View source: R/estimate_calculus.R
| normcdf | R Documentation |
Density (normpdf), distribution function (normcdf), and quantile function
(norminv) of the normal distribution for objects of class estimate (see
estimate.default) in which case the delta method is applied to obtain the
influence function (and hence robust standard errors) of the transformed
parameter.
normcdf(q, mean = 0, sd = 1, ...)
## Default S3 method:
normcdf(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE, ...)
normpdf(x, mean = 0, sd = 1, ...)
## Default S3 method:
normpdf(x, mean = 0, sd = 1, log = FALSE, ...)
norminv(p, mean = 0, sd = 1, ...)
## Default S3 method:
norminv(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE, ...)
mean |
mean of the normal distribution (numeric or |
sd |
standard deviation of the normal distribution (numeric or
|
... |
additional arguments to lower level functions (e.g., |
lower.tail |
if |
x, q |
vector of quantiles, or an |
log, log.p |
if |
p |
vector of probabilities, or an |
Any of the arguments x/q/p, mean and sd may be an estimate
object. When more than one of them is an estimate object the objects are
first merged (see merge.estimate) and the joint delta method is applied,
correctly propagating the dependence between them.
A numeric vector (default method) or an object of class estimate.
Klaus Kähler Holst
estimate.default stats::dnorm
e <- estimate(coef = c(a = 1, b = 2), vcov = diag(2) * 0.1)
normcdf(e)
normpdf(e)
norminv(normcdf(e)) # identity
## mean and sd may themselves be estimates
mu <- estimate(coef = c(mu = 0.5), vcov = 0.01)
normcdf(e, mean = mu, sd = 2)
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