R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8.2
+ ,3.7
+ ,5.1
+ ,6.8
+ ,4.9
+ ,8.5
+ ,4.3
+ ,5
+ ,5.7
+ ,4.9
+ ,4.3
+ ,5.3
+ ,7.9
+ ,8.2
+ ,4
+ ,3.9
+ ,8.9
+ ,4.5
+ ,4
+ ,4.5
+ ,7.4
+ ,9.2
+ ,4.6
+ ,5.4
+ ,4.8
+ ,3
+ ,4.1
+ ,8.8
+ ,4.7
+ ,6.4
+ ,3.6
+ ,4.3
+ ,7.1
+ ,3.5
+ ,3.5
+ ,6.8
+ ,6
+ ,9
+ ,4.5
+ ,4.5
+ ,4.7
+ ,3.3
+ ,4.7
+ ,8.5
+ ,4.3
+ ,6.5
+ ,9.5
+ ,3.6
+ ,5.7
+ ,2
+ ,4.2
+ ,8.9
+ ,2.3
+ ,6.9
+ ,2.5
+ ,2.1
+ ,6.3
+ ,3.7
+ ,6.3
+ ,6.9
+ ,3.6
+ ,6.2
+ ,4.8
+ ,4.3
+ ,7
+ ,4.6
+ ,6.1
+ ,9.3
+ ,5.9
+ ,5.8
+ ,4.4
+ ,4.4
+ ,5.5
+ ,4.4
+ ,5.8
+ ,8.4
+ ,5.7
+ ,6.4
+ ,5.3
+ ,4.1
+ ,7.4
+ ,4
+ ,3.7
+ ,6.8
+ ,6.8
+ ,8.7
+ ,7.5
+ ,3.8
+ ,6
+ ,3.2
+ ,4.9
+ ,8.2
+ ,3.9
+ ,6.1
+ ,5.9
+ ,3
+ ,8.4
+ ,4.4
+ ,4.5
+ ,7.6
+ ,6.9
+ ,9.5
+ ,5.3
+ ,5.1
+ ,7.6
+ ,4.2
+ ,2.6
+ ,7.1
+ ,8.4
+ ,9.2
+ ,3
+ ,4.5
+ ,8
+ ,5.2
+ ,6.2
+ ,8.8
+ ,6.8
+ ,6.3
+ ,5.4
+ ,4.8
+ ,6.6
+ ,4.5
+ ,3.9
+ ,4.9
+ ,7.8
+ ,8.7
+ ,5
+ ,4.3
+ ,6.4
+ ,4.5
+ ,6.2
+ ,6.2
+ ,5.5
+ ,5.7
+ ,5.4
+ ,4.2
+ ,7.4
+ ,4.8
+ ,5.8
+ ,8.4
+ ,6.4
+ ,5.9
+ ,6.3
+ ,5.7
+ ,6.8
+ ,4.5
+ ,6
+ ,9.1
+ ,5.7
+ ,5.6
+ ,6.1
+ ,5
+ ,7.6
+ ,4.4
+ ,6.1
+ ,8.4
+ ,5.3
+ ,9.1
+ ,6.7
+ ,4.5
+ ,5.4
+ ,3.3
+ ,4.9
+ ,8.4
+ ,4.3
+ ,5.2
+ ,4.6
+ ,3.3
+ ,9.9
+ ,4.3
+ ,3
+ ,4.5
+ ,8.3
+ ,9.6
+ ,6.5
+ ,4.3
+ ,7
+ ,4
+ ,3.4
+ ,3.7
+ ,7.3
+ ,8.6
+ ,6
+ ,4.8
+ ,8.6
+ ,4.5
+ ,4.4
+ ,6.2
+ ,7.2
+ ,9.3
+ ,4.2
+ ,6.7
+ ,4.8
+ ,4
+ ,5.3
+ ,8
+ ,5.3
+ ,6
+ ,3.9
+ ,4.7
+ ,6.6
+ ,3.9
+ ,6.6
+ ,7.1
+ ,3.9
+ ,6.4
+ ,3.7
+ ,5.6
+ ,6.3
+ ,4.4
+ ,3.8
+ ,4.8
+ ,7.6
+ ,8.5
+ ,6.7
+ ,5.3
+ ,5.4
+ ,3.7
+ ,5.2
+ ,9
+ ,4.8
+ ,7
+ ,5.9
+ ,4.3
+ ,6.3
+ ,4.4
+ ,3.8
+ ,4.8
+ ,7.6
+ ,8.5
+ ,6
+ ,5.7
+ ,5.4
+ ,3.5
+ ,5.5
+ ,7.7
+ ,4.2
+ ,7.6
+ ,7.2
+ ,4.7
+ ,6.1
+ ,3.3
+ ,2.7
+ ,5.2
+ ,6.4
+ ,6.9
+ ,3.3
+ ,3.7
+ ,6.4
+ ,3
+ ,3.5
+ ,6.6
+ ,5.1
+ ,8.1
+ ,6.1
+ ,3
+ ,5.4
+ ,3.4
+ ,4.5
+ ,9.2
+ ,5.1
+ ,6.7
+ ,4.2
+ ,3.5
+ ,7.3
+ ,4.2
+ ,6.6
+ ,8.7
+ ,4.6
+ ,8
+ ,3.8
+ ,4.7
+ ,6.3
+ ,3.5
+ ,4.3
+ ,8.4
+ ,5.4
+ ,6.7
+ ,6
+ ,2.5
+ ,5.4
+ ,2.5
+ ,2.9
+ ,5.6
+ ,6.1
+ ,8.7
+ ,6.5
+ ,3.1
+ ,7.1
+ ,3.5
+ ,3.5
+ ,6.8
+ ,6
+ ,9
+ ,4.3
+ ,3.9
+ ,8.7
+ ,4.9
+ ,4.6
+ ,7.7
+ ,7.7
+ ,9.6
+ ,4.4
+ ,5.2
+ ,7.6
+ ,4.5
+ ,6.9
+ ,9
+ ,4.9
+ ,8.2
+ ,7.1
+ ,4.7
+ ,6
+ ,3.2
+ ,4.9
+ ,8.2
+ ,3.9
+ ,6.1
+ ,6.8
+ ,4.5
+ ,7
+ ,3.9
+ ,5.8
+ ,9.1
+ ,4.6
+ ,8.3
+ ,1.7
+ ,4.6
+ ,7.6
+ ,4.1
+ ,4.5
+ ,8.5
+ ,6.5
+ ,9.4
+ ,6.2
+ ,4.1
+ ,8.9
+ ,4.3
+ ,4.6
+ ,7.4
+ ,6.6
+ ,9.3
+ ,4.1
+ ,4.6
+ ,7.6
+ ,4.5
+ ,6.3
+ ,5.9
+ ,5.4
+ ,5.1
+ ,5.2
+ ,4.9
+ ,5.5
+ ,4.7
+ ,4.2
+ ,5.2
+ ,7.7
+ ,8
+ ,3.9
+ ,4.3
+ ,7.4
+ ,4.8
+ ,5.8
+ ,8.4
+ ,6.4
+ ,5.9
+ ,5.1
+ ,5.2
+ ,7.1
+ ,3.5
+ ,4
+ ,3.8
+ ,5.4
+ ,10
+ ,3.7
+ ,5
+ ,7.6
+ ,5.2
+ ,7.3
+ ,8.2
+ ,5.7
+ ,5.7
+ ,4.8
+ ,6.5
+ ,8.7
+ ,3.9
+ ,3.4
+ ,6.8
+ ,7
+ ,9.9
+ ,7.2
+ ,4.5
+ ,8.6
+ ,4.3
+ ,4.2
+ ,4.7
+ ,6.9
+ ,7.9
+ ,3.6
+ ,4.1
+ ,5.4
+ ,2.8
+ ,3.6
+ ,7.2
+ ,4.7
+ ,6.7
+ ,5.3
+ ,4
+ ,5.7
+ ,4.9
+ ,4.3
+ ,5.3
+ ,7.9
+ ,8.2
+ ,5
+ ,4.5
+ ,8.7
+ ,4.6
+ ,4.6
+ ,6.3
+ ,7.3
+ ,9.4
+ ,9.2
+ ,4.7
+ ,6.1
+ ,3.3
+ ,2.7
+ ,5.2
+ ,6.4
+ ,6.9
+ ,4.4
+ ,3.2
+ ,7.3
+ ,4.2
+ ,6.6
+ ,8.7
+ ,4.6
+ ,8
+ ,4.2
+ ,4.9
+ ,7.7
+ ,3.4
+ ,3.2
+ ,7.4
+ ,6.4
+ ,9.3
+ ,5.9
+ ,4.1
+ ,9
+ ,5.5
+ ,6.5
+ ,9.6
+ ,7.2
+ ,7.4
+ ,7.4
+ ,5.7
+ ,8.2
+ ,4
+ ,3.9
+ ,4.4
+ ,6.6
+ ,7.6
+ ,6.4
+ ,4.6
+ ,7.1
+ ,3.5
+ ,4
+ ,3.8
+ ,5.4
+ ,10
+ ,4.5
+ ,3.7
+ ,7.9
+ ,4
+ ,4.9
+ ,5.4
+ ,5.8
+ ,9.9
+ ,7
+ ,5.6
+ ,6.6
+ ,4.5
+ ,3.9
+ ,4.9
+ ,7.8
+ ,8.7
+ ,4.5
+ ,5.4
+ ,8
+ ,3.6
+ ,5
+ ,6.7
+ ,4.7
+ ,8.4
+ ,4.2
+ ,2.7
+ ,6.3
+ ,2.9
+ ,3.7
+ ,5.8
+ ,4.7
+ ,8.8
+ ,7.2
+ ,4.4
+ ,6
+ ,2.6
+ ,3.1
+ ,6.2
+ ,4.7
+ ,7.7
+ ,4.7
+ ,3.3
+ ,5.4
+ ,2.8
+ ,3.6
+ ,7.2
+ ,4.7
+ ,6.6
+ ,3.9
+ ,3.5
+ ,7.6
+ ,5.2
+ ,7.3
+ ,8.2
+ ,5.7
+ ,5.7
+ ,5
+ ,4.7
+ ,6.4
+ ,4.5
+ ,6.2
+ ,6.2
+ ,5.5
+ ,5.7
+ ,6.4
+ ,5
+ ,6.1
+ ,4.3
+ ,5.9
+ ,6
+ ,5.3
+ ,5.5
+ ,2.5
+ ,4.5
+ ,5.2
+ ,3.4
+ ,5.4
+ ,7.6
+ ,4.1
+ ,7.5
+ ,5.2
+ ,4
+ ,6.6
+ ,3.9
+ ,6.6
+ ,7.1
+ ,3.9
+ ,6.4
+ ,5.5
+ ,4.7
+ ,7.6
+ ,4.4
+ ,6.1
+ ,8.4
+ ,5.3
+ ,9.1
+ ,5.7
+ ,5.4
+ ,5.8
+ ,3.1
+ ,2.6
+ ,5
+ ,6.3
+ ,6.7
+ ,2.5
+ ,2.9
+ ,7.9
+ ,4.6
+ ,5.6
+ ,8.7
+ ,6.3
+ ,6.5
+ ,6.3
+ ,4.6
+ ,8.6
+ ,3.9
+ ,3.4
+ ,6.8
+ ,7
+ ,9.9
+ ,4.6
+ ,4.1
+ ,8.2
+ ,3.7
+ ,5.1
+ ,6.8
+ ,4.9
+ ,8.5
+ ,3.6
+ ,4.4
+ ,7.1
+ ,3.8
+ ,4.3
+ ,4.9
+ ,5.9
+ ,9.9
+ ,7.6
+ ,3.1
+ ,6.4
+ ,3.9
+ ,5.8
+ ,7.4
+ ,4.6
+ ,7.6
+ ,6.6
+ ,4.5
+ ,7.6
+ ,4.1
+ ,4.5
+ ,8.5
+ ,6.5
+ ,9.4
+ ,2.4
+ ,4.3
+ ,8.9
+ ,4.6
+ ,4.1
+ ,4.6
+ ,7.5
+ ,9.3
+ ,3.1
+ ,5.2
+ ,5.7
+ ,2.7
+ ,3.1
+ ,7.8
+ ,5
+ ,7.1
+ ,3.5
+ ,2.6
+ ,7.1
+ ,3.8
+ ,4.3
+ ,4.9
+ ,5.9
+ ,9.9
+ ,6.9
+ ,3.2
+ ,7.4
+ ,4
+ ,3.7
+ ,6.8
+ ,6.8
+ ,8.7
+ ,5.1
+ ,4.3
+ ,6.6
+ ,3
+ ,3
+ ,6.3
+ ,5.6
+ ,8.6
+ ,4
+ ,2.7
+ ,5
+ ,1.6
+ ,3.7
+ ,8.4
+ ,2.9
+ ,6.4
+ ,6.5
+ ,2
+ ,8.2
+ ,4.3
+ ,3.9
+ ,5.9
+ ,7.2
+ ,7.7
+ ,4.1
+ ,4.7
+ ,5.2
+ ,3.4
+ ,5.4
+ ,7.6
+ ,4.1
+ ,7.5
+ ,2.8
+ ,3.4
+ ,5.2
+ ,3.1
+ ,4.8
+ ,8.2
+ ,4.2
+ ,5
+ ,7.6
+ ,2.4
+ ,8.2
+ ,4.3
+ ,3.9
+ ,5.9
+ ,7.2
+ ,7.7
+ ,7.7
+ ,5.1
+ ,7.3
+ ,3.9
+ ,4.3
+ ,8.3
+ ,6.2
+ ,9.1
+ ,4.1
+ ,4.6
+ ,8.2
+ ,4.9
+ ,6.7
+ ,6.3
+ ,5.7
+ ,5.5
+ ,4.9
+ ,5.5
+ ,7.4
+ ,3.3
+ ,3
+ ,7.3
+ ,6.3
+ ,9.1
+ ,4.6
+ ,4.4
+ ,4.8
+ ,2.4
+ ,4
+ ,9.9
+ ,3.3
+ ,7.1
+ ,3.5
+ ,2
+ ,7.6
+ ,4.2
+ ,2.6
+ ,7.1
+ ,8.4
+ ,9.2
+ ,6.6
+ ,4.4
+ ,8.9
+ ,4.6
+ ,4.1
+ ,4.6
+ ,7.5
+ ,9.3
+ ,4.9
+ ,4.8
+ ,7.7
+ ,3.4
+ ,3.2
+ ,7.4
+ ,6.4
+ ,9.3
+ ,4.8
+ ,3.6
+ ,7.3
+ ,3.6
+ ,3.6
+ ,6.7
+ ,6
+ ,8.6
+ ,3.6
+ ,4.9
+ ,6.3
+ ,3.7
+ ,5.6
+ ,7.2
+ ,4.4
+ ,7.4
+ ,6.4
+ ,4.2
+ ,5.4
+ ,2.5
+ ,2.9
+ ,5.6
+ ,6.1
+ ,8.7
+ ,4.3
+ ,3.1
+ ,6.4
+ ,3.9
+ ,4.9
+ ,7.9
+ ,5.3
+ ,7.8
+ ,5.7
+ ,4.3
+ ,6.4
+ ,3.5
+ ,5.4
+ ,9.7
+ ,4.2
+ ,7.9
+ ,5.8
+ ,3.4
+ ,5.4
+ ,3.5
+ ,5.5
+ ,7.7
+ ,4.2
+ ,7.6
+ ,5.1
+ ,3.1
+ ,8.7
+ ,4.2
+ ,4.6
+ ,7.3
+ ,6.5
+ ,9.2
+ ,8.6
+ ,5.1
+ ,6.1
+ ,3.7
+ ,4.7
+ ,7.7
+ ,5.2
+ ,7.7
+ ,5.4
+ ,4
+ ,8.4
+ ,4.4
+ ,4.5
+ ,7.6
+ ,6.9
+ ,9.5
+ ,4.4
+ ,5.6
+ ,7.9
+ ,4.6
+ ,5.6
+ ,8.7
+ ,6.3
+ ,6.5
+ ,6.9
+ ,5
+ ,7
+ ,3.9
+ ,5.8
+ ,9.1
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+ ,8.3
+ ,5.2
+ ,4.2
+ ,8.7
+ ,4.9
+ ,4.6
+ ,7.7
+ ,7.7
+ ,9.6
+ ,5.5
+ ,4.4
+ ,7.9
+ ,5.4
+ ,7.5
+ ,8.4
+ ,5.9
+ ,5.9
+ ,5.3
+ ,5.8
+ ,7.1
+ ,4.2
+ ,3.5
+ ,3.8
+ ,7.4
+ ,8.7
+ ,5.7
+ ,4.6
+ ,5.8
+ ,3.1
+ ,2.6
+ ,5
+ ,6.3
+ ,6.7
+ ,6.5
+ ,3.8
+ ,8.4
+ ,4.1
+ ,3.4
+ ,6.7
+ ,7.5
+ ,9.7
+ ,5.2
+ ,3.7
+ ,7.1
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+ ,2.3
+ ,6.7
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+ ,2.7
+ ,4
+ ,7.6
+ ,4.5
+ ,6.9
+ ,9
+ ,4.9
+ ,8.2
+ ,4.3
+ ,4.5
+ ,7.3
+ ,4.2
+ ,5.9
+ ,8.2
+ ,5.1
+ ,8.9
+ ,6.7
+ ,4.2
+ ,8
+ ,3.6
+ ,5
+ ,6.7
+ ,4.7
+ ,8.4
+ ,6.6
+ ,4
+ ,6.1
+ ,3.7
+ ,4.7
+ ,7.7
+ ,5.2
+ ,7.7
+ ,7.4
+ ,5.1
+ ,8.7
+ ,4.2
+ ,4.6
+ ,7.3
+ ,6.5
+ ,9.2
+ ,8.9
+ ,4.2
+ ,5.8
+ ,2.9
+ ,3.3
+ ,8
+ ,5.2
+ ,7.3
+ ,3.7
