sbiosampleerror
Sample error based on error model and add noise to input data
Description
adds noise to the input data using one or more error models and error
parameters.noisydata
= sbiosampleerror(inputdata
,errormodel
,errorparam
)
Examples
Add Noise to Simulation Data
This example adds noise (or error) to the simulation data from a radioactive decay model with the first-order reaction: , where x
and z
are species and c
is the forward rate constant.
Load the sample project containing the radiodecay model m1
.
sbioloadproject radiodecay;
Simulate the model.
[t,sd,names] = sbiosimulate(m1);
Plot the simulation results.
plot(t,sd); legend(names,'AutoUpdate','off'); hold on
Add noise to the simulation results using the constant error model with the error parameter set to 20.
noisydata = sbiosampleerror(sd,'constant',20);
Plot the noisy simulation data.
plot(t,noisydata);
Define a Custom Error Model Using a Function Handle
This example defines a custom error model using a function handle and adds noise to simulation data of a radioactive decay model with the first-order reaction , where
x
and z
are species, and c
is the forward rate constant.
Load the sample project containing the radiodecay model m1
.
sbioloadproject radiodecay;
Suppose you have a simple custom error model with a standard mean-zero and unit-variance (Gaussian) normal variable e
, simulation results f
, and two parameters p1
and p2
:
Define a function handle that represents the error model.
em = @(y,p1,p2) y+p1+p2*randn(size(y));
Simulate the model.
[t,sd,names] = sbiosimulate(m1);
Plot the simulation results and hold the plot.
plot(t,sd); legend(names,'AutoUpdate','off'); hold on
Sample the error using the previously defined custom function with two parameters set to 0.5 and 30, respectively.
noisydata = sbiosampleerror(sd,em,{0.5,30});
Plot the noisy simulation data.
plot(t,noisydata);
You can also apply a different error model to each state, which is a column in sd
. Suppose you want to apply the custom error model (em
) to the first column (species x
data) and the proportional error model to the second column (species z
data).
hold off noisydata = sbiosampleerror(sd,{em,'proportional'},{{0.5,30},0.3}); plot(t,sd); legend(names,'AutoUpdate','off'); hold on plot(t,noisydata);
Input Arguments
inputdata
— Input data
SimData
object | vector of SimData
objects | numeric matrix
Input data, specified as a SimData object
, vector of
SimData
objects, or numeric matrix. If it is a vector
of SimData
objects, the error is added to each of the
columns in the Data property. If it is a numeric matrix, the error is added
to each column in the matrix.
errormodel
— Error model
character vector | string | function handle | string vector | cell array of character vectors
Error model(s), specified as a character vector, string, function handle, string vector, cell array of character vectors, or cell array containing a mixture of character vectors and function handles.
If it is a string vector or cell array, its length must match the number of columns
(responses) in inputdata
, and each error model is
applied to the corresponding column in inputdata
. If it
is a single character vector, string, or function handle, the same error
model is applied to all columns in inputdata
.
The first argument of a function handle must be a matrix of
simulation results. The subsequent arguments are the parameters of
the error model supplied in the errorparam
input
argument. The output of the function handle must be a matrix of the
same size as the first input argument (simulation results).
For example, suppose you have a custom error model with a standard mean-zero and unit-variance (Gaussian) normal variable e, simulation results f, and two parameters p1 and p2: . You can define the corresponding function handle as follows.
em = @(y,p1,p2) y+p1+p2*randn(size(y));
y
is the matrix of simulation results and
p1
and p2
are the error
parameters. The output of the function handle must be the same size as
y
, which is the same as the simulation results
specified in the inputdata
input argument. The
parameters p1
and p2
are specified in
the errorparam
argument.
There are four built-in error models. Each model defines the error using a standard mean-zero and unit-variance (Gaussian) variable e, simulation results f, and one or two parameters a and b. The models are:
'constant'
:'proportional'
:'combined'
:'exponential'
:
errorparam
— Error model parameter
scalar | vector | cell array
Error model parameter(s), specified as a scalar, vector, or
cell array. If errormodel
is 'constant'
, 'proportional'
,
or 'exponential'
, then errorparam
is
specified as a numeric scalar. If it is 'combined'
,
then errorparam
is specified as a row vector
with two elements [a b]
.
If errormodel
is a cell array, then errorparam
must
be a cell array of the same length. In other words, errorparam
must
contain N elements, where N is the number of error models in errormodel
.
Each element must have the correct number of parameters for the corresponding
error model.
For example, suppose you have three columns in inputdata
, and you are
applying a different error model (constant
,
proportional
, and exponential
error models with error parameters 0.1
,
0.2
, and 0.5
, respectively) to
each column, then errormodel
and
errorparam
must be cell arrays with three elements
as follows.
errormodel = {'constant','proportional','exponential'}; errorparam = {0.1,0.2,0.5};
Output Arguments
noisydata
— Data with added noise
vector of SimData
objects | matrix
Data with added noise, returned as a vector of SimData
objects or numeric matrix. If inputdata
is a vector of
SimData
objects, noisydata
is
also a vector of SimData
objects, and the error is added
to each column in the inputdata.Data
property. If
inputdata
is specified as a matrix,
noisydata
is a matrix, and the error is added to
each column in the matrix.
Version History
Introduced in R2014a
See Also
sbiosampleparameters
| createSimFunction
| SimFunction object
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