search for: strains

We need to keep the discussion on the list. When I run your code, there are several problems. strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName ="strain", header = T, row.names = 1) str(strain.data) # lists 9 columns at the end with all NAs strain.data1 <- (strain.data, sqrt.dist = TRUE) # this is not a valid R line. I get Error: unexpected ',' in

...times and in y 25 times... strain "Chab1999" only appears 200 times in x and none in y (so i dont want that retained). I want to create a new data frame that has all 175 measurements for "Chab1405" and any other 'strain' that appears in both the two data sets.. but not strains that appear in only one data set...So i want the intersection of two data sets (maybe?). I've tried x %in% y, but that only gives TRUE/FALSE -- View this message in context: http://r.789695.n4.nabble.com/Combining-Overlapping-Data-tp4032719p4032719.html Sent from the R help mailing list arch...

...e individuals have higher fecundity at later ages compared to others. - The variance between individuals in strain B is low; therefore the variance between ind/lines and interactions can be ignored. These results, on its own, are interesting, but I would like to have a model where I include both strains (and still can make some interpretations) My first guess is m4 <- lmer(fecudnity ~ Age + (1 | strain/line/ind), method=?ML?) m5 <- lmer(fecudnity ~ Age + (Age | strain/line/ind), method=?ML?) Using LTR, I find that m5 describes better the data (X^2 [105 d.f] = 347.15, p-value < 2.2 e-1...

Hi, I have a data-frame, r (column names below), that needs subsetting: date, time, strain, gene, deltact When I try to subset r by applying selection criteria on two columns I get an empty data frame. For example I would like to extract all rows that have time == 0h and strain == ROC. So, t <- subset(r, (r$time == "0h" && r$strain == "ROC"), select= c(time,

...ple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second column....(I am only concerned with the strains, the second list is the estimates of 'Test'). However i have tried every possible way to unlist 'blups' so that is retains the names of my strain AND the blups... Theoretically it should...

Dear R user, I have a question on using R to analyze data with repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I would like to know how t...

Hello Sir I am getting problem in plotting in CCA . Could you please help me? I wrote the below command but I don't know why it is taking only first 5 env data rather than all 9. > strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName="strain", header = T, row.names = 1) > env.data <- read.xlsx("Dee rhiz.xlsx", sheetName="env", header = T,

Hello, I am new to R and currently have the following problem: I have successfully loaded my data in R which consists of two numeric columns (LI_F and female) and one character column (Strain). So far I can plot two different set of boxplots for each of the numeric columns plotted by the groups of the character column and the commands look like that: boxplot(LI_F~Strain, ylab="LI_F",

In the vcdExtra package on R-Forge, I have functions and generic methods for calculating log odds ratios for R x C x strata tables. I'd like to define methods for fitting weighted lm()s to the resulting loddsratio objects, but I'm having problems figuring out how to do this generally. # install.packages("vcdExtra", repos="http://R-Forge.R-Project.org")

...a version of SSmicmen that allows testing for group differences? The basic Michaelis-Menten equation is: (Bmax * X) / (Kd + X). The nlme package allows modeling of random effects for Bmax and Kd as needed, but I curious how I can build in group differences? I have receptor binding data for strains of mice, and following Pinheiro and Bates' lead in their book, a random effort for animal differences in Bmax seems reasonable, within strain. However, I'd like a self-start function, or even just the equation above, modified to allow testing for strain differences in Bmax and or Kd...

Hello, I seem unable to construct a legend which contains a substitution as well as math symbols. I'm trying to do the following: strain2 <- "YJG48" legend.txt <- c( substitute( strain * %==% * "YJG45, rpn10" * %Delta%, list(strain=strain2) ), "Verhulst/Logistic", "Malthus" ) legend( 100,2.5, legend.txt, cex=0.75,

Hi, I have some difficulty in figuring out whether I am doing correct or not. A brief introduction about the work: It is a light/dark choice test conducted in insect larvae.  The response is binary (0- present in dark area, 1-present in light area) and the experiment is run for 15 min, so there are 15 repeated measurements per individual larva at 1 min intervals.  The factors which affect

Dear R list, What is the proper way to specify a nested model so that the F values agree with the expected mean square errors? Specifically, suppose I have a design where "Heads" are nested within "Machines". I would like to model the following Y_ijk = Mu + Machine_i +Head_j(i) +Error_k(ij). Using the commands below, > summary(aov(Strain~Machine + Head%in%Machine ))

Andi - I have experienced the same issue you mention and gotten no reply as to a way to fix it. I finally implemented "blacklist" into my Asterisk and added "Anonymous", "anonymous", "unknown", "Unknown", etc., into my blacklist file. When those come in with an IP address instead of a phone number but have no real name, they get the

Dear List, I downloaded R for the first time yesterday, in the hopes that I might deal more effectively with a complex repeated measures experimental design involving inbred strains of laboratory mice. The design below, somewhat simplified, cannot be computed with standard ANOVA, because something called the X'X matrix is too large. The design has the following factors: Between-subject factors (levels): inbred mouse strain (20, "twenty") sex (2) Animals: 1...

I have assayed the concentrations of various metal elements in different anatomic regions of two strains of mice. Now, for each element, in each region, I want to do a t test to find whether there is any difference between the two strains. Here is what I did (using simulated data as an example): # create the data frame > elemconc = data.frame(expand.grid(id=1:3, geno=c('exp', 'wt'...

...2 "X strain-pairs being related, whereas the others are unrelated to each other and also to the 2 "X" strain-pairs. We want to take into account the fact that there are really only 4 parent populations of these 5 strain-pairs so as to decrease the weighting put on the "X" strains in the model. What would the most appropriate approach to this be and how would the model be written? Thanks, Mark Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry Indiana University School of Medicine 15032 Hunter Court, Westfield, IN 46074 (317) 490-5129 Work, & Mobile &am...

I am running into a problem using 'mix' for multiple imputation (over continuous and categorical variables). For the way I will be using this I would like to create an imputation model on some training data set and then use this model to impute missing values for a different set of individuals (i.e. I need to have a model in place before I receive their information). I expected that all

Dear R users, Here is the coxme output I obtain on my survival dataset having 3 strains and 2 infection status (i: infected, ni: non infected) coxme(Surv(lay) ~ infection*strain, data=datalay, random= ~1 |block) Cox mixed-effects model fit by maximum likelihood Data: datalay n= 1194 Iterations= 3 77 NULL Integrated Penalized Log-likelihood -7270.028 -7223.859...

Hello.... Trying to apply a model to each level of a factor For example, i have three levels of a variable I call 'Code'...I want to model the data under each level of code differently...I've attached a sample data set... http://r.789695.n4.nabble.com/file/n3913431/data.txt data.txt I.E for code 0HY0 I want to model y~ 1|strain + 1|code *both code and strain are random effects...