similar to: sapply question

All, In the function below I have 24 individuals and 6 calculations per individual. The 6 calculations are collected each time in a 1:24 loop when calculating "delta". I'd like to collect all 144 = 24*6 calculations in one vector ("delta.patient.comb"). The function works as is via indexing, but is there an easier way to collect the measurements via appendinng the 6

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

All, When saving plots to a pdf file via the pdf function, I would like to be able to automatically expand the graphics device to achieve the same result as when one does this manually (e.g., clicking the green expand button on the upper left of the graph on Mac OS). Consider simple example below: library("lattice") foo.frm = data.frame(Subject = rep(c(1:4), each = 9), Y =

All, I'm having problems w/ a simple attempt to subset an xyplot. The first plot below is a plot of y versus x for certain values of a third categorical variable z. Now I'd like to further restrict this to certain values of variable y. Neither of the two attempts below work. Any suggestions much appreciated. (note: I don't want to merely use ylim since I have a loess plot and I

All, How does one replace plot symbols with say subject IDs when using xyplot? Or superimpose them next to plot symbols? I searched the archives under various key words but haven't had much. Any suggestions or links much appreciated. Sample code below. David junk.frm = data.frame(ID = rep(1:16, each = 2), x, y, z = rep(c("D", "P"), 16)) y = c( 0.4, 0.6, -0.1,

I've been working with R for all of about 8 hours, so anyone with more experience than this should be able to help me. General comments about my methods of work are also welcomed. I have a table that I've imported thusly: > w <- read.table("woodford.data", header=T) > w start therms gas KWHs elect temp days 1 10-Jun-98 9 16.84 613 63.80 75 40 2

I am getting a difference in results when running some analysis using by and tapply compare to using a for loop. I've tried searching the web but had no luck with the keywords I used. I've attached a simple example below to illustrates my problem. I get a difference in the mean of yvar, diff and the p-value using tapply & by compared to a for loop. I cannot see what I am doing wrong.

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

All, Simple question but I don't seem to be able to find the answer in the documentation: When using "plot" within a loop, is there any way to supply the argument to "main" dynamically, i.e., so that the title is Patient k below as the loop cycles through each value of k? plot(x,y, xlim=c(0,250), ylim=c(0,1000), xlab="gamma", ylab="r1",

Dear colleagues, I have the following code. This code is to 'filter' the data set. It works on the data frame 'whole' with four numeric columns: a,b,d, and c. Every row in the data frame is considered as a point in 3-D space. Variables a,b, and d are the point's coordinates, and c is its value. This code looks at every point, builds a cube 'centered' at this

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

All, I have a repeated measures data frame and was wondering if the covariance matrix can be calculated via some created indexing or built-in R function. Specifically, say there are 3 variables, where potassium concentration is measured 6 times on each patient. Patient number (discrete) Time (1 to 6, discrete) Potassium (continuous variable) I want the covariance/correlation matrix for the

All, When I take a subset of a factor the reduced factor still maintains all the original levels of the factor when say forming the key in a plot. The data is correct, but the variable still "remembers" the original levels. See below for reproducible code. Does anyone know how to fix this? cheers, dave fact = as.factor(c(rep("A", 3),rep("B", 3), rep("C",

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

All, I'd like to plot the main relationship of a grouped data object for all levels of a factor in a single panel. The sample code below creates a separate panel for each level of the factor. I realize that this could be done in other ways, but I'd like to do it via plotting the grouped data object. thanks! dave z = rnorm(18, mean=0, sd=1) x = rep(1:6, 3) y =

Hi, I have a complex-valued vector X in C^n. Given another complex-valued vector Y in C^n, I want to find a permutation of Y, say, Y*, that minimizes ||X - Y*||, the distance between X and Y*. Note that this problem can be trivially solved for "Real" vectors, since real numbers possess the ordering property. Complex numbers, however, do not possess this property. Hence the

I am still searching for a solution to what i think is a simple problem i am having with building a vector in a for loop. I have built a more understandable example so hopefully that will help..help you, help me, if you know what i mean. dev=400 #test location model TAZs to reference cands=c(101,105,109) #Create Object of length of cands candslength=length(cands) #TEST TAZ Vector

All, I've coded a function and it works manually if I copy it line by line into R. However, when I try to "load" (copy and paste) the entire function into R, I get the following error after the listed line of code: + N.j.list = lapply(rej.hyp, length) Error: symbol print-name too long Does anyone you know what this error means? Strangely, when I copy the same line verbatim

Another question on functions - I have something that looks like plotter<-function(i){ temp.i<-rwb[rwb$vector1 <=(i*.10),] with(temp.i, plot(vector2, vector3, main=(i*.10),)) mod<-lm(vector3~vector3-1,data=temp.i) r2<-summary(mod)$adj.r.squared rsqrd[i]<-r2 legend("bottomright", legend=signif(r2), col="black") abline(mod) rsqrd<<-rsqrd } I'd