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(96.96,89.1,93.11,83.3,95.62,97.7,98.30,100.9,96.38,108.3,100.82,113.2,99.06,105,94.03,104,102.07,109.8,99.31,98.6,98.64,93.5,101.82,98.2,99.14,88,97.63,85.3,100.06,96.8,101.32,98.8,101.49,110.3,105.43,111.6,105.09,111.2,99.48,106.9,108.53,117.6,104.34,97,106.10,97.3,107.35,98.4,103.00,87.6,104.50,87.4,105.17,94.7,104.84,101.5,106.18,110.4,108.86,108.4,107.77,109.7,102.74,105.2,112.63,111.1,106.26,96.2,108.86,97.3,111.38,98.9,106.85,91.7,107.86,90.9,107.94,98.8,111.38,111.5,111.29,119,113.72,115.3,111.88,116.3,109.87,113.6,113.72,115.1,111.71,109.7,114.81,97.6,112.05,100.8,111.54,94,110.87,87.2,110.87,102.9,115.48,111.3,111.63,106.6,116.24,108.9,113.56,108.3,106.01,100.5,110.45,104,107.77,89.9,108.61,86.8,108.19,91.2),dim=c(2,60),dimnames=list(c('BESTC','INDUSTR'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('BESTC','INDUSTR'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
BESTC INDUSTR M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 96.96 89.1 1 0 0 0 0 0 0 0 0 0 0 1
2 93.11 83.3 0 1 0 0 0 0 0 0 0 0 0 2
3 95.62 97.7 0 0 1 0 0 0 0 0 0 0 0 3
4 98.30 100.9 0 0 0 1 0 0 0 0 0 0 0 4
5 96.38 108.3 0 0 0 0 1 0 0 0 0 0 0 5
6 100.82 113.2 0 0 0 0 0 1 0 0 0 0 0 6
7 99.06 105.0 0 0 0 0 0 0 1 0 0 0 0 7
8 94.03 104.0 0 0 0 0 0 0 0 1 0 0 0 8
9 102.07 109.8 0 0 0 0 0 0 0 0 1 0 0 9
10 99.31 98.6 0 0 0 0 0 0 0 0 0 1 0 10
11 98.64 93.5 0 0 0 0 0 0 0 0 0 0 1 11
12 101.82 98.2 0 0 0 0 0 0 0 0 0 0 0 12
13 99.14 88.0 1 0 0 0 0 0 0 0 0 0 0 13
14 97.63 85.3 0 1 0 0 0 0 0 0 0 0 0 14
15 100.06 96.8 0 0 1 0 0 0 0 0 0 0 0 15
16 101.32 98.8 0 0 0 1 0 0 0 0 0 0 0 16
17 101.49 110.3 0 0 0 0 1 0 0 0 0 0 0 17
18 105.43 111.6 0 0 0 0 0 1 0 0 0 0 0 18
19 105.09 111.2 0 0 0 0 0 0 1 0 0 0 0 19
20 99.48 106.9 0 0 0 0 0 0 0 1 0 0 0 20
21 108.53 117.6 0 0 0 0 0 0 0 0 1 0 0 21
22 104.34 97.0 0 0 0 0 0 0 0 0 0 1 0 22
23 106.10 97.3 0 0 0 0 0 0 0 0 0 0 1 23
24 107.35 98.4 0 0 0 0 0 0 0 0 0 0 0 24
25 103.00 87.6 1 0 0 0 0 0 0 0 0 0 0 25
26 104.50 87.4 0 1 0 0 0 0 0 0 0 0 0 26
27 105.17 94.7 0 0 1 0 0 0 0 0 0 0 0 27
28 104.84 101.5 0 0 0 1 0 0 0 0 0 0 0 28
29 106.18 110.4 0 0 0 0 1 0 0 0 0 0 0 29
30 108.86 108.4 0 0 0 0 0 1 0 0 0 0 0 30
31 107.77 109.7 0 0 0 0 0 0 1 0 0 0 0 31
32 102.74 105.2 0 0 0 0 0 0 0 1 0 0 0 32
33 112.63 111.1 0 0 0 0 0 0 0 0 1 0 0 33
34 106.26 96.2 0 0 0 0 0 0 0 0 0 1 0 34
35 108.86 97.3 0 0 0 0 0 0 0 0 0 0 1 35
36 111.38 98.9 0 0 0 0 0 0 0 0 0 0 0 36
37 106.85 91.7 1 0 0 0 0 0 0 0 0 0 0 37
38 107.86 90.9 0 1 0 0 0 0 0 0 0 0 0 38
39 107.94 98.8 0 0 1 0 0 0 0 0 0 0 0 39
40 111.38 111.5 0 0 0 1 0 0 0 0 0 0 0 40
41 111.29 119.0 0 0 0 0 1 0 0 0 0 0 0 41
42 113.72 115.3 0 0 0 0 0 1 0 0 0 0 0 42
43 111.88 116.3 0 0 0 0 0 0 1 0 0 0 0 43
44 109.87 113.6 0 0 0 0 0 0 0 1 0 0 0 44
45 113.72 115.1 0 0 0 0 0 0 0 0 1 0 0 45
46 111.71 109.7 0 0 0 0 0 0 0 0 0 1 0 46
47 114.81 97.6 0 0 0 0 0 0 0 0 0 0 1 47
48 112.05 100.8 0 0 0 0 0 0 0 0 0 0 0 48
49 111.54 94.0 1 0 0 0 0 0 0 0 0 0 0 49
50 110.87 87.2 0 1 0 0 0 0 0 0 0 0 0 50
51 110.87 102.9 0 0 1 0 0 0 0 0 0 0 0 51
52 115.48 111.3 0 0 0 1 0 0 0 0 0 0 0 52
53 111.63 106.6 0 0 0 0 1 0 0 0 0 0 0 53
54 116.24 108.9 0 0 0 0 0 1 0 0 0 0 0 54
55 113.56 108.3 0 0 0 0 0 0 1 0 0 0 0 55
56 106.01 100.5 0 0 0 0 0 0 0 1 0 0 0 56
57 110.45 104.0 0 0 0 0 0 0 0 0 1 0 0 57
