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(14.5,14.8,14.3,14.7,15.3,16,14.4,15.4,13.7,15,14.2,15.5,13.5,15.1,11.9,11.7,14.6,16.3,15.6,16.7,14.1,15,14.9,14.9,14.2,14.6,14.6,15.3,17.2,17.9,15.4,16.4,14.3,15.4,17.5,17.9,14.5,15.9,14.4,13.9,16.6,17.8,16.7,17.9,16.6,17.4,16.9,16.7,15.7,16,16.4,16.6,18.4,19.1,16.9,17.8,16.5,17.2,18.3,18.6,15.1,16.3,15.7,15.1,18.1,19.2,16.8,17.7,18.9,19.1,19,18,18.1,17.5,17.8,17.8,21.5,21.1,17.1,17.2,18.7,19.4,19,19.8,16.4,17.6,16.9,16.2,18.6,19.5,19.3,19.9,19.4,20,17.6,17.3,18.6,18.9,18.1,18.6,20.4,21.4,18.1,18.6,19.6,19.8,19.9,20.8,19.2,19.6,17.8,17.7,19.2,19.8,22,22.2,21.1,20.7,19.5,17.9,22.2,20.9,20.9,21.2,22.2,21.4,23.5,23,21.5,21.3,24.3,23.9,22.8,22.4,20.3,18.3,23.7,22.8,23.3,22.3,19.6,17.8,18,16.4,17.3,16,16.8,16.4,18.2,17.7,16.5,16.6,16,16.2,18.4,18.3),dim=c(2,78),dimnames=list(c('Y','X'),1:78))
> y <- array(NA,dim=c(2,78),dimnames=list(c('Y','X'),1:78))
> 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'
> #'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
1 14.5 14.8
2 14.3 14.7
3 15.3 16.0
4 14.4 15.4
5 13.7 15.0
6 14.2 15.5
7 13.5 15.1
8 11.9 11.7
9 14.6 16.3
10 15.6 16.7
11 14.1 15.0
12 14.9 14.9
13 14.2 14.6
14 14.6 15.3
15 17.2 17.9
16 15.4 16.4
17 14.3 15.4
18 17.5 17.9
19 14.5 15.9
20 14.4 13.9
21 16.6 17.8
22 16.7 17.9
23 16.6 17.4
24 16.9 16.7
25 15.7 16.0
26 16.4 16.6
27 18.4 19.1
28 16.9 17.8
29 16.5 17.2
30 18.3 18.6
31 15.1 16.3
32 15.7 15.1
33 18.1 19.2
34 16.8 17.7
35 18.9 19.1
36 19.0 18.0
37 18.1 17.5
38 17.8 17.8
39 21.5 21.1
40 17.1 17.2
41 18.7 19.4
42 19.0 19.8
43 16.4 17.6
44 16.9 16.2
45 18.6 19.5
46 19.3 19.9
47 19.4 20.0
48 17.6 17.3
49 18.6 18.9
50 18.1 18.6
51 20.4 21.4
52 18.1 18.6
53 19.6 19.8
54 19.9 20.8
55 19.2 19.6
56 17.8 17.7
57 19.2 19.8
58 22.0 22.2
59 21.1 20.7
60 19.5 17.9
61 22.2 20.9
62 20.9 21.2
63 22.2 21.4
64 23.5 23.0
65 21.5 21.3
66 24.3 23.9
67 22.8 22.4
68 20.3 18.3
69 23.7 22.8
70 23.3 22.3
71 19.6 17.8
72 18.0 16.4
73 17.3 16.0
74 16.8 16.4
75 18.2 17.7
76 16.5 16.6
77 16.0 16.2
78 18.4 18.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-1.536 1.074
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3716 -0.6040 -0.1366 0.4247 2.1801
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.53648 0.69584 -2.208 0.0303 *
X 1.07412 0.03839 27.976 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8172 on 76 degrees of freedom
Multiple R-squared: 0.9115, Adjusted R-squared: 0.9103
F-statistic: 782.7 on 1 and 76 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,] 0.177746941 0.35549388 0.82225306
[2,] 0.120413186 0.24082637 0.87958681
[3,] 0.157095258 0.31419052 0.84290474
[4,] 0.085673157 0.17134631 0.91432684
[5,] 0.061271393 0.12254279 0.93872861
[6,] 0.042002781 0.08400556 0.95799722
[7,] 0.021488683 0.04297737 0.97851132
[8,] 0.035487515 0.07097503 0.96451249
[9,] 0.021084749 0.04216950 0.97891525
[10,] 0.011720920 0.02344184 0.98827908
[11,] 0.019534341 0.03906868 0.98046566
