Syntax
outliers = excludedata(xdata,ydata,'MethodName',MethodValue)
where
xdata |
A column vector of predictor data |
ydata |
A column vector of response data |
'MethodName' |
The data exclusion method |
MethodValue |
The value associated with MethodName |
outliers |
A logical vector that defines data to be excluded from a fit |
Description
outliers = excludedata(xdata,ydata,'MethodName',MethodValue) identifies data to be excluded from a fit using the specified MethodName and MethodValue. outliers is a logical vector containing 1’s marking data points to exclude while fitting, and 0’s marking data points to include while fitting. The data exclusion methods are given below.
| Method | Description |
box |
A four-element vector that specifies a box of data to include in a fit. Data outside the box is excluded. You specify the box as [\(x_{min}\) \(x_{max}\) \(y_{min}\) \(y_{max}\)] |
domain |
A two-element vector that specifies the domain of data to include in a fit. Data outside this domain is excluded. You specify the domain as [\(x_{min}\) \(x_{max}\)] |
indices |
A vector specifying the indices of the data points to be excluded |
range |
A two-element vector that specifies the range of data to include in a fit. Data outside this range is excluded. You specify the range as [\(y_{min}\) \(y_{max}\)] |
You can combine data exclusion methods using logical operators. For example, to combine methods using the | (OR) operator
outliers = excludedata(xdata,ydata,'indices',[3 5]);
outliers = outliers|excludedata(xdata,ydata,'box',[1 10 0 90]);
In some cases, you may want to use the ~ (NOT) operator to specify a box that contains all the data to exclude.
outliers = ~excludedata(xdata,ydata,'box',[1 10 0 90]);
Example
Generate random data in the interval [0, 15], create a sine wave with noise, and add two outliers with the value 2.
rand('state',0);
x = 15*rand(150,1);
y = sin(x) + (rand(size(x))-0.5)*0.5;
y(ceil(length(x)*rand(2,1))) = 2;
Identify outliers that are outside the interval [-1.5, 1.5] using the range method.
outliers = excludedata(x,y,'range',[-1.5 1.5]);
Identify the same outliers using the indices method.
ind = find((y>1.5)|(y<-1.5));
outliers = excludedata(x,y,'indices',ind);
You can pass outliers to the fit function to exclude the specified data points from a fit.
ftype = fittype('a*sin(b*x)');
fresult = fit(x,y,ftype,'startpoint',[1 1],'exclude',outliers);