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(115.6,0,111.3,0,114.6,0,137.5,0,83.7,0,106.0,0,123.4,0,126.5,0,120.0,0,141.6,0,90.5,0,96.5,0,113.5,0,120.1,0,123.9,0,144.4,0,90.8,0,114.2,0,138.1,0,135.0,0,131.3,0,144.6,0,101.7,0,108.7,0,135.3,0,124.3,0,138.3,0,158.2,0,93.5,0,124.8,0,154.4,0,152.8,0,148.9,0,170.3,0,124.8,0,134.4,0,154.0,0,147.9,0,168.1,0,175.7,0,116.7,0,140.8,0,164.2,0,173.8,0,167.8,0,166.6,0,135.1,1,158.1,1,151.8,1,166.7,1,165.3,1,187.0,1,125.2,1,144.4,1,181.7,1,175.9,1,166.3,1,181.5,1,121.8,1,134.8,1,162.9,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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
1 115.6 0 1 0 0 0 0 0 0 0 0 0 0 1
2 111.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 114.6 0 0 0 1 0 0 0 0 0 0 0 0 3
4 137.5 0 0 0 0 1 0 0 0 0 0 0 0 4
5 83.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 106.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 123.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 126.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 120.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 141.6 0 0 0 0 0 0 0 0 0 0 1 0 10
11 90.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 96.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 113.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 120.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 123.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 144.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 90.8 0 0 0 0 0 1 0 0 0 0 0 0 17
18 114.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 138.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 135.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 131.3 0 0 0 0 0 0 0 0 0 1 0 0 21
22 144.6 0 0 0 0 0 0 0 0 0 0 1 0 22
23 101.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 108.7 0 0 0 0 0 0 0 0 0 0 0 0 24
25 135.3 0 1 0 0 0 0 0 0 0 0 0 0 25
26 124.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 138.3 0 0 0 1 0 0 0 0 0 0 0 0 27
28 158.2 0 0 0 0 1 0 0 0 0 0 0 0 28
29 93.5 0 0 0 0 0 1 0 0 0 0 0 0 29
30 124.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 154.4 0 0 0 0 0 0 0 1 0 0 0 0 31
32 152.8 0 0 0 0 0 0 0 0 1 0 0 0 32
33 148.9 0 0 0 0 0 0 0 0 0 1 0 0 33
34 170.3 0 0 0 0 0 0 0 0 0 0 1 0 34
35 124.8 0 0 0 0 0 0 0 0 0 0 0 1 35
36 134.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 154.0 0 1 0 0 0 0 0 0 0 0 0 0 37
38 147.9 0 0 1 0 0 0 0 0 0 0 0 0 38
39 168.1 0 0 0 1 0 0 0 0 0 0 0 0 39
40 175.7 0 0 0 0 1 0 0 0 0 0 0 0 40
41 116.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 140.8 0 0 0 0 0 0 1 0 0 0 0 0 42
43 164.2 0 0 0 0 0 0 0 1 0 0 0 0 43
44 173.8 0 0 0 0 0 0 0 0 1 0 0 0 44
45 167.8 0 0 0 0 0 0 0 0 0 1 0 0 45
46 166.6 0 0 0 0 0 0 0 0 0 0 1 0 46
47 135.1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 158.1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 151.8 1 1 0 0 0 0 0 0 0 0 0 0 49
50 166.7 1 0 1 0 0 0 0 0 0 0 0 0 50
51 165.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 187.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 125.2 1 0 0 0 0 1 0 0 0 0 0 0 53
54 144.4 1 0 0 0 0 0 1 0 0 0 0 0 54
55 181.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 175.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 166.3 1 0 0 0 0 0 0 0 0 1 0 0 57
