search for: d93

...n publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > anova(glm.D93,test="Chisq") Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL...

...Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) d.AD <- data.frame(treatment, outcome, counts) glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) opar <- par(mfrow=c(2,1)) plot(glm.D93, which=c(4,5)) par(opar) cooks.distance(glm.D93) The difference is reasonably moderate in this case. My suspicions were aroused by a case in which the plot showed five or size points greater than...

...istance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > influence.measures(glm.D93)$infmat[, 8] 1 2 3 4 5 6 0.288294276 0.309131723 0.011614584 0.030963844 0.304525117 0.444410274 7 8 9 0.459190432 0...

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

...t; inconsistent AIC value. We also get the same wrong value if we use > "extractAIC" o "AIC" functions. Inconsistent with what? It seems to me that it consistently gives the right answer, but you have not used the actual definition of AIC. > example(glm) > > glm.D93 > > extractAIC(glm.D93) > > #AIC of this model should be 15.129 (residual deviance + 2*effective > degrees of freedom), but the AIC which R returns is 56.76. Function > extractAIC returns the right number of effective degrees of freedom (5), > but anyway seems to fail in cal...

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At...

.... . . . . -1 . "arizona state" . 0 . ------snip----- the data descriptions is: variable names: year apps top25 ver500 mth500 stufac bowl btitle finfour lapps d93 avg500 cfinfour clapps cstufac cbowl cavg500 cbtitle lapps_1 school ctop25 bball cbball Obs: 118 1. year 1992 or 1993 2. apps # applics for admission 3. top25 perc frosh class in 25th high...

...problem. Consider this toy example Sweave file: \documentclass{article} \begin{document} <<echo=TRUE,results=verbatim>>= options(width=40) # Set width to 40 characters hide <- capture.output(example(glm)) # Create an example of the problem, but hide the output summary(glm.D93) # This is where the problem is evident @ \end{document} The intent of the options(width=40) command is to ensure the output does not extend to far to the right of the page, as per the Sweave manual Section A.14. And in most cases, this does the job. Now, I run Sweave on the above file, ru...

Greetings, I am running glm models for species counts using a poisson link function. Normal summary functions for this provide summary statistics in the form of the deviance, AIC, and p-values for individual predictors. I would like to obtain the p-value for the overall model. So far, I have been using an analysis of deviance table to check a model against the null model with the intercept as

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call:

...and not the number of > cells (Raftery, 1986a). Is there a reason why this should not/cannot be done that way? This behavior can be reproduced with with R 3.0.0 from SVN, using the example from ?glm: counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) nobs(glm.D93) # 9 == length(counts) # Should be 150 == sum(counts) FWIW, stats:::nobs.glm is currently defined as: nobs.glm <- function (object, ...) if (!is.null(w <- object$prior.weights)) sum(w != 0) else length(object$resid...

...ect$residuals^2)/ df.r } else Inf ## calculate scaled and unscaled covariance matrix ...... Here is an example: > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > print(d.AD <- data.frame(treatment, outcome, counts)) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > anova(glm.D93) > summary(glm.D93,dispersion=0) #current code ...... Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.045e+00 0.000e+00 Inf <2e-16 outcome2 -4.543e-01 0.000e+00 -Inf...

...t 0.380088737 1.622517536 am -0.614677730 0.926307310 > confint(fit, "wt") 2.5 % 97.5 % wt 0.3800887 1.622518 > > ## from example(glm) > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9); treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > confint(glm.D93) Error in loadNamespace(name) : there is no package called 'MASS' Execution halted But as there are no recommended packages this fails as indicated. So is there something wrong with what I'm doing (i.e. so...

Good afternoon, I REALLY try to answer to my question as an autonomous student searching in the huge pile of papers on my desk and on the Internet but I can't find out the solution. Would you mind giving me some help? Please. ######################################### I'm trying to use glm with factors: > Pyr.1.glm<-glm(Pyrale~Trait,DataRav,family=poisson) If I have correctly

...l mean and confidence intervals for a particular independent variable? For example in the following example: >> counts <- c(18,17,15,20,10,20,25,13,12) >> outcome <- gl(3,1,9) >> treatment <- gl(3,3) >> d.AD <- data.frame(treatment, outcome, counts) >> glm.D93 <- glm(counts ~ outcome + treatment, family=poisson, >> data=d.AD) How would one calculate the confidence intervals for the "treatment" variable? Is there something that can come after the 'se.fit=TRUE' command? Thanks very much! Rachel Rachel T. Buxton P...

...This combination of strwrap and writeLines >seems to "work" in one sense of the word: > > > ># Intro stuff: >wid <- 50 >option(width=wid) >hide <- capture.output(example(glm)) > ># The business end: >writeLines( strwrap( capture.output(summary(glm.D93)), wid)) > > > >Mind you, while it "works", it's an awful construct. I was actually >hoping to show this code in my book: > summary(glm.D93) >and get the nice output appearing in my book. I don't plan on replacing >the simple code above with >...

OK, I really dread to ask that .... much more that I know some discussion about p-values and if they are relevant for regressions were already on the list. I know to get p-val of regression coefficients - this is not a problem. But unfortunately one editor of a journal where i would like to publish some results insists in giving p-values for the squared deviance i get out from different glm and

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

...adapted from the GLM example): \documentclass{article} \begin{document} <<setup,include=FALSE,echo=FALSE>>= options(width=80) @ <<test>>= countsasdfasdfasdf <- c(18,17,15,20,10,20,25,13,12) outcomeasdfasdfasdf <- gl(3,1,9) treatmentasdfasdfasdfasdf <- gl(3,3) glm.D93 <- glm(countsasdfasdfasdf ~ outcomeasdfasdfasdf + treatmentasdfasdfasdfasdf, family=poisson()) @ \end{document} When I compile this, treatmentasdfasdfasdfasdf sticks out into the right margin. The linebreak is inserted after the last comma, so that the line as printed is 85 characters long, ev...

I am trying to build a package (GWASTools, submitted to Bioconductor) that uses the "sandwich" package. I have references to "sandwich" in DESCRIPTION: Imports: methods, DBI, RSQLite, sandwich, survival, DNAcopy and NAMESPACE: import(sandwich) In the code itself is a call to vcovHC: Vhat <- vcovHC(mod, type="HC0") I have sandwich version 2.2-7 installed.