R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(8715.1
+ ,0
+ ,8919.9
+ ,0
+ ,10085.8
+ ,0
+ ,9511.7
+ ,0
+ ,8991.3
+ ,0
+ ,10311.2
+ ,0
+ ,8895.4
+ ,0
+ ,7449.8
+ ,0
+ ,10084.0
+ ,0
+ ,9859.4
+ ,0
+ ,9100.1
+ ,0
+ ,8920.8
+ ,0
+ ,8502.7
+ ,0
+ ,8599.6
+ ,0
+ ,10394.4
+ ,0
+ ,9290.4
+ ,0
+ ,8742.2
+ ,0
+ ,10217.3
+ ,0
+ ,8639.0
+ ,0
+ ,8139.6
+ ,0
+ ,10779.1
+ ,0
+ ,10427.7
+ ,0
+ ,10349.1
+ ,0
+ ,10036.4
+ ,0
+ ,9492.1
+ ,0
+ ,10638.8
+ ,0
+ ,12054.5
+ ,0
+ ,10324.7
+ ,0
+ ,11817.3
+ ,0
+ ,11008.9
+ ,0
+ ,9996.6
+ ,0
+ ,9419.5
+ ,0
+ ,11958.8
+ ,0
+ ,12594.6
+ ,0
+ ,11890.6
+ ,0
+ ,10871.7
+ ,0
+ ,11835.7
+ ,0
+ ,11542.2
+ ,0
+ ,13093.7
+ ,0
+ ,11180.2
+ ,0
+ ,12035.7
+ ,0
+ ,12112.0
+ ,0
+ ,10875.2
+ ,0
+ ,9897.3
+ ,0
+ ,11672.1
+ ,1
+ ,12385.7
+ ,1
+ ,11405.6
+ ,1
+ ,9830.9
+ ,1
+ ,11025.1
+ ,1
+ ,10853.8
+ ,1
+ ,12252.6
+ ,1
+ ,11839.4
+ ,1
+ ,11669.1
+ ,1
+ ,11601.4
+ ,1
+ ,11178.4
+ ,1
+ ,9516.4
+ ,1
+ ,12102.8
+ ,1
+ ,12989.0
+ ,1
+ ,11610.2
+ ,1
+ ,10205.5
+ ,1
+ ,11356.2
+ ,1
+ ,11307.1
+ ,1
+ ,12648.6
+ ,1
+ ,11947.2
+ ,1
+ ,11714.1
+ ,1
+ ,12192.5
+ ,1
+ ,11268.8
+ ,1
+ ,9097.4
+ ,1
+ ,12639.8
+ ,1
+ ,13040.1
+ ,1
+ ,11687.3
+ ,1
+ ,11191.7
+ ,1
+ ,11391.9
+ ,1
+ ,11793.1
+ ,1
+ ,13933.2
+ ,1
+ ,12778.1
+ ,1
+ ,11810.3
+ ,1
+ ,13698.4
+ ,1
+ ,11956.6
+ ,1
+ ,10723.8
+ ,1
+ ,13938.9
+ ,1
+ ,13979.8
+ ,1
+ ,13807.4
+ ,1
+ ,12973.9
+ ,1
+ ,12509.8
+ ,1
+ ,12934.1
+ ,1
+ ,14908.3
+ ,1
+ ,13772.1
+ ,1
+ ,13012.6
+ ,1
+ ,14049.9
+ ,1
+ ,11816.5
+ ,1
+ ,11593.2
+ ,1
+ ,14466.2
+ ,1
+ ,13615.9
+ ,1
+ ,14733.9
+ ,1
+ ,13880.7
+ ,1
+ ,13527.5
+ ,1
+ ,13584.0
+ ,1
+ ,16170.2
+ ,1
+ ,13260.6
+ ,1
+ ,14741.9
+ ,1
+ ,15486.5
+ ,1
+ ,13154.5
+ ,1
+ ,12621.2
+ ,1
+ ,15031.6
+ ,1
+ ,15452.4
+ ,1
+ ,15428.0
+ ,1
+ ,13105.9
+ ,1
+ ,14716.8
+ ,1
+ ,14180.0
+ ,1
+ ,16202.2
+ ,1
+ ,14392.4
+ ,1
+ ,15140.6
+ ,1
+ ,15960.1
+ ,1
+ ,14351.3
+ ,1
+ ,13230.2
+ ,1
+ ,15202.1
+ ,1
+ ,17056.0
+ ,1
+ ,16077.7
+ ,1
+ ,13348.2
+ ,1
+ ,16402.4
+ ,1
+ ,16559.1
+ ,1
+ ,16579.0
+ ,1
+ ,17561.2
+ ,1
+ ,16129.6
+ ,1
+ ,18484.3
+ ,1
+ ,16402.6
+ ,1
+ ,14032.3
+ ,1
+ ,17109.1
+ ,1
+ ,17157.2
+ ,1
+ ,13879.8
+ ,1
+ ,12362.4
+ ,1)
+ ,dim=c(2
+ ,132)
+ ,dimnames=list(c('Uitvoer'
+ ,'Dummie')
+ ,1:132))
> y <- array(NA,dim=c(2,132),dimnames=list(c('Uitvoer','Dummie'),1:132))
> 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
Uitvoer Dummie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8715.1 0 1 0 0 0 0 0 0 0 0 0 0 1
2 8919.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 10085.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 9511.7 0 0 0 0 1 0 0 0 0 0 0 0 4
5 8991.3 0 0 0 0 0 1 0 0 0 0 0 0 5
6 10311.2 0 0 0 0 0 0 1 0 0 0 0 0 6
7 8895.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7449.8 0 0 0 0 0 0 0 0 1 0 0 0 8
9 10084.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 9859.4 0 0 0 0 0 0 0 0 0 0 1 0 10
11 9100.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 8920.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 8502.7 0 1 0 0 0 0 0 0 0 0 0 0 13
14 8599.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 10394.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 9290.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 8742.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 10217.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 8639.0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 8139.6 0 0 0 0 0 0 0 0 1 0 0 0 20
21 10779.1 0 0 0 0 0 0 0 0 0 1 0 0 21
22 10427.7 0 0 0 0 0 0 0 0 0 0 1 0 22
