dlogis(x, location=0, scale=1, log=F) plogis(q, location=0, scale=1) qlogis(p, location=0, scale=1) rlogis(n, location=0, scale=1, bigdata=F)
bdVector
of quantiles.
Missing values (
NA
s) are allowed.
bdVector
of quantiles.
Missing values (
NA
s) are allowed.
bdVector
of probabilities.
Missing values (
NA
s) are allowed.
length(n)
is larger than 1, then
length(n)
random values are returned.
bdVector
of location parameters.
This is replicated to be the same length as
p
or
q
or the number of
deviates generated.
bdVector
of scale parameters.
This is replicated to be the same length as
p
or
q
or the number of
deviates generated.
TRUE
, an object of type
bdVector
is returned.
Otherwise, a
vector
object is returned. This argument can be used only if the bigdata library section has been loaded.
TRUE
,
dlogis
will return
the log of the density, not the density itself.
dlogis
),
probability (
plogis
),
quantile (
qlogis
), or
random sample (
rlogis
)
for the logistic distribution with parameters
location
and
scale
.
rlogis
causes creation of the dataset
.Random.seed
if it does
not already exist, otherwise its value is updated.
Elements of
q
or
p
that are missing will cause the corresponding
elements of the result to be missing.
The logistic is a unimodal, symmetric distribution on the real line
with tails that are longer than the Gaussian distribution.
It is heavily used to model growth curves, but has also been used in
bioassay studies and other applications.
A motivation for using the logistic with growth curves is because the
logistic distribution function F satisfies:
the derivative of F with respect to x is proportional to [F(x)-A][B-F(x)]
with A < B. The interpretation is that the rate of growth is proportional
to the amount already grown times the amount of growth that is still
expected.
For details on the uniform random number generator implemented in S-PLUS,
see the
set.seed
help file.
Johnson, N. L. and Kotz, S. (1970).
Continuous Univariate Distributions, vol. 2.
Houghton-Mifflin, Boston.
Logistic Distribution. In
Encyclopedia of Statistical Sciences.
S. Kotz and N. L. Johnson, eds.
# comparison of the densities of the logistic and the normal with the # same variance xx <- seq(-6, 6, by = 0.1) plot(xx, dlogis(xx), type = "l", ylab="density", xlab="") lines(xx, dnorm(xx, s = pi/sqrt(3)), lty = 2) legend(3, .22, c("Logistic", "Gaussian"), lty=1:2)