R version 2.7.0 (2008-04-22)
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(540,0,522,0,526,0,527,0,516,0,503,0,489,0,479,0,475,0,524,0,552,0,532,0,511,0,492,0,492,0,493,0,481,0,462,0,457,0,442,0,439,0,488,0,521,0,501,0,485,0,464,0,460,0,467,0,460,0,448,0,443,0,436,0,431,0,484,0,510,0,513,0,503,0,471,0,471,0,476,0,475,0,470,0,461,0,455,0,456,1,517,1,525,1,523,1,519,1,509,1,512,1,519,1,517,1,510,1,509,1,501,1,507,1,569,1,580,1,578,1,565,1,547,1,555,1,562,1,561,1,555,1,544,1,537,1,543,1,594,1,611,1,613,1,611,1,594,1,595,1,591,1,589,1,584,1,573,1,567,1,569,1,621,1,629,1,628,1,612,1,595,1,597,1,593,1,590,1,580,1,574,1,573,1,573,1,620,1,626,1,620,1,588,1,566,1,557,1,561,1,549,1,532,1,526,1,511,1,499,1,555,1,565,1,542,1,527,1,510,1,514,1,517,1,508,1,493,1,490,1,469,1,478,1,528,1,534,1,518,1,506,1),dim=c(2,121),dimnames=list(c('X','Y'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('X','Y'),1:121))
> 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
X Y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 540 0 1 0 0 0 0 0 0 0 0 0 0 1
2 522 0 0 1 0 0 0 0 0 0 0 0 0 2
3 526 0 0 0 1 0 0 0 0 0 0 0 0 3
4 527 0 0 0 0 1 0 0 0 0 0 0 0 4
5 516 0 0 0 0 0 1 0 0 0 0 0 0 5
6 503 0 0 0 0 0 0 1 0 0 0 0 0 6
7 489 0 0 0 0 0 0 0 1 0 0 0 0 7
8 479 0 0 0 0 0 0 0 0 1 0 0 0 8
9 475 0 0 0 0 0 0 0 0 0 1 0 0 9
10 524 0 0 0 0 0 0 0 0 0 0 1 0 10
11 552 0 0 0 0 0 0 0 0 0 0 0 1 11
12 532 0 0 0 0 0 0 0 0 0 0 0 0 12
13 511 0 1 0 0 0 0 0 0 0 0 0 0 13
14 492 0 0 1 0 0 0 0 0 0 0 0 0 14
15 492 0 0 0 1 0 0 0 0 0 0 0 0 15
16 493 0 0 0 0 1 0 0 0 0 0 0 0 16
17 481 0 0 0 0 0 1 0 0 0 0 0 0 17
18 462 0 0 0 0 0 0 1 0 0 0 0 0 18
19 457 0 0 0 0 0 0 0 1 0 0 0 0 19
20 442 0 0 0 0 0 0 0 0 1 0 0 0 20
21 439 0 0 0 0 0 0 0 0 0 1 0 0 21
22 488 0 0 0 0 0 0 0 0 0 0 1 0 22
23 521 0 0 0 0 0 0 0 0 0 0 0 1 23
24 501 0 0 0 0 0 0 0 0 0 0 0 0 24
25 485 0 1 0 0 0 0 0 0 0 0 0 0 25
26 464 0 0 1 0 0 0 0 0 0 0 0 0 26
27 460 0 0 0 1 0 0 0 0 0 0 0 0 27
28 467 0 0 0 0 1 0 0 0 0 0 0 0 28
29 460 0 0 0 0 0 1 0 0 0 0 0 0 29
30 448 0 0 0 0 0 0 1 0 0 0 0 0 30
31 443 0 0 0 0 0 0 0 1 0 0 0 0 31
32 436 0 0 0 0 0 0 0 0 1 0 0 0 32
33 431 0 0 0 0 0 0 0 0 0 1 0 0 33
34 484 0 0 0 0 0 0 0 0 0 0 1 0 34
35 510 0 0 0 0 0 0 0 0 0 0 0 1 35
36 513 0 0 0 0 0 0 0 0 0 0 0 0 36
37 503 0 1 0 0 0 0 0 0 0 0 0 0 37
38 471 0 0 1 0 0 0 0 0 0 0 0 0 38
39 471 0 0 0 1 0 0 0 0 0 0 0 0 39
40 476 0 0 0 0 1 0 0 0 0 0 0 0 40
41 475 0 0 0 0 0 1 0 0 0 0 0 0 41
42 470 0 0 0 0 0 0 1 0 0 0 0 0 42
43 461 0 0 0 0 0 0 0 1 0 0 0 0 43
44 455 0 0 0 0 0 0 0 0 1 0 0 0 44
45 456 1 0 0 0 0 0 0 0 0 1 0 0 45
46 517 1 0 0 0 0 0 0 0 0 0 1 0 46
47 525 1 0 0 0 0 0 0 0 0 0 0 1 47
48 523 1 0 0 0 0 0 0 0 0 0 0 0 48
49 519 1 1 0 0 0 0 0 0 0 0 0 0 49
50 509 1 0 1 0 0 0 0 0 0 0 0 0 50
51 512 1 0 0 1 0 0 0 0 0 0 0 0 51
52 519 1 0 0 0 1 0 0 0 0 0 0 0 52
53 517 1 0 0 0 0 1 0 0 0 0 0 0 53
54 510 1 0 0 0 0 0 1 0 0 0 0 0 54
55 509 1 0 0 0 0 0 0 1 0 0 0 0 55
56 501 1 0 0 0 0 0 0 0 1 0 0 0 56
57 507 1 0 0 0 0 0 0 0 0 1 0 0 57
