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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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(110.5,55,110.8,48.7,104.2,70.3,88.9,94.8,89.8,58.5,90,62.4,93.9,56.7,91.3,65.1,87.8,114.4,99.7,50.7,73.5,44.5,79.2,72,96.9,61.2,95.2,68.4,95.6,78.7,89.7,64.1,92.8,64.6,88,71.9,101.1,71,92.7,76.4,95.8,117.3,103.8,66.1,81.8,57.3,87.1,75,105.9,63.8,108.1,62.2,102.6,75.4,93.7,58,103.5,62.1,100.6,99.2,113.3,70.7,102.4,73.3,102.1,111.2,106.9,68.9,87.3,57.6,93.1,72.9,109.1,75.9,120.3,79.4,104.9,96.9,92.6,75.2,109.8,60.3,111.4,88.9,117.9,90.5,121.6,79.9,117.8,116.3,124.2,95.2,106.8,81.5,102.7,89.1,116.8,76,113.6,100.5,96.1,83.9,85,75.1,83.2,69.5,84.9,95.1,83,90.1,79.6,78.4,83.2,113.8,83.8,73.6,82.8,56.5,71.4,97.7),dim=c(2,60),dimnames=list(c('prod','inv
'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('prod','inv
'),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
prod inv\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 110.5 55.0 1 0 0 0 0 0 0 0 0 0 0 1
2 110.8 48.7 0 1 0 0 0 0 0 0 0 0 0 2
3 104.2 70.3 0 0 1 0 0 0 0 0 0 0 0 3
4 88.9 94.8 0 0 0 1 0 0 0 0 0 0 0 4
5 89.8 58.5 0 0 0 0 1 0 0 0 0 0 0 5
6 90.0 62.4 0 0 0 0 0 1 0 0 0 0 0 6
7 93.9 56.7 0 0 0 0 0 0 1 0 0 0 0 7
8 91.3 65.1 0 0 0 0 0 0 0 1 0 0 0 8
9 87.8 114.4 0 0 0 0 0 0 0 0 1 0 0 9
10 99.7 50.7 0 0 0 0 0 0 0 0 0 1 0 10
11 73.5 44.5 0 0 0 0 0 0 0 0 0 0 1 11
12 79.2 72.0 0 0 0 0 0 0 0 0 0 0 0 12
13 96.9 61.2 1 0 0 0 0 0 0 0 0 0 0 13
14 95.2 68.4 0 1 0 0 0 0 0 0 0 0 0 14
15 95.6 78.7 0 0 1 0 0 0 0 0 0 0 0 15
16 89.7 64.1 0 0 0 1 0 0 0 0 0 0 0 16
17 92.8 64.6 0 0 0 0 1 0 0 0 0 0 0 17
18 88.0 71.9 0 0 0 0 0 1 0 0 0 0 0 18
19 101.1 71.0 0 0 0 0 0 0 1 0 0 0 0 19
20 92.7 76.4 0 0 0 0 0 0 0 1 0 0 0 20
21 95.8 117.3 0 0 0 0 0 0 0 0 1 0 0 21
22 103.8 66.1 0 0 0 0 0 0 0 0 0 1 0 22
23 81.8 57.3 0 0 0 0 0 0 0 0 0 0 1 23
24 87.1 75.0 0 0 0 0 0 0 0 0 0 0 0 24
25 105.9 63.8 1 0 0 0 0 0 0 0 0 0 0 25
26 108.1 62.2 0 1 0 0 0 0 0 0 0 0 0 26
27 102.6 75.4 0 0 1 0 0 0 0 0 0 0 0 27
28 93.7 58.0 0 0 0 1 0 0 0 0 0 0 0 28
29 103.5 62.1 0 0 0 0 1 0 0 0 0 0 0 29
30 100.6 99.2 0 0 0 0 0 1 0 0 0 0 0 30
31 113.3 70.7 0 0 0 0 0 0 1 0 0 0 0 31
32 102.4 73.3 0 0 0 0 0 0 0 1 0 0 0 32
33 102.1 111.2 0 0 0 0 0 0 0 0 1 0 0 33
34 106.9 68.9 0 0 0 0 0 0 0 0 0 1 0 34
35 87.3 57.6 0 0 0 0 0 0 0 0 0 0 1 35
36 93.1 72.9 0 0 0 0 0 0 0 0 0 0 0 36
37 109.1 75.9 1 0 0 0 0 0 0 0 0 0 0 37
38 120.3 79.4 0 1 0 0 0 0 0 0 0 0 0 38
39 104.9 96.9 0 0 1 0 0 0 0 0 0 0 0 39
40 92.6 75.2 0 0 0 1 0 0 0 0 0 0 0 40
41 109.8 60.3 0 0 0 0 1 0 0 0 0 0 0 41
42 111.4 88.9 0 0 0 0 0 1 0 0 0 0 0 42
43 117.9 90.5 0 0 0 0 0 0 1 0 0 0 0 43
44 121.6 79.9 0 0 0 0 0 0 0 1 0 0 0 44
45 117.8 116.3 0 0 0 0 0 0 0 0 1 0 0 45
46 124.2 95.2 0 0 0 0 0 0 0 0 0 1 0 46
47 106.8 81.5 0 0 0 0 0 0 0 0 0 0 1 47
48 102.7 89.1 0 0 0 0 0 0 0 0 0 0 0 48
49 116.8 76.0 1 0 0 0 0 0 0 0 0 0 0 49
50 113.6 100.5 0 1 0 0 0 0 0 0 0 0 0 50
51 96.1 83.9 0 0 1 0 0 0 0 0 0 0 0 51
52 85.0 75.1 0 0 0 1 0 0 0 0 0 0 0 52
53 83.2 69.5 0 0 0 0 1 0 0 0 0 0 0 53
54 84.9 95.1 0 0 0 0 0 1 0 0 0 0 0 54
55 83.0 90.1 0 0 0 0 0 0 1 0 0 0 0 55
56 79.6 78.4 0 0 0 0 0 0 0 1 0 0 0 56
57 83.2 113.8 0 0 0 0 0 0 0 0 1 0 0 57
