R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(7.977
+ ,0
+ ,8.241
+ ,0
+ ,8.444
+ ,0
+ ,8.49
+ ,0
+ ,8.388
+ ,0
+ ,8.099
+ ,0
+ ,7.984
+ ,0
+ ,7.786
+ ,0
+ ,8.086
+ ,0
+ ,9.315
+ ,0
+ ,9.113
+ ,0
+ ,9.023
+ ,0
+ ,9.026
+ ,1
+ ,9.787
+ ,1
+ ,9.536
+ ,1
+ ,9.49
+ ,1
+ ,9.736
+ ,1
+ ,9.694
+ ,1
+ ,9.647
+ ,1
+ ,9.753
+ ,1
+ ,10.07
+ ,1
+ ,10.137
+ ,1
+ ,9.984
+ ,1
+ ,9.732
+ ,1
+ ,9.103
+ ,1
+ ,9.155
+ ,1
+ ,9.308
+ ,1
+ ,9.394
+ ,1
+ ,9.948
+ ,1
+ ,10.177
+ ,1
+ ,10.002
+ ,1
+ ,9.728
+ ,1
+ ,10.002
+ ,1
+ ,10.063
+ ,1
+ ,10.018
+ ,1
+ ,9.96
+ ,1
+ ,10.236
+ ,1
+ ,10.893
+ ,1
+ ,10.756
+ ,1
+ ,10.94
+ ,1
+ ,10.997
+ ,1
+ ,10.827
+ ,1
+ ,10.166
+ ,1
+ ,10.186
+ ,1
+ ,10.457
+ ,1
+ ,10.368
+ ,1
+ ,10.244
+ ,1
+ ,10.511
+ ,1
+ ,10.812
+ ,1
+ ,10.738
+ ,1
+ ,10.171
+ ,1
+ ,9.721
+ ,1
+ ,9.897
+ ,1
+ ,9.828
+ ,1
+ ,9.924
+ ,1
+ ,10.371
+ ,1
+ ,10.846
+ ,1
+ ,10.413
+ ,1
+ ,10.709
+ ,1
+ ,10.662
+ ,1
+ ,10.57
+ ,1
+ ,10.297
+ ,1
+ ,10.635
+ ,1
+ ,10.872
+ ,1
+ ,10.296
+ ,1
+ ,10.383
+ ,1
+ ,10.431
+ ,1
+ ,10.574
+ ,1
+ ,10.653
+ ,1
+ ,10.805
+ ,1
+ ,10.872
+ ,1
+ ,10.625
+ ,1
+ ,10.407
+ ,1
+ ,10.463
+ ,1
+ ,10.556
+ ,1
+ ,10.646
+ ,1
+ ,10.702
+ ,1
+ ,11.353
+ ,1
+ ,11.346
+ ,1
+ ,11.451
+ ,1
+ ,11.964
+ ,1
+ ,12.574
+ ,1
+ ,13.031
+ ,1
+ ,13.812
+ ,1
+ ,14.544
+ ,1
+ ,14.931
+ ,1
+ ,14.886
+ ,1
+ ,16.005
+ ,1
+ ,17.064
+ ,1
+ ,15.168
+ ,1
+ ,16.05
+ ,1
+ ,15.839
+ ,1
+ ,15.137
+ ,1
+ ,14.954
+ ,1
+ ,15.648
+ ,1
+ ,15.305
+ ,1
+ ,15.579
+ ,1
+ ,16.348
+ ,1
+ ,15.928
+ ,1
+ ,16.171
+ ,1
+ ,15.937
+ ,1
+ ,15.713
+ ,1
+ ,15.594
+ ,1
+ ,15.683
+ ,1
+ ,16.438
+ ,1
+ ,17.032
+ ,1
+ ,17.696
+ ,1
+ ,17.745
+ ,1
+ ,19.394
+ ,1)
+ ,dim=c(2
+ ,109)
+ ,dimnames=list(c('prijs'
+ ,'dummy')
+ ,1:109))
> y <- array(NA,dim=c(2,109),dimnames=list(c('prijs','dummy'),1:109))
> 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
prijs dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.977 0 1 0 0 0 0 0 0 0 0 0 0 1
2 8.241 0 0 1 0 0 0 0 0 0 0 0 0 2
3 8.444 0 0 0 1 0 0 0 0 0 0 0 0 3
4 8.490 0 0 0 0 1 0 0 0 0 0 0 0 4
5 8.388 0 0 0 0 0 1 0 0 0 0 0 0 5
