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(93.3,121.8,97.3,127.6,127,129.9,111.7,128,96.4,123.5,133,124,72.2,127.4,95.8,127.6,124.1,128.4,127.6,131.4,110.7,135.1,104.6,134,112.7,144.5,115.3,147.3,139.4,150.9,119,148.7,97.4,141.4,154,138.9,81.5,139.8,88.8,145.6,127.7,147.9,105.1,148.5,114.9,151.1,106.4,157.5,104.5,167.5,121.6,172.3,141.4,173.5,99,187.5,126.7,205.5,134.1,195.1,81.3,204.5,88.6,204.5,132.7,201.7,132.9,207,134.4,206.6,103.7,210.6,119.7,211.1,115,215,132.9,223.9,108.5,238.2,113.9,238.9,142,229.6,97.7,232.2,92.2,222.1,128.8,221.6,134.9,227.3,128.2,221,114.8,213.6,117.9,243.4,119.1,253.8,120.7,265.3,129.1,268.2,117.6,268.5,129.2,266.9,100,268.4,87,250.8,128,231.2,127.7,192,93.4,171.4,84.1,160,71.7,148.1),dim=c(2,61),dimnames=list(c('IPtran','IGPic'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('IPtran','IGPic'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
IPtran IGPic M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 93.3 121.8 1 0 0 0 0 0 0 0 0 0 0
2 97.3 127.6 0 1 0 0 0 0 0 0 0 0 0
3 127.0 129.9 0 0 1 0 0 0 0 0 0 0 0
4 111.7 128.0 0 0 0 1 0 0 0 0 0 0 0
5 96.4 123.5 0 0 0 0 1 0 0 0 0 0 0
6 133.0 124.0 0 0 0 0 0 1 0 0 0 0 0
7 72.2 127.4 0 0 0 0 0 0 1 0 0 0 0
8 95.8 127.6 0 0 0 0 0 0 0 1 0 0 0
9 124.1 128.4 0 0 0 0 0 0 0 0 1 0 0
10 127.6 131.4 0 0 0 0 0 0 0 0 0 1 0
11 110.7 135.1 0 0 0 0 0 0 0 0 0 0 1
12 104.6 134.0 0 0 0 0 0 0 0 0 0 0 0
13 112.7 144.5 1 0 0 0 0 0 0 0 0 0 0
14 115.3 147.3 0 1 0 0 0 0 0 0 0 0 0
15 139.4 150.9 0 0 1 0 0 0 0 0 0 0 0
16 119.0 148.7 0 0 0 1 0 0 0 0 0 0 0
17 97.4 141.4 0 0 0 0 1 0 0 0 0 0 0
18 154.0 138.9 0 0 0 0 0 1 0 0 0 0 0
19 81.5 139.8 0 0 0 0 0 0 1 0 0 0 0
20 88.8 145.6 0 0 0 0 0 0 0 1 0 0 0
21 127.7 147.9 0 0 0 0 0 0 0 0 1 0 0
22 105.1 148.5 0 0 0 0 0 0 0 0 0 1 0
23 114.9 151.1 0 0 0 0 0 0 0 0 0 0 1
24 106.4 157.5 0 0 0 0 0 0 0 0 0 0 0
25 104.5 167.5 1 0 0 0 0 0 0 0 0 0 0
26 121.6 172.3 0 1 0 0 0 0 0 0 0 0 0
27 141.4 173.5 0 0 1 0 0 0 0 0 0 0 0
28 99.0 187.5 0 0 0 1 0 0 0 0 0 0 0
29 126.7 205.5 0 0 0 0 1 0 0 0 0 0 0
30 134.1 195.1 0 0 0 0 0 1 0 0 0 0 0
31 81.3 204.5 0 0 0 0 0 0 1 0 0 0 0
32 88.6 204.5 0 0 0 0 0 0 0 1 0 0 0
33 132.7 201.7 0 0 0 0 0 0 0 0 1 0 0
34 132.9 207.0 0 0 0 0 0 0 0 0 0 1 0
35 134.4 206.6 0 0 0 0 0 0 0 0 0 0 1
36 103.7 210.6 0 0 0 0 0 0 0 0 0 0 0
37 119.7 211.1 1 0 0 0 0 0 0 0 0 0 0
38 115.0 215.0 0 1 0 0 0 0 0 0 0 0 0
39 132.9 223.9 0 0 1 0 0 0 0 0 0 0 0
40 108.5 238.2 0 0 0 1 0 0 0 0 0 0 0
41 113.9 238.9 0 0 0 0 1 0 0 0 0 0 0
42 142.0 229.6 0 0 0 0 0 1 0 0 0 0 0
43 97.7 232.2 0 0 0 0 0 0 1 0 0 0 0
44 92.2 222.1 0 0 0 0 0 0 0 1 0 0 0
45 128.8 221.6 0 0 0 0 0 0 0 0 1 0 0
46 134.9 227.3 0 0 0 0 0 0 0 0 0 1 0
47 128.2 221.0 0 0 0 0 0 0 0 0 0 0 1
48 114.8 213.6 0 0 0 0 0 0 0 0 0 0 0
49 117.9 243.4 1 0 0 0 0 0 0 0 0 0 0
50 119.1 253.8 0 1 0 0 0 0 0 0 0 0 0
51 120.7 265.3 0 0 1 0 0 0 0 0 0 0 0
52 129.1 268.2 0 0 0 1 0 0 0 0 0 0 0
53 117.6 268.5 0 0 0 0 1 0 0 0 0 0 0
54 129.2 266.9 0 0 0 0 0 1 0 0 0 0 0
55 100.0 268.4 0 0 0 0 0 0 1 0 0 0 0
56 87.0 250.8 0 0 0 0 0 0 0 1 0 0 0
57 128.0 231.2 0 0 0 0 0 0 0 0 1 0 0
58 127.7 192.0 0 0 0 0 0 0 0 0 0 1 0
59 93.4 171.4 0 0 0 0 0 0 0 0 0 0 1
60 84.1 160.0 0 0 0 0 0 0 0 0 0 0 0