58 569 1 0 0 0 0 0 0 0 0 0 1 0 58
59 580 1 0 0 0 0 0 0 0 0 0 0 1 59
60 578 1 0 0 0 0 0 0 0 0 0 0 0 60
61 565 1 1 0 0 0 0 0 0 0 0 0 0 61
62 547 1 0 1 0 0 0 0 0 0 0 0 0 62
63 555 1 0 0 1 0 0 0 0 0 0 0 0 63
64 562 1 0 0 0 1 0 0 0 0 0 0 0 64
65 561 1 0 0 0 0 1 0 0 0 0 0 0 65
66 555 1 0 0 0 0 0 1 0 0 0 0 0 66
67 544 1 0 0 0 0 0 0 1 0 0 0 0 67
68 537 1 0 0 0 0 0 0 0 1 0 0 0 68
69 543 1 0 0 0 0 0 0 0 0 1 0 0 69
70 594 1 0 0 0 0 0 0 0 0 0 1 0 70
71 611 1 0 0 0 0 0 0 0 0 0 0 1 71
72 613 1 0 0 0 0 0 0 0 0 0 0 0 72
73 611 1 1 0 0 0 0 0 0 0 0 0 0 73
74 594 1 0 1 0 0 0 0 0 0 0 0 0 74
75 595 1 0 0 1 0 0 0 0 0 0 0 0 75
76 591 1 0 0 0 1 0 0 0 0 0 0 0 76
77 589 1 0 0 0 0 1 0 0 0 0 0 0 77
78 584 1 0 0 0 0 0 1 0 0 0 0 0 78
79 573 1 0 0 0 0 0 0 1 0 0 0 0 79
80 567 1 0 0 0 0 0 0 0 1 0 0 0 80
81 569 1 0 0 0 0 0 0 0 0 1 0 0 81
82 621 1 0 0 0 0 0 0 0 0 0 1 0 82
83 629 1 0 0 0 0 0 0 0 0 0 0 1 83
84 628 1 0 0 0 0 0 0 0 0 0 0 0 84
85 612 1 1 0 0 0 0 0 0 0 0 0 0 85
86 595 1 0 1 0 0 0 0 0 0 0 0 0 86
87 597 1 0 0 1 0 0 0 0 0 0 0 0 87
88 593 1 0 0 0 1 0 0 0 0 0 0 0 88
89 590 1 0 0 0 0 1 0 0 0 0 0 0 89
90 580 1 0 0 0 0 0 1 0 0 0 0 0 90
91 574 1 0 0 0 0 0 0 1 0 0 0 0 91
92 573 1 0 0 0 0 0 0 0 1 0 0 0 92
93 573 1 0 0 0 0 0 0 0 0 1 0 0 93
94 620 1 0 0 0 0 0 0 0 0 0 1 0 94
95 626 1 0 0 0 0 0 0 0 0 0 0 1 95
96 620 1 0 0 0 0 0 0 0 0 0 0 0 96
97 588 1 1 0 0 0 0 0 0 0 0 0 0 97
98 566 1 0 1 0 0 0 0 0 0 0 0 0 98
99 557 1 0 0 1 0 0 0 0 0 0 0 0 99
100 561 1 0 0 0 1 0 0 0 0 0 0 0 100
101 549 1 0 0 0 0 1 0 0 0 0 0 0 101
102 532 1 0 0 0 0 0 1 0 0 0 0 0 102
103 526 1 0 0 0 0 0 0 1 0 0 0 0 103
104 511 1 0 0 0 0 0 0 0 1 0 0 0 104
105 499 1 0 0 0 0 0 0 0 0 1 0 0 105
106 555 1 0 0 0 0 0 0 0 0 0 1 0 106
107 565 1 0 0 0 0 0 0 0 0 0 0 1 107
108 542 1 0 0 0 0 0 0 0 0 0 0 0 108
109 527 1 1 0 0 0 0 0 0 0 0 0 0 109
110 510 1 0 1 0 0 0 0 0 0 0 0 0 110
111 514 1 0 0 1 0 0 0 0 0 0 0 0 111
112 517 1 0 0 0 1 0 0 0 0 0 0 0 112
113 508 1 0 0 0 0 1 0 0 0 0 0 0 113
114 493 1 0 0 0 0 0 1 0 0 0 0 0 114
115 490 1 0 0 0 0 0 0 1 0 0 0 0 115
116 469 1 0 0 0 0 0 0 0 1 0 0 0 116
117 478 1 0 0 0 0 0 0 0 0 1 0 0 117
118 528 1 0 0 0 0 0 0 0 0 0 1 0 118
119 534 1 0 0 0 0 0 0 0 0 0 0 1 119
120 518 1 0 0 0 0 0 0 0 0 0 0 0 120
121 506 1 1 0 0 0 0 0 0 0 0 0 0 121
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y M1 M2 M3 M4
517.3215 84.0926 -10.4628 -24.3281 -23.1343 -20.1406
M5 M6 M7 M8 M9 M10
-25.8469 -36.4531 -43.2594 -52.5657 -60.6812 -7.3875
M11 t
8.2063 -0.2937
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-71.515 -21.956 -1.900 25.994 59.584
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 517.3215 12.0902 42.789 < 2e-16 ***
Y 84.0926 11.5084 7.307 5.15e-11 ***
M1 -10.4628 14.6518 -0.714 0.476723
M2 -24.3281 15.0162 -1.620 0.108150
M3 -23.1343 15.0104 -1.541 0.126216
M4 -20.1406 15.0064 -1.342 0.182392
M5 -25.8469 15.0039 -1.723 0.087837 .
M6 -36.4531 15.0032 -2.430 0.016776 *
M7 -43.2594 15.0041 -2.883 0.004760 **
M8 -52.5657 15.0067 -3.503 0.000673 ***
