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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(-3
+ ,-19
+ ,53
+ ,24
+ ,-2
+ ,-29
+ ,-4
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+ ,50
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+ ,54
+ ,40
+ ,-11
+ ,-40
+ ,-16
+ ,-41
+ ,58
+ ,50
+ ,-11
+ ,-41)
+ ,dim=c(6
+ ,82)
+ ,dimnames=list(c('Y_t'
+ ,'X_1t'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t')
+ ,1:82))
> y <- array(NA,dim=c(6,82),dimnames=list(c('Y_t','X_1t','X_2t','X_3t','X_4t','X_5t'),1:82))
> 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'
> 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, 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_t X_1t X_2t X_3t X_4t X_5t
1 -3 -19 53 24 -2 -29
2 -4 -20 50 24 -4 -29
3 -7 -21 50 31 -5 -27
4 -7 -19 51 25 -2 -29
5 -7 -17 53 28 -4 -24
6 -3 -16 49 24 -4 -29
7 0 -10 54 25 -5 -21
8 -5 -16 57 16 -7 -20
9 -3 -10 58 17 -5 -26
10 3 -8 56 11 -6 -19
11 2 -7 60 12 -4 -22
12 -7 -15 55 39 -2 -22
13 -1 -7 54 19 -3 -15
14 0 -6 52 14 0 -16
15 -3 -6 55 15 -4 -22
16 4 2 56 7 -3 -21
17 2 -4 54 12 -3 -11
18 3 -4 53 12 -3 -10
19 0 -8 59 14 -4 -6
20 -10 -10 62 9 -5 -8
21 -10 -16 63 8 -5 -15
22 -9 -14 64 4 -6 -16
23 -22 -30 75 7 -10 -24
24 -16 -33 77 3 -11 -27
25 -18 -40 79 5 -13 -33
26 -14 -38 77 0 -12 -29
27 -12 -39 82 -2 -13 -34
28 -17 -46 83 6 -12 -37
29 -23 -50 81 11 -15 -31
30 -28 -55 78 9 -14 -33
31 -31 -66 79 17 -16 -25
32 -21 -63 79 21 -16 -27
33 -19 -56 73 21 -12 -21
34 -22 -66 72 41 -16 -32
35 -22 -63 67 57 -15 -31
36 -25 -69 67 65 -17 -32
37 -16 -69 50 68 -15 -30
38 -22 -72 45 73 -14 -34
39 -21 -69 39 71 -15 -35
40 -10 -67 39 71 -14 -37
41 -7 -64 37 70 -16 -32
42 -5 -61 30 69 -11 -28
43 -4 -58 24 65 -14 -26
44 7 -47 27 57 -12 -24
45 6 -44 19 57 -11 -27
46 3 -42 19 57 -13 -26
47 10 -34 25 55 -12 -27
48 0 -38 16 65 -12 -27
49 -2 -41 20 65 -10 -24
50 -1 -38 25 64 -12 -28
51 2 -37 34 60 -11 -23
52 8 -22 39 43 -10 -23
53 -6 -37 40 47 -12 -29
54 -4 -36 38 40 -12 -25
55 4 -25 42 31 -11 -24
56 7 -15 46 27 -12 -20
57 3 -17 48 24 -9 -22
58 3 -19 51 23 -6 -24
59 8 -12 55 17 -7 -27
60 3 -17 52 16 -7 -25
61 -3 -21 55 15 -10 -26
62 4 -10 58 8 -8 -24
63 -5 -19 72 5 -11 -26
64 -1 -14 70 6 -12 -22
65 5 -8 70 5 -11 -20
66 0 -16 63 12 -11 -26
67 -6 -14 66 8 -9 -22
68 -13 -30 65 17 -9 -29
69 -15 -33 55 22 -12 -30
70 -8 -37 57 24 -10 -26
71 -20 -47 60 36 -10 -30
72 -10 -48 63 31 -13 -33
73 -22 -50 65 34 -13 -33
74 -25 -56 61 47 -12 -31
75 -10 -47 65 33 -14 -36
76 -8 -37 63 35 -9 -43
77 -9 -35 59 31 -12 -40
78 -5 -29 56 35 -10 -38
79 -7 -28 54 39 -13 -41
80 -11 -29 56 46 -11 -38
81 -11 -33 54 40 -11 -40
82 -16 -41 58 50 -11 -41
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
25.805037 0.428060 -0.450778 -0.077614 -0.816445 0.005186
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.8205 -2.9511 0.5013 3.1853 8.2359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.805037 4.064581 6.349 1.44e-08 ***
X_1t 0.428060 0.059689 7.172 4.17e-10 ***
X_2t -0.450778 0.064245 -7.017 8.17e-10 ***
X_3t -0.077614 0.062086 -1.250 0.215100
X_4t -0.816445 0.204637 -3.990 0.000151 ***
X_5t 0.005186 0.080931 0.064 0.949077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.068 on 76 degrees of freedom