[12,] 0.011481326 0.02296265 0.98851867
[13,] 0.007653369 0.01530674 0.99234663
[14,] 0.012405193 0.02481039 0.98759481
[15,] 0.012751252 0.02550250 0.98724875
[16,] 0.024998235 0.04999647 0.97500177
[17,] 0.018571364 0.03714273 0.98142864
[18,] 0.014072342 0.02814468 0.98592766
[19,] 0.010626955 0.02125391 0.98937304
[20,] 0.023904730 0.04780946 0.97609527
[21,] 0.018971649 0.03794330 0.98102835
[22,] 0.017464058 0.03492812 0.98253594
[23,] 0.014484546 0.02896909 0.98551545
[24,] 0.010962034 0.02192407 0.98903797
[25,] 0.007989534 0.01597907 0.99201047
[26,] 0.007727144 0.01545429 0.99227286
[27,] 0.009252821 0.01850564 0.99074718
[28,] 0.018591932 0.03718386 0.98140807
[29,] 0.017511159 0.03502232 0.98248884
[30,] 0.015828338 0.03165668 0.98417166
[31,] 0.016454345 0.03290869 0.98354565
[32,] 0.071412194 0.14282439 0.92858781
[33,] 0.099850770 0.19970154 0.90014923
[34,] 0.086605289 0.17321058 0.91339471
[35,] 0.092474716 0.18494943 0.90752528
[36,] 0.074954652 0.14990930 0.92504535
[37,] 0.065571590 0.13114318 0.93442841
[38,] 0.061141010 0.12228202 0.93885899
[39,] 0.092107978 0.18421596 0.90789202
[40,] 0.111217334 0.22243467 0.88878267
[41,] 0.120233693 0.24046739 0.87976631
[42,] 0.110207411 0.22041482 0.88979259
[43,] 0.102630210 0.20526042 0.89736979
[44,] 0.093850725 0.18770145 0.90614927
[45,] 0.080950056 0.16190011 0.91904994
[46,] 0.078124903 0.15624981 0.92187510
[47,] 0.104946786 0.20989357 0.89505321
[48,] 0.110848157 0.22169631 0.88915184
[49,] 0.100138344 0.20027669 0.89986166
[50,] 0.152037525 0.30407505 0.84796248
[51,] 0.167174186 0.33434837 0.83282581
[52,] 0.160878542 0.32175708 0.83912146
[53,] 0.229316161 0.45863232 0.77068384
[54,] 0.233365599 0.46673120 0.76663440
[55,] 0.215207752 0.43041550 0.78479225
[56,] 0.382453515 0.76490703 0.61754649
[57,] 0.458512862 0.91702572 0.54148714
[58,] 0.495102797 0.99020559 0.50489720
[59,] 0.447804237 0.89560847 0.55219576
[60,] 0.375551773 0.75110355 0.62444823
[61,] 0.329658947 0.65931789 0.67034105
[62,] 0.270837825 0.54167565 0.72916218
[63,] 0.232641193 0.46528239 0.76735881
[64,] 0.459654103 0.91930821 0.54034590
[65,] 0.364705879 0.72941176 0.63529412
[66,] 0.275802845 0.55160569 0.72419716
[67,] 0.524679344 0.95064131 0.47532066
[68,] 0.714233072 0.57153386 0.28576693
[69,] 0.927104917 0.14579017 0.07289508
> postscript(file="/var/www/html/rcomp/tmp/11igu1258656798.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2p8x61258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cw8o1258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41q2g1258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/538sc1258656798.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 = 78
Frequency = 1
1 2 3 4 5 6
0.13954673 0.04695843 -0.34939358 -0.60492342 -0.87527665 -0.91233511
7 8 9 10 11 12
-1.18268834 0.86930920 -1.37162865 -0.80127542 -0.47527665 0.43213504
13 14 15 16 17 18
0.05437012 -0.29751173 -0.49021573 -0.67904035 -0.70492342 -0.19021573
