InverseGaussianDistribution
statistics: InverseGaussianDistribution
Inverse Gaussian probability distribution object.
A InverseGaussianDistribution
object consists of parameters, a
model description, and sample data for a Inverse Gaussian probability
distribution.
The Inverse Gaussian distribution is a continuous probability distribution, which is often used to model non-negative positively skewed data. Is is defined by mean parameter mu and shape parameter lambda.
There are several ways to create a InverseGaussianDistribution
object.
fitdist
function.
makedist
function.
InverseGaussianDistribution (mu,
lambda)
to create a Inverse Gaussian distribution with fixed
parameter values mu and lambda.
InverseGaussianDistribution.fit
(x, alpha, censor, freq, options)
to fit a
distribution to the data in x using the same input arguments as the
invgfit
function.
It is highly recommended to use fitdist
and makedist
functions to create probability distribution objects, instead of the class
constructor or the aforementioned static method.
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, invgfit, invglike, invgstat
Source Code: InverseGaussianDistribution
A positive scalar value characterizing the mean of the
Inverse Gaussian distribution. You can access the mu
property using dot name assignment.
## Create an Inverse Gaussian distribution with default parameters pd = makedist ("InverseGaussian") ## Query parameter 'mu' (mean parameter) pd.mu ## Set parameter 'mu' pd.mu = 2 ## Use this to initialize or modify the mean parameter of an Inverse Gaussian ## distribution. The mean parameter must be a positive real scalar. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 1 ans = 1 pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 2 lambda = 1 |
## Create an Inverse Gaussian distribution object by calling its constructor pd = InverseGaussianDistribution (1.5, 2) ## Query parameter 'mu' pd.mu ## This demonstrates direct construction with a specific mean parameter, ## useful for modeling non-negative skewed data with a known mean. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1.5 lambda = 2 ans = 1.5000 |
A positive scalar value characterizing the shape of the
Inverse Gaussian distribution. You can access the lambda
property using dot name assignment.
## Create an Inverse Gaussian distribution with default parameters pd = makedist ("InverseGaussian") ## Query parameter 'lambda' (shape parameter) pd.lambda ## Set parameter 'lambda' pd.lambda = 3 ## Use this to initialize or modify the shape parameter in an Inverse Gaussian ## distribution. The shape parameter must be a positive real scalar. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 1 ans = 1 pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 3 |
## Create an Inverse Gaussian distribution object by calling its constructor pd = InverseGaussianDistribution (1.5, 2) ## Query parameter 'lambda' pd.lambda ## This shows how to set the shape parameter directly via the constructor, ## ideal for modeling specific variability in non-negative data. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1.5 lambda = 2 ans = 2 |
A character vector specifying the name of the probability distribution object. This property is read-only.
A scalar integer value specifying the number of parameters characterizing the probability distribution. This property is read-only.
A cell array of character vectors with each element containing the name of a distribution parameter. This property is read-only.
A cell array of character vectors with each element containing a short description of a distribution parameter. This property is read-only.
A numeric vector containing the values of the distribution
parameters. This property is read-only. You can change the distribution
parameters by assigning new values to the mu
and lambda
properties.
A numeric matrix containing the variance-covariance of the parameter estimates. Diagonal elements contain the variance of each estimated parameter, and non-diagonal elements contain the covariance between the parameter estimates. The covariance matrix is only meaningful when the distribution was fitted to data. If the distribution object was created with fixed parameters, or a parameter of a fitted distribution is modified, then all elements of the variance-covariance are zero. This property is read-only.
A logical vector specifying which parameters are fixed and
which are estimated. true
values correspond to fixed parameters,
false
values correspond to parameter estimates. This property is
read-only.
A numeric vector specifying the truncation interval for the
probability distribution. First element contains the lower boundary,
second element contains the upper boundary. This property is read-only.
You can only truncate a probability distribution with the
truncate
method.
A logical scalar value specifying whether a probability distribution is truncated or not. This property is read-only.
