Density, distribution function, quantile function
and random generation for the weighted normal distribution with
mean
, standard deviation sd
, steps steps
(or critical values) crit_x
), and weights omega
.
Usage
dwnorm(
x,
mean,
sd,
steps = if (!is.null(crit_x)) NULL,
omega,
crit_x = if (!is.null(steps)) NULL,
type = "two.sided",
log = FALSE
)
pwnorm(
q,
mean,
sd,
steps = if (!is.null(crit_x)) NULL,
omega,
crit_x = if (!is.null(steps)) NULL,
type = "two.sided",
lower.tail = TRUE,
log.p = FALSE
)
qwnorm(
p,
mean,
sd,
steps = if (!is.null(crit_x)) NULL,
omega,
crit_x = if (!is.null(steps)) NULL,
type = "two.sided",
lower.tail = TRUE,
log.p = FALSE
)
rwnorm(
n,
mean,
sd,
steps = if (!is.null(crit_x)) NULL,
omega,
crit_x = if (!is.null(steps)) NULL,
type = "two.sided"
)
Arguments
- x, q
vector of quantiles.
- mean
mean
- sd
standard deviation.
- steps
vector of steps for the weight function.
- omega
vector of weights defining the probability of observing a t-statistics between each of the two steps.
- crit_x
vector of critical values defining steps (if
steps
are not supplied).- type
type of weight function (defaults to
"two.sided"
).- log, log.p
logical; if
TRUE
, probabilitiesp
are given aslog(p)
.- lower.tail
logical; if
TRUE
(default), probabilities are \(P[X \le x]\), otherwise, \(P[X \ge x]\).- p
vector of probabilities.
- n
number of observations. If length(n) > 1, the length is taken to be the number required.
Value
dwnorm
gives the density, dwnorm
gives the
distribution function, qwnorm
gives the quantile function,
and rwnorm
generates random deviates.
Details
The mean
, sd
, steps
, omega
can be
supplied as a vectors (mean
, sd
) or matrices (steps
,
omega
) with length / number of rows equal to x
/q
/
p
. Otherwise, they are recycled to the length of the result.