Relative Difference between two Arrays or Data Frames

Calculates the relative difference, defined as $$\frac{\vert x - y \vert}{x} $$ between two arrays or data frames, where x are considered reference values.

Usage

rel_diff(x, y, pct = TRUE, epsilon = 10 * .Machine$double.eps)

Arguments

x

An array or data frame, considered the reference values

y

Comparison array or data frame

pct

Logical; if TRUE the relative differences are multiplied by 100, giving values in percent difference from x.

epsilon

Threshold for values near zero

Value

An array or data frame the same size as x and y containing the relative differences

Details

Beyond the obvious, a natural use case is to compare coefficients for alternative models for the same data, e.g., a classical and a robust model.

See also

link{robmlm}

Examples

# simple example
m1 <- cbind(c(0,1), c(1,1))
m2 <- cbind(c(0,1), c(1.01,1.11))
rel_diff(m1, m2, pct = FALSE) 
#>      [,1] [,2]
#> [1,]    0 0.01
#> [2,]    0 0.11
rel_diff(m1, m2) 
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    0   11

# compare coefficients
data(Skulls)

# fit manova model, classically and robustly
sk.mlm <- lm(cbind(mb, bh, bl, nh) ~ epoch, data=Skulls)
sk.rlm <- robmlm(cbind(mb, bh, bl, nh) ~ epoch, data=Skulls)
rel_diff(coef(sk.mlm),
         coef(sk.rlm))
#>                       mb          bh           bl           nh
#> (Intercept)   0.01446592  0.08977463   0.04729215   0.08496078
#> epoch.L       3.46042186 -0.59951299  -3.48922244   3.87957519
#> epoch.Q     -20.74713900 -3.30993862 -51.99508454 125.92364818
#> epoch.C     -25.78059257 -1.43305167   4.19962200  -0.62550036
#> epoch^4      85.33424274  9.19478314 -15.44695305 -49.00471013