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(0
+ ,9
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+ ,15)
+ ,dim=c(12
+ ,77)
+ ,dimnames=list(c('Gen'
+ ,'DoubtsAboutActions'
+ ,'ParentalExpectations'
+ ,'Expect_gen'
+ ,'ParentalCritism'
+ ,'Critism_gen'
+ ,'PersonalStandards'
+ ,'PersStand_gen'
+ ,'Popularity'
+ ,'Popular_gen'
+ ,'KnowingPeople'
+ ,'Knowing_gen')
+ ,1:77))
> y <- array(NA,dim=c(12,77),dimnames=list(c('Gen','DoubtsAboutActions','ParentalExpectations','Expect_gen','ParentalCritism','Critism_gen','PersonalStandards','PersStand_gen','Popularity','Popular_gen','KnowingPeople','Knowing_gen'),1:77))
> 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 = '2'
> #'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
DoubtsAboutActions Gen ParentalExpectations Expect_gen ParentalCritism
1 9 0 12 0 9
2 9 1 15 15 6
3 9 1 14 14 13
4 8 1 10 10 7
5 14 1 10 10 8
6 14 0 9 0 8
7 15 1 18 18 11
8 11 1 11 11 11
9 14 0 14 0 8
10 8 0 24 0 20
11 16 1 18 18 16
12 11 0 14 0 8
13 7 1 18 18 11
14 9 0 12 0 8
15 16 0 5 0 4
16 10 1 12 12 8
17 14 0 11 0 8
18 11 0 9 0 6
19 6 1 11 11 8
20 12 1 16 16 14
21 14 1 14 14 10
22 13 0 8 0 9
23 14 0 18 0 10
24 10 0 10 0 8
25 14 1 13 13 10
26 8 1 12 12 7
27 10 1 12 12 8
28 9 0 12 0 7
29 9 1 13 13 6
30 15 0 7 0 5
31 12 1 14 14 7
32 14 1 9 9 9
33 11 0 9 0 5
34 12 0 10 0 8
35 13 0 10 0 6
36 14 1 11 11 8
37 15 1 13 13 8
38 11 0 13 0 6
39 9 0 13 0 8
40 8 1 6 6 6
41 10 0 13 0 6
42 10 0 21 0 12
43 10 1 11 11 5
44 9 0 9 0 7
45 13 1 18 18 12
46 8 0 9 0 11
47 10 1 15 15 10
48 11 1 11 11 8
49 10 1 14 14 9
50 16 0 14 0 9
51 11 0 8 0 4
52 6 1 8 8 11
53 9 0 11 0 10
54 20 0 8 0 7
55 12 1 13 13 9
56 9 0 13 0 10
57 14 1 15 15 11
58 8 1 12 12 7
59 7 0 12 0 6
60 11 0 21 0 7
61 14 1 24 24 20
62 14 0 12 0 6
63 9 1 17 17 9
64 16 1 11 11 6
65 13 1 15 15 10
66 13 1 12 12 6
67 8 1 14 14 10
68 9 0 12 0 8
69 11 1 20 20 13
70 8 0 12 0 9
71 7 1 11 11 9
72 11 1 12 12 7
73 9 1 19 19 10
74 16 1 16 16 8
75 13 0 20 0 10
76 12 1 15 15 10
77 9 1 14 14 6
Critism_gen PersonalStandards PersStand_gen Popularity Popular_gen
1 0 24 0 13 0
2 6 25 25 12 12
3 13 19 19 15 15
4 7 18 18 12 12
5 8 18 18 10 10
6 0 23 0 12 0
7 11 23 23 15 15
8 11 23 23 9 9
9 0 17 0 7 0
10 0 30 0 11 0
11 16 26 26 10 10
12 0 23 0 14 0
13 11 35 35 11 11
14 0 21 0 15 0
15 0 23 0 12 0
16 8 20 20 14 14
17 0 24 0 15 0
18 0 20 0 9 0
19 8 17 17 13 13
20 14 27 27 16 16
21 10 18 18 13 13
22 0 24 0 12 0
23 0 26 0 11 0
24 0 26 0 16 0
25 10 25 25 12 12
26 7 20 20 13 13
27 8 26 26 16 16
28 0 18 0 14 0
29 6 19 19 15 15
30 0 21 0 8 0
31 7 24 24 17 17
32 9 23 23 13 13
33 0 31 0 6 0
34 0 23 0 8 0
35 0 19 0 14 0
36 8 26 26 12 12
37 8 14 14 11 11
38 0 25 0 16 0
39 0 27 0 8 0
40 6 20 20 15 15
41 0 24 0 16 0
42 0 32 0 14 0
43 5 26 26 16 16
44 0 21 0 9 0
45 12 21 21 14 14
46 0 24 0 13 0
47 10 23 23 15 15
48 8 24 24 15 15
49 9 21 21 13 13
50 0 21 0 11 0
51 0 13 0 11 0
52 11 29 29 12 12
53 0 21 0 7 0
54 0 19 0 12 0
55 9 21 21 12 12
56 0 19 0 16 0
57 11 22 22 14 14
58 7 14 14 10 10
59 0 19 0 12 0
60 0 29 0 10 0
61 20 21 21 8 8
62 0 15 0 11 0
63 9 25 25 16 16
64 6 27 27 9 9
65 10 22 22 14 14
66 6 19 19 8 8
67 10 20 20 8 8
68 0 16 0 11 0
69 13 24 24 12 12
70 0 21 0 15 0
71 9 26 26 16 16
72 7 17 17 12 12
73 10 20 20 4 4
74 8 24 24 10 10
75 0 26 0 15 0
76 10 29 29 7 7
77 6 19 19 19 19