+ ,2.8
+ ,6.4
+ ,3.1
+ ,3.9
+ ,6
+ ,4.8
+ ,9
+ ,4.9
+ ,3.3
+ ,6.4
+ ,3
+ ,3.5
+ ,6.6
+ ,5.1
+ ,8.1
+ ,6.2
+ ,2.6
+ ,9
+ ,5.5
+ ,6.5
+ ,9.6
+ ,7.2
+ ,7.4
+ ,4.3
+ ,5.7
+ ,6.4
+ ,3.5
+ ,5.4
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+ ,4.6
+ ,4.8
+ ,6
+ ,2.6
+ ,3.1
+ ,6.2
+ ,4.7
+ ,7.7
+ ,4.3
+ ,3.2
+ ,8.7
+ ,4.6
+ ,4.6
+ ,6.3
+ ,7.3
+ ,9.4
+ ,5.4
+ ,5.8
+ ,5
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+ ,4.1
+ ,10
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+ ,3.6
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+ ,2.9
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+ ,5.2
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+ ,4.6
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+ ,6
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+ ,5.3
+ ,6.6
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+ ,4.3
+ ,8
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+ ,5.8
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+ ,3.6
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+ ,4.4
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+ ,9.4
+ ,6
+ ,5.1
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+ ,2.1
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+ ,5.2
+ ,5.7
+ ,8.3
+ ,4.2
+ ,1.8
+ ,6.4
+ ,3.9
+ ,4.9
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+ ,5.3
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+ ,5.2
+ ,4.1
+ ,4.8
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+ ,4
+ ,9.9
+ ,3.3
+ ,7.1
+ ,3.1
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+ ,6.4
+ ,3.9
+ ,5.8
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+ ,4.6
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+ ,5.3
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+ ,6
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+ ,5.6
+ ,6
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+ ,4
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+ ,7.4
+ ,9.2
+ ,6.1
+ ,4.2
+ ,7.4
+ ,4.2
+ ,3.4
+ ,7.3
+ ,7.5
+ ,9.1
+ ,5.2
+ ,4.5
+ ,7
+ ,3.5
+ ,4
+ ,3.8
+ ,5.4
+ ,9.9
+ ,8
+ ,3.8
+ ,7
+ ,3.5
+ ,4
+ ,3.8
+ ,5.4
+ ,9.9
+ ,6.2
+ ,4.1
+ ,6
+ ,3.3
+ ,4.1
+ ,8.2
+ ,5.3
+ ,6.6
+ ,3.5
+ ,4.6
+ ,7.4
+ ,3.3
+ ,3
+ ,7.3
+ ,6.3
+ ,9.1
+ ,6.5
+ ,3.7
+ ,7.6
+ ,4.5
+ ,6.3
+ ,5.9
+ ,5.4
+ ,5.1
+ ,3.9
+ ,5.1
+ ,4.8
+ ,4
+ ,5.3
+ ,8
+ ,5.3
+ ,6
+ ,3.6
+ ,4.3
+ ,7.3
+ ,4.2
+ ,5.9
+ ,8.2
+ ,5.1
+ ,8.9
+ ,3.8
+ ,5
+ ,6.3
+ ,3.7
+ ,6.3
+ ,6.9
+ ,3.6
+ ,6.2
+ ,4.7
+ ,4
+ ,5
+ ,2.5
+ ,4.1
+ ,10
+ ,3.4
+ ,7.2
+ ,2.9
+ ,3
+ ,7.1
+ ,3.9
+ ,2.3
+ ,6.7
+ ,8.1
+ ,8.8
+ ,5.6
+ ,4.1
+ ,6.3
+ ,3.4
+ ,5.1
+ ,8.4
+ ,4.1
+ ,6.3
+ ,4.2
+ ,4.4
+ ,6.8
+ ,3.6
+ ,4.1
+ ,4.8
+ ,5.7
+ ,9.7
+ ,1.6
+ ,4
+ ,5.2
+ ,3.1
+ ,4.8
+ ,8.2
+ ,4.2
+ ,5
+ ,4
+ ,3.7
+ ,6.3
+ ,3.7
+ ,5.6
+ ,7.2
+ ,4.4
+ ,7.4
+ ,5.1
+ ,4
+ ,6.1
+ ,4.3
+ ,5.9
+ ,6
+ ,5.3
+ ,5.5
+ ,6
+ ,4.3
+ ,7.3
+ ,3.9
+ ,4.3
+ ,8.3
+ ,6.2
+ ,9.1
+ ,6.1
+ ,4.6
+ ,5.4
+ ,3.4
+ ,4.5
+ ,9.2
+ ,5.1
+ ,6.7
+ ,5.6
+ ,3.7
+ ,8
+ ,5.2
+ ,6.2
+ ,8.8
+ ,6.8
+ ,6.3
+ ,6.5
+ ,6.4
+ ,7.4
+ ,3.1
+ ,2.9
+ ,5.3
+ ,6.1
+ ,8.3
+ ,5.5
+ ,3.6
+ ,7.3
+ ,3
+ ,2.8
+ ,5.2
+ ,6
+ ,8.2
+ ,5.9
+ ,4.7
+ ,7.3
+ ,3
+ ,2.8
+ ,5.2
+ ,6
+ ,8.2
+ ,6.2
+ ,4
+ ,6.4
+ ,3.1
+ ,3.9
+ ,6
+ ,4.8
+ ,9
+ ,5.6
+ ,4.3
+ ,5.7
+ ,2.7
+ ,3.1
+ ,7.8
+ ,5
+ ,7.1
+ ,7.2
+ ,3.6
+ ,5.7
+ ,2
+ ,4.2
+ ,8.9
+ ,2.3
+ ,6.9
+ ,3.4
+ ,2.7
+ ,6.6
+ ,3
+ ,3
+ ,6.3
+ ,5.6
+ ,8.6
+ ,5.1
+ ,4
+ ,6.3
+ ,3.5
+ ,4.3
+ ,8.4
+ ,5.4
+ ,6.7
+ ,4
+ ,3.8
+ ,5.4
+ ,3.7
+ ,5.2
+ ,9
+ ,4.8
+ ,7
+ ,5.3
+ ,3.3
+ ,7.4
+ ,3.8
+ ,4.7
+ ,5.2
+ ,5.6
+ ,9.7
+ ,8.4
+ ,4.5
+ ,8.6
+ ,3.9
+ ,3.4
+ ,6.8
+ ,7
+ ,9.9
+ ,8
+ ,5
+ ,7.3
+ ,3.6
+ ,3.6
+ ,6.7
+ ,6
+ ,8.6
+ ,2.8
+ ,4.8
+ ,6.3
+ ,3.4
+ ,5.1
+ ,8.4
+ ,4.1
+ ,6.3
+ ,2.4
+ ,2.8
+ ,8.7
+ ,3.9
+ ,3.4
+ ,6.8
+ ,7
+ ,9.9
+ ,5.2
+ ,4.3
+ ,8.6
+ ,4.5
+ ,4.4
+ ,6.2
+ ,7.2
+ ,9.3
+ ,4.1
+ ,4
+ ,8.4
+ ,4.1
+ ,3.4
+ ,6.7
+ ,7.5
+ ,9.7
+ ,6.1
+ ,4.9
+ ,7.4
+ ,3.8
+ ,4.7
+ ,5.2
+ ,5.6
+ ,9.7
+ ,7.1
+ ,4.6
+ ,9.9
+ ,4.3
+ ,3
+ ,4.5
+ ,8.3
+ ,9.6
+ ,6.2
+ ,4
+ ,8
+ ,4.2
+ ,3.8
+ ,5.8
+ ,7.1
+ ,7.6
+ ,5.5
+ ,4.4
+ ,7.9
+ ,4.4
+ ,3.7
+ ,7.6
+ ,7.8
+ ,9.4
+ ,6.5
+ ,4.7
+ ,9.8
+ ,4.3
+ ,3
+ ,4.5
+ ,8.2
+ ,9.6
+ ,5.6
+ ,4.6
+ ,8.9
+ ,4.3
+ ,4.6
+ ,7.4
+ ,6.6
+ ,9.3
+ ,5.7
+ ,4.4
+ ,6.8
+ ,3.6
+ ,4.1
+ ,4.8
+ ,5.7
+ ,9.7
+ ,6.3
+ ,4.7
+ ,7.4
+ ,4.2
+ ,3.4
+ ,7.3
+ ,7.5
+ ,9.1
+ ,5.1
+ ,6
+ ,4.7
+ ,3.3
+ ,4.7
+ ,8.5
+ ,4.3
+ ,6.5
+ ,4.8
+ ,4.3
+ ,5.4
+ ,2.8
+ ,3.6
+ ,7.2
+ ,4.7
+ ,6.6
+ ,4.8
+ ,3.2
+ ,7
+ ,4.6
+ ,6.1
+ ,9.3
+ ,5.9
+ ,5.8
+ ,3.4
+ ,5.9
+ ,7.1
+ ,4.2
+ ,3.5
+ ,3.8
+ ,7.4
+ ,8.7
+ ,3.6
+ ,5.5
+ ,6.3
+ ,2.9
+ ,3.7
+ ,5.8
+ ,4.7
+ ,8.8
+ ,5.8
+ ,3.8
+ ,5.5
+ ,4.4
+ ,5.8
+ ,8.4
+ ,5.7
+ ,6.4
+ ,5
+ ,4
+ ,5.4
+ ,2.8
+ ,3.6
+ ,7.2
+ ,4.7
+ ,6.7
+ ,5
+ ,2.9
+ ,5.4
+ ,3.3
+ ,4.9
+ ,8.4
+ ,4.3
+ ,5.2
+ ,3.6
+ ,4.3
+ ,4.8
+ ,3
+ ,4.1
+ ,8.8
+ ,4.7
+ ,6.4
+ ,7
+ ,3.6
+ ,8.2
+ ,4
+ ,3.9
+ ,4.4
+ ,6.6
+ ,7.6
+ ,6.8
+ ,4.4
+ ,7.9
+ ,5.4
+ ,7.5
+ ,8.4
+ ,5.9
+ ,5.9
+ ,6.6
+ ,6
+ ,8.6
+ ,4.2
+ ,3.5
+ ,6.8
+ ,7.6
+ ,9.7
+ ,5.2
+ ,4.4
+ ,8.2
+ ,4.9
+ ,6.7
+ ,6.3
+ ,5.7
+ ,5.5
+ ,5.3
+ ,5.9
+ ,8.6
+ ,4.2
+ ,3.5
+ ,6.8
+ ,7.6
+ ,9.7
+ ,1.2
+ ,4.3)
+ ,dim=c(8
+ ,200)
+ ,dimnames=list(c('Klantentevredenheid'
+ ,'Leveringssnelheid'
+ ,'Prijsflexibiliteit'
+ ,'Prijszetting'
+ ,'Productgamma'
+ ,'Productkwaliteit'
+ ,'Productontwikkeling'
+ ,'Facturatie')
+ ,1:200))
> y <- array(NA,dim=c(8,200),dimnames=list(c('Klantentevredenheid','Leveringssnelheid','Prijsflexibiliteit','Prijszetting','Productgamma','Productkwaliteit','Productontwikkeling','Facturatie'),1:200))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Klantentevredenheid Leveringssnelheid Prijsflexibiliteit Prijszetting
1 8.2 3.7 5.1 6.8
2 5.7 4.9 4.3 5.3
3 8.9 4.5 4.0 4.5
4 4.8 3.0 4.1 8.8
5 7.1 3.5 3.5 6.8
6 4.7 3.3 4.7 8.5
7 5.7 2.0 4.2 8.9
8 6.3 3.7 6.3 6.9
9 7.0 4.6 6.1 9.3
10 5.5 4.4 5.8 8.4
11 7.4 4.0 3.7 6.8
12 6.0 3.2 4.9 8.2
13 8.4 4.4 4.5 7.6
14 7.6 4.2 2.6 7.1
15 8.0 5.2 6.2 8.8
16 6.6 4.5 3.9 4.9
17 6.4 4.5 6.2 6.2
18 7.4 4.8 5.8 8.4
19 6.8 4.5 6.0 9.1
20 7.6 4.4 6.1 8.4
21 5.4 3.3 4.9 8.4
22 9.9 4.3 3.0 4.5
23 7.0 4.0 3.4 3.7
24 8.6 4.5 4.4 6.2
25 4.8 4.0 5.3 8.0
26 6.6 3.9 6.6 7.1
27 6.3 4.4 3.8 4.8
28 5.4 3.7 5.2 9.0
29 6.3 4.4 3.8 4.8
30 5.4 3.5 5.5 7.7
31 6.1 3.3 2.7 5.2
32 6.4 3.0 3.5 6.6
33 5.4 3.4 4.5 9.2
34 7.3 4.2 6.6 8.7
35 6.3 3.5 4.3 8.4
36 5.4 2.5 2.9 5.6
37 7.1 3.5 3.5 6.8
38 8.7 4.9 4.6 7.7
39 7.6 4.5 6.9 9.0
40 6.0 3.2 4.9 8.2
41 7.0 3.9 5.8 9.1
42 7.6 4.1 4.5 8.5
43 8.9 4.3 4.6 7.4
44 7.6 4.5 6.3 5.9
45 5.5 4.7 4.2 5.2
46 7.4 4.8 5.8 8.4
47 7.1 3.5 4.0 3.8
48 7.6 5.2 7.3 8.2
49 8.7 3.9 3.4 6.8
50 8.6 4.3 4.2 4.7
51 5.4 2.8 3.6 7.2
52 5.7 4.9 4.3 5.3
53 8.7 4.6 4.6 6.3
54 6.1 3.3 2.7 5.2
55 7.3 4.2 6.6 8.7