58 83.8 73.6 0 0 0 0 0 0 0 0 0 1 0 58
59 82.8 56.5 0 0 0 0 0 0 0 0 0 0 1 59
60 71.4 97.7 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) `inv\r` M1 M2 M3 M4
66.61584 0.26187 24.68595 25.04994 13.75455 5.07851
M5 M6 M7 M8 M9 M10
13.68618 7.51170 16.42184 12.44463 1.82899 19.64633
M11 t
5.43062 -0.03378
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.7740 -5.0675 0.2919 5.8500 23.1027
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.61584 12.59727 5.288 3.32e-06 ***
`inv\r` 0.26187 0.16735 1.565 0.12450
M1 24.68595 7.36838 3.350 0.00162 **
M2 25.04994 7.20419 3.477 0.00112 **
M3 13.75455 7.16031 1.921 0.06095 .
M4 5.07851 7.17076 0.708 0.48238
M5 13.68618 7.57714 1.806 0.07742 .
M6 7.51170 7.15668 1.050 0.29938
M7 16.42184 7.13300 2.302 0.02590 *
M8 12.44463 7.15511 1.739 0.08868 .
M9 1.82899 9.15111 0.200 0.84247
M10 19.64633 7.27856 2.699 0.00969 **
M11 5.43062 7.95298 0.683 0.49813
t -0.03378 0.10958 -0.308 0.75928
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.22 on 46 degrees of freedom
Multiple R-squared: 0.3784, Adjusted R-squared: 0.2028
F-statistic: 2.154 on 13 and 46 DF, p-value: 0.0283
> 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,] 0.0964755949 0.192951190 0.9035244
[2,] 0.0481627202 0.096325440 0.9518373
[3,] 0.0707887892 0.141577578 0.9292112
[4,] 0.0433551546 0.086710309 0.9566448
[5,] 0.0416281728 0.083256346 0.9583718
[6,] 0.0255625175 0.051125035 0.9744375
[7,] 0.0272918534 0.054583707 0.9727081
[8,] 0.0203029136 0.040605827 0.9796971
[9,] 0.0116444021 0.023288804 0.9883556
[10,] 0.0062086626 0.012417325 0.9937913
[11,] 0.0027695198 0.005539040 0.9972305
[12,] 0.0011271627 0.002254325 0.9988728
[13,] 0.0010412217 0.002082443 0.9989588
[14,] 0.0015771580 0.003154316 0.9984228
[15,] 0.0016866650 0.003373330 0.9983133
[16,] 0.0011924449 0.002384890 0.9988076
[17,] 0.0010337907 0.002067581 0.9989662
[18,] 0.0005474077 0.001094815 0.9994526
[19,] 0.0018083412 0.003616682 0.9981917
[20,] 0.0021270269 0.004254054 0.9978730
[21,] 0.0162280587 0.032456117 0.9837719
[22,] 0.0299430891 0.059886178 0.9700569
[23,] 0.0823794601 0.164758920 0.9176205
[24,] 0.5266375782 0.946724844 0.4733624
[25,] 0.4779728608 0.955945722 0.5220271
[26,] 0.5197850357 0.960429929 0.4802150
[27,] 0.3640932816 0.728186563 0.6359067
> postscript(file="/var/www/html/rcomp/tmp/1d2qa1258638968.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yk7v1258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3geao1258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qp721258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dokr1258638968.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
4.8293461 6.4488934 5.5217516 -7.4841556 -5.6522939 -0.2653138
7 8 9 10 11 12
-3.7490463 -4.5377252 -10.2983188 0.4990066 -9.8279354 -5.8648611
13 14 15 16 17 18
-9.9888788 -13.9045261 -4.8725790 1.7604849 -3.8443321 -4.3476973
19 20 21 22 23 24
0.1116121 -5.6914680 -2.6523850 0.9716121 -4.4744776 1.6548861
25 26 27 28 29 30
-1.2643850 1.0243908 3.3969258 7.7632152 7.9156796 1.5086996
31 32 33 34 35 36
12.7955180 5.2256635 5.6503453 3.7437325 1.3523086 8.6101513
37 38 39 40 41 42
-0.8276208 9.1256370 0.4721470 2.5644614 15.0923850 15.4112682
43 44 45 46 47 48
