R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(10
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+ ,22)
+ ,dim=c(9
+ ,80)
+ ,dimnames=list(c('Perceived_happiness'
+ ,'Doubts_about_actions'
+ ,'Doubts_about_actions*G'
+ ,'Parental_expectations'
+ ,'Parental_expectations*G'
+ ,'Personal_standards'
+ ,'Personal_standards*G'
+ ,'Organization'
+ ,'Organization*G')
+ ,1:80))
> y <- array(NA,dim=c(9,80),dimnames=list(c('Perceived_happiness','Doubts_about_actions','Doubts_about_actions*G','Parental_expectations','Parental_expectations*G','Personal_standards','Personal_standards*G','Organization','Organization*G'),1:80))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
> 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
Perceived_happiness Doubts_about_actions Doubts_about_actions*G
1 10 14 0
2 14 11 11
3 18 6 6
4 15 12 0
5 18 8 8
6 11 10 10
7 17 10 10
8 19 11 11
9 7 16 16
10 12 11 11
11 13 13 0
12 15 12 12
13 14 8 8
14 14 12 12
15 16 11 0
16 16 4 0
17 12 9 9
18 12 8 0
19 13 8 0
20 16 14 14
21 9 15 15
22 11 11 0
23 14 8 8
24 11 9 0
25 17 9 9
26 14 8 8
27 15 9 9
28 11 16 0
29 15 11 0
30 14 16 0
31 11 12 12
32 12 12 0
33 9 10 0
34 16 9 9
35 13 10 0
36 15 12 0
37 10 14 0
38 13 14 14
39 16 10 10
40 15 6 6
41 13 13 13
42 16 11 0
43 15 7 0
44 16 15 15
45 15 9 0
46 13 10 0
47 11 10 10
48 17 10 0
49 10 11 0
50 17 8 0
51 14 13 0
52 15 11 11
53 16 9 9
54 12 12 12
55 11 12 0
56 16 8 8
57 9 14 0
58 15 11 0
59 15 10 0
60 13 11 0
61 15 10 10
62 15 12 12
63 18 8 8
64 16 14 0
65 12 14 14
66 15 8 8
67 13 6 6
68 13 8 8
69 13 14 0
70 14 11 11
71 15 11 11
72 11 14 14
73 14 11 0
74 17 8 8
75 13 11 11
76 12 8 8
77 13 13 13
78 16 12 12
79 13 9 9
80 19 7 7
Parental_expectations Parental_expectations*G Personal_standards
1 11 0 24
2 7 7 25
3 17 17 30
4 10 0 19
5 12 12 22
6 12 12 22
7 11 11 25
8 11 11 23
9 12 12 17
10 13 13 21
11 14 0 19
12 16 16 19
13 11 11 15
14 10 10 16
15 11 0 23
16 15 0 27
17 9 9 22
18 11 0 14
19 17 0 22
20 17 17 23
21 11 11 23
22 11 0 20
23 15 15 23
24 13 0 19
25 13 13 22
26 12 12 32
27 17 17 25
28 9 0 29
29 9 0 28
30 12 0 17
31 18 18 28
32 12 0 29
33 15 0 14
34 16 16 25
35 10 0 26
36 11 0 20
37 9 0 32
38 17 17 25
39 12 12 20
40 6 6 15
41 12 12 24
42 11 0 23
43 7 0 22
44 13 13 14
45 12 0 24
46 13 0 24
47 12 12 22
48 11 0 19
49 9 0 31
50 11 0 22
51 10 0 19
52 11 11 25
53 15 15 27
54 14 14 22
55 13 0 19
56 16 16 25
57 8 0 19
58 16 0 20
59 12 0 17
60 9 0 17
61 15 15 22
62 16 16 19
63 15 15 21
64 11 0 20
65 11 11 17
66 16 16 18
67 8 8 29
68 13 13 21
69 15 0 22
70 7 7 26
71 12 12 17
72 14 14 25
73 17 0 21
74 10 10 22
75 13 13 24
76 9 9 18
77 12 12 22
78 15 15 29
79 12 12 10
80 11 11 26
Personal_standards*G Organization Organization*G t
1 0 26 0 1
2 25 23 23 2
3 30 25 25 3
4 0 23 0 4
5 22 19 19 5
6 22 29 29 6
7 25 25 25 7
8 23 21 21 8
9 17 22 22 9
10 21 25 25 10
11 0 24 0 11
12 19 18 18 12
13 15 22 22 13
14 16 15 15 14
15 0 22 0 15
16 0 28 0 16
17 22 20 20 17
18 0 12 0 18
19 0 24 0 19
20 23 20 20 20
21 23 21 21 21
22 0 28 0 22
23 23 24 24 23
24 0 24 0 24
25 22 23 23 25
26 32 25 25 26
27 25 21 21 27
28 0 26 0 28
29 0 22 0 29
30 0 22 0 30
31 28 22 22 31
32 0 23 0 32
33 0 17 0 33
34 25 23 23 34
35 0 23 0 35
36 0 25 0 36
37 0 24 0 37
38 25 21 21 38
39 20 28 28 39
40 15 16 16 40
41 24 29 29 41
42 0 22 0 42
43 0 28 0 43
44 14 16 16 44
45 0 25 0 45
46 0 24 0 46
47 22 29 29 47
48 0 23 0 48
49 0 30 0 49
50 0 24 0 50
