 # buckleyjames stsdas.analysis.statistics

NAME · USAGE · DESCRIPTION · PARAMETERS · EXAMPLES · BUGS · SEE_ALSO

## NAME

buckleyjames -- Compute linear regression based on Kaplan-Meier residuals

## USAGE

buckleyjames input

## DESCRIPTION

The buckleyjames task calculates a regression with Kaplan-Meier residuals. It can treat several independent variables if the dependent variable contains only one kind of censoring (i.e., upper or lower limits). The task requires only that the censoring distribution about the fitted line is random. Of the three linear regression methods available in this package, `buckleyjames' is probably the most reliable.

If the iterations reach their limit (the default is 50), there are three possibilities: 1) The computation was done exactly at the limit; 2) The convergence has not been reached; or 3) The results are trapped in an oscillation. In the second case, you should increase the iteration limit and compute the results again. If the results are the same, then you have an oscillation. The result shown is the average of the last values and, because the difference between the values is much smaller than the errors, you can use it as the final estimate.

## PARAMETERS

input [string]
Input file(s); this can be a list of files. Following each file name is a list of column names in brackets. These column names specify which columns in the file contain the information used by this task. The brackets MUST contain at least three names in the following format: [censor_indicator, independent_vars, dependent_var]. If there is more than one independent variable, the column names all follow the censor indicator. The censor indicator specifies the censorship of the data point. The different kinds of censorship are explained in the `censor' help file. If the input file is an STSDAS table, the names in brackets are the table column names. If the input file is a text file, the names in brackets are the column numbers. A title string will be printed if the input file is a table containing the header parameter "TITLE".
(tolerance = 1.0000000000000E-5) [real, min=0.]
Tolerance for regression fit. If the sum of the square of the difference between the coefficients of two successive iterations is less than this value, the iteration stops.
(niter = 50) [integer, min=1]
Maximum number of iterations. The computation stops when the maximum number of iterations is performed even if convergence has not been achieved.
(verbose = no) [boolean]
Provide detailed output? The detailed output includes the number of data points and the number of censored points and type of censorship.

## EXAMPLES

1. Apply `buckleyjames' to the data in the table "kriss.tab", using the columns "Censor" for the censoring indicator, "LogL1mu" for the first independent variable column, "LogL2500A" for the second independent variable column, and "LogL2keV" for the dependent variable column. Then use the file "iraslum.dat" (text file), columns 1, 2, and 3 (censor, independent, dependent). Several files can be processed sequentially. The following example will compute the Buckley-James linear regression fit for the two files indicated:

```cl> buckleyjames kriss.tab[Censor,LogL1mu,LogL2500A,LogL2keV], \
>>> iraslum.dat[1,2,3]
```