DOH!

I thought that thread title looked familiar ;-)

DRAT...

I modified the macro further to utilize the new support for PIVOT TABLES in

ver 25.

Also changed the DO IF $CASENUM / PRINT business to use ECHO.

Other caveats are still operative regarding renaming of Residuals for

multiple runs.

see original thread

http://spssx-discussion.1045642.n5.nabble.com/BREUSCH-PAGAN-amp-KOENKER-TEST-MACRO-undefined-variables-td5727299.html#a5731408*------------.

* BREUSCH-PAGAN & KOENKER TEST MACRO *

* See 'Heteroscedasticity: Testing and correcting in SPSS'

* by Gwilym Pryce, for technical details.

* REVISION HISTORY *.

* Code by Marta Garcia-Granero 2002/10/28.

* Modified by David Marso 2014/09/18

* (changed AGGREGATE and MATCH to use MODE=ADDVARIABLES,

slight mods to MATRIX code, some formatting changes)

* Modified by David Marso 2018/02/23

* (Modified Output formatting to support new Pivot Table support in

MATRIX).

* The MACRO needs 3 arguments:

* the dependent, the number of predictors and the list of predictors

* (if they are consecutive, the keyword TO can be used) .

* (1) MACRO definition (select and run just ONCE).

DEFINE bpktest(

!POSITIONAL !TOKENS(1)

/!POSITIONAL !TOKENS(1)

/!POSITIONAL !CMDEND).

* Regression to GET the residuals and residual plots.

REGRESSION

/STATISTICS R ANOVA

/DEPENDENT !1

/METHOD=ENTER !3

/SCATTERPLOT=(*ZRESID,*ZPRED)

/RESIDUALS HIST(ZRESID) NORM(ZRESID)

/SAVE RESID(residual) .

ECHO "Examine the scatter plot of the residuals to detect model

misspecification and/or heteroscedasticity" .

ECHO "".

ECHO "Also, check the histogram and np plot of residuals to detect non

normality of residuals " .

ECHO "Skewness and kurtosis more than twice their SE indicate

non-normality".

* Checking normality of residuals.

DESCRIPTIVES VARIABLES=residual /STATISTICS=KURTOSIS SKEWNESS .

* New dependent variable (g) creation.

COMPUTE sq_res=residual**2.

AGGREGATE

/OUTFILE=* MODE ADDVARIABLES

/BREAK=

/rss = SUM(sq_res)

/N=N.

COMPUTE g=sq_res/(rss/n).

* BP&K tests.

* Regression of g on the predictors.

REGRESSION

/STATISTICS R ANOVA

/DEPENDENT g

/METHOD=ENTER !3

/SAVE RESID(resid) .

* Routine adapted from Gwilym Pryce.

MATRIX.

COMPUTE p=!2.

GET g

/ VARIABLES=g.

GET resid

/ VARIABLES=resid.

COMPUTE sq_res2 = resid&**2.

COMPUTE n = nrow(g).

COMPUTE rss = msum(sq_res2).

COMPUTE m0 = ident(n)-((1/n)*make(n,n,1)).

COMPUTE tss = transpos(g)*m0*g.

COMPUTE regss = tss-msum(sq_res2).

COMPUTE r_sq=1-(rss/tss).

COMPUTE bp_test=0.5*regss.

COMPUTE BP_sig=1-chicdf(bp_test,p).

COMPUTE k_test=n*r_sq.

COMPUTE K_sig=1-chicdf(k_test,p).

*Final report.

PRINT /TITLE " BP&K TESTS".

PRINT { regss , rss , tss, r_sq}

/TITLE "Sums of Squares Partitioning"

/FORMAT "F8.4"

/RLABELS " "

/CLABELS "Regression SS","Residual SS","Total SS","R-squared".

PRINT {n,p}

/TITLE "Problem Size"

/FORMAT "F4.0"

/RLABELS " "

/CLABELS "Sample size (N)","Number of predictors (P)".

PRINT {bp_test ,BP_sig ;k_test ,K_sig }

/TITLE " Breusch-Pagan and Koenker tests for Heteroscedasticity"

/FORMATS "F8.4"

/CLABELS "Test Statistic Chi Square (df=P)", "Significance level of

Chi-square df=(H0:homoscedasticity)"

/RLABELS "Breusch-Pagan","Koenker".

END MATRIX.

!ENDDEFINE.

* (2) Sample data (replace by your own)*.

INPUT PROGRAM.

- VECTOR x(20).

- LOOP #I = 1 TO 50.

- LOOP #J = 1 TO 20.

- COMPUTE x(#J) = NORMAL(1).

- END LOOP.

- END CASE.

- END LOOP.

- END FILE.

END INPUT PROGRAM.

EXECUTE.

* Sets the mode for displaying output in MATRIX.

SET MDISPLAY=TABLES.

* (3) MACRO CALL (select and run).

* x1 is the dependent and x2 TO x20 the predictors.

BPKTEST x1 19 x2 TO x20 .

PRogman wrote

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