similar to: null hypothesis for two-way anova

Hi! I would like to do Cox regression using the available routines in the survival package BUT I want to use an arbitrary link function, i.e. want to use the model h(t)=h_0(t)r(beta'z) with arbitrary function r, instead of h(t)=h_0(t)exp(beta'z) Grateful for any comment on this, Dragi ----------------------------------------------------------- Dragi Anevski, PhD Mathematical

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

Hi, In the following results I interpret exp(coef) as the factor that multiplies the base hazard rate if the corresponding variable is TRUE. For example, when the bucket is ks008 and fidelity <= 3, then the rate, compared to the base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case does the base hazard rate correspond to? I would expect the reference to be the first

I have a question and it's only relation to R is that I probably need R after I understand what to do. Both models are delta y_t = Beta + epslion and suppose I have a null hypothesis and alternative hypothesis H_0 : delta y_t = zero + epsilon epsilon is normal ( 0, sigmazero^2 ) H_1 delta y_t = beta + epsilon epsilon is normal ( sigmabeta^2 )

My question is, how to compute hazard function(H(t)) after building the coxph model. I even aware of the terminology that differs from hazard function(H(t)) and the hazard rate(h(t)). Here onward I wish to calculate both. Here what I have done in two different methods; ##########################################################################################

Dear All, I am currently working with the coxph function within the package survival. I have the model h_ij = h_0(t) exp(b1x1 + b2x2) where the indicator variables are as follows: x1 x2 VPS 0 0 LTG 1 0 TPM 0 1 [[alternative HTML version deleted]]

Hi, I'm using the ks.test() on two vectors. I looked up the reference and also coded up a version of the two sample Smirnov test. My question is that how can I decide from the output of R that the two vectors x & y come from the same distribution? Am I correct in assuming that smaller D values indicate that they come from the same distribution? In addition how can I use the p value that

Dear R-helpers, I was testing the truncgof CRAN package, found something that looked like a bug, and did my job: contacted the maintainer. But he did not reply, so I am resending my query here. I installed package truncgof and run the example for function ad.test. I got the following output: set.seed(123) treshold <- 10 xc <- rlnorm(100, 2, 2) # complete sample xt <- xc[xc >=

I don't know from the nortest package, but it should ***always*** be the case that you test hypotheses H_0: The data have a normal distribution. vs. H_a: The data do not have a normal distribution. So if you get a p-value < 0.05 you can say that ***there is evidence*** (at the 0.05 significance level) that the data are not from a normal distribution. If the nortest package does

Hello, I ran the coxph model and everything worked fine. When I extract the output from the basehaz(y) function and was wondering if that baseline is cumulative or not. I then do: F(t,t+1)=1-exp(h_0(t+1) exp(coeff(1)*covariate(1)+...)) Does this give me the probability of death from t to t+1 (in which case the baseline is not cumulative) or the probability of death before t+1 (in which case the

> Subject: [R] LRT in arima models > Date: Mon, 17 Feb 2003 11:53:04 +0100 > From: "vito muggeo" <vito.muggeo at giustizia.it> > To: <r-help at stat.math.ethz.ch> > > Dear all, > > For some reason I'm evaluating the size of the LRT testing for the effect of > some explanatory variable in arima models. > I performed three different simulations

Full_Name: Yves Rosseel Version: 2.2.1 OS: Submission from: (NULL) (157.193.116.152) The code below sketches a possible implementation of a function 'mlh.mlm' which I think would be a good complement to the 'anova.mlm' function in the stats package. It tests a single linear hypothesis of the form H_0: LBT'= 0 where B is the matrix of regression coefficients; L is a matrix

Dear Carlos, Duncan and everyone You may have already sorted the matter by now, but since I have not seen anything posted since Duncan's reply, here I go. I apologize in advance for the spam, if it turns out I've missed some post. I think the test and the implementation of the truncgof package are just fine. I've done Carlos' experiment (repeatedly generating samples and testing

Dear all, For analysis of a longitudinal data set with fixed measurement in time I built a gls model (nlme). For testing hypotheses in this model I used the linear.hypothesis function from the car package. A check with the results obtained in SAS proc MIXED with a repeated statement revealed an inconsistency in the results. The problem can be that the linear.hypothesis function (1) only gives the

Hello, I have a MANOVA model and I want to test the following hypothesis: LBM = 0 where B is the parameter estimates. Is there any function to do this in R? Cheers, Philippe -- Philippe Hup? Institut Curie, CNRS UMR 144, INSERM U900 26 rue d'Ulm 75005 Paris - France Email : Philippe.Hupe at curie.fr T?l : +33 (0)1 56 24 69 91 Fax: +33 (0)1 56 24 69 11 website :

I wish to test the hypothesis of mu equal to or less than 5 against the specific alternative mu equal to or greater than 7. I am unable to find how to persuade R to do this with any function (e.g. t.test). Suggestions? Michael Grant [[alternative HTML version deleted]]

Hi folks: Does anyone know of a way to do (linear) hypothesis tests of parameters after fitting a maximum-likelihood model w/ optim? I can't seem to find anything like a Wald test whose documentation says it applies to optim output. Also, thanks again to everyone who gave me feedback on the robustness of ML estimation in R! Peter ********************************

Dear R-users, Is there a way to get p-values for a one-sided hypothesis test about a poisson mean? Thanks, Jan Wijffels University Center for Statistics W. de Croylaan 54 3001 Heverlee Belgium tel: +32 (0)16 322784 fax: +32 (0)16 322831 <http://www.kuleuven.be/ucs> http://www.kuleuven.be/ucs Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm [[alternative HTML version

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 First of all, I'm a physicist and therefore I'm not much used to use statistics. So, please forgive me if this is a FAQ or stupid, but I failed to find the answer by myself. I want to use corr.test to test for the correlation of two data sets (actually I have a lot of data set and perform pairwise testing). But I wanted to find sets where the