R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(104.8
+ ,117.6
+ ,112.3
+ ,103.9
+ ,100.4
+ ,111.4
+ ,118.6
+ ,125.7
+ ,110.9
+ ,108.6
+ ,106.9
+ ,110.1
+ ,117.5
+ ,106.6
+ ,109.9
+ ,103.9
+ ,113.1
+ ,105.8
+ ,103.2
+ ,109.5
+ ,107.6
+ ,91.2
+ ,115.1
+ ,108.7
+ ,109.2
+ ,104.7
+ ,106.2
+ ,119.3
+ ,104.6
+ ,100.9
+ ,109.1
+ ,115.5
+ ,119.0
+ ,108.7
+ ,98.4
+ ,109.3
+ ,106.2
+ ,126.8
+ ,109.5
+ ,94.2
+ ,102.2
+ ,95.9
+ ,116.3
+ ,102.6
+ ,94.7
+ ,109.8
+ ,113.2
+ ,127.1
+ ,109.5
+ ,95.2
+ ,106.2
+ ,78.3
+ ,106.5
+ ,108.1
+ ,100.3
+ ,95.1
+ ,79.8
+ ,95.5
+ ,96.1
+ ,100.9
+ ,118.7
+ ,121.2
+ ,121.3
+ ,118.5
+ ,97.9
+ ,116.9
+ ,125.6
+ ,118.3
+ ,116.3
+ ,106.9
+ ,105.3
+ ,97.2
+ ,95.6
+ ,105.9
+ ,100.8
+ ,119.5
+ ,102.8
+ ,90.1
+ ,120.7
+ ,106.6
+ ,96.5
+ ,88.8
+ ,82.6
+ ,97.0
+ ,108.2
+ ,99.3
+ ,95.3
+ ,86.9
+ ,99.6
+ ,98.4
+ ,113.8
+ ,107.6
+ ,95.9
+ ,114.2
+ ,102.0
+ ,102.7
+ ,95.0
+ ,88.8
+ ,103.3
+ ,95.7
+ ,98.8
+ ,87.5
+ ,93.2
+ ,99.6
+ ,100.8
+ ,109.9
+ ,106.7
+ ,98.9
+ ,110.1
+ ,98.8
+ ,103.6
+ ,75.8
+ ,79.6
+ ,105.7
+ ,99.6
+ ,96.6
+ ,80.0
+ ,80.7
+ ,97.8
+ ,106.1
+ ,111.6
+ ,117.2
+ ,102.9
+ ,111.1
+ ,106.3
+ ,111.6
+ ,106.6
+ ,101.7
+ ,112.0
+ ,105.7
+ ,107.0
+ ,104.7
+ ,95.9
+ ,107.2
+ ,103.7
+ ,111.5
+ ,95.2
+ ,87.1
+ ,112.7
+ ,111.2
+ ,102.0
+ ,94.0
+ ,87.5
+ ,102.5
+ ,114.8
+ ,113.5
+ ,95.7
+ ,93.6
+ ,114.9
+ ,103.6
+ ,125.5
+ ,112.6
+ ,109.6
+ ,126.4
+ ,107.0
+ ,106.7
+ ,99.1
+ ,103.2
+ ,107.3
+ ,104.8
+ ,102.9
+ ,91.6
+ ,98.9
+ ,103.8
+ ,104.7
+ ,123.6
+ ,111.5
+ ,113.7
+ ,124.5
+ ,102.0
+ ,107.7
+ ,76.6
+ ,87.9
+ ,110.1
+ ,103.4
+ ,105.5
+ ,83.4
+ ,89.6
+ ,107.1
+ ,107.0
+ ,117.1
+ ,113.5
+ ,114.4
+ ,117.3
+ ,104.0
+ ,113.3
+ ,106.4
+ ,108.8
+ ,113.8
+ ,105.4
+ ,118.0
+ ,104.1
+ ,104.3
+ ,119.0
+ ,107.9
+ ,118.4
+ ,108.4
+ ,97.0
+ ,119.1
+ ,110.1
+ ,105.8
+ ,91.0
+ ,100.4
+ ,106.9
+ ,111.0
+ ,114.6
+ ,108.3
+ ,105.3
+ ,115.0
+ ,98.5
+ ,140.3
+ ,121.0
+ ,122.3
+ ,141.7
+ ,101.9
+ ,113.8
+ ,95.4
+ ,105.6
+ ,115.2
+ ,103.4
+ ,117.4
+ ,109.9
+ ,116.9
+ ,117.9
+ ,102.9
+ ,115.4
+ ,101.4
+ ,110.5
+ ,116.5
+ ,101.0
+ ,105.9
+ ,86.0
+ ,88.6
+ ,107.4
+ ,103.4
+ ,120.4
+ ,96.5
+ ,94.5
+ ,122.2
+ ,107.2
+ ,126.9
+ ,124.6
+ ,115.7
+ ,126.9
+ ,104.5
+ ,117.1
+ ,109.3
+ ,107.8
+ ,117.7
+ ,104.7
+ ,113.8
+ ,104.5
+ ,106.6
+ ,114.4
+ ,107.0
+ ,112.8
+ ,101.8
+ ,100.7
+ ,113.6
+ ,110.3
+ ,106.7
+ ,101.5
+ ,100.4
+ ,107.0
+ ,107.9
+ ,107.3
+ ,103.4
+ ,101.7
+ ,107.5
+ ,97.1
+ ,121.8
+ ,125.9
+ ,115.2
+ ,121.4
+ ,98.6
+ ,101.1
+ ,96.8
+ ,100.9
+ ,101.3
+ ,95.3
+ ,103.1
+ ,104.4
+ ,105.3
+ ,102.9
+ ,101.7
+ ,110.4
+ ,121.1
+ ,109.8
+ ,109.5
+ ,96.3
+ ,108.3
+ ,83.7
+ ,92.1
+ ,110.2
+ ,99.0
+ ,116.3
+ ,91.5
+ ,92.5
+ ,118.2
+ ,104.0)
+ ,dim=c(5
+ ,60)
