R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
Natural language support but running in an English locale
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(0
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+ ,15)
+ ,dim=c(7
+ ,77)
+ ,dimnames=list(c('Gen'
+ ,'DoubtsAboutActions'
+ ,'ParentalExpectations'
+ ,'ParentalCritism'
+ ,'PersonalStandards'
+ ,'Popularity'
+ ,'KnowingPeople')
+ ,1:77))
> y <- array(NA,dim=c(7,77),dimnames=list(c('Gen','DoubtsAboutActions','ParentalExpectations','ParentalCritism','PersonalStandards','Popularity','KnowingPeople'),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 ParentalCritism
1 9 0 12 9
2 9 1 15 6
3 9 1 14 13
4 8 1 10 7
5 14 1 10 8
6 14 0 9 8
7 15 1 18 11
8 11 1 11 11
9 14 0 14 8
10 8 0 24 20
11 16 1 18 16
12 11 0 14 8
13 7 1 18 11
14 9 0 12 8
15 16 0 5 4
16 10 1 12 8
17 14 0 11 8
18 11 0 9 6
19 6 1 11 8
20 12 1 16 14
21 14 1 14 10
22 13 0 8 9
23 14 0 18 10
24 10 0 10 8
25 14 1 13 10
26 8 1 12 7
27 10 1 12 8
28 9 0 12 7
29 9 1 13 6
30 15 0 7 5
31 12 1 14 7
32 14 1 9 9
33 11 0 9 5
34 12 0 10 8
35 13 0 10 6
36 14 1 11 8
37 15 1 13 8
38 11 0 13 6
39 9 0 13 8
40 8 1 6 6
41 10 0 13 6
42 10 0 21 12
43 10 1 11 5
44 9 0 9 7
45 13 1 18 12
46 8 0 9 11
47 10 1 15 10
48 11 1 11 8
49 10 1 14 9
50 16 0 14 9
51 11 0 8 4
52 6 1 8 11
53 9 0 11 10
54 20 0 8 7
55 12 1 13 9
56 9 0 13 10
57 14 1 15 11
58 8 1 12 7
59 7 0 12 6
60 11 0 21 7
61 14 1 24 20
62 14 0 12 6
63 9 1 17 9
64 16 1 11 6
65 13 1 15 10
66 13 1 12 6
67 8 1 14 10
68 9 0 12 8
69 11 1 20 13
70 8 0 12 9
71 7 1 11 9
72 11 1 12 7
73 9 1 19 10
74 16 1 16 8
75 13 0 20 10
76 12 1 15 10
77 9 1 14 6
PersonalStandards Popularity KnowingPeople
1 24 13 14
2 25 12 8
3 19 15 12
4 18 12 7
5 18 10 10
6 23 12 7
7 23 15 16
8 23 9 11
9 17 7 12
10 30 11 7
11 26 10 11
12 23 14 15
13 35 11 7
14 21 15 14
15 23 12 7
16 20 14 15
17 24 15 17
18 20 9 15
19 17 13 14
20 27 16 8
21 18 13 8
22 24 12 14
23 26 11 8
24 26 16 16
25 25 12 10
26 20 13 14
27 26 16 16
28 18 14 13
29 19 15 5
30 21 8 10
31 24 17 15
32 23 13 16
33 31 6 15
34 23 8 8
35 19 14 13
36 26 12 14
37 14 11 12
38 25 16 16
39 27 8 10
40 20 15 15
41 24 16 16
42 32 14 19
43 26 16 14
44 21 9 6
45 21 14 13
46 24 13 7
47 23 15 13
48 24 15 14
49 21 13 13
50 21 11 11
51 13 11 14
52 29 12 14
53 21 7 7
54 19 12 12
55 21 12 11
56 19 16 14
57 22 14 10
58 14 10 13
59 19 12 11
60 29 10 8
61 21 8 4
62 15 11 14
63 25 16 15
64 27 9 11
65 22 14 15
66 19 8 10
67 20 8 9
68 16 11 12
69 24 12 15
70 21 15 12
71 26 16 14
72 17 12 12
73 20 4 6
74 24 10 8
75 26 15 13
76 29 7 13
77 19 19 15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gen ParentalExpectations
13.7755022 -0.2059533 0.0223982
ParentalCritism PersonalStandards Popularity
-0.0005269 -0.0391623 -0.1831208
KnowingPeople
0.0329767
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.2271 -2.1807 -0.4136 2.0990 8.5948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.7755022 2.4479085 5.627 3.51e-07 ***
Gen -0.2059533 0.6969435 -0.296 0.768
ParentalExpectations 0.0223982 0.1204456 0.186 0.853