6 8.099 0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.984 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.786 0 0 0 0 0 0 0 0 1 0 0 0 8
9 8.086 0 0 0 0 0 0 0 0 0 1 0 0 9
10 9.315 0 0 0 0 0 0 0 0 0 0 1 0 10
11 9.113 0 0 0 0 0 0 0 0 0 0 0 1 11
12 9.023 0 0 0 0 0 0 0 0 0 0 0 0 12
13 9.026 1 1 0 0 0 0 0 0 0 0 0 0 13
14 9.787 1 0 1 0 0 0 0 0 0 0 0 0 14
15 9.536 1 0 0 1 0 0 0 0 0 0 0 0 15
16 9.490 1 0 0 0 1 0 0 0 0 0 0 0 16
17 9.736 1 0 0 0 0 1 0 0 0 0 0 0 17
18 9.694 1 0 0 0 0 0 1 0 0 0 0 0 18
19 9.647 1 0 0 0 0 0 0 1 0 0 0 0 19
20 9.753 1 0 0 0 0 0 0 0 1 0 0 0 20
21 10.070 1 0 0 0 0 0 0 0 0 1 0 0 21
22 10.137 1 0 0 0 0 0 0 0 0 0 1 0 22
23 9.984 1 0 0 0 0 0 0 0 0 0 0 1 23
24 9.732 1 0 0 0 0 0 0 0 0 0 0 0 24
25 9.103 1 1 0 0 0 0 0 0 0 0 0 0 25
26 9.155 1 0 1 0 0 0 0 0 0 0 0 0 26
27 9.308 1 0 0 1 0 0 0 0 0 0 0 0 27
28 9.394 1 0 0 0 1 0 0 0 0 0 0 0 28
29 9.948 1 0 0 0 0 1 0 0 0 0 0 0 29
30 10.177 1 0 0 0 0 0 1 0 0 0 0 0 30
31 10.002 1 0 0 0 0 0 0 1 0 0 0 0 31
32 9.728 1 0 0 0 0 0 0 0 1 0 0 0 32
33 10.002 1 0 0 0 0 0 0 0 0 1 0 0 33
34 10.063 1 0 0 0 0 0 0 0 0 0 1 0 34
35 10.018 1 0 0 0 0 0 0 0 0 0 0 1 35
36 9.960 1 0 0 0 0 0 0 0 0 0 0 0 36
37 10.236 1 1 0 0 0 0 0 0 0 0 0 0 37
38 10.893 1 0 1 0 0 0 0 0 0 0 0 0 38
39 10.756 1 0 0 1 0 0 0 0 0 0 0 0 39
40 10.940 1 0 0 0 1 0 0 0 0 0 0 0 40
41 10.997 1 0 0 0 0 1 0 0 0 0 0 0 41
42 10.827 1 0 0 0 0 0 1 0 0 0 0 0 42
43 10.166 1 0 0 0 0 0 0 1 0 0 0 0 43
44 10.186 1 0 0 0 0 0 0 0 1 0 0 0 44
45 10.457 1 0 0 0 0 0 0 0 0 1 0 0 45
46 10.368 1 0 0 0 0 0 0 0 0 0 1 0 46
47 10.244 1 0 0 0 0 0 0 0 0 0 0 1 47
48 10.511 1 0 0 0 0 0 0 0 0 0 0 0 48
49 10.812 1 1 0 0 0 0 0 0 0 0 0 0 49
50 10.738 1 0 1 0 0 0 0 0 0 0 0 0 50
51 10.171 1 0 0 1 0 0 0 0 0 0 0 0 51
52 9.721 1 0 0 0 1 0 0 0 0 0 0 0 52
53 9.897 1 0 0 0 0 1 0 0 0 0 0 0 53
54 9.828 1 0 0 0 0 0 1 0 0 0 0 0 54
55 9.924 1 0 0 0 0 0 0 1 0 0 0 0 55
56 10.371 1 0 0 0 0 0 0 0 1 0 0 0 56
57 10.846 1 0 0 0 0 0 0 0 0 1 0 0 57
58 10.413 1 0 0 0 0 0 0 0 0 0 1 0 58
59 10.709 1 0 0 0 0 0 0 0 0 0 0 1 59
60 10.662 1 0 0 0 0 0 0 0 0 0 0 0 60
61 10.570 1 1 0 0 0 0 0 0 0 0 0 0 61
62 10.297 1 0 1 0 0 0 0 0 0 0 0 0 62
63 10.635 1 0 0 1 0 0 0 0 0 0 0 0 63
64 10.872 1 0 0 0 1 0 0 0 0 0 0 0 64