M9 -60.6812 14.9972 -4.046 9.86e-05 ***
M10 -7.3875 14.9930 -0.493 0.623216
M11 8.2063 14.9905 0.547 0.585223
t -0.2937 0.1584 -1.855 0.066383 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 33.52 on 107 degrees of freedom
Multiple R-squared: 0.6012, Adjusted R-squared: 0.5527
F-statistic: 12.41 on 13 and 107 DF, p-value: 4.379e-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,] 3.621256e-04 7.242513e-04 9.996379e-01
[2,] 1.790692e-04 3.581384e-04 9.998209e-01
[3,] 1.583487e-05 3.166973e-05 9.999842e-01
[4,] 1.644882e-06 3.289765e-06 9.999984e-01
[5,] 1.372431e-07 2.744863e-07 9.999999e-01
[6,] 1.074132e-08 2.148263e-08 1.000000e+00
[7,] 1.097569e-09 2.195138e-09 1.000000e+00
[8,] 1.035662e-10 2.071323e-10 1.000000e+00
[9,] 2.043161e-10 4.086321e-10 1.000000e+00
[10,] 4.002341e-11 8.004682e-11 1.000000e+00
[11,] 3.687427e-12 7.374854e-12 1.000000e+00
[12,] 6.107569e-13 1.221514e-12 1.000000e+00
[13,] 3.667007e-13 7.334015e-13 1.000000e+00
[14,] 5.589055e-13 1.117811e-12 1.000000e+00
[15,] 1.716851e-12 3.433702e-12 1.000000e+00
[16,] 9.670447e-12 1.934089e-11 1.000000e+00
[17,] 1.139458e-11 2.278917e-11 1.000000e+00
[18,] 1.956675e-11 3.913350e-11 1.000000e+00
[19,] 9.458616e-12 1.891723e-11 1.000000e+00
[20,] 3.890057e-10 7.780113e-10 1.000000e+00
[21,] 9.299948e-09 1.859990e-08 1.000000e+00
[22,] 8.521831e-09 1.704366e-08 1.000000e+00
[23,] 6.128732e-09 1.225746e-08 1.000000e+00
[24,] 4.375324e-09 8.750647e-09 1.000000e+00
[25,] 5.987530e-09 1.197506e-08 1.000000e+00
[26,] 1.616757e-08 3.233514e-08 1.000000e+00
[27,] 2.486642e-08 4.973283e-08 1.000000e+00
[28,] 4.283907e-08 8.567814e-08 1.000000e+00
[29,] 2.946911e-08 5.893821e-08 1.000000e+00
[30,] 2.136345e-08 4.272690e-08 1.000000e+00
[31,] 2.103496e-08 4.206992e-08 1.000000e+00
[32,] 1.688278e-08 3.376557e-08 1.000000e+00
[33,] 1.366799e-08 2.733599e-08 1.000000e+00
[34,] 1.878092e-08 3.756184e-08 1.000000e+00
[35,] 2.822597e-08 5.645194e-08 1.000000e+00
[36,] 4.446728e-08 8.893456e-08 1.000000e+00
[37,] 8.910260e-08 1.782052e-07 9.999999e-01
[38,] 2.198754e-07 4.397509e-07 9.999998e-01
[39,] 6.950079e-07 1.390016e-06 9.999993e-01
[40,] 2.120974e-06 4.241947e-06 9.999979e-01
[41,] 2.721489e-05 5.442978e-05 9.999728e-01
[42,] 2.884089e-04 5.768178e-04 9.997116e-01
[43,] 9.162674e-04 1.832535e-03 9.990837e-01
[44,] 3.088360e-03 6.176720e-03 9.969116e-01
[45,] 7.575490e-03 1.515098e-02 9.924245e-01
[46,] 2.117124e-02 4.234248e-02 9.788288e-01
[47,] 5.379961e-02 1.075992e-01 9.462004e-01
[48,] 1.117585e-01 2.235169e-01 8.882415e-01
[49,] 2.086299e-01 4.172599e-01 7.913701e-01
[50,] 3.422951e-01 6.845902e-01 6.577049e-01
[51,] 5.168775e-01 9.662450e-01 4.831225e-01
[52,] 6.987157e-01 6.025686e-01 3.012843e-01
[53,] 8.757933e-01 2.484133e-01 1.242067e-01
[54,] 9.662362e-01 6.752762e-02 3.376381e-02
[55,] 9.916041e-01 1.679180e-02 8.395900e-03
[56,] 9.977569e-01 4.486296e-03 2.243148e-03
[57,] 9.989418e-01 2.116481e-03 1.058241e-03
