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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> x <- array(list(110.3672031
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+ ,0)
+ ,dim=c(9
+ ,104)
+ ,dimnames=list(c('BouwV'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'D1'
+ ,'D2'
+ ,'D3')
+ ,1:104))
> y <- array(NA,dim=c(9,104),dimnames=list(c('BouwV','X','Y1','Y2','Y3','Y4','D1','D2','D3'),1:104))
> 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
BouwV X Y1 Y2 Y3 Y4 D1 D2 D3 M1 M2 M3 M4 M5
1 110.36720 0 102.18803 114.01503 108.15603 100.00000 0 0 0 1 0 0 0 0
2 96.86025 0 110.36720 102.18803 114.01503 108.15603 0 0 0 0 1 0 0 0
3 94.19446 0 96.86025 110.36720 102.18803 114.01503 0 0 0 0 0 1 0 0
4 99.51622 0 94.19446 96.86025 110.36720 102.18803 0 0 0 0 0 0 1 0
5 94.06333 0 99.51622 94.19446 96.86025 110.36720 0 0 0 0 0 0 0 1
6 97.55415 0 94.06333 99.51622 94.19446 96.86025 0 0 0 0 0 0 0 0
7 78.15062 0 97.55415 94.06333 99.51622 94.19446 0 0 0 0 0 0 0 0
8 81.24346 0 78.15062 97.55415 94.06333 99.51622 0 0 0 0 0 0 0 0
9 92.36262 0 81.24346 78.15062 97.55415 94.06333 0 0 0 0 0 0 0 0
10 96.06324 0 92.36262 81.24346 78.15062 97.55415 0 0 0 0 0 0 0 0
11 114.05238 0 96.06324 92.36262 81.24346 78.15062 0 0 0 0 0 0 0 0
12 110.66167 0 114.05238 96.06324 92.36262 81.24346 0 0 0 0 0 0 0 0
13 104.91719 0 110.66167 114.05238 96.06324 92.36262 0 0 0 1 0 0 0 0
14 90.00187 0 104.91719 110.66167 114.05238 96.06324 0 0 0 0 1 0 0 0
15 95.70081 0 90.00187 104.91719 110.66167 114.05238 0 0 0 0 0 1 0 0
16 86.02741 0 95.70081 90.00187 104.91719 110.66167 0 0 0 0 0 0 1 0
17 84.85288 0 86.02741 95.70081 90.00187 104.91719 0 0 0 0 0 0 0 1
18 100.04328 0 84.85288 86.02741 95.70081 90.00187 0 0 0 0 0 0 0 0
19 80.91714 0 100.04328 84.85288 86.02741 95.70081 0 0 0 0 0 0 0 0
20 74.06540 0 80.91714 100.04328 84.85288 86.02741 0 0 0 0 0 0 0 0
21 77.30281 0 74.06540 80.91714 100.04328 84.85288 0 0 0 0 0 0 0 0
22 97.23043 0 77.30281 74.06540 80.91714 100.04328 0 0 0 0 0 0 0 0
23 90.75516 0 97.23043 77.30281 74.06540 80.91714 0 0 0 0 0 0 0 0
24 100.56145 0 90.75516 97.23043 77.30281 74.06540 0 0 0 0 0 0 0 0
25 92.01293 0 100.56145 90.75516 97.23043 77.30281 0 0 0 1 0 0 0 0
26 99.24012 0 92.01293 100.56145 90.75516 97.23043 0 0 0 0 1 0 0 0
27 105.86728 0 99.24012 92.01293 100.56145 90.75516 0 0 0 0 0 1 0 0
28 90.99205 0 105.86728 99.24012 92.01293 100.56145 0 0 0 0 0 0 1 0
29 93.30624 0 90.99205 105.86728 99.24012 92.01293 0 0 0 0 0 0 0 1
30 91.17419 0 93.30624 90.99205 105.86728 99.24012 0 0 0 0 0 0 0 0
31 77.33295 0 91.17419 93.30624 90.99205 105.86728 0 0 0 0 0 0 0 0
32 91.12777 0 77.33295 91.17419 93.30624 90.99205 0 0 0 0 0 0 0 0
33 85.01250 0 91.12777 77.33295 91.17419 93.30624 0 0 0 0 0 0 0 0
34 83.90390 0 85.01250 91.12777 77.33295 91.17419 0 0 0 0 0 0 0 0
35 104.86263 0 83.90390 85.01250 91.12777 77.33295 0 0 0 0 0 0 0 0
36 110.90391 0 104.86263 83.90390 85.01250 91.12777 0 0 0 0 0 0 0 0
37 95.43714 0 110.90391 104.86263 83.90390 85.01250 0 0 0 1 0 0 0 0
38 111.62387 0 95.43714 110.90391 104.86263 83.90390 0 0 0 0 1 0 0 0
39 108.89254 0 111.62387 95.43714 110.90391 104.86263 0 0 0 0 0 1 0 0
40 96.17512 0 108.89254 111.62387 95.43714 110.90391 0 0 0 0 0 0 1 0
41 101.97402 0 96.17512 108.89254 111.62387 95.43714 0 0 0 0 0 0 0 1
42 99.11953 0 101.97402 96.17512 108.89254 111.62387 0 0 0 0 0 0 0 0