23 10349.1 0 0 0 0 0 0 0 0 0 0 0 1 23
24 10036.4 0 0 0 0 0 0 0 0 0 0 0 0 24
25 9492.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 10638.8 0 0 1 0 0 0 0 0 0 0 0 0 26
27 12054.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 10324.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 11817.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 11008.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 9996.6 0 0 0 0 0 0 0 1 0 0 0 0 31
32 9419.5 0 0 0 0 0 0 0 0 1 0 0 0 32
33 11958.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 12594.6 0 0 0 0 0 0 0 0 0 0 1 0 34
35 11890.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 10871.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 11835.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 11542.2 0 0 1 0 0 0 0 0 0 0 0 0 38
39 13093.7 0 0 0 1 0 0 0 0 0 0 0 0 39
40 11180.2 0 0 0 0 1 0 0 0 0 0 0 0 40
41 12035.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 12112.0 0 0 0 0 0 0 1 0 0 0 0 0 42
43 10875.2 0 0 0 0 0 0 0 1 0 0 0 0 43
44 9897.3 0 0 0 0 0 0 0 0 1 0 0 0 44
45 11672.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 12385.7 1 0 0 0 0 0 0 0 0 0 1 0 46
47 11405.6 1 0 0 0 0 0 0 0 0 0 0 1 47
48 9830.9 1 0 0 0 0 0 0 0 0 0 0 0 48
49 11025.1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 10853.8 1 0 1 0 0 0 0 0 0 0 0 0 50
51 12252.6 1 0 0 1 0 0 0 0 0 0 0 0 51
52 11839.4 1 0 0 0 1 0 0 0 0 0 0 0 52
53 11669.1 1 0 0 0 0 1 0 0 0 0 0 0 53
54 11601.4 1 0 0 0 0 0 1 0 0 0 0 0 54
55 11178.4 1 0 0 0 0 0 0 1 0 0 0 0 55
56 9516.4 1 0 0 0 0 0 0 0 1 0 0 0 56
57 12102.8 1 0 0 0 0 0 0 0 0 1 0 0 57
58 12989.0 1 0 0 0 0 0 0 0 0 0 1 0 58
59 11610.2 1 0 0 0 0 0 0 0 0 0 0 1 59
60 10205.5 1 0 0 0 0 0 0 0 0 0 0 0 60
61 11356.2 1 1 0 0 0 0 0 0 0 0 0 0 61
62 11307.1 1 0 1 0 0 0 0 0 0 0 0 0 62
63 12648.6 1 0 0 1 0 0 0 0 0 0 0 0 63
64 11947.2 1 0 0 0 1 0 0 0 0 0 0 0 64
65 11714.1 1 0 0 0 0 1 0 0 0 0 0 0 65
66 12192.5 1 0 0 0 0 0 1 0 0 0 0 0 66
67 11268.8 1 0 0 0 0 0 0 1 0 0 0 0 67
68 9097.4 1 0 0 0 0 0 0 0 1 0 0 0 68
69 12639.8 1 0 0 0 0 0 0 0 0 1 0 0 69
70 13040.1 1 0 0 0 0 0 0 0 0 0 1 0 70
71 11687.3 1 0 0 0 0 0 0 0 0 0 0 1 71
72 11191.7 1 0 0 0 0 0 0 0 0 0 0 0 72
73 11391.9 1 1 0 0 0 0 0 0 0 0 0 0 73
74 11793.1 1 0 1 0 0 0 0 0 0 0 0 0 74
75 13933.2 1 0 0 1 0 0 0 0 0 0 0 0 75
76 12778.1 1 0 0 0 1 0 0 0 0 0 0 0 76
77 11810.3 1 0 0 0 0 1 0 0 0 0 0 0 77
78 13698.4 1 0 0 0 0 0 1 0 0 0 0 0 78
79 11956.6 1 0 0 0 0 0 0 1 0 0 0 0 79
80 10723.8 1 0 0 0 0 0 0 0 1 0 0 0 80
81 13938.9 1 0 0 0 0 0 0 0 0 1 0 0 81
82 13979.8 1 0 0 0 0 0 0 0 0 0 1 0 82
83 13807.4 1 0 0 0 0 0 0 0 0 0 0 1 83
84 12973.9 1 0 0 0 0 0 0 0 0 0 0 0 84
85 12509.8 1 1 0 0 0 0 0 0 0 0 0 0 85
86 12934.1 1 0 1 0 0 0 0 0 0 0 0 0 86
87 14908.3 1 0 0 1 0 0 0 0 0 0 0 0 87
88 13772.1 1 0 0 0 1 0 0 0 0 0 0 0 88
89 13012.6 1 0 0 0 0 1 0 0 0 0 0 0 89
90 14049.9 1 0 0 0 0 0 1 0 0 0 0 0 90
91 11816.5 1 0 0 0 0 0 0 1 0 0 0 0 91
92 11593.2 1 0 0 0 0 0 0 0 1 0 0 0 92
93 14466.2 1 0 0 0 0 0 0 0 0 1 0 0 93
94 13615.9 1 0 0 0 0 0 0 0 0 0 1 0 94
95 14733.9 1 0 0 0 0 0 0 0 0 0 0 1 95
96 13880.7 1 0 0 0 0 0 0 0 0 0 0 0 96
97 13527.5 1 1 0 0 0 0 0 0 0 0 0 0 97
98 13584.0 1 0 1 0 0 0 0 0 0 0 0 0 98
99 16170.2 1 0 0 1 0 0 0 0 0 0 0 0 99
100 13260.6 1 0 0 0 1 0 0 0 0 0 0 0 100
101 14741.9 1 0 0 0 0 1 0 0 0 0 0 0 101
102 15486.5 1 0 0 0 0 0 1 0 0 0 0 0 102
103 13154.5 1 0 0 0 0 0 0 1 0 0 0 0 103
104 12621.2 1 0 0 0 0 0 0 0 1 0 0 0 104
105 15031.6 1 0 0 0 0 0 0 0 0 1 0 0 105
106 15452.4 1 0 0 0 0 0 0 0 0 0 1 0 106
107 15428.0 1 0 0 0 0 0 0 0 0 0 0 1 107