[58,] 9.995073e-01 9.853586e-04 4.926793e-04
[59,] 9.997481e-01 5.038072e-04 2.519036e-04
[60,] 9.999038e-01 1.924056e-04 9.620278e-05
[61,] 9.999568e-01 8.644768e-05 4.322384e-05
[62,] 9.999708e-01 5.849918e-05 2.924959e-05
[63,] 9.999889e-01 2.226757e-05 1.113379e-05
[64,] 9.999945e-01 1.097627e-05 5.488136e-06
[65,] 9.999976e-01 4.796040e-06 2.398020e-06
[66,] 9.999991e-01 1.745777e-06 8.728886e-07
[67,] 9.999998e-01 4.494109e-07 2.247055e-07
[68,] 9.999998e-01 4.325228e-07 2.162614e-07
[69,] 9.999997e-01 5.518133e-07 2.759066e-07
[70,] 9.999994e-01 1.194264e-06 5.971318e-07
[71,] 9.999985e-01 3.066491e-06 1.533245e-06
[72,] 9.999974e-01 5.256346e-06 2.628173e-06
[73,] 9.999935e-01 1.305903e-05 6.529515e-06
[74,] 9.999818e-01 3.640486e-05 1.820243e-05
[75,] 9.999519e-01 9.617355e-05 4.808677e-05
[76,] 9.999198e-01 1.604740e-04 8.023699e-05
[77,] 9.998963e-01 2.074949e-04 1.037474e-04
[78,] 9.998141e-01 3.717522e-04 1.858761e-04
[79,] 9.996566e-01 6.868264e-04 3.434132e-04
[80,] 9.999383e-01 1.233115e-04 6.165575e-05
[81,] 9.999341e-01 1.318797e-04 6.593984e-05
[82,] 9.999642e-01 7.154669e-05 3.577334e-05
[83,] 9.999084e-01 1.832593e-04 9.162967e-05
[84,] 9.998147e-01 3.705307e-04 1.852654e-04
[85,] 9.995383e-01 9.234248e-04 4.617124e-04
[86,] 9.987331e-01 2.533898e-03 1.266949e-03
[87,] 9.956291e-01 8.741771e-03 4.370886e-03
[88,] 9.976350e-01 4.729948e-03 2.364974e-03
> postscript(file="/var/www/html/rcomp/tmp/1rqhy1227794992.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fhes1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3utzc1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j4951227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5czz91227794992.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 = 121
Frequency = 1
1 2 3 4 5 6
33.4350254 29.5940484 32.6940484 30.9940484 25.9940484 23.8940484
7 8 9 10 11 12
16.9940484 16.5940484 21.0033086 17.0033086 29.7033086 18.2033086
13 14 15 16 17 18
7.9598061 3.1188291 2.2188291 0.5188291 -5.4811709 -13.5811709
19 20 21 22 23 24
-11.4811709 -16.8811709 -11.4719107 -15.4719107 2.2280893 -9.2719107
25 26 27 28 29 30
-14.5154133 -21.3563903 -26.2563903 -21.9563903 -22.9563903 -24.0563903
31 32 33 34 35 36
-21.9563903 -19.3563903 -15.9471301 -15.9471301 -5.2471301 6.2528699
37 38 39 40 41 42
7.0093674 -10.8316096 -11.7316096 -9.4316096 -4.4316096 1.4683904
43 44 45 46 47 48
-0.4316096 3.1683904 -71.5149517 -63.5149517 -70.8149517 -64.3149517
49 50 51 52 53 54
-57.5584542 -53.3994312 -51.2994312 -46.9994312 -42.9994312 -39.0994312
55 56 57 58 59 60
-32.9994312 -31.3994312 -16.9901710 -7.9901710 -12.2901710 -5.7901710
61 62 63 64 65 66
-8.0336735 -11.8746506 -4.7746506 -0.4746506 4.5253494 9.4253494
67 68 69 70 71 72
5.5253494 8.1253494 22.5346097 20.5346097 22.2346097 32.7346097
73 74 75 76 77 78
41.4911071 38.6501301 38.7501301 32.0501301 36.0501301 41.9501301
79 80 81 82 83 84
38.0501301 41.6501301 52.0593903 51.0593903 43.7593903 51.2593903
