Description Usage Arguments Details Author(s) Examples
Functions to flag common statistical trouble spots.
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x |
A numeric vector. |
y |
A numeric vector. |
lmobj |
An object of class |
alpha |
Significance level or 1 - confidence level. |
bonf |
Factor to use in Bonferroni correction. |
usebonf |
If true, apply Bonferroni correction. |
These functions search for common statistical trouble spots, such as small p-values that are associated with very small, possibly practically insignificant, effects, and issues with multiple inference.
The package aims to move users more toward using confidence intervals than p-values. A narrow confidence interval near 0 but not containing it may indicate lack of practically significance, while an interval containing 0 but mostly in "interesting" territory may be of some relevance.
The function t.test.rv
is essentially a replacement for
t.test
, the standard R function for significance tests and
confidence intervals for means. (Only the two-sample case is handled
currently.) If the user anticipates making m intervals or tests, she
should set bonf
to m.
The function coef.rv
is like the R function coef
, which
extracts coefficients from objects such as the value returned by
lm
and glm
. This function again checks for misleadingly
small p-values. If a Bonferroni adjustment is specified, then the
Bonferroni factor is the length of the coefficient vector.
The output of coef.rv
includes a warning column, which indicates
that a small p-value may be of little or no practical significance in
this particular case.
Computation of Bonferroni p-values is rudimentary; see the entry for R's
p.adjust
for other methods.
Methods for multiple inference other than Bonferroni are planned.
norm Matloff
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