108 13105.9 1 0 0 0 0 0 0 0 0 0 0 0 108
109 14716.8 1 1 0 0 0 0 0 0 0 0 0 0 109
110 14180.0 1 0 1 0 0 0 0 0 0 0 0 0 110
111 16202.2 1 0 0 1 0 0 0 0 0 0 0 0 111
112 14392.4 1 0 0 0 1 0 0 0 0 0 0 0 112
113 15140.6 1 0 0 0 0 1 0 0 0 0 0 0 113
114 15960.1 1 0 0 0 0 0 1 0 0 0 0 0 114
115 14351.3 1 0 0 0 0 0 0 1 0 0 0 0 115
116 13230.2 1 0 0 0 0 0 0 0 1 0 0 0 116
117 15202.1 1 0 0 0 0 0 0 0 0 1 0 0 117
118 17056.0 1 0 0 0 0 0 0 0 0 0 1 0 118
119 16077.7 1 0 0 0 0 0 0 0 0 0 0 1 119
120 13348.2 1 0 0 0 0 0 0 0 0 0 0 0 120
121 16402.4 1 1 0 0 0 0 0 0 0 0 0 0 121
122 16559.1 1 0 1 0 0 0 0 0 0 0 0 0 122
123 16579.0 1 0 0 1 0 0 0 0 0 0 0 0 123
124 17561.2 1 0 0 0 1 0 0 0 0 0 0 0 124
125 16129.6 1 0 0 0 0 1 0 0 0 0 0 0 125
126 18484.3 1 0 0 0 0 0 1 0 0 0 0 0 126
127 16402.6 1 0 0 0 0 0 0 1 0 0 0 0 127
128 14032.3 1 0 0 0 0 0 0 0 1 0 0 0 128
129 17109.1 1 0 0 0 0 0 0 0 0 1 0 0 129
130 17157.2 1 0 0 0 0 0 0 0 0 0 1 0 130
131 13879.8 1 0 0 0 0 0 0 0 0 0 0 1 131
132 12362.4 1 0 0 0 0 0 0 0 0 0 0 0 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummie M1 M2 M3 M4
7642.02 -1159.29 865.75 930.75 2447.97 1249.25
M5 M6 M7 M8 M9 M10
1178.82 1960.31 386.77 -843.74 1856.42 2115.68
M11 t
1269.36 65.58
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2777.028 -405.397 6.324 388.239 1778.044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7642.023 238.458 32.048 < 2e-16 ***
Dummie -1159.290 222.751 -5.204 8.32e-07 ***
M1 865.747 296.322 2.922 0.004174 **
M2 930.747 296.207 3.142 0.002120 **
M3 2447.966 296.117 8.267 2.34e-13 ***
M4 1249.249 296.053 4.220 4.83e-05 ***
M5 1178.823 296.015 3.982 0.000118 ***
M6 1960.314 296.002 6.623 1.10e-09 ***
M7 386.770 296.015 1.307 0.193893
M8 -843.739 296.053 -2.850 0.005162 **
M9 1856.416 295.886 6.274 6.02e-09 ***
M10 2115.680 295.822 7.152 7.76e-11 ***
M11 1269.363 295.784 4.292 3.66e-05 ***
t 65.581 2.756 23.798 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 693.6 on 118 degrees of freedom
Multiple R-squared: 0.9214, Adjusted R-squared: 0.9127
F-statistic: 106.4 on 13 and 118 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,] 3.227208e-02 6.454415e-02 0.9677279
[2,] 7.799574e-03 1.559915e-02 0.9922004
[3,] 1.933877e-03 3.867753e-03 0.9980661
[4,] 1.067075e-02 2.134150e-02 0.9893293
[5,] 1.365146e-02 2.730291e-02 0.9863485
[6,] 1.020280e-02 2.040561e-02 0.9897972
[7,] 3.086613e-02 6.173227e-02 0.9691339
[8,] 3.844652e-02 7.689303e-02 0.9615535
[9,] 2.880970e-02 5.761939e-02 0.9711903
[10,] 8.443906e-02 1.688781e-01 0.9155609
[11,] 1.123995e-01 2.247990e-01 0.8876005
[12,] 7.823321e-02 1.564664e-01 0.9217668
[13,] 3.290025e-01 6.580051e-01 0.6709975
[14,] 2.845113e-01 5.690227e-01 0.7154887
[15,] 2.252593e-01 4.505185e-01 0.7747407
[16,] 1.892441e-01 3.784882e-01 0.8107559
[17,] 1.498266e-01 2.996533e-01 0.8501734
[18,] 1.973569e-01 3.947139e-01 0.8026431
[19,] 1.964616e-01 3.929233e-01 0.8035384
[20,] 1.603744e-01 3.207487e-01 0.8396256
[21,] 1.969503e-01 3.939006e-01 0.8030497
[22,] 1.579485e-01 3.158970e-01 0.8420515
[23,] 1.263638e-01 2.527275e-01 0.8736362
[24,] 1.063587e-01 2.127174e-01 0.8936413
[25,] 8.343406e-02 1.668681e-01 0.9165659
[26,] 6.535920e-02 1.307184e-01 0.9346408
[27,] 4.826948e-02 9.653896e-02 0.9517305
[28,] 3.593296e-02 7.186593e-02 0.9640670
[29,] 2.549154e-02 5.098308e-02 0.9745085
[30,] 2.158845e-02 4.317691e-02 0.9784115
[31,] 1.569999e-02 3.139998e-02 0.9843000
[32,] 1.950600e-02 3.901201e-02 0.9804940
[33,] 1.393499e-02 2.786999e-02 0.9860650
