CutCI {maCorrPlot} | R Documentation

## Calculate confidence intervals for grouped values

### Description

`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.

### Usage

    CutCI(dat, number = 10, func = mean, alpha=0.95)

    CIrho(rho, alpha = 0.95)

### Arguments

`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

### Details

The quantiles for the confidence interval are taken from the standard normal distribution, so a reasonable number of observations per interval would be good.

### Value

`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

`CIrho` returns a vector of length two, containing the lower and upper end of the confidence interval.

### See Also

`co.intervals`

### Examples

    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)

[Package maCorrPlot version 1.54.0 Index]