similar to: means and error bars on xyplot for binary data

For the following code is there a way to make the jitter all line up horizontally, instead of them being just randomly spread around a value. So for ex if there are multiple values at 63 for genotype wt then all the values should be plotted on the same y value of 63 but spaced apart by a certain factor or noise.. library(lattice); dataFrame <- as.data.frame(t(structure(c( 64,'wt',

-----Original Message----- From: Ghosh, Sandeep Sent: Thursday, June 30, 2005 5:43 PM To: 'Berton Gunter' Subject: plot legend outside the grid Thanks for the pointers... I managed to get everything to look and feel the way I want except for the legend to plot outside the grid... Thanks for the note on the par, but I'm not able to it to plot outside the plot grid.. dataFrame <-

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

Can anyone tell me how I can supply more than one graph to plotCI (gplots) at once? Below is what I tried, also with rbind instead of cbind. What is the way to do this (in general, I think)? Problem is that lines of 1-st and 2-nd series are mixed, while they have nothing to do with each other. I also tried calling plotCI with argument add=TRUE, which didn't seem to work (that is actually

I have a combined date and time. I would like to separate them out into two columns so I can do things such as take the mean by time across all dates. meas<-runif(435) nTime<-seq(1303975800, 1304757000, 1800) nDateT<-as.POSIXct(nTime, origin="1970-01-01") mat1<-cbind(nDateT,meas) means1<- aggregate(mat1$meas, list(nDateT), mean) This doesn't do anything as each day

#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, #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

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

Dear all, I have a data.frame that includes a series of demographic variables for a set of respondents plus a dependent variable (Theta). For example: Age Education Marital Familysize Income Housing Theta 1: 50 Associate degree Divorced 4 70K+ Owned with mortgage 9.147777 2: 65

Hello I have the following data.frame and xyplot. I need this plot without the borders. Does anybody know how to get this xyplot without borders o with white borders? Thank's in advance Juan Hernández my.label <- data.frame(quantil=rep(20,8), my.factor=factor(c('FA','FB','FC','FD','FE','FG','FH','FI'))) library(lattice)

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

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

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

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

All, On p.52 of Deepayan Sarkar's Lattice book there is a nice plot of showing residuals with median lines superimposed or various groups: library("lattice") stripplot(sqrt(abs(residuals(lm(yield~variety+year+site)))) ~ site, data = barley, groups = year, jitter.data = TRUE, type = c("p", "a"), fun = median) Suppose we wanted to make a similar plot for a

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 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

Hi All, I've been working on updating the '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 <-

Hi, (Please use ?dput() to share the example dataset. Avoid using images to show dataset. Also, please read the posting guide esp. regarding home work, assignments etc.) res <- sapply(Gene[,-1],function(x) tapply(x,list(Gene$Genotype),mean)) #or res2 <-? aggregate(.~Genotype, data=Gene,mean) #or library(plyr) ?res3 <- ddply(Gene,.(Genotype),numcolwise(mean)) identical(res2,res3)

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