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(21,2472.81,19,2407.6,25,2454.62,21,2448.05,23,2497.84,23,2645.64,19,2756.76,18,2849.27,19,2921.44,19,2981.85,22,3080.58,23,3106.22,20,3119.31,14,3061.26,14,3097.31,14,3161.69,15,3257.16,11,3277.01,17,3295.32,16,3363.99,20,3494.17,24,3667.03,23,3813.06,20,3917.96,21,3895.51,19,3801.06,23,3570.12,23,3701.61,23,3862.27,23,3970.1,27,4138.52,26,4199.75,17,4290.89,24,4443.91,26,4502.64,24,4356.98,27,4591.27,27,4696.96,26,4621.4,24,4562.84,23,4202.52,23,4296.49,24,4435.23,17,4105.18,21,4116.68,19,3844.49,22,3720.98,22,3674.4,18,3857.62,16,3801.06,14,3504.37,12,3032.6,14,3047.03,16,2962.34,8,2197.82,3,2014.45,0,1862.83,5,1905.41,1,1810.99,1,1670.07,3,1864.44),dim=c(2,61),dimnames=list(c('Consvertr','Aand'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Consvertr','Aand'),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
Consvertr Aand M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 21 2472.81 1 0 0 0 0 0 0 0 0 0 0 1
2 19 2407.60 0 1 0 0 0 0 0 0 0 0 0 2
3 25 2454.62 0 0 1 0 0 0 0 0 0 0 0 3
4 21 2448.05 0 0 0 1 0 0 0 0 0 0 0 4
5 23 2497.84 0 0 0 0 1 0 0 0 0 0 0 5
6 23 2645.64 0 0 0 0 0 1 0 0 0 0 0 6
7 19 2756.76 0 0 0 0 0 0 1 0 0 0 0 7
8 18 2849.27 0 0 0 0 0 0 0 1 0 0 0 8
9 19 2921.44 0 0 0 0 0 0 0 0 1 0 0 9
10 19 2981.85 0 0 0 0 0 0 0 0 0 1 0 10
11 22 3080.58 0 0 0 0 0 0 0 0 0 0 1 11
12 23 3106.22 0 0 0 0 0 0 0 0 0 0 0 12
13 20 3119.31 1 0 0 0 0 0 0 0 0 0 0 13
14 14 3061.26 0 1 0 0 0 0 0 0 0 0 0 14
15 14 3097.31 0 0 1 0 0 0 0 0 0 0 0 15
16 14 3161.69 0 0 0 1 0 0 0 0 0 0 0 16
17 15 3257.16 0 0 0 0 1 0 0 0 0 0 0 17
18 11 3277.01 0 0 0 0 0 1 0 0 0 0 0 18
19 17 3295.32 0 0 0 0 0 0 1 0 0 0 0 19
20 16 3363.99 0 0 0 0 0 0 0 1 0 0 0 20
21 20 3494.17 0 0 0 0 0 0 0 0 1 0 0 21
22 24 3667.03 0 0 0 0 0 0 0 0 0 1 0 22
23 23 3813.06 0 0 0 0 0 0 0 0 0 0 1 23
24 20 3917.96 0 0 0 0 0 0 0 0 0 0 0 24
25 21 3895.51 1 0 0 0 0 0 0 0 0 0 0 25
26 19 3801.06 0 1 0 0 0 0 0 0 0 0 0 26
27 23 3570.12 0 0 1 0 0 0 0 0 0 0 0 27
28 23 3701.61 0 0 0 1 0 0 0 0 0 0 0 28
29 23 3862.27 0 0 0 0 1 0 0 0 0 0 0 29
30 23 3970.10 0 0 0 0 0 1 0 0 0 0 0 30
31 27 4138.52 0 0 0 0 0 0 1 0 0 0 0 31
32 26 4199.75 0 0 0 0 0 0 0 1 0 0 0 32
33 17 4290.89 0 0 0 0 0 0 0 0 1 0 0 33
34 24 4443.91 0 0 0 0 0 0 0 0 0 1 0 34
35 26 4502.64 0 0 0 0 0 0 0 0 0 0 1 35
36 24 4356.98 0 0 0 0 0 0 0 0 0 0 0 36
37 27 4591.27 1 0 0 0 0 0 0 0 0 0 0 37
38 27 4696.96 0 1 0 0 0 0 0 0 0 0 0 38
39 26 4621.40 0 0 1 0 0 0 0 0 0 0 0 39
40 24 4562.84 0 0 0 1 0 0 0 0 0 0 0 40
41 23 4202.52 0 0 0 0 1 0 0 0 0 0 0 41
42 23 4296.49 0 0 0 0 0 1 0 0 0 0 0 42
43 24 4435.23 0 0 0 0 0 0 1 0 0 0 0 43
44 17 4105.18 0 0 0 0 0 0 0 1 0 0 0 44
45 21 4116.68 0 0 0 0 0 0 0 0 1 0 0 45
46 19 3844.49 0 0 0 0 0 0 0 0 0 1 0 46
47 22 3720.98 0 0 0 0 0 0 0 0 0 0 1 47
48 22 3674.40 0 0 0 0 0 0 0 0 0 0 0 48
49 18 3857.62 1 0 0 0 0 0 0 0 0 0 0 49
50 16 3801.06 0 1 0 0 0 0 0 0 0 0 0 50
51 14 3504.37 0 0 1 0 0 0 0 0 0 0 0 51
52 12 3032.60 0 0 0 1 0 0 0 0 0 0 0 52
53 14 3047.03 0 0 0 0 1 0 0 0 0 0 0 53
54 16 2962.34 0 0 0 0 0 1 0 0 0 0 0 54
55 8 2197.82 0 0 0 0 0 0 1 0 0 0 0 55
56 3 2014.45 0 0 0 0 0 0 0 1 0 0 0 56
57 0 1862.83 0 0 0 0 0 0 0 0 1 0 0 57
58 5 1905.41 0 0 0 0 0 0 0 0 0 1 0 58
59 1 1810.99 0 0 0 0 0 0 0 0 0 0 1 59
60 1 1670.07 0 0 0 0 0 0 0 0 0 0 0 60
