Chidf: Difference between revisions
imported>Jeremy (Importing text file) |
imported>Bob |
||
(3 intermediate revisions by one other user not shown) | |||
Line 1: | Line 1: | ||
===Purpose=== | ===Purpose=== | ||
Chi-squared distribution. | Chi-squared distribution. | ||
===Synopsis=== | ===Synopsis=== | ||
:prob = chidf(function,x,a) | :prob = chidf(function,x,a) | ||
===Description=== | ===Description=== | ||
Estimates cumulative distribution function (cumulative, cdf), probability density function (density, pdf), quantile (inverse of cdf), or random numbers for a Chi-sqared distribution. | Estimates cumulative distribution function (cumulative, cdf), probability density function (density, pdf), quantile (inverse of cdf), or random numbers for a Chi-sqared distribution. | ||
The chi-squared distribution usually models data that are positive (such as the sum of physical measurements). With integer degrees of freedom parameter v, it is equal to the sum of v normally distributed variates. This toolbox does not require that the degrees of freedom be integral and will ignore negative values in a sample. Chi-squared distributions have variance equal to twice the mean. | The chi-squared distribution usually models data that are positive (such as the sum of physical measurements). With integer degrees of freedom parameter v, it is equal to the sum of v normally distributed variates. This toolbox does not require that the degrees of freedom be integral and will ignore negative values in a sample. Chi-squared distributions have variance equal to twice the mean. | ||
==== | ::<math>f(x) = \frac {x^{\left (a-2 \right)/2 }\, exp \left( -x/2 \right)} {2^{a/2}\, \Gamma \left (a/2 \right )}</math> | ||
====Inputs==== | |||
* '''function''' = [ {'cumulative'} | 'density' | 'quantile' | 'random' ], defines the functionality to be used. Note that the function recognizes the first letter of each string so that the string could be: [ 'c' | 'd' | 'q' | 'r' ]. | * '''function''' = [ {'cumulative'} | 'density' | 'quantile' | 'random' ], defines the functionality to be used. Note that the function recognizes the first letter of each string so that the string could be: [ 'c' | 'd' | 'q' | 'r' ]. | ||
* '''x''' = matrix in which the sample data is stored, in the interval (0,inf). | * '''x''' = matrix in which the sample data is stored, in the interval (0,inf). | ||
:: for function=quantile - matrix with values in the interval (0,1). | |||
:: for function=random - vector indicating the size of the random matrix to create. | |||
* '''a''' = degrees of freedom parameter (positive integer). | * '''a''' = degrees of freedom parameter (positive integer). | ||
'''Note''': If inputs (x, a, and b) are not equal in size, the function will attempt to resize all inputs to the largest input using the RESIZE function. | '''Note''': If inputs (x, a, and b) are not equal in size, the function will attempt to resize all inputs to the largest input using the RESIZE function. | ||
'''Note''': Functions will typically allow input values outside of the acceptable range to be passed but such values will return NaN in the results. | '''Note''': Functions will typically allow input values outside of the acceptable range to be passed but such values will return NaN in the results. | ||
===Examples=== | ===Examples=== | ||
====Cumulative | |||
>> prob = chidf('c',[3.7942 4.6052],2) | ====Cumulative==== | ||
<pre>>> prob = chidf('c',[3.7942 4.6052],2) | |||
prob = | prob = | ||
0.8500 0.9000 | 0.8500 0.9000 | ||
>> x = 0:0.1:8; | >> x = 0:0.1:8; | ||
>> plot(x,chidf('c',x,2),'b',x,chidf('c',x,0.5),'r') | >> plot(x,chidf('c',x,2),'b',x,chidf('c',x,0.5),'r')</pre> | ||
====Density | |||
>> prob = chidf('d',[3.7942 4.6052],2) | ====Density==== | ||
<pre>>> prob = chidf('d',[3.7942 4.6052],2) | |||
prob = | prob = | ||
0.0750 0.0500 | 0.0750 0.0500 | ||
>> x = 0:0.1:8; | >> x = 0:0.1:8; | ||
>> plot(x,chidf('d',x,2),'b',x,chidf('d',x,0.5),'r') | >> plot(x,chidf('d',x,2),'b',x,chidf('d',x,0.5),'r')</pre> | ||
====Quantile | |||
>> prob = chidf('q',[0.85 0.9],2) | ====Quantile==== | ||
<pre>>> prob = chidf('q',[0.85 0.9],2) | |||
prob = | prob = | ||
3.7942 4.6052 | 3.7942 4.6052</pre> | ||
====Random | |||
>> prob = chidf('r',[4 1],2) | ====Random==== | ||
<pre>>> prob = chidf('r',[4 1],2) | |||
prob = | prob = | ||
0.1023 | 0.1023 | ||
Line 39: | Line 62: | ||
0.9990 | 0.9990 | ||
1.4432 | 1.4432 | ||
</pre> | |||
===See Also=== | ===See Also=== | ||
[[betadr]], [[cauchydf]], [[expdf]], [[gammadf]], [[gumbeldf]], [[laplacedf]], [[logisdf]], [[lognormdf]], [[normdf]], [[paretodf]], [[raydf]], [[triangledf]], [[unifdf]], [[weibulldf]] | [[betadr]], [[cauchydf]], [[expdf]], [[gammadf]], [[gumbeldf]], [[laplacedf]], [[logisdf]], [[lognormdf]], [[normdf]], [[paretodf]], [[raydf]], [[triangledf]], [[unifdf]], [[weibulldf]] |
Latest revision as of 06:04, 10 October 2008
Purpose
Chi-squared distribution.
Synopsis
- prob = chidf(function,x,a)
Description
Estimates cumulative distribution function (cumulative, cdf), probability density function (density, pdf), quantile (inverse of cdf), or random numbers for a Chi-sqared distribution.
The chi-squared distribution usually models data that are positive (such as the sum of physical measurements). With integer degrees of freedom parameter v, it is equal to the sum of v normally distributed variates. This toolbox does not require that the degrees of freedom be integral and will ignore negative values in a sample. Chi-squared distributions have variance equal to twice the mean.
Inputs
- function = [ {'cumulative'} | 'density' | 'quantile' | 'random' ], defines the functionality to be used. Note that the function recognizes the first letter of each string so that the string could be: [ 'c' | 'd' | 'q' | 'r' ].
- x = matrix in which the sample data is stored, in the interval (0,inf).
- for function=quantile - matrix with values in the interval (0,1).
- for function=random - vector indicating the size of the random matrix to create.
- a = degrees of freedom parameter (positive integer).
Note: If inputs (x, a, and b) are not equal in size, the function will attempt to resize all inputs to the largest input using the RESIZE function.
Note: Functions will typically allow input values outside of the acceptable range to be passed but such values will return NaN in the results.
Examples
Cumulative
>> prob = chidf('c',[3.7942 4.6052],2) prob = 0.8500 0.9000 >> x = 0:0.1:8; >> plot(x,chidf('c',x,2),'b',x,chidf('c',x,0.5),'r')
Density
>> prob = chidf('d',[3.7942 4.6052],2) prob = 0.0750 0.0500 >> x = 0:0.1:8; >> plot(x,chidf('d',x,2),'b',x,chidf('d',x,0.5),'r')
Quantile
>> prob = chidf('q',[0.85 0.9],2) prob = 3.7942 4.6052
Random
>> prob = chidf('r',[4 1],2) prob = 0.1023 2.9295 0.9990 1.4432
See Also
betadr, cauchydf, expdf, gammadf, gumbeldf, laplacedf, logisdf, lognormdf, normdf, paretodf, raydf, triangledf, unifdf, weibulldf