R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(102.1880309
+ ,0
+ ,114.0150276
+ ,108.1560276
+ ,100
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+ ,128.5991124
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+ ,140.9639674
+ ,1
+ ,126.8169313
+ ,149.7373327
+ ,128.5991124
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+ ,137.6691981
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+ ,149.7373327
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+ ,122.3095247
+ ,1
+ ,117.9402337
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+ ,140.9639674
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+ ,1
+ ,122.3095247
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+ ,122.3095247
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+ ,1
+ ,116.2405856
+ ,136.1677176
+ ,127.7804207
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+ ,116.3400234
+ ,1
+ ,123.1576893
+ ,116.2405856
+ ,136.1677176
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+ ,108.6119282
+ ,1
+ ,116.3400234
+ ,123.1576893
+ ,116.2405856
+ ,0
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+ ,125.8982264
+ ,1
+ ,108.6119282
+ ,116.3400234
+ ,123.1576893
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+ ,125.8982264
+ ,108.6119282
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+ ,135.0955413
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+ ,107.5182447
+ ,112.8003105
+ ,125.8982264
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+ ,0
+ ,115.5096488
+ ,1
+ ,135.0955413
+ ,107.5182447
+ ,112.8003105
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+ ,115.8640759
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+ ,115.5096488
+ ,135.0955413
+ ,107.5182447
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+ ,104.5883906
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+ ,115.8640759
+ ,115.5096488
+ ,135.0955413
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+ ,0
+ ,163.7213386
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+ ,104.5883906
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+ ,115.5096488
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+ ,113.4482275
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+ ,163.7213386
+ ,104.5883906
+ ,115.8640759
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+ ,98.0428844
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+ ,113.4482275
+ ,163.7213386
+ ,104.5883906
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+ ,116.7868521
+ ,1
+ ,98.0428844
+ ,113.4482275
+ ,163.7213386
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+ ,116.7868521
+ ,98.0428844
+ ,113.4482275
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+ ,113.0336597
+ ,1
+ ,126.5330444
+ ,116.7868521
+ ,98.0428844
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+ ,0
+ ,0
+ ,124.3392163
+ ,1
+ ,113.0336597
+ ,126.5330444
+ ,116.7868521
+ ,0
+ ,0
+ ,0
+ ,109.8298759
+ ,1
+ ,124.3392163
+ ,113.0336597
+ ,126.5330444
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+ ,0
+ ,124.4434777
+ ,1
+ ,109.8298759
+ ,124.3392163
+ ,113.0336597
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+ ,0
+ ,111.5039454
+ ,1
+ ,124.4434777
+ ,109.8298759
+ ,124.3392163
+ ,0
+ ,0
+ ,0
+ ,102.0350019
+ ,1
+ ,111.5039454
+ ,124.4434777
+ ,109.8298759
+ ,0
+ ,0
+ ,0
+ ,116.8726598
+ ,1
+ ,102.0350019
+ ,111.5039454
+ ,124.4434777
+ ,0
+ ,0
+ ,0
+ ,112.2073122
+ ,1
+ ,116.8726598
+ ,102.0350019
+ ,111.5039454
+ ,0
+ ,0
+ ,0
+ ,101.1513902
+ ,1
+ ,112.2073122
+ ,116.8726598
+ ,102.0350019
+ ,0
+ ,0
+ ,0
+ ,124.4255108
+ ,1
+ ,101.1513902
