similar to: Generalized additive models: Plots for Qualitative Data

This is my first time running GAMs in R. My csv file has these column headings: "X" "Y" "Sound" "Atlantic" "Blacktip" "Bonnet" "Bull" "Finetooth" "Lemon" "Scalloped" "Sandbar" "Spinner" "Abundance" "Diversity"

Hi there, My question is about the 'theta' parameter in specification of a NB GAMM. I have fit a GAM with an optimum structure of: SB.gam4<-gam(count~offset(vol_offset)+ s(Depth_m, by=StnF, bs="cs")+StageF*RegionF, family=negbin(1, link=log), data=Zoop_2011[Zoop_2011$SpeciesF=='SB',]) However, this GAM shows heterogeneity in the

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

I am using the read.table function to load an Excel data set into R. It has a few variables with very long qualitative (free response typically in sentences) response that I would like to keep, but would like to limit the "length" of the response that R shows. Is there some sort of string or column width command I can include in the read.table function to limit the length of words used

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Hi, I want to know is there other functions in R to estimate qualitative response model besides multinom() in library nnet, if this is the only possibility, I have a question about the application: for example: there is three transportation choice : car, bus , subway. each alternative has own characteristic variables, I want to apply conditional logit model to analysis the choice of three

Hi all, I?m trying to plot this data N M W I 10 106 II 124 484 III 321 874 IV 777 1140 V 896 996 VI 1706 1250 VII 635 433 VIII 1437 654 IX 693 333 X 1343 624 XI 1221 611 XII 25 15 XIII 3 XIV 7 8 So that in de Y axis will be the level (qualitative data) and in the X axis will be M and W variables. So x axis will be wwith a lenght between 0 and 2000. I would like to plot a line with M and other

Hi, I have been working towards developing compiler optimization tools targeting multi core processors while using LLVM IR as the starting point and building on top of the analysis and optimization passes available in the llvm source. Recently, I looked into Open64 and its intermediate representation WHIRL. Documentation for developers to use Open64 seems to be inadequate (when compared to LLVM

See ?barplot and set the horiz argument to TRUE. (This is in the base R plotting version. The ggplot2 and lattice systems have other ways of doing this) Note: if you search on e.g. "barplots in R" or similar, you should find numerous examples with code. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into

Hi, Arvind Sudarsanam: I know some of Open64. Above all, Open64 is designed for a high performance compiler. It is now supported by AMD, HP, ICT Chinese Academy of Science, etc. and has been ported to X86, Itanium, Loongson CPU etc. And to your questions 1, Open64 already have some main optimization phases, Inline for aggressive inline opt. LNO for loop opt, WOPT for machine independent opt(

Many thanks, My goal is to make a plott like attached but the Y axis starts in XIV and end at top in I. Generally for instance in excel X axis is categories but Y axis is numbers I want the contrary plotted in lines, your last help is near what I look but barplot is not needed. Hope you can help me thanks in advance. 2018-05-22 0:58 GMT+02:00 Jim Lemon <drjimlemon at gmail.com>: > Hi

Hi Pedro, melt() is probably working. The problem is I did not finish the copy and paste.? It would have been better if I had included the ggplot() command. Try ============================================================== library(reshape2) library(ggplot2) dat1? <- structure(list(N = c("I", "II", "III", "IV", "V", "VI",

Dear R experts: I have following problem: # myfunction mfun1 <- function(x) { if ( x == 2){ xv <- sample(c(2,1,0),100, replace = T, prob = c(0.6, 0.2, 0.2)) } if ( x == 1){ xv <- sample(c(1,0),100, replace = T, prob = c(0.6, 0.4)) } if ( x == 0) { xv <- sample(c(0,0),100, replace = T, prob = c(0.5, 0.5)) } return(xv) } # applying the function x = mfun1(1) # just what I

The problem remain unsolved. If you have any idea please do suggest .................... thank you; Ram H On Wed, Aug 31, 2011 at 12:06 PM, Ram H. Sharma <sharma.ram.h@gmail.com>wrote: > Dear R experts: > > I have following problem: > > # myfunction > mfun1 <- function(x) { > if ( x == 2){ > xv <- sample(c(2,1,0),100, replace = T, prob = c(0.6, 0.2,

Dear list, I am a first time user of this list and a ultra-novice "R" user. I am seeking the ability to graphically depict open-ended responses from a focus group determining data needs of community mental health staff members. These responses would be mapped based upon either Theme or concept or domain, then sorted by similar responses and grouped. Next we would like to use

Hi John, > A "volatile error" indicates a case where a compiler failed to respect > the volatile invariant. The volatile invariant is simply that changing > the optimization level of a strictly conforming C program must not > change the number of dynamic loads or stores to any variable that is > volatile-qualified in the compiler's input. We check this with a hacked

Hi Pedro, Not too hard. Just have to watch the order of the variables: ppdf<-read.table(text="N M W I 10 106 II 124 484 III 321 874 IV 777 1140 V 896 996 VI 1706 1250 VII 635 433 VIII 1437 654 IX 693 333 X 1343 624 XI 1221 611 XII 25 15 XIII 3 NA XIV 7 8", header=TRUE) plot(rev(ppdf[,2]),1:14,col="blue",lty=1,type="l",

Hi John, > > does this also check that writes are atomic: that they are performed in > > one processor operation? > > Can you elaborate a bit? I don't think volatile has any atomicity > requirements. Of course I can make a struct, an int128_t, or whatever > volatile (on AVR even an int16_t is updated non-atomically!). that's not entirely true in practice: if

Hi Duncan- > does this also check that writes are atomic: that they are performed in > one processor operation? Can you elaborate a bit? I don't think volatile has any atomicity requirements. Of course I can make a struct, an int128_t, or whatever volatile (on AVR even an int16_t is updated non-atomically!). Lack of atomicity is one of many problems with using volatile as a basis

Hi folks, We recently generated some data that seemed interesting enough to share here. This is a comparison between compilers that ignores the performance of the generated code and focuses only on compiler correctness. volatile checksum errors errors avr-gcc-3.4 1.879% 0.378% avr-gcc-4.1 0.037% 0.256% avr-gcc-4.2