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(104.89,124,105.15,118.63,105.24,121.86,105.57,119.97,105.62,125.03,106.17,130.09,106.27,126.65,106.41,121.7,106.94,119.24,107.16,122.63,107.32,116.66,107.32,114.12,107.35,113.11,107.55,112.61,107.87,113.4,108.37,115.18,108.38,121.01,107.92,119.44,108.03,116.68,108.14,117.07,108.3,117.41,108.64,119.58,108.66,120.92,109.04,117.09,109.03,116.77,109.03,119.39,109.54,122.49,109.75,124.08,109.83,118.29,109.65,112.94,109.82,113.79,109.95,114.43,110.12,118.7,110.15,120.36,110.21,118.27,109.99,118.34,110.14,117.82,110.14,117.65,110.81,118.18,110.97,121.02,110.99,124.78,109.73,131.16,109.81,130.14,110.02,131.75,110.18,134.73,110.21,135.35,110.25,140.32,110.36,136.35,110.51,131.6,110.6,128.9,110.95,133.89,111.18,138.25,111.19,146.23,111.69,144.76,111.7,149.3,111.83,156.8,111.77,159.08,111.73,165.12,112.01,163.14,111.86,153.43,112.04,151.01),dim=c(2,61),dimnames=list(c('AKW','AKB'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('AKW','AKB'),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
AKW AKB M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 104.89 124.00 1 0 0 0 0 0 0 0 0 0 0 1
2 105.15 118.63 0 1 0 0 0 0 0 0 0 0 0 2
3 105.24 121.86 0 0 1 0 0 0 0 0 0 0 0 3
4 105.57 119.97 0 0 0 1 0 0 0 0 0 0 0 4
5 105.62 125.03 0 0 0 0 1 0 0 0 0 0 0 5
6 106.17 130.09 0 0 0 0 0 1 0 0 0 0 0 6
7 106.27 126.65 0 0 0 0 0 0 1 0 0 0 0 7
8 106.41 121.70 0 0 0 0 0 0 0 1 0 0 0 8
9 106.94 119.24 0 0 0 0 0 0 0 0 1 0 0 9
10 107.16 122.63 0 0 0 0 0 0 0 0 0 1 0 10
11 107.32 116.66 0 0 0 0 0 0 0 0 0 0 1 11
12 107.32 114.12 0 0 0 0 0 0 0 0 0 0 0 12
13 107.35 113.11 1 0 0 0 0 0 0 0 0 0 0 13
14 107.55 112.61 0 1 0 0 0 0 0 0 0 0 0 14
15 107.87 113.40 0 0 1 0 0 0 0 0 0 0 0 15
16 108.37 115.18 0 0 0 1 0 0 0 0 0 0 0 16
17 108.38 121.01 0 0 0 0 1 0 0 0 0 0 0 17
18 107.92 119.44 0 0 0 0 0 1 0 0 0 0 0 18
19 108.03 116.68 0 0 0 0 0 0 1 0 0 0 0 19
20 108.14 117.07 0 0 0 0 0 0 0 1 0 0 0 20
21 108.30 117.41 0 0 0 0 0 0 0 0 1 0 0 21
22 108.64 119.58 0 0 0 0 0 0 0 0 0 1 0 22
23 108.66 120.92 0 0 0 0 0 0 0 0 0 0 1 23
24 109.04 117.09 0 0 0 0 0 0 0 0 0 0 0 24
25 109.03 116.77 1 0 0 0 0 0 0 0 0 0 0 25
26 109.03 119.39 0 1 0 0 0 0 0 0 0 0 0 26
27 109.54 122.49 0 0 1 0 0 0 0 0 0 0 0 27
28 109.75 124.08 0 0 0 1 0 0 0 0 0 0 0 28
29 109.83 118.29 0 0 0 0 1 0 0 0 0 0 0 29
30 109.65 112.94 0 0 0 0 0 1 0 0 0 0 0 30
31 109.82 113.79 0 0 0 0 0 0 1 0 0 0 0 31
32 109.95 114.43 0 0 0 0 0 0 0 1 0 0 0 32
33 110.12 118.70 0 0 0 0 0 0 0 0 1 0 0 33
34 110.15 120.36 0 0 0 0 0 0 0 0 0 1 0 34
35 110.21 118.27 0 0 0 0 0 0 0 0 0 0 1 35
36 109.99 118.34 0 0 0 0 0 0 0 0 0 0 0 36
37 110.14 117.82 1 0 0 0 0 0 0 0 0 0 0 37
38 110.14 117.65 0 1 0 0 0 0 0 0 0 0 0 38
39 110.81 118.18 0 0 1 0 0 0 0 0 0 0 0 39
40 110.97 121.02 0 0 0 1 0 0 0 0 0 0 0 40
41 110.99 124.78 0 0 0 0 1 0 0 0 0 0 0 41
42 109.73 131.16 0 0 0 0 0 1 0 0 0 0 0 42
43 109.81 130.14 0 0 0 0 0 0 1 0 0 0 0 43
44 110.02 131.75 0 0 0 0 0 0 0 1 0 0 0 44
45 110.18 134.73 0 0 0 0 0 0 0 0 1 0 0 45
46 110.21 135.35 0 0 0 0 0 0 0 0 0 1 0 46
47 110.25 140.32 0 0 0 0 0 0 0 0 0 0 1 47
48 110.36 136.35 0 0 0 0 0 0 0 0 0 0 0 48
49 110.51 131.60 1 0 0 0 0 0 0 0 0 0 0 49
50 110.60 128.90 0 1 0 0 0 0 0 0 0 0 0 50
51 110.95 133.89 0 0 1 0 0 0 0 0 0 0 0 51
52 111.18 138.25 0 0 0 1 0 0 0 0 0 0 0 52
53 111.19 146.23 0 0 0 0 1 0 0 0 0 0 0 53
54 111.69 144.76 0 0 0 0 0 1 0 0 0 0 0 54
55 111.70 149.30 0 0 0 0 0 0 1 0 0 0 0 55
56 111.83 156.80 0 0 0 0 0 0 0 1 0 0 0 56