KnowingPeople Knowing_gen
1 14 0
2 8 8
3 12 12
4 7 7
5 10 10
6 7 0
7 16 16
8 11 11
9 12 0
10 7 0
11 11 11
12 15 0
13 7 7
14 14 0
15 7 0
16 15 15
17 17 0
18 15 0
19 14 14
20 8 8
21 8 8
22 14 0
23 8 0
24 16 0
25 10 10
26 14 14
27 16 16
28 13 0
29 5 5
30 10 0
31 15 15
32 16 16
33 15 0
34 8 0
35 13 0
36 14 14
37 12 12
38 16 0
39 10 0
40 15 15
41 16 0
42 19 0
43 14 14
44 6 0
45 13 13
46 7 0
47 13 13
48 14 14
49 13 13
50 11 0
51 14 0
52 14 14
53 7 0
54 12 0
55 11 11
56 14 0
57 10 10
58 13 13
59 11 0
60 8 0
61 4 4
62 14 0
63 15 15
64 11 11
65 15 15
66 10 10
67 9 9
68 12 0
69 15 15
70 12 0
71 14 14
72 12 12
73 6 6
74 8 8
75 13 0
76 13 13
77 15 15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gen ParentalExpectations
15.258683 -5.711866 -0.039958
Expect_gen ParentalCritism Critism_gen
0.205269 -0.269755 0.376650
PersonalStandards PersStand_gen Popularity
0.005426 -0.030489 -0.084119
Popular_gen KnowingPeople Knowing_gen
-0.092835 -0.028836 0.120075
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.9371 -2.0123 -0.3626 2.2172 8.2016
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.258683 3.469091 4.398 4.13e-05 ***
Gen -5.711866 5.142306 -1.111 0.271
ParentalExpectations -0.039958 0.172009 -0.232 0.817
Expect_gen 0.205269 0.252377 0.813 0.419
ParentalCritism -0.269755 0.261671 -1.031 0.306
Critism_gen 0.376650 0.337174 1.117 0.268
PersonalStandards 0.005426 0.134226 0.040 0.968
PersStand_gen -0.030489 0.174907 -0.174 0.862
Popularity -0.084119 0.206976 -0.406 0.686
Popular_gen -0.092835 0.269905 -0.344 0.732
KnowingPeople -0.028836 0.180664 -0.160 0.874
Knowing_gen 0.120075 0.249418 0.481 0.632
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.918 on 65 degrees of freedom
Multiple R-squared: 0.1093, Adjusted R-squared: -0.04142
F-statistic: 0.7252 on 11 and 65 DF, p-value: 0.7104
> 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,] 0.09594040 0.1918808 0.9040596
[2,] 0.03516890 0.0703378 0.9648311
[3,] 0.22064298 0.4412860 0.7793570
[4,] 0.24057622 0.4811524 0.7594238
[5,] 0.51939296 0.9612141 0.4806070
[6,] 0.64200376 0.7159925 0.3579962
[7,] 0.56414798 0.8717040 0.4358520
[8,] 0.48263931 0.9652786 0.5173607
[9,] 0.42154766 0.8430953 0.5784523
[10,] 0.33989543 0.6797909 0.6601046
[11,] 0.44359838 0.8871968 0.5564016
[12,] 0.38312754 0.7662551 0.6168725
[13,] 0.36696850 0.7339370 0.6330315
[14,] 0.30815770 0.6163154 0.6918423
[15,] 0.25009689 0.5001938 0.7499031
[16,] 0.21011939 0.4202388 0.7898806
[17,] 0.19690932 0.3938186 0.8030907
[18,] 0.25322194 0.5064439 0.7467781
[19,] 0.25887502 0.5177500 0.7411250
[20,] 0.21557105 0.4311421 0.7844290
[21,] 0.16927503 0.3385501 0.8307250
[22,] 0.18723007 0.3744601 0.8127699
[23,] 0.22698062 0.4539612 0.7730194
[24,] 0.17177608 0.3435522 0.8282239
[25,] 0.16995745 0.3399149 0.8300425
[26,] 0.12870351 0.2574070 0.8712965
[27,] 0.09949958 0.1989992 0.9005004
[28,] 0.45059497 0.9011899 0.5494050
[29,] 0.40273067 0.8054613 0.5972693
[30,] 0.49912139 0.9982428 0.5008786
[31,] 0.46326473 0.9265295 0.5367353
[32,] 0.45620870 0.9124174 0.5437913
[33,] 0.39047451 0.7809490 0.6095255
[34,] 0.32154271 0.6430854 0.6784573
[35,] 0.26118765 0.5223753 0.7388124
[36,] 0.31891711 0.6378342 0.6810829
[37,] 0.35995988 0.7199198 0.6400401
[38,] 0.44048254 0.8809651 0.5595175
[39,] 0.41341868 0.8268374 0.5865813
