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I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

I am trying to replicate the repeated measures example from Dr.Faraway's book (Extending the linear model with R) as follows: data(vision) vision$npower <- rep(1:4,14) mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision) When I look at the fixed effects p-value, it is missing. Am I missing something here? Thanks. Fixed effects: Estimate Std. Error t value

I quote from "Principles of Digital Audio" by Ken C. Pohlmann: "Because perceptual coders tailor the coded signal to the ear's acuity, they similarly tailor the required response of the playback system itself. Live music does not pass through amplifiers and loudspeakers, it goes directly to the ear. But recorded music must pass through the playback signal chain. Much of the

Asterisk work does not pay all of my bills, so I have joined up with a company that has a very good payment plan. I have recently become a Mona Vie Independent Distributor. I am not going to go into a sales pitch. This is just an FYI to this opportunity. The company has grown into a Billion dollar company in just 2 years. This company's compensation plan is the best and quickest that I have

I am using psm to fit a survival model with a dataset that has missing values, e.g., DS <-psm(Surv(los,DSCHRG) ~AGE + SEX + ACUITY, data=LOS,dist='weibull',x=TRUE,y=TRUE) and I notice that when I look at the output there are 0 missing values and when I use the summary function e.g., summary(DS) plot(summary(DS)) the missing values are showing up as a category e.g., for Sex F: F:M