43 86.78158 0 99.11953 101.97402 96.17512 108.89254 0 0 0 0 0 0 0 0
44 118.41950 0 86.78158 99.11953 101.97402 96.17512 0 0 0 0 0 0 0 0
45 118.74414 0 118.41950 86.78158 99.11953 101.97402 0 0 0 0 0 0 0 0
46 106.52962 0 118.74414 118.41950 86.78158 99.11953 0 0 0 0 0 0 0 0
47 134.77727 0 106.52962 118.74414 118.41950 86.78158 0 0 0 0 0 0 0 0
48 104.67787 0 134.77727 106.52962 118.74414 118.41950 0 0 0 0 0 0 0 0
49 105.29543 0 104.67787 134.77727 106.52962 118.74414 0 0 0 1 0 0 0 0
50 139.41398 0 105.29543 104.67787 134.77727 106.52962 0 0 0 0 1 0 0 0
51 103.60605 0 139.41398 105.29543 104.67787 134.77727 0 0 0 0 0 1 0 0
52 99.78183 0 103.60605 139.41398 105.29543 104.67787 0 0 0 0 0 0 1 0
53 103.46103 0 99.78183 103.60605 139.41398 105.29543 0 0 0 0 0 0 0 1
54 120.05949 0 103.46103 99.78183 103.60605 139.41398 0 0 0 0 0 0 0 0
55 96.71377 0 120.05949 103.46103 99.78183 103.60605 0 0 0 0 0 0 0 0
56 107.13089 0 96.71377 120.05949 103.46103 99.78183 0 0 0 0 0 0 0 0
57 105.36084 0 107.13089 96.71377 120.05949 103.46103 0 0 0 0 0 0 0 0
58 111.69424 0 105.36084 107.13089 96.71377 120.05949 0 0 0 0 0 0 0 0
59 132.05200 0 111.69424 105.36084 107.13089 96.71377 0 0 0 0 0 0 0 0
60 126.80379 0 132.05200 111.69424 105.36084 107.13089 0 0 0 0 0 0 0 0
61 154.48243 0 126.80379 132.05200 111.69424 105.36084 1 0 0 1 0 0 0 0
62 141.55710 0 154.48243 126.80379 132.05200 111.69424 0 0 0 0 1 0 0 0
63 109.95069 0 141.55710 154.48243 126.80379 132.05200 0 0 0 0 0 1 0 0
64 127.90420 0 109.95069 141.55710 154.48243 126.80379 0 0 0 0 0 0 1 0
65 133.08886 0 127.90420 109.95069 141.55710 154.48243 0 0 0 0 0 0 0 1
66 120.07963 0 133.08886 127.90420 109.95069 141.55710 0 0 0 0 0 0 0 0
67 117.55571 0 120.07963 133.08886 127.90420 109.95069 0 0 0 0 0 0 0 0
68 143.03623 0 117.55571 120.07963 133.08886 127.90420 0 0 0 0 0 0 0 0
69 159.98293 1 143.03623 117.55571 120.07963 133.08886 0 1 0 0 0 0 0 0
70 128.59911 1 159.98293 143.03623 117.55571 120.07963 0 0 0 0 0 0 0 0
71 149.73733 1 128.59911 159.98293 143.03623 117.55571 0 0 0 0 0 0 0 0
72 126.81693 1 149.73733 128.59911 159.98293 143.03623 0 0 0 0 0 0 0 0
73 140.96397 1 126.81693 149.73733 128.59911 159.98293 0 0 0 1 0 0 0 0
74 137.66920 1 140.96397 126.81693 149.73733 128.59911 0 0 0 0 1 0 0 0
75 117.94023 1 137.66920 140.96397 126.81693 149.73733 0 0 0 0 0 1 0 0
76 122.30952 1 117.94023 137.66920 140.96397 126.81693 0 0 0 0 0 0 1 0
77 127.78042 1 122.30952 117.94023 137.66920 140.96397 0 0 0 0 0 0 0 1
78 136.16772 1 127.78042 122.30952 117.94023 137.66920 0 0 0 0 0 0 0 0
79 116.24059 1 136.16772 127.78042 122.30952 117.94023 0 0 0 0 0 0 0 0
80 123.15769 1 116.24059 136.16772 127.78042 122.30952 0 0 0 0 0 0 0 0
81 116.34002 1 123.15769 116.24059 136.16772 127.78042 0 0 0 0 0 0 0 0
82 108.61193 1 116.34002 123.15769 116.24059 136.16772 0 0 0 0 0 0 0 0
83 125.89823 1 108.61193 116.34002 123.15769 116.24059 0 0 0 0 0 0 0 0
84 112.80031 1 125.89823 108.61193 116.34002 123.15769 0 0 0 0 0 0 0 0
85 107.51824 1 112.80031 125.89823 108.61193 116.34002 0 0 0 1 0 0 0 0
86 135.09554 1 107.51824 112.80031 125.89823 108.61193 0 0 0 0 1 0 0 0
87 115.50965 1 135.09554 107.51824 112.80031 125.89823 0 0 0 0 0 1 0 0
88 115.86408 1 115.50965 135.09554 107.51824 112.80031 0 0 0 0 0 0 1 0
89 104.58839 1 115.86408 115.50965 135.09554 107.51824 0 0 0 0 0 0 0 1
