[GenABEL-dev] [Genabel-commits] r817 - pkg/VariABEL/man
Yurii Aulchenko
yurii.aulchenko at gmail.com
Tue Dec 6 10:54:14 CET 2011
Maksim,
I suggest adding a keyword 'htest', not 'manip'; also, I think an
example using a quantitative trait would be more appropriate.
Best wishes,
Yurii
----------------------
Yurii Aulchenko
Independent consultant
(sent from mobile device)
On 6 Dec 2011, at 10:41, "noreply at r-forge.wu-wien.ac.at"
<noreply at r-forge.wu-wien.ac.at> wrote:
> Author: maksim
> Date: 2011-12-06 10:41:02 +0100 (Tue, 06 Dec 2011)
> New Revision: 817
>
> Modified:
> pkg/VariABEL/man/var_test_gwaa.Rd
> Log:
> change ecodding from ISO-8859 to ASCII. Otherwise checking fail...
>
> Modified: pkg/VariABEL/man/var_test_gwaa.Rd
> ==================================================================--- pkg/VariABEL/man/var_test_gwaa.Rd 2011-12-06 01:56:31 UTC (rev 816)
> +++ pkg/VariABEL/man/var_test_gwaa.Rd 2011-12-06 09:41:02 UTC (rev 817)
> @@ -2,63 +2,65 @@
> \alias{var_test_gwaa}
> \title{Function performs compariosn of genotypic variances.}
> \description{
> -Presence of significant difference of genotypic variances points to possible
> - Significant variance difference points to possible presence of interaction between a tested SNP and a factor (or set of factors).
> + Presence of significant difference of genotypic variances points to possible
> + Significant variance difference points to possible presence of interaction between a tested SNP and a factor (or set of factors).
> }
> \usage{
> -var_test_gwaa(formula, genodata, phenodata, genodata_info=NULL, testname="sqlm", analysis_type="AAvsABvsBB")
> + var_test_gwaa(formula, genodata, phenodata, genodata_info=NULL, testname="sqlm", analysis_type="AAvsABvsBB")
> }
> \arguments{\item{formula}{Regression model used for analysis. In the first stage linear regression is run to exclude main snp effect. In this stage adjustment for covariates is performed.}
> -\item{genodata}{The genotypes data in format of genabel or databel object.}
> -\item{phenodata}{The phenotypes data in format of data.frame object.}
> -\item{genodata_info}{The file with snp information (name, position). Used if genodata is databel object.}
> -\item{testname}{Name of variance heterogeneity test to perform. sqlm (for imputed genotype data), levene, and bartlett test are supported.}
> -\item{analysis_type}{Type of analsysis to perform. AAvsABvsBB - additive model where B allele additivly increase risk, AAvsABandBB - group AA tested agains AB and BB, ABvsAAandBB - AB against AA and BB, BBvsAAandAB - BB against AA and AB. Only available for typed snps.}
> + \item{genodata}{The genotypes data in format of genabel or databel object.}
> + \item{phenodata}{The phenotypes data in format of data.frame object.}
> + \item{genodata_info}{The file with snp information (name, position). Used if genodata is databel object.}
> + \item{testname}{Name of variance heterogeneity test to perform. sqlm (for imputed genotype data), levene, and bartlett test are supported.}
> + \item{analysis_type}{Type of analsysis to perform. AAvsABvsBB - additive model where B allele additivly increase risk, AAvsABandBB - group AA tested agains AB and BB, ABvsAAandBB - AB against AA and BB, BBvsAAandAB - BB against AA and AB. Only available for typed snps.}
> }
> \details{
>
> -The function var_test_gwaa tests for difference in genotypic variances. This difference points to presence of possible interaction between the tested SNP
> -and some factor. In the case sqlm test the analysis consists of two stage: firstly the regular GWA id done where regression analysis is performed with
> -covariates specified in the input parameter formula, in the second stage the regression analysis is performed with using residuals from the first stage and
> -a sno as a covariate.
> + The function var_test_gwaa tests for difference in genotypic variances. This difference points to presence of possible interaction between the tested SNP
> + and some factor. In the case sqlm test the analysis consists of two stage: firstly the regular GWA id done where regression analysis is performed with
> + covariates specified in the input parameter formula, in the second stage the regression analysis is performed with using residuals from the first stage and
> + a sno as a covariate.
> }
>
> \value{
> - The ouput is a data.frame object. The table contains the chisq of variance heterogeneity test (the name is chisq) the effects and standart errors of all covariates included into regression model,
> - main snp effect (the names are snp_eff and snp_se). In the case of sqlm test the columns snp_eff_dispertion and snp_se_dispertion contain
> - effect of a snp on squared vallues of a trait.
> + The ouput is a data.frame object. The table contains the chisq of variance heterogeneity test (the name is chisq) the effects and standart errors of all covariates included into regression model,
> + main snp effect (the names are snp_eff and snp_se). In the case of sqlm test the columns snp_eff_dispertion and snp_se_dispertion contain
> + effect of a snp on squared vallues of a trait.
>
> - }
> + }
>
>
> %\references{ http://www.biomedcentral.com/1471-2156/11/92/abstract }
> \author{Maksim Struchalin}
> %\note{ ~~further notes~~
> -%}
> -%\seealso{
> -% \code{\link{var.meta}},
> -%}
> -\examples{
> - if (require(GenABEL)) {
> - data(srdta)
> - result1 <- var_test_gwaa(bt~qt1+qt2, genodata=gtdata(srdta), phenodata=phdata(srdta))
> -
> + %}
> + %\seealso{
> + % \code{\link{var.meta}},
> + %}
> + \examples{
> + if (require(GenABEL)) {
> + data(srdta)
> + result1 <- var_test_gwaa(bt~qt1+qt2, genodata=gtdata(srdta), phenodata=phdata(srdta))
> +
> #if there is no covariates needed:
> - result2 <- var_test_gwaa("bt", genodata=gtdata(srdta), phenodata=phdata(srdta))
> - }
> + result2 <- var_test_gwaa("bt", genodata=gtdata(srdta), phenodata=phdata(srdta))
> + }
>
> -}
> + }
>
> \references{
>
> - Struchalin et al., Variance heterogeneity analysis for detection of potentially
> - interacting genetic loci: method and its limitations.
> - BMC Genetics 2010, 11:92, doi:10.1186/1471-2156-11-92
> -
> - Struchalin et al., An R package �VariABEL� for genome-wide searching of
> - potentially interacting loci by testing genotypic variance heterogeneity. Submitted.
> -
> + Struchalin et al., Variance heterogeneity analysis for detection of potentially
> + interacting genetic loci: method and its limitations.
> + BMC Genetics 2010, 11:92, doi:10.1186/1471-2156-11-92
> +
> + Struchalin et al., An R package VariABEL for genome-wide searching of
> + potentially interacting loci by testing genotypic variance heterogeneity. Submitted.
> +
> }
> % Add one or more standard keywords, see file 'KEYWORDS' in the
> % R documentation directory.
> \keyword{manip}
> +
> +
>
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