search for: glh

Is there a function/package that will allow you to test the hypothesis beta1 = x in a simple linear regression, where x is a constant? The AOV tests if beta1 in different from 0 but what if I want to know if beta1 one is different from 1? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department of Range, Wildlife, and Fisheries Management Mail Stop 2125 Texas

...duals: Min Q1 Med Q3 Max -1.80200345 -0.64316591 0.05783115 0.70099706 1.63142054 Number of Observations: 36 Number of Groups: 9 ======== Now suppose I want to test the following hypothesis H0: TypeT1 =0 and TypeT2 = 0 I've tried estimable() and glh.test() in package gmodels, esticon() in package boBy, and linear.hypothesis() in package car, but it seems none of them would work with objects from lme: ======== > library(gmodels) > estimable(fm, rbind(c("TypeT1"=1), c("TypeT2"=1))) Error in FUN(newX[, i], ...) : `par...

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

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()

...28513 x2 0.13853332 0.4604842 How can I test whether x1 and x2 respectively have the same effect on y1 and y2? In other words, how can I test if coef(x.mlm)[2,1] is statistically equal to coef(x.mlm)[2,2] and coef(x.mlm)[3,1] to coef(x.mlm)[3,2]? I looked at linear.hypothesis {car} and glh.test {gmodels}, but these do not seem the apply to multivariate models. Thank you in advance, Uli Keller

...rary contrasts for regression objects estimable Compute and test estimable linear functions of the fitted coefficients (including contrasts) of regression objects factorial Compute factorial function glh.test Test a General Linear Hypothesis for a Regression Model hist2d Compute and Plot a 2-Dimensional Histogram interleave Interleave Rows of Data Frames or Matrices lowess Scatter Plot Smoothing nobs...

...rary contrasts for regression objects estimable Compute and test estimable linear functions of the fitted coefficients (including contrasts) of regression objects factorial Compute factorial function glh.test Test a General Linear Hypothesis for a Regression Model hist2d Compute and Plot a 2-Dimensional Histogram interleave Interleave Rows of Data Frames or Matrices lowess Scatter Plot Smoothing nobs...

...of the fitted coefficients (including contrasts) of regression objects factorial Compute factorial function fast.prcomp Efficient computation of principal components and singular value decompositions. glh.test Test a General Linear Hypothesis for a Regression Model hist2d Compute and Plot a 2-Dimensional Histogram interleave Interleave Rows of Data Frames or Matrices lowess Scatter Plot Smoothing make.contrasts...

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

...ion objects factorial Compute factorial function fast.prcomp Efficient computation of principal components and singular value decompositions. fit.contrast Compute and test arbitrary contrasts for regression objects glh.test Test a General Linear Hypothesis for a Regression Model hist2d Compute and Plot a 2-Dimensional Histogram interleave Interleave Rows of Data Frames or Matrices lowess Scatter Plot Smoothing make.contrasts...

Hi, I'd like to know if it's possible to specify different contrast matrices in lm() for a factor that is nested within another one. This is useful when we have a model where the nested factor has a different number of levels, depending on the main factor. Let me illustrate with an example to make it clearer. Consider the following data set: set.seed(1) y <-