similar to: singular.ok in lm

Hi, I have two questions regarding the cloud function in the lattice package: 1) Is there a way to not print the surrounding frame (i.e. the square surrounding the entire plot)? 2) Is there a way to italicize the text displayed with the key argument? Some sample code: data(iris) cloud(Sepal.Length~Petal.Length*Petal.Width,data=iris, groups=Species,screen=list(z=20,x=-70),

Hi, I am wondering if there is a way to change the value of the "record" option in a graphics device that is already open (and accepts this option). I don''t want to open a new device with, for example "dev.new(record=T)", but just want to change the settings of the current device. This can be done by pointing and clicking on the "history" tab of a

dear R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is

i am trying to use matinv from the Design package to compute the generalized inverse of the normal equations of a 3x3 design via the sweep operator. That is, for the linear model y = ? + x1 + x2 + x1*x2 where x1, x2 are 3-level factors and dummy coding is being used the matrix to be inverted is X'X = 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0 3 0 3 0 1 1 1 0 1 0 0 1

thank you, achim. I will try chol2inv. sandwich is a very nice package, but let me make some short suggestions. I am not a good econometrician, so I do not know what prewhitening is, and the vignette did not explain it. "?coeftest" did not work after I loaded the library. automatic bandwidth selection can be a good thing, but is not always. as to my own little function, I like the

Please help, I have a strange problem. I've got a balanced panel data set. I use dummy variable regression and I've got results with lm function. summary(lm(y ~ post + t19961 + t19962 + t19963 + t19964 + t19971 + t19972 + t19973 + t19974 + t19981+factor( id))) The problem is that I would like to get my standard errors clustered but then gets the following error message: f<-(lm(y ~

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

I have a problem finding how to use prior.weights or weights options when performing a stepwise GAM analysis (gam and step.gam functions) for covariate inclusions. Thanks beforehand for references or help on how to handle these 2 options. -- Paul Baverel, MSc, PhD student Div. of Pharmacokinetics and Drug Therapy, Dept. of Pharmaceutical Biosciences, Faculty of Pharmacy Uppsala University Box

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

Dear All, I have conducted an experiment in order to examine predation pressure in the surroundings of potential wildlife road-crossing structures. I have documented predation occurrence (binary?) in these structures and calculated several possible explanatory variables describing the spatial heterogeneity in several scales. At the landscape scale I have calculated the percentage of different

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

Hi, is there a function in R that will give me the variances of a predicted values obtained using predict.lm(). If no function is available I would need to calculate them myself - which involves taking the inverse of X'X (' indicating transpose) where X is my model matrix. I know that calculating an inverse directly is not a good idea in general - could anybody suggest a way around

Suppose I have the following hat matrix: H=X(X'X)^{-1}X' X is a n by p matrix, where n >= p and X_{i,1} = 1 I'm wondering why H1 = 1. (Here, 1 is column vector, whose each element is the number 1) Thank you!

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

Here's a skeletal example. Embellish as needed: p <- 5 n <- 300 set.seed(1) dat <- cbind(rnorm(n), matrix(runif(n * p), n, p)) write.table(dat, file="c:/temp/big.txt", row=FALSE, col=FALSE) xtx <- matrix(0, p + 1, p + 1) xty <- numeric(p + 1) f <- file("c:/temp/big.txt", open="r") for (i in 1:3) { x <- matrix(scan(f, nlines=100), 100,

The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less

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

It was a real surprise, but a student in my class found that the function eigen is buggy. He traced to the problem from his inability of getting principal component analysis to work on his data. Chong Gu Here is a matrix I generated through X'X, where X is 2x3. > jj [,1] [,2] [,3] [1,] 0.8288469 -1.269783 -0.7533517 [2,] -1.2697829 2.162132 2.0262917 [3,]

Hi, I have a problem with aggregate. x <- aggregate(t1,list(t2,t3,t4), mean) z<-x[,3] I want z to be a vector but it is a factor. I've tried to use as.vector(z,mode="numeric") but then the numbers get scrambeled. Any help is appriciated /anders

Please forgive a statistics question. I know that a simple bivariate linear regression, y=f(x) or in R parlance lm(y~x) can be solved using the variance-covariance matrix: beta(x)=covariance(x,y)/variance(x). I also know that a linear regression with multiple independent variables, for example y=f(x,z) can also be solved using the variance-covariance matrix, but I don't know how to do this.