search for: genotyp

#Hello, #I have a question about obtaining results from a loop I have written. #Below is a sample of individual genotypes from a genetic question I am working on called "P.genotype.sample ". P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3) P.genotype.sample[,4]<-c(2,8...

...ven and odd column numbers. # I can do it in a very clunky fashion and I know there # must be a better way. below is a sample matrix and my extremely # clunky code that gets the job done for a small matrix, but i plan to # do this on a much grander scale. any help would be very much appreciated. P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3) P.genotype.sample[,4]<-c(2,8,8,3,8,2,8,3,4,3) P.genotype.sample[,5]<-c(3,3,8,3,6,1,1,1,1,3) P.genotype.sample[,6]<...

...dering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor Gender.Age is Gender*Age Genotype.Age is Genotype*Age Gender.Genotype.Age is Gender*Genotype*Age If I have the intercept (but not the slope) as a random effect the fit converges OK fit1 <- lme(Outcome~Age + Gender +...

...lls are somewhere between novice and intermediary, and I am hoping that some of you very helpful forum members, whom I've seen work your magic on other peoples' problems/questions, can help me here. I have a matrix with the following format: (i) individual plants comprising many different genotype groups (i.e., a plant is genotype 1 or genotype 2 or genotype 3, etc). The column for genotypes is called "gen", and the plants are members of genotype class 1 - 309, with no overlaps (i.e., you're either a genotype 1 or a genotype something else, but not both) and no missing values....

Hi, I am conducting an association analysis of genotype and a phenotype such as cholesterol level as an outcome and the genotype as a regressor using multiple linear regression. There are 3 possibilities for the genotype AA, AG, GG. There are 5 people with the AA genotype, 100 with the AG genotype and 900 with the GG genotype. I coded GG genotype as 1,...

Hi, Could someone tell me if this is the correct model syntax for the following dataset: lme(height~treatment+genotype+treatment*genotype,drought,random=~genotyp e) The dataset has two factors: one fixed - treatment, and one random - genotype. I need to test the effect of both factors to identify their significance. There are multiple (but not equal) replicates at each level of genotype (the replicates are indepe...

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I performed a simple one-way anova with "genotype" as explanatory factor and "number of n...

...nel function in stead of the default as long as there are only points in the graphs. When I set type="b" things get messed up. Any idea why? I include sample code for illustration below. Thanks for your ideas. Geert dataset <- data.frame ( time = rep(1:5,times=9), genotype = factor(rep(rep (c("A","B","C"),each=5),times=3 )), location= factor(rep (paste("LOC",1:3),each=15)), color = rep (rep (c("red","green","blue"),each=5),times=3 ), result = rnorm (...

...ll the help. I got the legend figured out Friday but left early becoz of long weekend so didn't get a chance to reply.. I modified the plot margins a little bit and Here's what I finally had... par(mar=c(c(10, 6, 6, 10) + 0.1)); par(xpd=FALSE); with (dataFrame, stripchart(marbles_buried ~ genotype, method="jitter", vertical=TRUE, col = c('blue', 'red', 'green'), xlab='Genotype', ylab = "Marbles Buried", main='MBA WTs Vs HOMs', pch=c(1,4,2), jitter=1/3.5, cex=1)); meds <- as.vector(with(dataFrame, by(marbles_buried, genotype, m...

...#39;, 65,'hom', 64,'hom', 63,'hom', 65,'hom', 69,'hom', 61,'hom', 66,'hom', 65,'hom', 61,'hom', 63,'hom', 64,'hom', 67,'hom'), .Dim=c(2,98)))); colnames(dataFrame) <- c('marbles_buried', 'genotype'); png('mb.png', width=400, height=400, pointsize=8); dataFrame[c("marbles_buried")] <- lapply(dataFrame[c("marbles_buried")], function(x) as.numeric(levels(x)[x])); par(xpd=FALSE) with (dataFrame, stripchart(marbles_buried ~ genotype, method="jitter&quo...

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main model - it is a nesting factor etc to specify the...

Hello R Users, I am investigating the basic use of the LME function, using the following example; Response is Weight, covariate is Age, random factor is Genotype model.lme <- lme (Weight~Age, random=~ 1|Genotype) After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758. I am comparing the above model with the AOV function; model.aov <- aov (Weight~Age + Genotype) I find that the estimate of Age is also 0.098, and p=0.758 a...

...;genetics' package. As a consequence of the upgrade, .First.lib() looks like: .First.lib <- function(libname, pkgname) { if (!require(combinat)) warning("Unable to load 'combinat' library. Function `diseq.ci' will fail.") require(gregmisc) genotype <- get("genotype",pos="package:genetics") } the First.lib of the "gregmisc" package in turn does require("MASS") which defines a data set "genotype" which masks the "genotype" function in my genetics library. Is there any clean wa...

Hi, I am trying to test for pairwise associations between genotypes ( Rows=individuals, Columns =genes, data are up to 4 genotypes per gene, some with 2,3 or 4) where each chisquare comparison is different depending on the genes tested. The test is the observed multilocus (across columns for each individual) genotypes vs the expectation, which is the product of...

...[[3]] [1] 5 6 But how do I get the quote's that surround to.convert removed, given that it is an object of type character? Thanks in advance, Neil Background ======== This is a refined example from a larger problem that I'm working on. Given a data frame of pairs of columns containing genotype data I would like to convert them to genotype objects using makeGenotypes. The number of loci (i.e. ncol() / 2) is variable, so I like to be able to determine how many loci there are and then convert all of them. Thus an example data set would be... <-----Start test.dat------> ID,rs1.1,rs1...

Hello all, thanks for your time and help. Below are my commands, and it generates a really nice plot, however I am not happy with the reorder() function. I would like the order to be the same as they appear in the genotype variable "genotype <- c("CJ1450 NW 4/25/12","CJ1450 BAL 4/25/12","CJ1450 NW 4/27/12","CJ1450 BAL 4/27/12","CJ1721 NW 4/27/12","CJ1721 BAL 4/27/12","CJ1721 NW 4/29/12","CJ1721 BAL 4/29/12" )" and not as it...

I'm new to R (previously used SAS primarily) and I have a genetics data frame consisting of genotypes for each of 300+ subjects (ID1, ID2, ID3, ...) at 3000+ genetic locations (SNP1, SNP2, SNP3...). A small subset of the data is shown below: SNP_ID SNP1 SNP2 SNP3 SNP4 Maj_Allele C G C A Min_Allele T A T G ID1 CC GG CT AA ID2 CC GG CC AA ID3 CC GG nc AA...

...So, I'll paste my example here just to give it more publicity, :-) > library(gregmisc) Loading required package: MASS Attaching package `gregmisc': The following object(s) are masked from package:base : lowess > # Create example data set with two factors: Genotype and Time > set.seed(03215) > Genotype <- sample(c("WT","KO"), 1000, replace=TRUE) > Time <- factor(sample(1:3, 1000, replace=TRUE)) > y <- rnorm(1000) > data <- data.frame(y, Genotype, as.factor(Time)) > > cm.G &lt...

I have been merrily using the genetics package and more specifically have been using the makeGenotypes and genotypes function. I check my accomplishments by going > class(g2) [1] "genotype" "factor" and likewise > class(g1) [1] "genotype" "factor" Yet when I execute a command such as allele count I get this > allele.count(g1) D I [1,]...

Sequenom has an odd format of calling a SNP genotype gg [1] "C" "GA" "A" "C" "C" "AG" "C" "C" "T" "G" homozygous A is called A and heterozygous is called AT The genetics package cannot handle the fact that some genotypes are declared with 2...