90 163.72134 1 104.58839 115.86408 115.50965 135.09554 0 0 1 0 0 0 0 0
91 113.44823 1 163.72134 104.58839 115.86408 115.50965 0 0 0 0 0 0 0 0
92 98.04288 1 113.44823 163.72134 104.58839 115.86408 0 0 0 0 0 0 0 0
93 116.78685 1 98.04288 113.44823 163.72134 104.58839 0 0 0 0 0 0 0 0
94 126.53304 1 116.78685 98.04288 113.44823 163.72134 0 0 0 0 0 0 0 0
95 113.03366 1 126.53304 116.78685 98.04288 113.44823 0 0 0 0 0 0 0 0
96 124.33922 1 113.03366 126.53304 116.78685 98.04288 0 0 0 0 0 0 0 0
97 109.82988 1 124.33922 113.03366 126.53304 116.78685 0 0 0 1 0 0 0 0
98 124.44348 1 109.82988 124.33922 113.03366 126.53304 0 0 0 0 1 0 0 0
99 111.50395 1 124.44348 109.82988 124.33922 113.03366 0 0 0 0 0 1 0 0
100 102.03500 1 111.50395 124.44348 109.82988 124.33922 0 0 0 0 0 0 1 0
101 116.87266 1 102.03500 111.50395 124.44348 109.82988 0 0 0 0 0 0 0 1
102 112.20731 1 116.87266 102.03500 111.50395 124.44348 0 0 0 0 0 0 0 0
103 101.15139 1 112.20731 116.87266 102.03500 111.50395 0 0 0 0 0 0 0 0
104 124.42551 1 101.15139 112.20731 116.87266 102.03500 0 0 0 0 0 0 0 0
M6 M7 M8 M9 M10 M11 t
1 0 0 0 0 0 0 1
2 0 0 0 0 0 0 2
3 0 0 0 0 0 0 3
4 0 0 0 0 0 0 4
5 0 0 0 0 0 0 5
6 1 0 0 0 0 0 6
7 0 1 0 0 0 0 7
8 0 0 1 0 0 0 8
9 0 0 0 1 0 0 9
10 0 0 0 0 1 0 10
11 0 0 0 0 0 1 11
12 0 0 0 0 0 0 12
13 0 0 0 0 0 0 13
14 0 0 0 0 0 0 14
15 0 0 0 0 0 0 15
16 0 0 0 0 0 0 16
17 0 0 0 0 0 0 17
18 1 0 0 0 0 0 18
19 0 1 0 0 0 0 19
20 0 0 1 0 0 0 20
21 0 0 0 1 0 0 21
22 0 0 0 0 1 0 22
23 0 0 0 0 0 1 23
24 0 0 0 0 0 0 24
25 0 0 0 0 0 0 25
26 0 0 0 0 0 0 26
27 0 0 0 0 0 0 27
28 0 0 0 0 0 0 28
29 0 0 0 0 0 0 29
30 1 0 0 0 0 0 30
31 0 1 0 0 0 0 31
32 0 0 1 0 0 0 32
33 0 0 0 1 0 0 33
34 0 0 0 0 1 0 34
35 0 0 0 0 0 1 35
36 0 0 0 0 0 0 36
37 0 0 0 0 0 0 37
38 0 0 0 0 0 0 38
39 0 0 0 0 0 0 39
40 0 0 0 0 0 0 40
41 0 0 0 0 0 0 41
42 1 0 0 0 0 0 42
43 0 1 0 0 0 0 43
44 0 0 1 0 0 0 44
45 0 0 0 1 0 0 45
46 0 0 0 0 1 0 46
47 0 0 0 0 0 1 47
48 0 0 0 0 0 0 48
49 0 0 0 0 0 0 49
50 0 0 0 0 0 0 50
51 0 0 0 0 0 0 51
52 0 0 0 0 0 0 52
53 0 0 0 0 0 0 53
54 1 0 0 0 0 0 54
55 0 1 0 0 0 0 55
56 0 0 1 0 0 0 56
57 0 0 0 1 0 0 57
58 0 0 0 0 1 0 58
59 0 0 0 0 0 1 59
60 0 0 0 0 0 0 60
61 0 0 0 0 0 0 61
62 0 0 0 0 0 0 62
63 0 0 0 0 0 0 63
64 0 0 0 0 0 0 64
65 0 0 0 0 0 0 65
66 1 0 0 0 0 0 66
67 0 1 0 0 0 0 67
68 0 0 1 0 0 0 68
69 0 0 0 1 0 0 69
70 0 0 0 0 1 0 70
71 0 0 0 0 0 1 71
72 0 0 0 0 0 0 72
73 0 0 0 0 0 0 73
74 0 0 0 0 0 0 74
75 0 0 0 0 0 0 75
76 0 0 0 0 0 0 76
77 0 0 0 0 0 0 77
78 1 0 0 0 0 0 78
79 0 1 0 0 0 0 79
80 0 0 1 0 0 0 80
81 0 0 0 1 0 0 81
82 0 0 0 0 1 0 82
83 0 0 0 0 0 1 83
84 0 0 0 0 0 0 84
85 0 0 0 0 0 0 85
86 0 0 0 0 0 0 86
87 0 0 0 0 0 0 87
88 0 0 0 0 0 0 88
89 0 0 0 0 0 0 89
90 1 0 0 0 0 0 90
91 0 1 0 0 0 0 91
92 0 0 1 0 0 0 92
93 0 0 0 1 0 0 93
94 0 0 0 0 1 0 94
95 0 0 0 0 0 1 95
96 0 0 0 0 0 0 96
97 0 0 0 0 0 0 97
98 0 0 0 0 0 0 98
99 0 0 0 0 0 0 99
100 0 0 0 0 0 0 100
101 0 0 0 0 0 0 101
102 1 0 0 0 0 0 102
103 0 1 0 0 0 0 103
104 0 0 1 0 0 0 104
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
30.13519 -7.37173 0.21293 0.02160 0.37722 0.08139
D1 D2 D3 M1 M2 M3
35.92493 45.60778 47.62163 -3.09242 2.30992 -10.22268
M4 M5 M6 M7 M8 M9
-9.88127 -9.42118 -2.16497 -16.12343 -2.77630 -9.90267
M10 M11 t
-0.38975 11.30177 0.17956
