similar to: p value for mu: anova()

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

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(

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

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

Hi all R-users, I?m trying to assign colors on those p-value in my table output that fall above a certain critical value, let?s say a p-value >0.05. My table looks like this: Assets ADF-Level P-Value ADF-First D P-Value ADF-Second D P-Value [1,] Liabilities -2.3109 0.1988 -3.162 0.025 -6.0281

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

hi, In login_controller.rb when I do the fallowing code it works fine before_filter :authorize, :except => :login but I want to do the fallowing when creating the user so the authorization is not checked before_filter :authorize, :except => :login, :except => :create_user but when I do this webrick server appear to be slow or not connected and take lot of time to load? Could someone

I would like to write a function that runs GLMM using lmer on a user-input model containing interactions, but if the model doesn't produce significant results for the interaction, a reduced model will be run without the interaction. Doing this seems to require getting the p-values out of an lmer object, but I don't know how to do this. (The grand DF debate seems to be irrelevant since the

Hello, Rlisters I have to compute p-values that are on the tail of the distribution, P-values < 10^-20. However, my current implementations enable one to estimate P-values up to 10^-12, or so. A typical example is found below, where t is my critical value. ########### example - code adapted from Rassoc ####################### rho01 = 0.5 rho105 = 0.5 rho005 = 0.5 t = 8 z = 2

Hi, I have a program written in R which is good on the version 1.2, but for the fallowing versions of R, an error always is at the same place. That is at the level of the fallowing line: Sur<- getInitial(res2[m:M,2]~SSasymp(res2[m:M,1],Asymp,resp0,lrc),data=res2) Error in eval(expr,envir,enclos):numeric envir arg not of length one I don't know at all this langage for the instant.

I made an ANOVA with repeated mesures (aov(Mesure~Distance*Genre*Correct+Error(Sujet/(Distance*Genre*Correct)), data)) and I would like to extract the pvalues. The output is: ----------------------------------------------------------- Error: Sujet Df Sum Sq Mean Sq F value Pr(>F) Residuals 21 97.082 4.623 Error: Sujet:Distance Df Sum Sq Mean Sq F value Pr(>F) Distance

Hi R community I have a little problem, and I tried to solve it by myself but I couldn't. I building an if loop, and I want to check a value inside an interval. This would be the case: pvalue=0,2999 if(pvalue>0.05 or pvalue<0.1) as you can see I would like to check in that if loop if my pvalue is inside of that interval(from 0.05 to 0.1), and I tried these options: if(pvalue>0.05

I am trying to return the p value for a stat from the ECDF. That is the index of the first occurrence, on an ordered vector, of a value either greater than or equal to a given value. Ideally I would not have to order the vector beforehand. Currently I use: PValue<-function(stat, ECDF){ ###Get the length of the ECFD L<-length(ECDF) ###Loop through the ECDF until the p value is

Hello, I am trying to use the p.adjust function for multiple testing. here is what i have 9997 201674_s_at 0.327547396 9998 221013_s_at 0.834211067 9999 221685_s_at 0.185099475 I import them from excel have have the gene symbol as well as the pvalue here is the issue > pa<-p.adjust(pt,method="BH") Error in p[nna] : object is not

Hi all: I have a question about multi-comparison. The data is in the attachment. My purpose: Compare the predicted means of the 3 methods(a,b,c) pairwisely. I have 3 ideas: #idea1 result_aov<-aov(y~ method + x1 + x2) TukeyHSD(result_aov) diff lwr upr p adj b-a 0.845 0.5861098 1.1038902 0.0000001 c-a 0.790 0.5311098 1.0488902 0.0000002 c-b -0.055 -0.3138902

Sent this mail in rich text format before. Excuse me for this. ------------------------ Dear all, I'm using the lrm function from the package "Design", and I want to extract the p-values from the results of that function. Given an lrm object constructed as follows : fit <- lrm(Y~(X1+X2+X3+X4+X5+X6+X7)^2, data=dataset) I need the p-values for the coefficients printed by calling

I have created a list of tables with the same columns but different number of row. Example (actual list has ~200 elements): > temp1<- data.frame(ID=c("Herb","Shrub"),stat=c(4,5),pvalue=c(.03,.04)) > temp2<- data.frame(ID=c("Herb","Shrub", > "Tree"),stat=c(12,15,13),pvalue=c(.2,0.4,.3)) > L<-list(a=temp1,b=temp2) > L $a

Hi, people. I was a bit intrigued by the message quoted below. Indeed, if pt() is given a matrix, it returns a matrix. Should this feature be documented? ?pt speaks about "a vector of quantiles", and says nothing about the type of what it returns. The same might presumably apply to other distribution-related functions. ----- Forwarded message from Martyn Plummer <plummer at

Dear R-community, I have a short question: How do I interpret the result of a likelihood ratio test correctly? I am fitting a parametric survival model (with aftreg {eha}) and the output tells me the overall p-value of my model is < 0.001. My simple question is: Does the result mean my model fits the data well OR does it mean my model DOES NOT fit the data well? Some side information how the

Hello I am trying to get out of an lm model the fstatistics, however after I run the model I write > names(Model) and the fstatistic does not appear only these. names(Model) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" [6] "assign" "qr" "df.residual"