+ ,112.2073122
+ ,116.8726598
+ ,0
+ ,0
+ ,0)
+ ,dim=c(8
+ ,105)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'O1'
+ ,'O2'
+ ,'O3')
+ ,1:105))
> y <- array(NA,dim=c(8,105),dimnames=list(c('Y','X','Y1','Y2','Y3','O1','O2','O3'),1:105))
> 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 = 'Include Monthly 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
Y X Y1 Y2 Y3 O1 O2 O3 M1 M2 M3 M4 M5 M6 M7 M8
1 102.18803 0 114.01503 108.15603 100.00000 0 0 0 1 0 0 0 0 0 0 0
2 110.36720 0 102.18803 114.01503 108.15603 0 0 0 0 1 0 0 0 0 0 0
3 96.86025 0 110.36720 102.18803 114.01503 0 0 0 0 0 1 0 0 0 0 0
4 94.19446 0 96.86025 110.36720 102.18803 0 0 0 0 0 0 1 0 0 0 0
5 99.51622 0 94.19446 96.86025 110.36720 0 0 0 0 0 0 0 1 0 0 0
6 94.06333 0 99.51622 94.19446 96.86025 0 0 0 0 0 0 0 0 1 0 0
7 97.55415 0 94.06333 99.51622 94.19446 0 0 0 0 0 0 0 0 0 1 0
8 78.15062 0 97.55415 94.06333 99.51622 0 0 0 0 0 0 0 0 0 0 1
9 81.24346 0 78.15062 97.55415 94.06333 0 0 0 0 0 0 0 0 0 0 0
10 92.36262 0 81.24346 78.15062 97.55415 0 0 0 0 0 0 0 0 0 0 0
11 96.06324 0 92.36262 81.24346 78.15062 0 0 0 0 0 0 0 0 0 0 0
12 114.05238 0 96.06324 92.36262 81.24346 0 0 0 0 0 0 0 0 0 0 0
13 110.66167 0 114.05238 96.06324 92.36262 0 0 0 1 0 0 0 0 0 0 0
14 104.91719 0 110.66167 114.05238 96.06324 0 0 0 0 1 0 0 0 0 0 0
15 90.00187 0 104.91719 110.66167 114.05238 0 0 0 0 0 1 0 0 0 0 0
16 95.70081 0 90.00187 104.91719 110.66167 0 0 0 0 0 0 1 0 0 0 0
17 86.02741 0 95.70081 90.00187 104.91719 0 0 0 0 0 0 0 1 0 0 0
18 84.85288 0 86.02741 95.70081 90.00187 0 0 0 0 0 0 0 0 1 0 0
19 100.04328 0 84.85288 86.02741 95.70081 0 0 0 0 0 0 0 0 0 1 0
20 80.91714 0 100.04328 84.85288 86.02741 0 0 0 0 0 0 0 0 0 0 1
21 74.06540 0 80.91714 100.04328 84.85288 0 0 0 0 0 0 0 0 0 0 0
22 77.30281 0 74.06540 80.91714 100.04328 0 0 0 0 0 0 0 0 0 0 0
23 97.23043 0 77.30281 74.06540 80.91714 0 0 0 0 0 0 0 0 0 0 0
24 90.75516 0 97.23043 77.30281 74.06540 0 0 0 0 0 0 0 0 0 0 0
25 100.56145 0 90.75516 97.23043 77.30281 0 0 0 1 0 0 0 0 0 0 0
26 92.01293 0 100.56145 90.75516 97.23043 0 0 0 0 1 0 0 0 0 0 0
27 99.24012 0 92.01293 100.56145 90.75516 0 0 0 0 0 1 0 0 0 0 0
28 105.86728 0 99.24012 92.01293 100.56145 0 0 0 0 0 0 1 0 0 0 0
29 90.99205 0 105.86728 99.24012 92.01293 0 0 0 0 0 0 0 1 0 0 0
30 93.30624 0 90.99205 105.86728 99.24012 0 0 0 0 0 0 0 0 1 0 0
31 91.17419 0 93.30624 90.99205 105.86728 0 0 0 0 0 0 0 0 0 1 0
32 77.33295 0 91.17419 93.30624 90.99205 0 0 0 0 0 0 0 0 0 0 1
33 91.12777 0 77.33295 91.17419 93.30624 0 0 0 0 0 0 0 0 0 0 0
34 85.01250 0 91.12777 77.33295 91.17419 0 0 0 0 0 0 0 0 0 0 0
35 83.90390 0 85.01250 91.12777 77.33295 0 0 0 0 0 0 0 0 0 0 0
36 104.86263 0 83.90390 85.01250 91.12777 0 0 0 0 0 0 0 0 0 0 0
37 110.90391 0 104.86263 83.90390 85.01250 0 0 0 1 0 0 0 0 0 0 0
38 95.43714 0 110.90391 104.86263 83.90390 0 0 0 0 1 0 0 0 0 0 0
39 111.62387 0 95.43714 110.90391 104.86263 0 0 0 0 0 1 0 0 0 0 0
40 108.89254 0 111.62387 95.43714 110.90391 0 0 0 0 0 0 1 0 0 0 0
41 96.17512 0 108.89254 111.62387 95.43714 0 0 0 0 0 0 0 1 0 0 0
42 101.97402 0 96.17512 108.89254 111.62387 0 0 0 0 0 0 0 0 1 0 0
43 99.11953 0 101.97402 96.17512 108.89254 0 0 0 0 0 0 0 0 0 1 0
44 86.78158 0 99.11953 101.97402 96.17512 0 0 0 0 0 0 0 0 0 0 1
45 118.41950 0 86.78158 99.11953 101.97402 0 0 0 0 0 0 0 0 0 0 0
46 118.74414 0 118.41950 86.78158 99.11953 0 0 0 0 0 0 0 0 0 0 0
47 106.52962 0 118.74414 118.41950 86.78158 0 0 0 0 0 0 0 0 0 0 0
48 134.77727 0 106.52962 118.74414 118.41950 0 0 0 0 0 0 0 0 0 0 0
49 104.67787 0 134.77727 106.52962 118.74414 0 0 0 1 0 0 0 0 0 0 0