85 86 87 88 89 90
46.0158878 43.1749107 44.2749107 37.5749107 40.5749107 41.4749107
91 92 93 94 95 96
42.5749107 51.1749107 59.5841710 53.5841710 44.2841710 46.7841710
97 98 99 100 101 102
25.5406684 17.6996914 7.7996914 9.0996914 3.0996914 -3.0003086
103 104 105 106 107 108
-1.9003086 -7.3003086 -10.8910484 -7.8910484 -13.1910484 -27.6910484
109 110 111 112 113 114
-31.9345509 -34.7755280 -31.6755280 -31.3755280 -34.3755280 -38.4755280
115 116 117 118 119 120
-34.3755280 -45.7755280 -28.3662677 -31.3662677 -40.6662677 -48.1662677
121
-49.4097703
> postscript(file="/var/www/html/rcomp/tmp/66mmv1227794992.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 33.4350254 NA
1 29.5940484 33.4350254
2 32.6940484 29.5940484
3 30.9940484 32.6940484
4 25.9940484 30.9940484
5 23.8940484 25.9940484
6 16.9940484 23.8940484
7 16.5940484 16.9940484
8 21.0033086 16.5940484
9 17.0033086 21.0033086
10 29.7033086 17.0033086
11 18.2033086 29.7033086
12 7.9598061 18.2033086
13 3.1188291 7.9598061
14 2.2188291 3.1188291
15 0.5188291 2.2188291
16 -5.4811709 0.5188291
17 -13.5811709 -5.4811709
18 -11.4811709 -13.5811709
19 -16.8811709 -11.4811709
20 -11.4719107 -16.8811709
21 -15.4719107 -11.4719107
22 2.2280893 -15.4719107
23 -9.2719107 2.2280893
24 -14.5154133 -9.2719107
25 -21.3563903 -14.5154133
26 -26.2563903 -21.3563903
27 -21.9563903 -26.2563903
28 -22.9563903 -21.9563903
29 -24.0563903 -22.9563903
30 -21.9563903 -24.0563903
31 -19.3563903 -21.9563903
32 -15.9471301 -19.3563903
33 -15.9471301 -15.9471301
34 -5.2471301 -15.9471301
35 6.2528699 -5.2471301
36 7.0093674 6.2528699
37 -10.8316096 7.0093674
38 -11.7316096 -10.8316096
39 -9.4316096 -11.7316096
40 -4.4316096 -9.4316096
41 1.4683904 -4.4316096
42 -0.4316096 1.4683904
43 3.1683904 -0.4316096
44 -71.5149517 3.1683904
45 -63.5149517 -71.5149517
46 -70.8149517 -63.5149517
47 -64.3149517 -70.8149517
48 -57.5584542 -64.3149517
49 -53.3994312 -57.5584542
50 -51.2994312 -53.3994312
51 -46.9994312 -51.2994312
52 -42.9994312 -46.9994312
53 -39.0994312 -42.9994312
54 -32.9994312 -39.0994312
55 -31.3994312 -32.9994312
56 -16.9901710 -31.3994312
57 -7.9901710 -16.9901710
58 -12.2901710 -7.9901710
59 -5.7901710 -12.2901710
60 -8.0336735 -5.7901710
61 -11.8746506 -8.0336735
62 -4.7746506 -11.8746506
63 -0.4746506 -4.7746506
64 4.5253494 -0.4746506
65 9.4253494 4.5253494
66 5.5253494 9.4253494
67 8.1253494 5.5253494
68 22.5346097 8.1253494
69 20.5346097 22.5346097
70 22.2346097 20.5346097
71 32.7346097 22.2346097
72 41.4911071 32.7346097
73 38.6501301 41.4911071
74 38.7501301 38.6501301
75 32.0501301 38.7501301
76 36.0501301 32.0501301
77 41.9501301 36.0501301
78 38.0501301 41.9501301
79 41.6501301 38.0501301
80 52.0593903 41.6501301
81 51.0593903 52.0593903
82 43.7593903 51.0593903
83 51.2593903 43.7593903
84 46.0158878 51.2593903
85 43.1749107 46.0158878
86 44.2749107 43.1749107
87 37.5749107 44.2749107
88 40.5749107 37.5749107
89 41.4749107 40.5749107
