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(1.4,8.2,1.2,8.0,1.0,7.5,1.7,6.8,2.4,6.5,2.0,6.6,2.1,7.6,2.0,8.0,1.8,8.1,2.7,7.7,2.3,7.5,1.9,7.6,2.0,7.8,2.3,7.8,2.8,7.8,2.4,7.5,2.3,7.5,2.7,7.1,2.7,7.5,2.9,7.5,3.0,7.6,2.2,7.7,2.3,7.7,2.8,7.9,2.8,8.1,2.8,8.2,2.2,8.2,2.6,8.2,2.8,7.9,2.5,7.3,2.4,6.9,2.3,6.6,1.9,6.7,1.7,6.9,2.0,7.0,2.1,7.1,1.7,7.2,1.8,7.1,1.8,6.9,1.8,7.0,1.3,6.8,1.3,6.4,1.3,6.7,1.2,6.6,1.4,6.4,2.2,6.3,2.9,6.2,3.1,6.5,3.5,6.8,3.6,6.8,4.4,6.4,4.1,6.1,5.1,5.8,5.8,6.1,5.9,7.2,5.4,7.3,5.5,6.9,4.8,6.1,3.2,5.8,2.7,6.2,2.1,7.1,1.9,7.7,0.6,7.9,0.7,7.7),dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> y <- array(NA,dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> 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 1.4 8.2 1 0 0 0 0 0 0 0 0 0 0 1
2 1.2 8.0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 1.7 6.8 0 0 0 1 0 0 0 0 0 0 0 4
5 2.4 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 2.0 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 2.1 7.6 0 0 0 0 0 0 1 0 0 0 0 7
8 2.0 8.0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.8 8.1 0 0 0 0 0 0 0 0 1 0 0 9
10 2.7 7.7 0 0 0 0 0 0 0 0 0 1 0 10
11 2.3 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 1.9 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 2.0 7.8 1 0 0 0 0 0 0 0 0 0 0 13
14 2.3 7.8 0 1 0 0 0 0 0 0 0 0 0 14
15 2.8 7.8 0 0 1 0 0 0 0 0 0 0 0 15
16 2.4 7.5 0 0 0 1 0 0 0 0 0 0 0 16
17 2.3 7.5 0 0 0 0 1 0 0 0 0 0 0 17
18 2.7 7.1 0 0 0 0 0 1 0 0 0 0 0 18
19 2.7 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 2.9 7.5 0 0 0 0 0 0 0 1 0 0 0 20
21 3.0 7.6 0 0 0 0 0 0 0 0 1 0 0 21
22 2.2 7.7 0 0 0 0 0 0 0 0 0 1 0 22
23 2.3 7.7 0 0 0 0 0 0 0 0 0 0 1 23
24 2.8 7.9 0 0 0 0 0 0 0 0 0 0 0 24
25 2.8 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 2.8 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 2.2 8.2 0 0 1 0 0 0 0 0 0 0 0 27
28 2.6 8.2 0 0 0 1 0 0 0 0 0 0 0 28
29 2.8 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 2.5 7.3 0 0 0 0 0 1 0 0 0 0 0 30
31 2.4 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 2.3 6.6 0 0 0 0 0 0 0 1 0 0 0 32
33 1.9 6.7 0 0 0 0 0 0 0 0 1 0 0 33
34 1.7 6.9 0 0 0 0 0 0 0 0 0 1 0 34
35 2.0 7.0 0 0 0 0 0 0 0 0 0 0 1 35
36 2.1 7.1 0 0 0 0 0 0 0 0 0 0 0 36
37 1.7 7.2 1 0 0 0 0 0 0 0 0 0 0 37
38 1.8 7.1 0 1 0 0 0 0 0 0 0 0 0 38
39 1.8 6.9 0 0 1 0 0 0 0 0 0 0 0 39
40 1.8 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 1.3 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 1.3 6.4 0 0 0 0 0 1 0 0 0 0 0 42
43 1.3 6.7 0 0 0 0 0 0 1 0 0 0 0 43
44 1.2 6.6 0 0 0 0 0 0 0 1 0 0 0 44
45 1.4 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 2.2 6.3 0 0 0 0 0 0 0 0 0 1 0 46
47 2.9 6.2 0 0 0 0 0 0 0 0 0 0 1 47
48 3.1 6.5 0 0 0 0 0 0 0 0 0 0 0 48
49 3.5 6.8 1 0 0 0 0 0 0 0 0 0 0 49