19 20 21 22 23 24
-1.04198188 1.00625197 -0.98280404 -0.99021573 -0.55315727 0.49872458
25 26 27 28 29 30
0.05060642 0.10613627 -0.57915604 -0.68280404 -0.43833388 -0.14209758
31 32 33 34 35 36
-0.87162865 1.01731166 -0.98656773 -0.67539235 -0.07915604 1.20237258
37 38 39 40 41 42
0.83943104 0.21719596 0.37261011 0.16166612 -0.60139112 -0.73103789
43 44 45 46 47 48
-0.96798065 1.03578304 -0.80880281 -0.53844958 -0.54586127 0.55425442
49 50 51 52 53 54
-0.16433266 -0.34209758 -1.04962497 -0.34209758 -0.13103789 -0.90515481
55 56 57 58 59 60
-0.31621450 0.32460765 -0.53103789 -0.30891851 0.40225688 1.80978427
61 62 63 64 65 66
1.28743350 -0.33480158 0.75037503 0.33178795 0.15778673 0.16508272
67 68 69 70 71 72
0.27625811 2.18013750 0.74661134 0.88366980 2.01719596 1.92095965
73 74 75 76 77 78
1.65060642 0.72095965 0.72460765 0.20613627 0.13578304 0.28013750
> postscript(file="/var/www/html/rcomp/tmp/6zkj91258656798.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 = 78
Frequency = 1
lag(myerror, k = 1) myerror
0 0.13954673 NA
1 0.04695843 0.13954673
2 -0.34939358 0.04695843
3 -0.60492342 -0.34939358
4 -0.87527665 -0.60492342
5 -0.91233511 -0.87527665
6 -1.18268834 -0.91233511
7 0.86930920 -1.18268834
8 -1.37162865 0.86930920
9 -0.80127542 -1.37162865
10 -0.47527665 -0.80127542
11 0.43213504 -0.47527665
12 0.05437012 0.43213504
13 -0.29751173 0.05437012
14 -0.49021573 -0.29751173
15 -0.67904035 -0.49021573
16 -0.70492342 -0.67904035
17 -0.19021573 -0.70492342
18 -1.04198188 -0.19021573
19 1.00625197 -1.04198188
20 -0.98280404 1.00625197
21 -0.99021573 -0.98280404
22 -0.55315727 -0.99021573
23 0.49872458 -0.55315727
24 0.05060642 0.49872458
25 0.10613627 0.05060642
26 -0.57915604 0.10613627
27 -0.68280404 -0.57915604
28 -0.43833388 -0.68280404
29 -0.14209758 -0.43833388
30 -0.87162865 -0.14209758
31 1.01731166 -0.87162865
32 -0.98656773 1.01731166
33 -0.67539235 -0.98656773
34 -0.07915604 -0.67539235
35 1.20237258 -0.07915604
36 0.83943104 1.20237258
37 0.21719596 0.83943104
38 0.37261011 0.21719596
39 0.16166612 0.37261011
40 -0.60139112 0.16166612
41 -0.73103789 -0.60139112
42 -0.96798065 -0.73103789
43 1.03578304 -0.96798065
44 -0.80880281 1.03578304
45 -0.53844958 -0.80880281
46 -0.54586127 -0.53844958
47 0.55425442 -0.54586127
48 -0.16433266 0.55425442
49 -0.34209758 -0.16433266
50 -1.04962497 -0.34209758
51 -0.34209758 -1.04962497
52 -0.13103789 -0.34209758
53 -0.90515481 -0.13103789
54 -0.31621450 -0.90515481
55 0.32460765 -0.31621450
56 -0.53103789 0.32460765
57 -0.30891851 -0.53103789
58 0.40225688 -0.30891851
59 1.80978427 0.40225688
60 1.28743350 1.80978427
61 -0.33480158 1.28743350
62 0.75037503 -0.33480158
63 0.33178795 0.75037503
64 0.15778673 0.33178795
65 0.16508272 0.15778673
66 0.27625811 0.16508272
67 2.18013750 0.27625811
68 0.74661134 2.18013750
69 0.88366980 0.74661134
70 2.01719596 0.88366980
71 1.92095965 2.01719596
72 1.65060642 1.92095965