56 7.7 3.4 3.2 7.4
57 9.0 5.5 6.5 9.6
58 8.2 4.0 3.9 4.4
59 7.1 3.5 4.0 3.8
60 7.9 4.0 4.9 5.4
61 6.6 4.5 3.9 4.9
62 8.0 3.6 5.0 6.7
63 6.3 2.9 3.7 5.8
64 6.0 2.6 3.1 6.2
65 5.4 2.8 3.6 7.2
66 7.6 5.2 7.3 8.2
67 6.4 4.5 6.2 6.2
68 6.1 4.3 5.9 6.0
69 5.2 3.4 5.4 7.6
70 6.6 3.9 6.6 7.1
71 7.6 4.4 6.1 8.4
72 5.8 3.1 2.6 5.0
73 7.9 4.6 5.6 8.7
74 8.6 3.9 3.4 6.8
75 8.2 3.7 5.1 6.8
76 7.1 3.8 4.3 4.9
77 6.4 3.9 5.8 7.4
78 7.6 4.1 4.5 8.5
79 8.9 4.6 4.1 4.6
80 5.7 2.7 3.1 7.8
81 7.1 3.8 4.3 4.9
82 7.4 4.0 3.7 6.8
83 6.6 3.0 3.0 6.3
84 5.0 1.6 3.7 8.4
85 8.2 4.3 3.9 5.9
86 5.2 3.4 5.4 7.6
87 5.2 3.1 4.8 8.2
88 8.2 4.3 3.9 5.9
89 7.3 3.9 4.3 8.3
90 8.2 4.9 6.7 6.3
91 7.4 3.3 3.0 7.3
92 4.8 2.4 4.0 9.9
93 7.6 4.2 2.6 7.1
94 8.9 4.6 4.1 4.6
95 7.7 3.4 3.2 7.4
96 7.3 3.6 3.6 6.7
97 6.3 3.7 5.6 7.2
98 5.4 2.5 2.9 5.6
99 6.4 3.9 4.9 7.9
100 6.4 3.5 5.4 9.7
101 5.4 3.5 5.5 7.7
102 8.7 4.2 4.6 7.3
103 6.1 3.7 4.7 7.7
104 8.4 4.4 4.5 7.6
105 7.9 4.6 5.6 8.7
106 7.0 3.9 5.8 9.1
107 8.7 4.9 4.6 7.7
108 7.9 5.4 7.5 8.4
109 7.1 4.2 3.5 3.8
110 5.8 3.1 2.6 5.0
111 8.4 4.1 3.4 6.7
112 7.1 3.9 2.3 6.7
113 7.6 4.5 6.9 9.0
114 7.3 4.2 5.9 8.2
115 8.0 3.6 5.0 6.7
116 6.1 3.7 4.7 7.7
117 8.7 4.2 4.6 7.3
118 5.8 2.9 3.3 8.0
119 6.4 3.1 3.9 6.0
120 6.4 3.0 3.5 6.6
121 9.0 5.5 6.5 9.6
122 6.4 3.5 5.4 9.7
123 6.0 2.6 3.1 6.2
124 8.7 4.6 4.6 6.3
125 5.0 2.5 4.1 10.0
126 7.4 3.1 2.9 5.3
127 8.6 4.3 4.2 4.7
128 5.8 2.9 3.3 8.0
129 9.8 4.3 3.0 4.5
130 4.8 2.1 2.5 5.2
131 7.0 4.0 3.4 3.7
132 5.5 4.7 4.2 5.2
133 5.0 1.6 3.7 8.4
134 6.0 3.3 4.1 8.2
135 8.0 4.2 3.8 5.8
136 7.9 4.4 3.7 7.6
137 4.8 2.1 2.5 5.2
138 6.4 3.9 4.9 7.9
139 4.8 2.4 4.0 9.9
140 6.4 3.9 5.8 7.4
141 6.8 4.5 6.0 9.1
142 7.9 4.0 4.9 5.4
143 8.9 4.5 4.0 4.5
144 7.4 4.2 3.4 7.3
145 7.0 3.5 4.0 3.8
146 7.0 3.5 4.0 3.8
147 6.0 3.3 4.1 8.2
148 7.4 3.3 3.0 7.3
149 7.6 4.5 6.3 5.9
150 4.8 4.0 5.3 8.0
151 7.3 4.2 5.9 8.2
152 6.3 3.7 6.3 6.9
153 5.0 2.5 4.1 10.0
154 7.1 3.9 2.3 6.7
155 6.3 3.4 5.1 8.4
156 6.8 3.6 4.1 4.8
157 5.2 3.1 4.8 8.2
158 6.3 3.7 5.6 7.2
159 6.1 4.3 5.9 6.0
160 7.3 3.9 4.3 8.3
161 5.4 3.4 4.5 9.2
162 8.0 5.2 6.2 8.8
163 7.4 3.1 2.9 5.3
164 7.3 3.0 2.8 5.2
165 7.3 3.0 2.8 5.2
166 6.4 3.1 3.9 6.0
167 5.7 2.7 3.1 7.8
168 5.7 2.0 4.2 8.9
169 6.6 3.0 3.0 6.3
170 6.3 3.5 4.3 8.4
171 5.4 3.7 5.2 9.0
172 7.4 3.8 4.7 5.2
173 8.6 3.9 3.4 6.8
174 7.3 3.6 3.6 6.7
175 6.3 3.4 5.1 8.4
176 8.7 3.9 3.4 6.8
177 8.6 4.5 4.4 6.2
178 8.4 4.1 3.4 6.7
179 7.4 3.8 4.7 5.2
180 9.9 4.3 3.0 4.5
181 8.0 4.2 3.8 5.8
182 7.9 4.4 3.7 7.6
183 9.8 4.3 3.0 4.5
184 8.9 4.3 4.6 7.4
185 6.8 3.6 4.1 4.8
186 7.4 4.2 3.4 7.3
187 4.7 3.3 4.7 8.5
188 5.4 2.8 3.6 7.2
189 7.0 4.6 6.1 9.3
190 7.1 4.2 3.5 3.8
191 6.3 2.9 3.7 5.8
192 5.5 4.4 5.8 8.4
193 5.4 2.8 3.6 7.2
194 5.4 3.3 4.9 8.4
195 4.8 3.0 4.1 8.8
196 8.2 4.0 3.9 4.4
197 7.9 5.4 7.5 8.4
198 8.6 4.2 3.5 6.8
199 8.2 4.9 6.7 6.3
200 8.6 4.2 3.5 6.8
Productgamma Productkwaliteit Productontwikkeling Facturatie
1 4.9 8.5 4.3 5.0
2 7.9 8.2 4.0 3.9
3 7.4 9.2 4.6 5.4
4 4.7 6.4 3.6 4.3
5 6.0 9.0 4.5 4.5
6 4.3 6.5 9.5 3.6
7 2.3 6.9 2.5 2.1
8 3.6 6.2 4.8 4.3
9 5.9 5.8 4.4 4.4
10 5.7 6.4 5.3 4.1
11 6.8 8.7 7.5 3.8
12 3.9 6.1 5.9 3.0
13 6.9 9.5 5.3 5.1
14 8.4 9.2 3.0 4.5
15 6.8 6.3 5.4 4.8
16 7.8 8.7 5.0 4.3
17 5.5 5.7 5.4 4.2
18 6.4 5.9 6.3 5.7
19 5.7 5.6 6.1 5.0
20 5.3 9.1 6.7 4.5
21 4.3 5.2 4.6 3.3
22 8.3 9.6 6.5 4.3
23 7.3 8.6 6.0 4.8
24 7.2 9.3 4.2 6.7
25 5.3 6.0 3.9 4.7
26 3.9 6.4 3.7 5.6
27 7.6 8.5 6.7 5.3
28 4.8 7.0 5.9 4.3
29 7.6 8.5 6.0 5.7
30 4.2 7.6 7.2 4.7
31 6.4 6.9 3.3 3.7
32 5.1 8.1 6.1 3.0
33 5.1 6.7 4.2 3.5
34 4.6 8.0 3.8 4.7
35 5.4 6.7 6.0 2.5
36 6.1 8.7 6.5 3.1
37 6.0 9.0 4.3 3.9
38 7.7 9.6 4.4 5.2
39 4.9 8.2 7.1 4.7
40 3.9 6.1 6.8 4.5
41 4.6 8.3 1.7 4.6
42 6.5 9.4 6.2 4.1
43 6.6 9.3 4.1 4.6
44 5.4 5.1 5.2 4.9
45 7.7 8.0 3.9 4.3
46 6.4 5.9 5.1 5.2
47 5.4 10.0 3.7 5.0
48 5.7 5.7 4.8 6.5
49 7.0 9.9 7.2 4.5
50 6.9 7.9 3.6 4.1
51 4.7 6.7 5.3 4.0
52 7.9 8.2 5.0 4.5
53 7.3 9.4 9.2 4.7
54 6.4 6.9 4.4 3.2
55 4.6 8.0 4.2 4.9
56 6.4 9.3 5.9 4.1
57 7.2 7.4 7.4 5.7
58 6.6 7.6 6.4 4.6
59 5.4 10.0 4.5 3.7
60 5.8 9.9 7.0 5.6
61 7.8 8.7 4.5 5.4
62 4.7 8.4 4.2 2.7
63 4.7 8.8 7.2 4.4
64 4.7 7.7 4.7 3.3
65 4.7 6.6 3.9 3.5
66 5.7 5.7 5.0 4.7
67 5.5 5.7 6.4 5.0
68 5.3 5.5 2.5 4.5
69 4.1 7.5 5.2 4.0
70 3.9 6.4 5.5 4.7
71 5.3 9.1 5.7 5.4
72 6.3 6.7 2.5 2.9
73 6.3 6.5 6.3 4.6
74 7.0 9.9 4.6 4.1
75 4.9 8.5 3.6 4.4
76 5.9 9.9 7.6 3.1
77 4.6 7.6 6.6 4.5
78 6.5 9.4 2.4 4.3
79 7.5 9.3 3.1 5.2
80 5.0 7.1 3.5 2.6
81 5.9 9.9 6.9 3.2
82 6.8 8.7 5.1 4.3
83 5.6 8.6 4.0 2.7
84 2.9 6.4 6.5 2.0
85 7.2 7.7 4.1 4.7
86 4.1 7.5 2.8 3.4
87 4.2 5.0 7.6 2.4
88 7.2 7.7 7.7 5.1
89 6.2 9.1 4.1 4.6
90 5.7 5.5 4.9 5.5
91 6.3 9.1 4.6 4.4
92 3.3 7.1 3.5 2.0
93 8.4 9.2 6.6 4.4
94 7.5 9.3 4.9 4.8
95 6.4 9.3 4.8 3.6
96 6.0 8.6 3.6 4.9
97 4.4 7.4 6.4 4.2
98 6.1 8.7 4.3 3.1
99 5.3 7.8 5.7 4.3
100 4.2 7.9 5.8 3.4
101 4.2 7.6 5.1 3.1
102 6.5 9.2 8.6 5.1
103 5.2 7.7 5.4 4.0
104 6.9 9.5 4.4 5.6
105 6.3 6.5 6.9 5.0
106 4.6 8.3 5.2 4.2
107 7.7 9.6 5.5 4.4
108 5.9 5.9 5.3 5.8
109 7.4 8.7 5.7 4.6
110 6.3 6.7 6.5 3.8
111 7.5 9.7 5.2 3.7
112 8.1 8.8 2.7 4.0
113 4.9 8.2 4.3 4.5
114 5.1 8.9 6.7 4.2
115 4.7 8.4 6.6 4.0
116 5.2 7.7 7.4 5.1
117 6.5 9.2 8.9 4.2
118 5.2 7.3 3.7 2.8
119 4.8 9.0 4.9 3.3
120 5.1 8.1 6.2 2.6
121 7.2 7.4 4.3 5.7
122 4.2 7.9 4.6 4.8
123 4.7 7.7 4.3 3.2
124 7.3 9.4 5.4 5.8
125 3.4 7.2 3.6 3.2
126 6.1 8.3 7.4 4.1
127 6.9 7.9 6.7 4.6
128 5.2 7.3 2.9 3.3
129 8.2 9.6 4.8 4.4
130 5.7 8.3 2.8 1.2
131 7.3 8.6 5.2 5.0
132 7.7 8.0 6.8 4.6
133 2.9 6.4 7.0 2.4
134 5.3 6.6 2.9 4.3
135 7.1 7.6 6.2 3.6
136 7.8 9.4 6.0 5.1
137 5.7 8.3 4.2 1.8
138 5.3 7.8 5.2 4.1
139 3.3 7.1 3.1 2.8
140 4.6 7.6 5.3 4.4
141 5.7 5.6 6.0 4.5
142 5.8 9.9 6.8 4.0
143 7.4 9.2 6.1 4.2
144 7.5 9.1 5.2 4.5
145 5.4 9.9 8.0 3.8
146 5.4 9.9 6.2 4.1
147 5.3 6.6 3.5 4.6
148 6.3 9.1 6.5 3.7
149 5.4 5.1 3.9 5.1
150 5.3 6.0 3.6 4.3
151 5.1 8.9 3.8 5.0
152 3.6 6.2 4.7 4.0
153 3.4 7.2 2.9 3.0
154 8.1 8.8 5.6 4.1
155 4.1 6.3 4.2 4.4
156 5.7 9.7 1.6 4.0
157 4.2 5.0 4.0 3.7
158 4.4 7.4 5.1 4.0
159 5.3 5.5 6.0 4.3
160 6.2 9.1 6.1 4.6
161 5.1 6.7 5.6 3.7
162 6.8 6.3 6.5 6.4
163 6.1 8.3 5.5 3.6
164 6.0 8.2 5.9 4.7
165 6.0 8.2 6.2 4.0
166 4.8 9.0 5.6 4.3
167 5.0 7.1 7.2 3.6
168 2.3 6.9 3.4 2.7
169 5.6 8.6 5.1 4.0
170 5.4 6.7 4.0 3.8
171 4.8 7.0 5.3 3.3
172 5.6 9.7 8.4 4.5
173 7.0 9.9 8.0 5.0
174 6.0 8.6 2.8 4.8
175 4.1 6.3 2.4 2.8
176 7.0 9.9 5.2 4.3
177 7.2 9.3 4.1 4.0
178 7.5 9.7 6.1 4.9
179 5.6 9.7 7.1 4.6
180 8.3 9.6 6.2 4.0
181 7.1 7.6 5.5 4.4
182 7.8 9.4 6.5 4.7
183 8.2 9.6 5.6 4.6
184 6.6 9.3 5.7 4.4
185 5.7 9.7 6.3 4.7
186 7.5 9.1 5.1 6.0
187 4.3 6.5 4.8 4.3
188 4.7 6.6 4.8 3.2
189 5.9 5.8 3.4 5.9
190 7.4 8.7 3.6 5.5