65 10.296 1 0 0 0 0 1 0 0 0 0 0 0 65
66 10.383 1 0 0 0 0 0 1 0 0 0 0 0 66
67 10.431 1 0 0 0 0 0 0 1 0 0 0 0 67
68 10.574 1 0 0 0 0 0 0 0 1 0 0 0 68
69 10.653 1 0 0 0 0 0 0 0 0 1 0 0 69
70 10.805 1 0 0 0 0 0 0 0 0 0 1 0 70
71 10.872 1 0 0 0 0 0 0 0 0 0 0 1 71
72 10.625 1 0 0 0 0 0 0 0 0 0 0 0 72
73 10.407 1 1 0 0 0 0 0 0 0 0 0 0 73
74 10.463 1 0 1 0 0 0 0 0 0 0 0 0 74
75 10.556 1 0 0 1 0 0 0 0 0 0 0 0 75
76 10.646 1 0 0 0 1 0 0 0 0 0 0 0 76
77 10.702 1 0 0 0 0 1 0 0 0 0 0 0 77
78 11.353 1 0 0 0 0 0 1 0 0 0 0 0 78
79 11.346 1 0 0 0 0 0 0 1 0 0 0 0 79
80 11.451 1 0 0 0 0 0 0 0 1 0 0 0 80
81 11.964 1 0 0 0 0 0 0 0 0 1 0 0 81
82 12.574 1 0 0 0 0 0 0 0 0 0 1 0 82
83 13.031 1 0 0 0 0 0 0 0 0 0 0 1 83
84 13.812 1 0 0 0 0 0 0 0 0 0 0 0 84
85 14.544 1 1 0 0 0 0 0 0 0 0 0 0 85
86 14.931 1 0 1 0 0 0 0 0 0 0 0 0 86
87 14.886 1 0 0 1 0 0 0 0 0 0 0 0 87
88 16.005 1 0 0 0 1 0 0 0 0 0 0 0 88
89 17.064 1 0 0 0 0 1 0 0 0 0 0 0 89
90 15.168 1 0 0 0 0 0 1 0 0 0 0 0 90
91 16.050 1 0 0 0 0 0 0 1 0 0 0 0 91
92 15.839 1 0 0 0 0 0 0 0 1 0 0 0 92
93 15.137 1 0 0 0 0 0 0 0 0 1 0 0 93
94 14.954 1 0 0 0 0 0 0 0 0 0 1 0 94
95 15.648 1 0 0 0 0 0 0 0 0 0 0 1 95
96 15.305 1 0 0 0 0 0 0 0 0 0 0 0 96
97 15.579 1 1 0 0 0 0 0 0 0 0 0 0 97
98 16.348 1 0 1 0 0 0 0 0 0 0 0 0 98
99 15.928 1 0 0 1 0 0 0 0 0 0 0 0 99
100 16.171 1 0 0 0 1 0 0 0 0 0 0 0 100
101 15.937 1 0 0 0 0 1 0 0 0 0 0 0 101
102 15.713 1 0 0 0 0 0 1 0 0 0 0 0 102
103 15.594 1 0 0 0 0 0 0 1 0 0 0 0 103
104 15.683 1 0 0 0 0 0 0 0 1 0 0 0 104
105 16.438 1 0 0 0 0 0 0 0 0 1 0 0 105
106 17.032 1 0 0 0 0 0 0 0 0 0 1 0 106
107 17.696 1 0 0 0 0 0 0 0 0 0 0 1 107
108 17.745 1 0 0 0 0 0 0 0 0 0 0 0 108
109 19.394 1 1 0 0 0 0 0 0 0 0 0 0 109
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy M1 M2 M3 M4
7.94650 -0.91411 0.24412 0.07477 -0.07551 0.01221
M5 M6 M7 M8 M9 M10
0.06960 -0.20178 -0.29262 -0.34734 -0.17373 -0.03056
M11 t
0.07328 0.07994
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.7054 -1.1133 0.1143 0.8436 3.4037
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.946497 0.586923 13.539 <2e-16 ***
dummy -0.914112 0.495432 -1.845 0.0681 .
