R version 2.12.1 (2010-12-16)
Copyright (C) 2010 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
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+ ,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
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+ ,6.4
+ ,5.9
+ ,6.3
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+ ,6.8
+ ,4.5
+ ,6
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+ ,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
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+ ,3.9
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+ ,5.2
+ ,4.2
+ ,8.7
+ ,4.9
+ ,4.6
+ ,7.7
+ ,7.7
+ ,9.6
+ ,5.5
+ ,4.4
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+ ,5.4
+ ,7.5
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+ ,5.9
+ ,5.9
+ ,5.3
+ ,5.8
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+ ,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
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+ ,6.7
+ ,7.5
+ ,9.7
+ ,5.2
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+ ,4
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+ ,6.9
+ ,9
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+ ,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
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+ ,6.6
+ ,4
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+ ,3.7
+ ,4.7
+ ,7.7
+ ,5.2
+ ,7.7
+ ,7.4
+ ,5.1
+ ,8.7
+ ,4.2
+ ,4.6
+ ,7.3
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+ ,9.2
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+ ,4.2
+ ,5.8
+ ,2.9
+ ,3.3
+ ,8
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+ ,7.3
+ ,3.7
+ ,2.8
+ ,6.4
+ ,3.1
+ ,3.9
+ ,6
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+ ,9
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+ ,3.3
+ ,6.4
+ ,3
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+ ,6.6
+ ,5.1
+ ,8.1
+ ,6.2
+ ,2.6
+ ,9
+ ,5.5
+ ,6.5
+ ,9.6
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+ ,6
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+ ,3.1
+ ,6.2
+ ,4.7
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+ ,4.3
+ ,3.2
+ ,8.7
+ ,4.6
+ ,4.6
+ ,6.3
+ ,7.3
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+ ,5.4
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+ ,5
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+ ,8.3
+ ,4.2
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+ ,6.4
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+ ,4
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+ ,3.3
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+ ,4
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+ ,6.1
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+ ,7.4
+ ,4.2
+ ,3.4
+ ,7.3
+ ,7.5
+ ,9.1
+ ,5.2
+ ,4.5
+ ,7
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+ ,4
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+ ,5.4
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+ ,8
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+ ,7
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+ ,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 = '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 t
1 4.9 8.5 4.3 5.0 1
2 7.9 8.2 4.0 3.9 2
3 7.4 9.2 4.6 5.4 3
4 4.7 6.4 3.6 4.3 4
5 6.0 9.0 4.5 4.5 5
6 4.3 6.5 9.5 3.6 6
7 2.3 6.9 2.5 2.1 7
8 3.6 6.2 4.8 4.3 8
9 5.9 5.8 4.4 4.4 9
10 5.7 6.4 5.3 4.1 10
11 6.8 8.7 7.5 3.8 11
12 3.9 6.1 5.9 3.0 12
13 6.9 9.5 5.3 5.1 13
14 8.4 9.2 3.0 4.5 14
15 6.8 6.3 5.4 4.8 15
16 7.8 8.7 5.0 4.3 16
17 5.5 5.7 5.4 4.2 17
18 6.4 5.9 6.3 5.7 18
19 5.7 5.6 6.1 5.0 19
20 5.3 9.1 6.7 4.5 20
21 4.3 5.2 4.6 3.3 21
22 8.3 9.6 6.5 4.3 22
23 7.3 8.6 6.0 4.8 23
24 7.2 9.3 4.2 6.7 24
25 5.3 6.0 3.9 4.7 25
26 3.9 6.4 3.7 5.6 26
27 7.6 8.5 6.7 5.3 27
28 4.8 7.0 5.9 4.3 28
29 7.6 8.5 6.0 5.7 29
30 4.2 7.6 7.2 4.7 30
31 6.4 6.9 3.3 3.7 31
32 5.1 8.1 6.1 3.0 32
33 5.1 6.7 4.2 3.5 33
34 4.6 8.0 3.8 4.7 34
35 5.4 6.7 6.0 2.5 35
36 6.1 8.7 6.5 3.1 36
37 6.0 9.0 4.3 3.9 37
38 7.7 9.6 4.4 5.2 38
39 4.9 8.2 7.1 4.7 39
40 3.9 6.1 6.8 4.5 40
41 4.6 8.3 1.7 4.6 41
42 6.5 9.4 6.2 4.1 42
43 6.6 9.3 4.1 4.6 43
44 5.4 5.1 5.2 4.9 44
45 7.7 8.0 3.9 4.3 45
46 6.4 5.9 5.1 5.2 46
47 5.4 10.0 3.7 5.0 47
48 5.7 5.7 4.8 6.5 48
49 7.0 9.9 7.2 4.5 49
50 6.9 7.9 3.6 4.1 50
51 4.7 6.7 5.3 4.0 51
52 7.9 8.2 5.0 4.5 52
53 7.3 9.4 9.2 4.7 53
54 6.4 6.9 4.4 3.2 54
55 4.6 8.0 4.2 4.9 55
56 6.4 9.3 5.9 4.1 56
57 7.2 7.4 7.4 5.7 57
58 6.6 7.6 6.4 4.6 58
59 5.4 10.0 4.5 3.7 59
60 5.8 9.9 7.0 5.6 60
61 7.8 8.7 4.5 5.4 61
62 4.7 8.4 4.2 2.7 62
63 4.7 8.8 7.2 4.4 63
64 4.7 7.7 4.7 3.3 64
65 4.7 6.6 3.9 3.5 65
66 5.7 5.7 5.0 4.7 66
67 5.5 5.7 6.4 5.0 67
68 5.3 5.5 2.5 4.5 68
69 4.1 7.5 5.2 4.0 69
70 3.9 6.4 5.5 4.7 70
71 5.3 9.1 5.7 5.4 71
72 6.3 6.7 2.5 2.9 72
73 6.3 6.5 6.3 4.6 73
74 7.0 9.9 4.6 4.1 74
75 4.9 8.5 3.6 4.4 75
76 5.9 9.9 7.6 3.1 76
77 4.6 7.6 6.6 4.5 77
78 6.5 9.4 2.4 4.3 78
79 7.5 9.3 3.1 5.2 79
80 5.0 7.1 3.5 2.6 80