73 0.72095965 1.65060642
74 0.72460765 0.72095965
75 0.20613627 0.72460765
76 0.13578304 0.20613627
77 0.28013750 0.13578304
78 NA 0.28013750
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.04695843 0.13954673
[2,] -0.34939358 0.04695843
[3,] -0.60492342 -0.34939358
[4,] -0.87527665 -0.60492342
[5,] -0.91233511 -0.87527665
[6,] -1.18268834 -0.91233511
[7,] 0.86930920 -1.18268834
[8,] -1.37162865 0.86930920
[9,] -0.80127542 -1.37162865
[10,] -0.47527665 -0.80127542
[11,] 0.43213504 -0.47527665
[12,] 0.05437012 0.43213504
[13,] -0.29751173 0.05437012
[14,] -0.49021573 -0.29751173
[15,] -0.67904035 -0.49021573
[16,] -0.70492342 -0.67904035
[17,] -0.19021573 -0.70492342
[18,] -1.04198188 -0.19021573
[19,] 1.00625197 -1.04198188
[20,] -0.98280404 1.00625197
[21,] -0.99021573 -0.98280404
[22,] -0.55315727 -0.99021573
[23,] 0.49872458 -0.55315727
[24,] 0.05060642 0.49872458
[25,] 0.10613627 0.05060642
[26,] -0.57915604 0.10613627
[27,] -0.68280404 -0.57915604
[28,] -0.43833388 -0.68280404
[29,] -0.14209758 -0.43833388
[30,] -0.87162865 -0.14209758
[31,] 1.01731166 -0.87162865
[32,] -0.98656773 1.01731166
[33,] -0.67539235 -0.98656773
[34,] -0.07915604 -0.67539235
[35,] 1.20237258 -0.07915604
[36,] 0.83943104 1.20237258
[37,] 0.21719596 0.83943104
[38,] 0.37261011 0.21719596
[39,] 0.16166612 0.37261011
[40,] -0.60139112 0.16166612
[41,] -0.73103789 -0.60139112
[42,] -0.96798065 -0.73103789
[43,] 1.03578304 -0.96798065
[44,] -0.80880281 1.03578304
[45,] -0.53844958 -0.80880281
[46,] -0.54586127 -0.53844958
[47,] 0.55425442 -0.54586127
[48,] -0.16433266 0.55425442
[49,] -0.34209758 -0.16433266
[50,] -1.04962497 -0.34209758
[51,] -0.34209758 -1.04962497
[52,] -0.13103789 -0.34209758
[53,] -0.90515481 -0.13103789
[54,] -0.31621450 -0.90515481
[55,] 0.32460765 -0.31621450
[56,] -0.53103789 0.32460765
[57,] -0.30891851 -0.53103789
[58,] 0.40225688 -0.30891851
[59,] 1.80978427 0.40225688
[60,] 1.28743350 1.80978427
[61,] -0.33480158 1.28743350
[62,] 0.75037503 -0.33480158
[63,] 0.33178795 0.75037503
[64,] 0.15778673 0.33178795
[65,] 0.16508272 0.15778673
[66,] 0.27625811 0.16508272
[67,] 2.18013750 0.27625811
[68,] 0.74661134 2.18013750
[69,] 0.88366980 0.74661134
[70,] 2.01719596 0.88366980
[71,] 1.92095965 2.01719596
[72,] 1.65060642 1.92095965
[73,] 0.72095965 1.65060642
[74,] 0.72460765 0.72095965
[75,] 0.20613627 0.72460765
[76,] 0.13578304 0.20613627
[77,] 0.28013750 0.13578304
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.04695843 0.13954673
2 -0.34939358 0.04695843
3 -0.60492342 -0.34939358
4 -0.87527665 -0.60492342
5 -0.91233511 -0.87527665
6 -1.18268834 -0.91233511
7 0.86930920 -1.18268834
8 -1.37162865 0.86930920
9 -0.80127542 -1.37162865
10 -0.47527665 -0.80127542
11 0.43213504 -0.47527665
12 0.05437012 0.43213504
13 -0.29751173 0.05437012
14 -0.49021573 -0.29751173
15 -0.67904035 -0.49021573
16 -0.70492342 -0.67904035
17 -0.19021573 -0.70492342
18 -1.04198188 -0.19021573
