot.random.effects.R
@@ -11,8 +12,8 @@ bd4a5ef6aad4293f4ff514233e794b7e *data/sensory.rda
 3b4c692402cf955ad72ec62c0a1beb17 *inst/CITATION
 ca591a030c5407c420cdcba3a31c06f2 *man/ACGaussian.Rd
 e02f57831b3a1c651460698503742667 *man/ACt.Rd
-0d794c2437253de1db61ec284b8fb9b1 *man/Bayes.factor.Rd
-2c1b8d20e1bbfb0461662fc6029f988a *man/Bayesthresh.Rd
+bd6b443a851de7cb18ec62be750a9a68 *man/Bayes.factor.Rd
+6851787816acc28d1629c2dc2d42da67 *man/Bayesthresh.Rd
 5f1450d8675c66f5a4d7f56569408e4a *man/MCGaussian.Rd
 3f0ab3eb6a77313f0e041b975e9819ef *man/MCt.Rd
 f1ccbd0f52aa5675a6c605d736f9ab21 *man/NCGaussian.Rd

R/Bayes.factor.R

@@ -1,4 +1,4 @@
-Bayes.factor <- function(model1, model2)
+Bayes.factor <- function(model1, model2, inter=TRUE)
 {
 	if(!inherits(model1, "Bayesthresh"))
 		stop("Model1 does not belong a class Bayesthresh")
@@ -18,5 +18,19 @@ Bayes.factor <- function(model1, model2)
 	cat("Model 2:", deparse(model2$formula), "\n")
 	cat("\n \n")
 	print(result)
+	if(inter==TRUE){
+					cat("\n","Scale for interpretation of the Bayes factor","\n")
+					cat("--------------------------------------------","\n")
+					cat("B_ij                Evidence in favor of M_1","\n")
+					cat("--------------------------------------------","\n")
+					cat("  <1                 negative (favor of M_2)","\n")
+					cat("1 to 3                             doubtfull","\n")
+					cat("3 to 10                          substantial","\n")
+					cat("10 to 30                              strong","\n")
+					cat("30 to 100                        very strong","\n")
+					cat(" >100                               decisive","\n")
+					cat("--------------------------------------------","\n")
+					cat("Jeffreys(1961)","\n")
+		}
 }
 

R/Bayesthresh.R

@@ -1629,7 +1629,7 @@ print.Bayesthresh <- function(x,...)
 ### The main function
 Bayesthresh <-function(formula, data, subset, na.action, A=NULL, algor = list(algorithm = "NC", link = "Gaussian"),
 																						Write = FALSE, priors = list(ru=10, su=2, dre=20, dse=5),
-																						burn = 100, jump = 10, ef.iter = 4000, model=TRUE)
+																						burn = 50, jump = 2, ef.iter = 4000, model=TRUE)
 				### Linear Mixed-Effects in R with threshold models
 {
 				mc <- match.call()

man/Bayes.factor.Rd

@@ -5,11 +5,12 @@
 
 \description{Compares two models by evaluating their Bayes factor}
 
-\usage{Bayes.factor(model1, model2)}
+\usage{Bayes.factor(model1, model2, inter=TRUE)}
 
 \arguments{
   \item{model1}{object of the class model \code{"Bayesthresh"}} 
   \item{model2}{object of the class model \code{"Bayesthresh"}}
+		\item{inter}{If TRUE, print to scale for interpretation of the Bayes factor}
 }
 
 \details{
@@ -41,16 +42,15 @@ Sacarose <- factor(sensory$sacarose)
 # Model with Gaussian link
 
 dex1 <- Bayesthresh(flavor ~ (1|Consumer) + Sacarose, burn = 0, jump = 1,
-																		 ef.iter = 10, data=sensory) 
+        ef.iter = 10, data=sensory) 
 summary(dex1)
 
 #### Model 2
 
 # Model with t-Student link
 
-dex2 <- Bayesthresh(flavor ~ (1|Consumer) + Sacarose, burn = 0, jump = 1, 
-																			ef.iter = 10, algor=list(algorithm="NC", link="t"), 
-																			data=sensory) 
+dex2 <- Bayesthresh(flavor ~ (1|Consumer) + Sacarose, burn = 0, jump = 1,
+        ef.iter = 10, algor=list(algorithm="NC", link="t"),data=sensory) 
 summary(dex2)
 
 Bayes.factor(dex1,dex2)

man/Bayesthresh.Rd

@@ -11,11 +11,10 @@
 		Student's "t" link function and for saving the full chains.
 }
 
-\usage{
-       Bayesthresh(formula, data, subset, na.action, A=NULL,
-																		algor = list(algorithm="NC", link="Gaussian"),
-							           Write, priors = list(ru = 10, su = 2, dre = 20, dse = 5), 
-                  burn = 100, jump = 10, ef.iter = 4000, model=TRUE)
+\usage{Bayesthresh(formula, data, subset, na.action, A=NULL,
+ algor = list(algorithm="NC", link="Gaussian"),
+ Write, priors = list(ru = 10, su = 2, dre = 20, dse = 5), 
+ burn = 50, jump = 2, ef.iter = 4000, model=TRUE)
 }
 \arguments{
   \item{formula}{a two-sided linear formula object describing the fixed-effects part of the model, 
@@ -34,7 +33,7 @@
   \item{priors}{priors is a list that contains the parameters of the priors used to estimate the
 					 variance components of random effects}
   \item{burn, jump, ef.iter}{are of the arguments of iteraction. By default the burn, jump and ef.iter
-    (effective iteractions) are 100, 5 e 1000 respectively}
+    (effective iteractions) are 50, 2 e 4000 respectively}
   \item{model}{logical scalar.  If \code{FALSE} the model frame in slot \code{frame} is truncated to zero rows.}
 }
 \details{