90 42.5749107 41.4749107
91 51.1749107 42.5749107
92 59.5841710 51.1749107
93 53.5841710 59.5841710
94 44.2841710 53.5841710
95 46.7841710 44.2841710
96 25.5406684 46.7841710
97 17.6996914 25.5406684
98 7.7996914 17.6996914
99 9.0996914 7.7996914
100 3.0996914 9.0996914
101 -3.0003086 3.0996914
102 -1.9003086 -3.0003086
103 -7.3003086 -1.9003086
104 -10.8910484 -7.3003086
105 -7.8910484 -10.8910484
106 -13.1910484 -7.8910484
107 -27.6910484 -13.1910484
108 -31.9345509 -27.6910484
109 -34.7755280 -31.9345509
110 -31.6755280 -34.7755280
111 -31.3755280 -31.6755280
112 -34.3755280 -31.3755280
113 -38.4755280 -34.3755280
114 -34.3755280 -38.4755280
115 -45.7755280 -34.3755280
116 -28.3662677 -45.7755280
117 -31.3662677 -28.3662677
118 -40.6662677 -31.3662677
119 -48.1662677 -40.6662677
120 -49.4097703 -48.1662677
121 NA -49.4097703
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 29.5940484 33.4350254
[2,] 32.6940484 29.5940484
[3,] 30.9940484 32.6940484
[4,] 25.9940484 30.9940484
[5,] 23.8940484 25.9940484
[6,] 16.9940484 23.8940484
[7,] 16.5940484 16.9940484
[8,] 21.0033086 16.5940484
[9,] 17.0033086 21.0033086
[10,] 29.7033086 17.0033086
[11,] 18.2033086 29.7033086
[12,] 7.9598061 18.2033086
[13,] 3.1188291 7.9598061
[14,] 2.2188291 3.1188291
[15,] 0.5188291 2.2188291
[16,] -5.4811709 0.5188291
[17,] -13.5811709 -5.4811709
[18,] -11.4811709 -13.5811709
[19,] -16.8811709 -11.4811709
[20,] -11.4719107 -16.8811709
[21,] -15.4719107 -11.4719107
[22,] 2.2280893 -15.4719107
[23,] -9.2719107 2.2280893
[24,] -14.5154133 -9.2719107
[25,] -21.3563903 -14.5154133
[26,] -26.2563903 -21.3563903
[27,] -21.9563903 -26.2563903
[28,] -22.9563903 -21.9563903
[29,] -24.0563903 -22.9563903
[30,] -21.9563903 -24.0563903
[31,] -19.3563903 -21.9563903
[32,] -15.9471301 -19.3563903
[33,] -15.9471301 -15.9471301
[34,] -5.2471301 -15.9471301
[35,] 6.2528699 -5.2471301
[36,] 7.0093674 6.2528699
[37,] -10.8316096 7.0093674
[38,] -11.7316096 -10.8316096
[39,] -9.4316096 -11.7316096
[40,] -4.4316096 -9.4316096
[41,] 1.4683904 -4.4316096
[42,] -0.4316096 1.4683904
[43,] 3.1683904 -0.4316096
[44,] -71.5149517 3.1683904
[45,] -63.5149517 -71.5149517
[46,] -70.8149517 -63.5149517
[47,] -64.3149517 -70.8149517
[48,] -57.5584542 -64.3149517
[49,] -53.3994312 -57.5584542
[50,] -51.2994312 -53.3994312
[51,] -46.9994312 -51.2994312
[52,] -42.9994312 -46.9994312
[53,] -39.0994312 -42.9994312
[54,] -32.9994312 -39.0994312
[55,] -31.3994312 -32.9994312
[56,] -16.9901710 -31.3994312
[57,] -7.9901710 -16.9901710
[58,] -12.2901710 -7.9901710
[59,] -5.7901710 -12.2901710
[60,] -8.0336735 -5.7901710
[61,] -11.8746506 -8.0336735
[62,] -4.7746506 -11.8746506
[63,] -0.4746506 -4.7746506
[64,] 4.5253494 -0.4746506
[65,] 9.4253494 4.5253494
[66,] 5.5253494 9.4253494
[67,] 8.1253494 5.5253494
[68,] 22.5346097 8.1253494
[69,] 20.5346097 22.5346097
[70,] 22.2346097 20.5346097
[71,] 32.7346097 22.2346097
[72,] 41.4911071 32.7346097
[73,] 38.6501301 41.4911071
