similar to: get the wald chi square in binary logistic regression

Hi All, I want to extract elements of elements in a list. Here's an example of what I mean: If I create a list: x <- as.list(100) for(loop in c(1:100)) { x[[loop]] <- summary(runif(100)) } > head(x) [[1]] Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02271 0.25260 0.58130 0.52120 0.77270 0.99670 [[2]] Min. 1st Qu. Median Mean 3rd Qu. Max. 0.006796 0.259700

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

Hi I'm having some difficulty understanding my output (below) from GEE. the person who wrote the program included some comments about the '3-th term gives diff between hyp/ox at time..', and after created an L vector to use for a WALD test. I was wondering if someone could help me understand the GEE output, the programmers comment, how L was determined, and its use in the WALD

I have a question about the Wald test F-statistics that are calculated when the anova() command is used on a singular gls or lme object. As I recall from my linear models class, the Wald test examines H0: C'B = d0 vs Ha: C'B != d0. Does anybody know how this C matrix is constructed in R? Is there a way to see the C matrix that R is using? In my situation, I'm looking at

Hello, Might anyone know how to conduct Wald-type F-tests of the fixed effects estimated by glmmPQL? I see this implemented in SAS (GLIMMIX), and have seen it recommended in user group discussions, but haven't come across any code to accomplish it. I understand the anova function treats a glmmPQL fit as an lme fit, with the test assumptions based on maximum likelihood, which is inappropriate

Dear R-Helpers, I'm searching for an R package that will produce power analyses for linear Wald tests. I have conducted a thorough analysis and come up with a pile of negative results (I'm the King, baby). I found asypow, which applies to likelihood ratio tests. However, we generally have the power to do Wald tests, I just need to prove it so that we can publish it ;) If this is a

*H*i, I am trying to fit a GEE model with *geeglm* function. The model is the following: Modelo<-geeglm(sqrt ~Tra+ Mes, id=Lugar , data=datos, family=gaussian(identity), corstr="independence") *Tra( is a experimental treatment, 2 levels)*, *Mes* (is the month of take data, 4 levels) and *Lugar* (is the site of study, 3 levels) are categorical variables and *sqrt* (sqrt of Total

Dear R users,    my question concerns my interest in comparing the beta coefficients between two identical regression models in two (actually 3) groups. Disclaimer: I am quite new to R, so I might be missing some terminology that I have not come across.   I am trying to find out if I can easily implement a Wald test in R for this, but the only relevant thing that I came across is this link

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

I have a doubt about the calculation of tests for fixed effects in mixed-effects models. I have read that, except in well-balanced designs, the F statistic that is usually calculated for ANOVA tables may be far from being distributed as an exact F distribution, and that's the reason why the anova method on "mer" objects (calculated by lmer) do not calculate the denominator df nor a

Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.

Hola a todos y todas, Gracias por vuestro apoyo en cantidad de preguntas anteriores, de nuevo os escribo para compartir una duda: Estoy trabajando con un modelo bien sencillo, es una regresión simple, pero me gustaría comprobar la significación estadística de cada uno de los coeficientes de regresión en el modelo. La idea es hacer un contraste de hipótesis. Me he descargado el paquete

I have two models, controlled by dummy variables to see if the models can be combined into one model with similar intercepts and slopes. Has anyone tried to conduct this type of test in R. I am utilizing the econometric idea of hypothesis testing through the hypothesis of coincidence. I have tried to run an anova with test of Chisq, but I am not sure what the results are telling. In addition, I

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

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

Buenos días, Gracias Carlos, siguiendo el ejemplo que comentas, esto es lo que he introducido en el Scrip de RStudio: *library(xlsx)* *library(xlsxjars)* *library(rJava)* *library(aod)* *R<-read.csv("2002.CSV", sep=";", dec=",", header=T)* *attach(R)* *group<-gl(2,670,1340,labels= c("AVE", "Log.Imports.Value.in.1000.USD"))*

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

Dear R users, I am applying the augmented-Dickey-Fuller Unit Root Test (ur.df function of the urca package) to a time series of approximately 50 values. To be sure I understood what was going on with the ur.df function, I checked the critical values of the 3 test statistics (tau, phi2 and phi3 if a trend is included) or the 2 test statistics (tau and phi1 if only a drift is included) with the

Good Morning, I am trying to create a new column of character strings based on the first two letters in a string in another column. I believe that I need to use some combination of ifelse and grep but I am not totally sure how to combine them. I am not totally sure why the command below isn't working. Obviously it isn't finding anything that matches my criteria but I am not sure why. Any