57 111.77 159.08 0 0 0 0 0 0 0 0 1 0 0 57
58 111.73 165.12 0 0 0 0 0 0 0 0 0 1 0 58
59 112.01 163.14 0 0 0 0 0 0 0 0 0 0 1 59
60 111.86 153.43 0 0 0 0 0 0 0 0 0 0 0 60
61 112.04 151.01 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) AKB M1 M2 M3 M4
110.05590 -0.03801 -0.17477 -0.28536 0.07322 0.29970
M5 M6 M7 M8 M9 M10
0.33621 0.06389 0.01847 0.07642 0.19924 0.29525
M11 t
0.25339 0.12551
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.76984 -0.36250 0.09342 0.27811 0.67799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.055899 0.745913 147.545 < 2e-16 ***
AKB -0.038009 0.006538 -5.813 5.15e-07 ***
M1 -0.174771 0.273445 -0.639 0.526
M2 -0.285363 0.287650 -0.992 0.326
M3 0.073219 0.286692 0.255 0.800
M4 0.299697 0.286290 1.047 0.301
M5 0.336207 0.286672 1.173 0.247
M6 0.063887 0.286451 0.223 0.824
M7 0.018470 0.285883 0.065 0.949
M8 0.076418 0.285887 0.267 0.790
M9 0.199243 0.286190 0.696 0.490
M10 0.295251 0.287662 1.026 0.310
M11 0.253391 0.286609 0.884 0.381
t 0.125505 0.004867 25.786 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4507 on 47 degrees of freedom
Multiple R-squared: 0.9576, Adjusted R-squared: 0.9459
F-statistic: 81.69 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,] 0.02707354 0.05414708 0.97292646
[2,] 0.10665877 0.21331754 0.89334123
[3,] 0.10146153 0.20292305 0.89853847
[4,] 0.28248881 0.56497763 0.71751119
[5,] 0.50911365 0.98177269 0.49088635
[6,] 0.48893537 0.97787074 0.51106463
[7,] 0.41540262 0.83080524 0.58459738
[8,] 0.31118424 0.62236848 0.68881576
[9,] 0.22893360 0.45786721 0.77106640
[10,] 0.16006727 0.32013454 0.83993273
[11,] 0.13090605 0.26181211 0.86909395
[12,] 0.09897910 0.19795820 0.90102090
[13,] 0.07121116 0.14242232 0.92878884
[14,] 0.06664768 0.13329536 0.93335232
[15,] 0.04711736 0.09423472 0.95288264
[16,] 0.03268339 0.06536678 0.96731661
[17,] 0.02509756 0.05019512 0.97490244
[18,] 0.03128580 0.06257159 0.96871420
[19,] 0.05273021 0.10546041 0.94726979
[20,] 0.07126537 0.14253074 0.92873463
[21,] 0.05641139 0.11282278 0.94358861
[22,] 0.04693778 0.09387556 0.95306222
[23,] 0.07658122 0.15316244 0.92341878
[24,] 0.23941780 0.47883560 0.76058220
[25,] 0.98665570 0.02668859 0.01334430
[26,] 0.98687586 0.02624829 0.01312414
[27,] 0.98864830 0.02270340 0.01135170
[28,] 0.98276149 0.03447702 0.01723851
> postscript(file="/var/www/html/rcomp/tmp/138mz1258915493.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jvsx1258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bq9o1258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47bw61258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z69v1258915493.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
-0.403481248 -0.362503711 -0.633821184 -0.727642899 -0.647331167 0.241810211
7 8 9 10 11 12
0.130969628 -0.100630050 0.087537296 0.214875005 0.064314431 0.095655969
13 14 15 16 17 18
0.136531602 0.302614422 0.168554261 0.384226670 0.453805563 0.080945293
19 20 21 22 23 24
0.005951032 -0.052678993 -0.128085611 0.072880754 0.060168150 0.422477693
25 26 27 28 29 30
0.449579742 0.534251572 0.677992891 0.596443533 0.294354380 0.057818962
31 32 33 34 35 36
0.180038269 0.150910568 0.234880492 0.106462115 0.003377618 -0.086076575
37 38 39 40 41 42
0.093423613 0.072049502 0.278106924 0.194069189 0.194968834 -0.675717515
43 44 45 46 47 48
-0.714575596 -0.626834278 -0.601896349 -0.769844396 -0.624583247 -0.537595005
49 50 51 52 53 54
-0.518874148 -0.546411784 -0.490832892 -0.447096493 -0.295797610 0.295143050