+ ,dimnames=list(c('consumer_goods'
+ ,'durable_consumer_goods'
+ ,'intermediate_and_capital_goods'
+ ,'non-durable_consumer_goods'
+ ,'energy')
+ ,1:60))
> y <- array(NA,dim=c(5,60),dimnames=list(c('consumer_goods','durable_consumer_goods','intermediate_and_capital_goods','non-durable_consumer_goods','energy'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
consumer_goods durable_consumer_goods intermediate_and_capital_goods
1 104.8 117.6 112.3
2 111.4 118.6 125.7
3 106.9 110.1 117.5
4 103.9 113.1 105.8
5 107.6 91.2 115.1
6 104.7 106.2 119.3
7 109.1 115.5 119.0
8 109.3 106.2 126.8
9 102.2 95.9 116.3
10 109.8 113.2 127.1
11 106.2 78.3 106.5
12 95.1 79.8 95.5
13 118.7 121.2 121.3
14 116.9 125.6 118.3
15 105.3 97.2 95.6
16 119.5 102.8 90.1
17 96.5 88.8 82.6
18 99.3 95.3 86.9
19 113.8 107.6 95.9
20 102.7 95.0 88.8
21 98.8 87.5 93.2
22 109.9 106.7 98.9
23 103.6 75.8 79.6
24 96.6 80.0 80.7
25 111.6 117.2 102.9
26 111.6 106.6 101.7
27 107.0 104.7 95.9
28 111.5 95.2 87.1
29 102.0 94.0 87.5
30 113.5 95.7 93.6
31 125.5 112.6 109.6
32 106.7 99.1 103.2
33 102.9 91.6 98.9
34 123.6 111.5 113.7
35 107.7 76.6 87.9
36 105.5 83.4 89.6
37 117.1 113.5 114.4
38 113.3 106.4 108.8
39 118.0 104.1 104.3
40 118.4 108.4 97.0
41 105.8 91.0 100.4
42 114.6 108.3 105.3
43 140.3 121.0 122.3
44 113.8 95.4 105.6
45 117.4 109.9 116.9
46 115.4 101.4 110.5
47 105.9 86.0 88.6
48 120.4 96.5 94.5
49 126.9 124.6 115.7
50 117.1 109.3 107.8
51 113.8 104.5 106.6
52 112.8 101.8 100.7
53 106.7 101.5 100.4
54 107.3 103.4 101.7
55 121.8 125.9 115.2
56 101.1 96.8 100.9
57 103.1 104.4 105.3
58 110.4 121.1 109.8
59 108.3 83.7 92.1
60 116.3 91.5 92.5
non-durable_consumer_goods energy
1 103.9 100.4
2 110.9 108.6
3 106.6 109.9
4 103.2 109.5
5 108.7 109.2
6 104.6 100.9
7 108.7 98.4
8 109.5 94.2
9 102.6 94.7
10 109.5 95.2
11 108.1 100.3
12 96.1 100.9
13 118.5 97.9
14 116.3 106.9
15 105.9 100.8
16 120.7 106.6
17 97.0 108.2
18 99.6 98.4
19 114.2 102.0
20 103.3 95.7
21 99.6 100.8
22 110.1 98.8
23 105.7 99.6
24 97.8 106.1
25 111.1 106.3
26 112.0 105.7
27 107.2 103.7
28 112.7 111.2
29 102.5 114.8
30 114.9 103.6
31 126.4 107.0
32 107.3 104.8
33 103.8 104.7
34 124.5 102.0
35 110.1 103.4
36 107.1 107.0
37 117.3 104.0
38 113.8 105.4
39 119.0 107.9
40 119.1 110.1
41 106.9 111.0
42 115.0 98.5
43 141.7 101.9
44 115.2 103.4
45 117.9 102.9
46 116.5 101.0
47 107.4 103.4
48 122.2 107.2
49 126.9 104.5
50 117.7 104.7
51 114.4 107.0
52 113.6 110.3
53 107.0 107.9
54 107.5 97.1
55 121.4 98.6
56 101.3 95.3
57 102.9 101.7
58 109.5 96.3
59 110.2 99.0
60 118.2 104.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) durable_consumer_goods
-0.0429447 0.0694911
intermediate_and_capital_goods `non-durable_consumer_goods`