Multiple R-squared: 0.8351, Adjusted R-squared: 0.8242
F-statistic: 76.96 on 5 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,] 0.2028754613 0.405750923 0.7971245
[2,] 0.0971702778 0.194340556 0.9028297
[3,] 0.0460849200 0.092169840 0.9539151
[4,] 0.0181930656 0.036386131 0.9818069
[5,] 0.0177471682 0.035494336 0.9822528
[6,] 0.0089081990 0.017816398 0.9910918
[7,] 0.0201177751 0.040235550 0.9798822
[8,] 0.0094783545 0.018956709 0.9905216
[9,] 0.0043701820 0.008740364 0.9956298
[10,] 0.0021176328 0.004235266 0.9978824
[11,] 0.0009061939 0.001812388 0.9990938
[12,] 0.0179954122 0.035990824 0.9820046
[13,] 0.0123156302 0.024631260 0.9876844
[14,] 0.0089345572 0.017869114 0.9910654
[15,] 0.0088916023 0.017783205 0.9911084
[16,] 0.0250404995 0.050080999 0.9749595
[17,] 0.0310438940 0.062087788 0.9689561
[18,] 0.0431938051 0.086387610 0.9568062
[19,] 0.0899097418 0.179819484 0.9100903
[20,] 0.1126542171 0.225308434 0.8873458
[21,] 0.0808856433 0.161771287 0.9191144
[22,] 0.0752523490 0.150504698 0.9247477
[23,] 0.0738125471 0.147625094 0.9261875
[24,] 0.1714264341 0.342852868 0.8285736
[25,] 0.1902386330 0.380477266 0.8097614
[26,] 0.1754022100 0.350804420 0.8245978
[27,] 0.1345219266 0.269043853 0.8654781
[28,] 0.1015809882 0.203161976 0.8984190
[29,] 0.0910175065 0.182035013 0.9089825
[30,] 0.0781610628 0.156322126 0.9218389
[31,] 0.0854754050 0.170950810 0.9145246
[32,] 0.1246008275 0.249201655 0.8753992
[33,] 0.1567450708 0.313490142 0.8432549
[34,] 0.1837849874 0.367569975 0.8162150
[35,] 0.1464800990 0.292960198 0.8535199
[36,] 0.3329243660 0.665848732 0.6670756
[37,] 0.3363936110 0.672787222 0.6636064
[38,] 0.3125626091 0.625125218 0.6874374
[39,] 0.4161607732 0.832321546 0.5838392
[40,] 0.4844765710 0.968953142 0.5155234
[41,] 0.4666879072 0.933375814 0.5333121
[42,] 0.4247395151 0.849479030 0.5752605
[43,] 0.4240858107 0.848171621 0.5759142
[44,] 0.4762805898 0.952561180 0.5237194
[45,] 0.4281785056 0.856357011 0.5718215
[46,] 0.3733082032 0.746616406 0.6266918
[47,] 0.3434236234 0.686847247 0.6565764
[48,] 0.3354401419 0.670880284 0.6645599
[49,] 0.2930190484 0.586038097 0.7069810
[50,] 0.3135408858 0.627081772 0.6864591
[51,] 0.4557719700 0.911543940 0.5442280
[52,] 0.5034074333 0.993185133 0.4965926
[53,] 0.4292284895 0.858456979 0.5707715
[54,] 0.4386498116 0.877299623 0.5613502
[55,] 0.4178419344 0.835683869 0.5821581
[56,] 0.3448296585 0.689659317 0.6551703
[57,] 0.3583071016 0.716614203 0.6416929
[58,] 0.3407087143 0.681417429 0.6592913
[59,] 0.2658559436 0.531711887 0.7341441
[60,] 0.3447453447 0.689490689 0.6552547
[61,] 0.6576315078 0.684736984 0.3423685
[62,] 0.5755620894 0.848875821 0.4244379
[63,] 0.4752256323 0.950451265 0.5247744
[64,] 0.5589858702 0.882028260 0.4410141
[65,] 0.8534444259 0.293111148 0.1465556
> postscript(file="/var/wessaorg/rcomp/tmp/1an6z1355568920.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22cev1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/329j11355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4lhyt1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5bjhy1355568920.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 = 82
Frequency = 1
1 2 3 4 5 6