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.5290 -5.6621 0.0591 5.7635 17.6521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.13519 9.56874 3.149 0.002275 **
X -7.37173 3.61450 -2.039 0.044582 *
Y1 0.21293 0.08786 2.424 0.017539 *
Y2 0.02160 0.08429 0.256 0.798374
Y3 0.37722 0.08549 4.412 3.05e-05 ***
Y4 0.08139 0.08646 0.941 0.349239
D1 35.92493 10.27851 3.495 0.000763 ***
D2 45.60778 10.71864 4.255 5.46e-05 ***
D3 47.62163 10.22929 4.655 1.21e-05 ***
M1 -3.09242 4.96345 -0.623 0.534967
M2 2.30992 4.75841 0.485 0.628643
M3 -10.22268 4.78626 -2.136 0.035639 *
M4 -9.88127 5.01152 -1.972 0.051974 .
M5 -9.42118 5.00029 -1.884 0.063049 .
M6 -2.16497 5.10067 -0.424 0.672338
M7 -16.12343 4.63788 -3.476 0.000811 ***
M8 -2.77630 5.20366 -0.534 0.595094
M9 -9.90267 5.30200 -1.868 0.065330 .
M10 -0.38975 5.13066 -0.076 0.939630
M11 11.30177 4.85707 2.327 0.022409 *
t 0.17956 0.06378 2.815 0.006083 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.431 on 83 degrees of freedom
Multiple R-squared: 0.7922, Adjusted R-squared: 0.7421
F-statistic: 15.82 on 20 and 83 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,] 0.7089186 0.5821628 0.2910814
[2,] 0.5918129 0.8163742 0.4081871
[3,] 0.6332499 0.7335002 0.3667501
[4,] 0.7776645 0.4446711 0.2223355
[5,] 0.6903005 0.6193991 0.3096995
[6,] 0.6195048 0.7609904 0.3804952
[7,] 0.5329617 0.9340765 0.4670383
[8,] 0.4666500 0.9332999 0.5333500
[9,] 0.5794637 0.8410725 0.4205363
[10,] 0.5023089 0.9953822 0.4976911
[11,] 0.4930000 0.9860001 0.5070000
[12,] 0.4308281 0.8616561 0.5691719
[13,] 0.3997922 0.7995843 0.6002078
[14,] 0.3231622 0.6463245 0.6768378
[15,] 0.4302044 0.8604088 0.5697956
[16,] 0.3922825 0.7845650 0.6077175
[17,] 0.3343105 0.6686210 0.6656895
[18,] 0.2727369 0.5454738 0.7272631
[19,] 0.2595195 0.5190389 0.7404805
[20,] 0.2456527 0.4913055 0.7543473
[21,] 0.5214141 0.9571717 0.4785859
[22,] 0.6450097 0.7099806 0.3549903
[23,] 0.5964185 0.8071630 0.4035815
[24,] 0.5553467 0.8893066 0.4446533
[25,] 0.7689233 0.4621533 0.2310767
[26,] 0.7197745 0.5604510 0.2802255
[27,] 0.7984162 0.4031677 0.2015838
[28,] 0.7643609 0.4712781 0.2356391
[29,] 0.7180180 0.5639640 0.2819820
[30,] 0.7764197 0.4471605 0.2235803
[31,] 0.7860545 0.4278911 0.2139455
[32,] 0.7750618 0.4498764 0.2249382
[33,] 0.7756620 0.4486759 0.2243380
[34,] 0.7477765 0.5044469 0.2522235
[35,] 0.7225281 0.5549438 0.2774719
[36,] 0.6702694 0.6594612 0.3297306
[37,] 0.6143248 0.7713504 0.3856752
[38,] 0.5399237 0.9201525 0.4600763
[39,] 0.4859806 0.9719611 0.5140194
[40,] 0.4818223 0.9636446 0.5181777
[41,] 0.4170489 0.8340979 0.5829511
[42,] 0.3617538 0.7235076 0.6382462
[43,] 0.3334255 0.6668510 0.6665745
[44,] 0.3327155 0.6654310 0.6672845
[45,] 0.3061523 0.6123046 0.6938477
[46,] 0.2360133 0.4720267 0.7639867
[47,] 0.2239462 0.4478925 0.7760538
[48,] 0.3301372 0.6602744 0.6698628
[49,] 0.3500232 0.7000465 0.6499768
[50,] 0.5267165 0.9465669 0.4732835
[51,] 0.4272748 0.8545497 0.5727252
[52,] 0.3290285 0.6580569 0.6709715
[53,] 0.2369734 0.4739467 0.7630266
[54,] 0.1784633 0.3569265 0.8215367
[55,] 0.2666009 0.5332017 0.7333991
[56,] 0.2071791 0.4143582 0.7928209
[57,] 0.1425810 0.2851620 0.8574190
> postscript(file="/var/www/html/rcomp/tmp/1r61b1258646524.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2oyma1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t4fa1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a07e1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5isd91258646524.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 = 104
