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R/ttestBF.R
In BayesFactor: Computation of Bayes factors for common designs
##' This function computes Bayes factors, or samples from the posterior, for
##' one- and two-sample designs.
##'
##' The Bayes factor provided by \code{ttestBF} tests the null hypothesis that
##' the mean (or mean difference) of a normal population is \eqn{\mu_0}{mu0}
##' (argument \code{mu}). Specifically, the Bayes factor compares two
##' hypotheses: that the standardized effect size is 0, or that the standardized
##' effect size is not 0. For one-sample tests, the standardized effect size is
##' \eqn{(\mu-\mu_0)/\sigma}{(mu-mu0)/sigma}; for two sample tests, the
##' standardized effect size is \eqn{(\mu_2-\mu_1)/\sigma}{(mu2-mu1)/sigma}.
##'
##' A noninformative Jeffreys prior is placed on the variance of the normal
##' population, while a Cauchy prior is placed on the standardized effect size.
##' The \code{rscale} argument controls the scale of the prior distribution,
##' with \code{rscale=1} yielding a standard Cauchy prior. See the references
##' below for more details.
##'
##' For the \code{rscale} argument, several named values are recognized:
##' "medium", "wide", and "ultrawide". For the one-sample test, these correspond
##' to \eqn{r} scale values of 1/2, \eqn{\sqrt{2}/2}{sqrt(2)/2}, and 1, respectively. For the two-sample
##' test, they correspond to \eqn{\sqrt{2}/2}{sqrt(2)/2}, 1, and \eqn{\sqrt{2}/2}{sqrt(2)}
##' respectively.
##'
##' The Bayes factor is computed via Gaussian quadrature.
##' @title Function for Bayesian analysis of one- and two-sample designs
##' @param x a vector of observations for the first (or only) group
##' @param y a vector of observations for the second group (or condition, for
##' paired)
##' @param formula for independent-group designs, a (optional) formula
##' describing the model
##' @param mu for one-sample and paired designs, the null value of the mean (or
##' mean difference)
##' @param nullInterval optional vector of length 2 containing lower and upper bounds of an interval hypothesis to test, in standardized units
##' @param paired if \code{TRUE}, observations are paired
##' @param data for use with \code{formula}, a data frame containing all the
##' data
##' @param rscale prior scale. A number of preset values can be given as
##' strings; see Details.
##' @param posterior if \code{TRUE}, return samples from the posterior instead
##' of Bayes factor
##' @param ... further arguments to be passed to or from methods.
##' @return If \code{posterior} is \code{FALSE}, an object of class
##' \code{BFBayesFactor} containing the computed model comparisons is
##' returned. If \code{nullInterval} is defined, then two Bayes factors will
##' be computed: The Bayes factor for the interval against the null hypothesis
##' that the standardized effect is 0, and the corresponding Bayes factor for
##' the compliment of the interval.
##'
##' If \code{posterior} is \code{TRUE}, an object of class \code{BFmcmc},
##' containing MCMC samples from the posterior is returned.
##' @export
##' @keywords htest
##' @author Richard D. Morey (\email{richarddmorey@@gmail.com})
##' @references Morey, R. D., Rouder, J. N., Pratte, M. S., & Speckman, P. L.
##' (2011). Using MCMC chain outputs to efficiently estimate Bayes factors.
##' Journal of Mathematical Psychology, 55, 368-378
##'
##' Morey, R. D. \& Rouder, J. N. (2011). Bayes Factor Approaches for Testing
##' Interval Null Hypotheses. Psychological Methods, 16, 406-419
##'
##' Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G.
##' (2009). Bayesian t-tests for accepting and rejecting the null hypothesis.
##' Psychonomic Bulletin & Review, 16, 752-760
##'
##' Perception and Cognition Lab (University of Missouri): Bayes factor
##' calculators. \url{http://pcl.missouri.edu/bayesfactor}
##' @note The default priors have scale has changed from 1 to \eqn{\sqrt{2}/2} for the
##' two-sample t test, and 1/2 for the one-sample t test. The
##' factor of \eqn{\sqrt{2}} in the two-sample t test is to be consistent
##' with Morey et al. (2011) and
##' Rouder et al. (2012), and the factor of \eqn{1/2} in both is to better scale the
##' expected effect sizes; the previous scaling put more weight on larger
##' effect sizes. To obtain the same Bayes factors as Rouder et al. (2009),
##' change the prior scale to 1.
