R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(7.3,-0.8,7.6,-0.2,7.5,0.2,7.6,1,7.9,0,7.9,-0.2,8.1,1,8.2,0.4,8,1,7.5,1.7,6.8,3.1,6.5,3.3,6.6,3.1,7.6,3.5,8,6,8.1,5.7,7.7,4.7,7.5,4.2,7.6,3.6,7.8,4.4,7.8,2.5,7.8,-0.6,7.5,-1.9,7.5,-1.9,7.1,0.7,7.5,-0.9,7.5,-1.7,7.6,-3.1,7.7,-2.1,7.7,0.2,7.9,1.2,8.1,3.8,8.2,4,8.2,6.6,8.2,5.3,7.9,7.6,7.3,4.7,6.9,6.6,6.6,4.4,6.7,4.6,6.9,6,7,4.8,7.1,4,7.2,2.7,7.1,3,6.9,4.1,7,4,6.8,2.7,6.4,2.6,6.7,3.1,6.6,4.4,6.4,3,6.3,2,6.2,1.3,6.5,1.5,6.8,1.3,6.8,3.2,6.4,1.8,6.1,3.3,5.8,1,6.1,2.4,7.2,0.4,7.3,-0.1,6.9,1.3,6.1,-1.1,5.8,-4.4,6.2,-7.5,7.1,-12.2,7.7,-14.5,7.9,-16,7.7,-16.7,7.4,-16.3,7.5,-16.9),dim=c(2,73),dimnames=list(c('WGM','EcGr'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('WGM','EcGr'),1:73))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
WGM EcGr
1 7.3 -0.8
2 7.6 -0.2
3 7.5 0.2
4 7.6 1.0
5 7.9 0.0
6 7.9 -0.2
7 8.1 1.0
8 8.2 0.4
9 8.0 1.0
10 7.5 1.7
11 6.8 3.1
12 6.5 3.3
13 6.6 3.1
14 7.6 3.5
15 8.0 6.0
16 8.1 5.7
17 7.7 4.7
18 7.5 4.2
19 7.6 3.6
20 7.8 4.4
21 7.8 2.5
22 7.8 -0.6
23 7.5 -1.9
24 7.5 -1.9
25 7.1 0.7
26 7.5 -0.9
27 7.5 -1.7
28 7.6 -3.1
29 7.7 -2.1
30 7.7 0.2
31 7.9 1.2
32 8.1 3.8
33 8.2 4.0
34 8.2 6.6
35 8.2 5.3
36 7.9 7.6
37 7.3 4.7
38 6.9 6.6
39 6.6 4.4
40 6.7 4.6
41 6.9 6.0
42 7.0 4.8
43 7.1 4.0
44 7.2 2.7
45 7.1 3.0
46 6.9 4.1
47 7.0 4.0
48 6.8 2.7
49 6.4 2.6
50 6.7 3.1
51 6.6 4.4
52 6.4 3.0
53 6.3 2.0
54 6.2 1.3
55 6.5 1.5
56 6.8 1.3
57 6.8 3.2
58 6.4 1.8
59 6.1 3.3
60 5.8 1.0
61 6.1 2.4
62 7.2 0.4
63 7.3 -0.1
64 6.9 1.3
65 6.1 -1.1
66 5.8 -4.4
67 6.2 -7.5
68 7.1 -12.2
69 7.7 -14.5
70 7.9 -16.0
71 7.7 -16.7
72 7.4 -16.3
73 7.5 -16.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) EcGr
7.229693 -0.008373
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.46654 -0.41881 0.06947 0.47198 1.02557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.229693 0.076331 94.715 <2e-16 ***
EcGr -0.008373 0.013810 -0.606 0.546
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6483 on 71 degrees of freedom
Multiple R-squared: 0.005151, Adjusted R-squared: -0.008861
F-statistic: 0.3676 on 1 and 71 DF, p-value: 0.5462
> 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,] 0.052229536 0.104459072 0.947770464
[2,] 0.036552591 0.073105181 0.963447409
[3,] 0.023014340 0.046028679 0.976985660
[4,] 0.024822885 0.049645769 0.975177115
[5,] 0.010514937 0.021029874 0.989485063
[6,] 0.015054054 0.030108107 0.984945946
[7,] 0.045899858 0.091799716 0.954100142
[8,] 0.054636014 0.109272028 0.945363986
[9,] 0.040620200 0.081240399 0.959379800
[10,] 0.060917909 0.121835818 0.939082091
[11,] 0.204496405 0.408992811 0.795503595
[12,] 0.263141574 0.526283149 0.736858426
[13,] 0.213463183 0.426926366 0.786536817
[14,] 0.160889141 0.321778283 0.839110859
[15,] 0.120805671 0.241611342 0.879194329
[16,] 0.100295131 0.200590261 0.899704869
[17,] 0.081219001 0.162438002 0.918780999
[18,] 0.063380684 0.126761368 0.936619316
[19,] 0.045625484 0.091250968 0.954374516
[20,] 0.032011474 0.064022948 0.967988526
[21,] 0.027864257 0.055728514 0.972135743
[22,] 0.018985640 0.037971280 0.981014360
[23,] 0.012681976 0.025363952 0.987318024
[24,] 0.008514112 0.017028225 0.991485888
[25,] 0.006106229 0.012212459 0.993893771
[26,] 0.004496106 0.008992213 0.995503894
[27,] 0.004512070 0.009024141 0.995487930
[28,] 0.007729018 0.015458037 0.992270982
[29,] 0.017980106 0.035960212 0.982019894
[30,] 0.045591337 0.091182673 0.954408663
[31,] 0.124358067 0.248716133 0.875641933
[32,] 0.238871234 0.477742467 0.761128766
[33,] 0.277273652 0.554547304 0.722726348
[34,] 0.353389000 0.706778000 0.646611000
[35,] 0.455175406 0.910350812 0.544824594
[36,] 0.503640885 0.992718230 0.496359115
