Description Usage Arguments Value Note Examples
This function determines the critical values for isolating a central portion of a distribution with a specified probability. This is designed to work especially well for symmetric distributions, but it can be used with any distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  cdist(dist = "norm", p, plot = TRUE, verbose = FALSE,
invisible = FALSE, digits = 3L, xlim, ylim, resolution = 500L,
return = c("values", "plot"), pattern = c("rings", "stripes"), ...,
refinements = list())
xcgamma(p, shape, rate = 1, scale = 1/rate, lower.tail = TRUE,
log.p = FALSE, ...)
xct(p, df, ncp, lower.tail = TRUE, log.p = FALSE, ...)
xcchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
xcf(p, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)
xcbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE, ...)
xcpois(p, lambda, lower.tail = TRUE, log.p = FALSE, ...)
xcgeom(p, prob, lower.tail = TRUE, log.p = FALSE, ...)
xcnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)
xcbeta(p, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE,
...)

dist 
a character string naming a distribution family (e.g., "norm"). This will work for any family for which the usual d/p/q functions exist. 
p 
the proportion to be in the central region, with equal proportions in either "tail". 
plot 
a logical indicating whether a plot should be created 
verbose 
a logical indicating whether a more verbose output value should be returned. 
invisible 
a logical 
digits 
the number of digits desired 
xlim 
x limits. By default, these are chosen to show the central 99.8% of the distribution. 
ylim 
y limits 
resolution 
number of points used for detecting discreteness and generating plots. The default value of 5000 should work well except for discrete distributions that have many distinct values, especially if these values are not evenly spaced. 
return 
If 
pattern 
One of 
... 
additional arguments passed to the distribution functions. Typically these specify the parameters of the particular distribution desired. See the examples. 
refinements 
A list of refinements to the plot. See 
shape 
shape and scale parameters. Must be positive,

rate 
an alternative way to specify the scale. 
scale 
shape and scale parameters. Must be positive,

lower.tail 
logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x]. 
log.p 
logical; if 
df 
degrees of freedom (> 0, maybe noninteger). 
ncp 
noncentrality parameter delta;
currently except for 
df1 
degrees of freedom. 
df2 
degrees of freedom. 
size 
number of trials (zero or more). 
prob 
probability of success on each trial. 
lambda 
vector of (nonnegative) means. 
mu 
alternative parametrization via mean: see ‘Details’. 
shape1 
nonnegative parameters of the Beta distribution. 
shape2 
nonnegative parameters of the Beta distribution. 
a pair of numbers indicating the upper and lower bounds, unless verbose
is
TRUE
, in which case a 1row data frame is returned containing these bounds,
the central probability, the tail probabilities, and the name of the distribution.
This function is still experimental and changes the input or output formats are possible in future versions of the package.
1 2 3 4 5 6 7 8 9 10 11 12 13  cdist( "norm", .95)
cdist( "t", c(.90, .95, .99), df=5)
cdist( "t", c(.90, .95, .99), df=50)
# plotting doesn't work well when the parameters are not constant
cdist( "t", .95, df=c(3,5,10,20), plot = FALSE)
cdist( "norm", .95, mean=500, sd=100 )
cdist( "chisq", c(.90, .95), df=3 )
# CI
x < rnorm(23, mean = 10, sd = 2)
cdist("t", p = 0.95, df=22)
mean(x) + cdist("t", p = 0.95, df=22) * sd(x) / sqrt(23)
confint(t.test(x))
cdist("t", p = 0.95, df=22, verbose = TRUE)

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