Dear Splus users,
I do have an example of a gaussian additive model where the standard
error obtained with preplot.gam() are not the same as those obtained by
predict.gam() (cf. below). Has someone else observed this behavior?
(which seems strange to me, because I understood that preplot.gam() was
simply using predict.gam()).
I'm using Version 6.1.2 Release 2 for Sun SPARC, SunOS 5.6 : 2002.
Thank you in advance for any insight.
Eva Cantoni
--
Dr Eva Cantoni phone : (+41) 22 379 8240
Econométrie - Univ. Genève fax : (+41) 22 379 8299
40, Bd du Pont d'Arve e-mail : Eva.Cantoni@metri.unige.ch
CH-1211 Genève 4 http://www.unige.ch/ses/metri/cantoni
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xx1 <- sort(rnorm(50))
yy1 <- 2+5*xx12+2*rnorm(50)
gamfit1 <- gam(yy1~s(xx1,spar=0.005/(max(xx1)-min(xx1))3))
(preplot.gam(gamfit1)[[1]])$se.y
1 2 3 4 5 6 7 8
1.491961 1.316211 1.140203 1.048695 1.049141 0.951788 0.865604 0.843049
9 10 11 12 13 14
15 16
0.8321379 0.8358734 0.8541559 0.9144309 0.9891901 1.121024 0.9510405
0.878901
17 18 19 20 21 22 23
0.8619451 0.8121189 0.7051739 0.7020254 0.696297 0.6854458 0.7156899
24 25 26 27 28 29 30
0.7215519 0.8246976 0.8737922 0.9615788 0.8114632 0.688451 0.6600137
31 32 33 34 35 36
37 38
0.6147368 0.6138927 0.6145545 0.618617 0.631937 0.7366704 0.7574085
0.751308
39 40 41 42 43 44
45 46
0.7301003 0.7299721 0.7335928 0.8317819 0.8597737 0.9948622 1.066162
1.048952
47 48 49 50
1.048959 1.038358 1.046152 1.566741
predict.gam(gamfit1,se.fit=T)$se.fit
1 2 3 4 5 6 7 8
1.51961 1.347471 1.17615 1.087671 1.088102 0.9945692 0.9124351 0.8910663
9 10 11 12 13 14
15 16
0.8807503 0.8842804 0.9015819 0.9588803 1.03042 1.157568 0.9938539
0.9250592
17 18 19 20 21 22 23
0.9089647 0.8618612 0.7619303 0.7590173 0.7537221 0.7437093 0.7716732
24 25 26 27 28 29 30 31
0.7771131 0.873724 0.9202067 1.003943 0.8612433 0.74648 0.7203371 0.6790942
32 33 34 35 36 37 38
0.6783302 0.6789292 0.6826086 0.6947028 0.7911705 0.8105154 0.8048175
39 40 41 42 43 44 45 46
0.7850567 0.7849375 0.7883057 0.880414 0.906906 1.035866 1.104522 1.087919
47 48 49 50
1.087925 1.077708 1.08522 1.593093
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