0.0007872 0.9298108
energy
0.0006376
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.11013 -0.04453 -0.00012 0.04136 0.14145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0429447 0.2069829 -0.207 0.836
durable_consumer_goods 0.0694911 0.0009555 72.727 <2e-16 ***
intermediate_and_capital_goods 0.0007872 0.0009668 0.814 0.419
`non-durable_consumer_goods` 0.9298108 0.0011181 831.564 <2e-16 ***
energy 0.0006376 0.0017294 0.369 0.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05972 on 55 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 0.9999
F-statistic: 2.809e+05 on 4 and 55 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,] 0.25929111 0.51858222 0.74070889
[2,] 0.13027166 0.26054332 0.86972834
[3,] 0.24575105 0.49150210 0.75424895
[4,] 0.22512386 0.45024771 0.77487614
[5,] 0.19854633 0.39709265 0.80145367
[6,] 0.16938613 0.33877226 0.83061387
[7,] 0.21071906 0.42143812 0.78928094
[8,] 0.24504769 0.49009538 0.75495231
[9,] 0.17474774 0.34949548 0.82525226
[10,] 0.12573230 0.25146460 0.87426770
[11,] 0.09344932 0.18689863 0.90655068
[12,] 0.11133258 0.22266515 0.88866742
[13,] 0.13081557 0.26163115 0.86918443
[14,] 0.10630372 0.21260745 0.89369628
[15,] 0.09609544 0.19219088 0.90390456
[16,] 0.44723812 0.89447624 0.55276188
[17,] 0.42354362 0.84708724 0.57645638
[18,] 0.56046272 0.87907456 0.43953728
[19,] 0.63043154 0.73913692 0.36956846
[20,] 0.72681906 0.54636189 0.27318094
[21,] 0.68227448 0.63545105 0.31772552
[22,] 0.65879433 0.68241133 0.34120567
[23,] 0.82707507 0.34584987 0.17292493
[24,] 0.79414536 0.41170929 0.20585464
[25,] 0.85270297 0.29459406 0.14729703
[26,] 0.93707281 0.12585437 0.06292719
[27,] 0.91508990 0.16982020 0.08491010
[28,] 0.95004513 0.09990973 0.04995487
[29,] 0.96389686 0.07220628 0.03610314
[30,] 0.94974680 0.10050640 0.05025320
[31,] 0.93853735 0.12292530 0.06146265
[32,] 0.90735698 0.18528605 0.09264302
[33,] 0.86965024 0.26069953 0.13034976
[34,] 0.82899650 0.34200700 0.17100350
[35,] 0.78822325 0.42355351 0.21177675
[36,] 0.76467125 0.47065750 0.23532875
[37,] 0.70725780 0.58548440 0.29274220
[38,] 0.63055278 0.73889444 0.36944722
[39,] 0.72793711 0.54412578 0.27206289
[40,] 0.63982023 0.72035954 0.36017977
[41,] 0.56826399 0.86347203 0.43173601
[42,] 0.86813399 0.26373203 0.13186601
[43,] 0.93553499 0.12893001 0.06446501
[44,] 0.89497908 0.21004183 0.10502092
[45,] 0.78561580 0.42876839 0.21438420
> postscript(file="/var/fisher/rcomp/tmp/1o9cv1353444508.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/20kk01353444508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3806g1353444508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/48ynt1353444508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5mybp1353444508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.088971472 -0.082915104 0.011571908 -0.026079470 0.074687009 -0.053469504