58 181.5 1 0 0 0 0 0 0 0 0 0 1 0 58
59 121.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 134.8 1 0 0 0 0 0 0 0 0 0 0 0 60
61 162.9 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
89.135 -3.905 17.496 17.592 24.491 41.929
M5 M6 M7 M8 M9 M10
-17.732 5.247 30.485 29.844 22.823 35.802
M11 t
-10.639 1.081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.58814 -3.90991 0.08319 3.61296 20.96758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.13505 3.91895 22.745 < 2e-16 ***
X -3.90549 3.35854 -1.163 0.250759
M1 17.49618 4.45735 3.925 0.000282 ***
M2 17.59200 4.67799 3.761 0.000469 ***
M3 24.49069 4.67317 5.241 3.71e-06 ***
M4 41.92938 4.66979 8.979 9.19e-12 ***
M5 -17.73193 4.66784 -3.799 0.000417 ***
M6 5.24676 4.66733 1.124 0.266660
M7 30.48545 4.66826 6.530 4.22e-08 ***
M8 29.84414 4.67063 6.390 6.90e-08 ***
M9 22.82283 4.67443 4.882 1.25e-05 ***
M10 35.80152 4.67967 7.650 8.51e-10 ***
M11 -10.63869 4.64684 -2.289 0.026590 *
t 1.08131 0.08196 13.193 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.346 on 47 degrees of freedom
Multiple R-squared: 0.9362, Adjusted R-squared: 0.9185
F-statistic: 53.04 on 13 and 47 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,] 0.1080333452 0.216066690 0.8919667
[2,] 0.0415320794 0.083064159 0.9584679
[3,] 0.0406147034 0.081229407 0.9593853
[4,] 0.0160532538 0.032106508 0.9839467
[5,] 0.0071637913 0.014327583 0.9928362
[6,] 0.0038794074 0.007758815 0.9961206
[7,] 0.0016678804 0.003335761 0.9983321
[8,] 0.0009850352 0.001970070 0.9990150
[9,] 0.0012665690 0.002533138 0.9987334
[10,] 0.0014105182 0.002821036 0.9985895
[11,] 0.0015809567 0.003161913 0.9984190
[12,] 0.0010161590 0.002032318 0.9989838
[13,] 0.0026476998 0.005295400 0.9973523
[14,] 0.0015338502 0.003067700 0.9984661
[15,] 0.0040364514 0.008072903 0.9959635
[16,] 0.0114573151 0.022914630 0.9885427
[17,] 0.0391291026 0.078258205 0.9608709
[18,] 0.0616888056 0.123377611 0.9383112
[19,] 0.0777238753 0.155447751 0.9222761
[20,] 0.1308935386 0.261787077 0.8691065
[21,] 0.1037624201 0.207524840 0.8962376
[22,] 0.1021934681 0.204386936 0.8978065
[23,] 0.1826544110 0.365308822 0.8173456
[24,] 0.1212498025 0.242499605 0.8787502
[25,] 0.0708575389 0.141715078 0.9291425
[26,] 0.0401345948 0.080269190 0.9598654
[27,] 0.0321948031 0.064389606 0.9678052
[28,] 0.0178383598 0.035676720 0.9821616
> postscript(file="/var/www/html/rcomp/tmp/1xjr01258578617.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2te1g1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jm281258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43ynv1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5r31u1258578617.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 = 61
Frequency = 1
1 2 3 4 5 6
7.8874590 2.4103333 -2.2696667 2.1103333 6.8903333 5.1303333
7 8 9 10 11 12
-3.7896667 -1.1296667 -1.6896667 5.8503333 0.1092350 -5.6107650
13 14 15 16 17 18
-7.1882568 -1.7653825 -5.9453825 -3.9653825 1.0146175 0.3546175
19 20 21 22 23 24
-2.0653825 -5.6053825 -3.3653825 -4.1253825 -1.6664809 -6.3864809
25 26 27 28 29 30
1.6360273 -10.5410984 -4.5210984 -3.1410984 -9.2610984 -2.0210984
31 32 33 34 35 36
1.2589016 -0.7810984 1.2589016 8.5989016 8.4578033 6.3378033
37 38 39 40 41 42
7.3603115 0.0831858 12.3031858 1.3831858 0.9631858 1.0031858
43 44 45 46 47 48
-1.9168142 7.2431858 7.1831858 -8.0768142 9.6875792 20.9675792
49 50 51 52 53 54
-3.9099126 9.8129617 0.4329617 3.6129617 0.3929617 -4.4670383
55 56 57 58 59 60
6.5129617 0.2729617 -3.3870383 -2.2470383 -16.5881366 -15.3081366
61
-5.7856284
> postscript(file="/var/www/html/rcomp/tmp/6a3oe1258578617.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 7.8874590 NA
1 2.4103333 7.8874590
2 -2.2696667 2.4103333
3 2.1103333 -2.2696667
4 6.8903333 2.1103333
5 5.1303333 6.8903333
6 -3.7896667 5.1303333
7 -1.1296667 -3.7896667
8 -1.6896667 -1.1296667
9 5.8503333 -1.6896667
10 0.1092350 5.8503333
11 -5.6107650 0.1092350
12 -7.1882568 -5.6107650
13 -1.7653825 -7.1882568
14 -5.9453825 -1.7653825
15 -3.9653825 -5.9453825
16 1.0146175 -3.9653825
17 0.3546175 1.0146175
18 -2.0653825 0.3546175
19 -5.6053825 -2.0653825
20 -3.3653825 -5.6053825
21 -4.1253825 -3.3653825
22 -1.6664809 -4.1253825
23 -6.3864809 -1.6664809
24 1.6360273 -6.3864809
25 -10.5410984 1.6360273
26 -4.5210984 -10.5410984
27 -3.1410984 -4.5210984
28 -9.2610984 -3.1410984
29 -2.0210984 -9.2610984
30 1.2589016 -2.0210984
31 -0.7810984 1.2589016
32 1.2589016 -0.7810984
33 8.5989016 1.2589016
34 8.4578033 8.5989016
35 6.3378033 8.4578033
36 7.3603115 6.3378033
37 0.0831858 7.3603115
38 12.3031858 0.0831858
39 1.3831858 12.3031858
40 0.9631858 1.3831858
41 1.0031858 0.9631858
42 -1.9168142 1.0031858
43 7.2431858 -1.9168142
44 7.1831858 7.2431858
45 -8.0768142 7.1831858
46 9.6875792 -8.0768142
47 20.9675792 9.6875792
48 -3.9099126 20.9675792
49 9.8129617 -3.9099126
50 0.4329617 9.8129617
51 3.6129617 0.4329617
52 0.3929617 3.6129617
53 -4.4670383 0.3929617
54 6.5129617 -4.4670383
55 0.2729617 6.5129617
56 -3.3870383 0.2729617
57 -2.2470383 -3.3870383
58 -16.5881366 -2.2470383
59 -15.3081366 -16.5881366
60 -5.7856284 -15.3081366
61 NA -5.7856284
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.4103333 7.8874590
[2,] -2.2696667 2.4103333
[3,] 2.1103333 -2.2696667
[4,] 6.8903333 2.1103333
[5,] 5.1303333 6.8903333
[6,] -3.7896667 5.1303333
[7,] -1.1296667 -3.7896667
[8,] -1.6896667 -1.1296667
[9,] 5.8503333 -1.6896667
[10,] 0.1092350 5.8503333
[11,] -5.6107650 0.1092350
[12,] -7.1882568 -5.6107650
[13,] -1.7653825 -7.1882568
[14,] -5.9453825 -1.7653825
[15,] -3.9653825 -5.9453825
[16,] 1.0146175 -3.9653825
[17,] 0.3546175 1.0146175
[18,] -2.0653825 0.3546175
[19,] -5.6053825 -2.0653825
[20,] -3.3653825 -5.6053825
[21,] -4.1253825 -3.3653825
[22,] -1.6664809 -4.1253825
[23,] -6.3864809 -1.6664809
[24,] 1.6360273 -6.3864809
[25,] -10.5410984 1.6360273
[26,] -4.5210984 -10.5410984
[27,] -3.1410984 -4.5210984
[28,] -9.2610984 -3.1410984
[29,] -2.0210984 -9.2610984
[30,] 1.2589016 -2.0210984
[31,] -0.7810984 1.2589016
[32,] 1.2589016 -0.7810984
[33,] 8.5989016 1.2589016
[34,] 8.4578033 8.5989016
[35,] 6.3378033 8.4578033
[36,] 7.3603115 6.3378033
[37,] 0.0831858 7.3603115
[38,] 12.3031858 0.0831858
[39,] 1.3831858 12.3031858
[40,] 0.9631858 1.3831858
[41,] 1.0031858 0.9631858
[42,] -1.9168142 1.0031858
[43,] 7.2431858 -1.9168142
[44,] 7.1831858 7.2431858
[45,] -8.0768142 7.1831858
[46,] 9.6875792 -8.0768142
[47,] 20.9675792 9.6875792
[48,] -3.9099126 20.9675792
