fix doc, 1.1.2 on CRAN

R/VAmain.r

@@ -3,12 +3,11 @@
 #' @param data Input VA data, see \code{data.type} below for more information about the format.
 #' @param data.type There are four data input types currently supported by \code{codeVA} function as below. 
 #'\itemize{
-#' \item{\code{WHO2012}: }
-#'   {InterVA-4 input format using WHO 2012 questionnaire. For example see \code{data(RandomVA1)}. The first column should be death ID.}
-#' \item{\code{WHO2016}: }
-#'    {InterVA-5 input format using WHO 2016 questionnaire. For example see \code{data(RandomVA5)}. The first column should be death ID.} 
-#' \item{\code{PHMRC}: }{PHMRC data format. The raw PHMRC long format data will be processed internally following the steps described in McComirck et al. (2016). For example see \code{\link{ConvertData.phmrc}}} 
-#' \item{\code{customized}: }{Any dichotomized dataset with ``Y`` denote ``presence'', ``'' denote ``absence'', and ``.'' denote ``missing''. The first column should be death ID.}}
+#' \item \code{WHO2012}: InterVA-4 input format using WHO 2012 questionnaire. For example see \code{data(RandomVA1)}. The first column should be death ID.
+#' \item \code{WHO2016}: InterVA-5 input format using WHO 2016 questionnaire. For example see \code{data(RandomVA5)}. The first column should be death ID.
+#' \item \code{PHMRC}: PHMRC data format. The raw PHMRC long format data will be processed internally following the steps described in McComirck et al. (2016). For example see \code{\link{ConvertData.phmrc}}
+#' \item \code{customized}: Any dichotomized dataset with ``Y`` denote ``presence'', ``'' denote ``absence'', and ``.'' denote ``missing''. The first column should be death ID.
+#' }
 #' @param data.train Training data with the same columns as \code{data}, except for an additional column specifying cause-of-death label. It is not used if \code{data.type} is ``WHO'' and \code{model} is ``InterVA'' or ``InSilicoVA''.  The first column also has to be death ID for ``WHO'' and ``customized'' types.
 #' @param causes.train the column name of the cause-of-death assignment label in training data.
 #' @param causes.table list of causes to consider in the training data. Default to be NULL, which uses all the causes present in the training data.
@@ -21,7 +20,9 @@
 #' @param phmrc.type Which PHMRC data format is used. Currently supports only ``adult'' and ``child'', ``neonate'' will be supported in the next release.
 #' @param convert.type type of data conversion when calculating conditional probability (probability of each symptom given each cause of death) for InterVA and InSilicoVA models. Both ``quantile'' and ``fixed'' usually give similar results empirically. 
 #' \itemize{
-#' \item{\code{quantile}: }{the rankings of the P(S|C) are obtained by matching the same quantile distributions in the default InterVA P(S|C)}\item{\code{fixed}: }{P(S|C) are matched to the closest values in the default InterVA P(S|C) table.} \item{\code{empirical}: }{no ranking is calculated, but use the empirical conditional probabilities directly, which will force \code{updateCondProb} to be FALSE for InSilicoVA algorithm.}  
+#' \item \code{quantile}: the rankings of the P(S|C) are obtained by matching the same quantile distributions in the default InterVA P(S|C)
+#' \item \code{fixed}: P(S|C) are matched to the closest values in the default InterVA P(S|C) table.
+#' \item \code{empirical}: no ranking is calculated, but use the empirical conditional probabilities directly, which will force \code{updateCondProb} to be FALSE for InSilicoVA algorithm.  
 #'}
 #' @param ... other arguments passed to \code{\link[InSilicoVA]{insilico}}, \code{\link[InterVA4]{InterVA}}, \code{\link{interVA_train}}, \code{\link[Tariff]{tariff}}, and nbc function in the nbc4va package. See respective package documents for details. 
 #'

R/interVA.train.r

@@ -9,7 +9,9 @@
 #' @param thre numerical number between 0 and 1. Symptoms with missing rate higher than \code{thre} in the training data will be dropped from both training and testing data.
 #' @param type type of data conversion when calculating conditional probability (probability of each symptom given each cause of death) for InterVA and InSilicoVA models. Both ``quantile'' and ``fixed'' usually give similar results empirically. 
 #' \itemize{
-#' \item{\code{quantile}: }{the rankings of the P(S|C) are obtained by matching the same quantile distributions in the default InterVA P(S|C)}\item{\code{fixed}: }{P(S|C) are matched to the closest values in the default InterVA P(S|C) table.} \item{\code{empirical}: }{no ranking is calculated, but use the empirical conditional probabilities directly.}
+#' \item \code{quantile}: the rankings of the P(S|C) are obtained by matching the same quantile distributions in the default InterVA P(S|C)
+#' \item \code{fixed}: P(S|C) are matched to the closest values in the default InterVA P(S|C) table.
+#' \item \code{empirical}: no ranking is calculated, but use the empirical conditional probabilities directly.
 #'}
 #' @param prior The prior distribution of CSMF. ``uniform'' uses no prior information, i.e., 1/C for all C causes and ``train'' uses the CSMF in the training data as prior distribution of CSMF.
 #' @param ... not used