meta: General Package for Meta-Analysis

#' Elevated CO_2 and total biomass of woody plants
#' 
#' @description
#' Meta-analysis on effects of elevated CO_2 on total biomass of woody
#' plants
#' 
#' This dataset has been used as an example in Hedges et al. (1999) to
#' describe methods for the meta-analysis of response ratios. The
#' complete dataset with 102 observations and 26 variables is
#' available online as a supplement. Here only a subset of 10
#' variables is provided and used in the examples.
#' 
#' @name woodyplants
#' 
#' @docType data
#' 
#' @format A data frame with the following columns:
#' \tabular{rl}{
#' \bold{\emph{obsno}}\tab observation number \cr
#' \bold{\emph{papno}}\tab database paper number \cr
#' \bold{\emph{treat}}\tab treatment code \cr
#' \bold{\emph{level}}\tab treatment level \cr
#' \bold{\emph{n.elev}}\tab number of observations in experimental
#'   group (elevated CO_2-level) \cr
#' \bold{\emph{mean.elev}}\tab estimated mean in experimental group
#'   \cr
#' \bold{\emph{sd.elev}}\tab standard deviation in experimental group
#'   \cr
#' \bold{\emph{n.amb}}\tab number of observations in control group
#'   (ambient CO_2-level) \cr
#' \bold{\emph{mean.amb}}\tab estimated mean in control group \cr
#' \bold{\emph{sd.amb}}\tab standard deviation in control group
#' }
#' 
#' @references
#' Hedges LV, Gurevitch J, Curtis PS (1999):
#' The meta-analysis of response ratios in experimental ecology.
#' \emph{Ecology},
#' \bold{80}, 1150--6
#' 
#' @keywords datasets
#' 
#' @examples
#' data(woodyplants)
#' 
#' # Meta-analysis of response ratios (Hedges et al., 1999)
#' #
#' m1 <- metacont(n.elev, mean.elev, sd.elev, n.amb, mean.amb, sd.amb,
#'   data = woodyplants, sm = "ROM", studlab = paste(obsno, papno, sep = " / "))
#' print(m1, prediction = TRUE)
#' 
#' # Meta-analysis for plants grown with low soil fertility treatment
#' #
#' m2 <- update(m1, subset = (treat == "fert" & level == "low"))
#' print(m2, prediction = TRUE)
#' 
#' # Meta-analysis for plants grown under low light conditions
#' #
#' m3 <- update(m1, subset = (treat == "light" & level == "low"))
#' print(m3, prediction = TRUE)


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