[19129] in s-news-athena
[S] Data input for 3D displays
daemon@ATHENA.MIT.EDU (Steve Friedman)
Tue Sep 7 15:07:36 1999
Message-Id: <37D56175.83CD40A5@gis.umn.edu>
Date: Tue, 07 Sep 1999 14:03:17 -0500
From: Steve Friedman <friedman@gis.umn.edu>
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Is there an easy approach to reading in a data file with the following
format inorder to display the variance - covariance matices in 3D space?
The data represent a principal components analysis conducted in another
software package that only provides 2D display capabilities. Is a
list format the most appropriate?
I am working with Unix Splus 3.3 and Windows/NT 4.0(Rel 3) versions of
Splus.
Thanks in advance.
Steve Friedman
---------------------------------------------------------------------------------------------------------------------------------------------
The following is an abbreviation of the whole data set. There are a
total of 6 9x9 matrices that need to be read into Splus.
Signatures Produced by Clustering of
# Stack glo-pca9
# number_of_classes=9 max_iterations=20 min_class_size=20
# sampling interval=10
# Number of selected grids
/* 9
# Layer-Number Grid-name
/* 1 glo-pca9c1
/* 2 glo-pca9c2
/* 3 glo-pca9c3
/* 4 glo-pca9c4
/* 5 glo-pca9c5
/* 6 glo-pca9c6
/* 7 glo-pca9c7
/* 8 glo-pca9c8
/* 9 glo-pca9c9
# Type Number of Classes Number of Layers Number of Parametric
Layers
1 6 9 9
# ===================================================================
# Class ID Number of Cells Class Name
1 24
# Layers 1 2 3
4 5 6 7 8 9
# Means
22.00869 22.13729 21.78568 18.40838
30.42839 27.39980 29.62657 43.35511 14.05082
# Covariance
1 12.54341 -3.18291 4.63164
-7.12572 7.28921 1.08599 -2.86335 -0.40784
-0.18954
2 -3.18291 44.65911 -21.08728 -27.28617
39.56582 -3.99729 -2.16451 -2.03948 -0.27197
3 4.63164 -21.08728 22.60505 17.31201
-12.81606 7.74802 6.49254 -1.44548 -0.57784
4 -7.12572 -27.28617 17.31201 32.57132
-36.59619 6.73524 11.21372 -0.74242 0.15175
5 7.28921 39.56582 -12.81606 -36.59619
60.46747 -1.26129 -11.33028 -0.25687 -0.08171
6 1.08599 -3.99729 7.74802 6.73524
-1.26129 14.52963 3.38596 -3.37280 -0.24261
7 -2.86335 -2.16451 6.49254 11.21372
-11.33028 3.38596 19.60374 -3.00988 -1.10751
8 -0.40784 -2.03948 -1.44548 -0.74242
-0.25687 -3.37280 -3.00988 3.41226 1.30980
9 -0.18954 -0.27197 -0.57784 0.15175
-0.08171 -0.24261 -1.10751 1.30980 1.69580
# -------------------------------------------------------------------
# Class ID Number of Cells Class Name
2 74
# Layers 1 2 3
4 5 6 7 8 9
# Means
19.09742 31.78932 13.10741 9.75088
37.72881 24.55387 28.86406 42.52163 14.37483
# Covariance
1 11.43881 -3.68358 1.02217
-2.23663 3.48395 1.95301 -1.76695 3.24258
0.43129
2 -3.68358 15.21887 1.79738 0.91560
-1.78592 -0.70240 0.98778 -6.78782 2.96308
3 1.02217 1.79738 15.66270
3.83827 4.07835 4.60192 -2.46830 -0.16057
0.10128
4 -2.23663 0.91560 3.83827 10.37283
-3.94365 -3.44885 -2.42828 -3.67554 -0.77942
5 3.48395 -1.78592 4.07835 -3.94365
15.22436 12.46037 -1.23389 2.51850 -1.46522
6 1.95301 -0.70240 4.60192 -3.44885
12.46037 25.12691 -5.76019 -0.28795 -0.83940
7 -1.76695 0.98778 -2.46830 -2.42828
-1.23389 -5.76019 15.88285 1.78492 -0.73729
8 3.24258 -6.78782 -0.16057
-3.67554 2.51850 -0.28795 1.78492 11.31631
-1.82259
9 0.43129 2.96308 0.10128 -0.77942
-1.46522 -0.83940 -0.73729 -1.82259 6.67403
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