similar to: Repeated measures lme or anova

Hi, I am encountering a difficulty I don't understand. Be patient, I'm very new to analysis of variance. If I load this data: example12_7=read.table("http://msemac.redwoods.edu/~darnold/math15/data/chapter12/example12_7.dat",header=TRUE) The run the oneway.test: oneway.test(time~drug,data=example12_7,var.equal=TRUE) I get these results: data: time and drug F = 4.1881, num

Hi all, thank you for your patience. I am dealing with a large dataset detailing patients and medications Medications are hard to code, as they are (usually) meaningless unless matched with doses. I have a dataframe with vectors (Drug1, Drug2..... Drug 16) and individual patients are represented by rows. The vectors are actually factors, with 100s of possible levels (all the drugs the patient

Please help with this error message drugbook is an 885 x 32 dataframe >names(drugbook) [1] "DRUG1" "DRUG2" "DRUG3" "DRUG4" "DRUG5" [6] "DRUG6" "DRUG7" "DRUG8" "DRUG9" "DRUG10" [11] "DRUG11" "DRUG12"

All, Is there an efficient way to apply say "mean" or "median" to a dataframe according to say all combinations of two variables in the dataframe? Below is a simple example and the outline of a "manual" solution that will work but is not very efficient (could also generalize this to a function). Searched the archives and docs but didn't see anything close to

Hey all, I'm trying to replicate some plots with ggplot2. The problem is that I need to specify the color for every attribute (drug). If I use the code below the colors get automatically assigned but I need to plot drug1 in black drug2 in blue etc. How do I do that? q = qplot(days,vol,data = cellLine7064, color = drug, geom = c("line", "point")) best regards, Immanuel

Hi, I am running a mixed-effect model with a nested-random effect. I am interested in gut parasites in moose. I has three different type of treatment that I applied to moose which are from different "families". My response variable is gut parasites and the factors are moose families which is nested within treatment. My data is balanced. To answer this question, I used the lme function

All, When specifying colors to xyplot w/ a groups argument, using auto.key no longer maintains the colors properly. I've searched the docs and help but haven't found exactly what I need ... I saw a few examples in the archives involving par.settings but that doesn't seem to do it. I also saw some people using key instead of auto.key, but that didn't seem consistent. Is there a

Relative noob here, I have a data.frame and simply want to add an explicit column of names in column 1 of the form "trial_number01" for row 1, "trial_number02" for row 2 .... etc. It is simply for visual purposes and to explain data to others. I've tried Using row.names and other but still no luck, am sure it has been covered but I can't find it, can you please point

I'm a journalist, wondering what questions to ask about a study that contrasted the impact on serum cholesterol of two drugs. This was a 40 dog study: 5 treatment blocks of 4 dogs each, randomized to: a control block, two blocks at different doses of drug1 and two at corresponding doses of drug2. Analysis was 2-factor repeated measures ANOVA on treatment group and sampling time. Linear

Hello I simply want to plot two variables against one 'year' variable in qplot. Is any way of doing this without reshaping data in long format and using facet function afterwards? Thank you Denis

Hello fellow R-ers, I have spent some time on this and it is driving me NUTS! I am sure there is a solution, so please help. I am trying to create a function that will plot different lines for subsets of a dataset. For example, I am trying to look at different drug groups (drug2), let's say 1,2,3,4, and 5. The data has 2 different rates, college students and high school students (var names cs

Dear R-maillit, I have to perform a stastistical test to asses if two grug show the same or different effect on my prepartion. I tested on the same preparation only one drug and I have to use normalized data due to high variability in the control condictions. What type can I use? A non paramatric test right? And how can do it on R software? Thanks in advance Simone [[alternative HTML version

Dear List, Here is an example of survival data in counting process format (detailed record of each day) > data[data$Id == 11,] # extracted one person's record Id Event Fup Start Stop sex Drug1 601 11 0 6 0 1 0 0 602 11 0 6 1 2 0 0 603 11 0 6 2 3 0 0 604 11 0 6 3 4 0 0 605 11 0 6 4 5 0 1 606

Dear friends - We have 25 rats, 14 of these subjected to partial removal of kidney tissue, 11 to sham operation, and then followed for 6 weeks. So far we have data on 26 urine metabolites measured by NMR 7 times during the observation. I have smoothed the measurements by b.splines in fda including a roughness penalty, and inspecting the mean curves for nephrectomized and sham animals indicate

Dear R users,   I am new user for elastic net. I am trying to use elasticnet library. I have marker data with 359 markers and 168 samples, and response is metabolites. I am trying to do regression between a metabolite and markers.  But i am getting the following error:   > en<-enet(marker,as.numeric(vio),lambda=0.5,normalize=FALSE,intercept=TRUE) Error in one %*% x : requires numeric

Hello, I have this data: Time AMP 0 0.2000000 10 0.1958350 20 0.2914560 40 0.6763628 60 0.8494534 90 0.9874526 120 1.0477692 where AMP is the concentration of this metabolite with time. If you plot the data, you can see that it could be fitted using a logistic regression. For this purpose, I used this code: AMP.nls <- nls(AMP~SSlogis(Time,Asym, xmid, scal), data

Hello, I'm trying to do some differential equation modeling in R using the package 'deSolve.' Briefly, I'm trying to use the law of mass action (the details of which aren't really important) to structure a vector of rate equation which will be passed into an ODE function and solved with associated functions from 'deSolve.' Roughly what this entails is scanning

Dear all, My company located in Innsbruck, Austria, has an opening for a biostatistician. If you have any interest, feel free to contact me or Dr Markus Speiser (markus.speiser*at*biocrates.com). Best regards, David ################## Biostatistician (m/f) Job Description BIOCRATES Life Sciences AG is a leading metabolomics company using a mass-spectrometry based technology

We are looking for a MSc or PhD holder in Bioinformatics,Molecular/Computational Biology or Computer Science to support ourbiomarker team in the identification, validation and furtherdevelopment of biomarkers. The chosen candidate will be part of ourmultidisciplinary team conducting studies to discover and developdiagnostic biomarkers as well as biomarkers for the characterization ofdisease models

Dear All This is more of a statistics question than a question about help for R, so forgive me. I am using lda from the MASS package to perform linear discriminant function analysis. I have 14 cases belonging to two groups and have measured each of 37 variables. I want to find those variables that best discriminate between the two groups, and I want to visualise that and create a