51 0 25 0 51
52 25 25 25 52
53 27 26 26 53
54 22 24 24 54
55 0 22 0 55
56 25 24 24 56
57 0 27 0 57
58 0 24 0 58
59 0 21 0 59
60 0 23 0 60
61 22 20 20 61
62 19 18 18 62
63 21 22 22 63
64 0 29 0 64
65 17 15 15 65
66 18 24 24 66
67 29 23 23 67
68 21 24 24 68
69 0 24 0 69
70 26 22 22 70
71 17 16 16 71
72 25 19 19 72
73 0 23 0 73
74 22 24 24 74
75 24 18 18 75
76 18 23 23 76
77 22 15 15 77
78 29 22 22 78
79 10 13 13 79
80 26 22 22 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Doubts_about_actions
18.070351 -0.345269
`Doubts_about_actions*G` Parental_expectations
-0.169899 -0.055128
`Parental_expectations*G` Personal_standards
0.246452 -0.077070
`Personal_standards*G` Organization
0.172410 0.056920
`Organization*G` t
-0.200014 0.001510
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.72922 -1.72674 -0.08011 1.62980 5.29199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.07035 2.56525 7.044 1.04e-09 ***
Doubts_about_actions -0.34527 0.14774 -2.337 0.0223 *
`Doubts_about_actions*G` -0.16990 0.18298 -0.928 0.3563
Parental_expectations -0.05513 0.13856 -0.398 0.6919
`Parental_expectations*G` 0.24645 0.17258 1.428 0.1577
Personal_standards -0.07707 0.10012 -0.770 0.4440
`Personal_standards*G` 0.17241 0.13208 1.305 0.1960
Organization 0.05692 0.12335 0.461 0.6459
`Organization*G` -0.20001 0.14675 -1.363 0.1773
t 0.00151 0.01117 0.135 0.8929
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.252 on 70 degrees of freedom
Multiple R-squared: 0.2686, Adjusted R-squared: 0.1745
F-statistic: 2.856 on 9 and 70 DF, p-value: 0.006318
> 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,] 0.4377722 0.8755445 0.56222777
[2,] 0.5823815 0.8352370 0.41761852
[3,] 0.4585966 0.9171933 0.54140336
[4,] 0.3858734 0.7717467 0.61412663
[5,] 0.8292780 0.3414439 0.17072196
[6,] 0.8857716 0.2284568 0.11422842
[7,] 0.8298225 0.3403549 0.17017746
[8,] 0.8019358 0.3961285 0.19806425
[9,] 0.8312278 0.3375444 0.16877220
[10,] 0.8140587 0.3718827 0.18594133
[11,] 0.7532549 0.4934901 0.24674507
[12,] 0.7390199 0.5219603 0.26098015
[13,] 0.7970648 0.4058703 0.20293516
[14,] 0.7707706 0.4584589 0.22922944
[15,] 0.7159125 0.5681751 0.28408754
[16,] 0.6474914 0.7050173 0.35250863
[17,] 0.6219652 0.7560696 0.37803481
[18,] 0.7604820 0.4790360 0.23951802
[19,] 0.8273653 0.3452695 0.17263474
[20,] 0.7755581 0.4488839 0.22444194
[21,] 0.9064794 0.1870412 0.09352058
[22,] 0.8862983 0.2274034 0.11370172
[23,] 0.8467091 0.3065818 0.15329091
[24,] 0.8503732 0.2992537 0.14962685
[25,] 0.8277065 0.3445869 0.17229345
[26,] 0.8061175 0.3877649 0.19388246
[27,] 0.8638198 0.2723604 0.13618018
[28,] 0.8273934 0.3452133 0.17260665
[29,] 0.7951172 0.4097657 0.20488283
[30,] 0.8567790 0.2864421 0.14322103
[31,] 0.8459254 0.3081493 0.15407464
[32,] 0.9354240 0.1291519 0.06457596
[33,] 0.9113489 0.1773022 0.08865110
[34,] 0.8767524 0.2464953 0.12324764
[35,] 0.8716222 0.2567555 0.12837776
[36,] 0.8898659 0.2202682 0.11013410
[37,] 0.8930548 0.2138904 0.10694521
[38,] 0.8904771 0.2190458 0.10952292
[39,] 0.8922751 0.2154498 0.10772490
[40,] 0.8902929 0.2194141 0.10970705
[41,] 0.8542803 0.2914394 0.14571972
[42,] 0.8082227 0.3835546 0.19177731
[43,] 0.7838497 0.4323007 0.21615033
[44,] 0.7129593 0.5740814 0.28704069
[45,] 0.7759590 0.4480820 0.22404102
[46,] 0.7186348 0.5627304 0.28136522
[47,] 0.6404673 0.7190654 0.35953268
[48,] 0.5451656 0.9096688 0.45483438
