similar to: log-normal overlay

Hello, Using the following lines of code, I created the following graph:

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

The aggregate function for some reason will now work for me. The error I'm getting is: "Error in sort.list(y) : 'x' must be atomic for 'sort.list' Have you called 'sort' on a list?" agPriceList=aggregate(PriceList$Size, list(PriceList$bandNum),sum) *Price list dataframe:* dput(PriceList) structure(list(Price = c(0, 8.18, 8.27, 10.42, 10.5, 10.6, 11.13,

Dear List, i am trying to produce a 3d plot using wireframe using the code: wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE) As you can see when the code (using the data below) is run the plot area is set-up correctly but the actual surface is missing? Any help would be greatly appreciated. Chris #data Elevation

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

Dear R users, I have a txt file of the form 10092007 24.62 24.31 24.90 11092007 19.20 23.17 22.10 13092007 24.71 27.33 23.10 14092007 27.33 27.90 24.10 15092007 28.22 28.55 24.30 16092007 28.53 29.24 27.40 17092007 24.19 30.64 26.80 18092007 22.60 20.62 28.40 19092007 8.89 1.70 14.70 20092007 21.27 2.92 17.30 21092007 22.38 24.72 8.80 22092007 23.94 24.73 20.40 23092007 25.33 25.12 22.60

Hey, i want to define 3 ideal breaks (bin) for each variable one of those variables is attached in the previous email, i don't want to consider quartile method because quartile is not working ideally for that data set because data distribution is non normal. so i want you to suggest another method so that i can define 3 breaks with the ideal interval for Recency, frequency and monetary to

Hello Folks, Very new to R so bear with me, running 5.2 on XP. Trying to do a zero-inflated negative binomial regression on placental scar data as dependent. Lactation, location, number of tick larvae present and mass of mouse are independents. Dataframe and attributes below: Location Lac Scars Lar Mass Lacfac 1 Tullychurry 0 0 15 13.87 0 2 Somerset 0 0 0

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

Dear R-users, I have to deal with (txt) files of the form 10092007 24.62 24.31 24.90 11092007 19.20 23.17 22.10 13092007 24.71 27.33 23.10 14092007 27.33 27.90 24.10 15092007 28.22 28.55 24.30 16092007 28.53 29.24 27.40 17092007 24.19 30.64 26.80 18092007 22.60 20.62 28.40 19092007 18.89 21.70 14.70 20092007 21.27 22.92 17.30 21092007 22.38 24.72 18.80 22092007 23.94 24.73 20.40 23092007 25.33

Hi Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like. We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer. Cheers

Hi You expect us to solve your problem but you ignore advice already recieved. Your data are unreadable, use dput(yourdata) instead. see ?dput > test<-read.table("clipboard", heade=T) Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, : line 115 did not have 6 elements What is ?ideal interval? can you define it? Should it be such to provide eqal

Hello, I'm a bit new to the world of R so forgive my ignorance. I'm trying to do a zero-inflated negative binomial regression and have received an error message and i'm not sure what it means. I'm running R 2.5.1 on XP. I have just tried a really simple version of the model to see if it would run before I put all the variables in. I have attached all the variables to the

Hello, I'm working on RFM analysis and i wanted to define my own breaks but my frequency distribution is not normally distributed so when I'm using quartile its not giving the optimal results. so I'm looking for a better approach where i can define breaks dynamically because after visualization i can do it easily but i want to apply this model so that it can automatically define the

Hi [macOSX 10.4.6; R-2.3.0] I have encountered a difference in behaviour between R-2.2.1 and R-2.3.0 when performing a linear model. Transcript follows for R-2.3.0 (R-2.2.1 worked as expected). How to make R-2.3.0 perform as R-2.2.1 did? > dput(x) c(29.13, 29.88, 30.09, 29.99, 29.74, 29.64, 29.65, 29.7, 30.04, 29.89, 29.96, 29.65, 28.76, 28.41, 28.38, 29.55, 29.76, 29.75, 29.84,

maybe it's in the data? So here it comes. > sv.growth Grouped Data: length ~ meas | box_id meas spec comp water box_id sprouts leaves length long.sprout 1 1 Sv control moist 1 8.800000 37.80 211.2000 60.6 2 1 Sv xfull moist 2 7.000000 8.00 174.8000 62.8 3 1 Sv control moist 3 9.000000

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

All, I would like to apply a set of rules to each row of the sample data set below. The rule sets are the guidelines for determining an individual's date for retirement eligibility. The rules are found in this document, http://www.opm.gov/feddata/RetirementPaperFinal_v4.pdf. I am only interested in the top two categories for retirement eligibility, the CSRS and FERS plans. The data set has

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not