similar to: Two factor ANOVA in lme

Hi, I'm using R for a QTL analysis of SNP data. I was wondering if anyone had any advice on fitting a dominance effect into the following function; > myfun4 function (x) { x <- scan(con, nmax=169) y <- unique(x[which(!is.na(x))]) if(length(y)>1) { summary(lme(Ad ~ x, random= ~1|sire, na.action="na.omit")) } else {print("no.infomation")} } Con is the

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

Hi. I was wondering 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

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

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)

Hello R-users! I have a style question. I know that for loops are somewhat frowned upon in R, and I was trying to figure out a nice way to do something without using loops, but figured that i could get it done quickly using them. I am now looking to see what kind of tricks I can use to make this code a bit more aesthetically appealing to other R users (and learn something about R along the

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

#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)

Hello, My R skills 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

Hi, I ran a small data set from a factorial experiment through R, Minitab and JMP... the result from R is significantly different from what Minitab or JMP give... The data set is at the following link: http://www.personal.psu.edu/nug107/Uploads/2x3_16repsANOVA.txt The first 5 columns are the factors and the next three are responses. In particular, for the response beta11MSE, two of the

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, AG as 2 and AA as 3 and the p-value for the

Hi I am performing an analysis of variance with two factors, each with two levels. I have differing numbers of observations in each of the four combinations, but all four combinations *are* present (2 of the factor combinations have 3 observations, 1 has 4 and 1 has 5) I have used both anova(aov(...)) and anova(lm(...)) in R and it gave the same result - as expected. I then plugged this into

Hello... I just installed R version 1.4.0 on my laptop running Redhat Linux 7.1. I downloaded the RPM from CRAN and when I try to use a function I have used on other Redhat systems, I get the following error message. -------- > library(qtl) > gastritis <- read.cross(format="csv",dir="/home/sen/qtl/data/gastritis", +

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

Dear all, Lattice provides automatic coloring for subgroups on each panel by the simple use of a groups statement. For an application I want to change these colors to a predifined set. This works well using a panel 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

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,]

#Hello, #I have would like to paste a single column of a matrix # in pair wise fashion with other columns based upon # even 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

Dear R list, I think my question maybe easy for you but I really spent entire day to resolve it. Say I have a matrix, rows are 6000 genes, columns(1-6) are 3 genotypes (a,b,c) with 2 repeat. I have to use two groups each time for t-test, a vs. c or b vs. c, but I dont know how to write correct codes. Below is my codes, the last two lines are needed to be corrected....

I have a rather basic question. How can I get R to generate a ANOVA table and a f-value for a hypothesis test such as: Data: group1 values: 5.2 4.5 6.0 6.1 6.7 5.8 group2 values: 6.5 8.0 6.1 7.5 5.9 5.6 ... H0: mean1 = mean2 = mean3 = mean4 HA: at least two means different where I want to evaluate using a f test statistic? F = MSTr/MSE I'd like a table similar to one that