distdata |

Save distribution plot data to a matrix.

Saves the data used to construct a distribution plot to the workfile.

Syntax

groupname.distdata(dtype=dist_type, dist_options) matrix_name_pattern

saves the distribution plot data specified by dist_type where dist_type must be one of the following keywords:

kernfit | Kernel regression (default). |

nnfit | Nearest neighbor (local) regression. |

empqq | Empirical quantile-quantile plot. |

The matrix_name_pattern is used to define a naming pattern for the output matrices; if the pattern is “NAME”, the resulting matrices will be named “NAME01”, “NAME02”, … and so on, using the next available name.

Options

For the first two types (“kernfit” and “nnfit”), dist_options are any of the distribution type-specific options described in “Kernfit Options” and “Nnfit Options”, respectively. The empirical quantile-quantile plot type (“empqq”) takes the options described in qqplot under “Empirical Options”.

Note that the graph display specific options such as “fill,” “nofill,” “leg,” and “noline” are not relevant for this procedure.

In addition, you may use the “mult” option to specify multiple series handling

mult = mat_type | Multiple series or column handling: where mat_type may be: “pairs” or “p” - pairs, “mat” or “m” - scatterplot matrix, “lower” or “l” - lower triangular matrix. |

and the “prompt” option to force the dialog display

prompt | Force the dialog to appear from within a program. |

Examples

group g w x y z

g.distdata(mult=first, dtype=kernel, k=e, ngrid=100) m

creates a group called G from the series X, Y and Z, then creates three matrices, M01, M02 and M03, where the first matrix contains the kernel fit (with an Epanechnikov kernel and 100 grid points) of W on X, the second contains the fit of W on Y, and the third matrix contains the kernel fit of Won Z.

g.distdata(mult=pairs, dtype=local, b=0.3, d=1, neval=100, s) n

creates two matrices, N1 and N2, where N1 contains the nearest neighbor fit of W on X computed using a bandwidth of 0.3 and polynomial degree of 1, 100 evaluation points and symmetric neighbors, and N2 contains the data for the nearest neighbor fit of Y on Z.

group g.drop z

g.distdata(mult=all, dtype=empqq, q=r) mat

drops Z from the group, then creates 3 matrices; MAT01, MAT02, MAT03, where MAT01 contains the empirical quantile-quantile for W and X, computed using the rankit quantile method, and MAT02 contains the qq-plot data for W and Y, and MAT03 contains the qq-plot data for X and Y.

Cross-references