similar to: nls: formula length greater than 502

Hi , I am using nls(formula) where formula is y~ expression y is 1 character ~ is 1 character now i have an expression that is greater tha 502 characters. but R does not recognize anything greater than 502 characters. (Note the expression itself is created using paste and as.formula commands) Does anyone have a suggestion. (BTW i have checked my formula and its

Hi.. is there a limit on the number of explanatory variables in nls ? i have a dataframe with the columns names x1,x2..,x300 when i run nls it gives the error: " x181 not found" thought it does run when i have x1,x2,...,x170 variables. Thanks Harsh --------------------------------- [[alternative HTML version deleted]]

Hi, I have a large number of parameters to estimate in nls say 100: beta1--beta100 lets say i have 100 values in a vector is there a way where i can create the start vector for nls using a loop instead of individually filling the 100 values. Thanks Harsh --------------------------------- [[alternative HTML version deleted]]

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with:

Hi.., is there a function in R to fit bivariate splines ? I came across 'polymars' (POLSPLINE) and 'mars' (mda) packages. Are these the one to use or are there other specific commands? Thanks. Harsh

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hello, I am trying to bootstrap a function that extracts the log-likelihood value and the nls coefficients from an nls object. I want to sample my dataset (pdd) with replacement and for each sampled dataset, I want to run nls and output the nls coefficients and the log-likelihood value. Code: x<-c(1,2,3,4,5,6,7,8,9,10) y<-c(10,11,12,15,19,23,26,28,28,30) pdd<-data.frame(x,y)

Hi, I'm getting an error that I don't understand when updating an nls object. Here is a toy example. dd <- structure(list(Contrast = c(0.00376, 0.03759, 0.12782, 0.25564, 0.50376, 1), Response = c(0.29915, 6.13248, 29.01709, 30.0641, 29.46581, 27.67094)), .Names = c("Contrast", "Response"), class = "data.frame", row.names = c(NA, -6L)) m1 <-

Dear all, Here I come with another stupid question. Suppose I want to use nls() to fit a series of data (here modelled by generated points), then plot the points and the fitting curve. I figured out some way of doing it: x <- runif(1:20, 0, 10) y <- 0.1*x^2 - rep(3, length(x)) + rnorm(length(x), sd = 0.5) yfit <- nls(y ~ a*x^2 + b*x + c, start = list(a = 1, b = 1, c = 1),

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hello, I want to fit a Gompertz model for tree diameter growth that depends on a 4 levels edaphic factor (?Drain?) and I don?t manage to introduce the factor variable in the formula. Dinc is the annual diameter increment and D is the Diameter. >treestab > Dinc D Drain [1,] 0.03 26.10 2 [2,] 0.04 13.05 1 [3,] 0.00 24.83 1 [4,] 0.00 15.92 4

Hi.. I have to invert a 15000 x 15000 matrix (generalized inverse). I do run the process on a fairly powerful computer. but still complains indufficient memory. Is there a way one can invert a large matrix in some other efficient manner. Thanks Harsh --------------------------------- [[alternative HTML version deleted]]

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

So thanks for the help, I have a matrix (AB) which in the first column has my bin numbers so -4 - +4 in 0.1 bin units. Then I have in the second column the frequency from some data. I have plotted them and they look roughly Gaussian. So I want to fit them/ find/optimize "mu", "sigma", and "A". So I call the nls function : nls_AB <- nls(x ~ (A/sig*sqrt(2*pi))*

Hi, I'm a new R user, with a lot of questions. At the moment I'm stoped on an error traying to fit a model: > x <- sandeel ## numeric data (2500-60000) > y <- Noss ## numeric data (0-1.2) > A <- 0.8 > B <- 0.6 > C <- 1/40000 > nls( y ~ A-B*exp(-C*x)) Error in match.call(definition, call, expand.dots) : .Primitive... is not a function I'm

Hi! How to write in model's formula of type: nls(y~k/(x^n), data=data, start=list(k=1,n=1)) i.e the problem is on x^n, I(x^n) generate error thanks Jarek

I want to fit a nonlinear model of the form: Y=A+B*X1+C*X2+log(X3/(X3+D)) I think that the best way is to use the plinear algorithm, but I don't know how to specify the formula in the nls function. I've tried: Y~cbind(rep(1,times=length(Y)),X1,X2,log(X3/(X3+D))) But this fits the model: Y=A+B*X1+C*X2+D1*log(X3/(X3+D)) How can I specify the formula correctly? Thanks, M

Hi.. i have an expression of the form: model1<-nls(y~beta1*(x1+(k1*x2)+(k1*k1*x3)+(k2*x4)+(k2*k1*x5)+(k2*k2*x6)+(k3*x7)+(k3*k4*x8)+(k3*k2*x9)+(k3*k3*x10)+ (k4*x11)+(k4*k1*x12)+(k4*k2*x13)+(k4*k3*x14)+(k4*k4*x15)+(k5*x16)+(k5*k1*x17)+(k5*k2*x18)+(k5*k3*x19)+

I'm attempting to calculate a regression in R that I normally use Prism for, because the formula isn't pretty by any means. Prism presents the formula (which is in the Prism equation library as Heterologous competition with depletion, if anyone is curious) in these segments: KdCPM = KdnM*SpAct*Vol*1000 R=NS+1 S=(1+10^(X-LogKi))*KdCPM+Hot a=-1*R b=R*S+NS*Hot+BMax c = -1*Hot*(S*MS+BMax) Y