191 4.7 8.8 5.8 3.8
192 5.7 6.4 5.0 4.0
193 4.7 6.7 5.0 2.9
194 4.3 5.2 3.6 4.3
195 4.7 6.4 7.0 3.6
196 6.6 7.6 6.8 4.4
197 5.9 5.9 6.6 6.0
198 7.6 9.7 5.2 4.4
199 5.7 5.5 5.3 5.9
200 7.6 9.7 1.2 4.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Leveringssnelheid Prijsflexibiliteit
0.193996 0.985986 -0.124939
Prijszetting Productgamma Productkwaliteit
-0.005293 -0.025678 0.376652
Productontwikkeling Facturatie
0.034198 0.138601
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.46147 -0.37288 0.03398 0.48591 1.69574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.193996 0.963459 0.201 0.841
Leveringssnelheid 0.985986 0.481649 2.047 0.042 *
Prijsflexibiliteit -0.124939 0.252278 -0.495 0.621
Prijszetting -0.005293 0.042685 -0.124 0.901
Productgamma -0.025678 0.242638 -0.106 0.916
Productkwaliteit 0.376652 0.050330 7.484 2.54e-12 ***
Productontwikkeling 0.034198 0.037090 0.922 0.358
Facturatie 0.138601 0.094240 1.471 0.143
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.761 on 192 degrees of freedom
Multiple R-squared: 0.6372, Adjusted R-squared: 0.624
F-statistic: 48.18 on 7 and 192 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.93424538 0.131509235 0.0657546177
[2,] 0.94783974 0.104320519 0.0521602597
[3,] 0.92509694 0.149806117 0.0749030584
[4,] 0.88898305 0.222033909 0.1110169544
[5,] 0.86155698 0.276886045 0.1384430224
[6,] 0.83758706 0.324825870 0.1624129350
[7,] 0.83925086 0.321498284 0.1607491422
[8,] 0.78478432 0.430431362 0.2152156812
[9,] 0.71780770 0.564384596 0.2821922982
[10,] 0.72017048 0.559659030 0.2798295151
[11,] 0.66195146 0.676097074 0.3380485370
[12,] 0.97149327 0.057013457 0.0285067283
[13,] 0.96669455 0.066610892 0.0333054461
[14,] 0.95466183 0.090676331 0.0453381655
[15,] 0.97774304 0.044513916 0.0222569578
[16,] 0.96858578 0.062828443 0.0314142215
[17,] 0.98321782 0.033564367 0.0167821836
[18,] 0.98698590 0.026028197 0.0130140985
[19,] 0.99256384 0.014872313 0.0074361565
[20,] 0.99523065 0.009538709 0.0047693546
[21,] 0.99348676 0.013026476 0.0065132379
[22,] 0.99079072 0.018418570 0.0092092849
[23,] 0.98852842 0.022943163 0.0114715817
[24,] 0.98393191 0.032136182 0.0160680912
[25,] 0.98036233 0.039275333 0.0196376664
[26,] 0.97456836 0.050863279 0.0254316395
[27,] 0.96529645 0.069407102 0.0347035512
[28,] 0.95352740 0.092945191 0.0464725954
[29,] 0.93884310 0.122313805 0.0611569024
[30,] 0.92800657 0.143986866 0.0719934330
[31,] 0.91495264 0.170094718 0.0850473589
[32,] 0.89316339 0.213673215 0.1068366076
[33,] 0.90266540 0.194669195 0.0973345973
[34,] 0.94471531 0.110569387 0.0552846936
[35,] 0.99048495 0.019030092 0.0095150460
[36,] 0.98888348 0.022233049 0.0111165246
[37,] 0.98638041 0.027239177 0.0136195885
[38,] 0.98189707 0.036205865 0.0181029327
[39,] 0.98372153 0.032556946 0.0162784731
[40,] 0.99178078 0.016438442 0.0082192212
[41,] 0.98880908 0.022381843 0.0111909213
[42,] 0.99900239 0.001995227 0.0009976135
[43,] 0.99881652 0.002366970 0.0011834848
[44,] 0.99846349 0.003073029 0.0015365145
[45,] 0.99783492 0.004330163 0.0021650815
[46,] 0.99740613 0.005187736 0.0025938681
[47,] 0.99723785 0.005524297 0.0027621485
[48,] 0.99834277 0.003314457 0.0016572286
[49,] 0.99774614 0.004507723 0.0022538613
[50,] 0.99687314 0.006253711 0.0031268557
[51,] 0.99850898 0.002982045 0.0014910224
[52,] 0.99922936 0.001541279 0.0007706395
[53,] 0.99893854 0.002122921 0.0010614607
[54,] 0.99854293 0.002914140 0.0014570700
[55,] 0.99793169 0.004136627 0.0020683137
[56,] 0.99735147 0.005297054 0.0026485272
[57,] 0.99657750 0.006845001 0.0034225004
[58,] 0.99547263 0.009054733 0.0045273666
[59,] 0.99696469 0.006070625 0.0030353124
[60,] 0.99591878 0.008162441 0.0040812204
[61,] 0.99488751 0.010224979 0.0051124897
[62,] 0.99342736 0.013145285 0.0065726423
[63,] 0.99369139 0.012617217 0.0063086086
[64,] 0.99379367 0.012412669 0.0062063346
[65,] 0.99602153 0.007956944 0.0039784718
[66,] 0.99531838 0.009363247 0.0046816234
[67,] 0.99433577 0.011328462 0.0056642310
[68,] 0.99248460 0.015030799 0.0075153994
[69,] 0.99175038 0.016499246 0.0082496232
[70,] 0.98925958 0.021480837 0.0107404184
[71,] 0.98755867 0.024882665 0.0124413324
[72,] 0.98391549 0.032169011 0.0160845054
[73,] 0.97960163 0.040796746 0.0203983729
[74,] 0.98189750 0.036204992 0.0181024961
[75,] 0.98267623 0.034647548 0.0173237738
[76,] 0.98580695 0.028386097 0.0141930483
[77,] 0.98191113 0.036177745 0.0180888724
[78,] 0.98112208 0.037755842 0.0188779212
[79,] 0.97619208 0.047615844 0.0238079220
[80,] 0.98320260 0.033594809 0.0167974047
[81,] 0.97942166 0.041156689 0.0205783444
[82,] 0.97493816 0.050123681 0.0250618406
[83,] 0.97172850 0.056543000 0.0282715001
[84,] 0.96886556 0.062268881 0.0311344407
[85,] 0.96596840 0.068063205 0.0340316026
[86,] 0.95760639 0.084787218 0.0423936091
[87,] 0.94867784 0.102644311 0.0513221555
[88,] 0.94268522 0.114629565 0.0573147827
[89,] 0.93763798 0.124724043 0.0623620213
[90,] 0.92428822 0.151423561 0.0757117807
[91,] 0.92889272 0.142214567 0.0711072837
[92,] 0.92678932 0.146421353 0.0732106765
[93,] 0.92214452 0.155710962 0.0778554811
[94,] 0.90636379 0.187272415 0.0936362073
[95,] 0.90254152 0.194916961 0.0974584806
[96,] 0.88357108 0.232857831 0.1164289156
[97,] 0.86242735 0.275145301 0.1375726505
[98,] 0.84116653 0.317666935 0.1588334676
[99,] 0.84536778 0.309264442 0.1546322212
[100,] 0.82609077 0.347818459 0.1739092297
[101,] 0.80852616 0.382947678 0.1914738388
[102,] 0.79306266 0.413874673 0.2069373364
[103,] 0.76625548 0.467489038 0.2337445191
[104,] 0.73533503 0.529329933 0.2646649667
[105,] 0.77764223 0.444715530 0.2223577652
[106,] 0.78184117 0.436317670 0.2181588350
[107,] 0.79042323 0.419153549 0.2095767744
[108,] 0.75832101 0.483357980 0.2416789898
[109,] 0.72616895 0.547662092 0.2738310459
[110,] 0.69231296 0.615374073 0.3076870364
[111,] 0.69461987 0.610760262 0.3053801311
[112,] 0.66147387 0.677052266 0.3385261332
[113,] 0.62476339 0.750473212 0.3752366062
[114,] 0.58864845 0.822703110 0.4113515549
[115,] 0.55180317 0.896393668 0.4481968341
[116,] 0.54365396 0.912692088 0.4563460442
[117,] 0.56544701 0.869105976 0.4345529881
[118,] 0.52214733 0.955705332 0.4778526662
[119,] 0.63255602 0.734887959 0.3674439793
[120,] 0.61074396 0.778512081 0.3892560406
[121,] 0.61901093 0.761978148 0.3809890740
[122,] 0.95104449 0.097911027 0.0489555135
[123,] 0.96563465 0.068730703 0.0343653514
[124,] 0.95592162 0.088156756 0.0440783781
[125,] 0.94959861 0.100802788 0.0504013940
[126,] 0.94043280 0.119134398 0.0595671989
[127,] 0.94508358 0.109832833 0.0549164165
[128,] 0.93513559 0.129728814 0.0648644071
[129,] 0.92091148 0.158177036 0.0790885180
[130,] 0.90478404 0.190431928 0.0952159640