58 107.77 89.9 0 0 0 0 0 0 0 0 0 1 0 58
59 108.61 86.8 0 0 0 0 0 0 0 0 0 0 1 59
60 108.19 91.2 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) INDUSTR M1 M2 M3 M4
56.7056 0.4278 1.4913 1.9113 -2.0810 -2.8516
M5 M6 M7 M8 M9 M10
-6.6103 -3.5005 -4.7228 -8.3027 -3.8636 -2.0724
M11 t
0.8000 0.2707
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.76859 -0.85573 -0.06316 0.77715 3.32967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.70557 5.02610 11.282 7.65e-15 ***
INDUSTR 0.42778 0.05136 8.329 9.74e-11 ***
M1 1.49133 1.08712 1.372 0.176777
M2 1.91125 1.15349 1.657 0.104339
M3 -2.08103 1.02224 -2.036 0.047558 *
M4 -2.85161 1.09071 -2.614 0.012041 *
M5 -6.61031 1.23582 -5.349 2.70e-06 ***
M6 -3.50052 1.25070 -2.799 0.007469 **
M7 -4.72283 1.20941 -3.905 0.000306 ***
M8 -8.30267 1.10930 -7.485 1.72e-09 ***
M9 -3.86358 1.24773 -3.096 0.003331 **
M10 -2.07237 1.01638 -2.039 0.047223 *
M11 0.80000 1.02666 0.779 0.439833
t 0.27065 0.01230 22.007 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.605 on 46 degrees of freedom
Multiple R-squared: 0.9407, Adjusted R-squared: 0.924
F-statistic: 56.17 on 13 and 46 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.086903041 0.173806081 0.9130970
[2,] 0.078342666 0.156685331 0.9216573
[3,] 0.031030371 0.062060742 0.9689696
[4,] 0.013648370 0.027296740 0.9863516
[5,] 0.005539477 0.011078953 0.9944605
[6,] 0.005746332 0.011492663 0.9942537
[7,] 0.012607541 0.025215083 0.9873925
[8,] 0.007827692 0.015655384 0.9921723
[9,] 0.007361787 0.014723574 0.9926382
[10,] 0.007601227 0.015202454 0.9923988
[11,] 0.007858276 0.015716553 0.9921417
[12,] 0.016710546 0.033421091 0.9832895
[13,] 0.008930794 0.017861588 0.9910692
[14,] 0.005049867 0.010099734 0.9949501
[15,] 0.004273206 0.008546411 0.9957268
[16,] 0.002672300 0.005344600 0.9973277
[17,] 0.004009076 0.008018152 0.9959909
[18,] 0.004022634 0.008045269 0.9959774
[19,] 0.002439666 0.004879331 0.9975603
[20,] 0.005903043 0.011806087 0.9940970
[21,] 0.008667942 0.017335885 0.9913321
[22,] 0.005646584 0.011293168 0.9943534
[23,] 0.004575505 0.009151010 0.9954245
[24,] 0.002410138 0.004820276 0.9975899
[25,] 0.002992131 0.005984261 0.9970079
[26,] 0.007738856 0.015477712 0.9922611
[27,] 0.437290348 0.874580697 0.5627097
> postscript(file="/var/www/html/rcomp/tmp/1e4571258650780.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dziu1258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fjwe1258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/405711258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bda61258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.37686308 -1.68256444 -1.61102914 0.19999365 -1.39757044 -2.43415492
7 8 9 10 11 12
0.26533498 -1.02768541 -0.17857643 -0.20925670 -1.84058123 -0.14181479
13 14 15 16 17 18
-0.22039120 -1.26595048 -0.03384031 0.87052382 -0.39095648 -0.38751697
19 20 21 22 23 24
0.39525427 -0.06607746 -0.30311225 2.25738125 0.74602072 2.05481116
25 26 27 28 29 30
0.56290542 1.45788502 2.72668986 -0.01231134 1.00844791 1.16357609
31 32 33 34 35 36
0.46911378 0.67333893 3.32966947 1.27179164 0.25820356 2.62310178
37 38 39 40 41 42
-0.58882796 0.07282231 0.49495648 -0.99797293 -0.80831546 -0.17595374
43 44 45 46 47 48
-1.49208071 0.96213245 -0.53928547 -2.30111551 2.83205106 -0.76750582
