similar to: Failing to install the rgl package

Hi all, I tended to use rbind, or cbind to force a vector be be deemed as a column or row vector. This is very important if I want to do things like u' * A * u, where u' is a row vector and u is a column vector, regardless of what originall format the "u" is... I want to recast it to column vector or row vector... How can I do that?

In a multiple linear regression with two independent variables is there any function in R to test for the coefficients being different than some given values? Example: x1<-rnorm(100) x2<-rnorm(100) y<-3+0.6*x1+0.3*x2 lm(y~x1+x2) Obtain a test for the coefficients for x1 being different than 0.6 and for x2 different than 0.3 Thanks Manuel

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

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]]

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, everyone, Night. I have three questions about multiple linear regression in R. Q1: y=rnorm(10,mean=5) x1=rnorm(10,mean=2) x2=rnorm(10) lin=lm(y~x1+x2) summary(lin) ## In the summary, 'Residual standard error: 1.017 on 7 degrees of freedom', 1.017 is the estimate of the constance variance? Q2: beta0=lin$coefficients[1] beta1=lin$coefficients[2] beta2=lin$coefficients[3]

Hi, for testing if the slope of experimental data differs from a given slope I'm using the function "test_regression_against_slope" (see below). I am now confronted with the problem that I have data which requires a modelII regression (also called reduced major axes regression (RMA) or geometric mean regression). For this I use the function "modelII" (see below). What

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

Hi R-Users, I apologize if it is too simple question for all. I have a multivariate dataset having 7 variables as independent and 1 dependent variable. 248 data points are there. I want to do out sample forecast first considering 156 points. So I'll have to start from 157th point and calculate the 157th y_hat value. In this way it will go to 248th data point. Can any one tell me how I can

Hi, I am using Extrafont package to install more fonts for my graphs. My primary graphic tool is ggplot2. I seem to have problem installing the package, but could not pinpoint where it is. I try to follow the instruction here: https://github.com/wch/extrafont I guess (but am not sure) the fonts are successfully installed. Problem encountered: 1. The following line sometimes yields an

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients