R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(100.00,100.00,94.97,106.73,107.50,104.81,124.27,96.15,107.06,88.46,79.71,88.46,163.41,91.35,144.83,92.31,166.82,91.35,154.26,87.50,132.60,85.58,157.51,86.54,104.02,97.12,106.03,99.04,113.23,98.08,117.64,92.31,113.34,88.46,66.62,89.42,185.99,90.38,174.57,90.38,208.19,88.46,163.81,86.54,162.46,86.54,148.16,86.54,113.41,94.23,105.63,96.15,111.79,94.23,132.36,89.42,110.75,86.54,67.37,86.54,178.29,87.50,156.38,87.50,189.71,87.50,152.80,88.46,150.80,84.62,160.40,79.81,127.25,80.77,108.47,77.88,117.09,74.04,147.25,75.96,116.19,75.96,75.83,76.92,181.94,75.96,179.12,73.08,183.15,68.27,197.90,65.38,155.42,62.50,162.54,66.35,125.90,78.85,105.50,83.65,121.11,79.81,137.51,75.96,97.20,72.12,69.74,75.00,152.58,79.81,146.59,80.77,161.16,78.85,152.84,74.04,121.95,69.23,140.12,70.19),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.00 100.00 1 0 0 0 0 0 0 0 0 0 0 1
2 94.97 106.73 0 1 0 0 0 0 0 0 0 0 0 2
3 107.50 104.81 0 0 1 0 0 0 0 0 0 0 0 3
4 124.27 96.15 0 0 0 1 0 0 0 0 0 0 0 4
5 107.06 88.46 0 0 0 0 1 0 0 0 0 0 0 5
6 79.71 88.46 0 0 0 0 0 1 0 0 0 0 0 6
7 163.41 91.35 0 0 0 0 0 0 1 0 0 0 0 7
8 144.83 92.31 0 0 0 0 0 0 0 1 0 0 0 8
9 166.82 91.35 0 0 0 0 0 0 0 0 1 0 0 9
10 154.26 87.50 0 0 0 0 0 0 0 0 0 1 0 10
11 132.60 85.58 0 0 0 0 0 0 0 0 0 0 1 11
12 157.51 86.54 0 0 0 0 0 0 0 0 0 0 0 12
13 104.02 97.12 1 0 0 0 0 0 0 0 0 0 0 13
14 106.03 99.04 0 1 0 0 0 0 0 0 0 0 0 14
15 113.23 98.08 0 0 1 0 0 0 0 0 0 0 0 15
16 117.64 92.31 0 0 0 1 0 0 0 0 0 0 0 16
17 113.34 88.46 0 0 0 0 1 0 0 0 0 0 0 17
18 66.62 89.42 0 0 0 0 0 1 0 0 0 0 0 18
19 185.99 90.38 0 0 0 0 0 0 1 0 0 0 0 19
20 174.57 90.38 0 0 0 0 0 0 0 1 0 0 0 20
21 208.19 88.46 0 0 0 0 0 0 0 0 1 0 0 21
22 163.81 86.54 0 0 0 0 0 0 0 0 0 1 0 22
23 162.46 86.54 0 0 0 0 0 0 0 0 0 0 1 23
24 148.16 86.54 0 0 0 0 0 0 0 0 0 0 0 24
25 113.41 94.23 1 0 0 0 0 0 0 0 0 0 0 25
26 105.63 96.15 0 1 0 0 0 0 0 0 0 0 0 26
27 111.79 94.23 0 0 1 0 0 0 0 0 0 0 0 27
28 132.36 89.42 0 0 0 1 0 0 0 0 0 0 0 28
29 110.75 86.54 0 0 0 0 1 0 0 0 0 0 0 29
30 67.37 86.54 0 0 0 0 0 1 0 0 0 0 0 30
31 178.29 87.50 0 0 0 0 0 0 1 0 0 0 0 31
32 156.38 87.50 0 0 0 0 0 0 0 1 0 0 0 32
33 189.71 87.50 0 0 0 0 0 0 0 0 1 0 0 33
34 152.80 88.46 0 0 0 0 0 0 0 0 0 1 0 34
35 150.80 84.62 0 0 0 0 0 0 0 0 0 0 1 35
36 160.40 79.81 0 0 0 0 0 0 0 0 0 0 0 36
37 127.25 80.77 1 0 0 0 0 0 0 0 0 0 0 37
38 108.47 77.88 0 1 0 0 0 0 0 0 0 0 0 38
39 117.09 74.04 0 0 1 0 0 0 0 0 0 0 0 39
40 147.25 75.96 0 0 0 1 0 0 0 0 0 0 0 40
41 116.19 75.96 0 0 0 0 1 0 0 0 0 0 0 41
42 75.83 76.92 0 0 0 0 0 1 0 0 0 0 0 42
43 181.94 75.96 0 0 0 0 0 0 1 0 0 0 0 43
44 179.12 73.08 0 0 0 0 0 0 0 1 0 0 0 44
45 183.15 68.27 0 0 0 0 0 0 0 0 1 0 0 45
46 197.90 65.38 0 0 0 0 0 0 0 0 0 1 0 46
47 155.42 62.50 0 0 0 0 0 0 0 0 0 0 1 47
48 162.54 66.35 0 0 0 0 0 0 0 0 0 0 0 48
49 125.90 78.85 1 0 0 0 0 0 0 0 0 0 0 49
50 105.50 83.65 0 1 0 0 0 0 0 0 0 0 0 50
51 121.11 79.81 0 0 1 0 0 0 0 0 0 0 0 51
52 137.51 75.96 0 0 0 1 0 0 0 0 0 0 0 52
53 97.20 72.12 0 0 0 0 1 0 0 0 0 0 0 53
54 69.74 75.00 0 0 0 0 0 1 0 0 0 0 0 54
55 152.58 79.81 0 0 0 0 0 0 1 0 0 0 0 55
56 146.59 80.77 0 0 0 0 0 0 0 1 0 0 0 56
57 161.16 78.85 0 0 0 0 0 0 0 0 1 0 0 57
58 152.84 74.04 0 0 0 0 0 0 0 0 0 1 0 58
59 121.95 69.23 0 0 0 0 0 0 0 0 0 0 1 59
60 140.12 70.19 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
249.7438 -1.0506 -31.0307 -38.0108 -30.2153 -16.6076
M5 M6 M7 M8 M9 M10
-42.9485 -78.6002 24.2010 12.2491 32.1316 12.4128
M11 t
-9.6954 -0.3937
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-22.139 -8.550 1.281 5.672 27.515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 249.7438 32.8970 7.592 1.19e-09 ***
X -1.0506 0.3457 -3.039 0.003905 **
M1 -31.0307 8.0263 -3.866 0.000345 ***
M2 -38.0108 8.4001 -4.525 4.24e-05 ***
M3 -30.2153 8.1082 -3.726 0.000530 ***
M4 -16.6076 7.7253 -2.150 0.036867 *
M5 -42.9485 7.5659 -5.677 8.82e-07 ***
M6 -78.6002 7.6048 -10.336 1.41e-13 ***
M7 24.2010 7.7295 3.131 0.003025 **
M8 12.2491 7.7436 1.582 0.120540
M9 32.1316 7.6369 4.207 0.000118 ***
M10 12.4128 7.5472 1.645 0.106851
M11 -9.6954 7.5275 -1.288 0.204188
t -0.3937 0.1746 -2.255 0.028956 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.9 on 46 degrees of freedom
Multiple R-squared: 0.9044, Adjusted R-squared: 0.8773
F-statistic: 33.46 on 13 and 46 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1507184 0.3014369 0.8492816
[2,] 0.1530249 0.3060497 0.8469751
[3,] 0.4251495 0.8502990 0.5748505
[4,] 0.6182496 0.7635008 0.3817504
[5,] 0.8684916 0.2630167 0.1315084
[6,] 0.8265690 0.3468620 0.1734310
[7,] 0.8533640 0.2932721 0.1466360
[8,] 0.8744501 0.2510998 0.1255499
[9,] 0.8398844 0.3202313 0.1601156
[10,] 0.7875316 0.4249369 0.2124684
[11,] 0.7324033 0.5351934 0.2675967
[12,] 0.6765432 0.6469136 0.3234568
[13,] 0.6195159 0.7609681 0.3804841
[14,] 0.6295096 0.7409808 0.3704904
[15,] 0.5383717 0.9232566 0.4616283
[16,] 0.5356809 0.9286382 0.4643191
[17,] 0.5080378 0.9839244 0.4919622
[18,] 0.6344642 0.7310716 0.3655358
[19,] 0.5787465 0.8425069 0.4212535
[20,] 0.4817638 0.9635275 0.5182362
[21,] 0.4051059 0.8102117 0.5948941
[22,] 0.4869284 0.9738569 0.5130716
[23,] 0.8585473 0.2829054 0.1414527
[24,] 0.8309626 0.3380748 0.1690374
[25,] 0.7718752 0.4562496 0.2281248
[26,] 0.7409245 0.5181510 0.2590755
[27,] 0.5736013 0.8527974 0.4263987
> postscript(file="/var/www/html/rcomp/tmp/1typ41259349814.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ivlg1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33wka1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wrls1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n00z1259349814.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 = 60
Frequency = 1
1 2 3 4 5 6
-13.2636135 -3.8495543 -0.7384333 -6.2801996 -4.8343496 3.8611155
7 8 9 10 11 12
-11.8103393 -17.0360974 -15.5434613 -12.0356124 -13.2106834 3.4061446
13 14 15 16 17 18
-7.5445295 3.8563495 2.6460054 -12.2196505 6.1703392 -3.4956608
19 20 21 22 23 24
14.4753090 15.4010160 27.5151171 1.2305414 22.3825403 -1.2191666
25 26 27 28 29 30
3.5340489 5.1449280 1.8860489 4.1889280 6.2879581 -1.0465768
31 32 33 34 35 36
8.4743930 -1.0899000 12.7512710 -3.0376999 13.4301593 8.6752721
37 38 39 40 41 42
7.9582376 -6.4840636 -9.3000125 9.6631166 5.3377516 2.0317516
43 44 45 46 47 48
4.7256516 11.2257537 -9.2862555 22.5401283 -0.4634776 1.3994607
49 50 51 52 53 54
9.3158565 1.3323404 5.5063915 4.6478054 -12.9616993 -1.3506295
55 56 57 58 59 60
-15.8650144 -8.5007724 -15.4366713 -8.6973574 -22.1385387 -12.2617107
> postscript(file="/var/www/html/rcomp/tmp/6id2n1259349814.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -13.2636135 NA
1 -3.8495543 -13.2636135
2 -0.7384333 -3.8495543
3 -6.2801996 -0.7384333
4 -4.8343496 -6.2801996
5 3.8611155 -4.8343496
6 -11.8103393 3.8611155
7 -17.0360974 -11.8103393
8 -15.5434613 -17.0360974
9 -12.0356124 -15.5434613
10 -13.2106834 -12.0356124
11 3.4061446 -13.2106834
12 -7.5445295 3.4061446
13 3.8563495 -7.5445295
14 2.6460054 3.8563495
15 -12.2196505 2.6460054
16 6.1703392 -12.2196505
17 -3.4956608 6.1703392
18 14.4753090 -3.4956608
19 15.4010160 14.4753090
20 27.5151171 15.4010160
21 1.2305414 27.5151171
22 22.3825403 1.2305414
23 -1.2191666 22.3825403
24 3.5340489 -1.2191666
25 5.1449280 3.5340489
26 1.8860489 5.1449280
27 4.1889280 1.8860489
28 6.2879581 4.1889280
29 -1.0465768 6.2879581
30 8.4743930 -1.0465768
31 -1.0899000 8.4743930
32 12.7512710 -1.0899000
33 -3.0376999 12.7512710
34 13.4301593 -3.0376999
35 8.6752721 13.4301593
36 7.9582376 8.6752721
37 -6.4840636 7.9582376
38 -9.3000125 -6.4840636
39 9.6631166 -9.3000125
40 5.3377516 9.6631166
41 2.0317516 5.3377516
42 4.7256516 2.0317516
43 11.2257537 4.7256516
44 -9.2862555 11.2257537
45 22.5401283 -9.2862555
46 -0.4634776 22.5401283
47 1.3994607 -0.4634776
48 9.3158565 1.3994607
49 1.3323404 9.3158565
50 5.5063915 1.3323404
51 4.6478054 5.5063915
52 -12.9616993 4.6478054
53 -1.3506295 -12.9616993
54 -15.8650144 -1.3506295
55 -8.5007724 -15.8650144
56 -15.4366713 -8.5007724
57 -8.6973574 -15.4366713
58 -22.1385387 -8.6973574
59 -12.2617107 -22.1385387
60 NA -12.2617107
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.8495543 -13.2636135
[2,] -0.7384333 -3.8495543
[3,] -6.2801996 -0.7384333
[4,] -4.8343496 -6.2801996
[5,] 3.8611155 -4.8343496
[6,] -11.8103393 3.8611155
[7,] -17.0360974 -11.8103393
[8,] -15.5434613 -17.0360974
[9,] -12.0356124 -15.5434613
[10,] -13.2106834 -12.0356124
[11,] 3.4061446 -13.2106834
[12,] -7.5445295 3.4061446
[13,] 3.8563495 -7.5445295
[14,] 2.6460054 3.8563495
[15,] -12.2196505 2.6460054
[16,] 6.1703392 -12.2196505
[17,] -3.4956608 6.1703392
[18,] 14.4753090 -3.4956608
[19,] 15.4010160 14.4753090
[20,] 27.5151171 15.4010160
[21,] 1.2305414 27.5151171
[22,] 22.3825403 1.2305414
[23,] -1.2191666 22.3825403
[24,] 3.5340489 -1.2191666
[25,] 5.1449280 3.5340489
[26,] 1.8860489 5.1449280
[27,] 4.1889280 1.8860489
[28,] 6.2879581 4.1889280
[29,] -1.0465768 6.2879581
[30,] 8.4743930 -1.0465768
[31,] -1.0899000 8.4743930
[32,] 12.7512710 -1.0899000
[33,] -3.0376999 12.7512710
[34,] 13.4301593 -3.0376999
[35,] 8.6752721 13.4301593
[36,] 7.9582376 8.6752721
[37,] -6.4840636 7.9582376
[38,] -9.3000125 -6.4840636
[39,] 9.6631166 -9.3000125
[40,] 5.3377516 9.6631166
[41,] 2.0317516 5.3377516
[42,] 4.7256516 2.0317516
[43,] 11.2257537 4.7256516
[44,] -9.2862555 11.2257537
[45,] 22.5401283 -9.2862555
[46,] -0.4634776 22.5401283
[47,] 1.3994607 -0.4634776
[48,] 9.3158565 1.3994607
[49,] 1.3323404 9.3158565
[50,] 5.5063915 1.3323404
[51,] 4.6478054 5.5063915
[52,] -12.9616993 4.6478054
[53,] -1.3506295 -12.9616993
[54,] -15.8650144 -1.3506295
[55,] -8.5007724 -15.8650144
[56,] -15.4366713 -8.5007724
[57,] -8.6973574 -15.4366713
[58,] -22.1385387 -8.6973574
[59,] -12.2617107 -22.1385387
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.8495543 -13.2636135
2 -0.7384333 -3.8495543
3 -6.2801996 -0.7384333
4 -4.8343496 -6.2801996
5 3.8611155 -4.8343496
6 -11.8103393 3.8611155
7 -17.0360974 -11.8103393
8 -15.5434613 -17.0360974
9 -12.0356124 -15.5434613
10 -13.2106834 -12.0356124
11 3.4061446 -13.2106834
12 -7.5445295 3.4061446
13 3.8563495 -7.5445295
14 2.6460054 3.8563495
15 -12.2196505 2.6460054
16 6.1703392 -12.2196505
17 -3.4956608 6.1703392
18 14.4753090 -3.4956608
19 15.4010160 14.4753090
20 27.5151171 15.4010160
21 1.2305414 27.5151171
22 22.3825403 1.2305414
23 -1.2191666 22.3825403
24 3.5340489 -1.2191666
25 5.1449280 3.5340489
26 1.8860489 5.1449280
27 4.1889280 1.8860489
28 6.2879581 4.1889280
29 -1.0465768 6.2879581
30 8.4743930 -1.0465768
31 -1.0899000 8.4743930
32 12.7512710 -1.0899000
33 -3.0376999 12.7512710
34 13.4301593 -3.0376999
35 8.6752721 13.4301593
36 7.9582376 8.6752721
37 -6.4840636 7.9582376
38 -9.3000125 -6.4840636
39 9.6631166 -9.3000125
40 5.3377516 9.6631166
41 2.0317516 5.3377516
42 4.7256516 2.0317516
43 11.2257537 4.7256516
44 -9.2862555 11.2257537
45 22.5401283 -9.2862555
46 -0.4634776 22.5401283
47 1.3994607 -0.4634776
48 9.3158565 1.3994607
49 1.3323404 9.3158565
50 5.5063915 1.3323404
51 4.6478054 5.5063915
52 -12.9616993 4.6478054
53 -1.3506295 -12.9616993
54 -15.8650144 -1.3506295
55 -8.5007724 -15.8650144
56 -15.4366713 -8.5007724
57 -8.6973574 -15.4366713
58 -22.1385387 -8.6973574
59 -12.2617107 -22.1385387
> 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/7x4rl1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/899mu1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97uxk1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10osob1259349814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z7bu1259349814.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/12f1ik1259349814.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/13hoxh1259349814.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/14pjom1259349814.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/15ge7q1259349814.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/16shj21259349814.tab")
+ }
>
> system("convert tmp/1typ41259349814.ps tmp/1typ41259349814.png")
> system("convert tmp/2ivlg1259349814.ps tmp/2ivlg1259349814.png")
> system("convert tmp/33wka1259349814.ps tmp/33wka1259349814.png")
> system("convert tmp/4wrls1259349814.ps tmp/4wrls1259349814.png")
> system("convert tmp/5n00z1259349814.ps tmp/5n00z1259349814.png")
> system("convert tmp/6id2n1259349814.ps tmp/6id2n1259349814.png")
> system("convert tmp/7x4rl1259349814.ps tmp/7x4rl1259349814.png")
> system("convert tmp/899mu1259349814.ps tmp/899mu1259349814.png")
> system("convert tmp/97uxk1259349814.ps tmp/97uxk1259349814.png")
> system("convert tmp/10osob1259349814.ps tmp/10osob1259349814.png")
>
>
> proc.time()
user system elapsed
2.387 1.567 3.278