7 8 9 10 11 12
-0.110130888 -0.011174269 0.028225456 0.001514122 0.141453696 0.103223066
13 14 15 16 17 18
-0.019867688 -0.083421902 -0.018083017 0.032198513 0.046473542 -0.019863002
19 20 21 22 23 24
0.040778545 -0.039089661 0.015678044 0.015223558 -0.031650562 0.016981447
25 26 27 28 29 30
0.047825043 -0.051071597 -0.050105692 -0.001754194 0.063094807 -0.082354338
31 32 33 34 35 36
0.035658609 -0.060384472 -0.081414617 -0.021300075 -0.087367654 0.025891402
37 38 39 40 41 42
0.032529038 -0.016230629 0.010531214 0.023082127 -0.027331216 0.043118127
43 44 45 46 47 48
0.019082658 -0.049769135 0.023543975 -0.077796848 -0.030646087 -0.028569883
49 50 51 52 53 54
0.133651936 -0.042783386 0.058627669 -0.007357359 0.052007608 0.060932081
55 56 57 58 59 60
0.061428381 0.086177802 0.062803565 0.065451496 -0.074236366 -0.058256357
> postscript(file="/var/fisher/rcomp/tmp/6p7wz1353444508.ps",horizontal=F,onefile=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 -0.088971472 NA
1 -0.082915104 -0.088971472
2 0.011571908 -0.082915104
3 -0.026079470 0.011571908
4 0.074687009 -0.026079470
5 -0.053469504 0.074687009
6 -0.110130888 -0.053469504
7 -0.011174269 -0.110130888
8 0.028225456 -0.011174269
9 0.001514122 0.028225456
10 0.141453696 0.001514122
11 0.103223066 0.141453696
12 -0.019867688 0.103223066
13 -0.083421902 -0.019867688
14 -0.018083017 -0.083421902
15 0.032198513 -0.018083017
16 0.046473542 0.032198513
17 -0.019863002 0.046473542
18 0.040778545 -0.019863002
19 -0.039089661 0.040778545
20 0.015678044 -0.039089661
21 0.015223558 0.015678044
22 -0.031650562 0.015223558
23 0.016981447 -0.031650562
24 0.047825043 0.016981447
25 -0.051071597 0.047825043
26 -0.050105692 -0.051071597
27 -0.001754194 -0.050105692
28 0.063094807 -0.001754194
29 -0.082354338 0.063094807
30 0.035658609 -0.082354338
31 -0.060384472 0.035658609
32 -0.081414617 -0.060384472
33 -0.021300075 -0.081414617
34 -0.087367654 -0.021300075
35 0.025891402 -0.087367654
36 0.032529038 0.025891402
37 -0.016230629 0.032529038
38 0.010531214 -0.016230629
39 0.023082127 0.010531214
40 -0.027331216 0.023082127
41 0.043118127 -0.027331216
42 0.019082658 0.043118127
43 -0.049769135 0.019082658
44 0.023543975 -0.049769135
45 -0.077796848 0.023543975
46 -0.030646087 -0.077796848
47 -0.028569883 -0.030646087
48 0.133651936 -0.028569883
49 -0.042783386 0.133651936
50 0.058627669 -0.042783386
51 -0.007357359 0.058627669
52 0.052007608 -0.007357359
53 0.060932081 0.052007608
54 0.061428381 0.060932081
55 0.086177802 0.061428381
56 0.062803565 0.086177802