##' @examples
##' ## Sleep data from t test example
##' data(sleep)
##' plot(extra ~ group, data = sleep)
##'
##' ## paired t test
##' ttestBF(x = sleep$extra[sleep$group==1], y = sleep$extra[sleep$group==2], paired=TRUE)
##'
##' ## Sample from the corresponding posterior distribution
##' samples = ttestBF(x = sleep$extra[sleep$group==1], y = sleep$extra[sleep$group==2], paired=TRUE, posterior = TRUE, iterations = 1000)
##' plot(samples[,"mu"])
##' @seealso \code{\link{integrate}}, \code{\link{t.test}}
ttestBF <- function(x, y = NULL, formula = NULL, mu = 0, nullInterval = NULL,
paired = FALSE, data = NULL, rscale="medium", posterior=FALSE, ...){
if( (is.null(formula) & is.null(y)) | (!is.null(y) & paired) ){
if(paired){
# check that the two vectors have same length
if(length(x)!=length(y)) stop("Length of x and y must be the same if paired=TRUE.")
x = x - y
}
rscale = rpriorValues("ttestOne",,rscale)
if(is.null(nullInterval)){
modFull = BFoneSample(type = "JZS",
identifier = list(formula = "y ~ 1"),
prior=list(rscale=rscale, mu=mu),
shortName = paste("Alt., r=",round(rscale,3),sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, sep="")
)
if(posterior){
chains = posterior(modFull,data = data.frame(y=x), ...)
return(chains)
}else{
bf = compare(numerator = modFull, data = data.frame(y=x))
return(bf)
}
}else{
nullInterval = range(nullInterval)
modInterval = BFoneSample(type = "JZS",
identifier = list(formula = "y ~ 1",nullInterval = nullInterval),
prior=list(rscale=rscale, mu=mu, nullInterval = nullInterval),
shortName = paste("Alt., r=",round(rscale,3)," ",nullInterval[1],"<d<",nullInterval[2],sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, " ",nullInterval[1],"<d<",nullInterval[2],sep="")
)
if(posterior){
chains = posterior(modInterval, data = data.frame(y=x), ...)
return(chains)
}else{
bf = compare(numerator = modInterval, data = data.frame(y=x))
return(bf)
}
}
}else if(!is.null(y) & !paired){
data = data.frame(y = c(x,y),
group = factor(c(rep("x",length(x)),rep("y",length(y))))
)
formula = y ~ group
}
if(!is.null(formula)){ # formula
if(paired) stop("Cannot use 'paired' with formula.")
if(is.null(data)) stop("'data' needed for formula.")
ivs = attr(terms(formula, data = data),"term.labels")
if(length(ivs) > 1) stop("Only one independent variable allowed for t test.")
dataTypes = "fixed"
names(dataTypes) = ivs
if(mu != 0) stop("Use of nonzero null hypothesis not implemented for independent samples test.")
rscale = rpriorValues("ttestTwo",,rscale)
if(is.null(nullInterval)){
numerator = BFindepSample(type = "JZS",
identifier = list(formula = stringFromFormula(formula)),
prior=list(rscale=rscale, mu=mu),
shortName = paste("Alt., r=",round(rscale,3),sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu,sep="")
)
}else{
nullInterval = range(nullInterval)
numerator = BFindepSample(type = "JZS",
identifier = list(formula = stringFromFormula(formula),nullInterval = nullInterval),
prior=list(rscale=rscale, mu=mu, nullInterval = nullInterval),
shortName = paste("Alt., r=",round(rscale,3)," ",nullInterval[1],"<d<",nullInterval[2],sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, " ",nullInterval[1],"<d<",nullInterval[2],sep="")
)
}
if(posterior){
chains = posterior(numerator, data = data, ...)
return(chains)
}else{
bf = compare(numerator = numerator, data = data)
return(bf)
}
}
}
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BayesFactor documentation built on May 2, 2019, 5:54 p.m.