M1 0.244118 0.624445 0.391 0.6967
M2 0.074767 0.641928 0.116 0.9075
M3 -0.075510 0.641565 -0.118 0.9066
M4 0.012214 0.641241 0.019 0.9848
M5 0.069604 0.640954 0.109 0.9138
M6 -0.201784 0.640705 -0.315 0.7535
M7 -0.292616 0.640495 -0.457 0.6488
M8 -0.347338 0.640322 -0.542 0.5888
M9 -0.173725 0.640188 -0.271 0.7867
M10 -0.030558 0.640093 -0.048 0.9620
M11 0.073277 0.640035 0.114 0.9091
t 0.079943 0.004951 16.147 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.358 on 95 degrees of freedom
Multiple R-squared: 0.7786, Adjusted R-squared: 0.7483
F-statistic: 25.69 on 13 and 95 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,] 3.089758e-03 6.179516e-03 0.9969102
[2,] 8.366045e-04 1.673209e-03 0.9991634
[3,] 2.353142e-04 4.706285e-04 0.9997647
[4,] 1.720490e-04 3.440980e-04 0.9998280
[5,] 8.577968e-05 1.715594e-04 0.9999142
[6,] 5.230775e-05 1.046155e-04 0.9999477
[7,] 2.229846e-05 4.459692e-05 0.9999777
[8,] 1.248866e-05 2.497732e-05 0.9999875
[9,] 2.701442e-06 5.402884e-06 0.9999973
[10,] 8.156221e-07 1.631244e-06 0.9999992
[11,] 1.749035e-07 3.498070e-07 0.9999998
[12,] 3.592017e-08 7.184034e-08 1.0000000
[13,] 1.760688e-08 3.521376e-08 1.0000000
[14,] 2.815234e-08 5.630468e-08 1.0000000
[15,] 1.798149e-08 3.596297e-08 1.0000000
[16,] 5.676545e-09 1.135309e-08 1.0000000
[17,] 1.696987e-09 3.393973e-09 1.0000000
[18,] 7.349377e-10 1.469875e-09 1.0000000
[19,] 2.199979e-10 4.399959e-10 1.0000000
[20,] 5.673258e-11 1.134652e-10 1.0000000
[21,] 2.681083e-10 5.362166e-10 1.0000000
[22,] 1.574760e-09 3.149520e-09 1.0000000
[23,] 2.098838e-09 4.197675e-09 1.0000000
[24,] 3.313097e-09 6.626193e-09 1.0000000
[25,] 3.018766e-09 6.037533e-09 1.0000000
[26,] 2.616087e-09 5.232173e-09 1.0000000
[27,] 1.557405e-09 3.114811e-09 1.0000000
[28,] 8.577305e-10 1.715461e-09 1.0000000
[29,] 5.488270e-10 1.097654e-09 1.0000000
[30,] 7.948595e-10 1.589719e-09 1.0000000
[31,] 7.841136e-10 1.568227e-09 1.0000000
[32,] 5.208113e-10 1.041623e-09 1.0000000
[33,] 5.463781e-10 1.092756e-09 1.0000000
[34,] 4.489744e-10 8.979489e-10 1.0000000
[35,] 5.852172e-10 1.170434e-09 1.0000000
[36,] 2.019333e-09 4.038666e-09 1.0000000
[37,] 4.615422e-09 9.230843e-09 1.0000000
[38,] 9.124413e-09 1.824883e-08 1.0000000
[39,] 7.429632e-09 1.485926e-08 1.0000000
[40,] 6.455817e-09 1.291163e-08 1.0000000
[41,] 1.069184e-08 2.138368e-08 1.0000000
[42,] 1.366726e-08 2.733453e-08 1.0000000
[43,] 1.176170e-08 2.352339e-08 1.0000000
[44,] 1.079604e-08 2.159208e-08 1.0000000
[45,] 6.112674e-09 1.222535e-08 1.0000000
[46,] 4.107633e-09 8.215266e-09 1.0000000
[47,] 2.790333e-09 5.580666e-09 1.0000000
[48,] 1.876363e-09 3.752726e-09 1.0000000
[49,] 1.068830e-09 2.137660e-09 1.0000000
[50,] 6.263399e-10 1.252680e-09 1.0000000
[51,] 2.841970e-10 5.683940e-10 1.0000000
[52,] 1.482984e-10 2.965969e-10 1.0000000
[53,] 7.922539e-11 1.584508e-10 1.0000000
[54,] 3.865876e-11 7.731752e-11 1.0000000
[55,] 1.358310e-11 2.716619e-11 1.0000000
[56,] 5.079893e-12 1.015979e-11 1.0000000
[57,] 3.783549e-12 7.567098e-12 1.0000000
[58,] 5.456777e-12 1.091355e-11 1.0000000
[59,] 5.073964e-12 1.014793e-11 1.0000000
[60,] 1.882838e-11 3.765676e-11 1.0000000
[61,] 4.873535e-10 9.747069e-10 1.0000000
[62,] 6.997347e-10 1.399469e-09 1.0000000
[63,] 3.479077e-09 6.958153e-09 1.0000000
[64,] 2.019823e-08 4.039646e-08 1.0000000
[65,] 9.003360e-08 1.800672e-07 0.9999999
[66,] 6.212969e-07 1.242594e-06 0.9999994
[67,] 1.840929e-05 3.681858e-05 0.9999816
[68,] 4.058322e-04 8.116644e-04 0.9995942
[69,] 1.090068e-02 2.180135e-02 0.9890993
[70,] 3.522646e-02 7.045291e-02 0.9647735
