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(100.6,71.7,104.3,77.5,120.4,89.8,107.5,80.3,102.9,78.7,125.6,93.8,107.5,57.6,108.8,60.6,128.4,91,121.1,85.3,119.5,77.4,128.7,77.3,108.7,68.3,105.5,69.9,119.8,81.7,111.3,75.1,110.6,69.9,120.1,84,97.5,54.3,107.7,60,127.3,89.9,117.2,77,119.8,85.3,116.2,77.6,111,69.2,112.4,75.5,130.6,85.7,109.1,72.2,118.8,79.9,123.9,85.3,101.6,52.2,112.8,61.2,128,82.4,129.6,85.4,125.8,78.2,119.5,70.2,115.7,70.2,113.6,69.3,129.7,77.5,112,66.1,116.8,69,127,79.2,112.1,56.2,114.2,63.3,121.1,77.8,131.6,92,125,78.1,120.4,65.1,117.7,71.1,117.5,70.9,120.6,72,127.5,81.9,112.3,70.6,124.5,72.5,115.2,65.1,104.7,54.9,130.9,80,129.2,77.4,113.5,59.6,125.6,57.4,107.6,50.8),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100.6 71.7 1 0 0 0 0 0 0 0 0 0 0
2 104.3 77.5 0 1 0 0 0 0 0 0 0 0 0
3 120.4 89.8 0 0 1 0 0 0 0 0 0 0 0
4 107.5 80.3 0 0 0 1 0 0 0 0 0 0 0
5 102.9 78.7 0 0 0 0 1 0 0 0 0 0 0
6 125.6 93.8 0 0 0 0 0 1 0 0 0 0 0
7 107.5 57.6 0 0 0 0 0 0 1 0 0 0 0
8 108.8 60.6 0 0 0 0 0 0 0 1 0 0 0
9 128.4 91.0 0 0 0 0 0 0 0 0 1 0 0
10 121.1 85.3 0 0 0 0 0 0 0 0 0 1 0
11 119.5 77.4 0 0 0 0 0 0 0 0 0 0 1
12 128.7 77.3 0 0 0 0 0 0 0 0 0 0 0
13 108.7 68.3 1 0 0 0 0 0 0 0 0 0 0
14 105.5 69.9 0 1 0 0 0 0 0 0 0 0 0
15 119.8 81.7 0 0 1 0 0 0 0 0 0 0 0
16 111.3 75.1 0 0 0 1 0 0 0 0 0 0 0
17 110.6 69.9 0 0 0 0 1 0 0 0 0 0 0
18 120.1 84.0 0 0 0 0 0 1 0 0 0 0 0
19 97.5 54.3 0 0 0 0 0 0 1 0 0 0 0
20 107.7 60.0 0 0 0 0 0 0 0 1 0 0 0
21 127.3 89.9 0 0 0 0 0 0 0 0 1 0 0
22 117.2 77.0 0 0 0 0 0 0 0 0 0 1 0
23 119.8 85.3 0 0 0 0 0 0 0 0 0 0 1
24 116.2 77.6 0 0 0 0 0 0 0 0 0 0 0
25 111.0 69.2 1 0 0 0 0 0 0 0 0 0 0
26 112.4 75.5 0 1 0 0 0 0 0 0 0 0 0
27 130.6 85.7 0 0 1 0 0 0 0 0 0 0 0
28 109.1 72.2 0 0 0 1 0 0 0 0 0 0 0
29 118.8 79.9 0 0 0 0 1 0 0 0 0 0 0
30 123.9 85.3 0 0 0 0 0 1 0 0 0 0 0
31 101.6 52.2 0 0 0 0 0 0 1 0 0 0 0
32 112.8 61.2 0 0 0 0 0 0 0 1 0 0 0
33 128.0 82.4 0 0 0 0 0 0 0 0 1 0 0
34 129.6 85.4 0 0 0 0 0 0 0 0 0 1 0
35 125.8 78.2 0 0 0 0 0 0 0 0 0 0 1
36 119.5 70.2 0 0 0 0 0 0 0 0 0 0 0
37 115.7 70.2 1 0 0 0 0 0 0 0 0 0 0
38 113.6 69.3 0 1 0 0 0 0 0 0 0 0 0
39 129.7 77.5 0 0 1 0 0 0 0 0 0 0 0
40 112.0 66.1 0 0 0 1 0 0 0 0 0 0 0
41 116.8 69.0 0 0 0 0 1 0 0 0 0 0 0
42 127.0 79.2 0 0 0 0 0 1 0 0 0 0 0
43 112.1 56.2 0 0 0 0 0 0 1 0 0 0 0
44 114.2 63.3 0 0 0 0 0 0 0 1 0 0 0
45 121.1 77.8 0 0 0 0 0 0 0 0 1 0 0
46 131.6 92.0 0 0 0 0 0 0 0 0 0 1 0
47 125.0 78.1 0 0 0 0 0 0 0 0 0 0 1
48 120.4 65.1 0 0 0 0 0 0 0 0 0 0 0
49 117.7 71.1 1 0 0 0 0 0 0 0 0 0 0
50 117.5 70.9 0 1 0 0 0 0 0 0 0 0 0
51 120.6 72.0 0 0 1 0 0 0 0 0 0 0 0
52 127.5 81.9 0 0 0 1 0 0 0 0 0 0 0
53 112.3 70.6 0 0 0 0 1 0 0 0 0 0 0
54 124.5 72.5 0 0 0 0 0 1 0 0 0 0 0
55 115.2 65.1 0 0 0 0 0 0 1 0 0 0 0
56 104.7 54.9 0 0 0 0 0 0 0 1 0 0 0
57 130.9 80.0 0 0 0 0 0 0 0 0 1 0 0
58 129.2 77.4 0 0 0 0 0 0 0 0 0 1 0
59 113.5 59.6 0 0 0 0 0 0 0 0 0 0 1
60 125.6 57.4 0 0 0 0 0 0 0 0 0 0 0
61 107.6 50.8 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) X M1 M2 M3 M4
107.6869 0.2070 -11.3174 -12.0618 -0.3072 -9.7594
M5 M6 M7 M8 M9 M10
-10.6488 -0.6426 -12.7245 -10.4690 2.0166 0.7822