57 0.065451496 0.062803565
58 -0.074236366 0.065451496
59 -0.058256357 -0.074236366
60 NA -0.058256357
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.082915104 -0.088971472
[2,] 0.011571908 -0.082915104
[3,] -0.026079470 0.011571908
[4,] 0.074687009 -0.026079470
[5,] -0.053469504 0.074687009
[6,] -0.110130888 -0.053469504
[7,] -0.011174269 -0.110130888
[8,] 0.028225456 -0.011174269
[9,] 0.001514122 0.028225456
[10,] 0.141453696 0.001514122
[11,] 0.103223066 0.141453696
[12,] -0.019867688 0.103223066
[13,] -0.083421902 -0.019867688
[14,] -0.018083017 -0.083421902
[15,] 0.032198513 -0.018083017
[16,] 0.046473542 0.032198513
[17,] -0.019863002 0.046473542
[18,] 0.040778545 -0.019863002
[19,] -0.039089661 0.040778545
[20,] 0.015678044 -0.039089661
[21,] 0.015223558 0.015678044
[22,] -0.031650562 0.015223558
[23,] 0.016981447 -0.031650562
[24,] 0.047825043 0.016981447
[25,] -0.051071597 0.047825043
[26,] -0.050105692 -0.051071597
[27,] -0.001754194 -0.050105692
[28,] 0.063094807 -0.001754194
[29,] -0.082354338 0.063094807
[30,] 0.035658609 -0.082354338
[31,] -0.060384472 0.035658609
[32,] -0.081414617 -0.060384472
[33,] -0.021300075 -0.081414617
[34,] -0.087367654 -0.021300075
[35,] 0.025891402 -0.087367654
[36,] 0.032529038 0.025891402
[37,] -0.016230629 0.032529038
[38,] 0.010531214 -0.016230629
[39,] 0.023082127 0.010531214
[40,] -0.027331216 0.023082127
[41,] 0.043118127 -0.027331216
[42,] 0.019082658 0.043118127
[43,] -0.049769135 0.019082658
[44,] 0.023543975 -0.049769135
[45,] -0.077796848 0.023543975
[46,] -0.030646087 -0.077796848
[47,] -0.028569883 -0.030646087
[48,] 0.133651936 -0.028569883
[49,] -0.042783386 0.133651936
[50,] 0.058627669 -0.042783386
[51,] -0.007357359 0.058627669
[52,] 0.052007608 -0.007357359
[53,] 0.060932081 0.052007608
[54,] 0.061428381 0.060932081
[55,] 0.086177802 0.061428381
[56,] 0.062803565 0.086177802
[57,] 0.065451496 0.062803565
[58,] -0.074236366 0.065451496
[59,] -0.058256357 -0.074236366
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.082915104 -0.088971472
2 0.011571908 -0.082915104
3 -0.026079470 0.011571908
4 0.074687009 -0.026079470
5 -0.053469504 0.074687009
6 -0.110130888 -0.053469504
7 -0.011174269 -0.110130888
8 0.028225456 -0.011174269
9 0.001514122 0.028225456
10 0.141453696 0.001514122
11 0.103223066 0.141453696
12 -0.019867688 0.103223066
13 -0.083421902 -0.019867688
14 -0.018083017 -0.083421902
15 0.032198513 -0.018083017
16 0.046473542 0.032198513
17 -0.019863002 0.046473542
18 0.040778545 -0.019863002
19 -0.039089661 0.040778545
20 0.015678044 -0.039089661
21 0.015223558 0.015678044
22 -0.031650562 0.015223558
23 0.016981447 -0.031650562
24 0.047825043 0.016981447
