Diff for "FAQ/points" - CBU statistics Wiki
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Differences between revisions 3 and 5 (spanning 2 versions)
 ⇤ ← Revision 3 as of 2007-06-08 10:30:12 → Size: 681 Editor: PeterWatson Comment: ← Revision 5 as of 2007-06-08 10:45:05 → ⇥ Size: 916 Editor: PeterWatson Comment: Deletions are marked like this. Additions are marked like this. Line 1: Line 1: = How do I scatterplot observations which have the same set of co-ordinates?= = How do I scatterplot observations which have the same set of co-ordinates? = Line 5: Line 5: One way of disentangling this is to add a proportionately small amount to the observed values of one of the variables, say, y. The below syntax uses the rv.uniform function in SPSS to add a small random amount to the y values. The new values which are now all unique (ynew) can then be plotted against x. One way of disentangling this is to add a proportionately small amount to the observed values of one of the variables, say, y. The below syntax uses the rv.uniform function in SPSS to add a small random amount to the y values when an x,y combination has previously occurred. The new values which are now all unique (ynew) can then be plotted against x. Line 8: Line 8: COMPUTE YNEW = Y + RV.UNIFORM(Y*0.01,Y*0.015). sort cases by x.exe.sort cases by y.exe.COMPUTE copy=0. DO IF (\$CASENUM NE 1). IF (x EQ LAG(x) AND y EQ LAG(y)) copy = 1. END IF. EXECUTE. compute ynew = y.if (copy eq 1) YNEW = Y + RV.UNIFORM(Y*0.01,Y*0.015).

# How do I scatterplot observations which have the same set of co-ordinates?

Sometimes the same co-ordinates are shared by more than one observation. A scatterplot will only, however, show one point for each unique x,y combination regardless of the number of observations that share this combination.

One way of disentangling this is to add a proportionately small amount to the observed values of one of the variables, say, y. The below syntax uses the rv.uniform function in SPSS to add a small random amount to the y values when an x,y combination has previously occurred. The new values which are now all unique (ynew) can then be plotted against x.

```sort cases by x.
exe.
sort cases by y.
exe.
COMPUTE copy=0.
DO IF (\$CASENUM NE 1).
IF (x EQ LAG(x) AND y EQ LAG(y)) copy = 1.
END IF.
EXECUTE.

compute ynew = y.
if (copy eq 1) YNEW = Y + RV.UNIFORM(Y*0.01,Y*0.015).
EXE.```

None: FAQ/points (last edited 2013-03-08 10:17:37 by localhost)