50 3.6 6.8 0 1 0 0 0 0 0 0 0 0 0 50
51 4.4 6.4 0 0 1 0 0 0 0 0 0 0 0 51
52 4.1 6.1 0 0 0 1 0 0 0 0 0 0 0 52
53 5.1 5.8 0 0 0 0 1 0 0 0 0 0 0 53
54 5.8 6.1 0 0 0 0 0 1 0 0 0 0 0 54
55 5.9 7.2 0 0 0 0 0 0 1 0 0 0 0 55
56 5.4 7.3 0 0 0 0 0 0 0 1 0 0 0 56
57 5.5 6.9 0 0 0 0 0 0 0 0 1 0 0 57
58 4.8 6.1 0 0 0 0 0 0 0 0 0 1 0 58
59 3.2 5.8 0 0 0 0 0 0 0 0 0 0 1 59
60 2.7 6.2 0 0 0 0 0 0 0 0 0 0 0 60
61 2.1 7.1 1 0 0 0 0 0 0 0 0 0 0 61
62 1.9 7.7 0 1 0 0 0 0 0 0 0 0 0 62
63 0.6 7.9 0 0 1 0 0 0 0 0 0 0 0 63
64 0.7 7.7 0 0 0 1 0 0 0 0 0 0 0 64
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
3.198532 -0.200223 -0.073138 -0.063541 -0.247326 -0.231129
M5 M6 M7 M8 M9 M10
0.370890 0.390427 0.486116 0.349703 0.277272 0.216809
M11 t
-0.003631 0.020418
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.05577 -0.56951 -0.03006 0.61839 2.53397
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.198532 2.465007 1.298 0.2004
X -0.200223 0.308151 -0.650 0.5188
M1 -0.073138 0.729866 -0.100 0.9206
M2 -0.063541 0.734145 -0.087 0.9314
M3 -0.247326 0.727078 -0.340 0.7352
M4 -0.231129 0.720530 -0.321 0.7497
M5 0.370890 0.759008 0.489 0.6272
M6 0.390427 0.767117 0.509 0.6130
M7 0.486116 0.752540 0.646 0.5213
M8 0.349703 0.752364 0.465 0.6441
M9 0.277272 0.751841 0.369 0.7138
M10 0.216809 0.753179 0.288 0.7746
M11 -0.003631 0.755071 -0.005 0.9962
t 0.020418 0.010032 2.035 0.0471 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.188 on 50 degrees of freedom
Multiple R-squared: 0.2018, Adjusted R-squared: -0.005747
F-statistic: 0.9723 on 13 and 50 DF, p-value: 0.4905
> 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,] 6.003732e-02 1.200746e-01 0.9399627
[2,] 1.714960e-02 3.429920e-02 0.9828504
[3,] 5.233328e-03 1.046666e-02 0.9947667
[4,] 1.319566e-03 2.639131e-03 0.9986804
[5,] 3.178798e-04 6.357597e-04 0.9996821
[6,] 7.837367e-04 1.567473e-03 0.9992163
[7,] 3.328175e-04 6.656349e-04 0.9996672
[8,] 1.090579e-04 2.181157e-04 0.9998909
[9,] 3.265110e-05 6.530221e-05 0.9999673
[10,] 9.923243e-06 1.984649e-05 0.9999901
[11,] 4.831465e-06 9.662930e-06 0.9999952
[12,] 1.992543e-06 3.985085e-06 0.9999980
[13,] 7.595292e-07 1.519058e-06 0.9999992
[14,] 4.625141e-07 9.250282e-07 0.9999995
[15,] 7.479019e-07 1.495804e-06 0.9999993
[16,] 5.749440e-07 1.149888e-06 0.9999994
[17,] 4.202067e-07 8.404135e-07 0.9999996
[18,] 3.747344e-07 7.494689e-07 0.9999996
[19,] 1.903176e-07 3.806352e-07 0.9999998
[20,] 1.046339e-07 2.092678e-07 0.9999999
[21,] 4.489205e-08 8.978410e-08 1.0000000
[22,] 1.346173e-08 2.692347e-08 1.0000000
[23,] 3.637973e-09 7.275946e-09 1.0000000
[24,] 2.817279e-09 5.634558e-09 1.0000000
[25,] 5.317208e-09 1.063442e-08 1.0000000
[26,] 7.496510e-09 1.499302e-08 1.0000000
[27,] 1.010254e-07 2.020508e-07 0.9999999
