similar to: Chow Test

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

Hello all, I've tried to reproduce the contour plot that appears in the book of Venables and Ripley, at page 255. Is a F-statistic surface and a confidence region for the regression parameters of a non-linear model. It uses the stormer data that are in the MASS package. I haven't been able to reproduce the plot either in R ( version 1.5 ) and S. It makes the axes and it puts the

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

Hello! I was reading R-intro and I have some suggestions: R-intro.html#A-sample-session rm(fm, fm1, lrf, x, dummy) suggestion rm(fm, fm1, lrf, x, y, w, dummy) The next section will look at data from the classical experiment of Michaelson and Morley to measure the speed of light. file.show("morley.tab") mm <- read.table("morley.tab") suggestion mm <- data(morley)

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Dear All, I am trying to use stepAIC for an lmer object but it doesn't work. Here is an example: x1 <- gl(4,100) x2 <- gl(2,200) time <- rep(1:4,100) ID <- rep(1:100, each=4) Y <- runif(400) <=.5 levels(Y) <- c(1,0) dfr <- as.data.frame(cbind(ID,Y,time,x1,x2)) fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial)

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

Hi, I am wondering how I can specify no intercept in a mixed model using lmer(). Here is an example dataset attached ("test.txt"). There are 3 workers, in 5 days, measured a response variable "y" on independent variable "x". I want to use a quadratic term (x2 in the dataset) to model the relationship between y and x.

Dear All: I need some guidance in calculating a goodness-of-fit statistic for a Tobit Regression model. To develop the Tobit regression, I used the tobit() method from the AER package, which is basically a simpler interface to the survreg() method. I've read about pseudo R2 and C-index and was wondering if there is a package that calculates this for me. Also, is there a reason to select

ERRORLOG Just wanted to import a bulk of data into R via BLOOMBERG. And it crashed. Is there any useful "something" like an ERRORLOG? Checked the web but did not get useful information. Cheers, Bernd -- View this message in context: http://www.nabble.com/Errorlog-tf4548140.html#a12978580 Sent from the R help mailing list archive at Nabble.com.

Hi, I would like to compare to regression models - each model has a different dependent variable. The first model uses a number that represents the learning curve for reward. The second model uses a number that represents the learning curve from punishment stimuli. The first model is significant and the second isn't. I want to compare those two models and show that they are significantly

Hi, I have recently use the strucchange package in R with a single time series observation. I found it extremely useful in the testing of change points. Now, I am thinking of using the strucchange package with panel data (about 500 firms, with 73 monthly time series observations each). For each firm, I have to conduct the Fstats and breakpoints tests. Based on the test of each firm, I have to

k=lm(y~x) summary(k) returns R^2=0.9994 lm(y~x) is supposed to find coef. a anb b in y=a*x+b l=lm(y~x+0) summary(l) returns R^2=0.9998 lm(y~x+0) is supposed to find coef. a in y=a*x+b while setting b=0 The question is why do I get better R^2, when it should be otherwise? Im sorry to use the word "MS exel" here, but I verified it in exel and it gives: R^2=0.9994 when y=a*x+b is used

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

Good day R-listers, My question is more a statistical question than an R related question, so please bear with me i'm currently applying the chow test of poolability in fact i'm working with panel N=17 T=5 , and my model looks like this : Yit= a0+B1X1+B2X2+B3X3+B4X4+eit My question is the following when i'm Testing for the equality of the coefficients of the unpooled data (the

Suppose one estimates a classification or regression tree (CART) for one group or one time period; and then estimates a CART for another group or time period. Is there a way to test for a structural change or break across the two groups or between the two time periods, in other words, is there an analogue of a Chow Test for CART? Has anyone ever seen anything like this or have any ideas how one

Hi,   A question on something which normally should be easy !   I perform a linear regression using lm function:   > reg1 <- lm (a b+c+d, data = database1)   Then I try to perform the Chow (1960) test (structural change test) on my regression. I know the breakpoint date. I try the following code like it is described in the “Examples” section of the “strucchange” package :   > sctest(reg1,

>>>>> "MM" == Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 12:37:21 +0200 writes: >>>>> "GaGr" == Gabor Grothendieck <ggrothendieck at gmail.com> >>>>> on Mon, 17 May 2010 09:45:00 -0400 writes: GaGr> BIC seems like something that would logically go into stats

Hello, Im using strucchange package in R software in order to apply Bai and Peron (1998, 2003) structural break tests to a set of n=1671 observations with a constant term (no AR terms). For that purpose I have read several papers, for instance Validating Multiple Structural Change Models An Extended Case Study, in which its aim is to replicate the results from Bai and Perron (2003) in R