61 3 1864.44 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) Aand M1 M2 M3 M4
3.556717 0.006381 -0.338286 -2.247249 0.008222 -0.964857
M5 M6 M7 M8 M9 M10
0.077860 -0.493835 -0.083631 -2.520544 -3.124563 -0.332805
M11 t
0.349714 -0.191708
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.5223 -1.3912 0.2804 1.8307 6.3476
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.5567173 2.4708073 1.439 0.157
Aand 0.0063809 0.0005358 11.910 8.49e-16 ***
M1 -0.3382861 2.0456233 -0.165 0.869
M2 -2.2472489 2.1507266 -1.045 0.301
M3 0.0082218 2.1453916 0.004 0.997
M4 -0.9648571 2.1420509 -0.450 0.654
M5 0.0778597 2.1398064 0.036 0.971
M6 -0.4938347 2.1384652 -0.231 0.818
M7 -0.0836315 2.1362553 -0.039 0.969
M8 -2.5205445 2.1349289 -1.181 0.244
M9 -3.1245627 2.1338614 -1.464 0.150
M10 -0.3328051 2.1331916 -0.156 0.877
M11 0.3497138 2.1328260 0.164 0.870
t -0.1917082 0.0249267 -7.691 7.40e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.372 on 47 degrees of freedom
Multiple R-squared: 0.8103, Adjusted R-squared: 0.7578
F-statistic: 15.44 on 13 and 47 DF, p-value: 8.78e-13
> 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,] 0.6477555 0.704488952 0.352244476
[2,] 0.8307956 0.338408842 0.169204421
[3,] 0.8269862 0.346027639 0.173013820
[4,] 0.7568372 0.486325536 0.243162768
[5,] 0.7994725 0.401055046 0.200527523
[6,] 0.9721997 0.055600579 0.027800289
[7,] 0.9735848 0.052830470 0.026415235
[8,] 0.9699101 0.060179768 0.030089884
[9,] 0.9741943 0.051611343 0.025805671
[10,] 0.9805711 0.038857721 0.019428861
[11,] 0.9837208 0.032558413 0.016279206
[12,] 0.9869517 0.026096692 0.013048346
[13,] 0.9823559 0.035288204 0.017644102
[14,] 0.9813675 0.037265021 0.018632510
[15,] 0.9875905 0.024818976 0.012409488
[16,] 0.9970185 0.005963066 0.002981533
[17,] 0.9974550 0.005090069 0.002545034
[18,] 0.9948399 0.010320236 0.005160118
[19,] 0.9892038 0.021592425 0.010796213
[20,] 0.9837561 0.032487757 0.016243879
[21,] 0.9706789 0.058642191 0.029321096
[22,] 0.9627634 0.074473276 0.037236638
[23,] 0.9507985 0.098402931 0.049201465
[24,] 0.9057824 0.188435253 0.094217626
[25,] 0.8447764 0.310447281 0.155223641
[26,] 0.8130876 0.373824769 0.186912384
[27,] 0.7113926 0.577214865 0.288607432
[28,] 0.6860739 0.627852109 0.313926055
> postscript(file="/var/www/html/rcomp/tmp/1kft21258617496.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vkns1258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3uap71258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n1gy1258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a51r1258617496.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
2.19462405 2.71139099 6.34760046 3.55430981 4.38559823 4.20590992
7 8 9 10 11 12
-0.72162623 0.31670169 1.65192155 -1.33359556 0.54561146 1.92342827
13 14 15 16 17 18
-0.63010290 -4.15902291 -6.45281541 -5.69882804 -6.15901728 -9.52227463
19 20 21 22 23 24
-3.85760321 -2.66715561 1.29791060 1.59486585 -0.82774178 -3.95567188
25 26 27 28 29 30
-2.28242732 -1.57908404 1.83074911 2.15651701 0.28035955 0.35571418
31 32 33 34 35 36
3.06255480 4.30047599 -4.48534905 -1.06179755 0.07264385 -0.45649812
37 38 39 40 41 42
1.57852460 3.00480267 0.42317789 -0.03837188 0.40977080 0.57356414
43 44 45 46 47 48
0.46978866 -1.79558747 2.92675913 0.06351586 3.36080508 4.19944756
49 50 51 52 53 54