[49,] 0.4425117 0.8850235 0.55748826
[50,] 0.3587374 0.7174748 0.64126260
[51,] 0.4340530 0.8681059 0.56594703
[52,] 0.3462471 0.6924942 0.65375292
[53,] 0.2896742 0.5793484 0.71032580
[54,] 0.2167545 0.4335091 0.78324547
[55,] 0.2877372 0.5754744 0.71226278
> postscript(file="/var/www/rcomp/tmp/1x3gb1290530486.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2x3gb1290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/38cfw1290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/48cfw1290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/58cfw1290530486.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 = 80
Frequency = 1
1 2 3 4 5 6
-2.26193114 1.16184916 0.48074853 1.77328463 2.36884546 -2.17139332
7 8 9 10 11 12
3.16002568 5.29199019 -3.60986968 -1.33062160 0.27157788 0.79658111
13 14 15 16 17 18
-0.35524359 0.79824566 2.83173949 0.60061935 -2.41703606 -2.33302549
19 20 21 22 23 24
-1.07024857 2.52834187 -2.66696334 -2.75155812 -1.61217002 -3.18424888
25 26 27 28 29 30
2.23487303 -1.75769669 -1.10564969 -0.33705553 2.08570123 2.12814783
31 32 33 34 35 36
-3.90043649 -0.38802513 -4.72922001 0.36129467 -0.42455723 1.74333774
37 38 39 40 41 42
-1.69612825 -0.54641662 2.82637973 -0.32827776 1.13059546 2.79098249
43 44 45 46 47 48
-0.23070417 4.05826546 1.05735392 -0.48683890 -2.23328358 3.07145600
49 50 51 52 53 54
-3.16863963 2.55218960 0.93376586 1.60726490 0.76253765 -1.31163065
55 56 57 58 59 60
-2.08139733 -0.04398872 -3.95411825 1.69741860 1.06967906 -0.86578524
61 62 63 64 65 66
-0.11623051 0.72110518 2.23194284 3.16392841 -0.53506891 -0.39170136
67 68 69 70 71 72
-3.08479264 -2.10676990 0.81563275 0.82076797 0.86214102 -2.30995579
73 74 75 76 77 78
0.86389383 2.36280422 -1.71641634 -2.21062306 -0.73637637 1.50724736
79 80
-1.94216838 2.97970711
> postscript(file="/var/www/rcomp/tmp/6jmez1290530486.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 = 80
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.26193114 NA
1 1.16184916 -2.26193114
2 0.48074853 1.16184916
3 1.77328463 0.48074853
4 2.36884546 1.77328463
5 -2.17139332 2.36884546
6 3.16002568 -2.17139332
7 5.29199019 3.16002568
8 -3.60986968 5.29199019
9 -1.33062160 -3.60986968
10 0.27157788 -1.33062160
11 0.79658111 0.27157788
12 -0.35524359 0.79658111
13 0.79824566 -0.35524359
14 2.83173949 0.79824566
15 0.60061935 2.83173949
16 -2.41703606 0.60061935
17 -2.33302549 -2.41703606
18 -1.07024857 -2.33302549
19 2.52834187 -1.07024857
20 -2.66696334 2.52834187
21 -2.75155812 -2.66696334
22 -1.61217002 -2.75155812
23 -3.18424888 -1.61217002
24 2.23487303 -3.18424888
25 -1.75769669 2.23487303
26 -1.10564969 -1.75769669
27 -0.33705553 -1.10564969
28 2.08570123 -0.33705553
29 2.12814783 2.08570123
30 -3.90043649 2.12814783
31 -0.38802513 -3.90043649
32 -4.72922001 -0.38802513
33 0.36129467 -4.72922001
34 -0.42455723 0.36129467
35 1.74333774 -0.42455723
36 -1.69612825 1.74333774
37 -0.54641662 -1.69612825
38 2.82637973 -0.54641662
39 -0.32827776 2.82637973
40 1.13059546 -0.32827776
41 2.79098249 1.13059546
42 -0.23070417 2.79098249
43 4.05826546 -0.23070417
44 1.05735392 4.05826546
45 -0.48683890 1.05735392
46 -2.23328358 -0.48683890
47 3.07145600 -2.23328358
48 -3.16863963 3.07145600
49 2.55218960 -3.16863963
50 0.93376586 2.55218960
51 1.60726490 0.93376586
52 0.76253765 1.60726490
53 -1.31163065 0.76253765
54 -2.08139733 -1.31163065
