meanProbs | R Documentation |
The generic function meanProbs
produces expected confidence bands
under either the t distribution or the normal sampling
distribution. This uses qnorm()
or qt()
with the mean and
standard deviation.
meanProbs(x, ...)
## Default S3 method:
meanProbs(
x,
probs = seq(0, 1, 0.25),
na.rm = FALSE,
names = TRUE,
useT = TRUE,
onlyProbs = TRUE,
pred = FALSE,
n = 0L,
...
)
x |
numeric vector whose mean and probability based confidence values are wanted, NA and NaN values are not allowed in numeric vectors unless ‘na.rm’ is ‘TRUE’. |
... |
Arguments passed to default method, allows many different methods to be applied. |
probs |
numeric vector of probabilities with values in the interval from 0 to 1 . |
na.rm |
logical; if true, any NA and NaN's are removed from
|
names |
logical; if true, the result has a names attribute. |
useT |
logical; if true, use the t-distribution to calculate the confidence-based estimates. If false use the normal distribution to calculate the confidence based estimates. |
onlyProbs |
logical; if true, only return the probability based confidence interval estimates, otherwise return |
pred |
logical; if true use the prediction interval instead of the confidence interval |
n |
integer/integerish; this is the n used to calculate the
prediction or confidence interval. When |
For a single probability, p, it uses either:
mean + qt(p, df=n)*sd/sqrt(n)
or
mean + qnorm(p)*sd/sqrt(n)
The smallest observation corresponds to a probability of 0 and the largest to a probability of 1 and the mean corresponds to 0.5.
The mean and standard deviation of the sample is calculated based on Welford's method for a single pass.
This is meant to perform in the same way as quantile()
so it can
be a drop in replacement for code using quantile()
but using
distributional assumptions.
By default the return has the probabilities as names (if
named) with the points where the expected distribution are
located given the sampling mean and standard deviation. If
onlyProbs=FALSE
then it would prepend mean, variance, standard
deviation, minimum, maximum and number of non-NA observations.
Matthew L. Fidler
quantile(x<- rnorm(1001))
meanProbs(x)
# Can get some extra statistics if you request onlyProbs=FALSE
meanProbs(x, onlyProbs=FALSE)
x[2] <- NA_real_
meanProbs(x, onlyProbs=FALSE)
quantile(x<- rnorm(42))
meanProbs(x)
meanProbs(x, useT=FALSE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.