student_t | R Documentation |
Density, distribution function, quantile function and random generation for the
scaled and shifted Student's t distribution, parameterized by degrees of freedom (df
),
location (mu
), and scale (sigma
).
dstudent_t(x, df, mu = 0, sigma = 1, log = FALSE)
pstudent_t(q, df, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
qstudent_t(p, df, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
rstudent_t(n, df, mu = 0, sigma = 1)
x , q |
vector of quantiles. |
df |
degrees of freedom ( |
mu |
Location parameter (median) |
sigma |
Scale parameter |
log , log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
p |
vector of probabilities. |
n |
number of observations. If |
dstudent_t
gives the density
pstudent_t
gives the cumulative distribution function (CDF)
qstudent_t
gives the quantile function (inverse CDF)
rstudent_t
generates random draws.
The length of the result is determined by n
for rstudent_t
, and is the maximum of the lengths of
the numerical arguments for the other functions.
The numerical arguments other than n
are recycled to the length of the result. Only the first elements
of the logical arguments are used.
parse_dist()
and parsing distribution specs and the stat_slabinterval()
family of stats for visualizing them.
library(dplyr)
library(ggplot2)
expand.grid(
df = c(3,5,10,30),
scale = c(1,1.5)
) %>%
ggplot(aes(y = 0, dist = "student_t", arg1 = df, arg2 = 0, arg3 = scale, color = ordered(df))) +
stat_slab(p_limits = c(.01, .99), fill = NA) +
scale_y_continuous(breaks = NULL) +
facet_grid( ~ scale) +
labs(
title = "dstudent_t(x, df, 0, sigma)",
subtitle = "Scale (sigma)",
y = NULL,
x = NULL
) +
theme_ggdist() +
theme(axis.title = element_text(hjust = 0))
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