MLS {ALSM} R Documentation

## MLS

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))
}
```

[Package ALSM version 0.2.0 Index]