Categories &

Functions List

Class Definition: InverseGaussianDistribution

statistics: InverseGaussianDistribution

Logistic probability distribution object.

A InverseGaussianDistribution object consists of parameters, a model description, and sample data for a logistic probability distribution.

The logistic distribution uses the following parameters.

ParameterDescriptionSupport
muScale parametermu >= 0
lambdaShape parameterlambda > 0

There are several ways to create a InverseGaussianDistribution object.

  • Fit a distribution to data using the fitdist function.
  • Create a distribution with specified parameter values using the makedist function.
  • Use the constructor InverseGaussianDistribution (mu, lambda) to create a logistic distribution with specified parameter values.
  • Use the static method InverseGaussianDistribution.fit (x, censor, freq, options) to a distribution to data x.

It is highly recommended to use fitdist and makedist functions to create probability distribution objects, instead of the constructor and the aforementioned static method.

A InverseGaussianDistribution object contains the following properties, which can be accessed using dot notation.

DistributionNameDistributionCodeNumParametersParameterNames
ParameterDescriptionParameterValuesParameterValuesParameterCI
ParameterIsFixedTruncationIsTruncatedInputData

A InverseGaussianDistribution object contains the following methods: cdf, icdf, iqr, mean, median, negloglik, paramci, pdf, plot, proflik, random, std, truncate, var.

Further information about the inverse Gaussian distribution can be found at https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution

See also: fitdist, makedist, invgcdf, invginv, invgpdf, invgrnd, lognfit, invglike, invgstat

Source Code: InverseGaussianDistribution

Properties

InverseGaussianDistribution.mu is not documented.
InverseGaussianDistribution.lambda is not documented.
InverseGaussianDistribution.CensoringAllowed is not documented.
InverseGaussianDistribution.DistributionName is not documented.
InverseGaussianDistribution.DistributionCode is not documented.
InverseGaussianDistribution.NumParameters is not documented.
InverseGaussianDistribution.ParameterNames is not documented.
InverseGaussianDistribution.ParameterDescription is not documented.
InverseGaussianDistribution.ParameterRange is not documented.
InverseGaussianDistribution.ParameterLogCI is not documented.
InverseGaussianDistribution.ParameterValues is not documented.
InverseGaussianDistribution.ParameterCI is not documented.
InverseGaussianDistribution.ParameterCovariance is not documented.
InverseGaussianDistribution.ParameterIsFixed is not documented.
InverseGaussianDistribution.Truncation is not documented.
InverseGaussianDistribution.IsTruncated is not documented.
InverseGaussianDistribution.InputData is not documented.

Methods

InverseGaussianDistribution: p = cdf (pd, x)
InverseGaussianDistribution: p = cdf (pd, x, "upper")

p = cdf (pd, x) computes the CDF of the probability distribution object, pd, evaluated at the values in x.

p = cdf (…, "upper") returns the complement of the CDF of the probability distribution object, pd, evaluated at the values in x.

InverseGaussianDistribution: p = icdf (pd, p)

p = icdf (pd, x) computes the quantile (the inverse of the CDF) of the probability distribution object, pd, evaluated at the values in x.

InverseGaussianDistribution: r = iqr (pd)

r = iqr (pd) computes the interquartile range of the probability distribution object, pd.

InverseGaussianDistribution: m = mean (pd)

m = mean (pd) computes the mean of the probability distribution object, pd.

InverseGaussianDistribution: m = median (pd)

m = median (pd) computes the median of the probability distribution object, pd.

InverseGaussianDistribution: nlogL = negloglik (pd)

m = negloglik (pd) computes the negative loglikelihood of the probability distribution object, pd.

InverseGaussianDistribution: ci = paramci (pd)
InverseGaussianDistribution: ci = paramci (pd, Name, Value)

ci = paramci (pd) computes the lower and upper boundaries of the 95% confidence interval for each parameter of the probability distribution object, pd.

ci = paramci (pd, Name, Value) computes the confidence intervals with additional options specified specified by Name-Value pair arguments listed below.