[28,] 1.290356e-05 2.580712e-05 0.9999871
[29,] 5.399559e-02 1.079912e-01 0.9460044
[30,] 8.615717e-01 2.768566e-01 0.1384283
[31,] 7.952049e-01 4.095901e-01 0.2047951
> postscript(file="/var/www/html/rcomp/tmp/1k6rs1258664835.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2b8fz1258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t0w31258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bsi91258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c2yk1258664835.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 = 64
Frequency = 1
1 2 3 4 5 6
-0.10398027 -0.37403983 -0.51078466 0.01244445 0.02994067 -0.38999233
7 8 9 10 11 12
-0.20587620 -0.10979133 -0.23775559 0.62219974 0.38217741 -0.02184940
13 14 15 16 17 18
0.17091539 0.44090050 1.10426734 0.60758578 -0.11485100 0.16510434
19 20 21 22 23 24
0.12908647 0.44508200 0.61711774 -0.12281526 0.17720707 0.69320260
25 26 27 28 29 30
0.78596739 0.77597484 0.33934167 0.70272712 0.22022334 -0.23986600
31 32 33 34 35 36
-0.53606253 -0.58013400 -0.90809827 -1.02800893 -0.50796427 -0.41199107
37 38 39 40 41 42
-0.73924861 -0.68928584 -0.56596367 -0.58255589 -1.74503734 -1.86508200
43 44 45 46 47 48
-1.92112220 -1.92514900 -1.71318027 -0.89315794 -0.01315794 0.22285993
49 50 51 52 53 54
0.73564705 0.80563216 1.68890966 1.29222810 1.60972433 2.32983599
55 56 57 58 59 60
2.53397446 2.16999233 2.24191640 1.42178239 -0.03826227 -0.48222207
61 62 63 64
-0.84930095 -0.95918184 -2.05577033 -2.03242956
> postscript(file="/var/www/html/rcomp/tmp/6lrws1258664835.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 = 64
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.10398027 NA
1 -0.37403983 -0.10398027
2 -0.51078466 -0.37403983
3 0.01244445 -0.51078466
4 0.02994067 0.01244445
5 -0.38999233 0.02994067
6 -0.20587620 -0.38999233
7 -0.10979133 -0.20587620
8 -0.23775559 -0.10979133
9 0.62219974 -0.23775559
10 0.38217741 0.62219974
11 -0.02184940 0.38217741
12 0.17091539 -0.02184940
13 0.44090050 0.17091539
14 1.10426734 0.44090050
15 0.60758578 1.10426734
16 -0.11485100 0.60758578
17 0.16510434 -0.11485100
18 0.12908647 0.16510434
19 0.44508200 0.12908647
20 0.61711774 0.44508200
21 -0.12281526 0.61711774
22 0.17720707 -0.12281526
23 0.69320260 0.17720707
24 0.78596739 0.69320260
25 0.77597484 0.78596739
26 0.33934167 0.77597484
27 0.70272712 0.33934167
28 0.22022334 0.70272712
29 -0.23986600 0.22022334
30 -0.53606253 -0.23986600
31 -0.58013400 -0.53606253
32 -0.90809827 -0.58013400
33 -1.02800893 -0.90809827
34 -0.50796427 -1.02800893
35 -0.41199107 -0.50796427
36 -0.73924861 -0.41199107
37 -0.68928584 -0.73924861
38 -0.56596367 -0.68928584
39 -0.58255589 -0.56596367
40 -1.74503734 -0.58255589
41 -1.86508200 -1.74503734
42 -1.92112220 -1.86508200
