[Genabel-commits] r1811 - pkg/GenABEL/R

[noreply at r-forge.r-project.org]

Author: lckarssen
Date: 2014-08-27 15:43:43 +0200 (Wed, 27 Aug 2014)
New Revision: 1811

Modified:
   pkg/GenABEL/R/cocohet.R
   pkg/GenABEL/R/lilog.R
Log:
Fixed some small problems in the GenABEL roxygen documentation found by running devtools::document()
- the @returnItem is not supported by Roxygen2 3.0
- Addressing slots via @ in the lilog examples was not accepted.


Modified: pkg/GenABEL/R/cocohet.R
===================================================================
--- pkg/GenABEL/R/cocohet.R	2014-08-27 13:41:07 UTC (rev 1810)
+++ pkg/GenABEL/R/cocohet.R	2014-08-27 13:43:43 UTC (rev 1811)
@@ -42,13 +42,15 @@
 #' field is >min_expected_cut_off. Otherwise the specified test is performed.
 #' Object of class \code{\link{integer}} or \code{\link{numeric}}
 #' @return A list is returned.
-#' @returnItem chi2_max A vector where each element is a test statistic choosen
+#' \describe{
+#'  \item{chi2_max} A vector where each element is a test statistic choosen
 #' as a maximum chisq among tests where a SNP and SNPs on the right within a
 #' window are involved.
-#' @returnItem chi2_all Statistics of all tests done in the analysis. Each row
+#'  \item{chi2_all} Statistics of all tests done in the analysis. Each row
 #' of the matrix contains tests statistics for a SNP and all SNPs on the right
 #' of him within of a given window.  For example: the ellement chi2_all[1,1]
 #' stands for a test
+#' }
 #' @references 
 #' Fan Liu, Maksim V. Struchalin, Kate van Duijn, Albert Hofman, Andre G. 
 #' Uitterlinden, Yurii S. Aulchenko, and Manfred Kayser. Detecting Low 

Modified: pkg/GenABEL/R/lilog.R
===================================================================
--- pkg/GenABEL/R/lilog.R	2014-08-27 13:41:07 UTC (rev 1810)
+++ pkg/GenABEL/R/lilog.R	2014-08-27 13:43:43 UTC (rev 1811)
@@ -65,7 +65,6 @@
 #' while for tranforming the output from ProbABEL use \code{\link{palinear2LiLog}}
 #'
 #' @examples
-#'
 #' # simulate a snp with q = 0.3
 #' snp <- rbinom(n=1000, size=2, prob=0.3)
 #' # estimate the probability according to a logistic model with beta 0.4
@@ -92,12 +91,12 @@
 #' snp <- as.numeric(srdta[, 100])
 #' probabil <- 1/(1 + exp( -(snp * 0.4 - 1) ))
 #' bin.trait <- rbinom(n=nids(srdta), size=1, prob=probabil)
-#' srdta at phdata$binary <- bin.trait
+#' phdata(srdta)$binary <- bin.trait
 #' res <- qtscore(binary, data=srdta)
 #' sum <- summary(srdta)
 #'
-#' lilog.res <- LiLog(prev=mean(bin.trait), beta=res at results$effB,
-#'                    SEbeta=res at results$se_effB, q=sum$Q.2)
+#' lilog.res <- LiLog(prev=mean(bin.trait), beta=results(res)$effB,
+#'                    SEbeta=results(res)$se_effB, q=sum$Q.2)
 #' summary(glm(bin.trait~snp,
 #'         family=binomial)$coefficients)["snp"1:2,] # logistic regression
 #' lilog.res[100, ] # lilog result