Constructor Detail

• AbstractMultipleLinearRegression

  public AbstractMultipleLinearRegression()

Method Detail

• isNoIntercept

  public boolean isNoIntercept()

  Returns:

  true if the model has no intercept term; false otherwise

  Since:

  2.2

• setNoIntercept

  public void setNoIntercept(boolean noIntercept)

  Parameters:

  noIntercept - true means the model is to be estimated without an intercept term

  Since:

  2.2

• newSampleData

  public void newSampleData(double[] data,
                            int nobs,
                            int nvars)

  Loads model x and y sample data from a flat input array, overriding any previous sample.

  Assumes that rows are concatenated with y values first in each row. For example, an input data array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with nobs = 3 and nvars = 2 creates a regression dataset with two independent variables, as below:

     y   x[0]  x[1]
     --------------
     1     2     3
     4     5     6
     7     8     9
   

  Note that there is no need to add an initial unitary column (column of 1's) when specifying a model including an intercept term. If isNoIntercept() is true, the X matrix will be created without an initial column of "1"s; otherwise this column will be added.

  Throws IllegalArgumentException if any of the following preconditions fail:

  • data cannot be null
  • data.length = nobs * (nvars + 1)
  • nobs > nvars

  Parameters:

  data - input data array

  nobs - number of observations (rows)

  nvars - number of independent variables (columns, not counting y)

  Throws:

  NullArgumentException - if the data array is null

  DimensionMismatchException - if the length of the data array is not equal to nobs * (nvars + 1)

  InsufficientDataException - if nobs is less than nvars + 1