ParentalCritism -0.0005269 0.1615168 -0.003 0.997
PersonalStandards -0.0391623 0.0850101 -0.461 0.646
Popularity -0.1831208 0.1307536 -1.401 0.166
KnowingPeople 0.0329767 0.1210661 0.272 0.786
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.924 on 70 degrees of freedom
Multiple R-squared: 0.03681, Adjusted R-squared: -0.04575
F-statistic: 0.4459 on 6 and 70 DF, p-value: 0.8455
> 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,] 0.85510952 0.28978096 0.1448905
[2,] 0.88498924 0.23002153 0.1150108
[3,] 0.80776076 0.38447848 0.1922392
[4,] 0.77093088 0.45813824 0.2290691
[5,] 0.70021206 0.59957588 0.2997879
[6,] 0.83759694 0.32480611 0.1624031
[7,] 0.78510238 0.42979524 0.2148976
[8,] 0.73808606 0.52382789 0.2619139
[9,] 0.73890131 0.52219738 0.2610987
[10,] 0.85014013 0.29971974 0.1498599
[11,] 0.82076763 0.35846474 0.1792324
[12,] 0.82916458 0.34167085 0.1708354
[13,] 0.78010223 0.43979554 0.2198978
[14,] 0.76926215 0.46147570 0.2307378
[15,] 0.71067862 0.57864275 0.2893214
[16,] 0.70487967 0.59024067 0.2951203
[17,] 0.69508085 0.60983830 0.3049192
[18,] 0.62494505 0.75010990 0.3750549
[19,] 0.58655455 0.82689089 0.4134454
[20,] 0.52232608 0.95534785 0.4776739
[21,] 0.49963497 0.99926995 0.5003650
[22,] 0.47256774 0.94513547 0.5274323
[23,] 0.45899971 0.91799941 0.5410003
[24,] 0.41195561 0.82391122 0.5880444
[25,] 0.34956767 0.69913534 0.6504323
[26,] 0.31089444 0.62178888 0.6891056
[27,] 0.31267262 0.62534525 0.6873274
[28,] 0.32967788 0.65935576 0.6703221
[29,] 0.26987516 0.53975031 0.7301248
[30,] 0.25510570 0.51021140 0.7448943
[31,] 0.25486294 0.50972588 0.7451371
[32,] 0.20172981 0.40345962 0.7982702
[33,] 0.16035519 0.32071037 0.8396448
[34,] 0.12042503 0.24085006 0.8795750
[35,] 0.11316407 0.22632814 0.8868359
[36,] 0.09487489 0.18974979 0.9051251
[37,] 0.09402798 0.18805595 0.9059720
[38,] 0.06797963 0.13595925 0.9320204
[39,] 0.04738073 0.09476146 0.9526193
[40,] 0.03327104 0.06654209 0.9667290
[41,] 0.04915194 0.09830388 0.9508481
[42,] 0.03433928 0.06867857 0.9656607
[43,] 0.06355928 0.12711855 0.9364407
[44,] 0.07803981 0.15607961 0.9219602
[45,] 0.47477927 0.94955855 0.5252207
[46,] 0.39974450 0.79948900 0.6002555
[47,] 0.33443830 0.66887660 0.6655617
[48,] 0.34365021 0.68730043 0.6563498
[49,] 0.34033263 0.68066526 0.6596674
[50,] 0.36905348 0.73810697 0.6309465
[51,] 0.34774403 0.69548806 0.6522560
[52,] 0.66435755 0.67128490 0.3356425
[53,] 0.58186063 0.83627875 0.4181394
[54,] 0.58599698 0.82800604 0.4140030
[55,] 0.52886339 0.94227321 0.4711366
[56,] 0.58582244 0.82835513 0.4141776
[57,] 0.45408789 0.90817577 0.5459121
[58,] 0.31565248 0.63130495 0.6843475
> postscript(file="/var/www/html/freestat/rcomp/tmp/1424r1293203858.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2424r1293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3fclc1293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4fclc1293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5fclc1293203858.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
-2.18074549 -1.98966594 -1.78109731 -3.11830760 2.41704766 2.89447569
