similar to: lme slopes and intercepts differences

Hi all: I perform the linear mixed model for 300 persons, y is CD4 count,x is time. I randomized slope and intercept,so I can get 300 slopes and 300 intercepts.Now I wanna test wheter the variance of 300 slopes and 300 intercepts differs from zero. If the variance of 300 slopes(or intercepts) differs from zero at 0.05 significant level,I should randomize the slope(or intercept), and if not,I

Can you provide the summary(m2) results? > -----Original Message----- > From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] > Sent: Wednesday, August 16, 2006 7:14 AM > To: Doran, Harold > Cc: r-help at stat.math.ethz.ch > Subject: [SPAM] - RE: [R] REML with random slopes and random > intercepts giving strange results - Bayesian Filter detected spam > > Hi again,

I would like to be able to use lme to fit random effect models In which some but not all of the random effects are constrained to be independent. It seems as thought the pdMat options in lme are a promising avenue. However, none of the existing pdMat classes seem to allow what I want. As a specific example, I would like to fit a random intercept/slope mixed model to longitudinal observations in

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.

I fit the model fit<-lm(thresh~cond*Ne) where thresh is the reponse cond is a factor with levels a, b, and c Ne is a continuous indep var I think of this full model as having three lines: thresh as a function of Ne for each condition. Thus we have slopea, slopeb, slopec, inta, intb, intc. lm output my params ------------------------- (Intercept) inta condb intb - inta condc

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)

Hi everyone, I have been using REML to derive intercepts and coeficients for each individual in a growth study. So the code is m2 <- lmer(change.wt ~ newwt+(newwt|id), data = grow) Calling coef(model.lmer) gives a matrix with this information which is what I want. However, as a test I looked at each individual on its own and used a simple linear regression to obtain the same information, then

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

Dear list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I

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

Hello. This is not a technical question but rather an interpretational one, so I apologise if it is not appropriate for this discussion group. I am fitting a mixed model involving two predictor variables: x1 and x2. x1 varies between groups but is constant within groups while x2 varies both between and within groups. I fit two nested models. Model 1: lme(y~x1*x2, random=~1|groups). In this

Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables.

Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value < 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University

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

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

hello i have a mixed effect model which gives slope and intercept terms for 6 groups (diagnosis (3 levels) by risk group(2 levels)). the fixed part of the model is -- brain volume ~ Diagnosis + Risk Group + (Risk Group * age : Diagnosis) - 1 thus allowing risk group age/slope terms to vary within diagnosis and omitting a nonsignificant diagnosis by risk group intercept (age was centered)

Dear all, I'm trying to fit a model on ecological data in which I have measured a few biotic and abiotic factors over the course of a few days in several individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3, and 'factor' as independent variables. Because data suggests both slope and intercept (for y ~x1) might differ between individuals, I'd want to

Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope coefficients and sb1,b2 the pooled standard error of the slope (b) which can be calculated in R this way: > df1 <-

Not really r-specific: Z = (b1 - b2) / SQRT ( SEb1^2 + SEb2^2) -------Original Message------- From: Gijsbert Stoet <stoet at volition.wustl.edu> Sent: 07/20/03 09:51 PM To: r-help at stat.math.ethz.ch Subject: [R] how to test whether two slopes are sign. different? > > Hi, suppose I do want to test whether the slopes (e.g. determined with lsfit) of two different population are

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