search for: lambda2

...-&n\log\Gamma(\frac{\nu}{2})-\frac{\nu+1}{2}\sum_{i=1}^{n}\log(1+\frac{(x_i-\xi)^2}{\omega^2\nu})+\sum_{i=1}^{n}\log\Phi(\lambda_1\frac{(x_i-\xi)}{\sqrt{\omega^2+\lambda_2(x_i-\xi)^2}}) \end{eqnarray*} ############## G2StNV178<-function(a){ require(maxLik) II=0 nu<-(a[1]) lambda1<-a[2] lambda2<-a[3] ksi<-a[4] omega<-a[5] II<-log(prod((2*dt((x-ksi)/omega,nu)*pnorm((lambda1*(x-ksi)/omega)/sqrt(1+lambda2*((x-ksi)/omega)^2)))/omega)) II } ########definition of gradient  function gradlik<- function(a){ nu<-a[1] lambda1<-a[2] lambda2<-a[3] ksi<-a[4] omega<-a[5] ...

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` <- function(lambda1, lambda2) return(integrate(f1, lower=1, upper=Inf, lambda1=lambda1, lambda2=lambda2)$value) return(exp(log(lambda1) * z - lambda2 * lga...

...quot; 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., MAX.ITER., TRACE.){ lambda1 <- abs(LAMBDA1.) lambda2 <- abs(LAMBDA2.) P <- lambda1 * P1. + lambda2 * P2. a <- Estimate(A.INIT., X., V., OFF., D., P, TOL., MONITOR., MAX.ITER.) Ita <- OFF. + X. %*% a Mu <- c(exp(Ita)) Wt <- Mu * V. Bt.W.B &l...

...0,1) k<-c(3,2,2,2) x<-c(0.5,0.5,1.0,1.0) % estimate lambda (lambda=beta0+beta1*x) GLM_results <- glm(r/k ~ x, family=binomial(link='cloglog'), offset=log(verpi),weights=k) beta0<-GLM_results$coefficients[[1]] beta1]<-GLM_results$coefficients[[2]] lambda1<-beta0+beta1*x[1] lambda2<-beta0+beta1*x[2] % using lambda, estimate alpha (a=alpha) through ML estimation L<-function(a){ s1_f1<-(exp(-lambda1*(0^a-0^a))-exp(-lambda1*(5^a-0^a))) s2_f2<-(exp(-lambda1*(10^a)-lambda2*(14^a-10^a+14^a-14^a)) -exp(-lambda1*(10^a)-lambda2*(14^a-10^a+18^a-14^a))) logl<-log...

...following : ======================================================================================= local.whittle <- function(d, x, m) { n <- length(x) t <- matrix(c(0:n1), nrow = n, ncol=1) lambda <- (2*pi*t)/n wx <- (2*pi*n)^(-1/2)*Conj(fft( Conj(x)))*exp(1i*lambda) M1 <- m+1 lambda2 <- lambda[2:M1] wx2 <- wx[2:M1] ix <- wx2*Conj(wx2) g <- mean((lambda2^(2*d))*ix) r <- log(mean((lambda2^(2*d))*ix)) - 2*d*mean(log(lambda2)) } ======================================================================================= which seems to run, but when I am trying to call t...

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 message in c...

...ollowing: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updated alpha, replicate 1) and 2) until alpha is converged to a value My source code is alpha=rep(1,100) beta0=rep(0,100) beta1=rep(0,100) lambda1=rep(0,100) lambda2=rep(0,100) verpi=rep(0,100) L=rep(0,100) alpha[0]=1 i=1 while(i<=100){ repeat{ verpi[i]<-c(5^alpha[i-1],10^alpha[i-1]-5^alpha[i-1],14^alpha[i-1]-10^alpha[i-1],18^alpha[i-1]-14^alpha[i-1]) r<-c(1,0,0,1) k<-c(3,2,2,2) x<-c(0.5,0.5,1.0,1.0) GLM_results <- glm(r/k ~ x, family=binom...

...;, lambda = 1, alpha = 0, standardize = FALSE) > coef(g) 1 -0.1098361 x 0.2196721 > library(penalized) > fit = penalized(z ~ x) > coefficients(fit, "all") (Intercept) x -0.2224843 0.4449687 > fit = penalized(z ~ x, lambda2 = 1) > coefficients(fit, "all") (Intercept) x -0.2060658 0.4121315 > sessionInfo() R version 2.9.2 (2009-08-24) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_Unit...

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

...gn::lrm. however, I'm not sure wheter mle helps me at all and I am uncertain about the logLik call I have implemented: #cox&snell lambda<- -2*log((logLik(null.model)[1]/logLik(model)[1])) out<-1-exp(-lambda/n) #nagelkerke lambda<- -2*log( logLik(model)[1]/logLik(null.model)[1] ) lambda2<- -2*log( logLik(model)[1] ) out<-(1-exp(-lambda/n))/(1-exp(-lambda2/n)) can anyone help me out?