[131,] 0.88405998 0.231880032 0.1159400161
[132,] 0.85898782 0.282024363 0.1410121815
[133,] 0.84425633 0.311487341 0.1557436704
[134,] 0.83359381 0.332812382 0.1664061909
[135,] 0.81931147 0.361377068 0.1806885338
[136,] 0.81296243 0.374075147 0.1870375737
[137,] 0.77794392 0.444112164 0.2220560819
[138,] 0.74751299 0.504974030 0.2524870148
[139,] 0.76781592 0.464368157 0.2321840783
[140,] 0.88045735 0.239085290 0.1195426451
[141,] 0.85391197 0.292176056 0.1460880281
[142,] 0.82984979 0.340300422 0.1701502112
[143,] 0.79703635 0.405927305 0.2029636523
[144,] 0.83424885 0.331502291 0.1657511454
[145,] 0.82635449 0.347291010 0.1736455051
[146,] 0.83717599 0.325648027 0.1628240134
[147,] 0.80220571 0.395588579 0.1977942894
[148,] 0.76248966 0.475020677 0.2375103387
[149,] 0.75555688 0.488886246 0.2444431232
[150,] 0.71023810 0.579523801 0.2897619003
[151,] 0.68733875 0.625322504 0.3126612521
[152,] 0.65432305 0.691353897 0.3456769483
[153,] 0.61951977 0.760960458 0.3804802290
[154,] 0.60016971 0.799660587 0.3998302937
[155,] 0.58536175 0.829276494 0.4146382468
[156,] 0.53258378 0.934832432 0.4674162159
[157,] 0.47819810 0.956396195 0.5218019024
[158,] 0.75516825 0.489663504 0.2448317521
[159,] 0.70735134 0.585297313 0.2926486563
[160,] 0.65641553 0.687168944 0.3435844720
[161,] 0.67548115 0.649037708 0.3245188539
[162,] 0.61727922 0.765441570 0.3827207848
[163,] 0.61732409 0.765351818 0.3826759089
[164,] 0.57064399 0.858712024 0.4293560119
[165,] 0.56004449 0.879911021 0.4399555103
[166,] 0.58180391 0.836392184 0.4181960920
[167,] 0.51250482 0.974990363 0.4874951816
[168,] 0.44106871 0.882137422 0.5589312890
[169,] 0.36835718 0.736714363 0.6316428183
[170,] 0.34562673 0.691253457 0.6543732714
[171,] 0.27609983 0.552199663 0.7239001687
[172,] 0.23893047 0.477860941 0.7610695293
[173,] 0.24391901 0.487838028 0.7560809858
[174,] 0.32495736 0.649914723 0.6750426386
[175,] 0.29412472 0.588249441 0.7058752793
[176,] 0.20873538 0.417470757 0.7912646217
[177,] 0.14779193 0.295583861 0.8522080695
[178,] 0.08751486 0.175029728 0.9124851358
[179,] 0.04380733 0.087614656 0.9561926722
> postscript(file="/var/wessaorg/rcomp/tmp/1y1n11322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ko8a1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3flp71322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4grk31322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5upp91322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 200
Frequency = 1
1 2 3 4 5
1.1152616777 -2.3230629199 0.6117034934 -0.8021068308 -0.0850631717
6 7 8 9 10
-1.2772114806 1.1894890410 0.2785584303 0.2884269279 -1.2769466671
11 12 13 14 15
-0.1251038308 0.4914501765 0.1809883520 -0.3485167281 0.4518281688
16 17 18 19 20
-1.3612939886 -0.1959741949 0.1790001814 0.1023705545 -0.2700149145
21 22 23 24 25
0.1460456843 1.6438954755 -0.6157950029 0.1613752371 -1.5420327263
26 27 28 29 30
0.2097112449 -1.7022613748 -1.0558843884 -1.7337630925 -1.1693830184
31 32 33 34 35
-0.0431274478 0.1759324247 -0.5567707992 0.1590369500 0.3001558036
36 37 38 39 40
-0.6391845485 0.0049369829 0.0008139636 0.0218302608 0.2527705652
41 42 43 44 45
0.0296792490 -0.1832350782 0.9689873506 1.1481742185 -2.3207146993
46 47 48 49 50
0.2893383079 -0.4724751322 0.1687279688 0.7024046632 1.2261353936
51 52 53 54 55
-0.2054008541 -2.4404215244 0.2594078481 -0.0114448647 0.1176375443
56 57 58 59 60
0.4840683044 0.6005656136 1.0230986787 -0.3196524075 -0.1926311594
61 62 63 64 65
-1.4966559513 1.3555685572 -0.3103028696 0.2649205228 -0.0505578871
66 67 68 69 70
0.4113699826 -0.3410529801 -0.2095284305 -1.0832934908 0.2728955458
71 72 73 74 75
-0.3605576653 0.0515189272 0.7766994101 0.7467599867 1.2223608634
76 77 78 79 80
-0.5444929187 -0.4693734966 -0.0810026220 0.5700479972 0.2465445492
81 82 83 84 85
-0.5344143654 -0.1123289328 0.2497838852 0.8995677086 0.7777801266
86 87 88 89 90
-0.9180575990 0.2245995127 0.5992267407 -0.2042766052 1.1900142382
91 92 93 94 95
0.3327896267 -0.1945901316 -0.4577696622 0.5639318464 0.5909866248
96 97 98 99 100
0.1543016203 -0.2796080722 -0.5639488113 -0.5780293230 -0.0562271545
101 102 103 104 105
-0.8758056419 0.6789622750 -0.6199415452 0.1424661564 0.7007402113
106 107 108 109 110
-0.0345736079 0.0740768177 0.3073040292 -0.6970867265 -0.2100141354
111 112 113 114 115
0.4721243890 -0.3698026669 0.1453050161 -0.2870891026 1.0933120330
116 117 118 119 120
-0.8407986637 0.7934436694 0.0706392152 -0.2230996484 0.2279529799
121 122 123 124 125
0.7065796069 -0.2092307445 0.2924598381 0.2368994912 -0.2850037925
126 127 128 129 130
0.7489178433 1.0508209486 0.0286972133 1.5856043160 -0.3666189193
131 132 133 134 135
-0.6161567337 -2.4614693512 0.8270283161 0.0629362587 0.7790979599
136 137 138 139 140
-0.3821261772 -0.4976567487 -0.5332101111 -0.2917916294 -0.4110559252
141 142 143 144 145
0.1750908125 0.0359698986 0.7267274841 -0.5081874264 -0.5155404881
146 147 148 149 150
-0.4955642469 0.0008371502 0.3648339406 1.1649115189 -1.4763329462
151 152 153 154 155
-0.2987954445 0.3235585076 -0.2333449681 -0.4828371380 0.4142000768
156 157 158 159 160
-0.5221704681 0.1675313626 -0.2074304104 -0.3015014680 -0.2726727299
161 162 163 164 165
-0.6323682672 0.1924488546 0.8831946135 0.7377271652 0.8244883789
166 167 168 169 170
-0.3856391955 -0.0185891850 1.0755502428 0.0319848421 0.1883707538
171 172 173 174 175
-0.8967646475 -0.3467019119 0.5057457615 0.1955201606 0.6975180346
176 177 178 179 180
0.7985209686 0.5390174828 0.2750250487 -0.3161045212 1.6957351652
181 182 183 184 185
0.6921558808 -0.3437848470 1.5305256854 0.9419906316 -0.7799219936
186 187 188 189 190
-0.7126689754 -1.2135012200 -0.0397558721 0.1147236351 -0.7500116086
191 192 193 194 195
-0.1792650402 -1.2528271581 -0.0426804416 0.0416428431 -0.8213596103
196 197 198 199 200
1.0371396344 0.2351263674 0.4920962141 1.1208946518 0.6427485539
> postscript(file="/var/wessaorg/rcomp/tmp/6w51j1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 200
Frequency = 1
lag(myerror, k = 1) myerror
0 1.1152616777 NA
1 -2.3230629199 1.1152616777
2 0.6117034934 -2.3230629199
3 -0.8021068308 0.6117034934
4 -0.0850631717 -0.8021068308
5 -1.2772114806 -0.0850631717
6 1.1894890410 -1.2772114806
7 0.2785584303 1.1894890410
8 0.2884269279 0.2785584303
9 -1.2769466671 0.2884269279
10 -0.1251038308 -1.2769466671
11 0.4914501765 -0.1251038308
12 0.1809883520 0.4914501765
13 -0.3485167281 0.1809883520
14 0.4518281688 -0.3485167281
15 -1.3612939886 0.4518281688
16 -0.1959741949 -1.3612939886
17 0.1790001814 -0.1959741949
18 0.1023705545 0.1790001814
19 -0.2700149145 0.1023705545
20 0.1460456843 -0.2700149145
