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The logistic distribution is a continuous distribution where it’s cumulative distribution function is the logistic function.
Returns the value at x of the density function of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
\(a\) is the location parameter and \(b\) is the scale parameter.
The pdf is
Returns the value at x of the distribution function of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The cdf is
Returns the q-quantile of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\); in other words, this is the inverse of cdf_logistic
. Argument q must be an element of \([0,1]\). To make use of this function, write first load("distrib")
.
Returns the mean of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The mean is
Returns the variance of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The variance is
Returns the standard deviation of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The standard deviation is
Returns the skewness coefficient of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The skewness coefficient is
Returns the kurtosis coefficient of a
\({\it Logistic}(a,b)\)
random variable , with \(b>0\). To make use of this function, write first load("distrib")
.
The kurtosis coefficient is
Returns a
\({\it Logistic}(a,b)\)
random variate, with \(b>0\). Calling random_logistic
with a third argument n, a random sample of size n will be simulated.
The implemented algorithm is based on the general inverse method.
To make use of this function, write first load("distrib")
.