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##' This function computes Bayes factors, or samples from the posterior, for
##' one- and two-sample designs.
##'
##' The Bayes factor provided by \code{ttestBF} tests the null hypothesis that
##' the mean (or mean difference) of a normal population is \eqn{\mu_0}{mu0}
##' (argument \code{mu}). Specifically, the Bayes factor compares two
##' hypotheses: that the standardized effect size is 0, or that the standardized
##' effect size is not 0. For one-sample tests, the standardized effect size is
##' \eqn{(\mu-\mu_0)/\sigma}{(mu-mu0)/sigma}; for two sample tests, the
##' standardized effect size is \eqn{(\mu_2-\mu_1)/\sigma}{(mu2-mu1)/sigma}.
##'
##' A noninformative Jeffreys prior is placed on the variance of the normal
##' population, while a Cauchy prior is placed on the standardized effect size.
##' The \code{rscale} argument controls the scale of the prior distribution,
##' with \code{rscale=1} yielding a standard Cauchy prior. See the references
##' below for more details.
##'
##' For the \code{rscale} argument, several named values are recognized:
##' "medium", "wide", and "ultrawide". For the one-sample test, these correspond
##' to \eqn{r} scale values of 1/2, \eqn{\sqrt{2}/2}{sqrt(2)/2}, and 1, respectively. For the two-sample
##' test, they correspond to \eqn{\sqrt{2}/2}{sqrt(2)/2}, 1, and \eqn{\sqrt{2}/2}{sqrt(2)}
##' respectively.
##'
##' The Bayes factor is computed via Gaussian quadrature.
##' @title Function for Bayesian analysis of one- and two-sample designs
##' @param x a vector of observations for the first (or only) group
##' @param y a vector of observations for the second group (or condition, for
##' paired)
##' @param formula for independent-group designs, a (optional) formula
##' describing the model
##' @param mu for one-sample and paired designs, the null value of the mean (or
##' mean difference)
##' @param nullInterval optional vector of length 2 containing lower and upper bounds of an interval hypothesis to test, in standardized units
##' @param paired if \code{TRUE}, observations are paired
##' @param data for use with \code{formula}, a data frame containing all the
##' data
##' @param rscale prior scale. A number of preset values can be given as
##' strings; see Details.
##' @param posterior if \code{TRUE}, return samples from the posterior instead
##' of Bayes factor
##' @param ... further arguments to be passed to or from methods.
##' @return If \code{posterior} is \code{FALSE}, an object of class
##' \code{BFBayesFactor} containing the computed model comparisons is
##' returned. If \code{nullInterval} is defined, then two Bayes factors will
##' be computed: The Bayes factor for the interval against the null hypothesis
##' that the standardized effect is 0, and the corresponding Bayes factor for
##' the compliment of the interval.
##'
##' If \code{posterior} is \code{TRUE}, an object of class \code{BFmcmc},
##' containing MCMC samples from the posterior is returned.
##' @export
##' @keywords htest
##' @author Richard D. Morey (\email{richarddmorey@@gmail.com})
##' @references Morey, R. D., Rouder, J. N., Pratte, M. S., & Speckman, P. L.
##' (2011). Using MCMC chain outputs to efficiently estimate Bayes factors.
##' Journal of Mathematical Psychology, 55, 368-378
##'
##' Morey, R. D. \& Rouder, J. N. (2011). Bayes Factor Approaches for Testing
##' Interval Null Hypotheses. Psychological Methods, 16, 406-419
##'
##' Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G.
##' (2009). Bayesian t-tests for accepting and rejecting the null hypothesis.
##' Psychonomic Bulletin & Review, 16, 752-760
##'
##' Perception and Cognition Lab (University of Missouri): Bayes factor
##' calculators. \url{http://pcl.missouri.edu/bayesfactor}
##' @note The default priors have scale has changed from 1 to \eqn{\sqrt{2}/2} for the
##' two-sample t test, and 1/2 for the one-sample t test. The
##' factor of \eqn{\sqrt{2}} in the two-sample t test is to be consistent
##' with Morey et al. (2011) and
##' Rouder et al. (2012), and the factor of \eqn{1/2} in both is to better scale the
##' expected effect sizes; the previous scaling put more weight on larger
##' effect sizes. To obtain the same Bayes factors as Rouder et al. (2009),
##' change the prior scale to 1.
