denorm(x, mean = 0, sd = 1, log = FALSE)
penorm(q, mean = 0, sd = 1, log = FALSE)
qenorm(p, mean = 0, sd = 1, Maxit_nr = 10, Tol_nr = 1.0e-6)
renorm(n, mean = 0, sd = 1)
deunif
.rnorm
.deunif
.denorm(x)
gives the density function $g(x)$.
penorm(q)
gives the distribution function $G(q)$.
qenorm(p)
gives the expectile function:
the value $y$ such that $G(y)=p$.
renorm(n)
gives $n$ random variates from $G$.deunif
including
a note regarding the terminology used.
Here,
norm
corresponds to the distribution of interest, $F$, and
enorm
corresponds to $G$.
The addition of ``e
'' is for the `other'
distribution associated with the parent distribution.
Thus
denorm
is for $g$,
penorm
is for $G$,
qenorm
is for the inverse of $G$,
renorm
generates random variates from $g$.
For qenorm
the Newton-Raphson algorithm is used to solve for
$y$ satisfying $p = G(y)$.
Numerical problems may occur when values of p
are
very close to 0 or 1.
deunif
,
deexp
,
dnorm
,
amlnormal
,
lms.bcn
.my_p = 0.25; y = rnorm(nn <- 1000)
(myexp = qenorm(my_p))
sum(myexp - y[y <= myexp]) / sum(abs(myexp - y)) # Should be my_p
# Non-standard normal
mymean = 1; mysd = 2
yy = rnorm(nn, mymean, mysd)
(myexp = qenorm(my_p, mymean, mysd))
sum(myexp - yy[yy <= myexp]) / sum(abs(myexp - yy)) # Should be my_p
penorm(-Inf, mymean, mysd) # Should be 0
penorm( Inf, mymean, mysd) # Should be 1
penorm(mean(yy), mymean, mysd) # Should be 0.5
abs(qenorm(0.5, mymean, mysd) - mean(yy)) # Should be 0
abs(penorm(myexp, mymean, mysd) - my_p) # Should be 0
integrate(f = denorm, lower=-Inf, upper = Inf,
mymean, mysd) # Should be 1
par(mfrow = c(2, 1))
yy = seq(-3, 3, len = nn)
plot(yy, denorm(yy), type = "l", col="blue", xlab = "y", ylab = "g(y)",
main = "g(y) for N(0,1); dotted green is f(y) = dnorm(y)")
lines(yy, dnorm(yy), col="darkgreen", lty="dotted", lwd=2) # 'original'
plot(yy, penorm(yy), type = "l", col = "blue", ylim = 0:1,
xlab = "y", ylab = "G(y)", main = "G(y) for N(0,1)")
abline(v = 0, h = 0.5, col = "red", lty = "dashed")
lines(yy, pnorm(yy), col = "darkgreen", lty = "dotted", lwd = 2)
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