[74,] 38.7501301 38.6501301
[75,] 32.0501301 38.7501301
[76,] 36.0501301 32.0501301
[77,] 41.9501301 36.0501301
[78,] 38.0501301 41.9501301
[79,] 41.6501301 38.0501301
[80,] 52.0593903 41.6501301
[81,] 51.0593903 52.0593903
[82,] 43.7593903 51.0593903
[83,] 51.2593903 43.7593903
[84,] 46.0158878 51.2593903
[85,] 43.1749107 46.0158878
[86,] 44.2749107 43.1749107
[87,] 37.5749107 44.2749107
[88,] 40.5749107 37.5749107
[89,] 41.4749107 40.5749107
[90,] 42.5749107 41.4749107
[91,] 51.1749107 42.5749107
[92,] 59.5841710 51.1749107
[93,] 53.5841710 59.5841710
[94,] 44.2841710 53.5841710
[95,] 46.7841710 44.2841710
[96,] 25.5406684 46.7841710
[97,] 17.6996914 25.5406684
[98,] 7.7996914 17.6996914
[99,] 9.0996914 7.7996914
[100,] 3.0996914 9.0996914
[101,] -3.0003086 3.0996914
[102,] -1.9003086 -3.0003086
[103,] -7.3003086 -1.9003086
[104,] -10.8910484 -7.3003086
[105,] -7.8910484 -10.8910484
[106,] -13.1910484 -7.8910484
[107,] -27.6910484 -13.1910484
[108,] -31.9345509 -27.6910484
[109,] -34.7755280 -31.9345509
[110,] -31.6755280 -34.7755280
[111,] -31.3755280 -31.6755280
[112,] -34.3755280 -31.3755280
[113,] -38.4755280 -34.3755280
[114,] -34.3755280 -38.4755280
[115,] -45.7755280 -34.3755280
[116,] -28.3662677 -45.7755280
[117,] -31.3662677 -28.3662677
[118,] -40.6662677 -31.3662677
[119,] -48.1662677 -40.6662677
[120,] -49.4097703 -48.1662677
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 29.5940484 33.4350254
2 32.6940484 29.5940484
3 30.9940484 32.6940484
4 25.9940484 30.9940484
5 23.8940484 25.9940484
6 16.9940484 23.8940484
7 16.5940484 16.9940484
8 21.0033086 16.5940484
9 17.0033086 21.0033086
10 29.7033086 17.0033086
11 18.2033086 29.7033086
12 7.9598061 18.2033086
13 3.1188291 7.9598061
14 2.2188291 3.1188291
15 0.5188291 2.2188291
16 -5.4811709 0.5188291
17 -13.5811709 -5.4811709
18 -11.4811709 -13.5811709
19 -16.8811709 -11.4811709
20 -11.4719107 -16.8811709
21 -15.4719107 -11.4719107
22 2.2280893 -15.4719107
23 -9.2719107 2.2280893
24 -14.5154133 -9.2719107
25 -21.3563903 -14.5154133
26 -26.2563903 -21.3563903
27 -21.9563903 -26.2563903
28 -22.9563903 -21.9563903
29 -24.0563903 -22.9563903
30 -21.9563903 -24.0563903
31 -19.3563903 -21.9563903
32 -15.9471301 -19.3563903
33 -15.9471301 -15.9471301
34 -5.2471301 -15.9471301
35 6.2528699 -5.2471301
36 7.0093674 6.2528699
37 -10.8316096 7.0093674
38 -11.7316096 -10.8316096
39 -9.4316096 -11.7316096
40 -4.4316096 -9.4316096
41 1.4683904 -4.4316096
42 -0.4316096 1.4683904
43 3.1683904 -0.4316096
44 -71.5149517 3.1683904
45 -63.5149517 -71.5149517
46 -70.8149517 -63.5149517
47 -64.3149517 -70.8149517
48 -57.5584542 -64.3149517
49 -53.3994312 -57.5584542
50 -51.2994312 -53.3994312
51 -46.9994312 -51.2994312
52 -42.9994312 -46.9994312
53 -39.0994312 -42.9994312
54 -32.9994312 -39.0994312
55 -31.3994312 -32.9994312
56 -16.9901710 -31.3994312
57 -7.9901710 -16.9901710
58 -12.2901710 -7.9901710
59 -5.7901710 -12.2901710
60 -8.0336735 -5.7901710