61 71.7 148.1 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) IGPic M1 M2 M3 M4
87.40117 0.08747 0.79050 10.23502 28.37396 9.07989
M5 M6 M7 M8 M9 M10
5.89394 34.36153 -17.86985 -13.55024 24.57612 22.38646
M11
13.43381
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-29.4454 -4.7274 0.8202 6.9927 20.0882
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 87.40117 7.24278 12.067 3.81e-16 ***
IGPic 0.08747 0.03057 2.861 0.006232 **
M1 0.79050 6.60516 0.120 0.905237
M2 10.23502 6.90284 1.483 0.144683
M3 28.37396 6.91088 4.106 0.000156 ***
M4 9.07989 6.92280 1.312 0.195897
M5 5.89394 6.92663 0.851 0.399044
M6 34.36153 6.91524 4.969 8.96e-06 ***
M7 -17.86985 6.92368 -2.581 0.012964 *
M8 -13.55024 6.91362 -1.960 0.055823 .
M9 24.57612 6.90666 3.558 0.000853 ***
M10 22.38646 6.90096 3.244 0.002149 **
M11 13.43381 6.89869 1.947 0.057362 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.91 on 48 degrees of freedom
Multiple R-squared: 0.7276, Adjusted R-squared: 0.6595
F-statistic: 10.68 on 12 and 48 DF, p-value: 7.285e-10
> 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,] 0.04056295 0.08112589 0.9594371
[2,] 0.04573179 0.09146359 0.9542682
[3,] 0.06681798 0.13363596 0.9331820
[4,] 0.02834360 0.05668720 0.9716564
[5,] 0.07534240 0.15068479 0.9246576
[6,] 0.04789303 0.09578607 0.9521070
[7,] 0.26334111 0.52668222 0.7366589
[8,] 0.17984260 0.35968521 0.8201574
[9,] 0.13309911 0.26619821 0.8669009
[10,] 0.10003727 0.20007454 0.8999627
[11,] 0.08004309 0.16008617 0.9199569
[12,] 0.10323604 0.20647208 0.8967640
[13,] 0.23490106 0.46980211 0.7650989
[14,] 0.35641947 0.71283894 0.6435805
[15,] 0.45583925 0.91167851 0.5441607
[16,] 0.37029864 0.74059728 0.6297014
[17,] 0.33580958 0.67161916 0.6641904
[18,] 0.29000365 0.58000729 0.7099964
[19,] 0.23188421 0.46376843 0.7681158
[20,] 0.29587129 0.59174258 0.7041287
[21,] 0.23866851 0.47733703 0.7613315
[22,] 0.28686988 0.57373975 0.7131301
[23,] 0.22627187 0.45254375 0.7737281
[24,] 0.38975404 0.77950808 0.6102460
[25,] 0.33675766 0.67351532 0.6632423
[26,] 0.25595447 0.51190894 0.7440455
[27,] 0.55102082 0.89795835 0.4489792
[28,] 0.59314134 0.81371731 0.4068587
[29,] 0.81322124 0.37355753 0.1867788
[30,] 0.70775349 0.58449302 0.2922465
> postscript(file="/var/www/html/rcomp/tmp/1ktnq1259004889.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cvf81259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3npwv1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lwbq1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57d6g1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
-5.5450538 -11.4968781 -0.1369862 4.0232665 -7.6971841 0.3914902
7 8 9 10 11 12
-8.4745152 10.7883881 0.8920489 6.3193164 -1.9516667 5.4783603
13 14 15 16 17 18
11.8694630 4.7800373 10.4262231 9.5127157 -8.2628295 20.0882435
19 20 21 22 23 24
-0.2590964 2.2139961 2.7864576 -17.6763559 0.8488738 5.2229041
25 26 27 28 29 30
1.6577399 8.8933818 10.4494866 -13.8809736 15.4305858 -4.7273581
31 32 33 34 35 36
-6.1181609 -3.1377643 3.0807749 5.0068702 15.4944985 -2.1215522
37 38 39 40 41 42
13.0442127 -1.4414258 -2.4588110 -8.8155110 -0.2907860 0.1550573
43 44 45 46 47 48
7.8590248 -1.0771697 -2.5598029 5.2313059 8.0349849 8.7160492