7 8 9 10 11 12
4.15299866 -0.62405575 1.46702362 -3.34415836 4.52239947 -0.11508689
13 14 15 16 17 18
-3.81274698 -1.93251766 4.98196063 -0.98182418 3.10843735 -1.03724093
19 20 21 22 23 24
-5.22705705 1.80295919 2.94382468 1.72572633 2.59533548 -0.57474240
25 26 27 28 29 30
2.99128473 -3.13249525 -0.41358544 -2.20067562 -1.53155090 3.02795332
31 32 33 34 35 36
1.67886421 2.98728662 -1.15634508 0.10661757 1.88275607 2.94228279
37 38 39 40 41 42
3.31037146 0.31784697 -2.86988109 -2.66536825 -0.72131533 -1.04921237
43 44 45 46 47 48
-0.32681471 -2.70076161 1.99101021 -2.88166041 -0.68140375 0.41332066
49 50 51 52 53 54
-1.10409875 4.38965969 -0.89081437 -4.87145506 -3.14319547 8.59481436
55 56 57 58 59 60
0.80113195 -1.84906574 3.19577009 -3.88385481 -4.46232854 -0.53907372
61 62 63 64 65 66
2.05890183 2.09897143 -1.53123494 4.52995880 2.03035978 1.04411813
67 68 69 70 71 72
-3.92643151 -2.79485907 -0.36796730 -2.86603738 -3.32470700 -0.36714960
73 74 75 76 77
-3.67197557 4.58358581 2.11813905 0.08860346 -1.15123255
> postscript(file="/var/www/html/freestat/rcomp/tmp/68lkx1293203858.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 -2.18074549 NA
1 -1.98966594 -2.18074549
2 -1.78109731 -1.98966594
3 -3.11830760 -1.78109731
4 2.41704766 -3.11830760
5 2.89447569 2.41704766
6 4.15299866 2.89447569
7 -0.62405575 4.15299866
8 1.46702362 -0.62405575
9 -3.34415836 1.46702362
10 4.52239947 -3.34415836
11 -0.11508689 4.52239947
12 -3.81274698 -0.11508689
13 -1.93251766 -3.81274698
14 4.98196063 -1.93251766
15 -0.98182418 4.98196063
16 3.10843735 -0.98182418
17 -1.03724093 3.10843735
18 -5.22705705 -1.03724093
19 1.80295919 -5.22705705
20 2.94382468 1.80295919
21 1.72572633 2.94382468
22 2.59533548 1.72572633
23 -0.57474240 2.59533548
24 2.99128473 -0.57474240
25 -3.13249525 2.99128473
26 -0.41358544 -3.13249525
27 -2.20067562 -0.41358544
28 -1.53155090 -2.20067562
29 3.02795332 -1.53155090
30 1.67886421 3.02795332
31 2.98728662 1.67886421
32 -1.15634508 2.98728662
33 0.10661757 -1.15634508
34 1.88275607 0.10661757
35 2.94228279 1.88275607
36 3.31037146 2.94228279
37 0.31784697 3.31037146
38 -2.86988109 0.31784697
39 -2.66536825 -2.86988109
40 -0.72131533 -2.66536825
41 -1.04921237 -0.72131533
42 -0.32681471 -1.04921237
43 -2.70076161 -0.32681471
44 1.99101021 -2.70076161
45 -2.88166041 1.99101021
46 -0.68140375 -2.88166041
47 0.41332066 -0.68140375
48 -1.10409875 0.41332066
49 4.38965969 -1.10409875
50 -0.89081437 4.38965969
51 -4.87145506 -0.89081437
52 -3.14319547 -4.87145506
53 8.59481436 -3.14319547
54 0.80113195 8.59481436
55 -1.84906574 0.80113195
56 3.19577009 -1.84906574
57 -3.88385481 3.19577009
58 -4.46232854 -3.88385481
59 -0.53907372 -4.46232854
60 2.05890183 -0.53907372
61 2.09897143 2.05890183
62 -1.53123494 2.09897143
63 4.52995880 -1.53123494
64 2.03035978 4.52995880
65 1.04411813 2.03035978
66 -3.92643151 1.04411813
67 -2.79485907 -3.92643151
68 -0.36796730 -2.79485907
69 -2.86603738 -0.36796730
70 -3.32470700 -2.86603738
71 -0.36714960 -3.32470700
72 -3.67197557 -0.36714960
