DWin
• Member for 11 years, 1 month
• Last seen this week
• Alameda, CA

The name for that test seems to me to be far too encompassing. It harkens back to a relatively early (1941) publication from von Neumann and so should not be taken as a comprehensive definition of "...

I agreed with the currently top-voted answer that MASS4 was a pretty good fit to the request and have the same experience as another respondent with difficulty meeting its requirement of a fairly high ...

To answer the request for an example of two densities with the same integral (i.e. have the same distribution function) consider these functions defined on the real numbers: f(x) = 1 ; when x is ...

You should provide str() from that data.frame. One of these variables is an ordered factor. Ordered factors will cause a polynomial contrast to be estimated. The L stands for linear and the Q stands ...

The premise of the question is wrong. (So read Wikipedia with a critical eye. Most of the rest of that article appears correct, but that sentence is flawed as is the one immediately preceding it that ...

That appears to be from the SAS QUANTREG manual section: They then go on to call the sparsity function. the "reciprocal of the density function" (which is arguably a natural language version of the ...

Prior CV postings on the matter of GOF measures in generalized linear models: Find out pseudo R square value for a Logistic Regression analysis Which pseudo-$R^2$ measure is the one to report for ...

Most software that supports Poisson regression will support an offset and the resulting estimates will become log(rate) or more acccurately in this case log(proportions) if the offset is constructed ...

The approach generally taken is to regress the counts on features that were present during the intervals during which the counts accumulated. The length of the interval is used as an offset after ...

The fact that these are coefficients are represented entirely by factors in R means that the Intercept is the log-odds for the event, i.e log(the proportion with event / proportion without) for ...

Harrell would advise that you NOT do so: How to do ROC-analysis in R with a Cox model Doing model comparison with LR statistics is more powerful than using methods that depend on an asymptotic ...

The distribution of r_hat around rho is given by this R function adapted from Matlab code at the webpage of Xu Cui. It's not that difficult to turn this into an estimate for the probability that an ...

If Y is NB with mean $\mu$ then $var(Y) = \mu + \mu^2/\theta$. Skewness would not be an appropriate interpretation, but the departure from $\theta= 1$ is often taken as "extra-Poisson" dispersion....

I don't agree that you sampled on the outcome, since you sampled on company and enrollment is your outcome. You may want to deal with the company as a random effect and the other features as fixed ...

It means you have many test cases, or that you are using software that does smoothing. It also means you have a test result that is continuous rather than categorical.

I'm not sure why your particular dataset got that particular result since the data is not available, but there's a reason logistic regression calculations "blow up": complete separation. If your ...

A chi-square statistic is the sum of the squared deviations for some expected pattern. If there are minimal deviations, then the chi-squared is small and the p-value is "chance-like", i.e. it's not ...

I'm a committed fan of Frank Harrell's advice that analysts should resist premature discretization of continuous data. And I have several answers on CV and SO that demonstrate how to visualize ...

The help page for ?Weibull says: The Weibull distribution with shape parameter a and scale parameter b has density given by f(x) = (a/b) (x/b)^(a-1) exp(- (x/b)^a) And then the help page for ?...

This sounds a bit like the specific case of doing mammography before the decision to perform a biopsy in the detection of breast cancer. The prevalence of breast cancer in women at age 50 and above ...

You need to formulate a sequence of conditional probabilities which can then be multiplied by each other. Single flight survival Pr(S) = 1=Pr(crash). So id p= Pr(crash), the probability of surviving ...

After building a model based on general linear model (as you would typically be doing when you have a binary outcome), you have several methods available for checking for violations of the assumptions ...

There's a neat little article published decades ago in JAMA entitled "If nothing goes wrong, is everything all right?". The authors considered the possibilities of a binomial parameter being in a ...

This is a double integral in R (It's not done symbolically as Mathematica would do it but rather numerically): llimy <- 0; llimx=0 ulimy <- 2 ; ulimx=1 f <- function(x,y) 1/6*(x^2*y+...

Exactly? You want an exact answer to a statistical question? And you're willing to pay a service to provide it? Did you formerly work for AIG? Seriously, we will need to guess here. My guess is that ...

The large standard error and the rather large Intercept estimate are a sign of model failure. You have complete separation or some other pathological situation. The Poisson models are constructed on a ...

Because survival::survreg objects have a predict method you can use this: indiv.pred <- predict( surfit, type="response") The 'type' argument is not actually needed, because that is what survreg ...

The case of high-dose chemotherapy with bone-marrow-transplant rescue as treatment for advanced breast cancer in the 1990's is one such instance. A series of low-quality studies were used to push ...