50 105.29543 0 104.67787 134.77727 106.52962 0 0 0 0 1 0 0 0 0 0 0
51 139.41398 0 105.29543 104.67787 134.77727 0 0 0 0 0 1 0 0 0 0 0
52 103.60605 0 139.41398 105.29543 104.67787 0 0 0 0 0 0 1 0 0 0 0
53 99.78183 0 103.60605 139.41398 105.29543 0 0 0 0 0 0 0 1 0 0 0
54 103.46103 0 99.78183 103.60605 139.41398 0 0 0 0 0 0 0 0 1 0 0
55 120.05949 0 103.46103 99.78183 103.60605 0 0 0 0 0 0 0 0 0 1 0
56 96.71377 0 120.05949 103.46103 99.78183 0 0 0 0 0 0 0 0 0 0 1
57 107.13089 0 96.71377 120.05949 103.46103 0 0 0 0 0 0 0 0 0 0 0
58 105.36084 0 107.13089 96.71377 120.05949 0 0 0 0 0 0 0 0 0 0 0
59 111.69424 0 105.36084 107.13089 96.71377 0 0 0 0 0 0 0 0 0 0 0
60 132.05200 0 111.69424 105.36084 107.13089 0 0 0 0 0 0 0 0 0 0 0
61 126.80379 0 132.05200 111.69424 105.36084 0 0 0 1 0 0 0 0 0 0 0
62 154.48243 0 126.80379 132.05200 111.69424 1 0 0 0 1 0 0 0 0 0 0
63 141.55710 0 154.48243 126.80379 132.05200 0 0 0 0 0 1 0 0 0 0 0
64 109.95069 0 141.55710 154.48243 126.80379 0 0 0 0 0 0 1 0 0 0 0
65 127.90420 0 109.95069 141.55710 154.48243 0 0 0 0 0 0 0 1 0 0 0
66 133.08886 0 127.90420 109.95069 141.55710 0 0 0 0 0 0 0 0 1 0 0
67 120.07963 0 133.08886 127.90420 109.95069 0 0 0 0 0 0 0 0 0 1 0
68 117.55571 0 120.07963 133.08886 127.90420 0 0 0 0 0 0 0 0 0 0 1
69 143.03623 0 117.55571 120.07963 133.08886 0 0 0 0 0 0 0 0 0 0 0
70 159.98293 1 143.03623 117.55571 120.07963 0 1 0 0 0 0 0 0 0 0 0
71 128.59911 1 159.98293 143.03623 117.55571 0 0 0 0 0 0 0 0 0 0 0
72 149.73733 1 128.59911 159.98293 143.03623 0 0 0 0 0 0 0 0 0 0 0
73 126.81693 1 149.73733 128.59911 159.98293 0 0 0 1 0 0 0 0 0 0 0
74 140.96397 1 126.81693 149.73733 128.59911 0 0 0 0 1 0 0 0 0 0 0
75 137.66920 1 140.96397 126.81693 149.73733 0 0 0 0 0 1 0 0 0 0 0
76 117.94023 1 137.66920 140.96397 126.81693 0 0 0 0 0 0 1 0 0 0 0
77 122.30952 1 117.94023 137.66920 140.96397 0 0 0 0 0 0 0 1 0 0 0
78 127.78042 1 122.30952 117.94023 137.66920 0 0 0 0 0 0 0 0 1 0 0
79 136.16772 1 127.78042 122.30952 117.94023 0 0 0 0 0 0 0 0 0 1 0
80 116.24059 1 136.16772 127.78042 122.30952 0 0 0 0 0 0 0 0 0 0 1
81 123.15769 1 116.24059 136.16772 127.78042 0 0 0 0 0 0 0 0 0 0 0
82 116.34002 1 123.15769 116.24059 136.16772 0 0 0 0 0 0 0 0 0 0 0
83 108.61193 1 116.34002 123.15769 116.24059 0 0 0 0 0 0 0 0 0 0 0
84 125.89823 1 108.61193 116.34002 123.15769 0 0 0 0 0 0 0 0 0 0 0
85 112.80031 1 125.89823 108.61193 116.34002 0 0 0 1 0 0 0 0 0 0 0
86 107.51824 1 112.80031 125.89823 108.61193 0 0 0 0 1 0 0 0 0 0 0
87 135.09554 1 107.51824 112.80031 125.89823 0 0 0 0 0 1 0 0 0 0 0
88 115.50965 1 135.09554 107.51824 112.80031 0 0 0 0 0 0 1 0 0 0 0
89 115.86408 1 115.50965 135.09554 107.51824 0 0 0 0 0 0 0 1 0 0 0
90 104.58839 1 115.86408 115.50965 135.09554 0 0 0 0 0 0 0 0 1 0 0
91 163.72134 1 104.58839 115.86408 115.50965 0 0 1 0 0 0 0 0 0 1 0
92 113.44823 1 163.72134 104.58839 115.86408 0 0 0 0 0 0 0 0 0 0 1
93 98.04288 1 113.44823 163.72134 104.58839 0 0 0 0 0 0 0 0 0 0 0
94 116.78685 1 98.04288 113.44823 163.72134 0 0 0 0 0 0 0 0 0 0 0
95 126.53304 1 116.78685 98.04288 113.44823 0 0 0 0 0 0 0 0 0 0 0
96 113.03366 1 126.53304 116.78685 98.04288 0 0 0 0 0 0 0 0 0 0 0
97 124.33922 1 113.03366 126.53304 116.78685 0 0 0 1 0 0 0 0 0 0 0
98 109.82988 1 124.33922 113.03366 126.53304 0 0 0 0 1 0 0 0 0 0 0
99 124.44348 1 109.82988 124.33922 113.03366 0 0 0 0 0 1 0 0 0 0 0
100 111.50395 1 124.44348 109.82988 124.33922 0 0 0 0 0 0 1 0 0 0 0
101 102.03500 1 111.50395 124.44348 109.82988 0 0 0 0 0 0 0 1 0 0 0
102 116.87266 1 102.03500 111.50395 124.44348 0 0 0 0 0 0 0 0 1 0 0
103 112.20731 1 116.87266 102.03500 111.50395 0 0 0 0 0 0 0 0 0 1 0