[40,] 0.49739410 0.9947882 0.5026059
[41,] 0.40425870 0.8085174 0.5957413
[42,] 0.31858358 0.6371672 0.6814164
[43,] 0.28266566 0.5653313 0.7173343
[44,] 0.22500949 0.4500190 0.7749905
[45,] 0.17737021 0.3547404 0.8226298
[46,] 0.10804185 0.2160837 0.8919582
[47,] 0.16328877 0.3265775 0.8367112
[48,] 0.08830261 0.1766052 0.9116974
> postscript(file="/var/www/html/rcomp/tmp/1j0t61292673769.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j0t61292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3j0t61292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4urtr1292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5urtr1292673769.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 = 77
Frequency = 1
1 2 3 4 5 6
-1.98436082 -1.64774342 -2.21516245 -2.01228367 3.25319931 2.34546267
7 8 9 10 11 12
3.07268108 -0.37567493 2.30139392 0.05978844 3.18481685 -0.05581893
13 14 15 16 17 18
-4.51323961 -2.06959933 3.10661111 -0.77567286 2.96067405 -1.19943543
19 20 21 22 23 24
-4.77126537 1.08972832 3.09150428 1.77168927 3.17303112 -1.03485309
25 26 27 28 29 30
3.07282296 -2.75449377 -0.36262790 -2.43603189 -0.66292103 2.21716644
31 32 33 34 35 36
1.63171199 3.42036070 -1.78123536 0.07777999 1.20887144 3.27734398
37 38 39 40 41 42
3.65149385 -0.44906438 -2.76637848 -1.39306431 -1.44363828 0.36941655
43 44 45 46 47 48
0.30584359 -3.19463440 1.01242193 -2.76657860 -1.05077698 0.75808036
49 50 51 52 53 54
-1.18260422 4.85708507 -1.60151887 -4.47221991 -2.44485535 8.20163645
55 56 57 58 59 60
0.98822920 -1.39515983 2.91402644 -3.34449258 -4.93712451 -0.61675900
61 62 63 64 65 66
-0.07518054 2.08697005 -2.22990137 5.25904908 1.56473021 1.80752194
67 68 69 70 71 72
-3.83437799 -2.43661853 -1.88629090 -2.85751703 -3.12173538 0.17584104
73 74 75 76 77
-4.09503314 4.59418595 2.73360554 -0.31603229 -1.03279846
> postscript(file="/var/www/html/rcomp/tmp/6urtr1292673769.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 = 77
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.98436082 NA
1 -1.64774342 -1.98436082
2 -2.21516245 -1.64774342
3 -2.01228367 -2.21516245
4 3.25319931 -2.01228367
5 2.34546267 3.25319931
6 3.07268108 2.34546267
7 -0.37567493 3.07268108
8 2.30139392 -0.37567493
9 0.05978844 2.30139392
10 3.18481685 0.05978844
11 -0.05581893 3.18481685
12 -4.51323961 -0.05581893
13 -2.06959933 -4.51323961
14 3.10661111 -2.06959933
15 -0.77567286 3.10661111
16 2.96067405 -0.77567286
17 -1.19943543 2.96067405
18 -4.77126537 -1.19943543
19 1.08972832 -4.77126537
20 3.09150428 1.08972832
21 1.77168927 3.09150428
22 3.17303112 1.77168927
23 -1.03485309 3.17303112
24 3.07282296 -1.03485309
25 -2.75449377 3.07282296
26 -0.36262790 -2.75449377
27 -2.43603189 -0.36262790
28 -0.66292103 -2.43603189
29 2.21716644 -0.66292103
30 1.63171199 2.21716644
31 3.42036070 1.63171199
32 -1.78123536 3.42036070
33 0.07777999 -1.78123536
34 1.20887144 0.07777999
35 3.27734398 1.20887144
36 3.65149385 3.27734398
37 -0.44906438 3.65149385
38 -2.76637848 -0.44906438
39 -1.39306431 -2.76637848
40 -1.44363828 -1.39306431
41 0.36941655 -1.44363828
42 0.30584359 0.36941655
43 -3.19463440 0.30584359
44 1.01242193 -3.19463440
45 -2.76657860 1.01242193
46 -1.05077698 -2.76657860
47 0.75808036 -1.05077698
48 -1.18260422 0.75808036
49 4.85708507 -1.18260422
