DWin
  • Member for 11 years, 1 month
  • Last seen this week
  • Alameda, CA
Interpreting the results of two tests of randomness
4 votes

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 "...

View answer
The reference book for statistics with R – does it exist and what should it contain?
4 votes

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 ...

View answer
Why does a Cumulative Distribution Function (CDF) uniquely define a distribution?
4 votes

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 ...

View answer
Interpretation of interaction term in R lm(): .L .Q
Accepted answer
4 votes

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 ...

View answer
Why does the hazard ratio represent the magnitude of distance between the Kaplan-Meier plots?
4 votes

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 ...

View answer
Huber sandwich estimator in quantile regression
4 votes

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 ...

View answer
What is a "good fit" Brier score and Harrell's C Index
4 votes

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 ...

View answer
Regression for a Rate variable in R
Accepted answer
4 votes

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 ...

View answer
Cumulative counts or counts for Poisson regression
Accepted answer
4 votes

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 ...

View answer
Interpreting odds ratios
Accepted answer
4 votes

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 ...

View answer
How to compare Harrell C-index from different models in survival analysis?
4 votes

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 ...

View answer
How to test hypothesis that correlation is equal to given value using R?
4 votes

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 ...

View answer
Interpreting negative binomial regression output in R
4 votes

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....

View answer
How to decide between a logistic regression or conditional logistic regression?
Accepted answer
4 votes

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 ...

View answer
What does it mean if ROC curves (training ROCs) are very smooth?
Accepted answer
3 votes

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.

View answer
Chi sq = inf (Firth's logistic regression)
3 votes

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 ...

View answer
Meaning of chi-squared in R Kruskal-Wallis test
3 votes

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 ...

View answer
What is the benefit of breaking up a continuous predictor variable?
3 votes

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 ...

View answer
Weibull Survival Model in R
3 votes

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 ?...

View answer
Extrapolating from a filtered data set
3 votes

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 ...

View answer
If each airline flight has a 2.6 in 1,000,000 of an accident and a person goes on 1000 flights, what are the odds for that person?
Accepted answer
3 votes

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 ...

View answer
Quadratic terms in logistic regression
Accepted answer
3 votes

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 ...

View answer
What is a better way to construct a confidence interval for the probability of success in binomial distributions?
3 votes

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 ...

View answer
How to find pdf of a joint distribution in R?
3 votes

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+...

View answer
What is the most likely meaning of a confidence interval?
Accepted answer
3 votes

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 ...

View answer
intepreting Negative coefficients of Poisson model
3 votes

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 ...

View answer
Parametric modelling with accelerated failed-time models
3 votes

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 ...

View answer
What are the case studies in public health policy research where unreliable/confounded/invalid studies or models were misused?
3 votes

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 ...

View answer
Choosing between transformations in logistic regression
3 votes

With generalized linear modeling the mathematical measure that is minimized is called the "deviance" (-2*log-likelihood). There are several sorts of residuals that can be developed. The "deviance ...

View answer
Iterative proportional fitting in R
3 votes

Make a glm fit to the marginals with Poisson errors (yielding a log-linear model) and then use predict on expand.grid data.frame from the the row and column values based of the second sample. (There'...

View answer
1
2
3 4 5
7