Frequency = 1
1 2 3 4 5
9.985498e+00 -1.346342e+01 2.907736e+00 6.445165e+00 3.706427e+00
6 7 8 9 10
2.912495e+00 -5.128146e+00 -9.881951e+00 7.071597e+00 5.680543e+00
11 12 13 14 15
1.118296e+01 1.055796e+01 5.758843e+00 -2.052898e+01 6.380024e-01
16 17 18 19 20
-8.004783e+00 -1.788418e+00 5.489451e+00 1.182068e-01 -1.528542e+01
21 22 23 24 25
-8.863588e+00 6.808623e+00 -1.170967e+01 9.503589e+00 -5.860823e+00
26 27 28 29 30
-1.786384e+00 1.266746e+01 -1.869459e+00 7.988785e-01 -1.202848e+01
31 32 33 34 35
-6.614979e+00 -3.015832e+00 -4.206775e+00 -8.609006e+00 -3.230354e+00
36 37 38 39 40
1.067834e+01 -2.698804e+00 3.253107e+00 5.777566e+00 -1.886238e+00
41 42 43 44 45
1.192852e+00 -1.034457e+01 -3.401516e+00 1.624612e+01 1.765214e+01
46 47 48 49 50
-1.210075e-01 7.919176e+00 -1.950642e+01 -5.595914e+00 1.379800e+01
51 52 53 54 55
-7.880161e+00 -3.120907e+00 -1.141396e+01 7.778502e+00 -1.045258e+00
56 57 58 59 60
-6.189238e-01 -3.716707e+00 5.316110e-01 5.678469e+00 6.900682e+00
61 62 63 64 65
-8.881784e-16 3.442571e+00 -1.333362e+01 1.094417e+00 5.122254e+00
66 67 68 69 70
-3.840073e+00 5.872996e+00 1.522832e+01 -4.662937e-15 2.383408e+00
71 72 73 74 75
8.560819e+00 -1.552687e+01 1.641622e+01 -3.971787e-01 -4.515235e-01
76 77 78 79 80
4.197829e+00 8.616340e+00 1.601883e+01 7.924010e+00 2.957056e+00
81 82 83 84 85
-1.565332e+00 -1.084936e+01 -4.628738e+00 -8.109549e+00 -4.593127e+00
86 87 88 89 90
1.291818e+01 3.461200e+00 9.927926e+00 -1.161256e+01 -1.554312e-15
91 92 93 94 95
2.399872e-01 -1.504005e+01 -6.371332e+00 4.175192e+00 -1.377265e+01
96 97 98 99 100
5.502278e+00 -1.341190e+01 2.764097e+00 -3.786665e+00 -6.783951e+00
101 102 103 104
5.378188e+00 -5.986157e+00 2.034699e+00 9.410693e+00
> postscript(file="/var/www/html/rcomp/tmp/6kpze1258646524.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 = 104
Frequency = 1
lag(myerror, k = 1) myerror
0 9.985498e+00 NA
1 -1.346342e+01 9.985498e+00
2 2.907736e+00 -1.346342e+01
3 6.445165e+00 2.907736e+00
4 3.706427e+00 6.445165e+00
5 2.912495e+00 3.706427e+00
6 -5.128146e+00 2.912495e+00
7 -9.881951e+00 -5.128146e+00
8 7.071597e+00 -9.881951e+00
9 5.680543e+00 7.071597e+00
10 1.118296e+01 5.680543e+00
11 1.055796e+01 1.118296e+01
12 5.758843e+00 1.055796e+01
13 -2.052898e+01 5.758843e+00
14 6.380024e-01 -2.052898e+01
15 -8.004783e+00 6.380024e-01
16 -1.788418e+00 -8.004783e+00
17 5.489451e+00 -1.788418e+00
18 1.182068e-01 5.489451e+00
19 -1.528542e+01 1.182068e-01
20 -8.863588e+00 -1.528542e+01
21 6.808623e+00 -8.863588e+00
22 -1.170967e+01 6.808623e+00
23 9.503589e+00 -1.170967e+01
24 -5.860823e+00 9.503589e+00
25 -1.786384e+00 -5.860823e+00
26 1.266746e+01 -1.786384e+00
27 -1.869459e+00 1.266746e+01
28 7.988785e-01 -1.869459e+00
29 -1.202848e+01 7.988785e-01
30 -6.614979e+00 -1.202848e+01
31 -3.015832e+00 -6.614979e+00
32 -4.206775e+00 -3.015832e+00
33 -8.609006e+00 -4.206775e+00
34 -3.230354e+00 -8.609006e+00
35 1.067834e+01 -3.230354e+00
36 -2.698804e+00 1.067834e+01
37 3.253107e+00 -2.698804e+00
38 5.777566e+00 3.253107e+00
39 -1.886238e+00 5.777566e+00
40 1.192852e+00 -1.886238e+00
41 -1.034457e+01 1.192852e+00
42 -3.401516e+00 -1.034457e+01
43 1.624612e+01 -3.401516e+00