104 101.15139 1 112.20731 116.87266 102.03500 0 0 0 0 0 0 0 0 0 0 1
105 124.42551 1 101.15139 112.20731 116.87266 0 0 0 0 0 0 0 0 0 0 0
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
62 0 0 0 62
63 0 0 0 63
64 0 0 0 64
65 0 0 0 65
66 0 0 0 66
67 0 0 0 67
68 0 0 0 68
69 1 0 0 69
70 0 1 0 70
71 0 0 1 71
72 0 0 0 72
73 0 0 0 73
74 0 0 0 74
75 0 0 0 75
76 0 0 0 76
77 0 0 0 77
78 0 0 0 78
79 0 0 0 79
80 0 0 0 80
81 1 0 0 81
82 0 1 0 82
83 0 0 1 83
84 0 0 0 84
85 0 0 0 85
86 0 0 0 86
87 0 0 0 87
88 0 0 0 88
89 0 0 0 89
90 0 0 0 90
91 0 0 0 91
92 0 0 0 92
93 1 0 0 93
94 0 1 0 94
95 0 0 1 95
96 0 0 0 96
97 0 0 0 97
98 0 0 0 98
99 0 0 0 99
100 0 0 0 100
101 0 0 0 101
102 0 0 0 102
103 0 0 0 103
104 0 0 0 104
105 1 0 0 105
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 O1
42.67722 -7.03033 0.23895 0.02734 0.40219 35.16391
O2 O3 M1 M2 M3 M4
46.45027 48.36851 -11.05493 -13.58439 -8.58776 -20.10798
M5 M6 M7 M8 M9 M10
-20.02035 -19.57053 -11.63219 -26.71281 -13.18199 -20.66496
M11 t
-9.85179 0.18375
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.081e+01 -5.191e+00 5.224e-15 5.692e+00 1.919e+01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.67722 8.79758 4.851 5.50e-06 ***
X -7.03033 3.55240 -1.979 0.051049 .
Y1 0.23895 0.08279 2.886 0.004940 **
Y2 0.02734 0.08313 0.329 0.743066
Y3 0.40219 0.08073 4.982 3.27e-06 ***
O1 35.16391 10.17787 3.455 0.000861 ***
O2 46.45027 10.60648 4.379 3.37e-05 ***
O3 48.36851 10.12967 4.775 7.42e-06 ***
M1 -11.05493 4.69985 -2.352 0.020975 *
M2 -13.58439 4.75853 -2.855 0.005410 **
M3 -8.58776 4.70125 -1.827 0.071256 .
M4 -20.10798 4.66217 -4.313 4.32e-05 ***
M5 -20.02035 4.65453 -4.301 4.51e-05 ***
M6 -19.57053 4.75111 -4.119 8.79e-05 ***
M7 -11.63219 4.72272 -2.463 0.015794 *
M8 -26.71281 4.61759 -5.785 1.18e-07 ***
M9 -13.18199 4.73033 -2.787 0.006566 **
M10 -20.66496 5.19847 -3.975 0.000147 ***
M11 -9.85179 4.74002 -2.078 0.040686 *
t 0.18375 0.06167 2.980 0.003763 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.369 on 85 degrees of freedom
Multiple R-squared: 0.7903, Adjusted R-squared: 0.7434
F-statistic: 16.86 on 19 and 85 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,] 0.3529103 0.7058207 0.6470897
[2,] 0.6861454 0.6277093 0.3138546
[3,] 0.5623896 0.8752208 0.4376104
[4,] 0.5064512 0.9870975 0.4935488
[5,] 0.4700616 0.9401233 0.5299384
[6,] 0.6399836 0.7200329 0.3600164
[7,] 0.5416397 0.9167206 0.4583603
[8,] 0.4889622 0.9779245 0.5110378
[9,] 0.4282229 0.8564459 0.5717771
[10,] 0.3468308 0.6936617 0.6531692
[11,] 0.4389103 0.8778206 0.5610897
[12,] 0.3618227 0.7236453 0.6381773
[13,] 0.3550665 0.7101330 0.6449335
[14,] 0.3027948 0.6055896 0.6972052
[15,] 0.2898500 0.5796999 0.7101500
[16,] 0.2328506 0.4657013 0.7671494
[17,] 0.3344642 0.6689283 0.6655358
[18,] 0.2987172 0.5974343 0.7012828
[19,] 0.2467774 0.4935547 0.7532226
[20,] 0.2010296 0.4020593 0.7989704
[21,] 0.2067374 0.4134749 0.7932626
[22,] 0.1855944 0.3711889 0.8144056
[23,] 0.4374209 0.8748418 0.5625791
[24,] 0.5775692 0.8448617 0.4224308
[25,] 0.5176144 0.9647712 0.4823856
[26,] 0.4712207 0.9424414 0.5287793
[27,] 0.6909598 0.6180805 0.3090402
[28,] 0.6460101 0.7079799 0.3539899
[29,] 0.7271310 0.5457379 0.2728690
[30,] 0.6952491 0.6095018 0.3047509
[31,] 0.6500158 0.6999684 0.3499842
[32,] 0.7404781 0.5190437 0.2595219
[33,] 0.7255358 0.5489285 0.2744642
[34,] 0.7176951 0.5646097 0.2823049
[35,] 0.7336259 0.5327481 0.2663741
[36,] 0.7084183 0.5831634 0.2915817
[37,] 0.6932710 0.6134581 0.3067290
[38,] 0.6472255 0.7055491 0.3527745
[39,] 0.5869973 0.8260054 0.4130027
[40,] 0.5136097 0.9727806 0.4863903