##' @examples
##' ## Sleep data from t test example
##' data(sleep)
##' plot(extra ~ group, data = sleep)
##'
##' ## paired t test
##' ttestBF(x = sleep$extra[sleep$group==1], y = sleep$extra[sleep$group==2], paired=TRUE)
##'
##' ## Sample from the corresponding posterior distribution
##' samples = ttestBF(x = sleep$extra[sleep$group==1], y = sleep$extra[sleep$group==2], paired=TRUE, posterior = TRUE, iterations = 1000)
##' plot(samples[,"mu"])
##' @seealso \code{\link{integrate}}, \code{\link{t.test}}
ttestBF <- function(x, y = NULL, formula = NULL, mu = 0, nullInterval = NULL,
paired = FALSE, data = NULL, rscale="medium", posterior=FALSE, ...){
if( (is.null(formula) & is.null(y)) | (!is.null(y) & paired) ){
if(paired){
# check that the two vectors have same length
if(length(x)!=length(y)) stop("Length of x and y must be the same if paired=TRUE.")
x = x - y
}
rscale = rpriorValues("ttestOne",,rscale)
if(is.null(nullInterval)){
modFull = BFoneSample(type = "JZS",
identifier = list(formula = "y ~ 1"),
prior=list(rscale=rscale, mu=mu),
shortName = paste("Alt., r=",round(rscale,3),sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, sep="")
)
if(posterior){
chains = posterior(modFull,data = data.frame(y=x), ...)
return(chains)
}else{
bf = compare(numerator = modFull, data = data.frame(y=x))
return(bf)
}
}else{
nullInterval = range(nullInterval)
modInterval = BFoneSample(type = "JZS",
identifier = list(formula = "y ~ 1",nullInterval = nullInterval),
prior=list(rscale=rscale, mu=mu, nullInterval = nullInterval),
shortName = paste("Alt., r=",round(rscale,3)," ",nullInterval[1],"<d<",nullInterval[2],sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, " ",nullInterval[1],"<d<",nullInterval[2],sep="")
)
if(posterior){
chains = posterior(modInterval, data = data.frame(y=x), ...)
return(chains)
}else{
bf = compare(numerator = modInterval, data = data.frame(y=x))
return(bf)
}
}
}else if(!is.null(y) & !paired){
data = data.frame(y = c(x,y),
group = factor(c(rep("x",length(x)),rep("y",length(y))))
)
formula = y ~ group
}
if(!is.null(formula)){ # formula
if(paired) stop("Cannot use 'paired' with formula.")
if(is.null(data)) stop("'data' needed for formula.")
ivs = attr(terms(formula, data = data),"term.labels")
if(length(ivs) > 1) stop("Only one independent variable allowed for t test.")
dataTypes = "fixed"
names(dataTypes) = ivs
if(mu != 0) stop("Use of nonzero null hypothesis not implemented for independent samples test.")
rscale = rpriorValues("ttestTwo",,rscale)
if(is.null(nullInterval)){
numerator = BFindepSample(type = "JZS",
identifier = list(formula = stringFromFormula(formula)),
prior=list(rscale=rscale, mu=mu),
shortName = paste("Alt., r=",round(rscale,3),sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu,sep="")
)
}else{
nullInterval = range(nullInterval)
numerator = BFindepSample(type = "JZS",
identifier = list(formula = stringFromFormula(formula),nullInterval = nullInterval),
prior=list(rscale=rscale, mu=mu, nullInterval = nullInterval),
shortName = paste("Alt., r=",round(rscale,3)," ",nullInterval[1],"<d<",nullInterval[2],sep=""),
longName = paste("Alternative, r = ",rscale,", mu =/= ",mu, " ",nullInterval[1],"<d<",nullInterval[2],sep="")
)
}
if(posterior){
chains = posterior(numerator, data = data, ...)
return(chains)
}else{
bf = compare(numerator = numerator, data = data)
return(bf)
}
}
}
Try the BayesFactor package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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