similar to: Generate random variate from a non-parametric hazard function

Hi Can anyone please suggest me a package where I can estimate a non-linear regression model? One of the independent variables is latent or unobserved. I have an indicator variable for this unobserved variable; however the relationship is known to be non-linear also. In terms of equations my problem is y=f(latent, fixed) q=g(latent) where q is the indicator variable For me both f and g are

Hi, I want to plot non parametric estimates of the empirical hazard function for right censored data. I've tried many functions from different packages (muhaz, Design, survival, eha, event), but none of them gave me what I wanted. Am I missing something? Here's what I want. The data below is the same used by Kiefer (J. Economic Literature, 1988), which in turns use a subset of the data

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base

Greetings -- I would like to estimate a proportional hazard model with a weibull or lognormal baseline. I have looked at both the coxph() and survreg() functions and neither appear (to me ) to do it. Am I missing something in the docs or is there another terrific package out there that will do this. Many Thanks. Carl Mason

Hello everyone Is there a function/command to simulate from "matrix variate normal distribution" in R. A follow up question would be is there a function/command to obtain the density, distribution and quantile function of "matrix variate normal distribution" in R. Wikipedia has a good description of "matrix variate normal distribution" which is also alternatively

Folks, I wonder if anything could be said about the distribution of a random variate x, where x = N(0,1)/N(0,1) Obviously x is pathological because it could be 0/0. If we exclude this point, so the set is {x/(0/0)}, does x have a well defined distribution? or does it exist a distribution that approximates x. (The case could be generalized of course to N(mu1, sigma1)/N(mu2, sigma2) and one

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Dear all, I would like to generate bi-variate normal data given that the first column of the data is known. for example: I first generate a set of data using the command, x <- rmvnorm(10, c(0, 0), matrix(c(1, 0, 0, 1), 2)) then I would like to sum up the two columns of x: x.sum <- apply(x, 1, sum) now with x.sum I would like to generate another column of data, say y, that makes

Hello, I am trying to test for non-linearity in a set of non-parametric data. Furthermore, I would like to do so in a multi-variate model. Frankly, I don't even know if this is possible, and if it is possible, I don't know if it is implemented in an R package. Any advice would greatly appreciated. Thank you! Charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G.

Does anyone know how to get a meaningful estimate of the covariance matrix for the first canonical variate coefficients for the right-hand (X) side of a canonical regression? cancor returns coefficients but not precisions. This would have to be subject to some constraint because of identifiability problems (arbitrary scaling). I think that SPSS attempts to do this but seems to condition on

Hello, I have built a multi-variate probit model using the package "bayesm", which requires that the X data is constructed using the function "CreateX". I've gone through the documentation and run my model, but wanted to be sure about my interpretation of the results for the coefficients - beta. Steps: 1) I have 5 choices for the dependent variable Y, so p=5 2) I have 8

Hello, I have built a multi-variate probit model using the package "bayesm", which requires that the X data is constructed using the function "CreateX". I've gone through the documentation and run my model, but wanted to be sure about my interpretation of the results for the coefficients - beta. Steps: 1) I have 5 choices for the dependent variable Y, so p=5 2) I have 8

** Reply Requested When Convenient ** I will be attending a class during the week of November 29. I will get back to you as quickly as possible. If you need to reach me immediately by phone talk with Barb Severs or Diane Terry. Thank you David J. Krassen >>> r-help 11/24/99 10:46 >>> A new version of my DSE package for multi-variate time series analysis is now available

http://r.789695.n4.nabble.com/file/n3058505/file.csv file.csv Hi, Rusers I have a problem in making a rcspline.plot with a Hmisc package. My data is in the upload attachment. My programme as follows: library(Hmisc) A<-read.csv("file.csv",header=TRUE) attach(A)

Dear all, I think the coxph and survfit.coxph can give the cumulative hazard of cox model. But is there any method to calculate the hazard Lambda(t)=lambda_0(t)*exp{beta*X(t)}? Any suggestion will be great help. Thank you very much! Koshihaku -- View this message in context: http://r.789695.n4.nabble.com/How-to-get-the-hazard-of-coxph-not-cumulative-hazard-tp3873516p3873516.html Sent from the R

Hi, I would like to compare the hazard functions of two samples using the Cox proportional hazards model. For sample 1 I have individual time-to- event data. For sample 2 I don't have individual data, but grouped data that allows to obtain a hazard function. I am wondering if there is an R function that allows to obtain a hazard ratio of the two hazard funtions (under the

Dear R-users, I would like to fit a fully parametric proportional hazard model with a weibull baseline hazard and a logit link function. This is, the hazard function is: lambda_i (t) = lambda_0 (t) psi (x_i* beta) where lambda_0 is a weibull distribution and psi a logistic distribution. Does someone know a package and/or function on R to do this? Thanks. -- M.L. Avendaño [[alternative HTML

Dear R-users, I computed a simple coxph model and plotted survival over time with associated confidence intervals for 2 covariate levels (males and females). M1 <- coxph(survobject~sex, data=surv) M1 survsex <- survfit(survobject~sex,data=surv) summary(survsex) plot(survsex, conf.int=T, col=c("black","red"), lty = c(1,2), lwd=c(1,2), xlab="Time",

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 Forum, I have a question about interaction estimate in the Cox model: why the hazard ratio of the interaction is not produced in the summary of the model? (Instead, the estimate of the coefficient is given in the print of the model.) # Example: modINT <-cph( Surv(T_BASE, T_FIN,STATUS)~ NYHA + ASINI + RFP + FE_REC + XX_PR*XX_DISF) print(modINT) coef se(coef) z