44 1.765214e+01 1.624612e+01
45 -1.210075e-01 1.765214e+01
46 7.919176e+00 -1.210075e-01
47 -1.950642e+01 7.919176e+00
48 -5.595914e+00 -1.950642e+01
49 1.379800e+01 -5.595914e+00
50 -7.880161e+00 1.379800e+01
51 -3.120907e+00 -7.880161e+00
52 -1.141396e+01 -3.120907e+00
53 7.778502e+00 -1.141396e+01
54 -1.045258e+00 7.778502e+00
55 -6.189238e-01 -1.045258e+00
56 -3.716707e+00 -6.189238e-01
57 5.316110e-01 -3.716707e+00
58 5.678469e+00 5.316110e-01
59 6.900682e+00 5.678469e+00
60 -8.881784e-16 6.900682e+00
61 3.442571e+00 -8.881784e-16
62 -1.333362e+01 3.442571e+00
63 1.094417e+00 -1.333362e+01
64 5.122254e+00 1.094417e+00
65 -3.840073e+00 5.122254e+00
66 5.872996e+00 -3.840073e+00
67 1.522832e+01 5.872996e+00
68 -4.662937e-15 1.522832e+01
69 2.383408e+00 -4.662937e-15
70 8.560819e+00 2.383408e+00
71 -1.552687e+01 8.560819e+00
72 1.641622e+01 -1.552687e+01
73 -3.971787e-01 1.641622e+01
74 -4.515235e-01 -3.971787e-01
75 4.197829e+00 -4.515235e-01
76 8.616340e+00 4.197829e+00
77 1.601883e+01 8.616340e+00
78 7.924010e+00 1.601883e+01
79 2.957056e+00 7.924010e+00
80 -1.565332e+00 2.957056e+00
81 -1.084936e+01 -1.565332e+00
82 -4.628738e+00 -1.084936e+01
83 -8.109549e+00 -4.628738e+00
84 -4.593127e+00 -8.109549e+00
85 1.291818e+01 -4.593127e+00
86 3.461200e+00 1.291818e+01
87 9.927926e+00 3.461200e+00
88 -1.161256e+01 9.927926e+00
89 -1.554312e-15 -1.161256e+01
90 2.399872e-01 -1.554312e-15
91 -1.504005e+01 2.399872e-01
92 -6.371332e+00 -1.504005e+01
93 4.175192e+00 -6.371332e+00
94 -1.377265e+01 4.175192e+00
95 5.502278e+00 -1.377265e+01
96 -1.341190e+01 5.502278e+00
97 2.764097e+00 -1.341190e+01
98 -3.786665e+00 2.764097e+00
99 -6.783951e+00 -3.786665e+00
100 5.378188e+00 -6.783951e+00
101 -5.986157e+00 5.378188e+00
102 2.034699e+00 -5.986157e+00
103 9.410693e+00 2.034699e+00
104 NA 9.410693e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.346342e+01 9.985498e+00
[2,] 2.907736e+00 -1.346342e+01
[3,] 6.445165e+00 2.907736e+00
[4,] 3.706427e+00 6.445165e+00
[5,] 2.912495e+00 3.706427e+00
[6,] -5.128146e+00 2.912495e+00
[7,] -9.881951e+00 -5.128146e+00
[8,] 7.071597e+00 -9.881951e+00
[9,] 5.680543e+00 7.071597e+00
[10,] 1.118296e+01 5.680543e+00
[11,] 1.055796e+01 1.118296e+01
[12,] 5.758843e+00 1.055796e+01
[13,] -2.052898e+01 5.758843e+00
[14,] 6.380024e-01 -2.052898e+01
[15,] -8.004783e+00 6.380024e-01
[16,] -1.788418e+00 -8.004783e+00
[17,] 5.489451e+00 -1.788418e+00
[18,] 1.182068e-01 5.489451e+00
[19,] -1.528542e+01 1.182068e-01
[20,] -8.863588e+00 -1.528542e+01
[21,] 6.808623e+00 -8.863588e+00
[22,] -1.170967e+01 6.808623e+00
[23,] 9.503589e+00 -1.170967e+01
[24,] -5.860823e+00 9.503589e+00
[25,] -1.786384e+00 -5.860823e+00
[26,] 1.266746e+01 -1.786384e+00
[27,] -1.869459e+00 1.266746e+01
[28,] 7.988785e-01 -1.869459e+00
[29,] -1.202848e+01 7.988785e-01
[30,] -6.614979e+00 -1.202848e+01
[31,] -3.015832e+00 -6.614979e+00
[32,] -4.206775e+00 -3.015832e+00
[33,] -8.609006e+00 -4.206775e+00
[34,] -3.230354e+00 -8.609006e+00
[35,] 1.067834e+01 -3.230354e+00
[36,] -2.698804e+00 1.067834e+01
[37,] 3.253107e+00 -2.698804e+00
[38,] 5.777566e+00 3.253107e+00
[39,] -1.886238e+00 5.777566e+00
[40,] 1.192852e+00 -1.886238e+00
[41,] -1.034457e+01 1.192852e+00
[42,] -3.401516e+00 -1.034457e+01
[43,] 1.624612e+01 -3.401516e+00
[44,] 1.765214e+01 1.624612e+01