55 -0.04398872 -2.08139733
56 -3.95411825 -0.04398872
57 1.69741860 -3.95411825
58 1.06967906 1.69741860
59 -0.86578524 1.06967906
60 -0.11623051 -0.86578524
61 0.72110518 -0.11623051
62 2.23194284 0.72110518
63 3.16392841 2.23194284
64 -0.53506891 3.16392841
65 -0.39170136 -0.53506891
66 -3.08479264 -0.39170136
67 -2.10676990 -3.08479264
68 0.81563275 -2.10676990
69 0.82076797 0.81563275
70 0.86214102 0.82076797
71 -2.30995579 0.86214102
72 0.86389383 -2.30995579
73 2.36280422 0.86389383
74 -1.71641634 2.36280422
75 -2.21062306 -1.71641634
76 -0.73637637 -2.21062306
77 1.50724736 -0.73637637
78 -1.94216838 1.50724736
79 2.97970711 -1.94216838
80 NA 2.97970711
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.16184916 -2.26193114
[2,] 0.48074853 1.16184916
[3,] 1.77328463 0.48074853
[4,] 2.36884546 1.77328463
[5,] -2.17139332 2.36884546
[6,] 3.16002568 -2.17139332
[7,] 5.29199019 3.16002568
[8,] -3.60986968 5.29199019
[9,] -1.33062160 -3.60986968
[10,] 0.27157788 -1.33062160
[11,] 0.79658111 0.27157788
[12,] -0.35524359 0.79658111
[13,] 0.79824566 -0.35524359
[14,] 2.83173949 0.79824566
[15,] 0.60061935 2.83173949
[16,] -2.41703606 0.60061935
[17,] -2.33302549 -2.41703606
[18,] -1.07024857 -2.33302549
[19,] 2.52834187 -1.07024857
[20,] -2.66696334 2.52834187
[21,] -2.75155812 -2.66696334
[22,] -1.61217002 -2.75155812
[23,] -3.18424888 -1.61217002
[24,] 2.23487303 -3.18424888
[25,] -1.75769669 2.23487303
[26,] -1.10564969 -1.75769669
[27,] -0.33705553 -1.10564969
[28,] 2.08570123 -0.33705553
[29,] 2.12814783 2.08570123
[30,] -3.90043649 2.12814783
[31,] -0.38802513 -3.90043649
[32,] -4.72922001 -0.38802513
[33,] 0.36129467 -4.72922001
[34,] -0.42455723 0.36129467
[35,] 1.74333774 -0.42455723
[36,] -1.69612825 1.74333774
[37,] -0.54641662 -1.69612825
[38,] 2.82637973 -0.54641662
[39,] -0.32827776 2.82637973
[40,] 1.13059546 -0.32827776
[41,] 2.79098249 1.13059546
[42,] -0.23070417 2.79098249
[43,] 4.05826546 -0.23070417
[44,] 1.05735392 4.05826546
[45,] -0.48683890 1.05735392
[46,] -2.23328358 -0.48683890
[47,] 3.07145600 -2.23328358
[48,] -3.16863963 3.07145600
[49,] 2.55218960 -3.16863963
[50,] 0.93376586 2.55218960
[51,] 1.60726490 0.93376586
[52,] 0.76253765 1.60726490
[53,] -1.31163065 0.76253765
[54,] -2.08139733 -1.31163065
[55,] -0.04398872 -2.08139733
[56,] -3.95411825 -0.04398872
[57,] 1.69741860 -3.95411825
[58,] 1.06967906 1.69741860
[59,] -0.86578524 1.06967906
[60,] -0.11623051 -0.86578524
[61,] 0.72110518 -0.11623051
[62,] 2.23194284 0.72110518
[63,] 3.16392841 2.23194284
[64,] -0.53506891 3.16392841
[65,] -0.39170136 -0.53506891
[66,] -3.08479264 -0.39170136
[67,] -2.10676990 -3.08479264
[68,] 0.81563275 -2.10676990
[69,] 0.82076797 0.81563275
[70,] 0.86214102 0.82076797
[71,] -2.30995579 0.86214102
[72,] 0.86389383 -2.30995579
[73,] 2.36280422 0.86389383
[74,] -1.71641634 2.36280422
[75,] -2.21062306 -1.71641634
[76,] -0.73637637 -2.21062306
[77,] 1.50724736 -0.73637637
[78,] -1.94216838 1.50724736
[79,] 2.97970711 -1.94216838
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.16184916 -2.26193114
2 0.48074853 1.16184916
3 1.77328463 0.48074853
4 2.36884546 1.77328463
5 -2.17139332 2.36884546
6 3.16002568 -2.17139332
7 5.29199019 3.16002568
8 -3.60986968 5.29199019
9 -1.33062160 -3.60986968
10 0.27157788 -1.33062160
11 0.79658111 0.27157788
12 -0.35524359 0.79658111
13 0.79824566 -0.35524359