A scalar structure containing the following fields:
data
: a numeric vector containing the data used for
distribution fitting.
cens
: a numeric vector of logical values indicating
censoring information corresponding to the elements of the data used for
distribution fitting. If no censoring vector was used for distribution
fitting, then this field defaults to an empty array.
freq
: a numeric vector of non-negative integer values
containing the frequency information corresponding to the elements of the
data used for distribution fitting. If no frequency vector was used for
distribution fitting, then this field defaults to an empty array.
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 (…,
returns the complement of
the CDF of the probability distribution object, pd, evaluated at
the values in x.
"upper"
)
## Plot various CDFs from the Inverse Gaussian distribution x = 0:0.01:5; pd1 = makedist ("InverseGaussian", "mu", 1, "lambda", 1); pd2 = makedist ("InverseGaussian", "mu", 1, "lambda", 2); pd3 = makedist ("InverseGaussian", "mu", 1, "lambda", 3); p1 = cdf (pd1, x); p2 = cdf (pd2, x); p3 = cdf (pd3, x); plot (x, p1, "-b", x, p2, "-g", x, p3, "-r") grid on legend ({"mu = 1, lambda = 1", "mu = 1, lambda = 2", "mu = 1, lambda = 3"}, ... "location", "southeast") title ("Inverse Gaussian CDF") xlabel ("values") ylabel ("Cumulative probability") ## Use this to compute and visualize the cumulative distribution function ## for different Inverse Gaussian distributions, showing how probability ## accumulates over values. |
InverseGaussianDistribution: x = icdf (pd, p)
x = icdf (pd, p)
computes the quantile (the
inverse of the CDF) of the probability distribution object, pd,
evaluated at the values in p.
## Plot various iCDFs from the Inverse Gaussian distribution p = 0.001:0.001:0.999; pd1 = makedist ("InverseGaussian", "mu", 1, "lambda", 1); pd2 = makedist ("InverseGaussian", "mu", 1, "lambda", 2); pd3 = makedist ("InverseGaussian", "mu", 1, "lambda", 3); x1 = icdf (pd1, p); x2 = icdf (pd2, p); x3 = icdf (pd3, p); plot (p, x1, "-b", p, x2, "-g", p, x3, "-r") grid on legend ({"mu = 1, lambda = 1", "mu = 1, lambda = 2", "mu = 1, lambda = 3"}, ... "location", "northwest") title ("Inverse Gaussian iCDF") xlabel ("Probability") ylabel ("values") ## This demonstrates the inverse CDF (quantiles) for Inverse Gaussian ## distributions, useful for finding the value corresponding to ## given probabilities. |
InverseGaussianDistribution: r = iqr (pd)
r = iqr (pd)
computes the interquartile range of the
probability distribution object, pd.
## Compute the interquartile range for an Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) iqr_value = iqr (pd) ## Use this to calculate the interquartile range, which measures the spread ## of the middle 50% of the distribution, useful for understanding variability ## in non-negative data. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 iqr_value = 0.7451 |
InverseGaussianDistribution: m = mean (pd)
m = mean (pd)
computes the mean of the probability
distribution object, pd.
## Compute the mean for different Inverse Gaussian distributions pd1 = makedist ("InverseGaussian", "mu", 1, "lambda", 1); pd2 = makedist ("InverseGaussian", "mu", 1, "lambda", 2); mean1 = mean (pd1) mean2 = mean (pd2) ## This shows how to compute the expected value for Inverse Gaussian ## distributions with different shape parameters. mean1 = 1 mean2 = 1 |
InverseGaussianDistribution: m = median (pd)
m = median (pd)
computes the median of the probability
distribution object, pd.
## Compute the median for different Inverse Gaussian distributions pd1 = makedist ("InverseGaussian", "mu", 1, "lambda", 1); pd2 = makedist ("InverseGaussian", "mu", 1, "lambda", 2); median1 = median (pd1) median2 = median (pd2) ## Use this to find the median value, which splits the distribution ## into two equal probability halves. median1 = 0.6758 median2 = 0.8043 |
InverseGaussianDistribution: nlogL = negloglik (pd)
nlogL = negloglik (pd)
computes the negative
loglikelihood of the probability distribution object, pd.