[71,] 5.610727e-02 1.122145e-01 0.9438927
[72,] 1.180740e-01 2.361479e-01 0.8819260
[73,] 3.829292e-01 7.658583e-01 0.6170708
[74,] 3.494529e-01 6.989058e-01 0.6505471
[75,] 5.200622e-01 9.598756e-01 0.4799378
[76,] 7.527613e-01 4.944774e-01 0.2472387
> postscript(file="/var/www/html/rcomp/tmp/153rg1227523478.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qbks1227523478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vqrw1227523478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4m7hk1227523478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ec501227523478.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 = 109
Frequency = 1
1 2 3 4 5
-0.2935588953 0.0598487885 0.3331821218 0.2115154551 -0.0278178782
6 7 8 9 10
-0.1253734338 -0.2294845449 -0.4527067671 -0.4062623227 0.5996265662
11 12 13 14 15
0.2138487885 0.1171821218 0.7102328232 1.5606405069 1.3799738403
16 17 18 19 20
1.1663071736 1.2749738403 1.4244182847 1.3883071736 1.4690849514
21 22 23 24 25
1.5325293958 1.3764182847 1.0396405069 0.7809738403 -0.1720876355
26 27 28 29 30
-0.0306799518 0.1926533816 0.1109867149 0.5276533816 0.9480978260
31 32 33 34 35
0.7839867149 0.4847644927 0.5052089371 0.3430978260 0.1143200482
36 37 38 39 40
0.0496533816 0.0015919058 0.7479995895 0.6813329228 0.6976662562
41 42 43 44 45
0.6173329228 0.6387773673 -0.0113337438 -0.0165559660 0.0008884784
46 47 48 49 50
-0.3112226327 -0.6190004105 -0.3586670772 -0.3817285529 -0.3663208692
51 52 53 54 55
-0.8629875359 -1.4806542025 -1.4419875359 -1.3195430914 -1.2126542025
56 57 58 59 60
-0.7908764248 -0.5694319803 -1.2255430914 -1.1133208692 -1.1669875359
61 62 63 64 65
-1.5830490116 -1.7666413279 -1.3583079946 -1.2889746612 -2.0023079946
66 67 68 69 70
-1.7238635501 -1.6649746612 -1.5471968835 -1.7217524390 -1.7928635501
71 72 73 74 75
-1.9096413279 -2.1633079946 -2.7053694703 -2.5599617866 -2.3966284533
76 77 78 79 80
-2.4742951200 -2.5556284533 -1.7131840088 -1.7092951200 -1.6295173422
81 82 83 84 85
-1.3700728977 -0.9831840088 -0.7099617866 0.0643715467 0.4723100709
86 87 88 89 90
0.9487177547 0.9740510880 1.9253844213 2.8470510880 1.1424955324
91 92 93 94 95
2.0353844213 1.7991621991 0.8436066436 0.4374955324 0.9477177547
96 97 98 99 100
0.5980510880 0.5479896122 1.4063972960 1.0567306293 1.1320639626
101 102 103 104 105
0.7607306293 0.7281750737 0.6200639626 0.6838417404 1.1852861848
106 107 108 109
1.5561750737 2.0363972960 2.0787306293 3.4036691535
> postscript(file="/var/www/html/rcomp/tmp/6d74l1227523478.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 = 109
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.2935588953 NA
1 0.0598487885 -0.2935588953
2 0.3331821218 0.0598487885
3 0.2115154551 0.3331821218
4 -0.0278178782 0.2115154551
5 -0.1253734338 -0.0278178782
6 -0.2294845449 -0.1253734338
7 -0.4527067671 -0.2294845449
8 -0.4062623227 -0.4527067671
9 0.5996265662 -0.4062623227
10 0.2138487885 0.5996265662
11 0.1171821218 0.2138487885
12 0.7102328232 0.1171821218
13 1.5606405069 0.7102328232
14 1.3799738403 1.5606405069
15 1.1663071736 1.3799738403
16 1.2749738403 1.1663071736
17 1.4244182847 1.2749738403
18 1.3883071736 1.4244182847
19 1.4690849514 1.3883071736
20 1.5325293958 1.4690849514
21 1.3764182847 1.5325293958
22 1.0396405069 1.3764182847
23 0.7809738403 1.0396405069
24 -0.1720876355 0.7809738403
25 -0.0306799518 -0.1720876355
26 0.1926533816 -0.0306799518
27 0.1109867149 0.1926533816
28 0.5276533816 0.1109867149
29 0.9480978260 0.5276533816
30 0.7839867149 0.9480978260
31 0.4847644927 0.7839867149
