similar to: Time vs Concentration Graphs by ID

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

Hi Everyone, I have data in a long format e.g. there is one row per patient but each follow-up appointment is included in the row. So, a snippet of the data looks like this: TrialNo Drug Sex Rand Adate1 Date1 Dose1 Time1 Adate2 Date2 Dose2 Time2 B1001029 LTG M 15719 30/04/2003 15825 150 106 29/08/2003 15946 200 227 B1117003 LTG M 15734 30/04/2003 15825 200 91 03/09/2003 15951 250 217

Dear All, I have a data sets as shown below A (Patient ID ), B and C are the Concentration of drug in blood on day 1 and day 4, D is the difference in conc. To do this in R I have written a code as follows, identified the number of patients who have more concentration on day 4 . Here I want to identify specifically the patient ID (is he patient 1 or 2 or 5 and 7), whose concentration is more. How

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

I have 2 questions on ANOVA with 1 between subjects factor and 2 within factors. 1. I am confused on how to do the analysis with aov because I have seen two examples on the web with different solutions. a) Jon Baron (http://www.psych.upenn.edu/~baron/rpsych/rpsych.html) does 6.8.5 Example 5: Stevens pp. 468 - 474 (one between, two within) between: gp within: drug, dose aov(effect ~ gp * drug *

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in

Hi, there might be a bug in read.delim (also read.table) in R.1.6.2. I included the correct input done in R1.6.1 first, then the wrong input in R1.6.2. The discrepancies are highlighted in red. Michael Man ####### correct input in R1.6.1 ########################## > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386,

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

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

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"

Respected Sir/Madam, I have a question regarding calculation of LD50 (Lethal Dose) and IC50 (50% inhibitory concentration) of an antimicrobial experiment. I have used a compound isolated from a plant and observed its effect on the fungus *Fusarium oxysporum* by the food poisoning method. Solutions of the compound at concentrations of 0, 50, 100, 150, 200 and 250µg/ ml were added to

R Experts I wish to tabulate into one data frame statistics summarizing concentration data. The summary is to include mean, standard deviation, median, min and max. I wish to have summaries by Dose, Day and Time. I can do this by calling aggregate once for each of the statistics (mean, standard deviation, median, min and max) and then execute 4 merges to merging the 5 data frames into

Greetings, I cannot find solution for this problem (I was searching on web, but without success): I want to plot dose-response models for one concentration and many responses (lets say 200) and I don?t want to do it manually. So I use loop for this: for (i in mydata[,2:201]){ #first column is concentration pdf(paste("plot_",i,".pdf",sep = ""))

Dear Rxperts, Running the following code: ======================================================= twlo=10; twhi=20; wt=154; vd=0.5; cl=0.046; tau=6; t=3; F=1; wtkg <- wt/2.2 # convert lbs to kg vd.pt <- wtkg * vd # compute weight-based vd (L) cl.pt <- wtkg * cl # compute CL (L/hr) k <- cl.pt/vd.pt # compute k (hr^-1) cave <-

I am attempting to estimate LC50 (analogous to LD50, but uses exposure concentration rather than dose) by fitting a Weibull model; but I can't seem to get it to work. From what I can gather, I should be using survreg() from the survival package. The survreg() function relies on time-to-event data; my data result from 96 h exposures (i.e., dead or alive after a fixed period; 96 h). I've

Dear Rich, It depends how the data is generated. Although I am not an expert in ecology, I can explain it based on a biomedical example. Certain variables are generated geometrically (exponentially), e.g. MIC or Titer. MIC = Minimum Inhibitory Concentration for bacterial resistance Titer = dilution which still has an effect, e.g. serially diluting blood samples; Obviously, diluting the

Dear all, I'm trying reproduce an example of bioassays Yielding Concentration-Mortality Data particularly control - adjustment model from book Bioassay of Entomopathogenic Microbes and Nematodes chapter 7 with R. I used glm with family=binomial and link=probit, but I do not know how to implement parameter gamma (control mortality - mortality of the untreated control insect in this exaple)

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Hi, folks, ###### food=c('fruit','fruit','fruit','drink','drink','drink') type=c('apple','apple','orange','water','soda','soda') value=c(2,3,1,5,7,6) data=data.frame(food,type,value) share=c((2+3)/(2+3+1),5/6,1/6,5/(5+7+6),13/18,13/18)