MLS: MLS

In ALSM: Companion to Applied Linear Statistical Models

Description

MLS

Usage

MLS(MSE1, df1, c1, MSE2, df2, c2, alpha = 0.05)

Arguments

mse1

MSE1
df1	df1
c1	c1
MSE2	mse2
df2	df2
c2	c2
alpha	a

References

Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li. Applied Linear Statistical Models Fifth Edition. chapter 25.

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (MSE1, df1, c1, MSE2, df2, c2, alpha = 0.05)
{
    f1 = qf(1 - alpha/2, df1, Inf)
    f2 = qf(1 - alpha/2, df2, Inf)
    f3 = qf(1 - alpha/2, Inf, df1)
    f4 = qf(1 - alpha/2, Inf, df2)
    f5 = qf(1 - alpha/2, df1, df2)
    f6 = qf(1 - alpha/2, df2, df1)
    g1 <- 1 - 1/f1
    g2 <- 1 - 1/f2
    g3 <- (((f5 - 1)^2) - ((g1 * f5)^2) - ((f4 - 1)^2))/f5
    g4 <- f6 * ((((f6 - 1)/f6)^2) - 1 * (((f3 - 1)/f6)^2) - g2^2)
    hl <- sqrt(((g1 * c1 * MSE1)^2) + (((f4 - 1) * c2 * MSE2)^2) -
        1 * ((g3 * c1 * c2 * MSE1 * MSE2)))
    hu <- sqrt((((f3 - 1) * c1 * MSE1)^2) + ((g2 * c2 * MSE2)^2) -
        1 * ((g4 * c1 * c2 * MSE1 * MSE2)))
    l = c1 * MSE1 + c2 * MSE2
    L = sum(l)
    lower <- L - hl
    upper <- L + hu
    return(cbind(estimate = L, lower = lower, upper = upper))
  }

ALSM documentation built on May 2, 2019, 10:19 a.m.