3.59954721 0.04238554 -2.81307431 -1.22439409 -2.60493705 -1.12063322
7 8 9 10 11 12
0.78457483 -2.63132972 -1.00729584 1.91660041 4.01771088 -0.08323368
13 14 15 16 17 18
-0.36351422 1.37332165 -3.43139580 0.78524813 0.78826526 1.33230189
19 20 21 22 23 24
2.06724768 -6.91844089 -3.94061436 -4.46767131 -8.65160446 -1.57721066
25 26 27 28 29 30
-1.12578031 1.52417663 5.26038156 5.16049345 -2.12120079 -5.66164291
31 32 33 34 35 36
-4.55566816 4.48097758 4.01455406 3.18792001 1.70293029 0.26449855
37 38 39 40 41 42
3.45663965 -2.28781022 -6.24314264 4.72755313 3.80538479 5.34962762
43 44 45 46 47 48
-0.40937922 8.23589769 3.17749882 -2.31669705 4.62988865 -6.93873033
49 50 51 52 53 54
-4.23410743 -3.95416054 3.15483735 4.48483132 -3.93480588 -3.82846083
55 56 57 58 59 60
1.37872099 0.75358492 0.73812514 5.32867038 7.86878864 3.56877219
61 62 63 64 65 66
-1.88841602 2.83447464 1.32609905 1.52466813 5.68476555 1.52821666
67 68 69 70 71 72
-2.67388034 -2.54086830 -9.82054212 1.56062769 -3.85432832 5.10421924
73 74 75 76 77 78
-4.90526386 -5.32496036 4.93205428 6.02364807 -0.41092927 1.60134902
79 80 81 82
-3.85158793 -4.36134447 -4.00596907 -2.99705320
> postscript(file="/var/wessaorg/rcomp/tmp/6bh2j1355568920.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 = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 3.59954721 NA
1 0.04238554 3.59954721
2 -2.81307431 0.04238554
3 -1.22439409 -2.81307431
4 -2.60493705 -1.22439409
5 -1.12063322 -2.60493705
6 0.78457483 -1.12063322
7 -2.63132972 0.78457483
8 -1.00729584 -2.63132972
9 1.91660041 -1.00729584
10 4.01771088 1.91660041
11 -0.08323368 4.01771088
12 -0.36351422 -0.08323368
13 1.37332165 -0.36351422
14 -3.43139580 1.37332165
15 0.78524813 -3.43139580
16 0.78826526 0.78524813
17 1.33230189 0.78826526
18 2.06724768 1.33230189
19 -6.91844089 2.06724768
20 -3.94061436 -6.91844089
21 -4.46767131 -3.94061436
22 -8.65160446 -4.46767131
23 -1.57721066 -8.65160446
24 -1.12578031 -1.57721066
25 1.52417663 -1.12578031
26 5.26038156 1.52417663
27 5.16049345 5.26038156
28 -2.12120079 5.16049345
29 -5.66164291 -2.12120079
30 -4.55566816 -5.66164291
31 4.48097758 -4.55566816
32 4.01455406 4.48097758
33 3.18792001 4.01455406
34 1.70293029 3.18792001
35 0.26449855 1.70293029
36 3.45663965 0.26449855
37 -2.28781022 3.45663965
38 -6.24314264 -2.28781022
39 4.72755313 -6.24314264
40 3.80538479 4.72755313
41 5.34962762 3.80538479
42 -0.40937922 5.34962762
43 8.23589769 -0.40937922
44 3.17749882 8.23589769
45 -2.31669705 3.17749882
46 4.62988865 -2.31669705
47 -6.93873033 4.62988865
48 -4.23410743 -6.93873033
49 -3.95416054 -4.23410743
50 3.15483735 -3.95416054
51 4.48483132 3.15483735
52 -3.93480588 4.48483132
53 -3.82846083 -3.93480588
54 1.37872099 -3.82846083
55 0.75358492 1.37872099
56 0.73812514 0.75358492
57 5.32867038 0.73812514
58 7.86878864 5.32867038
59 3.56877219 7.86878864
60 -1.88841602 3.56877219
61 2.83447464 -1.88841602
62 1.32609905 2.83447464
63 1.52466813 1.32609905
64 5.68476555 1.52466813
65 1.52821666 5.68476555
66 -2.67388034 1.52821666
67 -2.54086830 -2.67388034
68 -9.82054212 -2.54086830
69 1.56062769 -9.82054212
70 -3.85432832 1.56062769
71 5.10421924 -3.85432832
72 -4.90526386 5.10421924
73 -5.32496036 -4.90526386