50 -1.60151887 4.85708507
51 -4.47221991 -1.60151887
52 -2.44485535 -4.47221991
53 8.20163645 -2.44485535
54 0.98822920 8.20163645
55 -1.39515983 0.98822920
56 2.91402644 -1.39515983
57 -3.34449258 2.91402644
58 -4.93712451 -3.34449258
59 -0.61675900 -4.93712451
60 -0.07518054 -0.61675900
61 2.08697005 -0.07518054
62 -2.22990137 2.08697005
63 5.25904908 -2.22990137
64 1.56473021 5.25904908
65 1.80752194 1.56473021
66 -3.83437799 1.80752194
67 -2.43661853 -3.83437799
68 -1.88629090 -2.43661853
69 -2.85751703 -1.88629090
70 -3.12173538 -2.85751703
71 0.17584104 -3.12173538
72 -4.09503314 0.17584104
73 4.59418595 -4.09503314
74 2.73360554 4.59418595
75 -0.31603229 2.73360554
76 -1.03279846 -0.31603229
77 NA -1.03279846
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.64774342 -1.98436082
[2,] -2.21516245 -1.64774342
[3,] -2.01228367 -2.21516245
[4,] 3.25319931 -2.01228367
[5,] 2.34546267 3.25319931
[6,] 3.07268108 2.34546267
[7,] -0.37567493 3.07268108
[8,] 2.30139392 -0.37567493
[9,] 0.05978844 2.30139392
[10,] 3.18481685 0.05978844
[11,] -0.05581893 3.18481685
[12,] -4.51323961 -0.05581893
[13,] -2.06959933 -4.51323961
[14,] 3.10661111 -2.06959933
[15,] -0.77567286 3.10661111
[16,] 2.96067405 -0.77567286
[17,] -1.19943543 2.96067405
[18,] -4.77126537 -1.19943543
[19,] 1.08972832 -4.77126537
[20,] 3.09150428 1.08972832
[21,] 1.77168927 3.09150428
[22,] 3.17303112 1.77168927
[23,] -1.03485309 3.17303112
[24,] 3.07282296 -1.03485309
[25,] -2.75449377 3.07282296
[26,] -0.36262790 -2.75449377
[27,] -2.43603189 -0.36262790
[28,] -0.66292103 -2.43603189
[29,] 2.21716644 -0.66292103
[30,] 1.63171199 2.21716644
[31,] 3.42036070 1.63171199
[32,] -1.78123536 3.42036070
[33,] 0.07777999 -1.78123536
[34,] 1.20887144 0.07777999
[35,] 3.27734398 1.20887144
[36,] 3.65149385 3.27734398
[37,] -0.44906438 3.65149385
[38,] -2.76637848 -0.44906438
[39,] -1.39306431 -2.76637848
[40,] -1.44363828 -1.39306431
[41,] 0.36941655 -1.44363828
[42,] 0.30584359 0.36941655
[43,] -3.19463440 0.30584359
[44,] 1.01242193 -3.19463440
[45,] -2.76657860 1.01242193
[46,] -1.05077698 -2.76657860
[47,] 0.75808036 -1.05077698
[48,] -1.18260422 0.75808036
[49,] 4.85708507 -1.18260422
[50,] -1.60151887 4.85708507
[51,] -4.47221991 -1.60151887
[52,] -2.44485535 -4.47221991
[53,] 8.20163645 -2.44485535
[54,] 0.98822920 8.20163645
[55,] -1.39515983 0.98822920
[56,] 2.91402644 -1.39515983
[57,] -3.34449258 2.91402644
[58,] -4.93712451 -3.34449258
[59,] -0.61675900 -4.93712451
[60,] -0.07518054 -0.61675900
[61,] 2.08697005 -0.07518054
[62,] -2.22990137 2.08697005
[63,] 5.25904908 -2.22990137
[64,] 1.56473021 5.25904908
[65,] 1.80752194 1.56473021
[66,] -3.83437799 1.80752194
[67,] -2.43661853 -3.83437799
[68,] -1.88629090 -2.43661853
[69,] -2.85751703 -1.88629090
[70,] -3.12173538 -2.85751703
[71,] 0.17584104 -3.12173538
[72,] -4.09503314 0.17584104
[73,] 4.59418595 -4.09503314
[74,] 2.73360554 4.59418595
[75,] -0.31603229 2.73360554
[76,] -1.03279846 -0.31603229
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.64774342 -1.98436082
2 -2.21516245 -1.64774342
3 -2.01228367 -2.21516245
4 3.25319931 -2.01228367
5 2.34546267 3.25319931
6 3.07268108 2.34546267
7 -0.37567493 3.07268108
8 2.30139392 -0.37567493
9 0.05978844 2.30139392
10 3.18481685 0.05978844
11 -0.05581893 3.18481685