32 0.5052089371 0.4847644927
33 0.3430978260 0.5052089371
34 0.1143200482 0.3430978260
35 0.0496533816 0.1143200482
36 0.0015919058 0.0496533816
37 0.7479995895 0.0015919058
38 0.6813329228 0.7479995895
39 0.6976662562 0.6813329228
40 0.6173329228 0.6976662562
41 0.6387773673 0.6173329228
42 -0.0113337438 0.6387773673
43 -0.0165559660 -0.0113337438
44 0.0008884784 -0.0165559660
45 -0.3112226327 0.0008884784
46 -0.6190004105 -0.3112226327
47 -0.3586670772 -0.6190004105
48 -0.3817285529 -0.3586670772
49 -0.3663208692 -0.3817285529
50 -0.8629875359 -0.3663208692
51 -1.4806542025 -0.8629875359
52 -1.4419875359 -1.4806542025
53 -1.3195430914 -1.4419875359
54 -1.2126542025 -1.3195430914
55 -0.7908764248 -1.2126542025
56 -0.5694319803 -0.7908764248
57 -1.2255430914 -0.5694319803
58 -1.1133208692 -1.2255430914
59 -1.1669875359 -1.1133208692
60 -1.5830490116 -1.1669875359
61 -1.7666413279 -1.5830490116
62 -1.3583079946 -1.7666413279
63 -1.2889746612 -1.3583079946
64 -2.0023079946 -1.2889746612
65 -1.7238635501 -2.0023079946
66 -1.6649746612 -1.7238635501
67 -1.5471968835 -1.6649746612
68 -1.7217524390 -1.5471968835
69 -1.7928635501 -1.7217524390
70 -1.9096413279 -1.7928635501
71 -2.1633079946 -1.9096413279
72 -2.7053694703 -2.1633079946
73 -2.5599617866 -2.7053694703
74 -2.3966284533 -2.5599617866
75 -2.4742951200 -2.3966284533
76 -2.5556284533 -2.4742951200
77 -1.7131840088 -2.5556284533
78 -1.7092951200 -1.7131840088
79 -1.6295173422 -1.7092951200
80 -1.3700728977 -1.6295173422
81 -0.9831840088 -1.3700728977
82 -0.7099617866 -0.9831840088
83 0.0643715467 -0.7099617866
84 0.4723100709 0.0643715467
85 0.9487177547 0.4723100709
86 0.9740510880 0.9487177547
87 1.9253844213 0.9740510880
88 2.8470510880 1.9253844213
89 1.1424955324 2.8470510880
90 2.0353844213 1.1424955324
91 1.7991621991 2.0353844213
92 0.8436066436 1.7991621991
93 0.4374955324 0.8436066436
94 0.9477177547 0.4374955324
95 0.5980510880 0.9477177547
96 0.5479896122 0.5980510880
97 1.4063972960 0.5479896122
98 1.0567306293 1.4063972960
99 1.1320639626 1.0567306293
100 0.7607306293 1.1320639626
101 0.7281750737 0.7607306293
102 0.6200639626 0.7281750737
103 0.6838417404 0.6200639626
104 1.1852861848 0.6838417404
105 1.5561750737 1.1852861848
106 2.0363972960 1.5561750737
107 2.0787306293 2.0363972960
108 3.4036691535 2.0787306293
109 NA 3.4036691535
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0598487885 -0.2935588953
[2,] 0.3331821218 0.0598487885
[3,] 0.2115154551 0.3331821218
[4,] -0.0278178782 0.2115154551
[5,] -0.1253734338 -0.0278178782
[6,] -0.2294845449 -0.1253734338
[7,] -0.4527067671 -0.2294845449
[8,] -0.4062623227 -0.4527067671
[9,] 0.5996265662 -0.4062623227
[10,] 0.2138487885 0.5996265662
[11,] 0.1171821218 0.2138487885
[12,] 0.7102328232 0.1171821218
[13,] 1.5606405069 0.7102328232
[14,] 1.3799738403 1.5606405069
[15,] 1.1663071736 1.3799738403
[16,] 1.2749738403 1.1663071736
[17,] 1.4244182847 1.2749738403
[18,] 1.3883071736 1.4244182847
[19,] 1.4690849514 1.3883071736
[20,] 1.5325293958 1.4690849514
[21,] 1.3764182847 1.5325293958
[22,] 1.0396405069 1.3764182847
[23,] 0.7809738403 1.0396405069
[24,] -0.1720876355 0.7809738403
[25,] -0.0306799518 -0.1720876355
[26,] 0.1926533816 -0.0306799518
[27,] 0.1109867149 0.1926533816
[28,] 0.5276533816 0.1109867149
[29,] 0.9480978260 0.5276533816
[30,] 0.7839867149 0.9480978260
[31,] 0.4847644927 0.7839867149
[32,] 0.5052089371 0.4847644927
[33,] 0.3430978260 0.5052089371
[34,] 0.1143200482 0.3430978260
[35,] 0.0496533816 0.1143200482
[36,] 0.0015919058 0.0496533816
