R/ttest_tstat.R

Defines functions ttest.tstat

Documented in ttest.tstat

##' Using the classical t test statistic for a one- or two-sample design, this
##' function computes the corresponding Bayes factor test.
##'
##' This function can be used to compute the Bayes factor corresponding to a
##' one-sample, a paired-sample, or an independent-groups t test, using the
##' classical t statistic.  It can be used when you don't have access to the
##' full data set for analysis by \code{\link{ttestBF}}, but you do have the
##' test statistic.
##'
##' For details about the model, see the help for \code{\link{ttestBF}}, and the
##' references therein.
##'
##' The Bayes factor is computed via Gaussian quadrature.
##' @title Use t statistic to compute Bayes factor for one- and two- sample designs
##' @param t classical t statistic
##' @param n1 size of first group (or only group, for one-sample tests)
##' @param n2 size of second group, for independent-groups tests
##' @param nullInterval optional vector of length 2 containing lower and upper bounds of an interval hypothesis to test, in standardized units
##' @param rscale numeric prior scale
##' @param complement if \code{TRUE}, compute the Bayes factor against the complement of the interval
##' @param simple if \code{TRUE}, return only the Bayes factor
##' @return If \code{simple} is \code{TRUE}, returns the Bayes factor (against the
##' null). If \code{FALSE}, the function returns a
##' vector of length 3 containing the computed log(e) Bayes factor,
##' along with a proportional error estimate on the Bayes factor and the method used to compute it.
##' @author Richard D. Morey (\email{richarddmorey@@gmail.com}) and Jeffrey N.
##'   Rouder (\email{rouderj@@missouri.edu})
##' @keywords htest
##' @export
##' @references 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, 225-237
##' @note In version 0.9.9, the behaviour of this function has changed in order to produce more uniform results. In
##' version 0.9.8 and before, this function returned two Bayes factors when \code{nullInterval} was
##' non-\code{NULL}: the Bayes factor for the interval versus the null, and the Bayes factor for the complement of
##' the interval versus the null. Starting in version 0.9.9, in order to get the Bayes factor for the complement, it is required to
##' set the \code{complement} argument to \code{TRUE}, and the function only returns one Bayes factor.
##' @seealso \code{\link{integrate}}, \code{\link{t.test}}; see
##'   \code{\link{ttestBF}} for the intended interface to this function, using
##'   the full data set.
##' @examples
##' ## Classical example: Student's sleep data
##' data(sleep)
##' plot(extra ~ group, data = sleep)
##'
##' ## t.test() gives a t value of -4.0621
##' t.test(extra ~ group, data = sleep, paired=TRUE)
##' ## Gives a Bayes factor of about 15
##' ## in favor of the alternative hypothesis
##' result <- ttest.tstat(t = -4.0621, n1 = 10)
##' exp(result[['bf']])

ttest.tstat=function(t,n1,n2=0,nullInterval=NULL,rscale="medium", complement=FALSE, simple = FALSE)
{
  if(n2){
    rscale = rpriorValues("ttestTwo",,rscale)
  }else{
    rscale = rpriorValues("ttestOne",,rscale)
  }

  stopifnot(length(t)==1 & length(n1)==1)

  nu=ifelse(n2==0 | is.null(n2),n1-1,n1+n2-2)
  n=ifelse(n2==0 | is.null(n2), n1, exp(log(n1)+log(n2)-log(n1+n2)) )

  if( (n < 1) | (nu < 1))
    stop("not enough observations")

  if(is.infinite(t))
    stop("data are essentially constant")

  r2=rscale^2
  log.marg.like.0= -(nu+1)/2 * log(1+t^2/(nu))
  res = list(bf=NA, properror=NA,method=NA)
  if(is.null(nullInterval)){
    BFtry({res = meta.t.bf(t,n,nu,rscale=rscale)})
  }else{
    BFtry({res = meta.t.bf(t,n,nu,interval=nullInterval,rscale=rscale,complement = complement)})
  }
  if(simple){
    return(c(B10=exp(res$bf)))
  }else{
    return(res)
  }


}

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BayesFactor documentation built on May 2, 2019, 7 a.m.