[41,] 0.4567977 0.9135954 0.5432023
[42,] 0.4574237 0.9148474 0.5425763
[43,] 0.3972197 0.7944394 0.6027803
[44,] 0.3491942 0.6983884 0.6508058
[45,] 0.3180888 0.6361776 0.6819112
[46,] 0.3303690 0.6607381 0.6696310
[47,] 0.3073948 0.6147897 0.6926052
[48,] 0.2390862 0.4781725 0.7609138
[49,] 0.1915034 0.3830068 0.8084966
[50,] 0.2345824 0.4691648 0.7654176
[51,] 0.2271934 0.4543868 0.7728066
[52,] 0.5564211 0.8871578 0.4435789
[53,] 0.4620077 0.9240154 0.5379923
[54,] 0.3759529 0.7519057 0.6240471
[55,] 0.2815644 0.5631289 0.7184356
[56,] 0.2353726 0.4707451 0.7646274
[57,] 0.3498619 0.6997239 0.6501381
[58,] 0.2853118 0.5706237 0.7146882
[59,] 0.2007499 0.4014999 0.7992501
[60,] 0.1197753 0.2395507 0.8802247
> postscript(file="/var/www/html/rcomp/tmp/1xaj41258654568.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21dhn1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3344r1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wvdr1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50nd91258654568.ps",horizontal=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 = 105
Frequency = 1
1 2 3 4 5
-3.837060e-02 9.872105e+00 -1.280277e+01 3.628543e+00 6.395571e+00
6 7 8 9 10
4.542760e+00 2.141129e+00 -5.190969e+00 -9.078514e+00 7.727316e+00
11 12 13 14 15
5.493480e+00 1.101490e+01 9.623594e+00 5.054872e+00 -2.081060e+01
16 17 18 19 20
1.309612e+00 -7.278777e+00 -9.323633e-01 4.388987e+00 4.526260e-01
21 22 23 24 25
-1.548636e+01 -8.899086e+00 7.137748e+00 -1.146759e+01 8.910283e+00
26 27 28 29 30
-7.473495e+00 -1.047784e+00 1.147856e+01 -2.011044e+00 1.361327e-01
31 32 33 34 35
-1.292972e+01 -5.445185e+00 -2.930015e+00 -3.806462e+00 -9.261011e+00
36 37 38 39 40
-3.453929e+00 1.094023e+01 -3.751526e+00 2.356000e+00 5.086363e+00
41 42 43 44 45
-1.471677e+00 2.969734e-01 -1.061906e+01 -2.421729e+00 1.619555e+01
46 47 48 49 50
1.774484e+01 -1.446896e+00 6.950447e+00 -1.882424e+01 -4.528330e+00
51 52 53 54 55
1.372413e+01 -6.811123e+00 -3.531497e+00 -1.231539e+01 9.788111e+00
56 57 58 59 60
-1.189488e+00 -8.419740e-01 -3.839551e+00 1.024584e+00 5.692120e+00
61 62 63 64 65
6.989314e+00 -1.104585e-15 2.400054e+00 -1.342727e+01 1.028457e+00
66 67 68 69 70
7.352095e+00 -2.797044e+00 5.321951e+00 1.596141e+01 5.223686e-15
71 72 73 74 75
3.385702e-01 8.229059e+00 -1.482904e+01 1.918505e+01 -5.456047e-01
76 77 78 79 80
6.808648e-01 3.893270e+00 9.551047e+00 1.632438e+01 7.383087e+00
81 82 83 84 85
2.917581e+00 -1.082251e+00 -1.035272e+01 -3.850952e+00 -7.254986e+00
86 87 88 89 90
-4.425975e+00 1.263874e+01 3.211973e+00 9.345582e+00 -1.320433e+01
91 92 93 94 95
3.387048e-16 -9.719497e-01 -1.516064e+01 -7.844805e+00 7.066248e+00
96 97 98 99 100
-1.311405e+01 4.483219e+00 -1.393270e+01 4.087839e+00 -5.157530e+00
101 102 103 104 105
-6.369885e+00 4.573072e+00 -6.296784e+00 2.061656e+00 8.422967e+00
> postscript(file="/var/www/html/rcomp/tmp/6nq2f1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.837060e-02 NA
1 9.872105e+00 -3.837060e-02
2 -1.280277e+01 9.872105e+00
3 3.628543e+00 -1.280277e+01
4 6.395571e+00 3.628543e+00
5 4.542760e+00 6.395571e+00
6 2.141129e+00 4.542760e+00
7 -5.190969e+00 2.141129e+00
8 -9.078514e+00 -5.190969e+00
9 7.727316e+00 -9.078514e+00
10 5.493480e+00 7.727316e+00
11 1.101490e+01 5.493480e+00
12 9.623594e+00 1.101490e+01
13 5.054872e+00 9.623594e+00
14 -2.081060e+01 5.054872e+00
15 1.309612e+00 -2.081060e+01
16 -7.278777e+00 1.309612e+00
17 -9.323633e-01 -7.278777e+00
18 4.388987e+00 -9.323633e-01
19 4.526260e-01 4.388987e+00
20 -1.548636e+01 4.526260e-01
21 -8.899086e+00 -1.548636e+01
22 7.137748e+00 -8.899086e+00