81 5.9 9.9 6.9 3.2 81
82 6.8 8.7 5.1 4.3 82
83 5.6 8.6 4.0 2.7 83
84 2.9 6.4 6.5 2.0 84
85 7.2 7.7 4.1 4.7 85
86 4.1 7.5 2.8 3.4 86
87 4.2 5.0 7.6 2.4 87
88 7.2 7.7 7.7 5.1 88
89 6.2 9.1 4.1 4.6 89
90 5.7 5.5 4.9 5.5 90
91 6.3 9.1 4.6 4.4 91
92 3.3 7.1 3.5 2.0 92
93 8.4 9.2 6.6 4.4 93
94 7.5 9.3 4.9 4.8 94
95 6.4 9.3 4.8 3.6 95
96 6.0 8.6 3.6 4.9 96
97 4.4 7.4 6.4 4.2 97
98 6.1 8.7 4.3 3.1 98
99 5.3 7.8 5.7 4.3 99
100 4.2 7.9 5.8 3.4 100
101 4.2 7.6 5.1 3.1 101
102 6.5 9.2 8.6 5.1 102
103 5.2 7.7 5.4 4.0 103
104 6.9 9.5 4.4 5.6 104
105 6.3 6.5 6.9 5.0 105
106 4.6 8.3 5.2 4.2 106
107 7.7 9.6 5.5 4.4 107
108 5.9 5.9 5.3 5.8 108
109 7.4 8.7 5.7 4.6 109
110 6.3 6.7 6.5 3.8 110
111 7.5 9.7 5.2 3.7 111
112 8.1 8.8 2.7 4.0 112
113 4.9 8.2 4.3 4.5 113
114 5.1 8.9 6.7 4.2 114
115 4.7 8.4 6.6 4.0 115
116 5.2 7.7 7.4 5.1 116
117 6.5 9.2 8.9 4.2 117
118 5.2 7.3 3.7 2.8 118
119 4.8 9.0 4.9 3.3 119
120 5.1 8.1 6.2 2.6 120
121 7.2 7.4 4.3 5.7 121
122 4.2 7.9 4.6 4.8 122
123 4.7 7.7 4.3 3.2 123
124 7.3 9.4 5.4 5.8 124
125 3.4 7.2 3.6 3.2 125
126 6.1 8.3 7.4 4.1 126
127 6.9 7.9 6.7 4.6 127
128 5.2 7.3 2.9 3.3 128
129 8.2 9.6 4.8 4.4 129
130 5.7 8.3 2.8 1.2 130
131 7.3 8.6 5.2 5.0 131
132 7.7 8.0 6.8 4.6 132
133 2.9 6.4 7.0 2.4 133
134 5.3 6.6 2.9 4.3 134
135 7.1 7.6 6.2 3.6 135
136 7.8 9.4 6.0 5.1 136
137 5.7 8.3 4.2 1.8 137
138 5.3 7.8 5.2 4.1 138
139 3.3 7.1 3.1 2.8 139
140 4.6 7.6 5.3 4.4 140
141 5.7 5.6 6.0 4.5 141
142 5.8 9.9 6.8 4.0 142
143 7.4 9.2 6.1 4.2 143
144 7.5 9.1 5.2 4.5 144
145 5.4 9.9 8.0 3.8 145
146 5.4 9.9 6.2 4.1 146
147 5.3 6.6 3.5 4.6 147
148 6.3 9.1 6.5 3.7 148
149 5.4 5.1 3.9 5.1 149
150 5.3 6.0 3.6 4.3 150
151 5.1 8.9 3.8 5.0 151
152 3.6 6.2 4.7 4.0 152
153 3.4 7.2 2.9 3.0 153
154 8.1 8.8 5.6 4.1 154
155 4.1 6.3 4.2 4.4 155
156 5.7 9.7 1.6 4.0 156
157 4.2 5.0 4.0 3.7 157
158 4.4 7.4 5.1 4.0 158
159 5.3 5.5 6.0 4.3 159
160 6.2 9.1 6.1 4.6 160
161 5.1 6.7 5.6 3.7 161
162 6.8 6.3 6.5 6.4 162
163 6.1 8.3 5.5 3.6 163
164 6.0 8.2 5.9 4.7 164
165 6.0 8.2 6.2 4.0 165
166 4.8 9.0 5.6 4.3 166
167 5.0 7.1 7.2 3.6 167
168 2.3 6.9 3.4 2.7 168
169 5.6 8.6 5.1 4.0 169
170 5.4 6.7 4.0 3.8 170
171 4.8 7.0 5.3 3.3 171
172 5.6 9.7 8.4 4.5 172
173 7.0 9.9 8.0 5.0 173
174 6.0 8.6 2.8 4.8 174
175 4.1 6.3 2.4 2.8 175
176 7.0 9.9 5.2 4.3 176
177 7.2 9.3 4.1 4.0 177
178 7.5 9.7 6.1 4.9 178
179 5.6 9.7 7.1 4.6 179
180 8.3 9.6 6.2 4.0 180
181 7.1 7.6 5.5 4.4 181
182 7.8 9.4 6.5 4.7 182
183 8.2 9.6 5.6 4.6 183
184 6.6 9.3 5.7 4.4 184
185 5.7 9.7 6.3 4.7 185
186 7.5 9.1 5.1 6.0 186
187 4.3 6.5 4.8 4.3 187
188 4.7 6.6 4.8 3.2 188
189 5.9 5.8 3.4 5.9 189
190 7.4 8.7 3.6 5.5 190
191 4.7 8.8 5.8 3.8 191
192 5.7 6.4 5.0 4.0 192
193 4.7 6.7 5.0 2.9 193
194 4.3 5.2 3.6 4.3 194
195 4.7 6.4 7.0 3.6 195
196 6.6 7.6 6.8 4.4 196
197 5.9 5.9 6.6 6.0 197
198 7.6 9.7 5.2 4.4 198
199 5.7 5.5 5.3 5.9 199
200 7.6 9.7 1.2 4.3 200
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Leveringssnelheid Prijsflexibiliteit
-0.017975 0.996389 -0.110001
Prijszetting Productgamma Productkwaliteit
-0.007349 -0.021565 0.375692
Productontwikkeling Facturatie t
0.032882 0.126630 0.001604
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.52217 -0.43080 0.06432 0.42603 1.77539
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0179746 0.9668238 -0.019 0.9852
Leveringssnelheid 0.9963894 0.4793340 2.079 0.0390 *
Prijsflexibiliteit -0.1100009 0.2511984 -0.438 0.6620
Prijszetting -0.0073493 0.0424937 -0.173 0.8629
Productgamma -0.0215651 0.2414641 -0.089 0.9289
Productkwaliteit 0.3756917 0.0500876 7.501 2.33e-12 ***
Productontwikkeling 0.0328820 0.0369167 0.891 0.3742
Facturatie 0.1266304 0.0940437 1.347 0.1797
t 0.0016042 0.0009436 1.700 0.0907 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7573 on 191 degrees of freedom
Multiple R-squared: 0.6427, Adjusted R-squared: 0.6277
F-statistic: 42.94 on 8 and 191 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,] 0.8612216 0.277556721 0.1387783604
[2,] 0.9235968 0.152806445 0.0764032227
[3,] 0.8669194 0.266161263 0.1330806313
[4,] 0.8317195 0.336560998 0.1682804989
[5,] 0.7715422 0.456915639 0.2284578196
[6,] 0.6987949 0.602410154 0.3012050771
[7,] 0.6303413 0.739317499 0.3696587497
[8,] 0.5429431 0.914113838 0.4570569188
[9,] 0.6782036 0.643592882 0.3217964411
[10,] 0.6016758 0.796648369 0.3983241844
[11,] 0.9188945 0.162210943 0.0811054713
[12,] 0.9393893 0.121221322 0.0606106609
[13,] 0.9479073 0.104185368 0.0520926839
[14,] 0.9841768 0.031646441 0.0158232204
[15,] 0.9779350 0.044130095 0.0220650476
[16,] 0.9884671 0.023065779 0.0115328896
[17,] 0.9899661 0.020067732 0.0100338661
[18,] 0.9932608 0.013478430 0.0067392149
[19,] 0.9947490 0.010501988 0.0052509941
[20,] 0.9941144 0.011771283 0.0058856414
[21,] 0.9922286 0.015542741 0.0077713704
[22,] 0.9895370 0.020925982 0.0104629909
[23,] 0.9849217 0.030156525 0.0150782626
[24,] 0.9829271 0.034145808 0.0170729042
[25,] 0.9782531 0.043493796 0.0217468982