-0.43965923 0.02191328 -2.14871206 0.02637309 1.08328870 4.38708639
55 56 57 58 59 60
1.04688598 -0.15443461 -1.39124222 0.73701140 -3.15131859 -1.71070583
61
-0.42095920
> postscript(file="/var/www/html/rcomp/tmp/6yxoy1258617496.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 2.19462405 NA
1 2.71139099 2.19462405
2 6.34760046 2.71139099
3 3.55430981 6.34760046
4 4.38559823 3.55430981
5 4.20590992 4.38559823
6 -0.72162623 4.20590992
7 0.31670169 -0.72162623
8 1.65192155 0.31670169
9 -1.33359556 1.65192155
10 0.54561146 -1.33359556
11 1.92342827 0.54561146
12 -0.63010290 1.92342827
13 -4.15902291 -0.63010290
14 -6.45281541 -4.15902291
15 -5.69882804 -6.45281541
16 -6.15901728 -5.69882804
17 -9.52227463 -6.15901728
18 -3.85760321 -9.52227463
19 -2.66715561 -3.85760321
20 1.29791060 -2.66715561
21 1.59486585 1.29791060
22 -0.82774178 1.59486585
23 -3.95567188 -0.82774178
24 -2.28242732 -3.95567188
25 -1.57908404 -2.28242732
26 1.83074911 -1.57908404
27 2.15651701 1.83074911
28 0.28035955 2.15651701
29 0.35571418 0.28035955
30 3.06255480 0.35571418
31 4.30047599 3.06255480
32 -4.48534905 4.30047599
33 -1.06179755 -4.48534905
34 0.07264385 -1.06179755
35 -0.45649812 0.07264385
36 1.57852460 -0.45649812
37 3.00480267 1.57852460
38 0.42317789 3.00480267
39 -0.03837188 0.42317789
40 0.40977080 -0.03837188
41 0.57356414 0.40977080
42 0.46978866 0.57356414
43 -1.79558747 0.46978866
44 2.92675913 -1.79558747
45 0.06351586 2.92675913
46 3.36080508 0.06351586
47 4.19944756 3.36080508
48 -0.43965923 4.19944756
49 0.02191328 -0.43965923
50 -2.14871206 0.02191328
51 0.02637309 -2.14871206
52 1.08328870 0.02637309
53 4.38708639 1.08328870
54 1.04688598 4.38708639
55 -0.15443461 1.04688598
56 -1.39124222 -0.15443461
57 0.73701140 -1.39124222
58 -3.15131859 0.73701140
59 -1.71070583 -3.15131859
60 -0.42095920 -1.71070583
61 NA -0.42095920
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.71139099 2.19462405
[2,] 6.34760046 2.71139099
[3,] 3.55430981 6.34760046
[4,] 4.38559823 3.55430981
[5,] 4.20590992 4.38559823
[6,] -0.72162623 4.20590992
[7,] 0.31670169 -0.72162623
[8,] 1.65192155 0.31670169
[9,] -1.33359556 1.65192155
[10,] 0.54561146 -1.33359556
[11,] 1.92342827 0.54561146
[12,] -0.63010290 1.92342827
[13,] -4.15902291 -0.63010290
[14,] -6.45281541 -4.15902291
[15,] -5.69882804 -6.45281541
[16,] -6.15901728 -5.69882804
[17,] -9.52227463 -6.15901728
[18,] -3.85760321 -9.52227463
[19,] -2.66715561 -3.85760321
[20,] 1.29791060 -2.66715561
[21,] 1.59486585 1.29791060
[22,] -0.82774178 1.59486585
[23,] -3.95567188 -0.82774178
[24,] -2.28242732 -3.95567188
[25,] -1.57908404 -2.28242732
[26,] 1.83074911 -1.57908404
[27,] 2.15651701 1.83074911
[28,] 0.28035955 2.15651701
[29,] 0.35571418 0.28035955
[30,] 3.06255480 0.35571418
[31,] 4.30047599 3.06255480
[32,] -4.48534905 4.30047599
[33,] -1.06179755 -4.48534905
[34,] 0.07264385 -1.06179755
[35,] -0.45649812 0.07264385
[36,] 1.57852460 -0.45649812
[37,] 3.00480267 1.57852460
[38,] 0.42317789 3.00480267
[39,] -0.03837188 0.42317789
[40,] 0.40977080 -0.03837188
[41,] 0.57356414 0.40977080
[42,] 0.46978866 0.57356414
[43,] -1.79558747 0.46978866
[44,] 2.92675913 -1.79558747
[45,] 0.06351586 2.92675913
[46,] 3.36080508 0.06351586
[47,] 4.19944756 3.36080508
[48,] -0.43965923 4.19944756
[49,] 0.02191328 -0.43965923
[50,] -2.14871206 0.02191328
