similar to: Print Summary

Why, in R, does (0.1 + 0.05) > 0.15 evaluate to True? What am I missing here? How can I ensure this (ostensibly incorrect) behavior doesn't introduce bugs into my code? Thanks for your time. Dave Mitchell [[alternative HTML version deleted]]

Users, I am currently running scripts on a rather large dataset. Many times I sort the data into smaller datasets and must analyze them individually. My instincts are to make a for loop, but I can't seem to find a way to index each sub-dataset (data frames as the case would have it). I would make an array, however, the data types of the columns are different and as I understand, an array

I've noticed that ff uses the unzip utility available on it's host OS to load datasets via ffload. It seems to work fine in linux, but when I try to use the package in Windows (hence dling Windows unzip utils) I get 2 errors, one telling me that the options aren't being passed to unzip correctly and another indicating that my filename is incorrect. First, does anyone know of an unzip

Dear all, Possibly a rudimentary question, however any help is greatly appreciated. I am sorting a large matrix into an array of dim(p(i),q,3). I put each entry into a corresponding matrix (1 of the 3) based on some criteria. I figure this will assist me in condensing code as I can loop through the 3rd dimension of the array instead of generating 3 separate matrices and using the same block of

He folks=) I want to check whether a coefficient has an impact on a quantile regression (by applying the sup-wald test for a given quantile range [0.05,0.95]. Therefore I am doing the following calculations: a=0; for (i in 5:95/100){ fitrestricted=rq(Y~X1+X2,tau=i) tifunrestrited=rq(Y~X1+X2+X3,tau=i) a[i]=anova(fitrestricted,fitunrestricted)$table$Tn) #gives the Test-Value } supW=max(a) As anova

Hello, I have spent a good deal of time searching for an answer to this but have come up empty-handed; I apologize if I missed something that is common knowledge. I am trying to figure out how to get an ANOVA table that shows the sum of squares. degrees of freedom, etc, for the full model versus the error (aka residuals). Here is an example of the kind of table I'd like to get:

Can anyone help me? I''m having problems with the following code where I want to test the null hypothesis that regression slopes are the same among regressions. Here''s the code I''ve written with comments that include the final error I get. ... initial.dir <- getwd() library(systemfit) library(multcomp) basdata <- read.table("data_into7_test.txt",

Hi All, I am trying to run several linear regressions and print out the summay and the anova reslts on the top of each other for each model. Below is a sample progarm that did not work. is it possible to print the anova below the summary of lm in one file? thanks for your help ###################################################### data<-read.table("data.txt", header=T,

Given this interactive session: > an<-ls(pat="anova.ag.m2529") > an [1] "anova.ag.m2529.az" "anova.ag.m2529.can" "anova.ag.m2529.fl" > print(anova.ag.m2529.az) Analysis of Variance Table Response: year Df Sum Sq Mean Sq F value Pr(>F) time 1 14.823 14.8235 10.598 0.004392 ** Residuals 18 25.177

I have a print method for a set of statistical tests, vcdExtra::CMHtest, for which I'd like to have more sensible printing of pvalues, as in print.anova(). [Testing this requires the latest version of vcdExtra, from R-Forge **|install.packages("vcdExtra", repos="http://R-Forge.R-project.org")|** ] With my current print method, I get results like this, but all Prob values

> y<-c(131.79, 131.79, 135.02, 135.55, 136.46, 136.83, 137.82, 138.00, 138.06, 138.04, 140.04, 142.44, 145.47, 144.34, 146.30, 147.54, 147.80) > x<-c(194.5, 194.3, 197.9, 198.4, 199.4, 199.9, 200.9, 201.1, 201.4, 201.3, 203.6, 204.6, 209.5,208.6, 210.7, 211.9, 212.2) > fitted.results<-lm(y~x) > summary(fitted.results) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max

Hi, I want to give a summary or anova for "arima" model in R, as "summary", and "anova" for "lm". As including various intervention factors in arima(xreg = ) part, I want to assess the significancy of thse factors. I can do it using interrupted analysis of time series by linear regression, but want to see whether arima model works for the data first.

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a

Dear All, Could anyone explain me how it is possible that one factor in a glmmPQL model is non-significant according to the anova() function, whereas it turns out to be significant (or at least some of its levels differ significantly from some other levels) according to the summary() function. What is the truth, which results shall I believe? And, is there any other way of testing for the

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16

I have two assignment problems... I have written this small code for regression with two regressors . n <- 50 x1 <- runif(n,1,10) x2 <- x1 + rnorm(n,0,0.5) plot(x1,x2) # x1 and x2 strongly correlated cor(x1,x2) y <- 3 + 0.5*x1 + 1.1*x2 + rnorm(n,0,2) intact.lm <- lm(y ~ x1 + x2) summary(intact.lm) anova(intact.lm) the questions are 1.The function summary() is convenient since

I have two assignment problems... I have written this small code for regression with two regressors . n <- 50 x1 <- runif(n,1,10) x2 <- x1 + rnorm(n,0,0.5) plot(x1,x2) # x1 and x2 strongly correlated cor(x1,x2) y <- 3 + 0.5*x1 + 1.1*x2 + rnorm(n,0,2) intact.lm <- lm(y ~ x1 + x2) summary(intact.lm) anova(intact.lm) the questions are 1.The function summary() is convenient since

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

Hi, folks, I would like to copy the output of summary(lm) and anova (lm) in R to my word file. But the output will be a mess if I just copy after I call summary and anova. ##################### x=rnorm(10) y=rnorm(10,mean=3) lm=lm(y~x) summary(lm) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.278567 -0.312017 0.001938 0.297578 1.310113

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error