similar to: Newbie programming help

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 - 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 - 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

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 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

Colleagues, I have a routine in package labdsv that calls a FORTRAN subroutine. Recently, I was informed that it sometimes gives different results on a PC and Mac, and that the PC version is clearly wrong. I tested it on linux (because I don't have a PC), and I get the same (incorrect) behavior as the PC. Simply by inserting debug WRITE statements in the FORTRAN I would get different,

What code can i use to convert a table like this: Tag# Date 1 1 2 1 3 1 4 1 2 2 4 2 1 3 2 3 4 4 Into one like this: Tag 1 2 3 4 #Date header 1 1 0 0 1 2 1 1 1 0 3 1 0 0 0 4 1 1 0 1 Thanks, Ben Cox Research Assistant (M.S.) Montana Cooperative

hi all, i'm trying to get elements in a matrix into a vector. i need a "streamlined" way to do it as the way i'm doing it is not very serviceable. an example is a 3x3 matrix like 0 0 3 2 0 0 0 4 0 to a vector like 3 2 4 thanks...mj [[alternative HTML version deleted]]

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

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

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

I have been trying to get a working version of ar, since I have used it in several calculations in the test suite for my time series library. The following limited version (order.max must be specified and other short comings) works more or less, but the results differ by more than I would expect from those given by Splus. I have tried several variations with no success. If anyone can see a reason

Hi there, I would like to find a more efficient way of permuting the rows and columns of a symmetrical matrix that represents ecological or actual distances between objects in space. The permutation is of the type used in a Mantel test. Specifically, the permutation has to accomplish something like this: Original matrix addresses: a11 a12 a13 a21 a22 a23 a31 a32 a33 Example

hello, is it possible to do something like splinefun(x,y), but with a 3rd dim? for example, if i have a 2-dim system like: x<-1:100 y<-rexp(100,1) func<-splinefun(x,sort(y)) func(n) # returns interpolated value of y (after sorting) given x=n # i can check this by: plot(x,sort(y)) lines(spline(x,sort(y))) Can i do the same thing with an x,y, and z? i have found the akima

Dear mailing group, I have loaded an Excel file into R by calling it ".csv" and using the "read.csv" function in R. However then I want to use the (limma package specific, I believe) function "hclust", which clusters data in a tree dendrogram, by similarity. However, I receive the errors msg.s: 1) "missing observations in cov/cor" 2) "In

hello R_team having perfomed a PCA on my fitted model with the function: data<- na.omit(dataset) data.pca<-prcomp(data,scale =TRUE), I´ve decided to aggregate two variables that are highly correlated. My first question is: How can I combine the two variables into one new predictor? and secondly: How can I predict with the newly created variable in a new dataset? Guess I need the