14 2.83173949 0.79824566
15 0.60061935 2.83173949
16 -2.41703606 0.60061935
17 -2.33302549 -2.41703606
18 -1.07024857 -2.33302549
19 2.52834187 -1.07024857
20 -2.66696334 2.52834187
21 -2.75155812 -2.66696334
22 -1.61217002 -2.75155812
23 -3.18424888 -1.61217002
24 2.23487303 -3.18424888
25 -1.75769669 2.23487303
26 -1.10564969 -1.75769669
27 -0.33705553 -1.10564969
28 2.08570123 -0.33705553
29 2.12814783 2.08570123
30 -3.90043649 2.12814783
31 -0.38802513 -3.90043649
32 -4.72922001 -0.38802513
33 0.36129467 -4.72922001
34 -0.42455723 0.36129467
35 1.74333774 -0.42455723
36 -1.69612825 1.74333774
37 -0.54641662 -1.69612825
38 2.82637973 -0.54641662
39 -0.32827776 2.82637973
40 1.13059546 -0.32827776
41 2.79098249 1.13059546
42 -0.23070417 2.79098249
43 4.05826546 -0.23070417
44 1.05735392 4.05826546
45 -0.48683890 1.05735392
46 -2.23328358 -0.48683890
47 3.07145600 -2.23328358
48 -3.16863963 3.07145600
49 2.55218960 -3.16863963
50 0.93376586 2.55218960
51 1.60726490 0.93376586
52 0.76253765 1.60726490
53 -1.31163065 0.76253765
54 -2.08139733 -1.31163065
55 -0.04398872 -2.08139733
56 -3.95411825 -0.04398872
57 1.69741860 -3.95411825
58 1.06967906 1.69741860
59 -0.86578524 1.06967906
60 -0.11623051 -0.86578524
61 0.72110518 -0.11623051
62 2.23194284 0.72110518
63 3.16392841 2.23194284
64 -0.53506891 3.16392841
65 -0.39170136 -0.53506891
66 -3.08479264 -0.39170136
67 -2.10676990 -3.08479264
68 0.81563275 -2.10676990
69 0.82076797 0.81563275
70 0.86214102 0.82076797
71 -2.30995579 0.86214102
72 0.86389383 -2.30995579
73 2.36280422 0.86389383
74 -1.71641634 2.36280422
75 -2.21062306 -1.71641634
76 -0.73637637 -2.21062306
77 1.50724736 -0.73637637
78 -1.94216838 1.50724736
79 2.97970711 -1.94216838
> 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/rcomp/tmp/7cdw21290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8cdw21290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9cdw21290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/104mvn1290530486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/1185ca1290530486.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/rcomp/tmp/12tnay1290530486.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/rcomp/tmp/13067s1290530486.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/rcomp/tmp/14sfod1290530486.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/rcomp/tmp/15egn11290530486.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/rcomp/tmp/16s8391290530486.tab")
+ }
> try(system("convert tmp/1x3gb1290530486.ps tmp/1x3gb1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x3gb1290530486.ps tmp/2x3gb1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/38cfw1290530486.ps tmp/38cfw1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/48cfw1290530486.ps tmp/48cfw1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/58cfw1290530486.ps tmp/58cfw1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jmez1290530486.ps tmp/6jmez1290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cdw21290530486.ps tmp/7cdw21290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cdw21290530486.ps tmp/8cdw21290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cdw21290530486.ps tmp/9cdw21290530486.png",intern=TRUE))
character(0)
> try(system("convert tmp/104mvn1290530486.ps tmp/104mvn1290530486.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.110 2.070 6.183