similar to: anova for plm objects

Dear All, I would like to analyse the residuals from a generalized linear mixed model (GLMM) that I estimated, with random effects, by means of the command glmmPQL, from the MASS package. It is not very clear to me what the actual residuals to analyse are (Y - Yhat): I obtain two columns of residuals, of which the first are population residuals, and the second refer to the grouping used in the

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 was wondering if it is possible to generate a regression summary (it does not matter at this stage if from an lm or for example a glm estimate) in which to obtain the joint significance of a set of regressors? Examples could be looking at the joint significance level of a polynomial, or of a set of exogenous variables of which is of interest the linear combination suggested by the

Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly

Dear All, I carry out negative binomial estimations using the glm.nb command from the MASS package. Is there a command or a simple procedure for computing marginal effects from a glm.nb fitted object? If these are the same as for a Poisson fitted object (glm), my question remains how to compute them. Thanks in advance for your help. Roberto Patuelli ******************** Roberto Patuelli, Ph.D.

Dear All, I've been using already for a year glm.nb() from the MASS package. But today, R gave me an error message when estimating one of my usual models: > depEsf.nb <- glm.nb(depE ~ manuf00E + corps00E + lngdp00E + lngdp00sqE + > lnpop00E + indshE + scishE + mechshE + elecshE + chemshE + drugshE + > urban_dummyE + aggl_dummyE + + eE1 + eE2 + eE3 + eE4 + eE5 + eE6 + eE7 +

Dear useRs, a new version of the lmtest package is available from CRAN. Thanks to Giovanni Millo who provided the initial versions of several new functions (and many helpful discussions), there is new functionality for the comparison of nested and non-nested linear models. For non-nested model comparisons, the Cox test, encompassing test and J test are now available. For nested model

Dear useRs, a new version of the lmtest package is available from CRAN. Thanks to Giovanni Millo who provided the initial versions of several new functions (and many helpful discussions), there is new functionality for the comparison of nested and non-nested linear models. For non-nested model comparisons, the Cox test, encompassing test and J test are now available. For nested model

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

Hi all, I have been working through the examples in one of the vignettes associated with the 'mlogit' package, 'Kenneth Train's exercises using the mlogit package for R.' In spite of using the code unchanged, as well as the data used in the examples, I have been unable to run a Wald test to test two models. Specifically, I have run the following command, where mc and mi2 are

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921

Hi, I am trying to run a panel regression using 88 observations and 9 variables. In-built Hausman Test did not work, then I found a code for auxiliary regression method for the Hausman test. The panel models are: fe=plm(gd ~ l+g+o+c+g1+h+n+r, model = "within", data = new.frame,index = c("id")) re=plm(gd ~ l+g+o+c+g1+h+n+r, model = "random", data = new.frame,index =

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

Hello, I'm moving from R 2.2.1 (Winows XP) to R 2.5.1 and have problems with installing "myfuncs", which worked OK in 2.2.1 R CMD INSTALL myfuns # gives installing to '' ---------- Making package myfuncs ------------ adding build stamp to DESCRIPTION installing R files installing man source files installing indices installing help >>>

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages

Dear all, I'm trying to run a Wald Test for the Penalized Fixed Effects using the rqpd package. Actually, I'd like to test if the coefficients associated to the same variable are statistically different among the quantiles. The usual wald.test, waldtest or anova.rq don't work for rqpd. I would appreciate any help. Thank you. Regards, Fernando Parmagnani, M.A., Economics University

Dear list members, I am working with a Cox ph model for the duration of unemployment. The event of interest in my analysis is getting employed. I have various background variables explaining this event: age, sex, education etc. I have multiple unemployment spells per person. I use a model with person-specific frailty terms in order to take into account the correlation of spells by the same

Dear R users, the systemfit package contains functions for fitting systems of simultaneous equations by various estimation methods (e.g. OLS, SUR, 2SLS, 3SLS). Currently version 0.8 of systemfit is available on CRAN. However, shortly we will upload version 1.0, which is NOT BACKWARD COMPATIBLE. The changes that broke backward compatibility were necessary to make systemfit() more similar to

Dear R users, the systemfit package contains functions for fitting systems of simultaneous equations by various estimation methods (e.g. OLS, SUR, 2SLS, 3SLS). Currently version 0.8 of systemfit is available on CRAN. However, shortly we will upload version 1.0, which is NOT BACKWARD COMPATIBLE. The changes that broke backward compatibility were necessary to make systemfit() more similar to