Posted by
Swank, Paul R on
Apr 20, 2011; 6:48pm
URL: http://spssx-discussion.165.s1.nabble.com/Discriminant-function-analysis-tp4315226p4328937.html
I hope you have a large sample because discriminant function analysis requires 20-30 subjects per variable per group to adequately estimate the parameters of the model. If you have a smaller sample size, multinomial logistic regression might be a better option.
Dr. Paul R. Swank,
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center-Houston
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of Pete
Sent: Wednesday, April 20, 2011 3:33 AM
To:
[hidden email]
Subject: Discriminant function analysis
I've done a discriminant function analysis in SPSS intended to classify 3
types of case using 30 variables. The discriminant function coefficients and
structure matrix are below. It is clear that the two functions do different
things with these data. I have the following questions:
1. How do I identify what each function is doing when cases are classified?
2. I want to calculate on a case by case basis the scores for truncated
forms of each discriminant function (f1 S1DUR - S3PIRANG and f2 S3INRANG
-RPVIS1). I assume I generate a linear equation using these coefficients to
weight each variable. Is that right?
3. Alternatively, is there a simple way of obtaining the scores from each
case for each discriminant function by procedures in SPSS? On reflection,
this might be the preferred option, but I will stand to be advised.
any other advice, observations would be gratefully received.
Many thanks
PH
Standardized Canonical Discriminant Function Coefficients
Function
1 2
S1DUR .106 -.090
S1PIRANG -.041 .074
S1INRENG .087 .009
S2DUR -.048 -.276
S2PIRANG .198 -.284
S2INRANG .211 -.113
S3DUR -1.906 .829
S3PIRANG .034 .016
S3INRANG .273 .437
S4DUR 1.546 -.843
S4PIRANG .123 .118
S4INRANG -.326 -.145
NPVIS1 -.246 -.002
NPVIS2 -.165 .343
NPVIS3 .096 -.201
RPVIS4 .253 .362
NPVIS4 .373 -.099
RPVIS1PI -.250 .234
RPVIS2PI .256 .345
RPVIS3PI .067 .107
RPVIS4PI .081 -.113
RPVIS1IN .061 .316
RPVIS2IN -.689 -.097
RPVIS3IN .640 -.269
RPVIS4IN .229 .239
RPVI1TO4 -.200 1.226
NPVI1TO4 .082 -1.588
Structure Matrix
Function
1 2
NPVIS4 .324 -.053
S4INRANG -.255 -.014
S2INRANG .245 .076
RPVIS3IN .230 -.029
S3DUR -.227 -.065
RPVIS3 -.227 -.065
NPVIS1 -.222 .047
S4DUR -.155 -.079
RPVIS2IN -.119 .068
S2DUR -.117 .012
RPVIS2 -.117 .012
RPVIS4PI .106 .053
RPVIS4 .103 .047
S1INRENG -.100 -.034
S3PIRANG .088 -.038
RPVI1TO4 -.077 .057
S4PIRANG .072 .049
S3INRANG .141 .354
RPVIS3PI .040 .318
RPVIS2PI .175 .313
NPVIS2 -.210 .306
RPVIS1PI -.124 .225
S2PIRANG .078 -.207
RPVIS4IN .119 .181
NPVI1TO4 -.064 -.168
NPVIS3 .111 -.167
RPVIS1IN .144 .158
S1PIRANG .069 -.082
S1DUR .033 -.062
RPVIS1 .033 -.062
Pooled within-groups correlations between discriminating variables and
standardized canonical discriminant functions
Variables ordered by absolute size of correlation within function.
* Largest absolute correlation between each variable and any discriminant
function
a This variable not used in the analysis.
--
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