[34,] 9.938609e-03 1.987722e-02 0.9900614
[35,] 7.003818e-03 1.400764e-02 0.9929962
[36,] 6.197767e-03 1.239553e-02 0.9938022
[37,] 4.462824e-03 8.925647e-03 0.9955372
[38,] 3.636338e-03 7.272677e-03 0.9963637
[39,] 2.989221e-03 5.978442e-03 0.9970108
[40,] 2.236331e-03 4.472662e-03 0.9977637
[41,] 1.667358e-03 3.334716e-03 0.9983326
[42,] 1.265766e-03 2.531531e-03 0.9987342
[43,] 9.643496e-04 1.928699e-03 0.9990357
[44,] 1.474662e-03 2.949324e-03 0.9985253
[45,] 9.893251e-04 1.978650e-03 0.9990107
[46,] 7.482631e-04 1.496526e-03 0.9992517
[47,] 6.296791e-04 1.259358e-03 0.9993703
[48,] 3.869670e-04 7.739341e-04 0.9996130
[49,] 3.012477e-04 6.024954e-04 0.9996988
[50,] 2.582191e-04 5.164382e-04 0.9997418
[51,] 1.615372e-04 3.230744e-04 0.9998385
[52,] 4.520598e-04 9.041197e-04 0.9995479
[53,] 3.152639e-04 6.305278e-04 0.9996847
[54,] 2.066741e-04 4.133482e-04 0.9997933
[55,] 2.482945e-04 4.965889e-04 0.9997517
[56,] 1.860466e-04 3.720931e-04 0.9998140
[57,] 2.048818e-04 4.097636e-04 0.9997951
[58,] 1.535108e-04 3.070216e-04 0.9998465
[59,] 9.089036e-05 1.817807e-04 0.9999091
[60,] 5.183041e-05 1.036608e-04 0.9999482
[61,] 7.777381e-05 1.555476e-04 0.9999222
[62,] 5.429836e-05 1.085967e-04 0.9999457
[63,] 3.062618e-05 6.125237e-05 0.9999694
[64,] 1.729710e-05 3.459419e-05 0.9999827
[65,] 1.021787e-05 2.043575e-05 0.9999898
[66,] 5.391752e-06 1.078350e-05 0.9999946
[67,] 4.597502e-06 9.195003e-06 0.9999954
[68,] 1.225903e-05 2.451806e-05 0.9999877
[69,] 8.407196e-06 1.681439e-05 0.9999916
[70,] 4.431603e-06 8.863207e-06 0.9999956
[71,] 2.503166e-06 5.006332e-06 0.9999975
[72,] 1.397331e-06 2.794661e-06 0.9999986
[73,] 9.760360e-07 1.952072e-06 0.9999990
[74,] 7.152316e-07 1.430463e-06 0.9999993
[75,] 1.717939e-06 3.435878e-06 0.9999983
[76,] 8.480555e-07 1.696111e-06 0.9999992
[77,] 4.055799e-07 8.111598e-07 0.9999996
[78,] 1.548981e-06 3.097961e-06 0.9999985
[79,] 1.746719e-06 3.493438e-06 0.9999983
[80,] 3.147890e-05 6.295781e-05 0.9999685
[81,] 2.086874e-05 4.173748e-05 0.9999791
[82,] 1.222964e-05 2.445928e-05 0.9999878
[83,] 1.513393e-05 3.026786e-05 0.9999849
[84,] 3.941806e-05 7.883613e-05 0.9999606
[85,] 2.437666e-05 4.875332e-05 0.9999756
[86,] 1.673491e-05 3.346982e-05 0.9999833
[87,] 1.684956e-05 3.369912e-05 0.9999832
[88,] 7.884998e-06 1.577000e-05 0.9999921
[89,] 3.646015e-06 7.292030e-06 0.9999964
[90,] 1.996895e-06 3.993790e-06 0.9999980
[91,] 4.132230e-06 8.264460e-06 0.9999959
[92,] 1.669711e-05 3.339423e-05 0.9999833
[93,] 9.080187e-06 1.816037e-05 0.9999909
[94,] 1.127964e-05 2.255928e-05 0.9999887
[95,] 4.922550e-06 9.845099e-06 0.9999951
[96,] 5.462022e-05 1.092404e-04 0.9999454
[97,] 2.122218e-05 4.244436e-05 0.9999788
[98,] 1.239387e-04 2.478774e-04 0.9998761
[99,] 5.967598e-04 1.193520e-03 0.9994032
> postscript(file="/var/www/html/rcomp/tmp/1c4de1260867555.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zsf11260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36uov1260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/419ve1260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5m1dv1260867555.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 = 132
Frequency = 1
1 2 3 4 5 6
141.749545 215.967727 -200.932273 358.104091 -157.450455 315.376818
7 8 9 10 11 12
407.540455 126.867727 -4.667727 -554.113182 -532.676818 491.805000
13 14 15 16 17 18
-857.622727 -891.304545 -679.304545 -650.168182 -1193.522727 -565.495455
19 20 21 22 23 24
-635.831818 29.695455 -96.540000 -772.785455 -70.649091 820.432727