12.6159119 23.1026921 20.4201734 14.5619957 14.9990508 14.3732638
49 50 51 52 53 54
7.2515386 -2.6943952 -4.5182454 -4.6040059 -13.5114387 -12.3069566
55 56 57 58 59 60
-21.7739956 -18.0991624 -13.1198149 -19.7763468 -2.0489464 -18.7734401
> postscript(file="/var/www/html/rcomp/tmp/6q5sy1258638968.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 4.8293461 NA
1 6.4488934 4.8293461
2 5.5217516 6.4488934
3 -7.4841556 5.5217516
4 -5.6522939 -7.4841556
5 -0.2653138 -5.6522939
6 -3.7490463 -0.2653138
7 -4.5377252 -3.7490463
8 -10.2983188 -4.5377252
9 0.4990066 -10.2983188
10 -9.8279354 0.4990066
11 -5.8648611 -9.8279354
12 -9.9888788 -5.8648611
13 -13.9045261 -9.9888788
14 -4.8725790 -13.9045261
15 1.7604849 -4.8725790
16 -3.8443321 1.7604849
17 -4.3476973 -3.8443321
18 0.1116121 -4.3476973
19 -5.6914680 0.1116121
20 -2.6523850 -5.6914680
21 0.9716121 -2.6523850
22 -4.4744776 0.9716121
23 1.6548861 -4.4744776
24 -1.2643850 1.6548861
25 1.0243908 -1.2643850
26 3.3969258 1.0243908
27 7.7632152 3.3969258
28 7.9156796 7.7632152
29 1.5086996 7.9156796
30 12.7955180 1.5086996
31 5.2256635 12.7955180
32 5.6503453 5.2256635
33 3.7437325 5.6503453
34 1.3523086 3.7437325
35 8.6101513 1.3523086
36 -0.8276208 8.6101513
37 9.1256370 -0.8276208
38 0.4721470 9.1256370
39 2.5644614 0.4721470
40 15.0923850 2.5644614
41 15.4112682 15.0923850
42 12.6159119 15.4112682
43 23.1026921 12.6159119
44 20.4201734 23.1026921
45 14.5619957 20.4201734
46 14.9990508 14.5619957
47 14.3732638 14.9990508
48 7.2515386 14.3732638
49 -2.6943952 7.2515386
50 -4.5182454 -2.6943952
51 -4.6040059 -4.5182454
52 -13.5114387 -4.6040059
53 -12.3069566 -13.5114387
54 -21.7739956 -12.3069566
55 -18.0991624 -21.7739956
56 -13.1198149 -18.0991624
57 -19.7763468 -13.1198149
58 -2.0489464 -19.7763468
59 -18.7734401 -2.0489464
60 NA -18.7734401
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.4488934 4.8293461
[2,] 5.5217516 6.4488934
[3,] -7.4841556 5.5217516
[4,] -5.6522939 -7.4841556
[5,] -0.2653138 -5.6522939
[6,] -3.7490463 -0.2653138
[7,] -4.5377252 -3.7490463
[8,] -10.2983188 -4.5377252
[9,] 0.4990066 -10.2983188
[10,] -9.8279354 0.4990066
[11,] -5.8648611 -9.8279354
[12,] -9.9888788 -5.8648611
[13,] -13.9045261 -9.9888788
[14,] -4.8725790 -13.9045261
[15,] 1.7604849 -4.8725790
[16,] -3.8443321 1.7604849
[17,] -4.3476973 -3.8443321
[18,] 0.1116121 -4.3476973
[19,] -5.6914680 0.1116121
[20,] -2.6523850 -5.6914680
[21,] 0.9716121 -2.6523850
[22,] -4.4744776 0.9716121
[23,] 1.6548861 -4.4744776
[24,] -1.2643850 1.6548861
[25,] 1.0243908 -1.2643850
[26,] 3.3969258 1.0243908
[27,] 7.7632152 3.3969258
[28,] 7.9156796 7.7632152
[29,] 1.5086996 7.9156796
[30,] 12.7955180 1.5086996
[31,] 5.2256635 12.7955180
[32,] 5.6503453 5.2256635
[33,] 3.7437325 5.6503453
[34,] 1.3523086 3.7437325
[35,] 8.6101513 1.3523086
[36,] -0.8276208 8.6101513
[37,] 9.1256370 -0.8276208
[38,] 0.4721470 9.1256370
[39,] 2.5644614 0.4721470
[40,] 15.0923850 2.5644614
[41,] 15.4112682 15.0923850
[42,] 12.6159119 15.4112682
[43,] 23.1026921 12.6159119
[44,] 20.4201734 23.1026921
[45,] 14.5619957 20.4201734
[46,] 14.9990508 14.5619957
[47,] 14.3732638 14.9990508
[48,] 7.2515386 14.3732638
[49,] -2.6943952 7.2515386