[49,] 9.8129617 -3.9099126
[50,] 0.4329617 9.8129617
[51,] 3.6129617 0.4329617
[52,] 0.3929617 3.6129617
[53,] -4.4670383 0.3929617
[54,] 6.5129617 -4.4670383
[55,] 0.2729617 6.5129617
[56,] -3.3870383 0.2729617
[57,] -2.2470383 -3.3870383
[58,] -16.5881366 -2.2470383
[59,] -15.3081366 -16.5881366
[60,] -5.7856284 -15.3081366
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.4103333 7.8874590
2 -2.2696667 2.4103333
3 2.1103333 -2.2696667
4 6.8903333 2.1103333
5 5.1303333 6.8903333
6 -3.7896667 5.1303333
7 -1.1296667 -3.7896667
8 -1.6896667 -1.1296667
9 5.8503333 -1.6896667
10 0.1092350 5.8503333
11 -5.6107650 0.1092350
12 -7.1882568 -5.6107650
13 -1.7653825 -7.1882568
14 -5.9453825 -1.7653825
15 -3.9653825 -5.9453825
16 1.0146175 -3.9653825
17 0.3546175 1.0146175
18 -2.0653825 0.3546175
19 -5.6053825 -2.0653825
20 -3.3653825 -5.6053825
21 -4.1253825 -3.3653825
22 -1.6664809 -4.1253825
23 -6.3864809 -1.6664809
24 1.6360273 -6.3864809
25 -10.5410984 1.6360273
26 -4.5210984 -10.5410984
27 -3.1410984 -4.5210984
28 -9.2610984 -3.1410984
29 -2.0210984 -9.2610984
30 1.2589016 -2.0210984
31 -0.7810984 1.2589016
32 1.2589016 -0.7810984
33 8.5989016 1.2589016
34 8.4578033 8.5989016
35 6.3378033 8.4578033
36 7.3603115 6.3378033
37 0.0831858 7.3603115
38 12.3031858 0.0831858
39 1.3831858 12.3031858
40 0.9631858 1.3831858
41 1.0031858 0.9631858
42 -1.9168142 1.0031858
43 7.2431858 -1.9168142
44 7.1831858 7.2431858
45 -8.0768142 7.1831858
46 9.6875792 -8.0768142
47 20.9675792 9.6875792
48 -3.9099126 20.9675792
49 9.8129617 -3.9099126
50 0.4329617 9.8129617
51 3.6129617 0.4329617
52 0.3929617 3.6129617
53 -4.4670383 0.3929617
54 6.5129617 -4.4670383
55 0.2729617 6.5129617
56 -3.3870383 0.2729617
57 -2.2470383 -3.3870383
58 -16.5881366 -2.2470383
59 -15.3081366 -16.5881366
60 -5.7856284 -15.3081366
> 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/77c5x1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8u7ei1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v67d1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104apj1258578617.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1114oy1258578617.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/128vg41258578617.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/13p1er1258578617.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/14fxo21258578617.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/15svwv1258578617.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/165ni51258578617.tab")
+ }
> system("convert tmp/1xjr01258578617.ps tmp/1xjr01258578617.png")
> system("convert tmp/2te1g1258578617.ps tmp/2te1g1258578617.png")
> system("convert tmp/3jm281258578617.ps tmp/3jm281258578617.png")
> system("convert tmp/43ynv1258578617.ps tmp/43ynv1258578617.png")
> system("convert tmp/5r31u1258578617.ps tmp/5r31u1258578617.png")
> system("convert tmp/6a3oe1258578617.ps tmp/6a3oe1258578617.png")
> system("convert tmp/77c5x1258578617.ps tmp/77c5x1258578617.png")
> system("convert tmp/8u7ei1258578617.ps tmp/8u7ei1258578617.png")
> system("convert tmp/9v67d1258578617.ps tmp/9v67d1258578617.png")
> system("convert tmp/104apj1258578617.ps tmp/104apj1258578617.png")
>
>
> proc.time()
user system elapsed
2.398 1.550 3.449