[26,] 0.9699748 0.060050427 0.0300252134
[27,] 0.9597324 0.080535170 0.0402675848
[28,] 0.9466238 0.106752304 0.0533761519
[29,] 0.9383417 0.123316561 0.0616582806
[30,] 0.9253414 0.149317288 0.0746586438
[31,] 0.9050750 0.189850029 0.0949250144
[32,] 0.9156624 0.168675150 0.0843375752
[33,] 0.9492112 0.101577534 0.0507887669
[34,] 0.9919576 0.016084733 0.0080423667
[35,] 0.9905625 0.018875071 0.0094375355
[36,] 0.9883288 0.023342433 0.0116712164
[37,] 0.9843756 0.031248884 0.0156244421
[38,] 0.9856637 0.028672662 0.0143363308
[39,] 0.9919326 0.016134751 0.0080673757
[40,] 0.9890666 0.021866777 0.0109333884
[41,] 0.9991374 0.001725142 0.0008625709
[42,] 0.9989458 0.002108409 0.0010542043
[43,] 0.9985798 0.002840328 0.0014201640
[44,] 0.9980100 0.003980095 0.0019900473
[45,] 0.9975122 0.004975643 0.0024878217
[46,] 0.9971692 0.005661645 0.0028308223
[47,] 0.9979966 0.004006812 0.0020034058
[48,] 0.9973947 0.005210589 0.0026052946
[49,] 0.9964747 0.007050573 0.0035252863
[50,] 0.9984670 0.003066091 0.0015330454
[51,] 0.9990845 0.001831094 0.0009155470
[52,] 0.9987966 0.002406804 0.0012034018
[53,] 0.9983057 0.003388636 0.0016943178
[54,] 0.9976301 0.004739882 0.0023699412
[55,] 0.9968112 0.006377518 0.0031887592
[56,] 0.9959649 0.008070176 0.0040350879
[57,] 0.9947636 0.010472851 0.0052364257
[58,] 0.9969044 0.006191158 0.0030955788
[59,] 0.9957763 0.008447491 0.0042237457
[60,] 0.9949239 0.010152268 0.0050761338
[61,] 0.9933589 0.013282213 0.0066411067
[62,] 0.9929826 0.014034720 0.0070173599
[63,] 0.9924248 0.015150457 0.0075752284
[64,] 0.9945403 0.010919379 0.0054596897
[65,] 0.9938643 0.012271439 0.0061357194
[66,] 0.9930287 0.013942618 0.0069713088
[67,] 0.9911104 0.017779281 0.0088896403
[68,] 0.9896417 0.020716668 0.0103583340
[69,] 0.9865182 0.026963554 0.0134817771
[70,] 0.9848278 0.030344325 0.0151721624
[71,] 0.9806495 0.038700959 0.0193504796
[72,] 0.9753228 0.049354462 0.0246772312
[73,] 0.9770494 0.045901189 0.0229505943
[74,] 0.9758305 0.048339096 0.0241695480
[75,] 0.9817059 0.036588203 0.0182941016
[76,] 0.9765487 0.046902695 0.0234513476
[77,] 0.9732543 0.053491484 0.0267457420
[78,] 0.9679549 0.064090148 0.0320450739
[79,] 0.9746081 0.050783763 0.0253918816
[80,] 0.9688797 0.062240685 0.0311203425
[81,] 0.9649256 0.070148788 0.0350743942
[82,] 0.9611703 0.077659401 0.0388297005
[83,] 0.9554716 0.089056826 0.0445284131
[84,] 0.9504118 0.099176359 0.0495881796
[85,] 0.9398396 0.120320895 0.0601604477
[86,] 0.9304252 0.139149500 0.0695747500
[87,] 0.9227254 0.154549121 0.0772745606
[88,] 0.9203721 0.159255869 0.0796279347
[89,] 0.9062581 0.187483869 0.0937419346
[90,] 0.9148856 0.170228761 0.0851143806
[91,] 0.9097905 0.180419009 0.0902095044
[92,] 0.9071747 0.185650554 0.0928252769
[93,] 0.8892887 0.221422680 0.1107113401
[94,] 0.8819860 0.236028062 0.1180140308
[95,] 0.8620156 0.275968738 0.1379843692
[96,] 0.8373210 0.325358076 0.1626790380
[97,] 0.8132191 0.373561833 0.1867809163
[98,] 0.8180940 0.363812097 0.1819060484
[99,] 0.7961860 0.407627912 0.2038139558
[100,] 0.7733665 0.453267020 0.2266335100
[101,] 0.7579947 0.484010651 0.2420053253
[102,] 0.7292777 0.541444560 0.2707222802
[103,] 0.6989781 0.602043898 0.3010219488
[104,] 0.7384954 0.523009173 0.2615045864
[105,] 0.7502160 0.499568097 0.2497840483
[106,] 0.7565924 0.486815216 0.2434076082
[107,] 0.7219655 0.556068978 0.2780344889
[108,] 0.6890284 0.621943189 0.3109715947
[109,] 0.6523032 0.695393527 0.3476967636
[110,] 0.6553968 0.689206332 0.3446031659
[111,] 0.6283369 0.743326142 0.3716630712
[112,] 0.5893115 0.821376973 0.4106884866
[113,] 0.5546250 0.890749941 0.4453749707
[114,] 0.5229985 0.954003007 0.4770015033
[115,] 0.5152825 0.969435039 0.4847175193
[116,] 0.5406481 0.918703867 0.4593519337
[117,] 0.4982366 0.996473282 0.5017633590
[118,] 0.6302803 0.739439340 0.3697196702
[119,] 0.6077342 0.784531589 0.3922657945
[120,] 0.6006780 0.798644050 0.3993220249
[121,] 0.9394431 0.121113843 0.0605569215
[122,] 0.9577378 0.084524307 0.0422621537
[123,] 0.9470241 0.105951704 0.0529758519
[124,] 0.9389236 0.122152809 0.0610764047
[125,] 0.9270897 0.145820563 0.0729102813
[126,] 0.9297266 0.140546798 0.0702733992
[127,] 0.9189923 0.162015309 0.0810076547
[128,] 0.9041319 0.191736264 0.0958681320
[129,] 0.8866675 0.226664921 0.1133324604
[130,] 0.8639330 0.272133964 0.1360669818
[131,] 0.8359429 0.328114232 0.1640571161
[132,] 0.8193281 0.361343765 0.1806718823
[133,] 0.8043290 0.391342030 0.1956710152
[134,] 0.7880804 0.423839207 0.2119196033
[135,] 0.7814999 0.437000230 0.2185001149
[136,] 0.7434538 0.513092429 0.2565462144
[137,] 0.7093401 0.581319833 0.2906599167
[138,] 0.7283002 0.543399637 0.2716998185
[139,] 0.8566960 0.286607955 0.1433039775
[140,] 0.8276605 0.344678997 0.1723394983
[141,] 0.7989969 0.402006190 0.2010030952
[142,] 0.7635432 0.472913690 0.2364568449
[143,] 0.8141193 0.371761420 0.1858807099
