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...psm returns. I have included the following code to illustrate code similar to what I am using on my data. I suppose that the coefficients are somehow rescaled , but I am not sure how to return them to the original scale and make sense out of the coefficients, e.g., estimate the the effect of higher acuity on time to event in minutes. Any explanation or direction on how to interpret the coefficient values would be greatly appreciated. this is from the documentation for survreg.object. coefficientsthe coefficients of the linear.predictors, which multiply the columns of the model matrix. It does not...

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 (Intercept) 112.6429 2.2348 50.40 power6/18 0.7857 1.5400 0.51 power6/36 -1.0000...

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 original signal present in a live recording merely d...

...and performance Fights cancerous cells Slows down the aging process Promotes healthier and younger-looking skin Alleviates diabetes Normalizes and regulates cholesterol levels Helps maintain healthy heart function Minimizes inflammation Improves circulation Prevents artherosclerosis Enhances visual acuity The income can be made in two ways (actually more, but two primary ways) 1. Reselling the product at a marked up price. This is something that I have no interest in, and do not personally know anyone doing this. 2. Team Commissions. a. You make back 5 percent of the sales that occu...

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 Do the missing values need to be...