Thank you, Wolfgang! Now that I know what the function does I can at
least search some literature to learn about those criteria.
Thanks,
Frank Tamborello
On Feb 22, 2010, at 7:00 AM, Viechtbauer Wolfgang (STAT) wrote:
I don't think this information can be found in the documentation,
but you can always just check the actual influence.measures() and
print.infl() code to find out. Most importantly, influence.measures
() incldues the following code:
function (model)
{
is.influential <- function(infmat, n) {
k <- ncol(infmat) - 4
if (n <= k)
stop("too few cases, n < k")
absmat <- abs(infmat)
result <- cbind(absmat[, 1L:k] > 1, absmat[, k + 1] >
3 * sqrt(k/(n - k)), abs(1 - infmat[, k + 2]) > (3 *
k)/(n - k), pf(infmat[, k + 3], k, n - k) > 0.5,
infmat[, k + 4] > (3 * k)/n)
dimnames(result) <- dimnames(infmat)
result
}
...
infmat <- cbind(dfbetas, dffit = dffits, cov.r = cov.ratio,
cook.d = cooks.d, hat = h)
...
is.inf <- is.influential(infmat, sum(h > 0))
...
}
So, a case is flagged if:
- any of its absolute dfbetas values are larger than 1, or
- its absolute dffits value is larger than 3*sqrt(k/(n-k)), or
- abs(1 - covratio) is larger than 3*k/(n-k), or
- its Cook's distance is larger than the 50% percentile of
an F-distributio with k and n-k degrees of freedom, or
- its hatvalue is larger than 3*k/n,
where k denotes the number of model coefficients (e.g., k = 2 for
simple regression with the intercept included in the model).
Best,
--
Wolfgang Viechtbauer http://www.wvbauer.com/
Department of Methodology and Statistics Tel: +31 (43) 388-2277
School for Public Health and Primary Care Office Location:
Maastricht University, P.O. Box 616 Room B2.01 (second floor)
6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck)
----Original Message----
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of Frank Tamborello
Sent: Monday, February 22, 2010 00:39 To: r-help@r-project.org
Subject: [R] tests for measures of influence in regression
influence.measures gives several measures of influence for each
observation (Cook's Distance, etc) and actually flags observations
that it determines are influential by any of the measures. Looks
good! But how does it discriminate between the influential and non-
influential observations by each of the measures? Like does it do a
Bonferroni-corrected t on the residuals identified by the influence
measures or some other test?
Cheers,
Frank Tamborello, PhD
W. M. Keck Postdoctoral Fellow
School of Health Information Sciences
University of Texas Health Science Center, Houston
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
