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I have a data frame and one separate vector that is a grouping variable for the data frame. I would like to take all rows of the data frame belonging to each group and then sum the columns with out using a for statement. Something like: take all rows of group 1 then apply(group1.data,1,sum), but do this without having to do it separately for each group. Any ideas? thank you. Dan

Full_Name: daniel t. O'Shea Version: 2.2.1 OS: xp Submission from: (NULL) (156.98.28.4) using the cor command in the base package. cor(x,y) x is a matrix (15 rows and 1000 columns). I did not specify a column and R crashed - shut down. error signature AppName:rgui.exe AppVer 2.21.51220.0 ModName: r.dll ModVer: 2.21.51220.0

I am trying to distribute the entries of a data frame (dat) to a vector (water) based on the values of two other vectors (region and year). region is also the columns and year the rows of the data frame (dat). I can write a bunch of ifelse statements or I created the for statement below - but there must be a simpler way?? The for statement probably won't work if I am distributing a non

I am trying to refer to a variable name in a lm regression where the variable name is in another variable, but it does seem to work. Here is an example: y<-rnorm(10) dat<-data.frame(x1=rnorm(10),x2=rnorm(10),x3=rnorm(10)) nam<-c('x1','x2','x3') library(gtools) com<-combinations(3,2,1:3) mod<-lm(y~nam[com[1,1]],data=dat) #error in model frame....:variable

Hello, I am comparing 2 nonlinear models one with a correlation structure. I used the gnls command in the nlme package to fit model 1 and then the update(mod1,corr=corExp(form=~lat+lon,nugget=T) to fit the same model but with a correlation structure. I am comparing models using AIC, but I am getting results that don't seem to match. I get matching results for the model with no correlation

I am having trouble figuring out the right form for the nlme arguments. I do have examples in Modern and Applied Statistics with S and from other sources, but I still can't figure it out. I am trying to estimate species richness (sr) in streams across minnesota. My predictor variables are depth (d), habitat diversity (habdiv), drainage area (da) and an indicator variable representing the

I have been using a nlme model and wish to specify the correlation structure. My data is grouped (bas), but I have no time component or adequate spatial description beyond the grouping variable. So I chose the simplest structure and updated my original nlme model by: update(model, corr=corCompSymm(~1|bas)) This is not significant. One of my variables measures area upstream of the sample. Area

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

I am examining the following nlme model. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) mod1<-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2), fixed=ah+ad+ads+ads2+at+th1+th2~1, random=th1+th2~1, start=c(ah=.9124,ad=.9252,ads=.5,ads2=-.1,at=-1,th1=2.842,th2=-6.917), data=pca1.grouped) However, the two random effects (th1 and th2)

I installed R (R-2.4.1-win32.exe) on a new computer with Windows Vista and a 64 bit operating system (hp dv9000 with intel core t7200). The base R runs fine, but I can not get any of the packages to load. From within R I choose install packages choose a site then a package. I tried installing 2 packages and get similar errors (see below), I just copied and pasted lines from R. Can anyone