49 50 51 52 53 54
-0.13054934 1.41780759 -1.57677690 -0.06023321 1.58839447 1.83404954
55 56 57 58 59 60
0.36237768 -0.54170851 -2.30869531 -1.01880069 -1.99569411 -3.76859233
> postscript(file="/var/www/html/rcomp/tmp/6k8bf1258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.37686308 NA
1 -1.68256444 0.37686308
2 -1.61102914 -1.68256444
3 0.19999365 -1.61102914
4 -1.39757044 0.19999365
5 -2.43415492 -1.39757044
6 0.26533498 -2.43415492
7 -1.02768541 0.26533498
8 -0.17857643 -1.02768541
9 -0.20925670 -0.17857643
10 -1.84058123 -0.20925670
11 -0.14181479 -1.84058123
12 -0.22039120 -0.14181479
13 -1.26595048 -0.22039120
14 -0.03384031 -1.26595048
15 0.87052382 -0.03384031
16 -0.39095648 0.87052382
17 -0.38751697 -0.39095648
18 0.39525427 -0.38751697
19 -0.06607746 0.39525427
20 -0.30311225 -0.06607746
21 2.25738125 -0.30311225
22 0.74602072 2.25738125
23 2.05481116 0.74602072
24 0.56290542 2.05481116
25 1.45788502 0.56290542
26 2.72668986 1.45788502
27 -0.01231134 2.72668986
28 1.00844791 -0.01231134
29 1.16357609 1.00844791
30 0.46911378 1.16357609
31 0.67333893 0.46911378
32 3.32966947 0.67333893
33 1.27179164 3.32966947
34 0.25820356 1.27179164
35 2.62310178 0.25820356
36 -0.58882796 2.62310178
37 0.07282231 -0.58882796
38 0.49495648 0.07282231
39 -0.99797293 0.49495648
40 -0.80831546 -0.99797293
41 -0.17595374 -0.80831546
42 -1.49208071 -0.17595374
43 0.96213245 -1.49208071
44 -0.53928547 0.96213245
45 -2.30111551 -0.53928547
46 2.83205106 -2.30111551
47 -0.76750582 2.83205106
48 -0.13054934 -0.76750582
49 1.41780759 -0.13054934
50 -1.57677690 1.41780759
51 -0.06023321 -1.57677690
52 1.58839447 -0.06023321
53 1.83404954 1.58839447
54 0.36237768 1.83404954
55 -0.54170851 0.36237768
56 -2.30869531 -0.54170851
57 -1.01880069 -2.30869531
58 -1.99569411 -1.01880069
59 -3.76859233 -1.99569411
60 NA -3.76859233
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.68256444 0.37686308
[2,] -1.61102914 -1.68256444
[3,] 0.19999365 -1.61102914
[4,] -1.39757044 0.19999365
[5,] -2.43415492 -1.39757044
[6,] 0.26533498 -2.43415492
[7,] -1.02768541 0.26533498
[8,] -0.17857643 -1.02768541
[9,] -0.20925670 -0.17857643
[10,] -1.84058123 -0.20925670
[11,] -0.14181479 -1.84058123
[12,] -0.22039120 -0.14181479
[13,] -1.26595048 -0.22039120
[14,] -0.03384031 -1.26595048
[15,] 0.87052382 -0.03384031
[16,] -0.39095648 0.87052382
[17,] -0.38751697 -0.39095648
[18,] 0.39525427 -0.38751697
[19,] -0.06607746 0.39525427
[20,] -0.30311225 -0.06607746
[21,] 2.25738125 -0.30311225
[22,] 0.74602072 2.25738125
[23,] 2.05481116 0.74602072
[24,] 0.56290542 2.05481116
[25,] 1.45788502 0.56290542
[26,] 2.72668986 1.45788502
[27,] -0.01231134 2.72668986
[28,] 1.00844791 -0.01231134
[29,] 1.16357609 1.00844791
[30,] 0.46911378 1.16357609
[31,] 0.67333893 0.46911378
[32,] 3.32966947 0.67333893
[33,] 1.27179164 3.32966947
[34,] 0.25820356 1.27179164
[35,] 2.62310178 0.25820356
[36,] -0.58882796 2.62310178
[37,] 0.07282231 -0.58882796
[38,] 0.49495648 0.07282231
[39,] -0.99797293 0.49495648
[40,] -0.80831546 -0.99797293
[41,] -0.17595374 -0.80831546
[42,] -1.49208071 -0.17595374
[43,] 0.96213245 -1.49208071
[44,] -0.53928547 0.96213245
[45,] -2.30111551 -0.53928547
[46,] 2.83205106 -2.30111551
[47,] -0.76750582 2.83205106
[48,] -0.13054934 -0.76750582
[49,] 1.41780759 -0.13054934