55 56 57 58 59 60
0.397616667 0.629232752 0.407564172 0.375626522 0.496723048 0.105537918
61
0.242820440
> postscript(file="/var/www/html/rcomp/tmp/6ak5l1258915493.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 -0.403481248 NA
1 -0.362503711 -0.403481248
2 -0.633821184 -0.362503711
3 -0.727642899 -0.633821184
4 -0.647331167 -0.727642899
5 0.241810211 -0.647331167
6 0.130969628 0.241810211
7 -0.100630050 0.130969628
8 0.087537296 -0.100630050
9 0.214875005 0.087537296
10 0.064314431 0.214875005
11 0.095655969 0.064314431
12 0.136531602 0.095655969
13 0.302614422 0.136531602
14 0.168554261 0.302614422
15 0.384226670 0.168554261
16 0.453805563 0.384226670
17 0.080945293 0.453805563
18 0.005951032 0.080945293
19 -0.052678993 0.005951032
20 -0.128085611 -0.052678993
21 0.072880754 -0.128085611
22 0.060168150 0.072880754
23 0.422477693 0.060168150
24 0.449579742 0.422477693
25 0.534251572 0.449579742
26 0.677992891 0.534251572
27 0.596443533 0.677992891
28 0.294354380 0.596443533
29 0.057818962 0.294354380
30 0.180038269 0.057818962
31 0.150910568 0.180038269
32 0.234880492 0.150910568
33 0.106462115 0.234880492
34 0.003377618 0.106462115
35 -0.086076575 0.003377618
36 0.093423613 -0.086076575
37 0.072049502 0.093423613
38 0.278106924 0.072049502
39 0.194069189 0.278106924
40 0.194968834 0.194069189
41 -0.675717515 0.194968834
42 -0.714575596 -0.675717515
43 -0.626834278 -0.714575596
44 -0.601896349 -0.626834278
45 -0.769844396 -0.601896349
46 -0.624583247 -0.769844396
47 -0.537595005 -0.624583247
48 -0.518874148 -0.537595005
49 -0.546411784 -0.518874148
50 -0.490832892 -0.546411784
51 -0.447096493 -0.490832892
52 -0.295797610 -0.447096493
53 0.295143050 -0.295797610
54 0.397616667 0.295143050
55 0.629232752 0.397616667
56 0.407564172 0.629232752
57 0.375626522 0.407564172
58 0.496723048 0.375626522
59 0.105537918 0.496723048
60 0.242820440 0.105537918
61 NA 0.242820440
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.362503711 -0.403481248
[2,] -0.633821184 -0.362503711
[3,] -0.727642899 -0.633821184
[4,] -0.647331167 -0.727642899
[5,] 0.241810211 -0.647331167
[6,] 0.130969628 0.241810211
[7,] -0.100630050 0.130969628
[8,] 0.087537296 -0.100630050
[9,] 0.214875005 0.087537296
[10,] 0.064314431 0.214875005
[11,] 0.095655969 0.064314431
[12,] 0.136531602 0.095655969
[13,] 0.302614422 0.136531602
[14,] 0.168554261 0.302614422
[15,] 0.384226670 0.168554261
[16,] 0.453805563 0.384226670
[17,] 0.080945293 0.453805563
[18,] 0.005951032 0.080945293
[19,] -0.052678993 0.005951032
[20,] -0.128085611 -0.052678993
[21,] 0.072880754 -0.128085611
[22,] 0.060168150 0.072880754
[23,] 0.422477693 0.060168150
[24,] 0.449579742 0.422477693
[25,] 0.534251572 0.449579742
[26,] 0.677992891 0.534251572
[27,] 0.596443533 0.677992891
[28,] 0.294354380 0.596443533
[29,] 0.057818962 0.294354380
[30,] 0.180038269 0.057818962
[31,] 0.150910568 0.180038269
[32,] 0.234880492 0.150910568
[33,] 0.106462115 0.234880492
[34,] 0.003377618 0.106462115
[35,] -0.086076575 0.003377618
[36,] 0.093423613 -0.086076575
[37,] 0.072049502 0.093423613
[38,] 0.278106924 0.072049502
[39,] 0.194069189 0.278106924
[40,] 0.194968834 0.194069189
[41,] -0.675717515 0.194968834
[42,] -0.714575596 -0.675717515
[43,] -0.626834278 -0.714575596
[44,] -0.601896349 -0.626834278
[45,] -0.769844396 -0.601896349
[46,] -0.624583247 -0.769844396
[47,] -0.537595005 -0.624583247
[48,] -0.518874148 -0.537595005
[49,] -0.546411784 -0.518874148
