similar to: Problem in Fitting model equation in "nls" function

Dear R users, I am struggling to fit expo-linear equation to my data using "nls" function. I am always getting error message as i highlighted below in yellow color:  Theexpo-linear equation which i am interested to fit my data:       response_variable =  (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable my response variable rl <-

This data are kilojoules of energy that are consumed in starving fish over a time period (Days). The KJ reach a lower asymptote and level off and I would like to use a non-linear plot to show this leveling off. The data are noisy and the sample sizes not the largest. I have tried selfstarting weibull curves and tried the following, both end with errors. Days<-c(12, 12, 12, 12, 22, 22, 22,

Greetings, We are performing a meta-analysis of mink pup survival data versus chemical concentration. We have modeled percent survival successfully using nls as shown below and the plot. What we need to do is construct a confidence interval on the concentration at which we get 50% survival (aka the EC50, although we may want other percent survivals in the future). My first question is, what seems

Dear all, can anyone please tell me how to generate a sequence of days continuously, however without considering weekends i.e. Saturday and Sunday? I am aware of following code: > seq(as.Date("2010-01-01"), as.Date("2010-02-01"), by="1 day")  [1] "2010-01-01" "2010-01-02" "2010-01-03" "2010-01-04" "2010-01-05"

Dear all,   I was using older version of R (installed early). I install new version of R (R.2.9.0) but i could not find package "xlsReadWrite" to read Excel file. Is there any alternatives to read Excel file ? I hope it should be. I need help although it is minor querry. Thanks in advance.   Sincerely, Ram Kumar Basnet Wageningen University, The Netherlands   [[alternative HTML

Dear R users, I am not good in R-language programming. So, i need your help. I want to convert my lower-triangle value of symmetry matrix into a vector with their row and column name. I found a function called "sm2vec" in "corpcor" package but it give only a vector of values but not row and column names. But i also want ROW and COLUMN name together with their corresponding

Hi R users,   I am doing multiscale bootstrapping for clustering through pvclust package. I have large data set (observations 182 and variables 5546). When i tried to make bootstrapping, then i got message as "Bootstrap (r = 0.5)............Error: cannot allocate vector of size 117.3 Mb". I am new R user and could not understand what is the problem and also don't know the easiet

Dear R-users, I have a dataset as like below, and I want to subtotal the values of rice,wheat and maize by year for each fid. fid      year     rice     wheat      maize ------------------------------------------------ 1        1995      5        NA           NA 1        1995      NA        3           NA 1        1995      NA       NA           2 1        1996      4        NA           NA 1  

Dear R users,   It may be very simple but it is being difficult for me. I have two vectors with some common string. And, i want to combine into a vector in such a way that it includes string from both vectors and make a unique.   For example:   x <- paste(rep("A",5),1:5,sep = ".") x [1] "A.1" "A.2" "A.3" "A.4" "A.5" y <-

Dear R users,   I am not so used to this R software. I have to use the package " Rgraphviz" but found some problem in the installation process. I download this package and store in R library but i am not getting this package in R installation list. I made review in google search net and use the following command:   ### source("http://bioconductor.org/biocLite.R")

Dear all, I am trying to make barplot from matrix with "beside=FALSE". I have 165 labels in my bar plot. When i plot the label horizontally below the barplot, then all labels are not appear in the plot due to lack of space. I used the following function: barplot(t(bar), main="Barplot based on LCMS", ylab="RF membership Probability",   

Dear R gurus, As part of finding initial values for a much more complicated fit I want to fit a function of the form y ~ a + bx + cx^d to fairly "noisy" data and have hit some problems. To demonstrate the specific R-related problem, here is an idealised data set, smaller and better fitting than reality: # idealised data set aDF <- data.frame( x= c(1.80, 9.27, 6.48, 2.61, 9.86,

Hi All. I've run into a problem with the plinear algorithm in nls that is confusing me. Assume the following reaction time data over 15 trials for a single unit. Trials are coded from 0-14 so that the intercept represents reaction time in the first trial. trl RT 0 1132.0 1 630.5 2 1371.5 3 704.0 4 488.5 5 575.5 6 613.0 7 824.5 8 509.0 9

Hello, i have a problem with the function nls(). This are my data in "k": V1 V2 [1,] 0 0.367 [2,] 85 0.296 [3,] 122 0.260 [4,] 192 0.244 [5,] 275 0.175 [6,] 421 0.140 [7,] 603 0.093 [8,] 831 0.068 [9,] 1140 0.043 With the nls()-function i want to fit following formula whereas a,b, and c are variables: y~1/(a*x^2+b*x+c) With the standardalgorithm

Dear all, I have querry on how to extract the data by matching between two data set where one has the same elements multiple times?   For example, I have two matrix X and Y. > X    [,1]    [,2]       [,3] 1  A     5          P 2  B      6         P 3  C     7         P 4  D     5         Q 5  E      6         Q 6  F      7         Q 7  G     5          R 8  H     6          R 9  I       7

Hi everybody,   I am doing principal component analysis (PCA) using "prcomp' function. When i did "Biplot", i did not found interesting result and it is based on Principal component (PC) 1 vs PC2. Now, i want to see"Biplot" in combination of either PC1 vs PC3 or PC2 vs PC 3. I did not get the ideas. Does any one have ideas ? I am optimistic on getting some idea.

Dear sirs It seems like there is a bug in `profile.nls` with `algorithm = "plinear"` when a matrix is supplied on the right hand side. Here is the bug and a potential fix ##### # example where profile.nls does not work with `plinear` but does with # `default` require(graphics) set.seed(1) DNase1 <- subset(DNase, Run == 1) x <- rnorm(nrow(DNase1)) f1 <- nls(density ~ b1/(1 +

hello all i havnt had a chance to read through the references provided for the "nls" function (since the libraries are closed now). can anyone shed some light on how the "plinear" algorithm works? also, how are the fitted values obtained? also, WHAT DOES THE ".lin" below REPRESENT? thanking you in advance ###################################### i have a quick

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

I was wondering if there is way to place constraints upon the "plinear" algorithm of nls, or rather is there a manner in which this can be achieved because nls does not allow this to be done. I only want to place constraints on one of the nonlinear parameters, a, such that it is between 0 and 1. I have attempted to use a=pnorm(a*) , but then the fitting procedure becomes