21 1.6438954755 0.1460456843
22 -0.6157950029 1.6438954755
23 0.1613752371 -0.6157950029
24 -1.5420327263 0.1613752371
25 0.2097112449 -1.5420327263
26 -1.7022613748 0.2097112449
27 -1.0558843884 -1.7022613748
28 -1.7337630925 -1.0558843884
29 -1.1693830184 -1.7337630925
30 -0.0431274478 -1.1693830184
31 0.1759324247 -0.0431274478
32 -0.5567707992 0.1759324247
33 0.1590369500 -0.5567707992
34 0.3001558036 0.1590369500
35 -0.6391845485 0.3001558036
36 0.0049369829 -0.6391845485
37 0.0008139636 0.0049369829
38 0.0218302608 0.0008139636
39 0.2527705652 0.0218302608
40 0.0296792490 0.2527705652
41 -0.1832350782 0.0296792490
42 0.9689873506 -0.1832350782
43 1.1481742185 0.9689873506
44 -2.3207146993 1.1481742185
45 0.2893383079 -2.3207146993
46 -0.4724751322 0.2893383079
47 0.1687279688 -0.4724751322
48 0.7024046632 0.1687279688
49 1.2261353936 0.7024046632
50 -0.2054008541 1.2261353936
51 -2.4404215244 -0.2054008541
52 0.2594078481 -2.4404215244
53 -0.0114448647 0.2594078481
54 0.1176375443 -0.0114448647
55 0.4840683044 0.1176375443
56 0.6005656136 0.4840683044
57 1.0230986787 0.6005656136
58 -0.3196524075 1.0230986787
59 -0.1926311594 -0.3196524075
60 -1.4966559513 -0.1926311594
61 1.3555685572 -1.4966559513
62 -0.3103028696 1.3555685572
63 0.2649205228 -0.3103028696
64 -0.0505578871 0.2649205228
65 0.4113699826 -0.0505578871
66 -0.3410529801 0.4113699826
67 -0.2095284305 -0.3410529801
68 -1.0832934908 -0.2095284305
69 0.2728955458 -1.0832934908
70 -0.3605576653 0.2728955458
71 0.0515189272 -0.3605576653
72 0.7766994101 0.0515189272
73 0.7467599867 0.7766994101
74 1.2223608634 0.7467599867
75 -0.5444929187 1.2223608634
76 -0.4693734966 -0.5444929187
77 -0.0810026220 -0.4693734966
78 0.5700479972 -0.0810026220
79 0.2465445492 0.5700479972
80 -0.5344143654 0.2465445492
81 -0.1123289328 -0.5344143654
82 0.2497838852 -0.1123289328
83 0.8995677086 0.2497838852
84 0.7777801266 0.8995677086
85 -0.9180575990 0.7777801266
86 0.2245995127 -0.9180575990
87 0.5992267407 0.2245995127
88 -0.2042766052 0.5992267407
89 1.1900142382 -0.2042766052
90 0.3327896267 1.1900142382
91 -0.1945901316 0.3327896267
92 -0.4577696622 -0.1945901316
93 0.5639318464 -0.4577696622
94 0.5909866248 0.5639318464
95 0.1543016203 0.5909866248
96 -0.2796080722 0.1543016203
97 -0.5639488113 -0.2796080722
98 -0.5780293230 -0.5639488113
99 -0.0562271545 -0.5780293230
100 -0.8758056419 -0.0562271545
101 0.6789622750 -0.8758056419
102 -0.6199415452 0.6789622750
103 0.1424661564 -0.6199415452
104 0.7007402113 0.1424661564
105 -0.0345736079 0.7007402113
106 0.0740768177 -0.0345736079
107 0.3073040292 0.0740768177
108 -0.6970867265 0.3073040292
109 -0.2100141354 -0.6970867265
110 0.4721243890 -0.2100141354
111 -0.3698026669 0.4721243890
112 0.1453050161 -0.3698026669
113 -0.2870891026 0.1453050161
114 1.0933120330 -0.2870891026
115 -0.8407986637 1.0933120330
116 0.7934436694 -0.8407986637
117 0.0706392152 0.7934436694
118 -0.2230996484 0.0706392152
119 0.2279529799 -0.2230996484
120 0.7065796069 0.2279529799
121 -0.2092307445 0.7065796069
122 0.2924598381 -0.2092307445
123 0.2368994912 0.2924598381
124 -0.2850037925 0.2368994912
125 0.7489178433 -0.2850037925
126 1.0508209486 0.7489178433
127 0.0286972133 1.0508209486
128 1.5856043160 0.0286972133
129 -0.3666189193 1.5856043160
130 -0.6161567337 -0.3666189193
131 -2.4614693512 -0.6161567337
132 0.8270283161 -2.4614693512
133 0.0629362587 0.8270283161
134 0.7790979599 0.0629362587
135 -0.3821261772 0.7790979599
136 -0.4976567487 -0.3821261772
137 -0.5332101111 -0.4976567487
138 -0.2917916294 -0.5332101111
139 -0.4110559252 -0.2917916294
140 0.1750908125 -0.4110559252
141 0.0359698986 0.1750908125
142 0.7267274841 0.0359698986
143 -0.5081874264 0.7267274841
144 -0.5155404881 -0.5081874264
145 -0.4955642469 -0.5155404881
146 0.0008371502 -0.4955642469
147 0.3648339406 0.0008371502
148 1.1649115189 0.3648339406
149 -1.4763329462 1.1649115189
150 -0.2987954445 -1.4763329462
151 0.3235585076 -0.2987954445
152 -0.2333449681 0.3235585076
153 -0.4828371380 -0.2333449681
154 0.4142000768 -0.4828371380
155 -0.5221704681 0.4142000768
156 0.1675313626 -0.5221704681
157 -0.2074304104 0.1675313626
158 -0.3015014680 -0.2074304104
159 -0.2726727299 -0.3015014680
160 -0.6323682672 -0.2726727299
161 0.1924488546 -0.6323682672
162 0.8831946135 0.1924488546
163 0.7377271652 0.8831946135
164 0.8244883789 0.7377271652
165 -0.3856391955 0.8244883789
166 -0.0185891850 -0.3856391955
167 1.0755502428 -0.0185891850
168 0.0319848421 1.0755502428
169 0.1883707538 0.0319848421
170 -0.8967646475 0.1883707538
171 -0.3467019119 -0.8967646475
172 0.5057457615 -0.3467019119
173 0.1955201606 0.5057457615
174 0.6975180346 0.1955201606
175 0.7985209686 0.6975180346
176 0.5390174828 0.7985209686
177 0.2750250487 0.5390174828
178 -0.3161045212 0.2750250487
179 1.6957351652 -0.3161045212
180 0.6921558808 1.6957351652
181 -0.3437848470 0.6921558808
182 1.5305256854 -0.3437848470
183 0.9419906316 1.5305256854
184 -0.7799219936 0.9419906316
185 -0.7126689754 -0.7799219936
186 -1.2135012200 -0.7126689754
187 -0.0397558721 -1.2135012200
188 0.1147236351 -0.0397558721
189 -0.7500116086 0.1147236351
190 -0.1792650402 -0.7500116086
191 -1.2528271581 -0.1792650402
192 -0.0426804416 -1.2528271581
193 0.0416428431 -0.0426804416
194 -0.8213596103 0.0416428431
195 1.0371396344 -0.8213596103
196 0.2351263674 1.0371396344
197 0.4920962141 0.2351263674
198 1.1208946518 0.4920962141
199 0.6427485539 1.1208946518
200 NA 0.6427485539
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.3230629199 1.1152616777
[2,] 0.6117034934 -2.3230629199
[3,] -0.8021068308 0.6117034934
[4,] -0.0850631717 -0.8021068308
[5,] -1.2772114806 -0.0850631717
[6,] 1.1894890410 -1.2772114806
[7,] 0.2785584303 1.1894890410
[8,] 0.2884269279 0.2785584303
[9,] -1.2769466671 0.2884269279
[10,] -0.1251038308 -1.2769466671
[11,] 0.4914501765 -0.1251038308
[12,] 0.1809883520 0.4914501765
[13,] -0.3485167281 0.1809883520
[14,] 0.4518281688 -0.3485167281
[15,] -1.3612939886 0.4518281688
[16,] -0.1959741949 -1.3612939886
[17,] 0.1790001814 -0.1959741949
[18,] 0.1023705545 0.1790001814
[19,] -0.2700149145 0.1023705545
[20,] 0.1460456843 -0.2700149145
[21,] 1.6438954755 0.1460456843
[22,] -0.6157950029 1.6438954755
[23,] 0.1613752371 -0.6157950029
[24,] -1.5420327263 0.1613752371
[25,] 0.2097112449 -1.5420327263
[26,] -1.7022613748 0.2097112449
[27,] -1.0558843884 -1.7022613748
[28,] -1.7337630925 -1.0558843884
[29,] -1.1693830184 -1.7337630925
[30,] -0.0431274478 -1.1693830184
[31,] 0.1759324247 -0.0431274478
[32,] -0.5567707992 0.1759324247
[33,] 0.1590369500 -0.5567707992