73 4.58358581 -3.67197557
74 2.11813905 4.58358581
75 0.08860346 2.11813905
76 -1.15123255 0.08860346
77 NA -1.15123255
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.98966594 -2.18074549
[2,] -1.78109731 -1.98966594
[3,] -3.11830760 -1.78109731
[4,] 2.41704766 -3.11830760
[5,] 2.89447569 2.41704766
[6,] 4.15299866 2.89447569
[7,] -0.62405575 4.15299866
[8,] 1.46702362 -0.62405575
[9,] -3.34415836 1.46702362
[10,] 4.52239947 -3.34415836
[11,] -0.11508689 4.52239947
[12,] -3.81274698 -0.11508689
[13,] -1.93251766 -3.81274698
[14,] 4.98196063 -1.93251766
[15,] -0.98182418 4.98196063
[16,] 3.10843735 -0.98182418
[17,] -1.03724093 3.10843735
[18,] -5.22705705 -1.03724093
[19,] 1.80295919 -5.22705705
[20,] 2.94382468 1.80295919
[21,] 1.72572633 2.94382468
[22,] 2.59533548 1.72572633
[23,] -0.57474240 2.59533548
[24,] 2.99128473 -0.57474240
[25,] -3.13249525 2.99128473
[26,] -0.41358544 -3.13249525
[27,] -2.20067562 -0.41358544
[28,] -1.53155090 -2.20067562
[29,] 3.02795332 -1.53155090
[30,] 1.67886421 3.02795332
[31,] 2.98728662 1.67886421
[32,] -1.15634508 2.98728662
[33,] 0.10661757 -1.15634508
[34,] 1.88275607 0.10661757
[35,] 2.94228279 1.88275607
[36,] 3.31037146 2.94228279
[37,] 0.31784697 3.31037146
[38,] -2.86988109 0.31784697
[39,] -2.66536825 -2.86988109
[40,] -0.72131533 -2.66536825
[41,] -1.04921237 -0.72131533
[42,] -0.32681471 -1.04921237
[43,] -2.70076161 -0.32681471
[44,] 1.99101021 -2.70076161
[45,] -2.88166041 1.99101021
[46,] -0.68140375 -2.88166041
[47,] 0.41332066 -0.68140375
[48,] -1.10409875 0.41332066
[49,] 4.38965969 -1.10409875
[50,] -0.89081437 4.38965969
[51,] -4.87145506 -0.89081437
[52,] -3.14319547 -4.87145506
[53,] 8.59481436 -3.14319547
[54,] 0.80113195 8.59481436
[55,] -1.84906574 0.80113195
[56,] 3.19577009 -1.84906574
[57,] -3.88385481 3.19577009
[58,] -4.46232854 -3.88385481
[59,] -0.53907372 -4.46232854
[60,] 2.05890183 -0.53907372
[61,] 2.09897143 2.05890183
[62,] -1.53123494 2.09897143
[63,] 4.52995880 -1.53123494
[64,] 2.03035978 4.52995880
[65,] 1.04411813 2.03035978
[66,] -3.92643151 1.04411813
[67,] -2.79485907 -3.92643151
[68,] -0.36796730 -2.79485907
[69,] -2.86603738 -0.36796730
[70,] -3.32470700 -2.86603738
[71,] -0.36714960 -3.32470700
[72,] -3.67197557 -0.36714960
[73,] 4.58358581 -3.67197557
[74,] 2.11813905 4.58358581
[75,] 0.08860346 2.11813905
[76,] -1.15123255 0.08860346
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.98966594 -2.18074549
2 -1.78109731 -1.98966594
3 -3.11830760 -1.78109731
4 2.41704766 -3.11830760
5 2.89447569 2.41704766
6 4.15299866 2.89447569
7 -0.62405575 4.15299866
8 1.46702362 -0.62405575
9 -3.34415836 1.46702362
10 4.52239947 -3.34415836
11 -0.11508689 4.52239947
12 -3.81274698 -0.11508689
13 -1.93251766 -3.81274698
14 4.98196063 -1.93251766
15 -0.98182418 4.98196063
16 3.10843735 -0.98182418
17 -1.03724093 3.10843735
18 -5.22705705 -1.03724093
19 1.80295919 -5.22705705
20 2.94382468 1.80295919
21 1.72572633 2.94382468
22 2.59533548 1.72572633
23 -0.57474240 2.59533548
24 2.99128473 -0.57474240
25 -3.13249525 2.99128473