[45,] -1.210075e-01 1.765214e+01
[46,] 7.919176e+00 -1.210075e-01
[47,] -1.950642e+01 7.919176e+00
[48,] -5.595914e+00 -1.950642e+01
[49,] 1.379800e+01 -5.595914e+00
[50,] -7.880161e+00 1.379800e+01
[51,] -3.120907e+00 -7.880161e+00
[52,] -1.141396e+01 -3.120907e+00
[53,] 7.778502e+00 -1.141396e+01
[54,] -1.045258e+00 7.778502e+00
[55,] -6.189238e-01 -1.045258e+00
[56,] -3.716707e+00 -6.189238e-01
[57,] 5.316110e-01 -3.716707e+00
[58,] 5.678469e+00 5.316110e-01
[59,] 6.900682e+00 5.678469e+00
[60,] -8.881784e-16 6.900682e+00
[61,] 3.442571e+00 -8.881784e-16
[62,] -1.333362e+01 3.442571e+00
[63,] 1.094417e+00 -1.333362e+01
[64,] 5.122254e+00 1.094417e+00
[65,] -3.840073e+00 5.122254e+00
[66,] 5.872996e+00 -3.840073e+00
[67,] 1.522832e+01 5.872996e+00
[68,] -4.662937e-15 1.522832e+01
[69,] 2.383408e+00 -4.662937e-15
[70,] 8.560819e+00 2.383408e+00
[71,] -1.552687e+01 8.560819e+00
[72,] 1.641622e+01 -1.552687e+01
[73,] -3.971787e-01 1.641622e+01
[74,] -4.515235e-01 -3.971787e-01
[75,] 4.197829e+00 -4.515235e-01
[76,] 8.616340e+00 4.197829e+00
[77,] 1.601883e+01 8.616340e+00
[78,] 7.924010e+00 1.601883e+01
[79,] 2.957056e+00 7.924010e+00
[80,] -1.565332e+00 2.957056e+00
[81,] -1.084936e+01 -1.565332e+00
[82,] -4.628738e+00 -1.084936e+01
[83,] -8.109549e+00 -4.628738e+00
[84,] -4.593127e+00 -8.109549e+00
[85,] 1.291818e+01 -4.593127e+00
[86,] 3.461200e+00 1.291818e+01
[87,] 9.927926e+00 3.461200e+00
[88,] -1.161256e+01 9.927926e+00
[89,] -1.554312e-15 -1.161256e+01
[90,] 2.399872e-01 -1.554312e-15
[91,] -1.504005e+01 2.399872e-01
[92,] -6.371332e+00 -1.504005e+01
[93,] 4.175192e+00 -6.371332e+00
[94,] -1.377265e+01 4.175192e+00
[95,] 5.502278e+00 -1.377265e+01
[96,] -1.341190e+01 5.502278e+00
[97,] 2.764097e+00 -1.341190e+01
[98,] -3.786665e+00 2.764097e+00
[99,] -6.783951e+00 -3.786665e+00
[100,] 5.378188e+00 -6.783951e+00
[101,] -5.986157e+00 5.378188e+00
[102,] 2.034699e+00 -5.986157e+00
[103,] 9.410693e+00 2.034699e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.346342e+01 9.985498e+00
2 2.907736e+00 -1.346342e+01
3 6.445165e+00 2.907736e+00
4 3.706427e+00 6.445165e+00
5 2.912495e+00 3.706427e+00
6 -5.128146e+00 2.912495e+00
7 -9.881951e+00 -5.128146e+00
8 7.071597e+00 -9.881951e+00
9 5.680543e+00 7.071597e+00
10 1.118296e+01 5.680543e+00
11 1.055796e+01 1.118296e+01
12 5.758843e+00 1.055796e+01
13 -2.052898e+01 5.758843e+00
14 6.380024e-01 -2.052898e+01
15 -8.004783e+00 6.380024e-01
16 -1.788418e+00 -8.004783e+00
17 5.489451e+00 -1.788418e+00
18 1.182068e-01 5.489451e+00
19 -1.528542e+01 1.182068e-01
20 -8.863588e+00 -1.528542e+01
21 6.808623e+00 -8.863588e+00
22 -1.170967e+01 6.808623e+00
23 9.503589e+00 -1.170967e+01
24 -5.860823e+00 9.503589e+00
25 -1.786384e+00 -5.860823e+00
26 1.266746e+01 -1.786384e+00
27 -1.869459e+00 1.266746e+01
28 7.988785e-01 -1.869459e+00
29 -1.202848e+01 7.988785e-01
30 -6.614979e+00 -1.202848e+01
31 -3.015832e+00 -6.614979e+00
32 -4.206775e+00 -3.015832e+00
33 -8.609006e+00 -4.206775e+00
34 -3.230354e+00 -8.609006e+00
35 1.067834e+01 -3.230354e+00
36 -2.698804e+00 1.067834e+01
37 3.253107e+00 -2.698804e+00
38 5.777566e+00 3.253107e+00
39 -1.886238e+00 5.777566e+00
40 1.192852e+00 -1.886238e+00
41 -1.034457e+01 1.192852e+00
42 -3.401516e+00 -1.034457e+01
43 1.624612e+01 -3.401516e+00
44 1.765214e+01 1.624612e+01
45 -1.210075e-01 1.765214e+01