43 -1.92514900 -1.92112220
44 -1.71318027 -1.92514900
45 -0.89315794 -1.71318027
46 -0.01315794 -0.89315794
47 0.22285993 -0.01315794
48 0.73564705 0.22285993
49 0.80563216 0.73564705
50 1.68890966 0.80563216
51 1.29222810 1.68890966
52 1.60972433 1.29222810
53 2.32983599 1.60972433
54 2.53397446 2.32983599
55 2.16999233 2.53397446
56 2.24191640 2.16999233
57 1.42178239 2.24191640
58 -0.03826227 1.42178239
59 -0.48222207 -0.03826227
60 -0.84930095 -0.48222207
61 -0.95918184 -0.84930095
62 -2.05577033 -0.95918184
63 -2.03242956 -2.05577033
64 NA -2.03242956
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.37403983 -0.10398027
[2,] -0.51078466 -0.37403983
[3,] 0.01244445 -0.51078466
[4,] 0.02994067 0.01244445
[5,] -0.38999233 0.02994067
[6,] -0.20587620 -0.38999233
[7,] -0.10979133 -0.20587620
[8,] -0.23775559 -0.10979133
[9,] 0.62219974 -0.23775559
[10,] 0.38217741 0.62219974
[11,] -0.02184940 0.38217741
[12,] 0.17091539 -0.02184940
[13,] 0.44090050 0.17091539
[14,] 1.10426734 0.44090050
[15,] 0.60758578 1.10426734
[16,] -0.11485100 0.60758578
[17,] 0.16510434 -0.11485100
[18,] 0.12908647 0.16510434
[19,] 0.44508200 0.12908647
[20,] 0.61711774 0.44508200
[21,] -0.12281526 0.61711774
[22,] 0.17720707 -0.12281526
[23,] 0.69320260 0.17720707
[24,] 0.78596739 0.69320260
[25,] 0.77597484 0.78596739
[26,] 0.33934167 0.77597484
[27,] 0.70272712 0.33934167
[28,] 0.22022334 0.70272712
[29,] -0.23986600 0.22022334
[30,] -0.53606253 -0.23986600
[31,] -0.58013400 -0.53606253
[32,] -0.90809827 -0.58013400
[33,] -1.02800893 -0.90809827
[34,] -0.50796427 -1.02800893
[35,] -0.41199107 -0.50796427
[36,] -0.73924861 -0.41199107
[37,] -0.68928584 -0.73924861
[38,] -0.56596367 -0.68928584
[39,] -0.58255589 -0.56596367
[40,] -1.74503734 -0.58255589
[41,] -1.86508200 -1.74503734
[42,] -1.92112220 -1.86508200
[43,] -1.92514900 -1.92112220
[44,] -1.71318027 -1.92514900
[45,] -0.89315794 -1.71318027
[46,] -0.01315794 -0.89315794
[47,] 0.22285993 -0.01315794
[48,] 0.73564705 0.22285993
[49,] 0.80563216 0.73564705
[50,] 1.68890966 0.80563216
[51,] 1.29222810 1.68890966
[52,] 1.60972433 1.29222810
[53,] 2.32983599 1.60972433
[54,] 2.53397446 2.32983599
[55,] 2.16999233 2.53397446
[56,] 2.24191640 2.16999233
[57,] 1.42178239 2.24191640
[58,] -0.03826227 1.42178239
[59,] -0.48222207 -0.03826227
[60,] -0.84930095 -0.48222207
[61,] -0.95918184 -0.84930095
[62,] -2.05577033 -0.95918184
[63,] -2.03242956 -2.05577033
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.37403983 -0.10398027
2 -0.51078466 -0.37403983
3 0.01244445 -0.51078466
4 0.02994067 0.01244445
5 -0.38999233 0.02994067
6 -0.20587620 -0.38999233
7 -0.10979133 -0.20587620
8 -0.23775559 -0.10979133
9 0.62219974 -0.23775559
10 0.38217741 0.62219974
11 -0.02184940 0.38217741