23 -1.146759e+01 7.137748e+00
24 8.910283e+00 -1.146759e+01
25 -7.473495e+00 8.910283e+00
26 -1.047784e+00 -7.473495e+00
27 1.147856e+01 -1.047784e+00
28 -2.011044e+00 1.147856e+01
29 1.361327e-01 -2.011044e+00
30 -1.292972e+01 1.361327e-01
31 -5.445185e+00 -1.292972e+01
32 -2.930015e+00 -5.445185e+00
33 -3.806462e+00 -2.930015e+00
34 -9.261011e+00 -3.806462e+00
35 -3.453929e+00 -9.261011e+00
36 1.094023e+01 -3.453929e+00
37 -3.751526e+00 1.094023e+01
38 2.356000e+00 -3.751526e+00
39 5.086363e+00 2.356000e+00
40 -1.471677e+00 5.086363e+00
41 2.969734e-01 -1.471677e+00
42 -1.061906e+01 2.969734e-01
43 -2.421729e+00 -1.061906e+01
44 1.619555e+01 -2.421729e+00
45 1.774484e+01 1.619555e+01
46 -1.446896e+00 1.774484e+01
47 6.950447e+00 -1.446896e+00
48 -1.882424e+01 6.950447e+00
49 -4.528330e+00 -1.882424e+01
50 1.372413e+01 -4.528330e+00
51 -6.811123e+00 1.372413e+01
52 -3.531497e+00 -6.811123e+00
53 -1.231539e+01 -3.531497e+00
54 9.788111e+00 -1.231539e+01
55 -1.189488e+00 9.788111e+00
56 -8.419740e-01 -1.189488e+00
57 -3.839551e+00 -8.419740e-01
58 1.024584e+00 -3.839551e+00
59 5.692120e+00 1.024584e+00
60 6.989314e+00 5.692120e+00
61 -1.104585e-15 6.989314e+00
62 2.400054e+00 -1.104585e-15
63 -1.342727e+01 2.400054e+00
64 1.028457e+00 -1.342727e+01
65 7.352095e+00 1.028457e+00
66 -2.797044e+00 7.352095e+00
67 5.321951e+00 -2.797044e+00
68 1.596141e+01 5.321951e+00
69 5.223686e-15 1.596141e+01
70 3.385702e-01 5.223686e-15
71 8.229059e+00 3.385702e-01
72 -1.482904e+01 8.229059e+00
73 1.918505e+01 -1.482904e+01
74 -5.456047e-01 1.918505e+01
75 6.808648e-01 -5.456047e-01
76 3.893270e+00 6.808648e-01
77 9.551047e+00 3.893270e+00
78 1.632438e+01 9.551047e+00
79 7.383087e+00 1.632438e+01
80 2.917581e+00 7.383087e+00
81 -1.082251e+00 2.917581e+00
82 -1.035272e+01 -1.082251e+00
83 -3.850952e+00 -1.035272e+01
84 -7.254986e+00 -3.850952e+00
85 -4.425975e+00 -7.254986e+00
86 1.263874e+01 -4.425975e+00
87 3.211973e+00 1.263874e+01
88 9.345582e+00 3.211973e+00
89 -1.320433e+01 9.345582e+00
90 3.387048e-16 -1.320433e+01
91 -9.719497e-01 3.387048e-16
92 -1.516064e+01 -9.719497e-01
93 -7.844805e+00 -1.516064e+01
94 7.066248e+00 -7.844805e+00
95 -1.311405e+01 7.066248e+00
96 4.483219e+00 -1.311405e+01
97 -1.393270e+01 4.483219e+00
98 4.087839e+00 -1.393270e+01
99 -5.157530e+00 4.087839e+00
100 -6.369885e+00 -5.157530e+00
101 4.573072e+00 -6.369885e+00
102 -6.296784e+00 4.573072e+00
103 2.061656e+00 -6.296784e+00
104 8.422967e+00 2.061656e+00
105 NA 8.422967e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.872105e+00 -3.837060e-02
[2,] -1.280277e+01 9.872105e+00
[3,] 3.628543e+00 -1.280277e+01
[4,] 6.395571e+00 3.628543e+00
[5,] 4.542760e+00 6.395571e+00
[6,] 2.141129e+00 4.542760e+00
[7,] -5.190969e+00 2.141129e+00
[8,] -9.078514e+00 -5.190969e+00
[9,] 7.727316e+00 -9.078514e+00
[10,] 5.493480e+00 7.727316e+00
[11,] 1.101490e+01 5.493480e+00
[12,] 9.623594e+00 1.101490e+01
[13,] 5.054872e+00 9.623594e+00
[14,] -2.081060e+01 5.054872e+00
[15,] 1.309612e+00 -2.081060e+01
[16,] -7.278777e+00 1.309612e+00
[17,] -9.323633e-01 -7.278777e+00
[18,] 4.388987e+00 -9.323633e-01
[19,] 4.526260e-01 4.388987e+00
[20,] -1.548636e+01 4.526260e-01
[21,] -8.899086e+00 -1.548636e+01
[22,] 7.137748e+00 -8.899086e+00
[23,] -1.146759e+01 7.137748e+00
[24,] 8.910283e+00 -1.146759e+01
[25,] -7.473495e+00 8.910283e+00
[26,] -1.047784e+00 -7.473495e+00
[27,] 1.147856e+01 -1.047784e+00
[28,] -2.011044e+00 1.147856e+01
[29,] 1.361327e-01 -2.011044e+00
[30,] -1.292972e+01 1.361327e-01
[31,] -5.445185e+00 -1.292972e+01
[32,] -2.930015e+00 -5.445185e+00
[33,] -3.806462e+00 -2.930015e+00
