similar to: In a SLR, Why Does the Hat Matrix Depend on the Weights?

When I run this calculation library(ISwR) library(simple.boot) data(thuesen) fit <- lm(thuesen$short.velocity~thuesen$blood.glucose) summary(fit) fit.sb <- lm.boot(fit,R=1000,rows=F) summary(fit.sb) I get the following error from the lm.boot routine: newdata' had 100 rows but variable(s) found have 24 rows I don't know what this error means. Also, this example is

Dear all, I have the following output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua > data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose) Call: lm(formula = short.velocity ~ blood.glucose)

I have a work around for this, but can someone explain why the first example does not work properly? I believed it worked in the previous version of R, by selecting just the rows=200525 and omitting the na's. I just upgraded to 2.13. I am also concern with the row numbers being different in the selections, should I be worried? FYI, I just selected the first few rows for demonstration, please

I'm trying to write some functions extending influence measures to multivariate linear models and also allow subsets of size m>=1 to be considered for deletion diagnostics. I'd like these to work roughly parallel to those functions for the univariate lm where only single case deletion (m=1) diagnostics are considered. Corresponding to stats::hatvalues.lm, the S3 method for class

Hi, I am new to R so forgive me if the following query is somewhat simple.  I have a small tab-separated file with n records of glucose values, one record per day, seven measurements per day. It looks like this: date    sober    no    vm    nm    va    na    vs 20091229    NA    6.8    NA    2.7    11.7    2.7    6.2 I'd like to make a graph on which the glucose day curves are plotted

I am struiggling a bit with this function 'hatvalues'. I would like a little more undrestanding than taking the black-box and using the values. I looked at the Fortran source and it is quite opaque to me. So I am asking for some help in understanding the theory. First, I take the simplest case of a single variant. For this I turn o John Fox's book, "Applied Regression Analysis

Hello: I am trying to understand the method 'hatvalues(...)', which returns something similar to the diagonals of the plain vanilla hat matrix [X(X'X)^(-1)X'], but not quite.  A Fortran programmer I am not, but tracing through the code it looks like perhaps some sort of correction based on the notion of 'leave-one-out' variance is being applied. Whatever the

Greetings R users, I am trying to renumber my groups within the file shown below. The groups are currently set as 8,9,10,etc. I would like to renumber this as 1,2,3,etc. I have searched the help files and only come across using the rownames to renumber the values but I need to match values. Any assistance is always appreciated, Regards, Charles structure(list(Group = structure(c(1L, 1L, 1L,

Hi all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Hi all, In using the identify command, I get the following message > plot(hatvalues(scireg3)) > abline(h=.0154,lty=2) # plots a reference line at (k + 1)/n > identify(1:1165, hatvalues(scireg3),row.names(sciach)) Error in xy.coords(x, y) : 'x' and 'y' lengths differ which doesn't allow me to see the observation number when I scroll over with the mouse. What

Dear useRs, I have some trouble with the calculation of Cook's distance in R. The formula for Cook's distance can be found for example here: http://en.wikipedia.org/wiki/Cook%27s_distance I tried to apply it in R: > y <- (1:400)^2 > x <- 1:100 > lm(y~x) -> linmod # just for the sake of a simple example >

Hi everybody! I want to find a closer neighbourins observation. This is my code: ########################## library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

Hi! I have used the Matrix package (Version: 0.995-10) successfully to obtain the OLS solution for a problem where the design matrix X is 44000x6000. X is very sparse (about 80000 non-zeros elements). Now I want to do WLS: (X'WX)^-1X'Wy I tried W=Diagonal(length(w),w) and wX=solve(X,W) but after various minutes R gives a not enough memory error (Im using a 64bit machine with 16Gigs

I forgot, you should also set.seed() before calling boot() to make the results reproducible. Rui Barradas On 5/22/2018 10:00 AM, Rui Barradas wrote: > Hello, > > If you want to bootstrap a statistic, I suggest you use base package boot. > You would need the data in a data.frame, see how you could do it. > > > library(boot) > > bootMedianSE <- function(data,

Dear R-experts, I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ? Here is the reproducible example. ############################# install.packages( "quantreg" ) library(quantreg) crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67) bmi

Hi! I'm a bit lost while performing multiple comparisons on a lme model of that type: lmeglu=lme(glucose~Ath*tim,random=~1|Vol,na.action=na.omit,data=data) multc = glht(lmeglu, linfct = mcp(Ath = "Tukey", tim = "Tukey")) This works fine for identifying the effect of each factor. However, when I look for their interactions, l only obtain error messages. For example this

Greetings, I am using rpart for classification with "class" method. The test data is the Indian diabetes data from package mlbench. I fitted a classification tree firstly using the original data, and then exchanged the order of Body mass and Plasma glucose which are the strongest/important variables in the growing phase. The second tree is a little different from the first one. The

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

I am using the Unix version of R (version 1.7.0), installed via fink on a G4 Macintosh. I recently upgraded from version 1.6.0 and found that the "car" library now has a problem: ---Begin transcript--- >library(car) Attaching package 'car': The following object(s) are masked from package:base : dfbeta dfbeta.lm dfbetas dfbetas.lm hatvalues hatvalues.lm