similar to: Estimate of variance and prediction for multiple linear regression

Hello Can anyone tell me what I am doing wrong below? My Y and y_hat are the same. A<-scale(stackloss) n1<- dim(A)[1];n2<-dim(A)[2] X<-svd(A) Y<- matrix(A[,"stack.loss"],nrow=n1) Y y_hat <-matrix((X$u%*% t(X$u))%*%Y,nrow=n1,byrow=T) y_hat [[alternative HTML version deleted]]

Hi, I try to execute the seven lines of code below to plot a graph. But I am failing as the messages below show. Where am I going wrong? install.packages("rgl") library(rgl) y_hat = X%*%B_hat open3d(windowRect = c(100,100,900,900),family = "serif") color = rainbow(length(y_hat))[rank(y_hat)] plot3d(educ,exper,wage,col = color,type = "s",size = 0.5,xlim =

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

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

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <-

Hi, Is it possible to create Rose Diagrams of wind data (speed & direction) with R?? Best regards, Lars Peters ----- Lars Peters University of Konstanz Limnological Institute D-78457 Konstanz Germany phone: +49 (0)7531 88-2930 fax: +49 (0)7531 88-3533 e-mail: Lars.Peters@Uni-Konstanz.de web: Lars Peters <http://www.uni-konstanz.de/sfb454/tp_eng/A1/doc/peters/peters.html>

Hello, suppose one has the following values x1 <- rnorm(10,5,1) x2 <- rgamma(10,5,1) y <- rnorm(10,4,1) mydat <- data.frame(y,x1,x2) then one can use glm like mod <- glm(y~x1+x2, data=mydat, family=gaussian) But how could I estimate y_hat? Thanks alot! Sam --------------------------------- [[alternative HTML version deleted]]

HI >i have a little problem please help me to solve it >this is the code in R: >> beta0 [1] 64.90614 > beta1 [1] 17.7025 > beta [1] 17 64 >her beta<- c(beta0, beta1) thank you in advance hafida -- View this message in context: http://r.789695.n4.nabble.com/Decimal-number-tp4639428.html Sent from the R help mailing list archive at Nabble.com.

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Hello R-Experts, I am facing the problem that I have to estimate several parameters for a lot of different dependent variables. One single regression looks something like this: y = beta0 + beta1 * x1 + beta2 * x2 + beta3 * x1 * x2 + beta4 * x4 + beta5 * lag(x4,-1) where y is the dependent variable and xi are the independent ones. Important to me are the different estimates of betai and their

Hi, folks, Here are the codes: ############## y=1:10 x=c(1:9,1) lin=lm(log(y)~x) ### log(y) is following Normal distribution x=5:14 prediction=predict(lin,newdata=x) ##prediction=predict(lin) ############### 1. The codes do not work, and give the error message: Error in eval(predvars, data, env) : numeric 'envir' arg not of length one. But if I use the code after the pound sign, it

Hi, I am trying to covert a Winbugs code into R code. Here is the winbugs code model{# model’s likelihoodfor (i in 1:n){time[i] ~ dnorm( mu[i], tau ) # stochastic componenent# link and linear predictormu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i]}# prior distributionstau ~ dgamma( 0.01, 0.01 )beta0 ~ dnorm( 0.0, 1.0E-4)beta1 ~ dnorm( 0.0, 1.0E-4)beta2 ~ dnorm( 0.0, 1.0E-4)#

Dear All, I am trying to fit a structural time series model using the StructTS function (package stats) with only 2 seasons (summer and winter). More than 2 seasons work fine but with 2 seasons I get this error: > fit <- StructTS(y.ts, type="BSM") Error in T[cbind(ind + 1L, ind)] <- 1 : subscript out of bounds I have looked at Prof. Ripley's 2002 RNews article but cannot

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf =

Dear all I used R2WinBUGS package's bugs() function to generate MCMC results. 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+

Estimados Hace unos días envié un correo porque tenía problemas para guardar los gráficos en el disco rígido, utilizando R server 9, comentaba que el código antes funcionaba pero que tenía fallas. No encuentro mi mensaje en la lista para continuar el hilo, encontré el problema, no lo comprendo del todo pero cambiando jpg por png funciona, aparentemente hay un inconveniente para guardar en jpg.

Hi, I'm learning more about SVMs and kernels in general. I've gotten used to using the svm function in the e1071 package. It works great. Now, I want to do/learn some more interesting stuff. (Perhaps my own kernel and/or scoring system). So I want to better understand 1) how calculation of the kernel happens. 2) how to calculate the predicted value (y_hat) given a list of support