similar to: lm() question

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

All - Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Specifically, I'm wondering if anyone has implemented the goodness-of-fit process developed by Koenker and Machado (1999) for R? Though I have used package quantreg in the past, I may have overlooked this function, if it is included. Citation: Koenker, R. and

I'm tyring to fit a relatively simple nls model to some data, but keep coming up against the same error (code follows): Oto=nls(Otolith ~ Linf*(1-exp(-k(AGE-to))), data = ages, start = list(Linf=1000, k=0.1, to=0.1), trace = TRUE) The error message I keep getting is "Error in eval(expr, envir, enclos) : could not find function "k"". I've used this

All - Not sure if this is a real programming question, but here goes: I have data that looks like Lake Length Weight 1 158 45 1 179 70 1 200 125 1 202 150 1 206 145 1 209 165 1 210 140 1 215 175 1 216 152 1 220 150 1 221 165 ... where lake goes from 1 - 84 and the number of rows for each lake is variable (but > ~20). I'm trying to do two things: 1) build a simple linear model of the

All - I have a number of rows that I am assigning length classes to via l.class<-with(wae, ifelse((Length>=120)&(Length<130),"125", ifelse((Length>=130)&(Length<140),"135", ifelse((Length>=140)&(Length<150),"145", ifelse((Length>=150)&(Length<160),"155",

I keep running up against the same error when I try to plot a line from a nls model. The data is fisheries length/weight data. Code follows: require(graphics) pow = nls(Weight~alpha*Length^beta, data=wae, start=list(alpha=0.0000001, beta=3.0), trace=TRUE) predict(pow) plot(Weight~Length,

All - I have data: TL age 388 4 418 4 438 4 428 5 539 10 432 4 444 7 421 4 438 4 419 4 463 6 423 4 ... [truncated] and I'm trying to fit a simple Von Bertalanffy growth curve with program: #Creates a Von Bertalanffy growth model VonB=nls(TL~Linf*(1-exp(-k*(age-t0))), data=box5.4, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) #Scatterplot of the data plot(TL~age, data=box5.4,

All - My question is a bit involved, so bear with me. I have some data that looks like: Lake LL LW 81 2.176091259 1.342422681 81 2.176091259 1.414973348 81 2.176091259 1.447158031 81 2.181843588 1.414973348 81 2.181843588 1.447158031 81 2.184691431 1.462397998 81 2.187520721 1.447158031 81 2.187520721 1.477121255 81 2.187520721 1.505149978 ... [truncated] I'm trying to: 1) fit a simple

I have data that looks like Year,Recruit,Spawner,Mtempcv 1958,4532,775,0.24125 1959,22996,2310,0.16319 1960,628,2990,0.46056 1961,879,1400,0.33028 1962,14747,1130,0.22618 1963,13205,790,0.20596 1964,31793,1195,0.19229 1965,10621,981,0.20363 1966,22271,870,0.3452 1967,8736,1104,0.27511 1968,8761,883,0.10884 1969,18885,1421,0.17799 1970,10098,1198,0.2106 1971,3394,760,0.22098 1972,1697,1354,0.39461

I have data that looks like O.lengthO.age 176 1 179 1 182 1 ... 493 5 494 5 514 5 606 5 462 6 491 6 537 6 553 6 432 7 522 7 625 8 661 8 687 10 704 10 615 12 (truncated) with a simple VonB growth model from within nls(): plot(O.length~O.age, data=OS) Oto = nls(O.length~Linf*(1-exp(-k*(O.age-t0))), data=OS, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) mod <- seq(0, 12)

[Env: R 2.14.2 / Win Xp] In the script below, I want to select some variables from rrcov::OsloTransect, delete cases with any missing data, and subset the data frame Oslo to remove cases for two levels of the factor litho that occur with low frequency. The checks I run on my new data frame Oslo look OK, but I when I try to fit a multivariate linear model with lm(), I am getting an error:

I am trying to produce frequencies in defined intervals however I can't seem to figure out how to get R to bin my data the way I want it to. I have several thousand lengths of fish that I want to be binned as follows: Ex. Length Bin 209 200 219 210 431 430 727 720 That is, bins with any length equal to or greater than the lower

Hello, I have error on compiling time I tried on HP-UX 11.00 system with gcc version 3.1 to compile samba source Samba latest version source was dowloaded from www.us1.samba.org/samba/ftp I attached log file with this error thank you. begin 600 samba_make.log.doc M(R!M86ME#0HO=7-R+VQO8V%L+W-A;6)A+V)I;B(@+410241$25(](B]U<W(O M;&]C86PO<V%M8F$O=F%R+VQO8VMS(B

Dear All, I have seldom needed to use loops in R, but now I need to code a loop with a stride different from one. In the R manual I downloaded I have the example: > xc <- split(x, ind) > yc <- split(y, ind) > for (i in 1:length(yc)) { plot(xc[[i]], yc[[i]]); abline(lsfit(xc[[i]], yc[[i]])) } but in my case I'd like to add a condition so that i varies by 4 from one go

Dear useRs, I'm trying to write a loop to sum my data in the following way: (the second - the first) + (the third - the second) + (the fourth - the third) + ... for each column. So, I wrote something like this:   c <- list()   for(i in 1:ncol(mydata)) {   for(j in 2:nrow(mydata)) {   c[[i]] <- sum(yc[j,i] - yc[(j-1),i])   }}} As for the columns it works pretty fine, but it only

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc

All - I think I'm being dense, but for the life of me, I can't figure out why I get error message with the code below. I have data that looks like param level perc.surv asin.tran DO 3 0.6864407 0.9764544 DO 3 0.1250000 0.3613671 DO 3 0.8738739 1.2077299 DO 4 0.4615385 0.7468986 DO 4 0.5691057 0.8547258 DO 4 0.8504673 1.1737517 DO 6 0.5494505 0.8349297 DO

I'm having a problem with aggregate.formula when I call it in a function and the function is converted from a string in the funtion I think my problem may also only occur when the left hand side of the formula is cbind(...) Here is example code that generates a dataset and then the error. The first function "agg2" fails > agg2(FALSE) do agg 2 Error in m[[2L]][[2L]] : object

All - I have data that looks like psd Species Lake Length Weight St.weight Wr Wr.1 vol 432 substock SMB Clear 150 41.00 0.01 95.12438 95.10118 0.0105 433 substock SMB Clear 152 39.00 0.01 86.72916 86.70692 0.0105 434 substock SMB Clear 152 40.00 3.11 88.95298 82.03689 3.2655 435 substock SMB Clear

R help list subscribers, I am a new user of R. I am attempting to use R to explore a set of equations specifying the dynamics of a three trophic level food chain. I have put together this code for the function that is to be evaluted by LSODA. My equations Rprime, Cprime, and Pprime are meant to describe the actual equation of the derivative. When I run LSODA, I do not get the output that