[Genabel-commits] r1977 - pkg/MixABEL/R

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

Author: lckarssen
Date: 2015-05-15 18:54:51 +0200 (Fri, 15 May 2015)
New Revision: 1977

Modified:
   pkg/MixABEL/R/fastmixmod.R
Log:
Summary: Cleaned up some white space issues and long lines in MixABEL's fastmixmod.R. Also removed some comments that are already incorporated in the roxygen documentation.


Modified: pkg/MixABEL/R/fastmixmod.R
===================================================================
--- pkg/MixABEL/R/fastmixmod.R	2015-05-15 16:45:35 UTC (rev 1976)
+++ pkg/MixABEL/R/fastmixmod.R	2015-05-15 16:54:51 UTC (rev 1977)
@@ -1,38 +1,41 @@
 #' fast mixed models
-#' 
+#'
 #' fast mixed models -- BETA VERSION
-#' If compiled against OMP this library can exploit multi-core parallelism 
-#' Does not cope with missing data at present 
-#' 
+#' If compiled against OMP this library can exploit multi-core parallelism
+#' Does not cope with missing data at present
+#'
 #' @param Response is an n dimensional vector of Responses
-#' @param Explan is an n*p matrix of Explanatory variables, each to be tested marginally (SNPS)
+#' @param Explan is an n*p matrix of Explanatory variables, each to be
+#' tested marginally (SNPS)
 #' @param Kin is the n*n kinship matrix
 #' @param Covariates is an n*k matrix of Covariates
-#' @param nu_naught (and gamma_naught) are hyperparameters which control the heaviness 
-#' of the tails of the test distribution (recommend leave them unchanged).
-#' @param gamma_naught (and nu_naught) are hyperparameters which control the heaviness 
-#' of the tails of the test distribution (recommend leave them unchanged).
-#' 
+#' @param nu_naught (and gamma_naught) are hyperparameters which
+#' control the heaviness of the tails of the test distribution
+#' (recommend leave them unchanged).
+#' @param gamma_naught (and nu_naught) are hyperparameters which
+#' control the heaviness of the tails of the test distribution
+#' (recommend leave them unchanged).
+#'
 #' @return a list with values ...
-#' 
-#' @export 
-#' 
+#'
+#' @export
+#'
 #' @seealso mmscore
-#' 
+#'
 #' @references reference to fill in
-#' 
+#'
 #' @author William Astle \email{fio@@where}
-#' 
-#' @examples 
+#'
+#' @examples
 #' require(mvtnorm)
 #' data(ge03d2.clean)
 #' df <- ge03d2.clean[1:250,autosomal(ge03d2.clean)]
 #' NSNPS <- nsnps(df)
 #' modh2 <- 0.8
 #' gkin <- ibs(df[,autosomal(df)],w="freq")
-#' 
+#'
 #' ngkin <- gkin
-#' ngkin[upper.tri(ngkin)] <- t(ngkin)[upper.tri(ngkin)] 
+#' ngkin[upper.tri(ngkin)] <- t(ngkin)[upper.tri(ngkin)]
 #' ngkin[1:5,1:5]
 #' mysig <- (modh2*2*ngkin+(1.-modh2)*diag(dim(ngkin)[1]))
 #' mysig[1:5,1:5]
@@ -40,57 +43,49 @@
 #' mytra[1:10]
 #' df@@phdata$mytra <- mytra
 #' df@@phdata[1:5,]
-#' 
+#'
 #' time0.h2 <- proc.time()
 #' h2 <- polygenic(mytra~sex+age,data=df,kin=gkin)
 #' time.h2 <- proc.time() - time0.h2
-#' 
+#'
 #' time0.mms <- proc.time()
 #' mms <- mmscore(h2,data=df)
 #' time.mms <- proc.time() - time0.mms
-#' 
+#'
 #' time0.grs <- proc.time()
 #' grs <- qtscore(h2$pgres,data=df)
 #' time.grs <- proc.time() - time0.grs
-#' 
+#'
 #' res <- mytra
 #' summary(res)
 #' expl <- as.numeric(df[,1:NSNPS])
 #' summary(res)
 #' covariates <- matrix(c(phdata(df)$sex,phdata(df)$age),ncol=2)
-#' summary(covariates) 
-#' 
+#' summary(covariates)
+#'
 #' time0.fmm <- proc.time()
 #' fmm <- FastMixedModel(Response=res,
-#' 						Explan=expl,
-#' 						Kin = gkin,
-#' 						Cov=covariates)
+#'                                              Explan=expl,
+#'                                              Kin = gkin,
+#'                                              Cov=covariates)
 #' time.fmm <- proc.time() - time0.fmm
-#' 
+#'
 #' time.h2
 #' time.h2+time.grs
 #' time.h2+time.mms
 #' time.fmm
-#' 
+#'
 #' h2$h2an
 #' #mms$effB
 #' #mms$chi2.1df
 #' fmm$null.herit
-#' 
+#'
 #' cor(mms[,"chi2.1df"],fmm$chi.sq)^2
 #' plot(mms[,"chi2.1df"],fmm$chi.sq)
-#' 
 #'
+#'
 
 FastMixedModel<-function(Response, Explan, Kin, Covariates=NULL, nu_naught=0, gamma_naught=0)
-# dyn.load("/home/wja/svn.lmm/src.freq/libtwovarcomp.so.0.01")
-# BETA VERSION
-# Response is an n dimensional vector of Responses
-# Explan is an n*p matrix of Explanatory variables, each to be tested marginally (SNPs)
-# Kin is the n*n kinship matrix
-# Covariates is an n*k matrix of Covariates
-# nu_naught and gamma_naught are hyperparameters which control the heaviness of the tails of the test distribution (recommend leave them unchanged).
-# If compiled against OMP this library can exploit multi-core parallelism 
 {
  Response=as.matrix(Response)
  Explan=as.matrix(Explan)
@@ -106,15 +101,13 @@
  Covariates=as.matrix(Covariates)
 
  GenVar=2*Kin
- ret=.Call("rint_flmm", as.double(Explan), as.double(Response), 
-		 as.integer(dim(Explan)[1]), as.integer(dim(Explan)[2]), 
-		 as.double(t(Covariates)), as.integer(dim(Covariates)[2]), 
-		 as.double(t(GenVar)), as.double(nu_naught), as.double(gamma_naught))
+ ret=.Call("rint_flmm", as.double(Explan), as.double(Response),
+                 as.integer(dim(Explan)[1]), as.integer(dim(Explan)[2]),
+                 as.double(t(Covariates)), as.integer(dim(Covariates)[2]),
+                 as.double(t(GenVar)), as.double(nu_naught), as.double(gamma_naught))
   ret
 }
 
 #Example to get chi.sq statistics
 #Result=FastMixedModel(Response, Explan, Kin)
 #Result$chi.sq
-
-