25 -0.051071597 0.047825043
26 -0.050105692 -0.051071597
27 -0.001754194 -0.050105692
28 0.063094807 -0.001754194
29 -0.082354338 0.063094807
30 0.035658609 -0.082354338
31 -0.060384472 0.035658609
32 -0.081414617 -0.060384472
33 -0.021300075 -0.081414617
34 -0.087367654 -0.021300075
35 0.025891402 -0.087367654
36 0.032529038 0.025891402
37 -0.016230629 0.032529038
38 0.010531214 -0.016230629
39 0.023082127 0.010531214
40 -0.027331216 0.023082127
41 0.043118127 -0.027331216
42 0.019082658 0.043118127
43 -0.049769135 0.019082658
44 0.023543975 -0.049769135
45 -0.077796848 0.023543975
46 -0.030646087 -0.077796848
47 -0.028569883 -0.030646087
48 0.133651936 -0.028569883
49 -0.042783386 0.133651936
50 0.058627669 -0.042783386
51 -0.007357359 0.058627669
52 0.052007608 -0.007357359
53 0.060932081 0.052007608
54 0.061428381 0.060932081
55 0.086177802 0.061428381
56 0.062803565 0.086177802
57 0.065451496 0.062803565
58 -0.074236366 0.065451496
59 -0.058256357 -0.074236366
> 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/fisher/rcomp/tmp/78tnz1353444508.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/8oxeo1353444508.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/9aq3o1353444508.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/10c29k1353444508.ps",horizontal=F,onefile=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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/118ose1353444508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12387o1353444508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13y6861353444508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1402eu1353444508.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/fisher/rcomp/tmp/15h1mk1353444508.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/fisher/rcomp/tmp/16x5fb1353444509.tab")
+ }
>
> try(system("convert tmp/1o9cv1353444508.ps tmp/1o9cv1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/20kk01353444508.ps tmp/20kk01353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/3806g1353444508.ps tmp/3806g1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/48ynt1353444508.ps tmp/48ynt1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mybp1353444508.ps tmp/5mybp1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p7wz1353444508.ps tmp/6p7wz1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/78tnz1353444508.ps tmp/78tnz1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oxeo1353444508.ps tmp/8oxeo1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/9aq3o1353444508.ps tmp/9aq3o1353444508.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c29k1353444508.ps tmp/10c29k1353444508.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.930 1.313 7.243