my.p <- 0.25; y <- rsc.t2(nn <- 5000)
(myexp <- qsc.t2(my.p))
sum(myexp - y[y <= myexp]) / sum(abs(myexp - y)) # Should be my.p
# Equivalently:
I1 <- mean(y <= myexp) * mean( myexp - y[y <= myexp])
I2 <- mean(y > myexp) * mean(-myexp + y[y > myexp])
I1 / (I1 + I2) # Should be my.p
# Or:
I1 <- sum( myexp - y[y <= myexp])
I2 <- sum(-myexp + y[y > myexp])
# Non-standard Koenker distribution
myloc <- 1; myscale <- 2
yy <- rsc.t2(nn, myloc, myscale)
(myexp <- qsc.t2(my.p, myloc, myscale))
sum(myexp - yy[yy <= myexp]) / sum(abs(myexp - yy)) # Should be my.p
psc.t2(mean(yy), myloc, myscale) # Should be 0.5
abs(qsc.t2(0.5, myloc, myscale) - mean(yy)) # Should be 0
abs(psc.t2(myexp, myloc, myscale) - my.p) # Should be 0
integrate(f = dsc.t2, lower = -Inf, upper = Inf,
locat = myloc, scale = myscale) # Should be 1
y <- seq(-7, 7, len = 201)
max(abs(dsc.t2(y) - dt(y / sqrt(2), df = 2) / sqrt(2))) # Should be 0
plot(y, dsc.t2(y), type = "l", col = "blue", las = 1,
ylim = c(0, 0.4), main = "Blue = Koenker; orange = N(0, 1)")
lines(y, dnorm(y), type = "l", col = "orange")
abline(h = 0, v = 0, lty = 2)
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