CutCI {maCorrPlot} | R Documentation |
CutCI
groups values of one variable into intervals with the same number of observations each and computes confidence intervals for the mean of another variable in each interval.
CIrho
computes the normal theory confidence interval for a vector of values.
CutCI(dat, number = 10, func = mean, alpha=0.95) CIrho(rho, alpha = 0.95)
dat |
a numerical data frame or matrix with two columns, the first of which gets averaged, and the second of which defines the grouping |
number |
the number of equal-count intervals |
func |
summary function for computing the mean |
rho |
a vector of measurements |
alpha |
the desired confidence level |
The quantiles for the confidence interval are taken from the standard normal distribution, so a reasonable number of observations per interval would be good.
CutCI
returns invisibly a list of length three:
x |
the midpoints of the grouping intervals |
y |
the means within each interval, as computed by func |
yci |
a matrix with two columns, giving the lower and upper end of the confidence interval respectively |
normal-bracket29bracket-normal
CIrho
returns a vector of length two, containing the lower and upper end of the confidence interval.
x = rnorm(100, mean=2) CIrho(x) y = 2 + 3*x + rnorm(100) cc = CutCI(cbind(x,y), number=5) print(cc) # Show it plot(cc$x, cc$y) arrows(cc$x, cc$yci[,1], cc$x, cc$yci[,2], length=0)