search for: xbeta

...returns reasonable answers for all of these compared with the identical optimization problems in STATA and LIMDEP. However, I'm having a problem with beta regression. The parameterization of beta regression that I am using solves for the second distributional parameter in terms of the first and xbeta. Therefore, there is only a single distribution parameter. This parameterization works fine in STATA, LIMDEP, and SHAZAM. However, the results returned by optim in R are not reasonable in terms of the value of the log likelihood and parameter estimates. Here is my code. Does anyone see a problem w...

...008666 3 0.3026852 1 MA 2295.891 0 4 1008666 4 46.7493287 1 MA 2296.193 0 5 1008666 5 61.9772917 1 MA 2342.943 0 6 1008666 5 100.9964815 1 MA 2404.920 0 If I estimate predict(m,newdata,type="lp") does the output include xbeta or does it include xbeta+frailty term? ? I want to predict the cumulative hazard for each person for their next event at a given time in future.What is the best way to compute it? Currently I am doing it by pulling the different elements and putting them together using my own code. For example, I...

...independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <- output$coef xbeta <- as.matrix(cbind(x1, x2, x3) predprob <- pnorm(xbeta%*%outcoef) now I have the predicted probabilities for y=1 as defined above. My problem is as follows Keep X2 at 20 and X3 at 2. Then compute the predicted probability (predprob) for the entire range of X1 ie from -10 to 10 with an incr...

...it to work... x<- cbind(1,x1,x2,x3) y<- as.vector(y) ones<- x[,1] # Calculate K and n for later use K <- ncol(x) K [1] 4 K1 <- K + 1 n <- nrow(x) n [1] 81 # Define the function to be optimized llik.regress <- function(par,X,Y) { X <- as.matrix(x) Y <- as.vector(y) xbeta <- X%*%par[1:K] Sig <- par[K1:K1] sum(-(1/2)*log(2*pi)-(1/2)*log(Sig^2)-(1/(2*Sig^2))*(Y-xbeta)^2) } llik.regress # Now let's use the above function to estimate the model. model <- optim(c(1,1,1,1), llik.regress, method="BFGS", control=list(fnscale=-1), hessi...

I have a dataset at a hospital level (as opposed to the patient level) that contains number of patients experiencing events (call this number y), and the number of patients eligible for such events (call this number n). I am trying to model logit(y/n) = XBeta. In SAS this can be done in PROC LOGISTIC or GENMOD with a model statement such as: model y/n = <predictors>;. Can this be done using lrm from the Hmisc library without restructuring the dataset so that for each hospital there is one row with y = 1 and one row with y = 0 and then using the...

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

...1 (1x1) y112 1 1 1 " y121 1 1 2 2 (1x2) y122 1 1 2 " y123 1 1 2 " y131 1 1 3 3 (1x3) . . . . . . . . . . 2) using the design matrix from Y = XBeta +Zb. That is, using the same first two columns as above, but substituting 1020 columns (10 for ai's, 10 for bj's and 100 for abij's) for the last three columns. I get the message: "Error in eval(predvars, data, env) : invalid envir argument" Is my data layout mispecified? D...