search for: lamdas

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. /#

Hi r-users;   I have this values: eign_val <- c(137.810447,3.538721,2.995161,1.685670) alp    <- 1.6549 ;  lamda <- eign_val lamda_m <- min(lamda)   First I calculated manually: delta0 <- 1 delta1 <- alp*delta0*(4-lamda_m*(1/lamda[1]+1/lamda[2]+1/lamda[3]+1/lamda[4]))  delta1 delta2 <- (alp/2)*(delta1*(delta1/alp) + delta0*((1-lamda_m/lamda[1])^2+

Dear Sir/madam, I'm getting a problem with a R-code which calculate Fisher Information Matrix for Hybrid Censored Weibull Distribution. My problem is that: when I take weibull(scale=1,shape=2) { i.e shape>1} I got my desired result but when I take weibull(scale=1,shape=0.5) { i.e shape<1} it gives error : Error in integrate(int2, lower = 0, upper = t) : the integral is probably

...solve.QP(Dmat=VCovMat*2, dvec= MeanVec*lamda,Amat=a,bvec=c(1,0,0),meq=1) #return(MeanVec, VCovMat, WtVec$solution)return(WtVec$solution)} #Opt(OriData+1, 1, 5, 0) ##############################set.seed(4114)bs=1000 ###number of bootstrap samplesRegion<-5 ###Region indecies, check above.lamdaseq<-seq(0,1,.05) ###the lamda sequence. currently from 0 to 1 by .05. x<-numeric(bs*length(lamdaseq)) ###w1<-matrix(x, bs, length(lamdaseq)) ###To initialise the matrices.w5<-matrix(x, bs, length(lamdaseq)) ###1, 5, 10 denote the horizon.w10<-matrix(x, bs, length(lamdaseq)) ###...

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]]

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 =

Hi,all: I met a problem of nls. My data: x y 60 0.8 80 6.5 100 20.5 120 45.9 I want to fit exp curve of data. My code: > nls(y ~ exp(a + b*x)+d,start=list(a=0,b=0,d=1)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates I can't find out the reason for the error. Any suggesions are welcome. Many thanks. [[alternative HTML

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, 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)

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 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 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) >

Dear R-users, I'm wondering how to obtain Wilks-lambda values when discriminant analyses have only one discriminant function (i.e. 2 categories to discriminate between). The use of manova(predictions~groups, test="Wilks") asks for multiple response and the use of anova(lm(predictions~groups), test="Wilks") simply does not consider the last term. (The alternative

Hi - I am trying to dynamically create my partition folders via attachment_fu. I have read about a hack that allows you to do this on the attachment model: :partition => lambda {|a| a.article_id} Which passes the model''s object into the lambda and uses the ''article_id'' attribute as its directory name. However, this isn''t work for me. I think the lambda

Hi, I need to generate correlated (positive as well as negative) bivariate exponential distribution with rate of 1/5 or any rate I need some guidance here. Please help. Regards, Sunny

Hello R-world, I am facing a peculiar problem and hope someone out there can comment on it. In goodness-of-fit tests for evaluation of distributions, there are three well-known methods: 1. Chi-square 2. Anderson-Darling 3. Kolmogorov-Sminrov I am trying to use the second test. Many researchers have reported results using this test. I wrote programs in C and now in R to do this. I run into

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)

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

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"

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: