Description Usage Arguments Details Value Author(s) References See Also Examples
Computes a t-test for multiple groups using a given set of contrast weights.
1 2 3 4 5 6 7 8 9 10 11 | tContrast(IV, ...)
## Default Method
## Default S3 method:
tContrast(IV, DV, wgt = c(1, -1),
alpha = .05, EQVAR = FALSE, alternative = "unequal", ...)
## Method for class 'formula'
## S3 method for class 'formula'
tContrast(formula, data = NULL, wgt = c(1, -1),
alpha = .05, EQVAR = FALSE, alternative = "unequal", ...)
|
IV |
A factor of the same length as DV containing the independent variable codes. |
DV |
A numeric vector of the same length as IV containing the measured values. |
formula |
A formula of the form lhs ~ rhs where lhs is a numeric vector containing the data values and rhs is a variable containing the corresponding groups. |
data |
An optional data frame containing the variables in the formula. |
wgt |
A numeric vector containing the contrast weights corresponding to each successive level of the IV. Defaults to c(1, -1), implying that the first group is expected to have a higher mean than the second. |
alpha |
A numeric element > .00 and < 1.00 specifying the Type I error rate. |
EQVAR |
A logical indicating whether equal variances amongst the groups should be assumed. Defaults to FALSE (Welch's Method). |
alternative |
A character vector specifying the alternative hypothesis. Must be one of "unequal", "greater", or "less". |
... |
Further arguments to be passed to or from methods. |
This function computes a t-contrast for any number of groups based on the
specificed constrast weights (Rosenthal, Rosnow, & Rubin, 2000). By setting the
EQVAR option to TRUE degrees of freedom are consistent with Student's method.
If EQVAR is FALSE (default) then degrees of freedom are calculated using the
Welch-Sattertwaite approximation. The wgt option allows one to specify contrast
weights to test hypotheses with more than 2 levels of an IV. By default it tests
the hypothesis that two means are unequal. If a directional hypothesis is known
ahead of time, use "greater" to predict that higher contrast weights have higher
means and "less" to predict the opposite. For a robust version of this function
see yuenContrast
. The entire family of possible T-test equations
can be found here:
http://rynesherman.com/T-Family.doc
A list containing...
Ms |
A data.frame with the sample size, mean, and weight for each group. |
test |
A data.frame with the test statistic (stat), the degrees of freedom (df), the critical value for the test statistic (crit), the p-value, and an r-contrast (effect size). |
Ryne A. Sherman
Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge, UK: Cambridge University Press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | dv <- c(rnorm(30, mean=1, sd=2), rnorm(20))
iv <- c(rep(1,30),rep(2,20))
# Student's t-test (assuming equal variances)
t.test(dv ~ iv, var.equal=TRUE)
# Welch's t-test (not assuming equal variance)
t.test(dv ~ iv, var.equal=FALSE)
# tContrast assuming equal variances
tContrast(iv, dv, EQVAR=TRUE)
# tContrast not assuming equal variances
tContrast(iv, dv, EQVAR=FALSE)
# Contrast with 3 Groups
dv <- c(rnorm(30), rnorm(20, mean=-.5), rnorm(10, mean=-1))
iv <- c(rep("c",30), rep("b", 20), rep("a", 10))
# t-contrast with Welch-Sattertwaite DFs
tContrast(iv, dv, wgt=c(1, 0, -1))
# Compare with yuenContrast with no trimming
yuenContrast(iv, dv, wgt=c(1, 0, -1), tr=0)
# With the formula method
yuenContrast(dv ~ iv, wgt = c(1, 0, -1))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.