12 0.17091539 -0.02184940
13 0.44090050 0.17091539
14 1.10426734 0.44090050
15 0.60758578 1.10426734
16 -0.11485100 0.60758578
17 0.16510434 -0.11485100
18 0.12908647 0.16510434
19 0.44508200 0.12908647
20 0.61711774 0.44508200
21 -0.12281526 0.61711774
22 0.17720707 -0.12281526
23 0.69320260 0.17720707
24 0.78596739 0.69320260
25 0.77597484 0.78596739
26 0.33934167 0.77597484
27 0.70272712 0.33934167
28 0.22022334 0.70272712
29 -0.23986600 0.22022334
30 -0.53606253 -0.23986600
31 -0.58013400 -0.53606253
32 -0.90809827 -0.58013400
33 -1.02800893 -0.90809827
34 -0.50796427 -1.02800893
35 -0.41199107 -0.50796427
36 -0.73924861 -0.41199107
37 -0.68928584 -0.73924861
38 -0.56596367 -0.68928584
39 -0.58255589 -0.56596367
40 -1.74503734 -0.58255589
41 -1.86508200 -1.74503734
42 -1.92112220 -1.86508200
43 -1.92514900 -1.92112220
44 -1.71318027 -1.92514900
45 -0.89315794 -1.71318027
46 -0.01315794 -0.89315794
47 0.22285993 -0.01315794
48 0.73564705 0.22285993
49 0.80563216 0.73564705
50 1.68890966 0.80563216
51 1.29222810 1.68890966
52 1.60972433 1.29222810
53 2.32983599 1.60972433
54 2.53397446 2.32983599
55 2.16999233 2.53397446
56 2.24191640 2.16999233
57 1.42178239 2.24191640
58 -0.03826227 1.42178239
59 -0.48222207 -0.03826227
60 -0.84930095 -0.48222207
61 -0.95918184 -0.84930095
62 -2.05577033 -0.95918184
63 -2.03242956 -2.05577033
> 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/7h1ta1258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wv1n1258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m2i31258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10n4g21258664835.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hk9n1258664835.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/12cnll1258664835.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/13zs0h1258664835.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/14ivww1258664835.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/1547j91258664835.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/16xt091258664835.tab")
+ }
>
> system("convert tmp/1k6rs1258664835.ps tmp/1k6rs1258664835.png")
> system("convert tmp/2b8fz1258664835.ps tmp/2b8fz1258664835.png")
> system("convert tmp/3t0w31258664835.ps tmp/3t0w31258664835.png")
> system("convert tmp/4bsi91258664835.ps tmp/4bsi91258664835.png")
> system("convert tmp/5c2yk1258664835.ps tmp/5c2yk1258664835.png")
> system("convert tmp/6lrws1258664835.ps tmp/6lrws1258664835.png")
> system("convert tmp/7h1ta1258664835.ps tmp/7h1ta1258664835.png")
> system("convert tmp/8wv1n1258664835.ps tmp/8wv1n1258664835.png")
> system("convert tmp/9m2i31258664835.ps tmp/9m2i31258664835.png")
> system("convert tmp/10n4g21258664835.ps tmp/10n4g21258664835.png")
>
>
> proc.time()
user system elapsed
2.460 1.557 2.840