M11
-2.6436
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.6139 -3.8826 -0.1437 4.0836 12.6163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 107.6869 8.5702 12.565 < 2e-16 ***
X 0.2070 0.1181 1.753 0.08590 .
M1 -11.3174 3.3509 -3.377 0.00146 **
M2 -12.0618 3.5040 -3.442 0.00120 **
M3 -0.3072 3.7539 -0.082 0.93513
M4 -9.7594 3.5470 -2.751 0.00835 **
M5 -10.6488 3.5183 -3.027 0.00397 **
M6 -0.6426 3.8291 -0.168 0.86744
M7 -12.7245 3.7817 -3.365 0.00151 **
M8 -10.4690 3.6616 -2.859 0.00627 **
M9 2.0166 3.8931 0.518 0.60685
M10 0.7822 3.8519 0.203 0.83994
M11 -2.6436 3.5609 -0.742 0.46146
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.51 on 48 degrees of freedom
Multiple R-squared: 0.6838, Adjusted R-squared: 0.6047
F-statistic: 8.649 on 12 and 48 DF, p-value: 1.951e-08
> 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,] 0.2876917 0.5753833 0.7123083
[2,] 0.2099956 0.4199913 0.7900044
[3,] 0.3508571 0.7017142 0.6491429
[4,] 0.5806119 0.8387762 0.4193881
[5,] 0.4619827 0.9239655 0.5380173
[6,] 0.3528525 0.7057050 0.6471475
[7,] 0.3657377 0.7314754 0.6342623
[8,] 0.3018249 0.6036497 0.6981751
[9,] 0.6635552 0.6728896 0.3364448
[10,] 0.6876201 0.6247598 0.3123799
[11,] 0.7394533 0.5210935 0.2605467
[12,] 0.8048431 0.3903138 0.1951569
[13,] 0.8434792 0.3130417 0.1565208
[14,] 0.8875023 0.2249953 0.1124977
[15,] 0.8855788 0.2288423 0.1144212
[16,] 0.8981124 0.2037751 0.1018876
[17,] 0.8664169 0.2671663 0.1335831
[18,] 0.8067876 0.3864249 0.1932124
[19,] 0.8066259 0.3867482 0.1933741
[20,] 0.7702647 0.4594705 0.2297353
[21,] 0.8160112 0.3679776 0.1839888
[22,] 0.7996029 0.4007941 0.2003971
[23,] 0.7622866 0.4754268 0.2377134
[24,] 0.7621104 0.4757793 0.2378896
[25,] 0.7811307 0.4377386 0.2188693
[26,] 0.7559429 0.4881142 0.2440571
[27,] 0.6459268 0.7081463 0.3540732
[28,] 0.5631212 0.8737576 0.4368788
[29,] 0.5055983 0.9888033 0.4944017
[30,] 0.6022785 0.7954430 0.3977215
> postscript(file="/var/www/html/rcomp/tmp/1mwrd1258729588.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bzq01258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yx8w1258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46fh51258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nur71258729588.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
-10.6138897 -7.3703353 -5.5715240 -7.0524461 -10.4317425 -0.8642695
7 8 9 10 11 12
0.6123413 -0.9642216 -0.1437036 -5.0292275 -1.5678204 5.0092605
13 14 15 16 17 18
-1.8099676 -4.5968623 -4.4945329 -2.1758593 -0.9098263 -4.3353174
19 20 21 22 23 24
-8.7044401 -1.9400000 -1.0159641 -7.2108293 -2.9034042 -7.5528503
25 26 27 28 29 30
0.3037001 1.1437365 5.4773233 -3.7754551 5.2198144 -0.8044641
31 32 33 34 35 36
-4.1696647 2.9115569 1.2368054 3.4500689 4.5665509 -2.7207844
37 38 39 40 41 42
4.7966642 3.6273593 6.2750180 0.3874641 5.4765060 3.5584551
43 44 45 46 47 48
5.5021916 3.8767814 -4.7108293 4.0836317 3.7872545 -0.7649012
49 50 51 52 53 54
6.6103318 7.1961018 -1.6862844 12.6162964 0.6452485 2.4455958
55 56 57 58 59 60
6.7595718 -3.8841168 4.6336916 4.7063563 -3.8825808 6.0292755