NameValue
"Alpha"A scalar value in the range (0,1) specifying the significance level for the confidence interval. The default value 0.05 corresponds to a 95% confidence interval.
"Parameter"A character vector or a cell array of character vectors specifying the parameter names for which to compute confidence intervals. By default, paramci computes confidence intervals for all distribution parameters.

paramci is meaningful only when pd is fitted to data, otherwise an empty array, [], is returned.

InverseGaussianDistribution: y = pdf (pd, x)

y = pdf (pd, x) computes the PDF of the probability distribution object, pd, evaluated at the values in x.

InverseGaussianDistribution: plot (pd)
InverseGaussianDistribution: plot (pd, Name, Value)
InverseGaussianDistribution: h = plot (…)

plot (pd plots a probability density function (PDF) of the probability distribution object pd. If pd contains data, which have been fitted by fitdist, the PDF is superimposed over a histogram of the data.

plot (pd, Name, Value) specifies additional options with the Name-Value pair arguments listed below.

NameValue
"PlotType"A character vector specifying the plot type. "pdf" plots the probability density function (PDF). When pd is fit to data, the PDF is superimposed on a histogram of the data. "cdf" plots the cumulative density function (CDF). When pd is fit to data, the CDF is superimposed over an empirical CDF. "probability" plots a probability plot using a CDF of the data and a CDF of the fitted probability distribution. This option is available only when pd is fitted to data.
"Discrete"A logical scalar to specify whether to plot the PDF or CDF of a discrete distribution object as a line plot or a stem plot, by specifying false or true, respectively. By default, it is true for discrete distributions and false for continuous distributions. When pd is a continuous distribution object, option is ignored.
"Parent"An axes graphics object for plot. If not specified, the plot function plots into the current axes or creates a new axes object if one does not exist.

h = plot (…) returns a graphics handle to the plotted objects.

InverseGaussianDistribution: [nlogL, param] = proflik (pd, pnum)
InverseGaussianDistribution: [nlogL, param] = proflik (pd, pnum, "Display", display)
InverseGaussianDistribution: [nlogL, param] = proflik (pd, pnum, setparam)
InverseGaussianDistribution: [nlogL, param] = proflik (pd, pnum, setparam, "Display", display)

[nlogL, param] = proflik (pd, pnum) returns a vector nlogL of negative loglikelihood values and a vector param of corresponding parameter values for the parameter in the position indicated by pnum. By default, proflik uses the lower and upper bounds of the 95% confidence interval and computes 100 equispaced values for the selected parameter. pd must be fitted to data.

[nlogL, param] = proflik (pd, pnum, "Display", "on") also plots the profile likelihood against the default range of the selected parameter.

[nlogL, param] = proflik (pd, pnum, setparam) defines a user-defined range of the selected parameter.

[nlogL, param] = proflik (pd, pnum, setparam, "Display", "on") also plots the profile likelihood against the user-defined range of the selected parameter.

For the inverse Gaussian distribution, pnum = 1 selects the parameter mu and pnum = 2 selects the parameter lambda.

When opted to display the profile likelihood plot, proflik also plots the baseline loglikelihood computed at the lower bound of the 95% confidence interval and estimated maximum likelihood. The latter might not be observable if it is outside of the used-defined range of parameter values.

InverseGaussianDistribution: y = random (pd)
InverseGaussianDistribution: y = random (pd, rows)
InverseGaussianDistribution: y = random (pd, rows, cols, …)
InverseGaussianDistribution: y = random (pd, [sz])

r = random (pd) returns a random number from the distribution object pd.

When called with a single size argument, betarnd returns a square matrix with the dimension specified. When called with more than one scalar argument, the first two arguments are taken as the number of rows and columns and any further arguments specify additional matrix dimensions. The size may also be specified with a row vector of dimensions, sz.

InverseGaussianDistribution: s = std (pd)

s = std (pd) computes the standard deviation of the probability distribution object, pd.

InverseGaussianDistribution: t = truncate (pd, lower, upper)

t = truncate (pd) returns a probability distribution t, which is the probability distribution pd truncated to the specified interval with lower limit, lower, and upper limit, upper. If pd is fitted to data with fitdist, the returned probability distribution t is not fitted, does not contain any data or estimated values, and it is as it has been created with the makedist function, but it includes the truncation interval.

InverseGaussianDistribution: v = var (pd)

v = var (pd) computes the standard deviation of the probability distribution object, pd.