[50,] -4.5182454 -2.6943952
[51,] -4.6040059 -4.5182454
[52,] -13.5114387 -4.6040059
[53,] -12.3069566 -13.5114387
[54,] -21.7739956 -12.3069566
[55,] -18.0991624 -21.7739956
[56,] -13.1198149 -18.0991624
[57,] -19.7763468 -13.1198149
[58,] -2.0489464 -19.7763468
[59,] -18.7734401 -2.0489464
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.4488934 4.8293461
2 5.5217516 6.4488934
3 -7.4841556 5.5217516
4 -5.6522939 -7.4841556
5 -0.2653138 -5.6522939
6 -3.7490463 -0.2653138
7 -4.5377252 -3.7490463
8 -10.2983188 -4.5377252
9 0.4990066 -10.2983188
10 -9.8279354 0.4990066
11 -5.8648611 -9.8279354
12 -9.9888788 -5.8648611
13 -13.9045261 -9.9888788
14 -4.8725790 -13.9045261
15 1.7604849 -4.8725790
16 -3.8443321 1.7604849
17 -4.3476973 -3.8443321
18 0.1116121 -4.3476973
19 -5.6914680 0.1116121
20 -2.6523850 -5.6914680
21 0.9716121 -2.6523850
22 -4.4744776 0.9716121
23 1.6548861 -4.4744776
24 -1.2643850 1.6548861
25 1.0243908 -1.2643850
26 3.3969258 1.0243908
27 7.7632152 3.3969258
28 7.9156796 7.7632152
29 1.5086996 7.9156796
30 12.7955180 1.5086996
31 5.2256635 12.7955180
32 5.6503453 5.2256635
33 3.7437325 5.6503453
34 1.3523086 3.7437325
35 8.6101513 1.3523086
36 -0.8276208 8.6101513
37 9.1256370 -0.8276208
38 0.4721470 9.1256370
39 2.5644614 0.4721470
40 15.0923850 2.5644614
41 15.4112682 15.0923850
42 12.6159119 15.4112682
43 23.1026921 12.6159119
44 20.4201734 23.1026921
45 14.5619957 20.4201734
46 14.9990508 14.5619957
47 14.3732638 14.9990508
48 7.2515386 14.3732638
49 -2.6943952 7.2515386
50 -4.5182454 -2.6943952
51 -4.6040059 -4.5182454
52 -13.5114387 -4.6040059
53 -12.3069566 -13.5114387
54 -21.7739956 -12.3069566
55 -18.0991624 -21.7739956
56 -13.1198149 -18.0991624
57 -19.7763468 -13.1198149
58 -2.0489464 -19.7763468
59 -18.7734401 -2.0489464
> 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/717id1258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89b251258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dtm71258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wwpa1258638968.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n3to1258638968.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/12eecz1258638968.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/13c29h1258638968.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/14t2ys1258638968.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/15l3fw1258638968.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/16n66z1258638968.tab")
+ }
>
> system("convert tmp/1d2qa1258638968.ps tmp/1d2qa1258638968.png")
> system("convert tmp/2yk7v1258638968.ps tmp/2yk7v1258638968.png")
> system("convert tmp/3geao1258638968.ps tmp/3geao1258638968.png")
> system("convert tmp/4qp721258638968.ps tmp/4qp721258638968.png")
> system("convert tmp/5dokr1258638968.ps tmp/5dokr1258638968.png")
> system("convert tmp/6q5sy1258638968.ps tmp/6q5sy1258638968.png")
> system("convert tmp/717id1258638968.ps tmp/717id1258638968.png")
> system("convert tmp/89b251258638968.ps tmp/89b251258638968.png")
> system("convert tmp/9dtm71258638968.ps tmp/9dtm71258638968.png")
> system("convert tmp/10wwpa1258638968.ps tmp/10wwpa1258638968.png")
>
>
> proc.time()
user system elapsed
2.417 1.569 3.463