26 -0.41358544 -3.13249525
27 -2.20067562 -0.41358544
28 -1.53155090 -2.20067562
29 3.02795332 -1.53155090
30 1.67886421 3.02795332
31 2.98728662 1.67886421
32 -1.15634508 2.98728662
33 0.10661757 -1.15634508
34 1.88275607 0.10661757
35 2.94228279 1.88275607
36 3.31037146 2.94228279
37 0.31784697 3.31037146
38 -2.86988109 0.31784697
39 -2.66536825 -2.86988109
40 -0.72131533 -2.66536825
41 -1.04921237 -0.72131533
42 -0.32681471 -1.04921237
43 -2.70076161 -0.32681471
44 1.99101021 -2.70076161
45 -2.88166041 1.99101021
46 -0.68140375 -2.88166041
47 0.41332066 -0.68140375
48 -1.10409875 0.41332066
49 4.38965969 -1.10409875
50 -0.89081437 4.38965969
51 -4.87145506 -0.89081437
52 -3.14319547 -4.87145506
53 8.59481436 -3.14319547
54 0.80113195 8.59481436
55 -1.84906574 0.80113195
56 3.19577009 -1.84906574
57 -3.88385481 3.19577009
58 -4.46232854 -3.88385481
59 -0.53907372 -4.46232854
60 2.05890183 -0.53907372
61 2.09897143 2.05890183
62 -1.53123494 2.09897143
63 4.52995880 -1.53123494
64 2.03035978 4.52995880
65 1.04411813 2.03035978
66 -3.92643151 1.04411813
67 -2.79485907 -3.92643151
68 -0.36796730 -2.79485907
69 -2.86603738 -0.36796730
70 -3.32470700 -2.86603738
71 -0.36714960 -3.32470700
72 -3.67197557 -0.36714960
73 4.58358581 -3.67197557
74 2.11813905 4.58358581
75 0.08860346 2.11813905
76 -1.15123255 0.08860346
> 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/freestat/rcomp/tmp/70cj01293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/80cj01293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/90cj01293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10t3jl1293203858.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11fmh91293203858.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/freestat/rcomp/tmp/12i4yx1293203858.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/freestat/rcomp/tmp/1375v81293203858.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/freestat/rcomp/tmp/1475v81293203858.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/freestat/rcomp/tmp/15aotw1293203858.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/freestat/rcomp/tmp/166yr51293203858.tab")
+ }
>
> try(system("convert tmp/1424r1293203858.ps tmp/1424r1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/2424r1293203858.ps tmp/2424r1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fclc1293203858.ps tmp/3fclc1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fclc1293203858.ps tmp/4fclc1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fclc1293203858.ps tmp/5fclc1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/68lkx1293203858.ps tmp/68lkx1293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/70cj01293203858.ps tmp/70cj01293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/80cj01293203858.ps tmp/80cj01293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/90cj01293203858.ps tmp/90cj01293203858.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t3jl1293203858.ps tmp/10t3jl1293203858.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.179 2.525 4.699