search for: collinl

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

...tion. For parametric models a generalized likelihood ratio test can be used to test whether the heterogeity is needed. Alternatively a generalized Akaike information criterion (GAIC) can be used to compare models. Robert Rigby On Date: Sat, 26 Oct 2013 23:57:51 -0400 (EDT Collin Lynch < collinl@cs.pitt.edu> wrote Subject: [R] Heteroscedasticity and mgcv. I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Mod...

My apologies for the statistical naivete of my question but... Is there an established method or calulating the statistical power of a correlation test? And if so is there a method in R for it? Thank you, Collin Lynch.

I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

Hi, I trying to determine the best way to compute the power for a one-sample one-sided binomial test. Specifically I need to sample a population of individuals and ask whether a sample rate of 0% is compatable with a minimum threshold of 3% and how many samples are needed. I have made use of power.prop.test but I am not sure if a) that is the correct (or best) function to use and b) if the

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,