[37,] 0.7479995895 0.0015919058
[38,] 0.6813329228 0.7479995895
[39,] 0.6976662562 0.6813329228
[40,] 0.6173329228 0.6976662562
[41,] 0.6387773673 0.6173329228
[42,] -0.0113337438 0.6387773673
[43,] -0.0165559660 -0.0113337438
[44,] 0.0008884784 -0.0165559660
[45,] -0.3112226327 0.0008884784
[46,] -0.6190004105 -0.3112226327
[47,] -0.3586670772 -0.6190004105
[48,] -0.3817285529 -0.3586670772
[49,] -0.3663208692 -0.3817285529
[50,] -0.8629875359 -0.3663208692
[51,] -1.4806542025 -0.8629875359
[52,] -1.4419875359 -1.4806542025
[53,] -1.3195430914 -1.4419875359
[54,] -1.2126542025 -1.3195430914
[55,] -0.7908764248 -1.2126542025
[56,] -0.5694319803 -0.7908764248
[57,] -1.2255430914 -0.5694319803
[58,] -1.1133208692 -1.2255430914
[59,] -1.1669875359 -1.1133208692
[60,] -1.5830490116 -1.1669875359
[61,] -1.7666413279 -1.5830490116
[62,] -1.3583079946 -1.7666413279
[63,] -1.2889746612 -1.3583079946
[64,] -2.0023079946 -1.2889746612
[65,] -1.7238635501 -2.0023079946
[66,] -1.6649746612 -1.7238635501
[67,] -1.5471968835 -1.6649746612
[68,] -1.7217524390 -1.5471968835
[69,] -1.7928635501 -1.7217524390
[70,] -1.9096413279 -1.7928635501
[71,] -2.1633079946 -1.9096413279
[72,] -2.7053694703 -2.1633079946
[73,] -2.5599617866 -2.7053694703
[74,] -2.3966284533 -2.5599617866
[75,] -2.4742951200 -2.3966284533
[76,] -2.5556284533 -2.4742951200
[77,] -1.7131840088 -2.5556284533
[78,] -1.7092951200 -1.7131840088
[79,] -1.6295173422 -1.7092951200
[80,] -1.3700728977 -1.6295173422
[81,] -0.9831840088 -1.3700728977
[82,] -0.7099617866 -0.9831840088
[83,] 0.0643715467 -0.7099617866
[84,] 0.4723100709 0.0643715467
[85,] 0.9487177547 0.4723100709
[86,] 0.9740510880 0.9487177547
[87,] 1.9253844213 0.9740510880
[88,] 2.8470510880 1.9253844213
[89,] 1.1424955324 2.8470510880
[90,] 2.0353844213 1.1424955324
[91,] 1.7991621991 2.0353844213
[92,] 0.8436066436 1.7991621991
[93,] 0.4374955324 0.8436066436
[94,] 0.9477177547 0.4374955324
[95,] 0.5980510880 0.9477177547
[96,] 0.5479896122 0.5980510880
[97,] 1.4063972960 0.5479896122
[98,] 1.0567306293 1.4063972960
[99,] 1.1320639626 1.0567306293
[100,] 0.7607306293 1.1320639626
[101,] 0.7281750737 0.7607306293
[102,] 0.6200639626 0.7281750737
[103,] 0.6838417404 0.6200639626
[104,] 1.1852861848 0.6838417404
[105,] 1.5561750737 1.1852861848
[106,] 2.0363972960 1.5561750737
[107,] 2.0787306293 2.0363972960
[108,] 3.4036691535 2.0787306293
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0598487885 -0.2935588953
2 0.3331821218 0.0598487885
3 0.2115154551 0.3331821218
4 -0.0278178782 0.2115154551
5 -0.1253734338 -0.0278178782
6 -0.2294845449 -0.1253734338
7 -0.4527067671 -0.2294845449
8 -0.4062623227 -0.4527067671
9 0.5996265662 -0.4062623227
10 0.2138487885 0.5996265662
11 0.1171821218 0.2138487885
12 0.7102328232 0.1171821218
13 1.5606405069 0.7102328232
14 1.3799738403 1.5606405069
15 1.1663071736 1.3799738403
16 1.2749738403 1.1663071736
17 1.4244182847 1.2749738403
18 1.3883071736 1.4244182847
19 1.4690849514 1.3883071736
20 1.5325293958 1.4690849514
21 1.3764182847 1.5325293958
22 1.0396405069 1.3764182847
23 0.7809738403 1.0396405069
24 -0.1720876355 0.7809738403
25 -0.0306799518 -0.1720876355
26 0.1926533816 -0.0306799518
27 0.1109867149 0.1926533816
28 0.5276533816 0.1109867149
29 0.9480978260 0.5276533816
30 0.7839867149 0.9480978260
31 0.4847644927 0.7839867149
32 0.5052089371 0.4847644927
33 0.3430978260 0.5052089371
34 0.1143200482 0.3430978260
35 0.0496533816 0.1143200482
36 0.0015919058 0.0496533816
37 0.7479995895 0.0015919058
38 0.6813329228 0.7479995895
