similar to: Contextstack overlow

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 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

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 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

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

Hello, Are there any R functions available for performing a serial correlation test for short time series (e.g, series having between 10-14 observations)? Many thanks! Isabella R. Ghement, Ph.D. Ghement Statistical Consulting Company 301-7031 Blundell Road, Richmond, B.C., Canada, V6Y 1J5 Tel: 604-767-1250 Fax: 604-270-3922 E-mail: isabella at ghement.ca Web: www.ghement.ca -----Original

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,

I have data that looks like YC Age Num 82 11 2 83 10 0 84 9 8 85 8 21 86 7 49 87 6 18 88 5 79 89 4 28 90 3 273 91 2 175 with a program mod1=lm(log(Num+1)~YC, data=box44) plot(log(Num+1)~YC, data=box44, pch=19, xlab="Year Class", ylab="Loge Number at age", ylim=c(0,6), xlim=c(91,82)) abline(lm(log(Num+1)~YC), col="blue", lwd=2) summary(mod1) I need to

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

Hi all, I´m an absolute beginner in R programming…I have a really long R-code with many variables. When I run the program I always get an error at the same place called “contextstack overflow”. Does anybody know what to do in this case? Are there so strict limitations in R? If yes, is it possible to handle it? How? Thank you for any help! [[alternative HTML version deleted]]

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

Hello! I written several implementations in R of R?my's algorithm for generating random ordered strictly binary trees on 2n+1 vertices. One implementation involves manufacturing character strings like: "X <- list(0,list(0,0))" for the case n=2. If I perform the following two steps: cmd <- "X <- list(0,list(0,0))" eval(parse(text=cmd)) then X becomes a true

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)

I need to debug a call quality issue with remote users on the other end of a satellite link. The symptoms are: we here on the Internet side can hear them just fine. On their end, things work sorta OK most times, but they often suffer from severe dropouts and digital warbling, both of which I attribute to them missing packets. Often times they can't make out a word we are saying while we can

All - I have a data frame data.a ID valueA valueB 6 12 12 17 15 14 58 18 16 98 11 12 73 19 20 84 19 14 58 20 14 24 11 12 81 15 16 21 15 14 62 14 12 67 13 14 78 13 17 35 10 13 13 11 15 14 17 18 85 16 15 35 13 9 18 15 16 and a data frame data.b ID valueA valueB 6 84 21 78 14 I'd like to have R find the data.b$ID in data.a$ID and insert the corresponding data.a$valueA and

All - I have a column of SiteNames: SiteName OYS-PIA2-FL-1 OYS-PIA2-LA-1 OYS-PI-LA-BB-1 OYS-PIA2-LA-10 ... [truncated] and I want to include only the last few digits into a new column. I tried substr(data$SiteName, 13, 20) but because some SiteName values are of a different length, the final hyphen (i.e., "-") was included: "1" "1" "-1" "10"

All - I have an example data frame x l.c.1 43.38812035 085 47.55710661 085 47.55710661 085 51.99211429 085 51.99211429 095 54.78449958 095 54.78449958 095 56.70201864 095 56.70201864 105 59.66361903 105 61.69573564 105 61.69573564 105 63.77469479 115 64.83191994 115 64.83191994 115 66.98222118 115 66.98222118 125 66.98222118 125 66.98222118 125 66.98222118 125 and I'd like to get the 3rd

All – I’m having a problem with the rJava package. I can download it to my machine (Win 7 64-bit) but when I try to load the package into R (2.15.1, 64-bit version), I get the following error: > require(rJava) Loading required package: rJava Error : .onLoad failed in loadNamespace() for 'rJava', details: call: inDL(x, as.logical(local), as.logical(now), ...) error: unable