25 26 27 28 29 30
-655.195000 360.923182 193.823182 -402.840455 1094.605000 -560.867727
31 32 33 34 35 36
-65.204091 522.623182 296.187727 607.142273 683.878636 868.760455
37 38 39 40 41 42
901.432727 477.350909 446.050909 -334.312727 526.032727 -244.740000
43 44 45 46 47 48
26.423636 213.450909 381.805455 770.560000 571.196364 200.278182
49 50 51 52 53 54
463.150455 161.268636 -22.731364 697.205000 531.750455 -383.022273
55 56 57 58 59 60
701.941364 204.868636 25.533182 586.887727 -11.175909 -212.094091
61 62 63 64 65 66
7.278182 -172.403636 -413.703636 18.032727 -210.221818 -578.894545
67 68 69 70 71 72
5.369091 -1001.103636 -224.439091 -148.984545 -721.048182 -12.866364
73 74 75 76 77 78
-743.994091 -473.375909 83.924091 61.960455 -900.994091 140.033182
79 80 81 82 83 84
-93.803182 -161.675909 287.688636 3.743182 612.079545 982.361364
85 86 87 88 89 90
-413.066364 -119.348182 272.051818 268.988182 -485.666364 -295.439091
91 92 93 94 95 96
-1020.875455 -79.248182 28.016364 -1147.129091 751.607273 1102.189091
97 98 99 100 101 102
-182.338636 -256.420455 746.979545 -1029.484091 456.661364 354.188636
103 104 105 106 107 108
-469.847727 161.779545 -193.555909 -97.601364 658.735000 -459.583182
109 110 111 112 113 114
219.989091 -447.392727 -7.992727 -684.656364 68.389091 40.816364
115 116 117 118 119 120
-60.020000 -16.192727 -810.028182 719.026364 521.462727 -1004.255455
121 122 123 124 125 126
1118.616818 1144.735000 -418.165000 1697.171364 270.416818 1778.044091
127 128 129 130 131 132
1204.307727 -1.065000 309.999545 33.254091 -2463.409545 -2777.027727
> postscript(file="/var/www/html/rcomp/tmp/6yp2c1260867555.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 141.749545 NA
1 215.967727 141.749545
2 -200.932273 215.967727
3 358.104091 -200.932273
4 -157.450455 358.104091
5 315.376818 -157.450455
6 407.540455 315.376818
7 126.867727 407.540455
8 -4.667727 126.867727
9 -554.113182 -4.667727
10 -532.676818 -554.113182
11 491.805000 -532.676818
12 -857.622727 491.805000
13 -891.304545 -857.622727
14 -679.304545 -891.304545
15 -650.168182 -679.304545
16 -1193.522727 -650.168182
17 -565.495455 -1193.522727
18 -635.831818 -565.495455
19 29.695455 -635.831818
20 -96.540000 29.695455
21 -772.785455 -96.540000
22 -70.649091 -772.785455
23 820.432727 -70.649091
24 -655.195000 820.432727
25 360.923182 -655.195000
26 193.823182 360.923182
27 -402.840455 193.823182
28 1094.605000 -402.840455
29 -560.867727 1094.605000
30 -65.204091 -560.867727
31 522.623182 -65.204091
32 296.187727 522.623182
33 607.142273 296.187727
34 683.878636 607.142273
35 868.760455 683.878636
36 901.432727 868.760455
37 477.350909 901.432727
38 446.050909 477.350909
39 -334.312727 446.050909
40 526.032727 -334.312727
41 -244.740000 526.032727
42 26.423636 -244.740000
43 213.450909 26.423636
44 381.805455 213.450909
45 770.560000 381.805455
46 571.196364 770.560000
47 200.278182 571.196364
48 463.150455 200.278182
49 161.268636 463.150455
50 -22.731364 161.268636
51 697.205000 -22.731364
52 531.750455 697.205000
53 -383.022273 531.750455
54 701.941364 -383.022273
55 204.868636 701.941364
56 25.533182 204.868636
57 586.887727 25.533182
58 -11.175909 586.887727
59 -212.094091 -11.175909
60 7.278182 -212.094091
61 -172.403636 7.278182
62 -413.703636 -172.403636
63 18.032727 -413.703636
64 -210.221818 18.032727
65 -578.894545 -210.221818
66 5.369091 -578.894545
67 -1001.103636 5.369091
68 -224.439091 -1001.103636
69 -148.984545 -224.439091
70 -721.048182 -148.984545