46 7.919176e+00 -1.210075e-01
47 -1.950642e+01 7.919176e+00
48 -5.595914e+00 -1.950642e+01
49 1.379800e+01 -5.595914e+00
50 -7.880161e+00 1.379800e+01
51 -3.120907e+00 -7.880161e+00
52 -1.141396e+01 -3.120907e+00
53 7.778502e+00 -1.141396e+01
54 -1.045258e+00 7.778502e+00
55 -6.189238e-01 -1.045258e+00
56 -3.716707e+00 -6.189238e-01
57 5.316110e-01 -3.716707e+00
58 5.678469e+00 5.316110e-01
59 6.900682e+00 5.678469e+00
60 -8.881784e-16 6.900682e+00
61 3.442571e+00 -8.881784e-16
62 -1.333362e+01 3.442571e+00
63 1.094417e+00 -1.333362e+01
64 5.122254e+00 1.094417e+00
65 -3.840073e+00 5.122254e+00
66 5.872996e+00 -3.840073e+00
67 1.522832e+01 5.872996e+00
68 -4.662937e-15 1.522832e+01
69 2.383408e+00 -4.662937e-15
70 8.560819e+00 2.383408e+00
71 -1.552687e+01 8.560819e+00
72 1.641622e+01 -1.552687e+01
73 -3.971787e-01 1.641622e+01
74 -4.515235e-01 -3.971787e-01
75 4.197829e+00 -4.515235e-01
76 8.616340e+00 4.197829e+00
77 1.601883e+01 8.616340e+00
78 7.924010e+00 1.601883e+01
79 2.957056e+00 7.924010e+00
80 -1.565332e+00 2.957056e+00
81 -1.084936e+01 -1.565332e+00
82 -4.628738e+00 -1.084936e+01
83 -8.109549e+00 -4.628738e+00
84 -4.593127e+00 -8.109549e+00
85 1.291818e+01 -4.593127e+00
86 3.461200e+00 1.291818e+01
87 9.927926e+00 3.461200e+00
88 -1.161256e+01 9.927926e+00
89 -1.554312e-15 -1.161256e+01
90 2.399872e-01 -1.554312e-15
91 -1.504005e+01 2.399872e-01
92 -6.371332e+00 -1.504005e+01
93 4.175192e+00 -6.371332e+00
94 -1.377265e+01 4.175192e+00
95 5.502278e+00 -1.377265e+01
96 -1.341190e+01 5.502278e+00
97 2.764097e+00 -1.341190e+01
98 -3.786665e+00 2.764097e+00
99 -6.783951e+00 -3.786665e+00
100 5.378188e+00 -6.783951e+00
101 -5.986157e+00 5.378188e+00
102 2.034699e+00 -5.986157e+00
103 9.410693e+00 2.034699e+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/7k35f1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nerh1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yt751258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: Not plotting observations with leverage one:
61, 69, 90
2: Not plotting observations with leverage one:
61, 69, 90
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gvua1258646524.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z6ow1258646524.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/12qxfh1258646524.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/13vgl21258646524.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/14xzbu1258646524.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/150mk41258646524.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/1635011258646524.tab")
+ }
>
> system("convert tmp/1r61b1258646524.ps tmp/1r61b1258646524.png")
> system("convert tmp/2oyma1258646524.ps tmp/2oyma1258646524.png")
> system("convert tmp/3t4fa1258646524.ps tmp/3t4fa1258646524.png")
> system("convert tmp/4a07e1258646524.ps tmp/4a07e1258646524.png")
> system("convert tmp/5isd91258646524.ps tmp/5isd91258646524.png")
> system("convert tmp/6kpze1258646524.ps tmp/6kpze1258646524.png")
> system("convert tmp/7k35f1258646524.ps tmp/7k35f1258646524.png")
> system("convert tmp/8nerh1258646524.ps tmp/8nerh1258646524.png")
> system("convert tmp/9yt751258646524.ps tmp/9yt751258646524.png")
> system("convert tmp/10gvua1258646524.ps tmp/10gvua1258646524.png")
>
>
> proc.time()
user system elapsed
3.173 1.694 3.654