I was correct in my comment that you can levy the use of the deletion statistics available in the REGRESSION
procedure to get the info you need without having to loop through the dataset yourself.
What you have to do is calculate your own constant value of 1 and force the REGRESSION
through the origin (as SPSS does not let you specify an empty regression equation) predicting your variable of interest. Then have the regression procedure save the deletion residuals. The difference between these deletion residuals and your original variable are the jackknifed means with that observation deleted.
So in a nutshell this code would provide that info - just replace X
with your variable of interest.
COMPUTE Const = 1.
REGRESSION
/ORIGIN
/DEPENDENT X
/METHOD=ENTER Const
/SAVE DRESID (MeanResid).
COMPUTE JackknifeMeanX = X - MeanResid.
Full example (with fake data and checking via aggregate) is below:
INPUT PROGRAM.
LOOP Id = 1 TO 10.
END CASE.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME Sim.
COMPUTE X = RV.NORMAL(10,5).
COMPUTE Const = 1.
FORMATS Id Const (F2.0).
EXECUTE.
*Using deletion residuals in linear regression to calculate Jackknifed mean.
*Here I calculate my own intercept and force through origin.
REGRESSION
/ORIGIN
/DEPENDENT X
/METHOD=ENTER Const
/SAVE DRESID (MeanResid).
COMPUTE JackknifeMeanX = X - MeanResid.
*Checking to make sure this agrees with data.
VECTOR XMis(10).
LOOP #i = 1 TO 10.
IF $casenum <>#i XMis(#i) = X.
END LOOP.
AGGREGATE OUTFILE = * OVERWRITE=YES MODE=ADDVARIABLES
/BREAK
/XMis1 TO XMis10=MEAN(Xmis1 TO XMis10).