71 -12.866364 -721.048182
72 -743.994091 -12.866364
73 -473.375909 -743.994091
74 83.924091 -473.375909
75 61.960455 83.924091
76 -900.994091 61.960455
77 140.033182 -900.994091
78 -93.803182 140.033182
79 -161.675909 -93.803182
80 287.688636 -161.675909
81 3.743182 287.688636
82 612.079545 3.743182
83 982.361364 612.079545
84 -413.066364 982.361364
85 -119.348182 -413.066364
86 272.051818 -119.348182
87 268.988182 272.051818
88 -485.666364 268.988182
89 -295.439091 -485.666364
90 -1020.875455 -295.439091
91 -79.248182 -1020.875455
92 28.016364 -79.248182
93 -1147.129091 28.016364
94 751.607273 -1147.129091
95 1102.189091 751.607273
96 -182.338636 1102.189091
97 -256.420455 -182.338636
98 746.979545 -256.420455
99 -1029.484091 746.979545
100 456.661364 -1029.484091
101 354.188636 456.661364
102 -469.847727 354.188636
103 161.779545 -469.847727
104 -193.555909 161.779545
105 -97.601364 -193.555909
106 658.735000 -97.601364
107 -459.583182 658.735000
108 219.989091 -459.583182
109 -447.392727 219.989091
110 -7.992727 -447.392727
111 -684.656364 -7.992727
112 68.389091 -684.656364
113 40.816364 68.389091
114 -60.020000 40.816364
115 -16.192727 -60.020000
116 -810.028182 -16.192727
117 719.026364 -810.028182
118 521.462727 719.026364
119 -1004.255455 521.462727
120 1118.616818 -1004.255455
121 1144.735000 1118.616818
122 -418.165000 1144.735000
123 1697.171364 -418.165000
124 270.416818 1697.171364
125 1778.044091 270.416818
126 1204.307727 1778.044091
127 -1.065000 1204.307727
128 309.999545 -1.065000
129 33.254091 309.999545
130 -2463.409545 33.254091
131 -2777.027727 -2463.409545
132 NA -2777.027727
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 215.967727 141.749545
[2,] -200.932273 215.967727
[3,] 358.104091 -200.932273
[4,] -157.450455 358.104091
[5,] 315.376818 -157.450455
[6,] 407.540455 315.376818
[7,] 126.867727 407.540455
[8,] -4.667727 126.867727
[9,] -554.113182 -4.667727
[10,] -532.676818 -554.113182
[11,] 491.805000 -532.676818
[12,] -857.622727 491.805000
[13,] -891.304545 -857.622727
[14,] -679.304545 -891.304545
[15,] -650.168182 -679.304545
[16,] -1193.522727 -650.168182
[17,] -565.495455 -1193.522727
[18,] -635.831818 -565.495455
[19,] 29.695455 -635.831818
[20,] -96.540000 29.695455
[21,] -772.785455 -96.540000
[22,] -70.649091 -772.785455
[23,] 820.432727 -70.649091
[24,] -655.195000 820.432727
[25,] 360.923182 -655.195000
[26,] 193.823182 360.923182
[27,] -402.840455 193.823182
[28,] 1094.605000 -402.840455
[29,] -560.867727 1094.605000
[30,] -65.204091 -560.867727
[31,] 522.623182 -65.204091
[32,] 296.187727 522.623182
[33,] 607.142273 296.187727
[34,] 683.878636 607.142273
[35,] 868.760455 683.878636
[36,] 901.432727 868.760455
[37,] 477.350909 901.432727
[38,] 446.050909 477.350909
[39,] -334.312727 446.050909
[40,] 526.032727 -334.312727
[41,] -244.740000 526.032727
[42,] 26.423636 -244.740000
[43,] 213.450909 26.423636
[44,] 381.805455 213.450909
[45,] 770.560000 381.805455
[46,] 571.196364 770.560000
[47,] 200.278182 571.196364
[48,] 463.150455 200.278182
[49,] 161.268636 463.150455
[50,] -22.731364 161.268636
[51,] 697.205000 -22.731364
[52,] 531.750455 697.205000
[53,] -383.022273 531.750455
[54,] 701.941364 -383.022273
[55,] 204.868636 701.941364
[56,] 25.533182 204.868636
[57,] 586.887727 25.533182
[58,] -11.175909 586.887727
[59,] -212.094091 -11.175909
[60,] 7.278182 -212.094091
[61,] -172.403636 7.278182
[62,] -413.703636 -172.403636
[63,] 18.032727 -413.703636
[64,] -210.221818 18.032727
