similar to: Help : glm p-values for a factor predictor

It might help if you provided the code you used. It's possible that you didn't use direction="backward" in stepAIC(). Or if you did, it was still running, so whatever else you try will still be slow. The statement "R provides only the pvalues for each level" is wrong: look at the anova() function. Bob On 29 June 2017 at 11:13, Beno?t PELE <benoit.pele at

Hello, i am a newby on R and i am trying to make a backward selection on a binomial-logit glm on a large dataset (69000 lines for 145 predictors). After 3 days working, the stepAIC function did not terminate. I do not know if that is normal but i would like to try computing a "homemade" backward with a repeated glm ; at each step, the predictor with the max pvalue would be

How can I get the p values from a glm ? I want to get the p values so I can add them to a custom report fitwean<- glm(data[,"JWean"]~data[,"Group"],data=data,family=binomial(link ="logit")) summary(fitwean) # This lists the coefficeints, SEs, z and p values, but I can't isolate the pvalues. names(summary(fitwean)) # I see the coefficients,

On 01/04/2016 6:14 PM, John Sorkin wrote: > How can I get the p values from a glm ? I want to get the p values so I can add them to a custom report > > > fitwean<- glm(data[,"JWean"]~data[,"Group"],data=data,family=binomial(link ="logit")) > summary(fitwean) # This lists the coefficeints, SEs, z and p values, but I can't

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

Dear All, I am trying to combine dependent p-values in R. May you please help me with this? For independent pvalue combination, one of the popular way is fisher's method which I found the R code here (http://r.789695.n4.nabble.com/fisher-s-posthock-test-or-fisher-s-combination-test-td2195964.html#a2305025): fisher.comb <- function (pvalues) { df=length(pvalues)

Full_Name: Jason Polak Version: R version 2.5.0 (2007-04-23) OS: Xubuntu 7.04 Submission from: (NULL) (137.122.144.35) Dear R group, I have noticed a strange anomaly with the shapiro.test() function. Unfortunately I do not know how to calculate the shapiro test P values manually so I don't know if this is an actual bug. So, to produce the results, run the following code: pvalues = 0; for

Hi, for example one could do it this way: v <- summary(fit)$coefficients[,4] the coefficient attribute is a matrix, and with the "4" you refere to the pvalue (at least in lm - don't know if summary(glm) produces sligthely different output). to skip the intercept (1st row): v <- summary(glmfit)$coefficients[-1,4] hope this helps, Arne -- Arne Muller, Ph.D.

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

Bert Gunter wrote on 01.04.2016 23:46: > ... of course, whether one **should** get them is questionable... > > http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503#/ref-link-1 > This paper repeats the common place statement that a small p-value does not necessarily indicate an important finding. Agreed, but maybe I overlooked examples of important

This is a question at the border between stats and r. When I do a glm with many potential effects, and select a model using stepAIC, many independent variables are selected even if there are no relationship between dependent variable and the effects (all are random numbers). Do someone has a solution to prevent this effect ? Is it related to Bonferoni correction ? Is there is a ratio of

How do we get 2-tailed p-values for the rlm summary? I'm using the following: > fit <- rlm(oatRT ~ oatoacData$erp, psi=psi.bisquare, maxit=100, na.action='na.omit') > fitsum <- summary(fit, cor=F) > print(fitsum) Call: rlm(formula = oatRT ~ oatoacData$erp, psi = psi.bisquare, maxit = 100, na.action = "na.omit") Residuals: Min 1Q Median

Dear all, I observer a strange behavior of the pvalues of the t-test under the null hypothesis. Specifically, I obtain 2 samples of 3 individuals each from a normal distribution of mean 0 and variance 1. Then, I calculate the pvalue using the t-test (var.equal=TRUE, samples are independent). When I make a histogram of pvalues I see that consistently the bin of the smallest pvalues has a lower

On 01/04/2016 6:46 PM, Bert Gunter wrote: > ... of course, whether one **should** get them is questionable... They're just statistics. How could it hurt to look at them? Duncan Murdoch > > http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503#/ref-link-1 > > > Cheers, > Bert > > > > Bert Gunter > > "The trouble

How small does a p-value need to be to warrant attention, however? Witness Fisher?s comment that: ?. . . we may, if we prefer it, draw the line at one in fifty (the 2 per cent point), or one in a hundred (the 1 per cent point). Personally, the writer prefers to set a low standard of significance at the 5 per cent point, and ignore entirely all results which fail to reach this level. A scientific

Hi Ted, yes this was the problem. Thank you very much. best idaios On Wed, Jan 9, 2013 at 4:51 PM, Ted Harding <Ted.Harding@wlandres.net>wrote: > Ah! You have aqssigned a parameter "equal.var=TRUE", and "equal.var" > is not a listed paramater for t.test() -- see ?t.test : > > t.test(x, y = NULL, > alternative = c("two.sided",

Full_Name: Martha Nason Version: 2.0.1 OS: Windows XP Submission from: (NULL) (137.187.154.154) I am running simulations using fisher's test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c(

Hi, I have sent this message to this list the July, 7th. It was about a problem in MASS package. Until now there is no change in the devel version. As the problem occurs in a package and not in the R-core, I don't know if the message should have been sent here. Anyway, I have added a copy to Pr Ripley. I hope it could have been fixed. Sincerely Marc Le 09/07/2017 ? 16:05, Marc Girondot via

Hi R user, I am a kind of an intermediate user of R. Now I am using GLM model (library MASS, VEGUS). I used a backward stepwise logistic regression, but i got a problem in removing those predictors which are above 0.05. I don't want to include those variables which were above 0.05 in final backward stepwise logetsic regression model. for example: first I run the model,

inputfille snpid indid genotype gvariable probeid gene geneexpression rs1040480 CHB_NA18524 C/T 2 GI_19743926-I PTPRT 5.850586 rs1040480 CHB_NA18526 C/C 1 GI_19743926-I PTPRT 6.028641 rs1040480 CHB_NA18529 C/C 3 GI_19743926-I PTPRT 5.944392 rs1040481 CHB_NA18532 C/C 1 GI_19743926-I PTPRT 5.938578 rs1040481 CHB_NA18537 C/C 2 GI_19743926-I PTPRT 5.874439 rs1040481 CHB_NA18540 C/C 3 GI_19743926-I