[50,] -1.57677690 1.41780759
[51,] -0.06023321 -1.57677690
[52,] 1.58839447 -0.06023321
[53,] 1.83404954 1.58839447
[54,] 0.36237768 1.83404954
[55,] -0.54170851 0.36237768
[56,] -2.30869531 -0.54170851
[57,] -1.01880069 -2.30869531
[58,] -1.99569411 -1.01880069
[59,] -3.76859233 -1.99569411
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.68256444 0.37686308
2 -1.61102914 -1.68256444
3 0.19999365 -1.61102914
4 -1.39757044 0.19999365
5 -2.43415492 -1.39757044
6 0.26533498 -2.43415492
7 -1.02768541 0.26533498
8 -0.17857643 -1.02768541
9 -0.20925670 -0.17857643
10 -1.84058123 -0.20925670
11 -0.14181479 -1.84058123
12 -0.22039120 -0.14181479
13 -1.26595048 -0.22039120
14 -0.03384031 -1.26595048
15 0.87052382 -0.03384031
16 -0.39095648 0.87052382
17 -0.38751697 -0.39095648
18 0.39525427 -0.38751697
19 -0.06607746 0.39525427
20 -0.30311225 -0.06607746
21 2.25738125 -0.30311225
22 0.74602072 2.25738125
23 2.05481116 0.74602072
24 0.56290542 2.05481116
25 1.45788502 0.56290542
26 2.72668986 1.45788502
27 -0.01231134 2.72668986
28 1.00844791 -0.01231134
29 1.16357609 1.00844791
30 0.46911378 1.16357609
31 0.67333893 0.46911378
32 3.32966947 0.67333893
33 1.27179164 3.32966947
34 0.25820356 1.27179164
35 2.62310178 0.25820356
36 -0.58882796 2.62310178
37 0.07282231 -0.58882796
38 0.49495648 0.07282231
39 -0.99797293 0.49495648
40 -0.80831546 -0.99797293
41 -0.17595374 -0.80831546
42 -1.49208071 -0.17595374
43 0.96213245 -1.49208071
44 -0.53928547 0.96213245
45 -2.30111551 -0.53928547
46 2.83205106 -2.30111551
47 -0.76750582 2.83205106
48 -0.13054934 -0.76750582
49 1.41780759 -0.13054934
50 -1.57677690 1.41780759
51 -0.06023321 -1.57677690
52 1.58839447 -0.06023321
53 1.83404954 1.58839447
54 0.36237768 1.83404954
55 -0.54170851 0.36237768
56 -2.30869531 -0.54170851
57 -1.01880069 -2.30869531
58 -1.99569411 -1.01880069
59 -3.76859233 -1.99569411
> 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/7aja21258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cwv71258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9byhj1258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107d061258650780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bohn1258650780.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/1226761258650780.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/131lbb1258650780.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/143gnn1258650781.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/156yxe1258650781.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/16ysxa1258650781.tab")
+ }
> system("convert tmp/1e4571258650780.ps tmp/1e4571258650780.png")
> system("convert tmp/2dziu1258650780.ps tmp/2dziu1258650780.png")
> system("convert tmp/3fjwe1258650780.ps tmp/3fjwe1258650780.png")
> system("convert tmp/405711258650780.ps tmp/405711258650780.png")
> system("convert tmp/5bda61258650780.ps tmp/5bda61258650780.png")
> system("convert tmp/6k8bf1258650780.ps tmp/6k8bf1258650780.png")
> system("convert tmp/7aja21258650780.ps tmp/7aja21258650780.png")
> system("convert tmp/8cwv71258650780.ps tmp/8cwv71258650780.png")
> system("convert tmp/9byhj1258650780.ps tmp/9byhj1258650780.png")
> system("convert tmp/107d061258650780.ps tmp/107d061258650780.png")
>
>
> proc.time()
user system elapsed
2.422 1.587 2.885