Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.
3
votes
Comparing two models using anova() function in R
The Pirate's Guide to R", Chap.15.3:
The anova() function will take the model objects as arguments, and
return an ANOVA testing whether the more complex model is
significantly better at capturing the …
3
votes
Comparing two ECDFs using Kolmogorov-Smirnov test (alternative hypothesis)
First, please note that your understanding of what the p-values mean is not correct. E.g.:
Hypothesis #1: two-sided (equal)
The probability that both distributions are the same is 2.03% (p-va …
0
votes
Comparision of multiple curves in R using linear mixed models
I am not sure whether there's an easy way in R to do this with e.g. orthogonal polynomials, but maybe this idea will inspire you to try it out :-) …
0
votes
Raw or orthogonal polynomial regression?
: 0.984, Adjusted R-squared: 0.9779
F-statistic: 160.1 on 5 and 13 DF, p-value: 3.34e-11
Points to note:
The residual standard error, the $R^2$ values, the F-stats are exactly the same as before … As regards the model quality:
Residual standard error: 0.05044 on 15 degrees of freedom
Multiple R-squared: 0.9829, Adjusted R-squared: 0.9795
F-statistic: 288.1 on 3 and 15 DF, p-value: 1.768e- …
0
votes
Why do we say "Residual standard error"?
For the nls (nonlinear least squares fit) R function, the "Residual standard error" seems to be:
$$\sqrt{\frac{\mathrm{RSS}}{n-p}}$$
where RSS is the "residual sum-of-squares", n is the number of observations …
1
vote
Poisson model for non-integer
@jbowman said that "You can in fact run a Poisson regression on non-integer data, at least in R; you'll still get the "right" coefficient estimates etc.". … : 0.9976, Adjusted R-squared: 0.9976
F-statistic: 1.228e+04 on 1 and 29 DF, p-value: < 2.2e-16
If I run a Poisson GLM in R using log(N) as the offset I get a very similar result:
Call:
glm(formula …