[65,] -578.894545 -210.221818
[66,] 5.369091 -578.894545
[67,] -1001.103636 5.369091
[68,] -224.439091 -1001.103636
[69,] -148.984545 -224.439091
[70,] -721.048182 -148.984545
[71,] -12.866364 -721.048182
[72,] -743.994091 -12.866364
[73,] -473.375909 -743.994091
[74,] 83.924091 -473.375909
[75,] 61.960455 83.924091
[76,] -900.994091 61.960455
[77,] 140.033182 -900.994091
[78,] -93.803182 140.033182
[79,] -161.675909 -93.803182
[80,] 287.688636 -161.675909
[81,] 3.743182 287.688636
[82,] 612.079545 3.743182
[83,] 982.361364 612.079545
[84,] -413.066364 982.361364
[85,] -119.348182 -413.066364
[86,] 272.051818 -119.348182
[87,] 268.988182 272.051818
[88,] -485.666364 268.988182
[89,] -295.439091 -485.666364
[90,] -1020.875455 -295.439091
[91,] -79.248182 -1020.875455
[92,] 28.016364 -79.248182
[93,] -1147.129091 28.016364
[94,] 751.607273 -1147.129091
[95,] 1102.189091 751.607273
[96,] -182.338636 1102.189091
[97,] -256.420455 -182.338636
[98,] 746.979545 -256.420455
[99,] -1029.484091 746.979545
[100,] 456.661364 -1029.484091
[101,] 354.188636 456.661364
[102,] -469.847727 354.188636
[103,] 161.779545 -469.847727
[104,] -193.555909 161.779545
[105,] -97.601364 -193.555909
[106,] 658.735000 -97.601364
[107,] -459.583182 658.735000
[108,] 219.989091 -459.583182
[109,] -447.392727 219.989091
[110,] -7.992727 -447.392727
[111,] -684.656364 -7.992727
[112,] 68.389091 -684.656364
[113,] 40.816364 68.389091
[114,] -60.020000 40.816364
[115,] -16.192727 -60.020000
[116,] -810.028182 -16.192727
[117,] 719.026364 -810.028182
[118,] 521.462727 719.026364
[119,] -1004.255455 521.462727
[120,] 1118.616818 -1004.255455
[121,] 1144.735000 1118.616818
[122,] -418.165000 1144.735000
[123,] 1697.171364 -418.165000
[124,] 270.416818 1697.171364
[125,] 1778.044091 270.416818
[126,] 1204.307727 1778.044091
[127,] -1.065000 1204.307727
[128,] 309.999545 -1.065000
[129,] 33.254091 309.999545
[130,] -2463.409545 33.254091
[131,] -2777.027727 -2463.409545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 215.967727 141.749545
2 -200.932273 215.967727
3 358.104091 -200.932273
4 -157.450455 358.104091
5 315.376818 -157.450455
6 407.540455 315.376818
7 126.867727 407.540455
8 -4.667727 126.867727
9 -554.113182 -4.667727
10 -532.676818 -554.113182
11 491.805000 -532.676818
12 -857.622727 491.805000
13 -891.304545 -857.622727
14 -679.304545 -891.304545
15 -650.168182 -679.304545
16 -1193.522727 -650.168182
17 -565.495455 -1193.522727
18 -635.831818 -565.495455
19 29.695455 -635.831818
20 -96.540000 29.695455
21 -772.785455 -96.540000
22 -70.649091 -772.785455
23 820.432727 -70.649091
24 -655.195000 820.432727
25 360.923182 -655.195000
26 193.823182 360.923182
27 -402.840455 193.823182
28 1094.605000 -402.840455
29 -560.867727 1094.605000
30 -65.204091 -560.867727
31 522.623182 -65.204091
32 296.187727 522.623182
33 607.142273 296.187727
34 683.878636 607.142273
35 868.760455 683.878636
36 901.432727 868.760455
37 477.350909 901.432727
38 446.050909 477.350909
39 -334.312727 446.050909
40 526.032727 -334.312727
41 -244.740000 526.032727
42 26.423636 -244.740000
43 213.450909 26.423636
44 381.805455 213.450909
45 770.560000 381.805455
46 571.196364 770.560000
47 200.278182 571.196364
48 463.150455 200.278182
49 161.268636 463.150455
50 -22.731364 161.268636
51 697.205000 -22.731364
52 531.750455 697.205000
53 -383.022273 531.750455
54 701.941364 -383.022273
55 204.868636 701.941364
56 25.533182 204.868636
57 586.887727 25.533182
58 -11.175909 586.887727