39 0.6976662562 0.6813329228
40 0.6173329228 0.6976662562
41 0.6387773673 0.6173329228
42 -0.0113337438 0.6387773673
43 -0.0165559660 -0.0113337438
44 0.0008884784 -0.0165559660
45 -0.3112226327 0.0008884784
46 -0.6190004105 -0.3112226327
47 -0.3586670772 -0.6190004105
48 -0.3817285529 -0.3586670772
49 -0.3663208692 -0.3817285529
50 -0.8629875359 -0.3663208692
51 -1.4806542025 -0.8629875359
52 -1.4419875359 -1.4806542025
53 -1.3195430914 -1.4419875359
54 -1.2126542025 -1.3195430914
55 -0.7908764248 -1.2126542025
56 -0.5694319803 -0.7908764248
57 -1.2255430914 -0.5694319803
58 -1.1133208692 -1.2255430914
59 -1.1669875359 -1.1133208692
60 -1.5830490116 -1.1669875359
61 -1.7666413279 -1.5830490116
62 -1.3583079946 -1.7666413279
63 -1.2889746612 -1.3583079946
64 -2.0023079946 -1.2889746612
65 -1.7238635501 -2.0023079946
66 -1.6649746612 -1.7238635501
67 -1.5471968835 -1.6649746612
68 -1.7217524390 -1.5471968835
69 -1.7928635501 -1.7217524390
70 -1.9096413279 -1.7928635501
71 -2.1633079946 -1.9096413279
72 -2.7053694703 -2.1633079946
73 -2.5599617866 -2.7053694703
74 -2.3966284533 -2.5599617866
75 -2.4742951200 -2.3966284533
76 -2.5556284533 -2.4742951200
77 -1.7131840088 -2.5556284533
78 -1.7092951200 -1.7131840088
79 -1.6295173422 -1.7092951200
80 -1.3700728977 -1.6295173422
81 -0.9831840088 -1.3700728977
82 -0.7099617866 -0.9831840088
83 0.0643715467 -0.7099617866
84 0.4723100709 0.0643715467
85 0.9487177547 0.4723100709
86 0.9740510880 0.9487177547
87 1.9253844213 0.9740510880
88 2.8470510880 1.9253844213
89 1.1424955324 2.8470510880
90 2.0353844213 1.1424955324
91 1.7991621991 2.0353844213
92 0.8436066436 1.7991621991
93 0.4374955324 0.8436066436
94 0.9477177547 0.4374955324
95 0.5980510880 0.9477177547
96 0.5479896122 0.5980510880
97 1.4063972960 0.5479896122
98 1.0567306293 1.4063972960
99 1.1320639626 1.0567306293
100 0.7607306293 1.1320639626
101 0.7281750737 0.7607306293
102 0.6200639626 0.7281750737
103 0.6838417404 0.6200639626
104 1.1852861848 0.6838417404
105 1.5561750737 1.1852861848
106 2.0363972960 1.5561750737
107 2.0787306293 2.0363972960
108 3.4036691535 2.0787306293
> 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/7xoy61227523478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wwfw1227523478.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r7901227523479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10taw51227523479.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11glbi1227523479.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/12zxfb1227523479.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/13t8rw1227523479.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/14bgoy1227523479.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/158a951227523479.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/16ssf11227523479.tab")
+ }
>
> system("convert tmp/153rg1227523478.ps tmp/153rg1227523478.png")
> system("convert tmp/2qbks1227523478.ps tmp/2qbks1227523478.png")
> system("convert tmp/3vqrw1227523478.ps tmp/3vqrw1227523478.png")
> system("convert tmp/4m7hk1227523478.ps tmp/4m7hk1227523478.png")
> system("convert tmp/5ec501227523478.ps tmp/5ec501227523478.png")
> system("convert tmp/6d74l1227523478.ps tmp/6d74l1227523478.png")
> system("convert tmp/7xoy61227523478.ps tmp/7xoy61227523478.png")
> system("convert tmp/8wwfw1227523478.ps tmp/8wwfw1227523478.png")
> system("convert tmp/9r7901227523479.ps tmp/9r7901227523479.png")
> system("convert tmp/10taw51227523479.ps tmp/10taw51227523479.png")
>
>
> proc.time()
user system elapsed
3.077 1.600 3.550