...n dataset 'amacrine' provided in the spatstat package. The dataset has points of two types labelled "on" and "off". data(amacrine) K12 <- Kcross(amacrine, "on", "off") g12 <- pcf(K12, method="d", spar=0.7) lambda2 <- summary(amacrine)$marks["off","intensity"] Oring <- eval.fv(lambda2 * g12) plot(Oring, ylab="Oring(r)") regards Adrian Baddeley

...ponential 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 + (1-c1-c2)*exp(-x/lambda3)/lambda3) ) I've used optimizers such as ms and nlminb (SPLUS 5.0 for UNIX), and provided parameters constrains (0 <= c1, c2 <= 1) to nlminb via lower and upper. But these constrains provide no protection for (1-c1-c2) being between interval [0,1], whi...

On Fri, 25 Aug 2023 22:17:05 +0530 ASHLIN VARKEY <ashlinvarkey at gmail.com> wrote: > Sir, Please note that r-help is a mailing list, not a knight! ?? > I want to solve the equation Q(u)=mean, where Q(u) represents the > quantile function. Here my Q(u)=(c*u^lamda1)/((1-u)^lamda2), which is > the quantile function of Davies (Power-pareto) distribution. Hence I > want to

...ng to understand is where do I get the variance explained information from and how do I determine the relative importance of the 4 variables selected? It does not appear to be a part of the penalized procedure. I submit the final call to 'penalized' with the estimated values of lambda1 and lambda2 > fitfinal <- penalized(CHAB~.,data=chabun,lambda1=356.0856,lambda2=3.458605,model = "linear",steps=1,standardize = TRUE) # nonzero coefficients: 5 > fitfinal Penalized linear regression object 10 regression coefficients of which 5 are non-zero Loglikelihood = -154.1055 L1...

...how to adapt "glmnet" to accomplish our task. The extract of the code used is reproduced as follows; cv.ridge<- glmnet(x, y, family="gaussian", alpha=0, lambda=lambda1, standardize=TRUE) cv.lasso<- glmnet(x, y, family="gaussian", alpha=1, lambda=lambda2, standardize=TRUE) ##weight a=1/abs(matrix(coef(cv.ridge, s=lambda1)[, 1][2:(ncol(x)+1)] ))^1 b=1/abs(matrix(coef(cv.lasso, s=lambda2)[, 1][2:(ncol(x)+1)] ))^1 c=a*b w4 <-a+b+c w4[w4[,1] == Inf] <- 9 # Fit modified procedure fit<- glmnet(x, y, family...

...lts. Then I tried to save the simulation draws in R, using read.bugs() function. Here is a simple test: ###################### library(coda) library(R2WinBUGS) #fake some data to test beta0=1 beta1=1.5 beta2=-1 beta3=2 N=200 x1=rnorm(N, mean=0,sd=1) x2=rnorm(N, mean=0,sd=1) x3=rnorm(N, mean=0,sd=1) lambda2= exp(beta0+ beta1*x1+ beta2 * x2 + beta3 * x3) y=rep(NA,N) for (i in 1: N) { y[i]=rpois(1,lambda2[i]) } #generate inputs required by winbugs data <- list(Y20=y,x1=x1, x2=x2, x3=x3, N=N) inits<- function(){ list(beta0=0, beta1=0, beta2=0, beta3=0)} parameters <- c("beta0", "b...

...d), disc = b*b - dot(v, v) + r*r, t1, t2; if (disc < 0.0) return INFINITY; disc = sqrt(disc); t2 = b + disc; if (t2 < 0.0) return INFINITY; t1 = b - disc; return (t1 > 0.0 ? t1 : t2); } void intersect(Vec o, Vec d, real *lambda, struct Scene **t, struct Scene *scene) { real lambda2 = ray_sphere(o, d, scene->center, scene->radius); if (lambda2 < *lambda) { if (scene->child) { int i; for (i=0; i<5; ++i) intersect(o, d, lambda, t, &scene->child[i]); } else { *lambda = lambda2; *t = scene; } } } Vec neglight;...

...n unit second central moment. In your example: x = c(-2, -2, -2, -2, -1, -1, -1, 2, 2, 2, 3, 3, 3, 3) z = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1) You got: > g = lrm(z ~ x, penalty = 1) > g$coefficients Intercept x -0.1620727 0.3241454 Now: > coef(penalized(z ~ x, lambda2 = 1,standardize=T)) (Intercept) x -0.1651565 0.3303130 is already more similar. To counter the difference between dividing by n (penalized) or n-1 (lrm), do > coef(penalized(z ~ x, lambda2 = 14/13,standardize=T)) (Intercept) x -0.1620727 0.3241454 The glmn...

Hey all, I am trying to work under a State Space form, but I didn't get the help exactly. Have anyone eles used this functions? I was used to work with S-PLUS, but I have some codes I need to adpt. Thanks alot, Bernardo [[alternative HTML version deleted]]

FYI. http://leonardo-m.livejournal.com/73732.html If anyone is motivated, please file bugs for the losing cases. Also, it might make sense to incorporate the tests into our nightly tester test suite. Thanks, Evan