[50,] -0.490832892 -0.546411784
[51,] -0.447096493 -0.490832892
[52,] -0.295797610 -0.447096493
[53,] 0.295143050 -0.295797610
[54,] 0.397616667 0.295143050
[55,] 0.629232752 0.397616667
[56,] 0.407564172 0.629232752
[57,] 0.375626522 0.407564172
[58,] 0.496723048 0.375626522
[59,] 0.105537918 0.496723048
[60,] 0.242820440 0.105537918
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.362503711 -0.403481248
2 -0.633821184 -0.362503711
3 -0.727642899 -0.633821184
4 -0.647331167 -0.727642899
5 0.241810211 -0.647331167
6 0.130969628 0.241810211
7 -0.100630050 0.130969628
8 0.087537296 -0.100630050
9 0.214875005 0.087537296
10 0.064314431 0.214875005
11 0.095655969 0.064314431
12 0.136531602 0.095655969
13 0.302614422 0.136531602
14 0.168554261 0.302614422
15 0.384226670 0.168554261
16 0.453805563 0.384226670
17 0.080945293 0.453805563
18 0.005951032 0.080945293
19 -0.052678993 0.005951032
20 -0.128085611 -0.052678993
21 0.072880754 -0.128085611
22 0.060168150 0.072880754
23 0.422477693 0.060168150
24 0.449579742 0.422477693
25 0.534251572 0.449579742
26 0.677992891 0.534251572
27 0.596443533 0.677992891
28 0.294354380 0.596443533
29 0.057818962 0.294354380
30 0.180038269 0.057818962
31 0.150910568 0.180038269
32 0.234880492 0.150910568
33 0.106462115 0.234880492
34 0.003377618 0.106462115
35 -0.086076575 0.003377618
36 0.093423613 -0.086076575
37 0.072049502 0.093423613
38 0.278106924 0.072049502
39 0.194069189 0.278106924
40 0.194968834 0.194069189
41 -0.675717515 0.194968834
42 -0.714575596 -0.675717515
43 -0.626834278 -0.714575596
44 -0.601896349 -0.626834278
45 -0.769844396 -0.601896349
46 -0.624583247 -0.769844396
47 -0.537595005 -0.624583247
48 -0.518874148 -0.537595005
49 -0.546411784 -0.518874148
50 -0.490832892 -0.546411784
51 -0.447096493 -0.490832892
52 -0.295797610 -0.447096493
53 0.295143050 -0.295797610
54 0.397616667 0.295143050
55 0.629232752 0.397616667
56 0.407564172 0.629232752
57 0.375626522 0.407564172
58 0.496723048 0.375626522
59 0.105537918 0.496723048
60 0.242820440 0.105537918
> 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/7726t1258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jevc1258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/914lc1258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bg651258915493.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11r51w1258915493.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/125fao1258915493.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/13jj5r1258915493.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/146fft1258915493.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/155f171258915493.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/16fm6y1258915493.tab")
+ }
>
> system("convert tmp/138mz1258915493.ps tmp/138mz1258915493.png")
> system("convert tmp/2jvsx1258915493.ps tmp/2jvsx1258915493.png")
> system("convert tmp/3bq9o1258915493.ps tmp/3bq9o1258915493.png")
> system("convert tmp/47bw61258915493.ps tmp/47bw61258915493.png")
> system("convert tmp/5z69v1258915493.ps tmp/5z69v1258915493.png")
> system("convert tmp/6ak5l1258915493.ps tmp/6ak5l1258915493.png")
> system("convert tmp/7726t1258915493.ps tmp/7726t1258915493.png")
> system("convert tmp/8jevc1258915493.ps tmp/8jevc1258915493.png")
> system("convert tmp/914lc1258915493.ps tmp/914lc1258915493.png")
> system("convert tmp/10bg651258915493.ps tmp/10bg651258915493.png")
>
>
> proc.time()
user system elapsed
2.361 1.519 2.745