[144,] 0.7979332 0.404133556 0.2020667779
[145,] 0.8220106 0.355978827 0.1779894134
[146,] 0.7836827 0.432634584 0.2163172921
[147,] 0.7449149 0.510170119 0.2550850594
[148,] 0.7756494 0.448701264 0.2243506318
[149,] 0.7350064 0.529987186 0.2649935930
[150,] 0.7415802 0.516839567 0.2584197833
[151,] 0.6921410 0.615717938 0.3078589689
[152,] 0.6495517 0.700896568 0.3504482841
[153,] 0.6068053 0.786389411 0.3931947055
[154,] 0.5649161 0.870167729 0.4350838643
[155,] 0.5194296 0.961140726 0.4805703631
[156,] 0.4582931 0.916586279 0.5417068603
[157,] 0.7180338 0.563932319 0.2819661596
[158,] 0.6617313 0.676537336 0.3382686679
[159,] 0.6007072 0.798585596 0.3992927979
[160,] 0.6415740 0.716851987 0.3584259933
[161,] 0.5909025 0.818195043 0.4090975216
[162,] 0.5724043 0.855191476 0.4275957380
[163,] 0.5138569 0.972286164 0.4861430819
[164,] 0.4888868 0.977773613 0.5111131934
[165,] 0.5089658 0.982068467 0.4910342335
[166,] 0.4340203 0.868040624 0.5659796880
[167,] 0.3641352 0.728270317 0.6358648416
[168,] 0.2933435 0.586686901 0.7066565496
[169,] 0.2726203 0.545240675 0.7273796625
[170,] 0.2122940 0.424587956 0.7877060220
[171,] 0.1716623 0.343324655 0.8283376726
[172,] 0.2155979 0.431195869 0.7844020654
[173,] 0.6356613 0.728677482 0.3643387408
[174,] 0.5229070 0.954185996 0.4770929980
[175,] 0.4129577 0.825915318 0.5870423412
[176,] 0.2955856 0.591171135 0.7044144323
[177,] 0.2697179 0.539435841 0.7302820793
> postscript(file="/var/www/rcomp/tmp/1v3it1322145840.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xbgl1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3p1px1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4i39e1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5sswq1322145840.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 6
1.278454724 -2.191280541 0.770642314 -0.627889314 0.087929046 -1.117776440
7 8 9 10 11 12
1.350577355 0.408217765 0.405867537 -1.157406131 0.022064263 0.628036635
13 14 15 16 17 18
0.323504261 -0.194836360 0.553762764 -1.235660107 -0.097722724 0.298655374
19 20 21 22 23 24
0.215942270 -0.160128389 0.266935797 1.775389174 -0.481924965 0.300099442
25 26 27 28 29 30
-1.429146340 0.317416312 -1.577106790 -0.940278924 -1.607949955 -1.052516115
31 32 33 34 35 36
0.084079422 0.297388821 -0.448533119 0.242092849 0.394652268 -0.513848800
37 38 39 40 41 42
0.119149300 0.093553273 0.093180768 0.363579730 0.113727010 -0.091506575
43 44 45 46 47 48
1.055989810 1.208952826 -2.249256868 0.356511361 -0.372135236 0.223566457
49 50 51 52 53 54
0.802449725 1.293110972 -0.101385877 -2.380350685 0.330112053 0.074327862
55 56 57 58 59 60
0.169925808 0.577699119 0.641983213 1.094842405 -0.253071718 -0.118718361
61 62 63 64 65 66
-1.430701481 1.396296712 -0.221644623 0.347366428 0.023074458 0.416049153
67 68 69 70 71 72
-0.312118942 -0.186535179 -1.037358082 0.301611342 -0.323027894 0.105706905
73 74 75 76 77 78
0.801628832 0.798490036 1.258739612 -0.515779572 -0.441992462 -0.049632306
79 80 81 82 83 84
0.600055654 0.293803409 -0.513446222 -0.076232306 0.288347436 0.948854787
85 86 87 88 89 90
0.801971614 -0.909734500 0.236169841 0.628131759 -0.179121148 1.173623212
91 92 93 94 95 96
0.376195276 -0.172778305 -0.427280144 0.567457266 0.614620721 0.181791460
97 98 99 100 101 102
-0.282877773 -0.540968787 -0.577733833 -0.063164727 -0.894753493 0.684374668
103 104 105 106 107 108
-0.625076419 0.143800752 0.679913134 -0.054980680 0.047997676 0.252881637
109 110 111 112 113 114
-0.707650926 -0.200747887 0.455965729 -0.372084097 0.091865653 -0.326300814
115 116 117 118 119 120
1.067737947 -0.850988364 0.764414428 0.054308222 -0.245347483 0.203583270
121 122 123 124 125 126
0.641248605 -0.236281255 0.278534520 0.201872025 -0.304278093 0.735787368
127 128 129 130 131 132
1.004338349 0.001256626 1.544819698 -0.409116162 -0.654198940 -2.522169010
133 134 135 136 137 138
0.803155905 0.021056532 0.715319512 -0.427852476 -0.542358542 -0.598530528
139 140 141 142 143 144
-0.336327161 -0.487647388 0.086833388 -0.041077679 0.648687954 -0.568091467
145 146 147 148 149 150
-0.581213602 -0.561619359 -0.057516350 0.310921448 1.057932435 -1.569154461
151 152 153 154 155 156
-0.391602745 0.218490434 -0.300852206 -0.547481215 0.330619868 -0.613422879
157 158 159 160 161 162
0.077631542 -0.312661264 -0.422278115 -0.358783219 -0.725231417 0.079166758
163 164 165 166 167 168
0.802222947 0.671489128 0.748661598 -0.470392586 -0.094055574 0.986729332
169 170 171 172 173 174
-0.050403326 0.079229882 -1.023319808 -0.458496832 0.413908296 0.095632563
175 176 177 178 179 180
0.560332041 0.689806490 0.399847216 0.166934179 -0.439642694 1.569779453
181 182 183 184 185 186
0.563239447 -0.467434463 1.406561303 0.802512680 -0.903131189 -0.822125183
187 188 189 190 191 192
-1.342232420 -0.165846671 -0.039951849 -0.882506225 -0.304969093 -1.426842701
193 194 195 196 197 198
