similar to: Test if beta is different from something other than 0

Hello I want to compare the slope (let's say 'b') of a linear model obtained with lm() to a theoretical value (let's say 'th'). To do so, I think I should compute a 't value' using something like : (b - 'th')/standard.deviation(b) and then look at the p-value of this computed t. I don't understand how to do this in a simple way, just using lm()

The cygwin 'cygpath' command can now convert arbitrary windows and unix paths into the short ms-dos 8.3 equivalents. This tools might allow the windows build system to better deal with those messy long Windows file and directory names that contain spaces. EG: warneg@GRDGROL99X1028: src [1]$ pwd /cygdrive/c/Documents and Settings/WarnesGR/My Documents/cygwin_home/src/gregmisc/src

I am trying to reproduce an analysis done in SAS from which Type III Sums of squares are used. How can you get Type III sums of squares in an aov similar to what SAS provides? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department of Range, Wildlife, and Fisheries Management Mail Stop 2125 Texas Tech University Lubbock, Texas 79409-2125 (806) 742-2843 Voice

I have some data that was created for import into a Lotus 1-2-3 spreadsheet and on of the columns is time. The time is akin to Julian were the value 1 is mapped "01-Jan-00 12:00:00 AM" in Lotus 1-2-3. Is there a function in an R package that can convert this numeric vector to a POSIXct vector? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department

Using the "ergoStool" data cited in Mixed-Effects Models in S and S-PLUS by Pinheiro and Bates as an example, we have ======== > library(nlme) > fm <- lme(effort~Type-1, data=ergoStool, random=~1|Subject) > summary(fm) Linear mixed-effects model fit by REML Data: ergoStool AIC BIC logLik 133.1308 141.9252 -60.5654 Random effects: Formula: ~1 | Subject

I have a function for plotting soil texture that I am having a problem with. The function is: plot.psd <- function(sand, clay, ...) { conv.ter <- function(x, y) { x.con <- 100 - x - y*cos(1/3*pi) y.con <- y*sin(1/3*pi) data.frame(x=x.con, y=y.con) } plot(conv.ter(sand, clay), xlim = c(0,100), ylim = c(0,100*sin(1/3*pi)), axes = FALSE, xlab="",

Hello All, Is there a way of producing an ANOVA table split into contrasts, thus showing the contrasts sums of squares and associated p-values? Thanks, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle

Dear R-help: Does anyone know of a package in R that will do Welch's modified F-test for heterogeneous error variances? Are there other statistical techniques available in R that test the equality of means when homoscedastisity is violated? 't.test' does this in the pairwise sense when var.equal = TRUE. With best wishes and kind regards I am Sincerely, Corey A. Moffet Support

Dear R-Help, Does anyone know of a way to use CGIwithR in Windows? David Firth the author of CGIwithR states "It ought to be possible to adapt it for use also in conjunction with web servers running under Windows NT ... but the author has no plans for that." I have developed some applications in R that I would like to make available through a web interface using CGIwithR but at this

I am having difficulty with plot.formula. For example: ------------------- dat <- data.frame(y = c(1,1,1,2,2,2,3,3,3,4,4,4), x1 = as.ordered(c(1,1,1,1,1,1,2,2,2,2,2,2)), x2 = as.ordered(c(1,1,1,2,2,2,1,1,1,2,2,2))) plot(y ~ x1 + x2, data = dat) # Works fine "interactively" asking: # "Hit <Return> to see next plot:" #

Hello, suppose I have a multivariate multiple regression model such as the following: > DF<-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50)) > tmp<-rnorm(100) > DF$y1<-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5) > DF$y2<-tmp+DF$x1*.5+DF$x2*.7+rnorm(100,0,.5) > x.mlm<-lm(cbind(y1,y2)~x1+x2,data=DF) > coef(x.mlm) y1 y2 (Intercept)

Dear R-Help, I have a generated file that looks like the following: ----- Begin file ----- # # Output File # float Version 2002.700000000000 int Numdays 31 int NumOFEs 1 # # Hillslope-specific variables # char HillVarNames[ 3 ] {Days In Simulation} {Hillslope: Precipitation (mm)}

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Dear R-help, Does any one know of a function/package in R that will test the assumption of spericity in a split-plot analysis of variance? How are people dealing with this issue in R? With best wishes and kind regards I am Sincerely, Corey A. Moffet Support Scientist University of Idaho Northwest Watershed Research Center 800 Park Blvd, Plaza IV, Suite 105 Boise, ID 83712-7716 (208) 422-0718

Full_Name: Corey A. Moffet Version: 1.8.1 OS: Windows 2000 and RHL 9 Submission from: (NULL) (199.133.140.156) The function model.tables.aov and perhaps other model.tables methods refer the user to use the function se.contrasts when the design is unbalanced. The function name is se.contrast (singular).

I have some Bernoulli data something like this: x<-sort(runif(100,1,20)) p<-pnorm(x,10,3) y<-as.numeric(runif(x)<p) plot(x,y) lines(x,p) This plot is not very satisfactory because the ogive does not visually fit the (0,1) points very well, and also because the points tend to fall on top of one another. The second problem can be eliminated by adding vertical jitter. However I was

Hello all, I am trying to solve a problem, and my solution is rather ugly and not very general. The posts for "[R] help with gsub and grep functions" seemed relevent and gave me hope for a more refined and more general solution. The Problem: line <- "'this text has spaces' 'thisNot' 3 4 5 6 7 8 9 10" bad.line <- "'this text has spaces'

I'm looking for a tutorial or notes on the use of contrasts factor in linear model in R, I've found some mails and infos about in various documents about R, but I've probably missed a good review on this subject. -- Robert Espesser Laboratoire Parole et Langage UMR 6057, CNRS 29 Av. Robert Schuman 13621 AIX (FRANCE) Tel: +33 (0)4 42 95 36 26 Fax: +33 (0)4 42 59 50

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

I have an older Splus script that generated some regression graphics for me. In it, I used the commands key(10,800,text="<blah1>",border=0) key(10,750,text="<blah2>", border=0, background = 9) text(locator(1),"95% Confidence Limits") locator(n=2,type="l") text(locator(1),"Model for Group 1") locator(n=2,type="l") There is