49 50 51 52 53 54
8.4190537 -0.7351152 -18.2799125 9.1605024 0.8202139 -15.9074328
55 56 57 58 59 60
6.9927476 -8.7874503 -4.1994786 1.1188635 -22.4266905 -17.2957614
61
-29.4454154
> postscript(file="/var/www/html/rcomp/tmp/6b9s01259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.5450538 NA
1 -11.4968781 -5.5450538
2 -0.1369862 -11.4968781
3 4.0232665 -0.1369862
4 -7.6971841 4.0232665
5 0.3914902 -7.6971841
6 -8.4745152 0.3914902
7 10.7883881 -8.4745152
8 0.8920489 10.7883881
9 6.3193164 0.8920489
10 -1.9516667 6.3193164
11 5.4783603 -1.9516667
12 11.8694630 5.4783603
13 4.7800373 11.8694630
14 10.4262231 4.7800373
15 9.5127157 10.4262231
16 -8.2628295 9.5127157
17 20.0882435 -8.2628295
18 -0.2590964 20.0882435
19 2.2139961 -0.2590964
20 2.7864576 2.2139961
21 -17.6763559 2.7864576
22 0.8488738 -17.6763559
23 5.2229041 0.8488738
24 1.6577399 5.2229041
25 8.8933818 1.6577399
26 10.4494866 8.8933818
27 -13.8809736 10.4494866
28 15.4305858 -13.8809736
29 -4.7273581 15.4305858
30 -6.1181609 -4.7273581
31 -3.1377643 -6.1181609
32 3.0807749 -3.1377643
33 5.0068702 3.0807749
34 15.4944985 5.0068702
35 -2.1215522 15.4944985
36 13.0442127 -2.1215522
37 -1.4414258 13.0442127
38 -2.4588110 -1.4414258
39 -8.8155110 -2.4588110
40 -0.2907860 -8.8155110
41 0.1550573 -0.2907860
42 7.8590248 0.1550573
43 -1.0771697 7.8590248
44 -2.5598029 -1.0771697
45 5.2313059 -2.5598029
46 8.0349849 5.2313059
47 8.7160492 8.0349849
48 8.4190537 8.7160492
49 -0.7351152 8.4190537
50 -18.2799125 -0.7351152
51 9.1605024 -18.2799125
52 0.8202139 9.1605024
53 -15.9074328 0.8202139
54 6.9927476 -15.9074328
55 -8.7874503 6.9927476
56 -4.1994786 -8.7874503
57 1.1188635 -4.1994786
58 -22.4266905 1.1188635
59 -17.2957614 -22.4266905
60 -29.4454154 -17.2957614
61 NA -29.4454154
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.4968781 -5.5450538
[2,] -0.1369862 -11.4968781
[3,] 4.0232665 -0.1369862
[4,] -7.6971841 4.0232665
[5,] 0.3914902 -7.6971841
[6,] -8.4745152 0.3914902
[7,] 10.7883881 -8.4745152
[8,] 0.8920489 10.7883881
[9,] 6.3193164 0.8920489
[10,] -1.9516667 6.3193164
[11,] 5.4783603 -1.9516667
[12,] 11.8694630 5.4783603
[13,] 4.7800373 11.8694630
[14,] 10.4262231 4.7800373
[15,] 9.5127157 10.4262231
[16,] -8.2628295 9.5127157
[17,] 20.0882435 -8.2628295
[18,] -0.2590964 20.0882435
[19,] 2.2139961 -0.2590964
[20,] 2.7864576 2.2139961
[21,] -17.6763559 2.7864576
[22,] 0.8488738 -17.6763559
[23,] 5.2229041 0.8488738
[24,] 1.6577399 5.2229041
[25,] 8.8933818 1.6577399
[26,] 10.4494866 8.8933818
[27,] -13.8809736 10.4494866
[28,] 15.4305858 -13.8809736
[29,] -4.7273581 15.4305858
[30,] -6.1181609 -4.7273581
[31,] -3.1377643 -6.1181609
[32,] 3.0807749 -3.1377643
[33,] 5.0068702 3.0807749
[34,] 15.4944985 5.0068702
[35,] -2.1215522 15.4944985
[36,] 13.0442127 -2.1215522
[37,] -1.4414258 13.0442127
[38,] -2.4588110 -1.4414258
[39,] -8.8155110 -2.4588110
[40,] -0.2907860 -8.8155110
[41,] 0.1550573 -0.2907860
[42,] 7.8590248 0.1550573
[43,] -1.0771697 7.8590248
[44,] -2.5598029 -1.0771697
[45,] 5.2313059 -2.5598029
[46,] 8.0349849 5.2313059
[47,] 8.7160492 8.0349849
[48,] 8.4190537 8.7160492
[49,] -0.7351152 8.4190537
[50,] -18.2799125 -0.7351152
