ci.c {MBESS} | R Documentation |
Function to calculate the exact confidence interval for a contrast in a fixed effects analysis of variance context.
ci.c(means = NULL, error.variance = NULL, c.weights = NULL, n = NULL, N = NULL, Psi = NULL, conf.level = 0.95, alpha.lower = NULL, alpha.upper = NULL, df.error = NULL, ...)
means |
a vector of the group means or the means of the particular level of the effect (for fixed effect designs) |
error.variance |
the common variance of the error (i.e., the mean square error) |
c.weights |
the contrast weights (the sum of the contrast weights should be zero) |
n |
sample sizes per group or level of the particular factor (if length 1 it is assumed that the per group/level sample sizes are equal) |
N |
total sample size |
Psi |
the (unstandardized) contrast effect, obtained by multiplying the jth mean by the jth contrast weight (this is the unstandardized effect) |
conf.level |
confidence interval coverage (i.e., 1- Type I error rate); default is .95 |
alpha.lower |
Type I error for the lower confidence limit |
alpha.upper |
Type I error for the upper confidence limit |
df.error |
the degrees of freedom for the error. In one-way designs, this is simply N-length (means) and need not be specified; it must be specified if the design has multiple factors. |
... |
allows one to potentially include parameter values for inner functions |
Returns the confidence limits for the contrast:
Lower.Conf.Limit.Contrast |
The lower confidence limit for the contrast effect |
Contrast |
the value of the estimated unstandardized contrast effect |
Upper.Conf.Limit.Contrast |
The upper confidence limit for the contrast effect |
Be sure to use the error varaince and not its square root (i.e., the standard deviation of the errors).
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1-24.
Steiger, J. H. (2004). Beyond the F Test: Effect size confidence intervals and tests of close fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9, 164–182.
conf.limits.nct
, ci.sc
, ci.src
, ci.smd
, ci.smd.c
, ci.sm
ci.c(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), n=c(3, 3, 3, 3), N=12, conf.level=.95) ci.c(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), n=c(3, 3, 3, 3), N=12, conf.level=.95) ci.c(means=c(1.6, 0), error.variance=1, c.weights=c(1, -1), n=c(10, 10), N=20, conf.level=.95) # An example given by Maxwell and Delaney (2004, pp. 155--171) : # 24 subjects of mild hypertensives are assigned to one of four treatments: drug # therapy, biofeedback, dietary modification, and a treatment combining all the # three previous treatments. Subjects' blood pressure is measured two weeks # after the termination of treatment. Now we want to form a 95 # confidence interval for the difference in blood pressure between subjects # received drug treatment and those received biofeedback treatment ## Drug group's mean = 94; group size=4 ## Biofeedback group's mean = 91; group size=6 ## Diet group's mean = 92; group size=5 ## Combination group's mean = 83; group size=5 ## Mean Square Within (i.e., 'error.variance') = 67.375 ci.c(means=c(94, 91, 92, 83), error.variance=67.375, c.weights=c(1, -1, 0, 0), n=c(4, 6, 5, 5), N=20, conf.level=.95)