search for: betahat

Hola, buenas tardes estoy intentando hacer un bootstrap de un modelo, pero me da el siguiente error: "Error in FUN(newX[, i], ...) : unused argument(s) (list(age = c(33, 47, 49, 56, 60, 64, 64, 66, 68, 69, 71, 71, 72, 73, 74, 75, 75, 76, 78, 81, 83, 83, 36, 43, 46, 47, 49, 49, 51, 51, 52, 52, 53, 54, 54, 54, 55, 56, 56, 57, 57, 58, 58, 58, 58, 59, 59, 60, 61, 62, 63, 64, 65, 65, 66, 66,

...imated a generalized pareto distribution parameters (xi, beta, threshold) (I've used fExtremes pkg). I'm in doubt on what of the following command is the most appropriate: Let: x<-sample t<-threshold xt<-x[x>t] xihat<-gpdFit(x, threshold=t, type = "pwm")$par.ests[1] betahat<-gpdFit(x, threshold=t, type = "pwm")$par.ests[2] (1) ks.test(xt,"pgpd",list(xi=xihat,beta=betahat),H=t,estfun = "as.list(gpdFit(x, 0)$par.ests)", tol = 1e-02) (2) ks.test(xt,"pgpd",list(xi=xihat,beta=betahat),H=t,estfun = "as.list(gpdFit(x, t)$par.es...

...hows that in most cases the calculation involved was something like solve(t(X) %*% X) This is not good numerical linear algebra. Admittedly we all learn that, for example, least squares estimates can be written as (X'X)^{-1} X'y and it is seems reasonable to code this calculation as betahat <- solve(t(X) %*% X) %*% t(X) %*% y but you shouldn't do that. In terms of numerical stability and also in terms of efficiency, that is an remarkably bad way of determining least squares estimates. To begin with, you don't invert a matrix just to solve a system of equations. If A is a...

...,T,C) event <- ifelse(T<=C,1,0) mean(event) phmmd <- data.frame(Z) phmmd$cluster <- clusters phmmd$time <- time phmmd$event <- event fmla <- as.formula("Surv(time, event) ~ Z1 + Z2") BaseFun <- function(x){ start.coxph <- coxph(x, phmmd) print(start.coxph) betahat <- start.coxph$coefficient print(betahat) print(333) print(survfit(start.coxph))...

In the output of the code below, I want to do the following: - get hats over some of the betas - get the polygons stippled, not coloured grey - remove the tick marks at the ends of the axes. If I put tick = false, the whole axis disappears. betahat <- c(0.04*0:150) betahatdens <- dnorm(betahat, 3, 1) plot(betahat, betahatdens, xlim = c(-0.2, 6.2), ylim = c(-0.09, 1), type = "l", lwd = 2, xlab = NA, ylab = NA, axes = FALSE) axis(1, pos = 0, labels = FALSE, tick = TRUE, at = c(0, 2, 3, 4, 6)) axis(2, pos = 0, labels = F...

hi all a technical question for those bright statisticians. my question involves ridge regression. definition: n=sample size of a data set X is the matrix of data with , say p variables Y is the y matrix i.e the response variable Z(i,j) = ( X(i,j)- xbar(j) / [ (n-1)^0.5* std(x(j))] Y_new(i)=( Y(i)- ybar(j) ) / [ (n-1)^0.5* std(Y(i))] (note that i have scaled the Y matrix as well) k is

Dear R users, I am looking for a reference to an algorithm for estimation of sample quantiles which does not require bringing the whole data into memory (more precisely its memory complexity should be much less than linear, ideally constant). I realize that such an algorithm can only be approximate and actually quite wrong for some samples, but that's fine with me. Thank you, Vadim