similar to: R-beta: Stars again

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System: R 2.3.1 on Windows XP machine. I am building a logistic regression model for a sample of 100 cases in dataframe "d", in which there are 3 binary covariates: x1, x2 and x3. ---------------- > summary(d) y x1 x2 x3 0:54 0:50 0:64 0:78 1:46 1:50 1:36 1:22 > fit <- glm(y ~ x1 + x2 + x3, data=d, family=binomial(link=logit)) >

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2

List: I'm helping a colleague with some Poisson regression modeling. He uses SAS proc GENMOD and I'm using glm() in R. Note on the SAS and R output below that our estimates, standard errors, and deviances are identical but what we get for likelihoods differs considerably. I'm assuming that these must differ just by some constant but it would be nice to have some confirmation

hello, I work since a few time on R and i wanted to know how to obtain the Wald chi square value when you make a binary logistic regression. In fact, i have the z value and the signification but is there a script to see what is the value of Wald chi square. You can see my model below, Best regards, S??verine Erhel [Previously saved workspace restored] > m3 = glm(reponse2 ~ form +

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Dear R-Devel, As I am sure many of you know, a special issue of The American Statistician just came out, and its theme is the [mis]use of P values and the many common ways in which they are abused. The lead editorial in that issue mentions the 2014 ASA guidelines on P values, and goes one step further, by now recommending that the words "statistically significant" and related simplistic

Today's GNU R tutorial in http://how-to.linuxcareer.com/a-quick-gnu-r-tutorial-to-statistical-models-and-graphics points out how bad statistical practice is being further perpetuated, by virtue of "significance stars" still being the default in printed output from lm models. ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context:

I am estimating a multinomial model with two quantitative predictors, X1 and X2, and 3 responses. The responses are called neutral, positive and negative with neutral being the baseline. There are actually many models being estimated because I estimate the model over time and also for various parameter sets but that's not important. When I estimate a model, since neutral is the baseline

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

I read through the editorial. This is the one of the most mega-ultra-super-biased articles I've ever read. e.g. The authors encourage Baysian methods, and literally encourage subjective approaches. However, there's only one reference to robust methods and one reference to nonparametric methods, both of which are labelled as purely exploratory methods, which I regard as extremely

Greetings. I'm trying to recreate in R some regression models I've done in SAS, but I'm not getting the same results. My advisor suspects this may be due to differences in precision between R and SAS. Does anyone know where I can find specifications for R's type double? (It doesn't seem to be in the R Language Definition.) Thanks in advance for any help anyone can

Hi, We have an experiment with pass/fail outcome, and a continuous parameter which may contribute to the outcome. First, we've analyzed it by: p=c(F,T,F,F,F,T,T,T,T,T,T,T,F,T,T,T,T); w=c(53,67,59,59,53,89,72,56,65,63,62,58,59,72,61,68,63); l<-glm(p~w,family=binomial) summary(l) Which turned out to be non significant. Then, we thought of comparing the parameters of the two groups

How do you get the design matrix R used when calculating ANOVA? -- View this message in context: http://www.nabble.com/how-to-get-design-matrix--tp23464638p23464638.html Sent from the R help mailing list archive at Nabble.com.

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Full_Name: S?ren Faurby Version: 2.4.1 and 2.7.2 OS: Submission from: (NULL) (192.38.46.92) There appear to be a bug in the estimation of significance in the binomial model in GLM. This bug apparently appears when the correlation between two variables is to strong. Such as this dummy example c(0,0,0,0,0,1,1,1,1,1)->a a->b m1<-glm(a~b, binomial) summary(m1) It is sufficient that all

Hi I don't know if you can help. I am a 2nd year Bsc Cosmetic Science student and in R I need some help in drawing stars. The problem that I have is I want to recreate a radar diagram similar to the one in excel. I have put in these commands in a script window: stars(shampoo1[, 1:6], locations = c(0,0), radius = TRUE, key.loc=c(0,0), main = "Ranked Results for the Sensory

Hi! I have a dataset with three columns -the first two refer to x and y coordinates, the last one are odds ratios. I'd like to plot the data with x and y coordinates and the odds ratio shown as a fourfold plot, which I prefer to do using the stars function. Unfortunately the stars option in symbols is not as cool as the stars function on its own, and now i can't figure out how to do it!

Hi, I am just wondering if there is a test available for testing if a linear fit of an independent variable in a Cox regression is enough? Thanks for any suggestions. John Zhang ____________________________________________________________________________________ [[elided Yahoo spam]]