[34,] -9.261011e+00 -3.806462e+00
[35,] -3.453929e+00 -9.261011e+00
[36,] 1.094023e+01 -3.453929e+00
[37,] -3.751526e+00 1.094023e+01
[38,] 2.356000e+00 -3.751526e+00
[39,] 5.086363e+00 2.356000e+00
[40,] -1.471677e+00 5.086363e+00
[41,] 2.969734e-01 -1.471677e+00
[42,] -1.061906e+01 2.969734e-01
[43,] -2.421729e+00 -1.061906e+01
[44,] 1.619555e+01 -2.421729e+00
[45,] 1.774484e+01 1.619555e+01
[46,] -1.446896e+00 1.774484e+01
[47,] 6.950447e+00 -1.446896e+00
[48,] -1.882424e+01 6.950447e+00
[49,] -4.528330e+00 -1.882424e+01
[50,] 1.372413e+01 -4.528330e+00
[51,] -6.811123e+00 1.372413e+01
[52,] -3.531497e+00 -6.811123e+00
[53,] -1.231539e+01 -3.531497e+00
[54,] 9.788111e+00 -1.231539e+01
[55,] -1.189488e+00 9.788111e+00
[56,] -8.419740e-01 -1.189488e+00
[57,] -3.839551e+00 -8.419740e-01
[58,] 1.024584e+00 -3.839551e+00
[59,] 5.692120e+00 1.024584e+00
[60,] 6.989314e+00 5.692120e+00
[61,] -1.104585e-15 6.989314e+00
[62,] 2.400054e+00 -1.104585e-15
[63,] -1.342727e+01 2.400054e+00
[64,] 1.028457e+00 -1.342727e+01
[65,] 7.352095e+00 1.028457e+00
[66,] -2.797044e+00 7.352095e+00
[67,] 5.321951e+00 -2.797044e+00
[68,] 1.596141e+01 5.321951e+00
[69,] 5.223686e-15 1.596141e+01
[70,] 3.385702e-01 5.223686e-15
[71,] 8.229059e+00 3.385702e-01
[72,] -1.482904e+01 8.229059e+00
[73,] 1.918505e+01 -1.482904e+01
[74,] -5.456047e-01 1.918505e+01
[75,] 6.808648e-01 -5.456047e-01
[76,] 3.893270e+00 6.808648e-01
[77,] 9.551047e+00 3.893270e+00
[78,] 1.632438e+01 9.551047e+00
[79,] 7.383087e+00 1.632438e+01
[80,] 2.917581e+00 7.383087e+00
[81,] -1.082251e+00 2.917581e+00
[82,] -1.035272e+01 -1.082251e+00
[83,] -3.850952e+00 -1.035272e+01
[84,] -7.254986e+00 -3.850952e+00
[85,] -4.425975e+00 -7.254986e+00
[86,] 1.263874e+01 -4.425975e+00
[87,] 3.211973e+00 1.263874e+01
[88,] 9.345582e+00 3.211973e+00
[89,] -1.320433e+01 9.345582e+00
[90,] 3.387048e-16 -1.320433e+01
[91,] -9.719497e-01 3.387048e-16
[92,] -1.516064e+01 -9.719497e-01
[93,] -7.844805e+00 -1.516064e+01
[94,] 7.066248e+00 -7.844805e+00
[95,] -1.311405e+01 7.066248e+00
[96,] 4.483219e+00 -1.311405e+01
[97,] -1.393270e+01 4.483219e+00
[98,] 4.087839e+00 -1.393270e+01
[99,] -5.157530e+00 4.087839e+00
[100,] -6.369885e+00 -5.157530e+00
[101,] 4.573072e+00 -6.369885e+00
[102,] -6.296784e+00 4.573072e+00
[103,] 2.061656e+00 -6.296784e+00
[104,] 8.422967e+00 2.061656e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.872105e+00 -3.837060e-02
2 -1.280277e+01 9.872105e+00
3 3.628543e+00 -1.280277e+01
4 6.395571e+00 3.628543e+00
5 4.542760e+00 6.395571e+00
6 2.141129e+00 4.542760e+00
7 -5.190969e+00 2.141129e+00
8 -9.078514e+00 -5.190969e+00
9 7.727316e+00 -9.078514e+00
10 5.493480e+00 7.727316e+00
11 1.101490e+01 5.493480e+00
12 9.623594e+00 1.101490e+01
13 5.054872e+00 9.623594e+00
14 -2.081060e+01 5.054872e+00
15 1.309612e+00 -2.081060e+01
16 -7.278777e+00 1.309612e+00
17 -9.323633e-01 -7.278777e+00
18 4.388987e+00 -9.323633e-01
19 4.526260e-01 4.388987e+00
20 -1.548636e+01 4.526260e-01
21 -8.899086e+00 -1.548636e+01
22 7.137748e+00 -8.899086e+00
23 -1.146759e+01 7.137748e+00
24 8.910283e+00 -1.146759e+01
25 -7.473495e+00 8.910283e+00
26 -1.047784e+00 -7.473495e+00
27 1.147856e+01 -1.047784e+00
28 -2.011044e+00 1.147856e+01
29 1.361327e-01 -2.011044e+00
30 -1.292972e+01 1.361327e-01
31 -5.445185e+00 -1.292972e+01
32 -2.930015e+00 -5.445185e+00
33 -3.806462e+00 -2.930015e+00
34 -9.261011e+00 -3.806462e+00
35 -3.453929e+00 -9.261011e+00
36 1.094023e+01 -3.453929e+00
37 -3.751526e+00 1.094023e+01
38 2.356000e+00 -3.751526e+00
39 5.086363e+00 2.356000e+00
40 -1.471677e+00 5.086363e+00
