similar to: data.frame within a function (PR#9294) (cont'd)

Hi all, I have a (hopefully) simple newbie-level question.   # I have data like this: dtf <- data.frame(read.table(textConnection("var  value   company  9887.1   company  91117.0   blaah  91.1   etc  11   etc  97111"), header=TRUE))   # I would like to have output like this (the index number may vary): var  value.1 value.2 company 9887.1 91117.0 blah  91.1 NA etc 11 97111   # I

Although this should be trivial, I'm having a spot of trouble. I want to make a lattice plot of the format y1+y2 ~ x | grp but then fit a lm to each y variable and add an abline of those models in different colors. If the xyplot followed y~x|grp I would write a panel function as below, but I'm unsure of how to do that with y1 and y2 without reshaping the data before hand. Thoughts

R-help, I'm trying to test the significance of two regression lines , i.e. the significance of the slopes from two samples originated from the same population. Is it correct if I fit a liner model for each sample and then test the slope signicance with 'anova'. Something like this: lm1 <- lm(Y~ a1 + b1*X) # sample 1 lm2 <- lm(Y~ a2 + b2*X) # sample 2 anova(lm1, lm2)

Dear all, A few months ago, I asked for your help on the following problem: I have a list with three (named) numeric vectors: > lst = list(a=c(A=1,B=8) , b=c(A=2,B=3,C=0), c=c(B=2,D=0) ) > lst $a A B 1 8 $b A B C 2 3 0 $c B D 2 0 Now, I'd love to use this list to create the following data frame: > dtf = data.frame(a=c(A=1,B=8,C=NA,D=NA), +

Dear all, I have a list with three (named) numeric vectors: > lst = list(a=c(A=1,B=8) , b=c(A=2,B=3,C=0), c=c(B=2,D=0) ) > lst $a A B 1 8 $b A B C 2 3 0 $c B D 2 0 Now, I'd love to use this list to create the following data frame: > dtf = data.frame(a=c(A=1,B=8,C=NA,D=NA), + b=c(A=2,B=3,C=0,D=NA), + c=c(A=NA,B=2,C=NA,D=0) ) > dtf a b

Hi All, I have tried every fix on my try or tryCatch that I have found on the internet, but so far have not been able to get my R code to continue with the "for loop" after the lmer model results in an error. Here is two attemps of my code, the input is a 3D array file, but really any function would do.... metatrialstry<-function(mydata){ a<-matrix(data=NA, nrow=dim(mydata)[3],

Hello, I've written a simple (although probably overly roundabout) function to test whether two regression slope coefficients from two linear models on independent data sets are significantly different. I'm a bit concerned, because when I test it on simulated data with different sample sizes and variances, the function seems to be extremely sensitive both of these. I am wondering if

Dear R users, Suppose I have two different response variables y1, y2 that I regress separately on the same explanatory variable, x; sample sizes are n1=n2. Is it legitimate to compare the regression slopes (equal variances assumed) by using lm(y~x*FACTOR), where FACTOR gets "y1" if y1 is the response, and "y2" if y2 is the response? The problem I see here is that the

Dear All, For function: rmvnorm{mvtnorm} in (library mvtnorm, not splus2R), if I generate 2 bivariate normal samples as follows: > rmvnorm(2,mean=rep(0,2),sigma=diag(2)) [,1] [,2] [1,] 2.0749459 1.4932752 [2,] -0.9886333 0.3832266 Where is the first sample, it is stored in the first row or the first column? Does this function store samples row-wise or column-wise? Thank

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Could someone please tell me how to stop the package/function name from being included before the command prompt? This started happening today after I made some changes to my Rprofile.site file and I don't know why. For example: if I enter example(AIC), instead of just getting the regular '>' before each output line, I get 'AIC>' instead. I only want the '>'

Hi, all! I'm pretty sure I'm missing something about this. Is there a smart way of typping hat(R)^2 and it's value from a linear regression? I've just found this tricky one: # Sample data x <- sample(1:100,10) y <- 2+3*x+rnorm(10) # Run the regression lm1 <- lm(y~x) # Plotting plot(x,y, main="Linear Regression", col="red")

Hi, I have two data frames. One is like > dtf = data.frame(y=c(rep(2002,4), rep(2003,5)), + m=c(9:12, 1:5), + def=c(.74,.75,.76,.78,.80,.82,.85,.85,.87)) and the other dtf2 = data.frame(y=rep( c(2002,2003),20), m=c(trunc(runif(20,1,5)),trunc(runif(20,9,12))), inc=rnorm(40,mean=300,sd=150) ) What I want is to divide

Excuse the simple question... I'm not sure what I'm doing wrong with predict, but let me use this example: Suppose I do: dat<-matrix(c(0,0,10,20),2,byrow=T) lm1<-lm(dat[,2]~dat[,1]) Suppose I want to generate the linearly-interpolated y-values between the point (0,0) and (0,20) at every unit interval. I thought I just do: predict(lm1, data.frame(seq(0,10,1))) to get

Dear R-helpers, I have run the code below which I expected to make an object called dd1, but that object does not exist. So, in summary, my problem is that my function is meant to make an object (dd1), and it does indeed make that object (I know that the last line of the function prints it out) but then, after the function has run, the object has disappeared. It's late on a Friday so I may

Dear R users, I have some trouble using the allEffects() function to compute and display effect plots for a linear model. My data is quite simple, it concerns effects of 3 treatments on the tumoral volume of mice. vTum codes for the qualitative initial volume, from small to big, temps is the time in month since beginning of treatment, and S?rie codes for the batch. Data is unbalanced. >

Hi, My R version is 2.4.1 and I installed the the packages MASS and run command library("MASS"), however when I type ?rmvnorm, no help topic found, it worked before. I tried to ype ?rinvgamma from "MCMCpack" which works great. Anybody have idea? I also reinstalled MASS package, but when I try to type rmvnorm(), no functions found. Pat

Dear List, I want to ask a AIC question based on package library(MuMIn) The relative importance of 16 explanatory variables are assessed using delta AIC in a generalized linear model. Please kindly advise if it is possible to show models with any two only certain variables. Thank you. Elaine I asked a similar question and got a great help for models with only one variable as below.