similar to: question about nls

Hi I?m trying to fit a nonlinear model to a derivative of the logistic function y = a/(1+exp((b-x)/c)) (this is the parametrization for the SSlogis function with nls) The derivative calculated with D function is: > logis<- expression(a/(1+exp((b-x)/c))) > D(logis, "x") a * (exp((b - x)/c) * (1/c))/(1 + exp((b - x)/c))^2 So I enter this expression in the nls function:

I am trying to it a particular nonlinear model common in Soil Science to moisture release data from soil. I have written the function as shown below according to the logist example in Ch8 of Pinheiro & Bates. I am getting the following error (R version 2.1.1) *Error in qr(attr(rhs, "gradient")) : NA/NaN/Inf in foreign function call (arg 1)* Below is the function and data. /#

I have a set of data for US t-bill returns and US stock returns frm 1980-2012. I am trying to bootstrap the data and obtain the minimum variance portfolio and repeat this portfolio 1000 times. However I am unable to get the correct code function for the minimum variance portfolio. When I tried to enter Opt(OriData+1, 1, 5, 0), I get "error:subscript out of bounds" Please help!

Hi all, I am a bit mystified by this error message that I get when I try to apply lmer() to a simple dataset with one between factor (age) and one within factor (item): "$ operator is invalid for atomic vectors" I'll just provide the code, because I don't see where the problem is: library(lme4) options(contrasts=c("contr.helmert","contr.poly")) data =

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then

Hi list, I have a group of data. It looks like they follow a exponential distribution. In R, how can I esimate lamda, that is the rate in pexp, of the distribution and can I use Kolmogorov-Smirnov for hypothesis testing in such a situation? I have read the "8.2 Examing the distribution of a set of data" of "An Introduction to R" but I did not find any clues on this issue.

Hi, I have been using maxLik to do some MLE of Geometric Brownian Motion Process and everything has been going fine, but know I have tried to do it with jumps. I have create a vector of jumps and then added this into my log-likelihood equation, know I am getting a message: NA in the initial gradient My codes is hear # n<-length(combinedlr) j<-c(1,2,3,4,5,6,7,8,9,10)

Dear All, In Storey's estimator of the proportion of true nulls, the estimator depends on the tuning parameter lamda. Suppose now that an estimator of this proportion has been obtained by the qvalue package, what is the lamda that corresponds to the estimate? How to get this lamda? Thanks, -Chee [[alternative HTML version deleted]]

I've finally gotten around to playing with the advanced menu, though the complex example is definitely still beyond me. I'm trying to figure out a few basics from it, such as the tab key for line editing (don't know what I've done wrong here since it seems like I copied that part right) and the timeout. It looks to me like the only ontimeout implemented so far is an option to not

hi all: Here's a dataframe(dat) and a vector(z): dat: x1 x2 x3 0.2 1.2 2.5 0.5 2 5 0.8 3 6.2 > z [1] 10 100 100 I wanna do the following: 10*x1,100*x2,1000*x3 My solution is using the loop for z and dat(since the length of z is the same as ncol of dat),which is tedious. I wanna an efficient solution to do it . Any help? Many thanks! My best

Hello guys.I am working on implementing the PL2 weighting scheme of the DFR framework by Gianni Amati. It uses the Poisson approximation of the Binomial as the probabilistic model (P), the Laplace law of succession to calculate the after effect of sampling or the risk gain (L) and within document frequency normalization H2(2) (as proposed by Amati in his PHD thesis). The formula for w(t,d) in

Hi I have been having issue with a ML estimator for Jump diffusion process but know I am get little error I didn't notice before like I am try to create a vector > #GBMPJ MLE Combined Ph 1 LR > # > n<-length(combinedlrph1) > j<-c(1,2,3,4,5,6,7,8,9,10) Error in c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) : unused argument(s) (3, 4, 5, 6, 7, 8, 9, 10) >

If there are many character variables,and I want to get the mosaic plot of every pair of each variable,how to do then? If the variables are numeric, I can use pairs to get paired scatter plot. But as to the character variables, how to get the "paired mosaic plot"? Many thanks. -- QQ: 1733768559 At 2015-02-07 17:04:26,"Jim Lemon" <drjimlemon at gmail.com>

Hi all, I am currently using the code snippet below to minimize a function which I wasn't able to minimize properly with optim (cliffy surface, constraints, poles). Obviously this iteration is probably the poorest way of implementing such a function and it is expectedly slow. I've already written some vectorized functions, but I'm afraid that I'm missing the "big

Hello. Here is my R code. I used the functional data . Now I need to use the functional data by applying the kernels instead of the xi, yi functions. Bonjour. Voici mon code en R . J'ai utiliser les donn?es fonctionnelles . Maintenant j'ai besoin d'utiliser les donn?es fonctionnelles en appliquant les noyaux ? la place des fontions xi, yi library(MASS)

Hi all: My data is in the attachment. I want to analysis the mean difference of y between 2 sex. My code: result_lm<-lm(y~factor(sex) + x1 + x2) summary(result_lm) The result of "factor(sex)m" 136.83, is the mean difference of y between 2 sex,and the corresponding p value is 0.07618. My question is: how to get the mean y of sex(m) and sex(f) respectively via lm function? Many

Merry Xmas to all, I am writing a function and curiously this runs sometimes on one data set and fails on another and i cannot figure out why. Any help much appreciated. If i run the code below with data <- iris[ ,1:4] The code runs fine, but if i run on a large dataset i get the following error (showing data structures as matrix is large) > str(cluster.data) num [1:9985, 1:811] 0 0 0 0

Hm. Google tells me that kernel function is in stats package which comes with base installation and is invoked when you start R. search() [1] ".GlobalEnv" "package:stats" "package:graphics" [4] "package:grDevices" "package:utils" "package:datasets" [7] "package:methods" "Autoloads"

WARNING: This is not compatible with the previous lz4 patchset. If you''re using experimental compression that isn''t in mainline kernels, be prepared to backup and restore or decompress before upgrading, and have backups in case it eats data (which appears not to be a problem any more, but has been during development). These patches add lz4 and lz4hc compression

Good evening dear administrators, It is with pleasure that I am writing to you to ask for help to finalize my R programming algorithm. Indeed, I attach this note to my code which deals with a case of independence test statistic . My request is to introduce the kernels using the functional data for this same code that I am sending you. So I list the lines for which we need to introduce the