Normal parameter estimates
[muhat,sigmahat] = normfit(data)
[muhat,sigmahat,muci,sigmaci] = normfit(data)
[muhat,sigmahat,muci,sigmaci] = normfit(data,alpha)
[...] = normfit(data,alpha,censoring)
[...] = normfit(data,alpha,censoring,freq)
[...] = normfit(data,alpha,censoring,freq,options)
[muhat,sigmahat] = normfit(data)
returns
an estimate of the mean μ in muhat
,
and an estimate of the standard deviation σ in sigmahat
,
of the normal distribution given the data in data
.
[muhat,sigmahat,muci,sigmaci] = normfit(data)
returns
95% confidence intervals for the parameter estimates on the mean and
standard deviation in the arrays muci
and sigmaci
,
respectively. The first row of muci
contains the
lower bounds of the confidence intervals for μ the
second row contains the upper bounds. The first row of sigmaci
contains
the lower bounds of the confidence intervals for σ,
and the second row contains the upper bounds.
[muhat,sigmahat,muci,sigmaci] = normfit(data,alpha)
returns
100(1 - alpha
)
% confidence intervals for the parameter estimates, where alpha
is
a value in the range [0 1]
specifying
the width of the confidence intervals. By default, alpha
is 0.05
,
which corresponds to 95% confidence intervals.
[...] = normfit(data,alpha,censoring)
accepts
a Boolean vector, censoring
, of the same size as data
,
which is 1
for observations that are right-censored
and 0
for observations that are observed exactly. data
must
be a vector in order to pass in the argument censoring
.
[...] = normfit(data,alpha,censoring,freq)
accepts
a frequency vector, freq
, of the same size as data
.
Typically, freq
contains integer frequencies for
the corresponding elements in data
, but can contain
any nonnegative values. Pass in []
for alpha
, censoring
,
or freq
to use their default values.
[...] = normfit(data,alpha,censoring,freq,options)
accepts
a structure, options
, that specifies control parameters
for the iterative algorithm the function uses to compute maximum likelihood
estimates when there is censoring. The normal fit function accepts
an options
structure which you can create using
the function statset
. Enter statset('normfit')
to
see the names and default values of the parameters that normfit
accepts
in the options
structure. See the reference page
for statset
for more information
about these options.
Note:
With no censoring, |
In this example the data is a two-column random normal matrix. Both columns have µ = 10 and σ = 2. Note that the confidence intervals below contain the "true values."
data = normrnd(10,2,100,2); [mu,sigma,muci,sigmaci] = normfit(data) mu = 10.1455 10.0527 sigma = 1.9072 2.1256 muci = 9.7652 9.6288 10.5258 10.4766 sigmaci = 1.6745 1.8663 2.2155 2.4693