-0.180024117 -0.104339010 -0.957448756 0.885636242 0.042035294 0.342011753
199 200
0.934960594 0.482994291
> postscript(file="/var/www/rcomp/tmp/6d0dc1322145840.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 1.278454724 NA
1 -2.191280541 1.278454724
2 0.770642314 -2.191280541
3 -0.627889314 0.770642314
4 0.087929046 -0.627889314
5 -1.117776440 0.087929046
6 1.350577355 -1.117776440
7 0.408217765 1.350577355
8 0.405867537 0.408217765
9 -1.157406131 0.405867537
10 0.022064263 -1.157406131
11 0.628036635 0.022064263
12 0.323504261 0.628036635
13 -0.194836360 0.323504261
14 0.553762764 -0.194836360
15 -1.235660107 0.553762764
16 -0.097722724 -1.235660107
17 0.298655374 -0.097722724
18 0.215942270 0.298655374
19 -0.160128389 0.215942270
20 0.266935797 -0.160128389
21 1.775389174 0.266935797
22 -0.481924965 1.775389174
23 0.300099442 -0.481924965
24 -1.429146340 0.300099442
25 0.317416312 -1.429146340
26 -1.577106790 0.317416312
27 -0.940278924 -1.577106790
28 -1.607949955 -0.940278924
29 -1.052516115 -1.607949955
30 0.084079422 -1.052516115
31 0.297388821 0.084079422
32 -0.448533119 0.297388821
33 0.242092849 -0.448533119
34 0.394652268 0.242092849
35 -0.513848800 0.394652268
36 0.119149300 -0.513848800
37 0.093553273 0.119149300
38 0.093180768 0.093553273
39 0.363579730 0.093180768
40 0.113727010 0.363579730
41 -0.091506575 0.113727010
42 1.055989810 -0.091506575
43 1.208952826 1.055989810
44 -2.249256868 1.208952826
45 0.356511361 -2.249256868
46 -0.372135236 0.356511361
47 0.223566457 -0.372135236
48 0.802449725 0.223566457
49 1.293110972 0.802449725
50 -0.101385877 1.293110972
51 -2.380350685 -0.101385877
52 0.330112053 -2.380350685
53 0.074327862 0.330112053
54 0.169925808 0.074327862
55 0.577699119 0.169925808
56 0.641983213 0.577699119
57 1.094842405 0.641983213
58 -0.253071718 1.094842405
59 -0.118718361 -0.253071718
60 -1.430701481 -0.118718361
61 1.396296712 -1.430701481
62 -0.221644623 1.396296712
63 0.347366428 -0.221644623
64 0.023074458 0.347366428
65 0.416049153 0.023074458
66 -0.312118942 0.416049153
67 -0.186535179 -0.312118942
68 -1.037358082 -0.186535179
69 0.301611342 -1.037358082
70 -0.323027894 0.301611342
71 0.105706905 -0.323027894
72 0.801628832 0.105706905
73 0.798490036 0.801628832
74 1.258739612 0.798490036
75 -0.515779572 1.258739612
76 -0.441992462 -0.515779572
77 -0.049632306 -0.441992462
78 0.600055654 -0.049632306
79 0.293803409 0.600055654
80 -0.513446222 0.293803409
81 -0.076232306 -0.513446222
82 0.288347436 -0.076232306
83 0.948854787 0.288347436
84 0.801971614 0.948854787
85 -0.909734500 0.801971614
86 0.236169841 -0.909734500
87 0.628131759 0.236169841
88 -0.179121148 0.628131759
89 1.173623212 -0.179121148
90 0.376195276 1.173623212
91 -0.172778305 0.376195276
92 -0.427280144 -0.172778305
93 0.567457266 -0.427280144
94 0.614620721 0.567457266
95 0.181791460 0.614620721
96 -0.282877773 0.181791460
97 -0.540968787 -0.282877773
98 -0.577733833 -0.540968787
99 -0.063164727 -0.577733833
100 -0.894753493 -0.063164727
101 0.684374668 -0.894753493
102 -0.625076419 0.684374668
103 0.143800752 -0.625076419
104 0.679913134 0.143800752
105 -0.054980680 0.679913134
106 0.047997676 -0.054980680
107 0.252881637 0.047997676
108 -0.707650926 0.252881637
109 -0.200747887 -0.707650926
110 0.455965729 -0.200747887
111 -0.372084097 0.455965729
112 0.091865653 -0.372084097
113 -0.326300814 0.091865653
114 1.067737947 -0.326300814
115 -0.850988364 1.067737947
116 0.764414428 -0.850988364
117 0.054308222 0.764414428
118 -0.245347483 0.054308222
119 0.203583270 -0.245347483
120 0.641248605 0.203583270
121 -0.236281255 0.641248605
122 0.278534520 -0.236281255
123 0.201872025 0.278534520
124 -0.304278093 0.201872025
125 0.735787368 -0.304278093
126 1.004338349 0.735787368
127 0.001256626 1.004338349
128 1.544819698 0.001256626
129 -0.409116162 1.544819698
130 -0.654198940 -0.409116162
131 -2.522169010 -0.654198940
132 0.803155905 -2.522169010
133 0.021056532 0.803155905
134 0.715319512 0.021056532
135 -0.427852476 0.715319512
136 -0.542358542 -0.427852476
137 -0.598530528 -0.542358542
138 -0.336327161 -0.598530528
139 -0.487647388 -0.336327161
140 0.086833388 -0.487647388
141 -0.041077679 0.086833388
142 0.648687954 -0.041077679
143 -0.568091467 0.648687954
144 -0.581213602 -0.568091467
145 -0.561619359 -0.581213602
146 -0.057516350 -0.561619359
147 0.310921448 -0.057516350
148 1.057932435 0.310921448
149 -1.569154461 1.057932435
150 -0.391602745 -1.569154461
151 0.218490434 -0.391602745
152 -0.300852206 0.218490434
153 -0.547481215 -0.300852206
154 0.330619868 -0.547481215
155 -0.613422879 0.330619868
156 0.077631542 -0.613422879
157 -0.312661264 0.077631542
158 -0.422278115 -0.312661264
159 -0.358783219 -0.422278115
160 -0.725231417 -0.358783219
161 0.079166758 -0.725231417
162 0.802222947 0.079166758
163 0.671489128 0.802222947
164 0.748661598 0.671489128
165 -0.470392586 0.748661598
