similar to: after lm-fit: equality of two regression coefficients test

Hi, First of all, thanks for the great work on R in general, and MASS in particular. It's been a life saver for me many times. However, I think I've discovered a bug. It seems that, when I use weights during an initial least-squares regression fit, and later try to add terms using stepAIC(), it uses the weights when looking to remove terms, but not when looking to add them:

I am running R 1.2.1 under Windows 98SE. termplot() currently fails when there are composite terms, thus: > library(mass) > data(hills) > hills.lm <- lm(time ~ climb + poly(dist, 2), data = hills) > termplot(hills.lm) Hit <Return> to see next plot: Error in eval(expr, envir, enclos) : Object "dist" not found The call >

Hello, I've downloaded the package "mass" and I've tried to install it, but it didn't work. Can you help me (I use Windows)? Katrin -------------- next part -------------- An HTML attachment was scrubbed... URL: https://stat.ethz.ch/pipermail/r-help/attachments/20020707/e560f42b/attachment.html

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

Hi, I had a question about specifying the Null hypothesis in a significance test. Advance apologies if this has already been asked previously or is a naive question. I have two samples A and B, and I want to test whether A and B come from the same distribution. The default Null hypothesis would be H0: A=B But since I am trying to prove that A and B indeed come from the same distribution, I think

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

Hi everybody, i'm dealing with some percentage tables, of which i should test rowwise if the entries are sgnificantly equal or not. Namely, on row 1, test H0: element 1= element2, H0: element 1= element3...H0: element 2= element3...H0: element n-1= element n. The same on the other rows. Anybody knows how this can be done in quick way? I don't have large matrices, but it seems quite

dear all. Would someone be kind and willing to explain the code below for a person who has never used R? ( that is if one has enough time and inclination) It implements gillepsie's stochastic algorithm for Lotka Volterra model. What would help me tremendously is to see the breakdown of the line by line code into plain english. thanks for any insights or other comments. sean

Hi There, Using t.test to test hypothesis about which one is greater, A or B? where A={10,20,30},B={25,30,15}. My question is which of the following conclusions is right? #################hypothesis testing 1 h0: A greater than or equal to B h1: A less than B below is splus code A=c(10,20,30) B=c(25,30,15) t.test(c(10,20,30),c(25,30,15),alternative="less") output: p-value=0.3359

Thank you in advance. Now I want to make comparison of the different bandwidth h in a normal distribution graph. This is the table of bandwidth h: thumb rule (normal)--0.00205; thumb rule(Epanech.)--0.00452; Plug-in (normal)--0.0009; Plug-in(Epanech.)--0.002. this is the condition: N=1010 data sample is from normal distribution N(0,0.0077^2). The grid points are taken to be [-0.05,0.05] and

I want to extra the part of the formula not including the response variable from an lm object. For example if the lm object ABx.lm was created by the call ABx.lm <- lm( y ~ A + B + x, ...) Then ACx.lm is saved as part of a workspace. I wish to extract "~ A + B + x". Later in my code I will fit another linear model of the form z ~ A + B + x for some other response variable z. I

Hi, is there a test for the H0 skewness=0 (or with skewness as test statistic and normality as H0) implemented in R? Thank you, Christian *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/

Problem as follows: 1) Plug in an external USB drive. 2) Mount it anywhere. Doesn't matter how. 3) Copy a few GB of data to the drive from a non-USB disk. 4) Watch the load average "climb" to 5.x, sometimes 10.x or more. Why? This on an otherwise unloaded system. Doesn't matter how many cores, how much RAM, 32/64 bit, etc. Why should copying some files to a USB drive cause

Dear Community, this is my first programming in R and I am stuck with a problem. I have the following code which automatically calculates Granger causalities from a variable, say e.g. "bs" as below, to all other variables in the data frame: log.returns<-as.data.frame( lapply(daten, function(x) diff(log(ts(x))))) y1<-log.returns$bs y2<- log.returns[,!(names(log.returns) %in%

Dear R users, I'm coming back to R after while. I have a data frame with 200 columns, each column has a name. How to extract all columns to a new dataset, but the specified (by names) ones? I was playing with that for a little bit using the vector syntax but got several syntax errors. Thanks, Rado

On Jul 23, 2019, at 9:17 AM, JF Bastien via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> On Jul 23, 2019, at 8:30 AM, James Y Knight via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> >> As a very frequent explorer of history, I really don't think this is >> as big an issue as it may seem. Even absent refactorings, you often >> run into the

Dear All, I am estimating a Cox proportional hazard model, with several interactions of the type a*z + a*y + a*x + b*z + b*y + b*x. I need to know if the first three (the "a"s) are jointly significantly different from the last three (the "b"s). I have tried several approaches, but have been unsuccessful. Here's the model, and the code I came up with, with the obvious

Hi I was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Hi All This is a general stats problem that I am dealing with using R, so any help is greater appreciated. I have two lognormal distributions with means M1 and M2. If we have: H0: log(M1/M2)=0 H1: log(M1/M2) !=0 equivalent to log(M1/M2)=log(1+P) where P<0 or P>0. If we calculate the power for a value of P=0.1 or P=-0.1 (i.e. a 10% difference) and say assume SE{log(M1/M2)}=0.05, and

Hello, I have a simple theorical question about regresion... Let's suppose I have this: Model 1: Y = B0 + B1*X1 + B2*X2 + B3*X3 and Model 2: Y = B0 + B2*X2 + B3*X3 I.E. Model1 = lm(Y~X1+X2+X3) Model2 = lm(Y~X2+X3) The Ajusted R-Square for Model1 is 0.9 and the Ajusted R-Square for Model2 is 0.99, among many other significant improvements. And I want to do the anova test to choose the best