74 4.93205428 -5.32496036
75 6.02364807 4.93205428
76 -0.41092927 6.02364807
77 1.60134902 -0.41092927
78 -3.85158793 1.60134902
79 -4.36134447 -3.85158793
80 -4.00596907 -4.36134447
81 -2.99705320 -4.00596907
82 NA -2.99705320
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.04238554 3.59954721
[2,] -2.81307431 0.04238554
[3,] -1.22439409 -2.81307431
[4,] -2.60493705 -1.22439409
[5,] -1.12063322 -2.60493705
[6,] 0.78457483 -1.12063322
[7,] -2.63132972 0.78457483
[8,] -1.00729584 -2.63132972
[9,] 1.91660041 -1.00729584
[10,] 4.01771088 1.91660041
[11,] -0.08323368 4.01771088
[12,] -0.36351422 -0.08323368
[13,] 1.37332165 -0.36351422
[14,] -3.43139580 1.37332165
[15,] 0.78524813 -3.43139580
[16,] 0.78826526 0.78524813
[17,] 1.33230189 0.78826526
[18,] 2.06724768 1.33230189
[19,] -6.91844089 2.06724768
[20,] -3.94061436 -6.91844089
[21,] -4.46767131 -3.94061436
[22,] -8.65160446 -4.46767131
[23,] -1.57721066 -8.65160446
[24,] -1.12578031 -1.57721066
[25,] 1.52417663 -1.12578031
[26,] 5.26038156 1.52417663
[27,] 5.16049345 5.26038156
[28,] -2.12120079 5.16049345
[29,] -5.66164291 -2.12120079
[30,] -4.55566816 -5.66164291
[31,] 4.48097758 -4.55566816
[32,] 4.01455406 4.48097758
[33,] 3.18792001 4.01455406
[34,] 1.70293029 3.18792001
[35,] 0.26449855 1.70293029
[36,] 3.45663965 0.26449855
[37,] -2.28781022 3.45663965
[38,] -6.24314264 -2.28781022
[39,] 4.72755313 -6.24314264
[40,] 3.80538479 4.72755313
[41,] 5.34962762 3.80538479
[42,] -0.40937922 5.34962762
[43,] 8.23589769 -0.40937922
[44,] 3.17749882 8.23589769
[45,] -2.31669705 3.17749882
[46,] 4.62988865 -2.31669705
[47,] -6.93873033 4.62988865
[48,] -4.23410743 -6.93873033
[49,] -3.95416054 -4.23410743
[50,] 3.15483735 -3.95416054
[51,] 4.48483132 3.15483735
[52,] -3.93480588 4.48483132
[53,] -3.82846083 -3.93480588
[54,] 1.37872099 -3.82846083
[55,] 0.75358492 1.37872099
[56,] 0.73812514 0.75358492
[57,] 5.32867038 0.73812514
[58,] 7.86878864 5.32867038
[59,] 3.56877219 7.86878864
[60,] -1.88841602 3.56877219
[61,] 2.83447464 -1.88841602
[62,] 1.32609905 2.83447464
[63,] 1.52466813 1.32609905
[64,] 5.68476555 1.52466813
[65,] 1.52821666 5.68476555
[66,] -2.67388034 1.52821666
[67,] -2.54086830 -2.67388034
[68,] -9.82054212 -2.54086830
[69,] 1.56062769 -9.82054212
[70,] -3.85432832 1.56062769
[71,] 5.10421924 -3.85432832
[72,] -4.90526386 5.10421924
[73,] -5.32496036 -4.90526386
[74,] 4.93205428 -5.32496036
[75,] 6.02364807 4.93205428
[76,] -0.41092927 6.02364807
[77,] 1.60134902 -0.41092927
[78,] -3.85158793 1.60134902
[79,] -4.36134447 -3.85158793
[80,] -4.00596907 -4.36134447
[81,] -2.99705320 -4.00596907
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.04238554 3.59954721
2 -2.81307431 0.04238554
3 -1.22439409 -2.81307431
4 -2.60493705 -1.22439409
5 -1.12063322 -2.60493705
6 0.78457483 -1.12063322
7 -2.63132972 0.78457483
8 -1.00729584 -2.63132972
9 1.91660041 -1.00729584
10 4.01771088 1.91660041
11 -0.08323368 4.01771088
12 -0.36351422 -0.08323368
13 1.37332165 -0.36351422
14 -3.43139580 1.37332165
15 0.78524813 -3.43139580
16 0.78826526 0.78524813
17 1.33230189 0.78826526
18 2.06724768 1.33230189
19 -6.91844089 2.06724768
20 -3.94061436 -6.91844089
21 -4.46767131 -3.94061436
22 -8.65160446 -4.46767131