166 -0.094055574 -0.470392586
167 0.986729332 -0.094055574
168 -0.050403326 0.986729332
169 0.079229882 -0.050403326
170 -1.023319808 0.079229882
171 -0.458496832 -1.023319808
172 0.413908296 -0.458496832
173 0.095632563 0.413908296
174 0.560332041 0.095632563
175 0.689806490 0.560332041
176 0.399847216 0.689806490
177 0.166934179 0.399847216
178 -0.439642694 0.166934179
179 1.569779453 -0.439642694
180 0.563239447 1.569779453
181 -0.467434463 0.563239447
182 1.406561303 -0.467434463
183 0.802512680 1.406561303
184 -0.903131189 0.802512680
185 -0.822125183 -0.903131189
186 -1.342232420 -0.822125183
187 -0.165846671 -1.342232420
188 -0.039951849 -0.165846671
189 -0.882506225 -0.039951849
190 -0.304969093 -0.882506225
191 -1.426842701 -0.304969093
192 -0.180024117 -1.426842701
193 -0.104339010 -0.180024117
194 -0.957448756 -0.104339010
195 0.885636242 -0.957448756
196 0.042035294 0.885636242
197 0.342011753 0.042035294
198 0.934960594 0.342011753
199 0.482994291 0.934960594
200 NA 0.482994291
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.191280541 1.278454724
[2,] 0.770642314 -2.191280541
[3,] -0.627889314 0.770642314
[4,] 0.087929046 -0.627889314
[5,] -1.117776440 0.087929046
[6,] 1.350577355 -1.117776440
[7,] 0.408217765 1.350577355
[8,] 0.405867537 0.408217765
[9,] -1.157406131 0.405867537
[10,] 0.022064263 -1.157406131
[11,] 0.628036635 0.022064263
[12,] 0.323504261 0.628036635
[13,] -0.194836360 0.323504261
[14,] 0.553762764 -0.194836360
[15,] -1.235660107 0.553762764
[16,] -0.097722724 -1.235660107
[17,] 0.298655374 -0.097722724
[18,] 0.215942270 0.298655374
[19,] -0.160128389 0.215942270
[20,] 0.266935797 -0.160128389
[21,] 1.775389174 0.266935797
[22,] -0.481924965 1.775389174
[23,] 0.300099442 -0.481924965
[24,] -1.429146340 0.300099442
[25,] 0.317416312 -1.429146340
[26,] -1.577106790 0.317416312
[27,] -0.940278924 -1.577106790
[28,] -1.607949955 -0.940278924
[29,] -1.052516115 -1.607949955
[30,] 0.084079422 -1.052516115
[31,] 0.297388821 0.084079422
[32,] -0.448533119 0.297388821
[33,] 0.242092849 -0.448533119
[34,] 0.394652268 0.242092849
[35,] -0.513848800 0.394652268
[36,] 0.119149300 -0.513848800
[37,] 0.093553273 0.119149300
[38,] 0.093180768 0.093553273
[39,] 0.363579730 0.093180768
[40,] 0.113727010 0.363579730
[41,] -0.091506575 0.113727010
[42,] 1.055989810 -0.091506575
[43,] 1.208952826 1.055989810
[44,] -2.249256868 1.208952826
[45,] 0.356511361 -2.249256868
[46,] -0.372135236 0.356511361
[47,] 0.223566457 -0.372135236
[48,] 0.802449725 0.223566457
[49,] 1.293110972 0.802449725
[50,] -0.101385877 1.293110972
[51,] -2.380350685 -0.101385877
[52,] 0.330112053 -2.380350685
[53,] 0.074327862 0.330112053
[54,] 0.169925808 0.074327862
[55,] 0.577699119 0.169925808
[56,] 0.641983213 0.577699119
[57,] 1.094842405 0.641983213
[58,] -0.253071718 1.094842405
[59,] -0.118718361 -0.253071718
[60,] -1.430701481 -0.118718361
[61,] 1.396296712 -1.430701481
[62,] -0.221644623 1.396296712
[63,] 0.347366428 -0.221644623
[64,] 0.023074458 0.347366428
[65,] 0.416049153 0.023074458
[66,] -0.312118942 0.416049153
[67,] -0.186535179 -0.312118942
[68,] -1.037358082 -0.186535179
[69,] 0.301611342 -1.037358082
[70,] -0.323027894 0.301611342
[71,] 0.105706905 -0.323027894
[72,] 0.801628832 0.105706905
[73,] 0.798490036 0.801628832
[74,] 1.258739612 0.798490036
[75,] -0.515779572 1.258739612
[76,] -0.441992462 -0.515779572
[77,] -0.049632306 -0.441992462
[78,] 0.600055654 -0.049632306
[79,] 0.293803409 0.600055654
[80,] -0.513446222 0.293803409
[81,] -0.076232306 -0.513446222
[82,] 0.288347436 -0.076232306
[83,] 0.948854787 0.288347436
[84,] 0.801971614 0.948854787
[85,] -0.909734500 0.801971614
[86,] 0.236169841 -0.909734500
[87,] 0.628131759 0.236169841
[88,] -0.179121148 0.628131759
[89,] 1.173623212 -0.179121148
[90,] 0.376195276 1.173623212
[91,] -0.172778305 0.376195276
[92,] -0.427280144 -0.172778305
[93,] 0.567457266 -0.427280144
[94,] 0.614620721 0.567457266
[95,] 0.181791460 0.614620721
[96,] -0.282877773 0.181791460
[97,] -0.540968787 -0.282877773
[98,] -0.577733833 -0.540968787
[99,] -0.063164727 -0.577733833
[100,] -0.894753493 -0.063164727
[101,] 0.684374668 -0.894753493
[102,] -0.625076419 0.684374668
[103,] 0.143800752 -0.625076419
[104,] 0.679913134 0.143800752
[105,] -0.054980680 0.679913134
[106,] 0.047997676 -0.054980680
[107,] 0.252881637 0.047997676
[108,] -0.707650926 0.252881637
[109,] -0.200747887 -0.707650926
[110,] 0.455965729 -0.200747887
[111,] -0.372084097 0.455965729
[112,] 0.091865653 -0.372084097
[113,] -0.326300814 0.091865653
[114,] 1.067737947 -0.326300814
[115,] -0.850988364 1.067737947
[116,] 0.764414428 -0.850988364
[117,] 0.054308222 0.764414428
[118,] -0.245347483 0.054308222
[119,] 0.203583270 -0.245347483
[120,] 0.641248605 0.203583270
[121,] -0.236281255 0.641248605
[122,] 0.278534520 -0.236281255
[123,] 0.201872025 0.278534520
[124,] -0.304278093 0.201872025
