by An Uncommon Lab


Returns the normalized error squared for signals in x with covariance C. For multiple runs, returns the n-run average normalized error squared. Returns also the fraction of points within the 100% * p probability bounds based on the number of runs. This can be used to determine if errors in x correspond appropriately to the covariance.

[err, f, b, errs] = normerr(x, C)
[err, f, b, errs] = normerr(x, C, p)
[err, f, b, errs] = normerr(x, C, p, type)
[err, f, b, errs] = normerr(x, C, p, type, (etc.))



Residuals. For estimation error, x - x_hat. For measurement error, z - z_hat. Dimensions are nx-ns-nr (number of states, number of samples, and number of runs).


Covariance stored as nx-nx-ns-nr matrix. Alternately, for constant covariance across all runs and time stamps, just nx-by-nx.


Probability region (0 to 1) to examine. E.g., to compare the normalized residuals with a 95% probability region, use 0.95. Default is 0.95.


For type 1, the error x.' * C * x is used, giving a single normalized error magnitude for that sample. For type 2, the error x ./ sqrt(diag(C)) is used, giving an error per state, normalized by each state's standard deviation. Default is 1.


Option-value pairs:

'OneSided' - True to use a one-sided distribution test (only 
             used for type 1)                                
'Plot'     - True to plot results                            
'Xdata'    - Data to use for x values when plotting



N-run average normalized error (1-by-ns)


Fraction of points in err that are within the probability bounds


Probability bounds stored as [low bound, high bound]


Matrix containing normalized error for each run individually (1-by-ns-by-nr), useful for finding which runs have excessive error


Generate a large number of runs and samples per run of a 3-dimensional state with a different covariance matrix everywhere. See that the theoretical bounds and the empirical bounds determined by normerr match well.

% Define the sizes and preallocate space for the errors and covariance
% matrices.
nx = 3;                          % 3 states
nr = 25;                         % 25 runs
ns = 1000;                       % 1000 samples each
x  = zeros(nx, ns, nr);          % A place for all the residual data
C  = zeros(nx, nx, ns, nr);      % A place for each covariance matrix
% For each sample of each run, generate a random positive-definite
% covariance matrix and make a random draw from it.
for r = 1:nr
    for s = 1:ns
        C(:, :, s, r) = randcov(nx);
        x(:, s, r)    = mnddraw(C(:, :, s, r), 1);

Examine what fraction of the data lay within the 90% confidence bounds (should be close to 0.90).

% For type 1 -- total state error
subplot(2, 1, 1);
[err1, f1] = normerr(x, C, 0.90, 1, 'Plot', true);
% For type 2 -- individual state error
subplot(2, 1, 2);
[err2, f2] = normerr(x, C, 0.90, 2, 'Plot', true);
Percent of data in theoretical 90.0% bounds: 88.1%
Percent of data in theoretical 90.0% bounds: 89.6%

See Also

autocorrelate, kffmct

Table of Contents

  1. Inputs
  2. Outputs
  3. Example
  4. See Also