61
0.7131612
> postscript(file="/var/www/html/rcomp/tmp/6rpve1258729588.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 -10.6138897 NA
1 -7.3703353 -10.6138897
2 -5.5715240 -7.3703353
3 -7.0524461 -5.5715240
4 -10.4317425 -7.0524461
5 -0.8642695 -10.4317425
6 0.6123413 -0.8642695
7 -0.9642216 0.6123413
8 -0.1437036 -0.9642216
9 -5.0292275 -0.1437036
10 -1.5678204 -5.0292275
11 5.0092605 -1.5678204
12 -1.8099676 5.0092605
13 -4.5968623 -1.8099676
14 -4.4945329 -4.5968623
15 -2.1758593 -4.4945329
16 -0.9098263 -2.1758593
17 -4.3353174 -0.9098263
18 -8.7044401 -4.3353174
19 -1.9400000 -8.7044401
20 -1.0159641 -1.9400000
21 -7.2108293 -1.0159641
22 -2.9034042 -7.2108293
23 -7.5528503 -2.9034042
24 0.3037001 -7.5528503
25 1.1437365 0.3037001
26 5.4773233 1.1437365
27 -3.7754551 5.4773233
28 5.2198144 -3.7754551
29 -0.8044641 5.2198144
30 -4.1696647 -0.8044641
31 2.9115569 -4.1696647
32 1.2368054 2.9115569
33 3.4500689 1.2368054
34 4.5665509 3.4500689
35 -2.7207844 4.5665509
36 4.7966642 -2.7207844
37 3.6273593 4.7966642
38 6.2750180 3.6273593
39 0.3874641 6.2750180
40 5.4765060 0.3874641
41 3.5584551 5.4765060
42 5.5021916 3.5584551
43 3.8767814 5.5021916
44 -4.7108293 3.8767814
45 4.0836317 -4.7108293
46 3.7872545 4.0836317
47 -0.7649012 3.7872545
48 6.6103318 -0.7649012
49 7.1961018 6.6103318
50 -1.6862844 7.1961018
51 12.6162964 -1.6862844
52 0.6452485 12.6162964
53 2.4455958 0.6452485
54 6.7595718 2.4455958
55 -3.8841168 6.7595718
56 4.6336916 -3.8841168
57 4.7063563 4.6336916
58 -3.8825808 4.7063563
59 6.0292755 -3.8825808
60 0.7131612 6.0292755
61 NA 0.7131612
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.3703353 -10.6138897
[2,] -5.5715240 -7.3703353
[3,] -7.0524461 -5.5715240
[4,] -10.4317425 -7.0524461
[5,] -0.8642695 -10.4317425
[6,] 0.6123413 -0.8642695
[7,] -0.9642216 0.6123413
[8,] -0.1437036 -0.9642216
[9,] -5.0292275 -0.1437036
[10,] -1.5678204 -5.0292275
[11,] 5.0092605 -1.5678204
[12,] -1.8099676 5.0092605
[13,] -4.5968623 -1.8099676
[14,] -4.4945329 -4.5968623
[15,] -2.1758593 -4.4945329
[16,] -0.9098263 -2.1758593
[17,] -4.3353174 -0.9098263
[18,] -8.7044401 -4.3353174
[19,] -1.9400000 -8.7044401
[20,] -1.0159641 -1.9400000
[21,] -7.2108293 -1.0159641
[22,] -2.9034042 -7.2108293
[23,] -7.5528503 -2.9034042
[24,] 0.3037001 -7.5528503
[25,] 1.1437365 0.3037001
[26,] 5.4773233 1.1437365
[27,] -3.7754551 5.4773233
[28,] 5.2198144 -3.7754551
[29,] -0.8044641 5.2198144
[30,] -4.1696647 -0.8044641
[31,] 2.9115569 -4.1696647
[32,] 1.2368054 2.9115569
[33,] 3.4500689 1.2368054
[34,] 4.5665509 3.4500689
[35,] -2.7207844 4.5665509
[36,] 4.7966642 -2.7207844
[37,] 3.6273593 4.7966642
[38,] 6.2750180 3.6273593
[39,] 0.3874641 6.2750180
[40,] 5.4765060 0.3874641
[41,] 3.5584551 5.4765060
[42,] 5.5021916 3.5584551
[43,] 3.8767814 5.5021916
[44,] -4.7108293 3.8767814
[45,] 4.0836317 -4.7108293
[46,] 3.7872545 4.0836317
[47,] -0.7649012 3.7872545
[48,] 6.6103318 -0.7649012
[49,] 7.1961018 6.6103318
[50,] -1.6862844 7.1961018
[51,] 12.6162964 -1.6862844
[52,] 0.6452485 12.6162964
[53,] 2.4455958 0.6452485
