search for: straining

...4 0.6 0.8 1.0 1 Accumulated constrained eigenvalues Importance of components: CCA1 CCA2 CCA3 CCA4 CCA5 Eigenvalue 1.0 1.0 1.0 1.0 1.0 Proportion Explained 0.2 0.2 0.2 0.2 0.2 Cumulative Proportion 0.2 0.4 0.6 0.8 1.0 Biplot scores for constraining variables CCA1 CCA2 CCA3 CCA4 CCA5 Ph 0.29757 0.85775 -0.3364 0.2242 -0.11088 TotalN -0.01537 -0.67797 0.6132 0.2831 0.28985 Organicmatter 0.14618 0.06462 0.8320 0.3515 0.39827 Ca 0.08310 -0.41940 0.3858 0.8116 0.098...

I've scoured the archives but have found no concrete answer to my question. Problem: Two data sets 1st data set(x) = 20,000 rows 2nd data set(y) = 5,000 rows Both have the same column names, the column of interest to me is a variable called strain. For example, a strain named "Chab1405" appears in x 150 times and in y 25 times... strain "Chab1999" only appears 200

hello everyone, I would like to as for advice for the use of ?lmer? (package ?lme4?) and writing the proper syntax to best describe my data using a mixed-effects model. I have just started to use these models, and although I have read some good examples (Extending the Linear Model with R, Faraway 2005; and the R book, Crawley 2007), I am still not sure of the syntax to test my hypothesis.

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,

dfhfsdhf at ghghgr.com I ran a simple 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

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

...4 0.6 0.8 1.0 1 Accumulated constrained eigenvalues Importance of components: CCA1 CCA2 CCA3 CCA4 CCA5 Eigenvalue 1.0 1.0 1.0 1.0 1.0 Proportion Explained 0.2 0.2 0.2 0.2 0.2 Cumulative Proportion 0.2 0.4 0.6 0.8 1.0 Biplot scores for constraining variables CCA1 CCA2 CCA3 CCA4 CCA5 Ph 0.29757 0.85775 -0.3364 0.2242 -0.11088 TotalN -0.01537 -0.67797 0.6132 0.2831 0.28985 Organicmatter 0.14618 0.06462 0.8320 0.3515 0.39827 Ca 0.08310 -0.41940 0.3858 0.8116 0.098...

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")

Dear list, Has anyone created 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

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

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',

I need to compare gene expression differences between multiple line pairs of alcohol preferring and non-preferring rat lines. I have 5 such line pairs, 3 are unrelated but two were derived independently from the same parent stock. For each line, there are 10 samples. I'll be testing multiple genes, but for simplicity assume just one gene whose expression is measures as geneExpression. Alcohol

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 -7218.175

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...