## Compute the negative loglikelihood for a fitted Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); data = random (pd, 100, 1); pd_fitted = fitdist (data, "InverseGaussian") nlogL = negloglik (pd_fitted) ## This is useful for assessing the fit of an Inverse Gaussian distribution to ## data, lower values indicate a better fit. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 pd_fitted = InverseGaussianDistribution Inverse Gaussian distribution mu = 0.991317 [0.841261, 1.14137] lambda = 1.66197 [1.2013, 2.12263] nlogL = -77.493 |
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 by
Name-Value
pair arguments listed below.
Name | Value | |
---|---|---|
"Alpha" | A scalar value in the range 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.
## Compute confidence intervals for parameters of a fitted Inverse Gaussian ## distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); data = random (pd, 1000, 1); pd_fitted = fitdist (data, "InverseGaussian") ci = paramci (pd_fitted, "Alpha", 0.05) ## Use this to obtain confidence intervals for the estimated parameters (mu ## and lambda), providing a range of plausible values given the data. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 pd_fitted = InverseGaussianDistribution Inverse Gaussian distribution mu = 1.00566 [0.961629, 1.0497] lambda = 2.01487 [1.83826, 2.19148] ci = 0.9616 1.8383 1.0497 2.1915 |
InverseGaussianDistribution: y = pdf (pd, x)
y = pdf (pd, x)
computes the PDF of the
probability distribution object, pd, evaluated at the values in
x.
## Plot various PDFs from the Inverse Gaussian distribution x = 0:0.01:5; pd1 = makedist ("InverseGaussian", "mu", 1, "lambda", 1); pd2 = makedist ("InverseGaussian", "mu", 1, "lambda", 2); pd3 = makedist ("InverseGaussian", "mu", 1, "lambda", 3); y1 = pdf (pd1, x); y2 = pdf (pd2, x); y3 = pdf (pd3, x); plot (x, y1, "-b", x, y2, "-g", x, y3, "-r") grid on legend ({"mu = 1, lambda = 1", "mu = 1, lambda = 2", "mu = 1, lambda = 3"}, ... "location", "northeast") title ("Inverse Gaussian PDF") xlabel ("values") ylabel ("Probability density") ## This visualizes the probability density function for Inverse Gaussian ## distributions, showing the likelihood of different values. |
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.
Name | Value | |
---|---|---|
"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.
## Create an Inverse Gaussian distribution with fixed parameters μ = 1 and ## λ = 2 and plot its PDF. pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) plot (pd) title ("Fixed Inverse Gaussian distribution with mu = 1 and lambda = 2") pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 |
## Generate a data set of 100 random samples from an Inverse Gaussian ## distribution with parameters μ = 1 and λ = 2. Fit an Inverse Gaussian ## distribution to this data and plot its CDF superimposed over an empirical ## CDF. pd_fixed = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); data = random (pd_fixed, 100, 1); pd_fitted = fitdist (data, "InverseGaussian") plot (pd_fitted, "PlotType", "cdf") txt = "Fitted Inverse Gaussian distribution with μ = %0.2f and λ = %0.2f"; title (sprintf (txt, pd_fitted.mu, pd_fitted.lambda)) legend ({"empirical CDF", "fitted CDF"}, "location", "southeast") ## Use this to visualize the fitted CDF compared to the empirical CDF of the ## data, useful for assessing model fit. pd_fixed = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 pd_fitted = InverseGaussianDistribution Inverse Gaussian distribution mu = 0.978108 [0.845309, 1.11091] lambda = 2.0383 [1.47332, 2.60328] |
## Generate a data set of 200 random samples from an Inverse Gaussian ## distribution with parameters μ = 1 and λ = 2. Display a probability ## plot for the Inverse Gaussian distribution fit to the data. pd_fixed = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); data = random (pd_fixed, 200, 1); pd_fitted = fitdist (data, "InverseGaussian") plot (pd_fitted, "PlotType", "probability") txt = strcat ("Probability plot of fitted Inverse Gaussian", ... " distribution with μ = %0.2f and λ = %0.2f"); title (sprintf (txt, pd_fitted.mu, pd_fitted.lambda)) legend ({"empirical CDF", "fitted CDF"}, "location", "southeast") ## This creates a probability plot to compare the fitted distribution to the ## data, useful for checking if the Inverse Gaussian model is appropriate. pd_fixed = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 pd_fitted = InverseGaussianDistribution Inverse Gaussian distribution mu = 0.97456 [0.881299, 1.06782] lambda = 2.04403 [1.64341, 2.44465] |
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,
also plots the profile likelihood
against the default range of the selected parameter.
