similar to: testing slopes different than a given value

Hi all, I tended to use rbind, or cbind to force a vector be be deemed as a column or row vector. This is very important if I want to do things like u' * A * u, where u' is a row vector and u is a column vector, regardless of what originall format the "u" is... I want to recast it to column vector or row vector... How can I do that?

Hi, I try to execute the seven lines of code below to plot a graph. But I am failing as the messages below show. Where am I going wrong? install.packages("rgl") library(rgl) y_hat = X%*%B_hat open3d(windowRect = c(100,100,900,900),family = "serif") color = rainbow(length(y_hat))[rank(y_hat)] plot3d(educ,exper,wage,col = color,type = "s",size = 0.5,xlim =

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

Hello all, I need to infer if the slopes and intercept of each group of my lme analyze, showed below, are different from each other. Like a Tukey test. I don't need to compare the slope and interceptof each subject of my fixed data, but the slope and intercept of each group of them. > m1<-lme(fvfm~term*sp*env*est*time,random=~term|id,table) You know how can I make it? Thanks for

Hi, I'd like to add lines to the panels of a bwplot, showing (and connecting) the means of the displayed distributions. I didn't find anything in the documentation or the examples that suggests how to accomplish this easily. Has anybody done this already, and willing to share? Thanks, RenE J.V. Bertin College de France/LPPA 11, place Marcelin Berthelot 75005 Paris, France

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Some of my students have uncovered some apparent graphics bugs in R. I think they may manifest slightly different on different platforms/ with different display devices (they sometimes report a Windows graph that looks OK but odd results on printing). I'm able to replicate similar problems on my Linux (RH 6.0) system. 1. abline() plots nothing, or a wrong line, when the slope is too

Dear all I have the following data (a shortened extract shown; some replictates of time deleted) to which I fitted the linear model given below: time group mass 11 control 0.019 11 control 0.014 14 control 0.0306 14 control 0.0289 14 control 0.0236 17 control 0.0469 17 control 0.0709 11 five 0.0077 11 five

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in

Hi, Sorry if this is an obvious one, but is there a simple way to modify the lm function to test whether a slope coefficient is significantly different from 1 instead of different from 0? Thanks, Martin Martin Biuw SEA MAMMAL RESEARCH UNIT Gatty Marine Laboratory School of Environmental and Evolutionary Biology University of St Andrews Fife, Scotland KY16 8LB UK phone +44 (0)1334 462630 fax

Hi I have (8 measures * 96 groups) = 768 datasets for which I did linear regressions using lm(). Now I want to compare the slopes for each of the 8 measures in each of the 96 groups. As I understand , I can not use > anova(lm1, ..., lm8) as the lm1 ... lm8 are based on different datasets. I also read in previous discussions in this list, that I can see if the slope +- stddev(slope)

Hello Instead of testing against 0 i would like to test regression slopes against -1. Any idea if there's an R script (package?) available. Thanks for any hint. Cheers Lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of Aquatic Science and Technology ECO - Department of Aquatic Ecology ?berlandstrasse 133 CH-8600 D?bendorf Switzerland Phone: +41 (0) 71 220

Hi, I read almost all I found in prior R-Help list about How to compare two regression line slopes. So, I made a didactic example to illustrate a solution cited by Ben Bolker: =============================================== Subject: Re: [R] How to compare two regression line slopes From: Ben Bolker (bol... at ufl.edu) Date: Jan 27, 2009 1:52:20 pm List: org.r-project.r-help

Hi, all, I am thinking to compare a group of slopes from regression lines to see if they are different overall, and then make specific comparisons between groups. How can I achieve that in R? I searched the archives and there are only discussions about comparing two lines a time. Thanks. A sample data set is like the following. I would like to compare the regression slopes between the five

Dear r-helpers, I am working with plm package. I am trying to fit a fixed effects (or a 'within') model of the form y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific intercept and an individual- specific slope. Does plm support this directly? Thanks in advance! Otto Kassi

Hello all, I using lmer to develop a mixed effects model. I start with an overly parameterized model (as suggested in Zuur et al. Mixed Effects Models and Extension in Ecology with R) that looks something like this: m1 <- lmer( Y ~ aS + bS + c + d + e + (c|SpeciesId) + (d|SpeciesId) + (e|SpeciesId)) aS and bS are species level predictors an so do not vary within a SpeciesId. However, c, d, and

Hi I want to extract the random slopes from a lmer (I am doing a random regression), but are the answers obtained from ranef or coef? My model is: mod1<-lmer(B~ A +(A|bird), family=quasibinomial) And I want to obtain a slope for each individual bird but am not sure which output I need and can't find the answer anywhere. Thanks Sam Dr Samantha Patrick EU INTERREG Post Doc Davy 618

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Can anyone show me how to test for significant simple slopes of a 3-way interaction, with covariates. my equation tmod<-(glm(PCL~ rank.f + gender.f + MONTHS + CEXPOSE.M + bf.m + MONTHS*CEXPOSE.M*bf.m, data=mhatv, family=gaussian ,na.action=na.omit)) Thank you Mike [[alternative HTML version deleted]]