[34,] 0.3001558036 0.1590369500
[35,] -0.6391845485 0.3001558036
[36,] 0.0049369829 -0.6391845485
[37,] 0.0008139636 0.0049369829
[38,] 0.0218302608 0.0008139636
[39,] 0.2527705652 0.0218302608
[40,] 0.0296792490 0.2527705652
[41,] -0.1832350782 0.0296792490
[42,] 0.9689873506 -0.1832350782
[43,] 1.1481742185 0.9689873506
[44,] -2.3207146993 1.1481742185
[45,] 0.2893383079 -2.3207146993
[46,] -0.4724751322 0.2893383079
[47,] 0.1687279688 -0.4724751322
[48,] 0.7024046632 0.1687279688
[49,] 1.2261353936 0.7024046632
[50,] -0.2054008541 1.2261353936
[51,] -2.4404215244 -0.2054008541
[52,] 0.2594078481 -2.4404215244
[53,] -0.0114448647 0.2594078481
[54,] 0.1176375443 -0.0114448647
[55,] 0.4840683044 0.1176375443
[56,] 0.6005656136 0.4840683044
[57,] 1.0230986787 0.6005656136
[58,] -0.3196524075 1.0230986787
[59,] -0.1926311594 -0.3196524075
[60,] -1.4966559513 -0.1926311594
[61,] 1.3555685572 -1.4966559513
[62,] -0.3103028696 1.3555685572
[63,] 0.2649205228 -0.3103028696
[64,] -0.0505578871 0.2649205228
[65,] 0.4113699826 -0.0505578871
[66,] -0.3410529801 0.4113699826
[67,] -0.2095284305 -0.3410529801
[68,] -1.0832934908 -0.2095284305
[69,] 0.2728955458 -1.0832934908
[70,] -0.3605576653 0.2728955458
[71,] 0.0515189272 -0.3605576653
[72,] 0.7766994101 0.0515189272
[73,] 0.7467599867 0.7766994101
[74,] 1.2223608634 0.7467599867
[75,] -0.5444929187 1.2223608634
[76,] -0.4693734966 -0.5444929187
[77,] -0.0810026220 -0.4693734966
[78,] 0.5700479972 -0.0810026220
[79,] 0.2465445492 0.5700479972
[80,] -0.5344143654 0.2465445492
[81,] -0.1123289328 -0.5344143654
[82,] 0.2497838852 -0.1123289328
[83,] 0.8995677086 0.2497838852
[84,] 0.7777801266 0.8995677086
[85,] -0.9180575990 0.7777801266
[86,] 0.2245995127 -0.9180575990
[87,] 0.5992267407 0.2245995127
[88,] -0.2042766052 0.5992267407
[89,] 1.1900142382 -0.2042766052
[90,] 0.3327896267 1.1900142382
[91,] -0.1945901316 0.3327896267
[92,] -0.4577696622 -0.1945901316
[93,] 0.5639318464 -0.4577696622
[94,] 0.5909866248 0.5639318464
[95,] 0.1543016203 0.5909866248
[96,] -0.2796080722 0.1543016203
[97,] -0.5639488113 -0.2796080722
[98,] -0.5780293230 -0.5639488113
[99,] -0.0562271545 -0.5780293230
[100,] -0.8758056419 -0.0562271545
[101,] 0.6789622750 -0.8758056419
[102,] -0.6199415452 0.6789622750
[103,] 0.1424661564 -0.6199415452
[104,] 0.7007402113 0.1424661564
[105,] -0.0345736079 0.7007402113
[106,] 0.0740768177 -0.0345736079
[107,] 0.3073040292 0.0740768177
[108,] -0.6970867265 0.3073040292
[109,] -0.2100141354 -0.6970867265
[110,] 0.4721243890 -0.2100141354
[111,] -0.3698026669 0.4721243890
[112,] 0.1453050161 -0.3698026669
[113,] -0.2870891026 0.1453050161
[114,] 1.0933120330 -0.2870891026
[115,] -0.8407986637 1.0933120330
[116,] 0.7934436694 -0.8407986637
[117,] 0.0706392152 0.7934436694
[118,] -0.2230996484 0.0706392152
[119,] 0.2279529799 -0.2230996484
[120,] 0.7065796069 0.2279529799
[121,] -0.2092307445 0.7065796069
[122,] 0.2924598381 -0.2092307445
[123,] 0.2368994912 0.2924598381
[124,] -0.2850037925 0.2368994912
[125,] 0.7489178433 -0.2850037925
[126,] 1.0508209486 0.7489178433
[127,] 0.0286972133 1.0508209486
[128,] 1.5856043160 0.0286972133
[129,] -0.3666189193 1.5856043160
[130,] -0.6161567337 -0.3666189193
[131,] -2.4614693512 -0.6161567337
[132,] 0.8270283161 -2.4614693512
[133,] 0.0629362587 0.8270283161
[134,] 0.7790979599 0.0629362587
[135,] -0.3821261772 0.7790979599
[136,] -0.4976567487 -0.3821261772
[137,] -0.5332101111 -0.4976567487
[138,] -0.2917916294 -0.5332101111
[139,] -0.4110559252 -0.2917916294
[140,] 0.1750908125 -0.4110559252
[141,] 0.0359698986 0.1750908125
[142,] 0.7267274841 0.0359698986
[143,] -0.5081874264 0.7267274841
[144,] -0.5155404881 -0.5081874264
[145,] -0.4955642469 -0.5155404881
[146,] 0.0008371502 -0.4955642469
[147,] 0.3648339406 0.0008371502
[148,] 1.1649115189 0.3648339406
[149,] -1.4763329462 1.1649115189
[150,] -0.2987954445 -1.4763329462
[151,] 0.3235585076 -0.2987954445
[152,] -0.2333449681 0.3235585076
[153,] -0.4828371380 -0.2333449681
[154,] 0.4142000768 -0.4828371380
[155,] -0.5221704681 0.4142000768
[156,] 0.1675313626 -0.5221704681
[157,] -0.2074304104 0.1675313626
[158,] -0.3015014680 -0.2074304104
[159,] -0.2726727299 -0.3015014680
[160,] -0.6323682672 -0.2726727299
[161,] 0.1924488546 -0.6323682672
[162,] 0.8831946135 0.1924488546
[163,] 0.7377271652 0.8831946135
[164,] 0.8244883789 0.7377271652
[165,] -0.3856391955 0.8244883789
[166,] -0.0185891850 -0.3856391955
[167,] 1.0755502428 -0.0185891850
[168,] 0.0319848421 1.0755502428
[169,] 0.1883707538 0.0319848421
[170,] -0.8967646475 0.1883707538
[171,] -0.3467019119 -0.8967646475
[172,] 0.5057457615 -0.3467019119
[173,] 0.1955201606 0.5057457615
[174,] 0.6975180346 0.1955201606
[175,] 0.7985209686 0.6975180346
[176,] 0.5390174828 0.7985209686
[177,] 0.2750250487 0.5390174828
[178,] -0.3161045212 0.2750250487
[179,] 1.6957351652 -0.3161045212
[180,] 0.6921558808 1.6957351652
[181,] -0.3437848470 0.6921558808
[182,] 1.5305256854 -0.3437848470
[183,] 0.9419906316 1.5305256854
[184,] -0.7799219936 0.9419906316
[185,] -0.7126689754 -0.7799219936
[186,] -1.2135012200 -0.7126689754
[187,] -0.0397558721 -1.2135012200
[188,] 0.1147236351 -0.0397558721
[189,] -0.7500116086 0.1147236351
[190,] -0.1792650402 -0.7500116086
[191,] -1.2528271581 -0.1792650402
[192,] -0.0426804416 -1.2528271581
[193,] 0.0416428431 -0.0426804416
[194,] -0.8213596103 0.0416428431
[195,] 1.0371396344 -0.8213596103
[196,] 0.2351263674 1.0371396344
[197,] 0.4920962141 0.2351263674
[198,] 1.1208946518 0.4920962141
[199,] 0.6427485539 1.1208946518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.3230629199 1.1152616777
2 0.6117034934 -2.3230629199
3 -0.8021068308 0.6117034934
4 -0.0850631717 -0.8021068308
5 -1.2772114806 -0.0850631717
6 1.1894890410 -1.2772114806
7 0.2785584303 1.1894890410
8 0.2884269279 0.2785584303
9 -1.2769466671 0.2884269279
10 -0.1251038308 -1.2769466671
11 0.4914501765 -0.1251038308
12 0.1809883520 0.4914501765
13 -0.3485167281 0.1809883520
14 0.4518281688 -0.3485167281
15 -1.3612939886 0.4518281688
16 -0.1959741949 -1.3612939886
17 0.1790001814 -0.1959741949
18 0.1023705545 0.1790001814
19 -0.2700149145 0.1023705545
20 0.1460456843 -0.2700149145
21 1.6438954755 0.1460456843
22 -0.6157950029 1.6438954755
23 0.1613752371 -0.6157950029
24 -1.5420327263 0.1613752371
25 0.2097112449 -1.5420327263
26 -1.7022613748 0.2097112449
27 -1.0558843884 -1.7022613748
28 -1.7337630925 -1.0558843884
29 -1.1693830184 -1.7337630925
30 -0.0431274478 -1.1693830184
31 0.1759324247 -0.0431274478
32 -0.5567707992 0.1759324247
33 0.1590369500 -0.5567707992
34 0.3001558036 0.1590369500