[125,] 0.735787368 -0.304278093
[126,] 1.004338349 0.735787368
[127,] 0.001256626 1.004338349
[128,] 1.544819698 0.001256626
[129,] -0.409116162 1.544819698
[130,] -0.654198940 -0.409116162
[131,] -2.522169010 -0.654198940
[132,] 0.803155905 -2.522169010
[133,] 0.021056532 0.803155905
[134,] 0.715319512 0.021056532
[135,] -0.427852476 0.715319512
[136,] -0.542358542 -0.427852476
[137,] -0.598530528 -0.542358542
[138,] -0.336327161 -0.598530528
[139,] -0.487647388 -0.336327161
[140,] 0.086833388 -0.487647388
[141,] -0.041077679 0.086833388
[142,] 0.648687954 -0.041077679
[143,] -0.568091467 0.648687954
[144,] -0.581213602 -0.568091467
[145,] -0.561619359 -0.581213602
[146,] -0.057516350 -0.561619359
[147,] 0.310921448 -0.057516350
[148,] 1.057932435 0.310921448
[149,] -1.569154461 1.057932435
[150,] -0.391602745 -1.569154461
[151,] 0.218490434 -0.391602745
[152,] -0.300852206 0.218490434
[153,] -0.547481215 -0.300852206
[154,] 0.330619868 -0.547481215
[155,] -0.613422879 0.330619868
[156,] 0.077631542 -0.613422879
[157,] -0.312661264 0.077631542
[158,] -0.422278115 -0.312661264
[159,] -0.358783219 -0.422278115
[160,] -0.725231417 -0.358783219
[161,] 0.079166758 -0.725231417
[162,] 0.802222947 0.079166758
[163,] 0.671489128 0.802222947
[164,] 0.748661598 0.671489128
[165,] -0.470392586 0.748661598
[166,] -0.094055574 -0.470392586
[167,] 0.986729332 -0.094055574
[168,] -0.050403326 0.986729332
[169,] 0.079229882 -0.050403326
[170,] -1.023319808 0.079229882
[171,] -0.458496832 -1.023319808
[172,] 0.413908296 -0.458496832
[173,] 0.095632563 0.413908296
[174,] 0.560332041 0.095632563
[175,] 0.689806490 0.560332041
[176,] 0.399847216 0.689806490
[177,] 0.166934179 0.399847216
[178,] -0.439642694 0.166934179
[179,] 1.569779453 -0.439642694
[180,] 0.563239447 1.569779453
[181,] -0.467434463 0.563239447
[182,] 1.406561303 -0.467434463
[183,] 0.802512680 1.406561303
[184,] -0.903131189 0.802512680
[185,] -0.822125183 -0.903131189
[186,] -1.342232420 -0.822125183
[187,] -0.165846671 -1.342232420
[188,] -0.039951849 -0.165846671
[189,] -0.882506225 -0.039951849
[190,] -0.304969093 -0.882506225
[191,] -1.426842701 -0.304969093
[192,] -0.180024117 -1.426842701
[193,] -0.104339010 -0.180024117
[194,] -0.957448756 -0.104339010
[195,] 0.885636242 -0.957448756
[196,] 0.042035294 0.885636242
[197,] 0.342011753 0.042035294
[198,] 0.934960594 0.342011753
[199,] 0.482994291 0.934960594
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.191280541 1.278454724
2 0.770642314 -2.191280541
3 -0.627889314 0.770642314
4 0.087929046 -0.627889314
5 -1.117776440 0.087929046
6 1.350577355 -1.117776440
7 0.408217765 1.350577355
8 0.405867537 0.408217765
9 -1.157406131 0.405867537
10 0.022064263 -1.157406131
11 0.628036635 0.022064263
12 0.323504261 0.628036635
13 -0.194836360 0.323504261
14 0.553762764 -0.194836360
15 -1.235660107 0.553762764
16 -0.097722724 -1.235660107
17 0.298655374 -0.097722724
18 0.215942270 0.298655374
19 -0.160128389 0.215942270
20 0.266935797 -0.160128389
21 1.775389174 0.266935797
22 -0.481924965 1.775389174
23 0.300099442 -0.481924965
24 -1.429146340 0.300099442
25 0.317416312 -1.429146340
26 -1.577106790 0.317416312
27 -0.940278924 -1.577106790
28 -1.607949955 -0.940278924
29 -1.052516115 -1.607949955
30 0.084079422 -1.052516115
31 0.297388821 0.084079422
32 -0.448533119 0.297388821
33 0.242092849 -0.448533119
34 0.394652268 0.242092849
35 -0.513848800 0.394652268
36 0.119149300 -0.513848800
37 0.093553273 0.119149300
38 0.093180768 0.093553273
39 0.363579730 0.093180768
40 0.113727010 0.363579730
41 -0.091506575 0.113727010
42 1.055989810 -0.091506575
43 1.208952826 1.055989810
44 -2.249256868 1.208952826
45 0.356511361 -2.249256868
46 -0.372135236 0.356511361
47 0.223566457 -0.372135236
48 0.802449725 0.223566457
49 1.293110972 0.802449725
50 -0.101385877 1.293110972
51 -2.380350685 -0.101385877
52 0.330112053 -2.380350685
53 0.074327862 0.330112053
54 0.169925808 0.074327862
55 0.577699119 0.169925808
56 0.641983213 0.577699119
57 1.094842405 0.641983213
58 -0.253071718 1.094842405
59 -0.118718361 -0.253071718
60 -1.430701481 -0.118718361
61 1.396296712 -1.430701481
62 -0.221644623 1.396296712
63 0.347366428 -0.221644623
64 0.023074458 0.347366428
65 0.416049153 0.023074458
66 -0.312118942 0.416049153
67 -0.186535179 -0.312118942
68 -1.037358082 -0.186535179
69 0.301611342 -1.037358082
70 -0.323027894 0.301611342
71 0.105706905 -0.323027894
72 0.801628832 0.105706905
73 0.798490036 0.801628832
74 1.258739612 0.798490036
75 -0.515779572 1.258739612
76 -0.441992462 -0.515779572
77 -0.049632306 -0.441992462
78 0.600055654 -0.049632306
79 0.293803409 0.600055654
80 -0.513446222 0.293803409
81 -0.076232306 -0.513446222
82 0.288347436 -0.076232306
83 0.948854787 0.288347436
84 0.801971614 0.948854787
85 -0.909734500 0.801971614
86 0.236169841 -0.909734500
87 0.628131759 0.236169841
88 -0.179121148 0.628131759
