search for: h_0

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 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 Statistics Chalmers University of Technology G?teborg University SE-412 9...

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects. Starting with the model y = a_i + b_j + f_ij , i in A and j in B is the tested null hypothesis H_0: f_ij = 0 for all i in A and j in B or H_0: f_ij = f_mn for any i and...

Dear All I am trying to replicate a numerical application (not computed on R) from an article. Using, ks.test() I computed the exact D value shown in the article but the p-values I obtain are quite different from the one shown in the article. The tests are performed on a sample of 37 values (please see "[0] DATA" below) for truncated Exponential, Pareto and truncated LogNormal

Dear All, I am using wilcox.test to test two samples, data_a and data_b, earch sample has 3 replicates, suppose data_a and data_b are 20*3 matrix. Then I used the following to test the null hypothesis (they are from same distribution.): wilcox.test(x=data_a, y=data_b, alternative="g") I got pvalue = 1.90806170863311e-09. When I switched data_a and data_b by doing the following:

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 factor value, i.e. bucket jpc001 with fidelity <= 3, but its exp(coef) is not one. I verified the contrasts, and the row corresponding to the first...

...L.COXPH) # Obtaining the object "surv" from the survfit object MODEL.COXPH.SURVFIT.SURV <- MODEL.COXPH.SURVFIT$surv # Computing the hazard function ## ## I am pretty confuse in this; whether result from this, is a hazard function(H(t)) or baseline cumulative hazard function(H_0(t)) ## Hazard.Function <- -log(MODEL.COXPH.SURVFIT.SURV) ################################################ ################################################ # Baseline hazard function can also be calculated explicitly ## MODEL.COXPH.BASELINEHAZ <- basehaz(MODEL....

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

...assumed as being time dependent in "fg2" ? Question 2: both summaries give me the following that I dont understand at all, is there a mistake in my script ? Competing risks Model Test for nonparametric terms Test for non-significant effects sup| hat B(t)/SD(t) | p-value H_0: B(t)=0 (Intercept) 0 0 random 0 0 Test for time invariant effects supremum test p-value H_0: B(t)=b t (Intercept) 0 0 random 0 0...

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 ) ------------------------------------------------------------------------ ----------------------------------------------------...

...on? 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 is supplied in the output? In my code I decided (as described by Conover) by calculating the 1-alpha quantile and if D was greater than this value, the H_0 is rejected. However I dont calculate a P value. Is this method significantly different from the method used in R? Since I get the same value of D in both methods is there any reason to prefer one over the other? Thanks, ------------------------------------------------------------------- Rajar...

...2 0.21 0.22 0.26 0.27 0.28 0.3 0.31 0.32 0.33 0.36 0.38 0.4 0.44 0.49 0.54 0.55 2 2 1 3 1 2 1 1 1 2 1 2 1 1 2 1 0.56 0.57 0.62 0.7 0.76 0.78 0.96 0.98 1 2 1 1 1 1 1 1 This is, in a 45% of the cases, you would reject the H_0 hypothesis, which happens to be true, at the 5% "standard" confidence level. Do you think this behaviour is buggy? If so, given that the maintainer does not seem to be contactable, what would be the next step to take? Best regards, Carlos J. Gil Bellosta http://www.datanalytics.com

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B),

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 it differentl...

...y h_i(t) = exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that predict(mwa, type='linear') is \beta'x_i. b) since I need the coefficient intercept from the model to obtain the scale parameter to obtain the base hazard function as defined in Collett (h_0(t)=\lambda*\gamma*t^{\gamma-1}), I am concerned that this coefficient intercept changes depending on the reference level of the factor entered in the model. The change is very important when I have more than one predictor in the model. Any help would be greatly appreciated, David Biau....

...mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12 %*% b_12 where b are random effect vectors and the hypothesis to be tested is H_0: Var(b_12)=0 (<=> b_12_i == 0 for all i) the problem: gamm() does not seem to support the use of nested tensor product splines, does anybody know how to work around this? example code: (you'll need to source the P-spline constructor from ?p.spline beforehand) ########### test1<-fun...

...y h_i(t) = exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that predict(mwa, type='linear') is \beta'x_i. b) since I need the coefficient intercept from the model to obtain the scale parameter to obtain the base hazard function as defined in Collett (h_0(t)=\lambda*\gamma*t^{\gamma-1}), I am concerned that this coefficient intercept changes depending on the reference level of the factor entered in the model. The change is very important when I have more than one predictor in the model. Any help would be greatly appreciated, David Biau.

..._i(t) = F_0( exp((t+beta'x_i)/scale) ) So you need to multiply by the scale parameter and change sign to get the log hazard ratios. > b) since I need the coefficient intercept from the model to obtain the scale > parameter to obtain the base hazard function as defined in Collett > (h_0(t)=\lambda*\gamma*t^{\gamma-1}), I am concerned that this coefficient > intercept changes depending on the reference level of the factor entered in the > model. The change is very important when I have more than one predictor in the > model. As Terry Therneau pointed out recently in the c...

...ng for the effect of > some explanatory variable in arima models. > I performed three different simulations > y<-2+arima.sim(n, model=list(ar=ar.p,ma=0),sd=1) > with different ar parameter, namely 0.7, 0.5, and 0.2. n=100. > > Out of 1000 replications performed for each ar, the H_0 (no effect of > x<-1:n) was rejected 77, 67 and 53 times with ar=0.7, 0.5 and 0.2 > respectively. (sigma was assumed known in calculating the statistic test and > the nominal significance level is 0.05) > > That is, the LRT seems to become somewhat anti-conservative when the ar &g...

hello, I am looking for a step() function for GAM's. In the book Statistical Computing by Crawley and a removal of predictors has been done "by hand" model <- gam(y ~s(x1) +s(x2) + s(x3)) summary(model) model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable #then comparing these two models if an significant increase occurs. anova(model, model2,