similar to: Analysis of Variance

Hi, I am very new to R. I am trying to perform an Anova Test and see if it differs or not. Basically, i have 4 tests and 1 control. Tester Test1 Test2 Test3 Test4 Control 20 25 15 10 17 The inference is at alpha=0.05. they are all independent. I am trying to find if they differ or the same. > test1<-c(20) > test2<-c(25) > test3<-c(15) >

Hi! Let us define random variable: > x = seq(0,1,length=100) If we calculate variance following definition E[(x-E(x))^2] we get: > mean( (x - mean(x))^2 ) # == mean(x^2) - mean(x)^2 0.08501684 And if we use internal R function var: > var(x) 0.08587559 Can anyone tells me why the difference?

Hello all, I'm running very simple regression but face a problem of non-homogeneity of variance, but with a decreasing variance with increasing mean...I do not know how to deal with that. this relationship doesn't seem to be strong, but it's my first time to see something like that, and would like to know what to do if one day it becomes stronger. I tested just for fun some

Hello, I have been looking for functions for calculating the within-groups variance and between-groups variance, for the case where you have several numerical variables describing samples from a number of groups. I didn't find such functions in R, so wrote my own versions myself (see below). I can calculate the within- and between-groups variance for the Sepal.length variable (iris[1]) in

Hi, I have a question about the output for variance of random effect from a gamma frailty model using coxph in R. Is it the vairance of frailties themselves or variance of log frailties? Thanks. Guanghui

Hi all i have two data sets, spot and futures cash market prices. to estimate the minimum variance hedge ratio, i first had a glance on the correlation coefficient of relative price change (ln(St / St-1). surprizingly the value is just 0.2 compared to actual price correlation of 0.9. (i did regress the spot change on future change, co-effi is 0.3, and R2 is only 0.025 a) in such scenario can

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

I have been modelling spatial data using function likfit in package geoR. Now that I have the spatial regression models, I would like to calculate the amount of variance explained by both the trend and the spatial parts of the model. Any advice on how to do this would be much appreciated. Cheers, Ailsa

Hi all, I am trying to calculate the variance ratio of a time series under heteroskedasticity. So I know that the variance ratio should be calculated as a weighted average of autocorrelations. But I don't find the same results when I calculate the variance ratio manually and when I compute the M2 (M2 for heteroskedasticity) variance ratio using Lo.Mac function in R. Anybody knows what

hello all - i was searching for theoretical articles/vector equations regarding variance inflation factor (or generalization) for the linear mixed effects model (repeated measures data) sincerely, tom -- View this message in context: http://n4.nabble.com/variance-inflation-factor-for-linear-mixed-effects-tp1009143p1009143.html Sent from the R help mailing list archive at Nabble.com.

Dear forum, we have a 2 dimensional normal distribution of (x, y) which is consistent with a linear regression modell of type y = ax + b. It has a heteroscedastic variance. Is there a straight and simple way with R to get the variance of the marginal distribution W(y|x=xi) where xi may be randomly chosen? Maybe a single line command? Thank you best regards -- View this message in context:

Hi, does anyone know which of the scaled or unscaled version of the variance-covariance matrix of regression coefficients (as produced by ls.diag) is the correct one, or when one is better than the other? I am interested in univariate linear regression (lm). Thank you very much. Dipl.-Psych. Johannes Ullrich Philipps-Universit?t Marburg Fachbereich

I am estimating daily electricity prices using GARCH (1,1). What I would like to see is whether there is some kind of daily or seasonal effect in variance of the price series. For instance, variance of electricity prices might be different (higher) during weekdays as opposed to during weekend. Thus, I would like to include some dummy variables in variance equation -but I don't know how to

Is there any package or operation in R designed to conduct or facilitate Quadrat Variance analysis of spatial data? Any leads would be much appreciated as I have found very little in my searches thus far. Tyler -- View this message in context: http://www.nabble.com/Quadrat-Variance-analysis-tp22752517p22752517.html Sent from the R help mailing list archive at Nabble.com.

Dear R-help members, I have a question regarding how to use varComb function to specify a variance function for the "weights" in the gls. I need to fit a linear model with heteroscedasticity. The variance function is exp(c0+nu0*W +nu1*W^2) where W is a covariate. Initially I want to use varFunc to define my own variance function following the instruction in the Pinheiro and Bates

Dear R-help users, I'd like to use the R-package "pls" and want to extract the explained Y-variance to identify the important (PLS-) principal components in my model, related to the y-data. For explained X-variance there is a function: "explvar()". If I understand it right, the summary() function gives an overview, where the y-variance is shown, but I can't

Hi, All! To calculate Levene's Test for Homogeneity of Variance I use R Commander, and this is the output: > levene.test(Dataset$age, Dataset$sex) Levene's Test for Homogeneity of Variance Df F value Pr(>F) group 1 0.8739 0.3567 33 I am not sure what means "Pr(>F)"? Can anyone explain/translate this? Regards, Iurie Malai Department of Psychology and

Hi all, I've been attempting to fit a logistic glmm using glmmPQL in order to estimate variance components for a score test, where the model is of the form logit(mu) = X*a+ Z1*b1 + Z2*b2. Z1 and Z2 are actually reduced rank square root matrices of the assumed covariance structure (up to a constant) of random effects c1 and c2, respectively, such that b1 ~ N(0,sig.1^2*I) and c1 ~

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

Hello, I am looking for a code in R for the variance ratio test statistic (the Lo and Mackinlay version or any other versions). Does anybody have such a code they can share or know a library in which I can find this function? Basically I have a number of time series which I need to check for persistence. One other test I can use is the runs test in the tseries package. Any help will be greatly