[51,] 0.02637309 -2.14871206
[52,] 1.08328870 0.02637309
[53,] 4.38708639 1.08328870
[54,] 1.04688598 4.38708639
[55,] -0.15443461 1.04688598
[56,] -1.39124222 -0.15443461
[57,] 0.73701140 -1.39124222
[58,] -3.15131859 0.73701140
[59,] -1.71070583 -3.15131859
[60,] -0.42095920 -1.71070583
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.71139099 2.19462405
2 6.34760046 2.71139099
3 3.55430981 6.34760046
4 4.38559823 3.55430981
5 4.20590992 4.38559823
6 -0.72162623 4.20590992
7 0.31670169 -0.72162623
8 1.65192155 0.31670169
9 -1.33359556 1.65192155
10 0.54561146 -1.33359556
11 1.92342827 0.54561146
12 -0.63010290 1.92342827
13 -4.15902291 -0.63010290
14 -6.45281541 -4.15902291
15 -5.69882804 -6.45281541
16 -6.15901728 -5.69882804
17 -9.52227463 -6.15901728
18 -3.85760321 -9.52227463
19 -2.66715561 -3.85760321
20 1.29791060 -2.66715561
21 1.59486585 1.29791060
22 -0.82774178 1.59486585
23 -3.95567188 -0.82774178
24 -2.28242732 -3.95567188
25 -1.57908404 -2.28242732
26 1.83074911 -1.57908404
27 2.15651701 1.83074911
28 0.28035955 2.15651701
29 0.35571418 0.28035955
30 3.06255480 0.35571418
31 4.30047599 3.06255480
32 -4.48534905 4.30047599
33 -1.06179755 -4.48534905
34 0.07264385 -1.06179755
35 -0.45649812 0.07264385
36 1.57852460 -0.45649812
37 3.00480267 1.57852460
38 0.42317789 3.00480267
39 -0.03837188 0.42317789
40 0.40977080 -0.03837188
41 0.57356414 0.40977080
42 0.46978866 0.57356414
43 -1.79558747 0.46978866
44 2.92675913 -1.79558747
45 0.06351586 2.92675913
46 3.36080508 0.06351586
47 4.19944756 3.36080508
48 -0.43965923 4.19944756
49 0.02191328 -0.43965923
50 -2.14871206 0.02191328
51 0.02637309 -2.14871206
52 1.08328870 0.02637309
53 4.38708639 1.08328870
54 1.04688598 4.38708639
55 -0.15443461 1.04688598
56 -1.39124222 -0.15443461
57 0.73701140 -1.39124222
58 -3.15131859 0.73701140
59 -1.71070583 -3.15131859
60 -0.42095920 -1.71070583
> 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/7at7q1258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81ory1258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wwg41258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1050i21258617496.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cuka1258617496.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/12a90i1258617496.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/13yb1j1258617496.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/149rd01258617496.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/15lcv71258617496.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/16bpoa1258617496.tab")
+ }
>
> system("convert tmp/1kft21258617496.ps tmp/1kft21258617496.png")
> system("convert tmp/2vkns1258617496.ps tmp/2vkns1258617496.png")
> system("convert tmp/3uap71258617496.ps tmp/3uap71258617496.png")
> system("convert tmp/4n1gy1258617496.ps tmp/4n1gy1258617496.png")
> system("convert tmp/5a51r1258617496.ps tmp/5a51r1258617496.png")
> system("convert tmp/6yxoy1258617496.ps tmp/6yxoy1258617496.png")
> system("convert tmp/7at7q1258617496.ps tmp/7at7q1258617496.png")
> system("convert tmp/81ory1258617496.ps tmp/81ory1258617496.png")
> system("convert tmp/9wwg41258617496.ps tmp/9wwg41258617496.png")
> system("convert tmp/1050i21258617496.ps tmp/1050i21258617496.png")
>
>
> proc.time()
user system elapsed
2.352 1.557 3.389