61 -11.8746506 -8.0336735
62 -4.7746506 -11.8746506
63 -0.4746506 -4.7746506
64 4.5253494 -0.4746506
65 9.4253494 4.5253494
66 5.5253494 9.4253494
67 8.1253494 5.5253494
68 22.5346097 8.1253494
69 20.5346097 22.5346097
70 22.2346097 20.5346097
71 32.7346097 22.2346097
72 41.4911071 32.7346097
73 38.6501301 41.4911071
74 38.7501301 38.6501301
75 32.0501301 38.7501301
76 36.0501301 32.0501301
77 41.9501301 36.0501301
78 38.0501301 41.9501301
79 41.6501301 38.0501301
80 52.0593903 41.6501301
81 51.0593903 52.0593903
82 43.7593903 51.0593903
83 51.2593903 43.7593903
84 46.0158878 51.2593903
85 43.1749107 46.0158878
86 44.2749107 43.1749107
87 37.5749107 44.2749107
88 40.5749107 37.5749107
89 41.4749107 40.5749107
90 42.5749107 41.4749107
91 51.1749107 42.5749107
92 59.5841710 51.1749107
93 53.5841710 59.5841710
94 44.2841710 53.5841710
95 46.7841710 44.2841710
96 25.5406684 46.7841710
97 17.6996914 25.5406684
98 7.7996914 17.6996914
99 9.0996914 7.7996914
100 3.0996914 9.0996914
101 -3.0003086 3.0996914
102 -1.9003086 -3.0003086
103 -7.3003086 -1.9003086
104 -10.8910484 -7.3003086
105 -7.8910484 -10.8910484
106 -13.1910484 -7.8910484
107 -27.6910484 -13.1910484
108 -31.9345509 -27.6910484
109 -34.7755280 -31.9345509
110 -31.6755280 -34.7755280
111 -31.3755280 -31.6755280
112 -34.3755280 -31.3755280
113 -38.4755280 -34.3755280
114 -34.3755280 -38.4755280
115 -45.7755280 -34.3755280
116 -28.3662677 -45.7755280
117 -31.3662677 -28.3662677
118 -40.6662677 -31.3662677
119 -48.1662677 -40.6662677
120 -49.4097703 -48.1662677
> 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/74v2t1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8d6xr1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ufih1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10p3em1227794992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nx611227794992.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/12wzm61227794992.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/13a7aa1227794992.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/14qyra1227794992.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/15rimg1227794992.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/16i1mv1227794992.tab")
+ }
>
> system("convert tmp/1rqhy1227794992.ps tmp/1rqhy1227794992.png")
> system("convert tmp/2fhes1227794992.ps tmp/2fhes1227794992.png")
> system("convert tmp/3utzc1227794992.ps tmp/3utzc1227794992.png")
> system("convert tmp/4j4951227794992.ps tmp/4j4951227794992.png")
> system("convert tmp/5czz91227794992.ps tmp/5czz91227794992.png")
> system("convert tmp/66mmv1227794992.ps tmp/66mmv1227794992.png")
> system("convert tmp/74v2t1227794992.ps tmp/74v2t1227794992.png")
> system("convert tmp/8d6xr1227794992.ps tmp/8d6xr1227794992.png")
> system("convert tmp/9ufih1227794992.ps tmp/9ufih1227794992.png")
> system("convert tmp/10p3em1227794992.ps tmp/10p3em1227794992.png")
>
>
> proc.time()
user system elapsed
6.452 2.839 6.850