23 -1.57721066 -8.65160446
24 -1.12578031 -1.57721066
25 1.52417663 -1.12578031
26 5.26038156 1.52417663
27 5.16049345 5.26038156
28 -2.12120079 5.16049345
29 -5.66164291 -2.12120079
30 -4.55566816 -5.66164291
31 4.48097758 -4.55566816
32 4.01455406 4.48097758
33 3.18792001 4.01455406
34 1.70293029 3.18792001
35 0.26449855 1.70293029
36 3.45663965 0.26449855
37 -2.28781022 3.45663965
38 -6.24314264 -2.28781022
39 4.72755313 -6.24314264
40 3.80538479 4.72755313
41 5.34962762 3.80538479
42 -0.40937922 5.34962762
43 8.23589769 -0.40937922
44 3.17749882 8.23589769
45 -2.31669705 3.17749882
46 4.62988865 -2.31669705
47 -6.93873033 4.62988865
48 -4.23410743 -6.93873033
49 -3.95416054 -4.23410743
50 3.15483735 -3.95416054
51 4.48483132 3.15483735
52 -3.93480588 4.48483132
53 -3.82846083 -3.93480588
54 1.37872099 -3.82846083
55 0.75358492 1.37872099
56 0.73812514 0.75358492
57 5.32867038 0.73812514
58 7.86878864 5.32867038
59 3.56877219 7.86878864
60 -1.88841602 3.56877219
61 2.83447464 -1.88841602
62 1.32609905 2.83447464
63 1.52466813 1.32609905
64 5.68476555 1.52466813
65 1.52821666 5.68476555
66 -2.67388034 1.52821666
67 -2.54086830 -2.67388034
68 -9.82054212 -2.54086830
69 1.56062769 -9.82054212
70 -3.85432832 1.56062769
71 5.10421924 -3.85432832
72 -4.90526386 5.10421924
73 -5.32496036 -4.90526386
74 4.93205428 -5.32496036
75 6.02364807 4.93205428
76 -0.41092927 6.02364807
77 1.60134902 -0.41092927
78 -3.85158793 1.60134902
79 -4.36134447 -3.85158793
80 -4.00596907 -4.36134447
81 -2.99705320 -4.00596907
> 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/wessaorg/rcomp/tmp/7l50a1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/86i3v1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/90fjx1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pirm1355568920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11hrgm1355568920.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/wessaorg/rcomp/tmp/12i89k1355568920.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/wessaorg/rcomp/tmp/13p58v1355568920.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/wessaorg/rcomp/tmp/14k5mx1355568920.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/wessaorg/rcomp/tmp/15rex21355568920.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/wessaorg/rcomp/tmp/16utg21355568920.tab")
+ }
>
> try(system("convert tmp/1an6z1355568920.ps tmp/1an6z1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/22cev1355568920.ps tmp/22cev1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/329j11355568920.ps tmp/329j11355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lhyt1355568920.ps tmp/4lhyt1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bjhy1355568920.ps tmp/5bjhy1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bh2j1355568920.ps tmp/6bh2j1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l50a1355568920.ps tmp/7l50a1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/86i3v1355568920.ps tmp/86i3v1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/90fjx1355568920.ps tmp/90fjx1355568920.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pirm1355568920.ps tmp/10pirm1355568920.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.575 1.213 7.779