Estimate quantiles of a logistic distribution.
eqlogis(x, p = 0.5, method = "mle", digits = 0)
a numeric vector of observations, or an object resulting from a call to an
estimating function that assumes a logistic distribution
numeric vector of probabilities for which quantiles will be estimated.
All values of
character string specifying the method to use to estimate the distribution parameters.
Possible values are
an integer indicating the number of decimal places to round to when printing out
the value of
eqlogis returns estimated quantiles as well as
estimates of the location and scale parameters.
Quantiles are estimated by 1) estimating the location and scale parameters by
elogis, and then 2) calling the function
qlogis and using the estimated values for
location and scale.
x is a numeric vector,
eqlogis returns a
list of class
"estimate" containing the estimated quantile(s) and other
estimate.object for details.
x is the result of calling an estimation function,
returns a list whose class is the same as
x. The list
contains the same components as
x, as well as components called
The logistic distribution is defined on the real line and is unimodal and symmetric about its location parameter (the mean). It has longer tails than a normal (Gaussian) distribution. It is used to model growth curves and bioassay data.
Steven P. Millard ([email protected])
Forbes, C., M. Evans, N. Hastings, and B. Peacock. (2011). Statistical Distributions. Fourth Edition. John Wiley and Sons, Hoboken, NJ.
Johnson, N. L., S. Kotz, and N. Balakrishnan. (1995). Continuous Univariate Distributions, Volume 2. Second Edition. John Wiley and Sons, New York.
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# Generate 20 observations from a logistic distribution with # parameters location=0 and scale=1, then estimate the parameters # and estimate the 90th percentile. # (Note: the call to set.seed simply allows you to reproduce this example.) set.seed(250) dat <- rlogis(20) eqlogis(dat, p = 0.9) #Results of Distribution Parameter Estimation #-------------------------------------------- # #Assumed Distribution: Logistic # #Estimated Parameter(s): location = -0.2181845 # scale = 0.8152793 # #Estimation Method: mle # #Estimated Quantile(s): 90'th %ile = 1.573167 # #Quantile Estimation Method: Quantile(s) Based on # mle Estimators # #Data: dat # #Sample Size: 20 #---------- # Clean up rm(dat)
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