59 -212.094091 -11.175909
60 7.278182 -212.094091
61 -172.403636 7.278182
62 -413.703636 -172.403636
63 18.032727 -413.703636
64 -210.221818 18.032727
65 -578.894545 -210.221818
66 5.369091 -578.894545
67 -1001.103636 5.369091
68 -224.439091 -1001.103636
69 -148.984545 -224.439091
70 -721.048182 -148.984545
71 -12.866364 -721.048182
72 -743.994091 -12.866364
73 -473.375909 -743.994091
74 83.924091 -473.375909
75 61.960455 83.924091
76 -900.994091 61.960455
77 140.033182 -900.994091
78 -93.803182 140.033182
79 -161.675909 -93.803182
80 287.688636 -161.675909
81 3.743182 287.688636
82 612.079545 3.743182
83 982.361364 612.079545
84 -413.066364 982.361364
85 -119.348182 -413.066364
86 272.051818 -119.348182
87 268.988182 272.051818
88 -485.666364 268.988182
89 -295.439091 -485.666364
90 -1020.875455 -295.439091
91 -79.248182 -1020.875455
92 28.016364 -79.248182
93 -1147.129091 28.016364
94 751.607273 -1147.129091
95 1102.189091 751.607273
96 -182.338636 1102.189091
97 -256.420455 -182.338636
98 746.979545 -256.420455
99 -1029.484091 746.979545
100 456.661364 -1029.484091
101 354.188636 456.661364
102 -469.847727 354.188636
103 161.779545 -469.847727
104 -193.555909 161.779545
105 -97.601364 -193.555909
106 658.735000 -97.601364
107 -459.583182 658.735000
108 219.989091 -459.583182
109 -447.392727 219.989091
110 -7.992727 -447.392727
111 -684.656364 -7.992727
112 68.389091 -684.656364
113 40.816364 68.389091
114 -60.020000 40.816364
115 -16.192727 -60.020000
116 -810.028182 -16.192727
117 719.026364 -810.028182
118 521.462727 719.026364
119 -1004.255455 521.462727
120 1118.616818 -1004.255455
121 1144.735000 1118.616818
122 -418.165000 1144.735000
123 1697.171364 -418.165000
124 270.416818 1697.171364
125 1778.044091 270.416818
126 1204.307727 1778.044091
127 -1.065000 1204.307727
128 309.999545 -1.065000
129 33.254091 309.999545
130 -2463.409545 33.254091
131 -2777.027727 -2463.409545
> 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/7m17p1260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83yqd1260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9b6cy1260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u0711260867555.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vcb61260867555.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/12wy1t1260867555.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/13sufd1260867555.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/14xznf1260867555.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/154vlc1260867555.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/166n681260867556.tab")
+ }
>
> try(system("convert tmp/1c4de1260867555.ps tmp/1c4de1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zsf11260867555.ps tmp/2zsf11260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/36uov1260867555.ps tmp/36uov1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/419ve1260867555.ps tmp/419ve1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m1dv1260867555.ps tmp/5m1dv1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yp2c1260867555.ps tmp/6yp2c1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m17p1260867555.ps tmp/7m17p1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/83yqd1260867555.ps tmp/83yqd1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b6cy1260867555.ps tmp/9b6cy1260867555.png",intern=TRUE))
character(0)
> try(system("convert tmp/10u0711260867555.ps tmp/10u0711260867555.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.367 1.716 4.864