41 2.969734e-01 -1.471677e+00
42 -1.061906e+01 2.969734e-01
43 -2.421729e+00 -1.061906e+01
44 1.619555e+01 -2.421729e+00
45 1.774484e+01 1.619555e+01
46 -1.446896e+00 1.774484e+01
47 6.950447e+00 -1.446896e+00
48 -1.882424e+01 6.950447e+00
49 -4.528330e+00 -1.882424e+01
50 1.372413e+01 -4.528330e+00
51 -6.811123e+00 1.372413e+01
52 -3.531497e+00 -6.811123e+00
53 -1.231539e+01 -3.531497e+00
54 9.788111e+00 -1.231539e+01
55 -1.189488e+00 9.788111e+00
56 -8.419740e-01 -1.189488e+00
57 -3.839551e+00 -8.419740e-01
58 1.024584e+00 -3.839551e+00
59 5.692120e+00 1.024584e+00
60 6.989314e+00 5.692120e+00
61 -1.104585e-15 6.989314e+00
62 2.400054e+00 -1.104585e-15
63 -1.342727e+01 2.400054e+00
64 1.028457e+00 -1.342727e+01
65 7.352095e+00 1.028457e+00
66 -2.797044e+00 7.352095e+00
67 5.321951e+00 -2.797044e+00
68 1.596141e+01 5.321951e+00
69 5.223686e-15 1.596141e+01
70 3.385702e-01 5.223686e-15
71 8.229059e+00 3.385702e-01
72 -1.482904e+01 8.229059e+00
73 1.918505e+01 -1.482904e+01
74 -5.456047e-01 1.918505e+01
75 6.808648e-01 -5.456047e-01
76 3.893270e+00 6.808648e-01
77 9.551047e+00 3.893270e+00
78 1.632438e+01 9.551047e+00
79 7.383087e+00 1.632438e+01
80 2.917581e+00 7.383087e+00
81 -1.082251e+00 2.917581e+00
82 -1.035272e+01 -1.082251e+00
83 -3.850952e+00 -1.035272e+01
84 -7.254986e+00 -3.850952e+00
85 -4.425975e+00 -7.254986e+00
86 1.263874e+01 -4.425975e+00
87 3.211973e+00 1.263874e+01
88 9.345582e+00 3.211973e+00
89 -1.320433e+01 9.345582e+00
90 3.387048e-16 -1.320433e+01
91 -9.719497e-01 3.387048e-16
92 -1.516064e+01 -9.719497e-01
93 -7.844805e+00 -1.516064e+01
94 7.066248e+00 -7.844805e+00
95 -1.311405e+01 7.066248e+00
96 4.483219e+00 -1.311405e+01
97 -1.393270e+01 4.483219e+00
98 4.087839e+00 -1.393270e+01
99 -5.157530e+00 4.087839e+00
100 -6.369885e+00 -5.157530e+00
101 4.573072e+00 -6.369885e+00
102 -6.296784e+00 4.573072e+00
103 2.061656e+00 -6.296784e+00
104 8.422967e+00 2.061656e+00
> 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/html/rcomp/tmp/73paw1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gpqj1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9flh41258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: Not plotting observations with leverage one:
62, 70, 91
2: Not plotting observations with leverage one:
62, 70, 91
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106vqr1258654568.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11k3ep1258654568.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/html/rcomp/tmp/12i7qh1258654568.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/html/rcomp/tmp/13r66o1258654568.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/html/rcomp/tmp/149yjj1258654568.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/html/rcomp/tmp/15s45x1258654568.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/html/rcomp/tmp/16j5ah1258654568.tab")
+ }
> system("convert tmp/1xaj41258654568.ps tmp/1xaj41258654568.png")
> system("convert tmp/21dhn1258654568.ps tmp/21dhn1258654568.png")
> system("convert tmp/3344r1258654568.ps tmp/3344r1258654568.png")
> system("convert tmp/4wvdr1258654568.ps tmp/4wvdr1258654568.png")
> system("convert tmp/50nd91258654568.ps tmp/50nd91258654568.png")
> system("convert tmp/6nq2f1258654568.ps tmp/6nq2f1258654568.png")
> system("convert tmp/73paw1258654568.ps tmp/73paw1258654568.png")
> system("convert tmp/8gpqj1258654568.ps tmp/8gpqj1258654568.png")
> system("convert tmp/9flh41258654568.ps tmp/9flh41258654568.png")
> system("convert tmp/106vqr1258654568.ps tmp/106vqr1258654568.png")
>
>
> proc.time()
user system elapsed
3.219 1.602 3.637