[51,] 9.1605024 -18.2799125
[52,] 0.8202139 9.1605024
[53,] -15.9074328 0.8202139
[54,] 6.9927476 -15.9074328
[55,] -8.7874503 6.9927476
[56,] -4.1994786 -8.7874503
[57,] 1.1188635 -4.1994786
[58,] -22.4266905 1.1188635
[59,] -17.2957614 -22.4266905
[60,] -29.4454154 -17.2957614
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.4968781 -5.5450538
2 -0.1369862 -11.4968781
3 4.0232665 -0.1369862
4 -7.6971841 4.0232665
5 0.3914902 -7.6971841
6 -8.4745152 0.3914902
7 10.7883881 -8.4745152
8 0.8920489 10.7883881
9 6.3193164 0.8920489
10 -1.9516667 6.3193164
11 5.4783603 -1.9516667
12 11.8694630 5.4783603
13 4.7800373 11.8694630
14 10.4262231 4.7800373
15 9.5127157 10.4262231
16 -8.2628295 9.5127157
17 20.0882435 -8.2628295
18 -0.2590964 20.0882435
19 2.2139961 -0.2590964
20 2.7864576 2.2139961
21 -17.6763559 2.7864576
22 0.8488738 -17.6763559
23 5.2229041 0.8488738
24 1.6577399 5.2229041
25 8.8933818 1.6577399
26 10.4494866 8.8933818
27 -13.8809736 10.4494866
28 15.4305858 -13.8809736
29 -4.7273581 15.4305858
30 -6.1181609 -4.7273581
31 -3.1377643 -6.1181609
32 3.0807749 -3.1377643
33 5.0068702 3.0807749
34 15.4944985 5.0068702
35 -2.1215522 15.4944985
36 13.0442127 -2.1215522
37 -1.4414258 13.0442127
38 -2.4588110 -1.4414258
39 -8.8155110 -2.4588110
40 -0.2907860 -8.8155110
41 0.1550573 -0.2907860
42 7.8590248 0.1550573
43 -1.0771697 7.8590248
44 -2.5598029 -1.0771697
45 5.2313059 -2.5598029
46 8.0349849 5.2313059
47 8.7160492 8.0349849
48 8.4190537 8.7160492
49 -0.7351152 8.4190537
50 -18.2799125 -0.7351152
51 9.1605024 -18.2799125
52 0.8202139 9.1605024
53 -15.9074328 0.8202139
54 6.9927476 -15.9074328
55 -8.7874503 6.9927476
56 -4.1994786 -8.7874503
57 1.1188635 -4.1994786
58 -22.4266905 1.1188635
59 -17.2957614 -22.4266905
60 -29.4454154 -17.2957614
> 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/7nre91259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qc5j1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qjgc1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jyrs1259004889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jgbt1259004889.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/12wu6v1259004889.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/13wamw1259004889.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/14wew31259004889.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/15afgq1259004889.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/165o6h1259004889.tab")
+ }
>
> system("convert tmp/1ktnq1259004889.ps tmp/1ktnq1259004889.png")
> system("convert tmp/2cvf81259004889.ps tmp/2cvf81259004889.png")
> system("convert tmp/3npwv1259004889.ps tmp/3npwv1259004889.png")
> system("convert tmp/4lwbq1259004889.ps tmp/4lwbq1259004889.png")
> system("convert tmp/57d6g1259004889.ps tmp/57d6g1259004889.png")
> system("convert tmp/6b9s01259004889.ps tmp/6b9s01259004889.png")
> system("convert tmp/7nre91259004889.ps tmp/7nre91259004889.png")
> system("convert tmp/8qc5j1259004889.ps tmp/8qc5j1259004889.png")
> system("convert tmp/9qjgc1259004889.ps tmp/9qjgc1259004889.png")
> system("convert tmp/10jyrs1259004889.ps tmp/10jyrs1259004889.png")
>
>
> proc.time()
user system elapsed
2.479 1.579 3.360