"Display"
, "on"
)
[nlogL, param] = proflik (pd, pnum,
setparam)
defines a user-defined range of the selected parameter.
[nlogL, param] = proflik (pd, pnum,
setparam,
also plots the profile
likelihood against the user-defined range of the selected parameter.
"Display"
, "on"
)
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.
## Compute and plot the profile likelihood for the shape parameter of a fitted ## Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); data = random (pd, 1000, 1); pd_fitted = fitdist (data, "InverseGaussian") [nlogL, param] = proflik (pd_fitted, 2, "Display", "on"); ## Use this to analyze the profile likelihood of the shape parameter (lambda), ## helping to understand the uncertainty in parameter estimates. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 pd_fitted = InverseGaussianDistribution Inverse Gaussian distribution mu = 0.993918 [0.948235, 1.0396] lambda = 1.80738 [1.64896, 1.9658] |
InverseGaussianDistribution: r = random (pd)
InverseGaussianDistribution: r = random (pd, rows)
InverseGaussianDistribution: r = random (pd, rows, cols, …)
InverseGaussianDistribution: r = random (pd, [sz])
r = random (pd)
returns a random number from the
distribution object pd.
When called with a single size argument, invgrnd
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.
## Generate random samples from an Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) rand ("seed", 21); samples = random (pd, 500, 1); hist (samples, 50) title ("Histogram of 500 random samples from Inverse Gaussian(mu=1, lambda=2)") xlabel ("values") ylabel ("Frequency") ## This generates random samples from an Inverse Gaussian distribution, useful ## for simulating non-negative skewed data like repair times. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 |
InverseGaussianDistribution: s = std (pd)
s = std (pd)
computes the standard deviation of the
probability distribution object, pd.
## Compute the standard deviation for an Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) std_value = std (pd) ## Use this to calculate the standard deviation, which measures the variability ## in values. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 std_value = 0.7071 |
InverseGaussianDistribution: t = truncate (pd, lower, upper)
t = truncate (pd, lower, upper)
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.
## Plot the PDF of an Inverse Gaussian distribution, with parameters mu = 1 ## and lambda = 2, truncated at [0.5, 2] intervals. Generate 10000 random ## samples from this truncated distribution and superimpose a histogram scaled ## accordingly pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) t = truncate (pd, 0.5, 2) rand ("seed", 21); data = random (t, 10000, 1); ## Plot histogram and fitted PDF plot (t) hold on hist (data, 100, 50) hold off title ("Inverse Gaussian distribution (mu = 1, lambda = 2) truncated at [0.5, 2]") legend ("Truncated PDF", "Histogram") ## This demonstrates truncating an Inverse Gaussian distribution to a specific ## range and visualizing the resulting distribution with random samples. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 t = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 Truncated to the interval [0.5, 2] |
InverseGaussianDistribution: v = var (pd)
v = var (pd)
computes the variance of the
probability distribution object, pd.
## Compute the variance for an Inverse Gaussian distribution pd = makedist ("InverseGaussian", "mu", 1, "lambda", 2) var_value = var (pd) ## Use this to calculate the variance, which quantifies the spread of the ## values in the distribution. pd = InverseGaussianDistribution Inverse Gaussian distribution mu = 1 lambda = 2 var_value = 0.5000 |