[54,] 6.7595718 2.4455958
[55,] -3.8841168 6.7595718
[56,] 4.6336916 -3.8841168
[57,] 4.7063563 4.6336916
[58,] -3.8825808 4.7063563
[59,] 6.0292755 -3.8825808
[60,] 0.7131612 6.0292755
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.3703353 -10.6138897
2 -5.5715240 -7.3703353
3 -7.0524461 -5.5715240
4 -10.4317425 -7.0524461
5 -0.8642695 -10.4317425
6 0.6123413 -0.8642695
7 -0.9642216 0.6123413
8 -0.1437036 -0.9642216
9 -5.0292275 -0.1437036
10 -1.5678204 -5.0292275
11 5.0092605 -1.5678204
12 -1.8099676 5.0092605
13 -4.5968623 -1.8099676
14 -4.4945329 -4.5968623
15 -2.1758593 -4.4945329
16 -0.9098263 -2.1758593
17 -4.3353174 -0.9098263
18 -8.7044401 -4.3353174
19 -1.9400000 -8.7044401
20 -1.0159641 -1.9400000
21 -7.2108293 -1.0159641
22 -2.9034042 -7.2108293
23 -7.5528503 -2.9034042
24 0.3037001 -7.5528503
25 1.1437365 0.3037001
26 5.4773233 1.1437365
27 -3.7754551 5.4773233
28 5.2198144 -3.7754551
29 -0.8044641 5.2198144
30 -4.1696647 -0.8044641
31 2.9115569 -4.1696647
32 1.2368054 2.9115569
33 3.4500689 1.2368054
34 4.5665509 3.4500689
35 -2.7207844 4.5665509
36 4.7966642 -2.7207844
37 3.6273593 4.7966642
38 6.2750180 3.6273593
39 0.3874641 6.2750180
40 5.4765060 0.3874641
41 3.5584551 5.4765060
42 5.5021916 3.5584551
43 3.8767814 5.5021916
44 -4.7108293 3.8767814
45 4.0836317 -4.7108293
46 3.7872545 4.0836317
47 -0.7649012 3.7872545
48 6.6103318 -0.7649012
49 7.1961018 6.6103318
50 -1.6862844 7.1961018
51 12.6162964 -1.6862844
52 0.6452485 12.6162964
53 2.4455958 0.6452485
54 6.7595718 2.4455958
55 -3.8841168 6.7595718
56 4.6336916 -3.8841168
57 4.7063563 4.6336916
58 -3.8825808 4.7063563
59 6.0292755 -3.8825808
60 0.7131612 6.0292755
> 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/7oj931258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8q1qm1258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9f7ut1258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108c361258729588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111kg41258729588.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/1256bv1258729588.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/13puhb1258729588.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/148jdl1258729588.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/15x0lr1258729588.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/16ocx71258729588.tab")
+ }
> system("convert tmp/1mwrd1258729588.ps tmp/1mwrd1258729588.png")
> system("convert tmp/2bzq01258729588.ps tmp/2bzq01258729588.png")
> system("convert tmp/3yx8w1258729588.ps tmp/3yx8w1258729588.png")
> system("convert tmp/46fh51258729588.ps tmp/46fh51258729588.png")
> system("convert tmp/5nur71258729588.ps tmp/5nur71258729588.png")
> system("convert tmp/6rpve1258729588.ps tmp/6rpve1258729588.png")
> system("convert tmp/7oj931258729588.ps tmp/7oj931258729588.png")
> system("convert tmp/8q1qm1258729588.ps tmp/8q1qm1258729588.png")
> system("convert tmp/9f7ut1258729588.ps tmp/9f7ut1258729588.png")
> system("convert tmp/108c361258729588.ps tmp/108c361258729588.png")
>
>
> proc.time()
user system elapsed
2.453 1.578 2.907