similar to: lasso and ridge regression

Hi, I run into problem when writing a syntax, I don't know syntax that will return true or false if an integer is odd or even. Thanks OYEYEMI, Gafar Matanmi Department of Statistics University of Ilorin P.M.B 1515 Ilorin, Kwara State Nigeria Tel: +2348052278655 Tel: +2348068241885 [[alternative HTML version deleted]]

I am trying to read a csv file using the code; contol <- read.csv("RBS.csv") This is the error message I got; Error in file(file, "r") : unable to open connection In addition: Warning message: In file(file, "r") : cannot open file 'RBS.csv', reason 'No such file or directory' Where was the mistake? -- OYEYEMI, Gafar Matanmi (Ph.D) Senior

Dear R-users,                       I have problem or at cross-road on using discrminant analysis for my set of data. All my dependent variables are all categorical, hence the normalty assumption is no longer valid. Is it still right to use discrminant analysis or is there any non-parametric technique or approach to discriminant analysis? Thanks OYEYEMI, Gafar Matanmi Department of Statistics

Dear R-helpers                         I am having problem or reservation analyzing my data using discrminant analysis. The problem is that all my dependent variables are categorical which means that the normalty assumption is no longer valid. Can one still use discrminant analysis for such data? Is there non-parametric technique of doing discrminant analysis in R. Thanks  OYEYEMI, Gafar

Hi everyone. My problem is about multivariate analysis. Given a multivariate data X, of dimension (n by p), n is the number of observations while p is the number of variables. I wanted a sample of size p+1 that will have its product of the eigen values of the covariance matrix to be the minimum among all the possible samples (n combination (p+1)). I have written a syntax with worked well, but the

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

Dear R users, i would like to transform the following function from R-code to C-code and call it from R in order to speed up the computation because in my other functions this function is called many times. `dgcpois` <- function(z, lambda1, lambda2) { `f1` <- function(alpha, lambda1, lambda2) return(exp(log(lambda1) * (alpha - 1) - lambda2 * lgamma(alpha))) `f2` <-

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

How to convert the following ms() in Splus to Optim in R? The "Calc" function is also attached. ms(~ Calc(a.init, B, v, off, d, P.a, lambda.a, P.y, lambda.y, 10^(-8), FALSE, 20, TRUE)$Bic, start = list(lambda.a = 0.5, lambda.y = 240), control = list(maxiter = 10, tol = 0.1)) Calc <- function(A.INIT., X., V., OFF., D., P1., LAMBDA1., P2., LAMBDA2., TOL., MONITOR.,

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

I have an error for a simple optimization problem. Is there anyone knowing about this error? lambda1=-9 lambda2=-6 L<-function(a){ s2i2f<-(exp(-lambda1*(250^a)-lambda2*(275^a-250^a)) -exp(-lambda1*(250^a)-lambda2*(300^a-250^a))) logl<-log(s2i2f) return(-logl)} optim(1,L) Error in optim(1, L) : function cannot be evaluated at initial parameters Thank you in advance -- View this

Hi All, I am using the package 'penalized' to perform a multiple regression on a dataset of 33 samples and 9 explanatory variables. The analysis appears to have performed as outlined and I have ended up with 4 explanatory variables and their respective regression coefficients. What I am struggling to understand is where do I get the variance explained information from and how do I

Dear R and S users, I've been working on fitting finite mixture of negative exponential distributions using maximum likelihood based on the example given in MASS. So far I had much success in fitting two components. The problem started when I tried to extend the procedure to fit three components. More specifically, likelihood = sum( ln(c1*exp(-x/lambda1)/lambda1 + c2*exp(-x/lambda2)/lambda2

Hi - I have a question about the following code from Bayesian Computation with R (Jim Albert). par(mfrow=c(2,2)) m = 500 alphas = c(5, 20, 80, 400) for (j in 1:4) { mu = rgamma(m, shape=10, rate=10) lambda1 = rgamma(m, shape=alphas[j], rate=alphas[j]/mu) lambda2 = rgamma(m, shape=alphas[j], rate=alphas[j]/mu) plot(lambda1, lambda2) title(main=paste('alpha=',

Dear R users,? ? ? ? ? ? ? ? ? ? ? ? ? I am using "Glmnet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for?LASSO and lambda2 for ridge ( zou, 2005) penalty.?How can I? write the code to? pre-chose the? lambda1 for?LASSO and lambda2 for ridge without using cross-validation Thanks in advance? Tayo? [[alternative

Many thanks to those took time replied to my question. They were very helpful and I solved my problem by reparameterization. With the help of optim() the fitting procedure is very robust and insensitive to initial starting value. Once again, many thanks. Kevin -------- Original post --------- >Dear R and S users, > >I've been working on fitting finite mixture of negative

Dear all, I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and f2(p1,p2,p3) respectively. The goal is to maximize f1(p1, p2, p3) subject to two constraints: (1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known constants (2) p4 = f2(p1, p2, p3) In addition, each parameter

Dear R users, I am using "enet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the analysis, I realised tht, I optimised only one lambda. ( even when I looked at the example in R, they used only one penality) So, I am

Hi, I have reconstructed ancestral character states on a phylogeny using MuSSE in the diversitree package and plotted the character state probabilities as pie charts on the nodes. I would, however, like to colour the character states of my extant species, i.e. the tip labels, the same colours as my pie charts, such that all species in state 1 are e.g. blue, species in state 2 red and species in

The following R code using different packages gives the same results for a simple logistic regression without regularization, but different results with regularization. This may just be a matter of different scaling of the regularization parameters, but if anyone familiar with these packages has insight into why the results differ, I'd appreciate hearing about it. I'm new to