12 -4.51323961 -0.05581893
13 -2.06959933 -4.51323961
14 3.10661111 -2.06959933
15 -0.77567286 3.10661111
16 2.96067405 -0.77567286
17 -1.19943543 2.96067405
18 -4.77126537 -1.19943543
19 1.08972832 -4.77126537
20 3.09150428 1.08972832
21 1.77168927 3.09150428
22 3.17303112 1.77168927
23 -1.03485309 3.17303112
24 3.07282296 -1.03485309
25 -2.75449377 3.07282296
26 -0.36262790 -2.75449377
27 -2.43603189 -0.36262790
28 -0.66292103 -2.43603189
29 2.21716644 -0.66292103
30 1.63171199 2.21716644
31 3.42036070 1.63171199
32 -1.78123536 3.42036070
33 0.07777999 -1.78123536
34 1.20887144 0.07777999
35 3.27734398 1.20887144
36 3.65149385 3.27734398
37 -0.44906438 3.65149385
38 -2.76637848 -0.44906438
39 -1.39306431 -2.76637848
40 -1.44363828 -1.39306431
41 0.36941655 -1.44363828
42 0.30584359 0.36941655
43 -3.19463440 0.30584359
44 1.01242193 -3.19463440
45 -2.76657860 1.01242193
46 -1.05077698 -2.76657860
47 0.75808036 -1.05077698
48 -1.18260422 0.75808036
49 4.85708507 -1.18260422
50 -1.60151887 4.85708507
51 -4.47221991 -1.60151887
52 -2.44485535 -4.47221991
53 8.20163645 -2.44485535
54 0.98822920 8.20163645
55 -1.39515983 0.98822920
56 2.91402644 -1.39515983
57 -3.34449258 2.91402644
58 -4.93712451 -3.34449258
59 -0.61675900 -4.93712451
60 -0.07518054 -0.61675900
61 2.08697005 -0.07518054
62 -2.22990137 2.08697005
63 5.25904908 -2.22990137
64 1.56473021 5.25904908
65 1.80752194 1.56473021
66 -3.83437799 1.80752194
67 -2.43661853 -3.83437799
68 -1.88629090 -2.43661853
69 -2.85751703 -1.88629090
70 -3.12173538 -2.85751703
71 0.17584104 -3.12173538
72 -4.09503314 0.17584104
73 4.59418595 -4.09503314
74 2.73360554 4.59418595
75 -0.31603229 2.73360554
76 -1.03279846 -0.31603229
> 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/7njac1292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xsrf1292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xsrf1292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xsrf1292673769.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111s8k1292673769.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/124t681292673769.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/13tu321292673769.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/1433ln1292673769.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/157mjt1292673769.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/163vz11292673769.tab")
+ }
>
> try(system("convert tmp/1j0t61292673769.ps tmp/1j0t61292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j0t61292673769.ps tmp/2j0t61292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j0t61292673769.ps tmp/3j0t61292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/4urtr1292673769.ps tmp/4urtr1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/5urtr1292673769.ps tmp/5urtr1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/6urtr1292673769.ps tmp/6urtr1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/7njac1292673769.ps tmp/7njac1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xsrf1292673769.ps tmp/8xsrf1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xsrf1292673769.ps tmp/9xsrf1292673769.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xsrf1292673769.ps tmp/10xsrf1292673769.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.883 1.727 8.753