89 1.173623212 -0.179121148
90 0.376195276 1.173623212
91 -0.172778305 0.376195276
92 -0.427280144 -0.172778305
93 0.567457266 -0.427280144
94 0.614620721 0.567457266
95 0.181791460 0.614620721
96 -0.282877773 0.181791460
97 -0.540968787 -0.282877773
98 -0.577733833 -0.540968787
99 -0.063164727 -0.577733833
100 -0.894753493 -0.063164727
101 0.684374668 -0.894753493
102 -0.625076419 0.684374668
103 0.143800752 -0.625076419
104 0.679913134 0.143800752
105 -0.054980680 0.679913134
106 0.047997676 -0.054980680
107 0.252881637 0.047997676
108 -0.707650926 0.252881637
109 -0.200747887 -0.707650926
110 0.455965729 -0.200747887
111 -0.372084097 0.455965729
112 0.091865653 -0.372084097
113 -0.326300814 0.091865653
114 1.067737947 -0.326300814
115 -0.850988364 1.067737947
116 0.764414428 -0.850988364
117 0.054308222 0.764414428
118 -0.245347483 0.054308222
119 0.203583270 -0.245347483
120 0.641248605 0.203583270
121 -0.236281255 0.641248605
122 0.278534520 -0.236281255
123 0.201872025 0.278534520
124 -0.304278093 0.201872025
125 0.735787368 -0.304278093
126 1.004338349 0.735787368
127 0.001256626 1.004338349
128 1.544819698 0.001256626
129 -0.409116162 1.544819698
130 -0.654198940 -0.409116162
131 -2.522169010 -0.654198940
132 0.803155905 -2.522169010
133 0.021056532 0.803155905
134 0.715319512 0.021056532
135 -0.427852476 0.715319512
136 -0.542358542 -0.427852476
137 -0.598530528 -0.542358542
138 -0.336327161 -0.598530528
139 -0.487647388 -0.336327161
140 0.086833388 -0.487647388
141 -0.041077679 0.086833388
142 0.648687954 -0.041077679
143 -0.568091467 0.648687954
144 -0.581213602 -0.568091467
145 -0.561619359 -0.581213602
146 -0.057516350 -0.561619359
147 0.310921448 -0.057516350
148 1.057932435 0.310921448
149 -1.569154461 1.057932435
150 -0.391602745 -1.569154461
151 0.218490434 -0.391602745
152 -0.300852206 0.218490434
153 -0.547481215 -0.300852206
154 0.330619868 -0.547481215
155 -0.613422879 0.330619868
156 0.077631542 -0.613422879
157 -0.312661264 0.077631542
158 -0.422278115 -0.312661264
159 -0.358783219 -0.422278115
160 -0.725231417 -0.358783219
161 0.079166758 -0.725231417
162 0.802222947 0.079166758
163 0.671489128 0.802222947
164 0.748661598 0.671489128
165 -0.470392586 0.748661598
166 -0.094055574 -0.470392586
167 0.986729332 -0.094055574
168 -0.050403326 0.986729332
169 0.079229882 -0.050403326
170 -1.023319808 0.079229882
171 -0.458496832 -1.023319808
172 0.413908296 -0.458496832
173 0.095632563 0.413908296
174 0.560332041 0.095632563
175 0.689806490 0.560332041
176 0.399847216 0.689806490
177 0.166934179 0.399847216
178 -0.439642694 0.166934179
179 1.569779453 -0.439642694
180 0.563239447 1.569779453
181 -0.467434463 0.563239447
182 1.406561303 -0.467434463
183 0.802512680 1.406561303
184 -0.903131189 0.802512680
185 -0.822125183 -0.903131189
186 -1.342232420 -0.822125183
187 -0.165846671 -1.342232420
188 -0.039951849 -0.165846671
189 -0.882506225 -0.039951849
190 -0.304969093 -0.882506225
191 -1.426842701 -0.304969093
192 -0.180024117 -1.426842701
193 -0.104339010 -0.180024117
194 -0.957448756 -0.104339010
195 0.885636242 -0.957448756
196 0.042035294 0.885636242
197 0.342011753 0.042035294
198 0.934960594 0.342011753
199 0.482994291 0.934960594
> 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/www/rcomp/tmp/70fjo1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8e66a1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/99o371322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10zktp1322145840.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/11lin41322145840.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/www/rcomp/tmp/12mxie1322145840.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/www/rcomp/tmp/13pf521322145840.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/www/rcomp/tmp/14fjfg1322145840.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/www/rcomp/tmp/15yz5c1322145840.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/www/rcomp/tmp/165bmf1322145840.tab")
+ }
>
> try(system("convert tmp/1v3it1322145840.ps tmp/1v3it1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xbgl1322145840.ps tmp/2xbgl1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p1px1322145840.ps tmp/3p1px1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/4i39e1322145840.ps tmp/4i39e1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sswq1322145840.ps tmp/5sswq1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/6d0dc1322145840.ps tmp/6d0dc1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/70fjo1322145840.ps tmp/70fjo1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e66a1322145840.ps tmp/8e66a1322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/99o371322145840.ps tmp/99o371322145840.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zktp1322145840.ps tmp/10zktp1322145840.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.168 1.052 37.872