19 1.00625197 -1.04198188
20 -0.98280404 1.00625197
21 -0.99021573 -0.98280404
22 -0.55315727 -0.99021573
23 0.49872458 -0.55315727
24 0.05060642 0.49872458
25 0.10613627 0.05060642
26 -0.57915604 0.10613627
27 -0.68280404 -0.57915604
28 -0.43833388 -0.68280404
29 -0.14209758 -0.43833388
30 -0.87162865 -0.14209758
31 1.01731166 -0.87162865
32 -0.98656773 1.01731166
33 -0.67539235 -0.98656773
34 -0.07915604 -0.67539235
35 1.20237258 -0.07915604
36 0.83943104 1.20237258
37 0.21719596 0.83943104
38 0.37261011 0.21719596
39 0.16166612 0.37261011
40 -0.60139112 0.16166612
41 -0.73103789 -0.60139112
42 -0.96798065 -0.73103789
43 1.03578304 -0.96798065
44 -0.80880281 1.03578304
45 -0.53844958 -0.80880281
46 -0.54586127 -0.53844958
47 0.55425442 -0.54586127
48 -0.16433266 0.55425442
49 -0.34209758 -0.16433266
50 -1.04962497 -0.34209758
51 -0.34209758 -1.04962497
52 -0.13103789 -0.34209758
53 -0.90515481 -0.13103789
54 -0.31621450 -0.90515481
55 0.32460765 -0.31621450
56 -0.53103789 0.32460765
57 -0.30891851 -0.53103789
58 0.40225688 -0.30891851
59 1.80978427 0.40225688
60 1.28743350 1.80978427
61 -0.33480158 1.28743350
62 0.75037503 -0.33480158
63 0.33178795 0.75037503
64 0.15778673 0.33178795
65 0.16508272 0.15778673
66 0.27625811 0.16508272
67 2.18013750 0.27625811
68 0.74661134 2.18013750
69 0.88366980 0.74661134
70 2.01719596 0.88366980
71 1.92095965 2.01719596
72 1.65060642 1.92095965
73 0.72095965 1.65060642
74 0.72460765 0.72095965
75 0.20613627 0.72460765
76 0.13578304 0.20613627
77 0.28013750 0.13578304
> 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/7x4x21258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cfhb1258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9btre1258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103p0q1258656798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nqk61258656798.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/12ld3q1258656798.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/13bpse1258656798.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/14m4la1258656798.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/15nem71258656798.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/16ggh01258656798.tab")
+ }
>
> system("convert tmp/11igu1258656798.ps tmp/11igu1258656798.png")
> system("convert tmp/2p8x61258656798.ps tmp/2p8x61258656798.png")
> system("convert tmp/3cw8o1258656798.ps tmp/3cw8o1258656798.png")
> system("convert tmp/41q2g1258656798.ps tmp/41q2g1258656798.png")
> system("convert tmp/538sc1258656798.ps tmp/538sc1258656798.png")
> system("convert tmp/6zkj91258656798.ps tmp/6zkj91258656798.png")
> system("convert tmp/7x4x21258656798.ps tmp/7x4x21258656798.png")
> system("convert tmp/8cfhb1258656798.ps tmp/8cfhb1258656798.png")
> system("convert tmp/9btre1258656798.ps tmp/9btre1258656798.png")
> system("convert tmp/103p0q1258656798.ps tmp/103p0q1258656798.png")
>
>
> proc.time()
user system elapsed
2.617 1.613 3.058