35 -0.6391845485 0.3001558036
36 0.0049369829 -0.6391845485
37 0.0008139636 0.0049369829
38 0.0218302608 0.0008139636
39 0.2527705652 0.0218302608
40 0.0296792490 0.2527705652
41 -0.1832350782 0.0296792490
42 0.9689873506 -0.1832350782
43 1.1481742185 0.9689873506
44 -2.3207146993 1.1481742185
45 0.2893383079 -2.3207146993
46 -0.4724751322 0.2893383079
47 0.1687279688 -0.4724751322
48 0.7024046632 0.1687279688
49 1.2261353936 0.7024046632
50 -0.2054008541 1.2261353936
51 -2.4404215244 -0.2054008541
52 0.2594078481 -2.4404215244
53 -0.0114448647 0.2594078481
54 0.1176375443 -0.0114448647
55 0.4840683044 0.1176375443
56 0.6005656136 0.4840683044
57 1.0230986787 0.6005656136
58 -0.3196524075 1.0230986787
59 -0.1926311594 -0.3196524075
60 -1.4966559513 -0.1926311594
61 1.3555685572 -1.4966559513
62 -0.3103028696 1.3555685572
63 0.2649205228 -0.3103028696
64 -0.0505578871 0.2649205228
65 0.4113699826 -0.0505578871
66 -0.3410529801 0.4113699826
67 -0.2095284305 -0.3410529801
68 -1.0832934908 -0.2095284305
69 0.2728955458 -1.0832934908
70 -0.3605576653 0.2728955458
71 0.0515189272 -0.3605576653
72 0.7766994101 0.0515189272
73 0.7467599867 0.7766994101
74 1.2223608634 0.7467599867
75 -0.5444929187 1.2223608634
76 -0.4693734966 -0.5444929187
77 -0.0810026220 -0.4693734966
78 0.5700479972 -0.0810026220
79 0.2465445492 0.5700479972
80 -0.5344143654 0.2465445492
81 -0.1123289328 -0.5344143654
82 0.2497838852 -0.1123289328
83 0.8995677086 0.2497838852
84 0.7777801266 0.8995677086
85 -0.9180575990 0.7777801266
86 0.2245995127 -0.9180575990
87 0.5992267407 0.2245995127
88 -0.2042766052 0.5992267407
89 1.1900142382 -0.2042766052
90 0.3327896267 1.1900142382
91 -0.1945901316 0.3327896267
92 -0.4577696622 -0.1945901316
93 0.5639318464 -0.4577696622
94 0.5909866248 0.5639318464
95 0.1543016203 0.5909866248
96 -0.2796080722 0.1543016203
97 -0.5639488113 -0.2796080722
98 -0.5780293230 -0.5639488113
99 -0.0562271545 -0.5780293230
100 -0.8758056419 -0.0562271545
101 0.6789622750 -0.8758056419
102 -0.6199415452 0.6789622750
103 0.1424661564 -0.6199415452
104 0.7007402113 0.1424661564
105 -0.0345736079 0.7007402113
106 0.0740768177 -0.0345736079
107 0.3073040292 0.0740768177
108 -0.6970867265 0.3073040292
109 -0.2100141354 -0.6970867265
110 0.4721243890 -0.2100141354
111 -0.3698026669 0.4721243890
112 0.1453050161 -0.3698026669
113 -0.2870891026 0.1453050161
114 1.0933120330 -0.2870891026
115 -0.8407986637 1.0933120330
116 0.7934436694 -0.8407986637
117 0.0706392152 0.7934436694
118 -0.2230996484 0.0706392152
119 0.2279529799 -0.2230996484
120 0.7065796069 0.2279529799
121 -0.2092307445 0.7065796069
122 0.2924598381 -0.2092307445
123 0.2368994912 0.2924598381
124 -0.2850037925 0.2368994912
125 0.7489178433 -0.2850037925
126 1.0508209486 0.7489178433
127 0.0286972133 1.0508209486
128 1.5856043160 0.0286972133
129 -0.3666189193 1.5856043160
130 -0.6161567337 -0.3666189193
131 -2.4614693512 -0.6161567337
132 0.8270283161 -2.4614693512
133 0.0629362587 0.8270283161
134 0.7790979599 0.0629362587
135 -0.3821261772 0.7790979599
136 -0.4976567487 -0.3821261772
137 -0.5332101111 -0.4976567487
138 -0.2917916294 -0.5332101111
139 -0.4110559252 -0.2917916294
140 0.1750908125 -0.4110559252
141 0.0359698986 0.1750908125
142 0.7267274841 0.0359698986
143 -0.5081874264 0.7267274841
144 -0.5155404881 -0.5081874264
145 -0.4955642469 -0.5155404881
146 0.0008371502 -0.4955642469
147 0.3648339406 0.0008371502
148 1.1649115189 0.3648339406
149 -1.4763329462 1.1649115189
150 -0.2987954445 -1.4763329462
151 0.3235585076 -0.2987954445
152 -0.2333449681 0.3235585076
153 -0.4828371380 -0.2333449681
154 0.4142000768 -0.4828371380
155 -0.5221704681 0.4142000768
156 0.1675313626 -0.5221704681
157 -0.2074304104 0.1675313626
158 -0.3015014680 -0.2074304104
159 -0.2726727299 -0.3015014680
160 -0.6323682672 -0.2726727299
161 0.1924488546 -0.6323682672
162 0.8831946135 0.1924488546
163 0.7377271652 0.8831946135
164 0.8244883789 0.7377271652
165 -0.3856391955 0.8244883789
166 -0.0185891850 -0.3856391955
167 1.0755502428 -0.0185891850
168 0.0319848421 1.0755502428
169 0.1883707538 0.0319848421
170 -0.8967646475 0.1883707538
171 -0.3467019119 -0.8967646475
172 0.5057457615 -0.3467019119
173 0.1955201606 0.5057457615
174 0.6975180346 0.1955201606
175 0.7985209686 0.6975180346
176 0.5390174828 0.7985209686
177 0.2750250487 0.5390174828
178 -0.3161045212 0.2750250487
179 1.6957351652 -0.3161045212
180 0.6921558808 1.6957351652
181 -0.3437848470 0.6921558808
182 1.5305256854 -0.3437848470
183 0.9419906316 1.5305256854
184 -0.7799219936 0.9419906316
185 -0.7126689754 -0.7799219936
186 -1.2135012200 -0.7126689754
187 -0.0397558721 -1.2135012200
188 0.1147236351 -0.0397558721
189 -0.7500116086 0.1147236351
190 -0.1792650402 -0.7500116086
191 -1.2528271581 -0.1792650402
192 -0.0426804416 -1.2528271581
193 0.0416428431 -0.0426804416
194 -0.8213596103 0.0416428431
195 1.0371396344 -0.8213596103
196 0.2351263674 1.0371396344
197 0.4920962141 0.2351263674
198 1.1208946518 0.4920962141
199 0.6427485539 1.1208946518
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7zxeb1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/871z61322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/93n7o1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10cgzr1322083116.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/111ozi1322083116.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12gjma1322083116.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/138bk41322083116.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14gmog1322083116.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15j2rr1322083116.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/164rx81322083116.tab")
+ }
>
> try(system("convert tmp/1y1n11322083116.ps tmp/1y1n11322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ko8a1322083116.ps tmp/2ko8a1322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/3flp71322083116.ps tmp/3flp71322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/4grk31322083116.ps tmp/4grk31322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/5upp91322083116.ps tmp/5upp91322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/6w51j1322083116.ps tmp/6w51j1322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zxeb1322083116.ps tmp/7zxeb1322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/871z61322083116.ps tmp/871z61322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/93n7o1322083116.ps tmp/93n7o1322083116.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cgzr1322083116.ps tmp/10cgzr1322083116.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.079 0.619 6.826