[37,] 0.518894793 0.962210414 0.481105207
[38,] 0.526339685 0.947320630 0.473660315
[39,] 0.540063953 0.919872095 0.459936047
[40,] 0.565300464 0.869399072 0.434699536
[41,] 0.588366745 0.823266509 0.411633255
[42,] 0.605810704 0.788378593 0.394189296
[43,] 0.641803230 0.716393539 0.358196770
[44,] 0.650936637 0.698126726 0.349063363
[45,] 0.681201336 0.637597328 0.318798664
[46,] 0.678713707 0.642572586 0.321286293
[47,] 0.678357267 0.643285466 0.321642733
[48,] 0.677294340 0.645411319 0.322705660
[49,] 0.683154702 0.633690596 0.316845298
[50,] 0.701447667 0.597104666 0.298552333
[51,] 0.666980910 0.666038181 0.333019090
[52,] 0.632495860 0.735008279 0.367504140
[53,] 0.625974650 0.748050701 0.374025350
[54,] 0.582796507 0.834406986 0.417203493
[55,] 0.556276149 0.887447702 0.443723851
[56,] 0.633382729 0.733234543 0.366617271
[57,] 0.605943154 0.788113692 0.394056846
[58,] 0.618685300 0.762629399 0.381314700
[59,] 0.769750323 0.460499354 0.230249677
[60,] 0.976287015 0.047425970 0.023712985
[61,] 0.991361655 0.017276689 0.008638345
[62,] 0.978680123 0.042639753 0.021319877
[63,] 0.963836771 0.072326459 0.036163229
[64,] 0.957087288 0.085825424 0.042912712
> postscript(file="/var/www/html/rcomp/tmp/1r2391258730055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vlc71258730055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3pkqf1258730055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vzv61258730055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z5a01258730055.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 = 73
Frequency = 1
1 2 3 4 5 6
0.063608392 0.368632365 0.271981680 0.378680311 0.670307022 0.668632365
7 8 9 10 11 12
0.878680311 0.973656338 0.778680311 0.284541613 -0.403735783 -0.702061126
13 14 15 16 17 18
-0.603735783 0.399613532 0.820546753 0.918034766 0.509661478 0.305474834
19 20 21 22 23 24
0.400450861 0.607149492 0.591240244 0.565283049 0.254397774 0.254397774
25 26 27 28 29 30
-0.123831676 0.262771063 0.256072432 0.344349828 0.452723117 0.471981680
31 32 33 34 35 36
0.680354969 0.902125518 1.003800176 1.025570726 1.014685451 0.733944014
37 38 39 40 41 42
0.109661478 -0.274429274 -0.592850508 -0.491175851 -0.279453247 -0.189501193
43 44 45 46 47 48
-0.096199824 -0.007085099 -0.104573112 -0.295362495 -0.196199824 -0.407085099
49 50 51 52 53 54
-0.807922428 -0.503735783 -0.592850508 -0.804573112 -0.912946401 -1.018807703
55 56 57 58 59 60
-0.717133045 -0.418807703 -0.402898455 -0.814621058 -1.102061126 -1.421319689
61 62 63 64 65 66
-1.109597085 -0.026343662 0.069469694 -0.318807703 -1.138903595 -1.466535447
67 68 69 70 71 72
-1.092492641 -0.231847096 0.348894340 0.536334408 0.330473106 0.033822421
73
0.128798448
> postscript(file="/var/www/html/rcomp/tmp/6d86v1258730055.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 0.063608392 NA
1 0.368632365 0.063608392
2 0.271981680 0.368632365
3 0.378680311 0.271981680
4 0.670307022 0.378680311
5 0.668632365 0.670307022
6 0.878680311 0.668632365
7 0.973656338 0.878680311
8 0.778680311 0.973656338
9 0.284541613 0.778680311
10 -0.403735783 0.284541613
11 -0.702061126 -0.403735783
12 -0.603735783 -0.702061126
13 0.399613532 -0.603735783
14 0.820546753 0.399613532
15 0.918034766 0.820546753
16 0.509661478 0.918034766
17 0.305474834 0.509661478
18 0.400450861 0.305474834
19 0.607149492 0.400450861
20 0.591240244 0.607149492
21 0.565283049 0.591240244
22 0.254397774 0.565283049
23 0.254397774 0.254397774
24 -0.123831676 0.254397774
25 0.262771063 -0.123831676
26 0.256072432 0.262771063
27 0.344349828 0.256072432
28 0.452723117 0.344349828
29 0.471981680 0.452723117
30 0.680354969 0.471981680
31 0.902125518 0.680354969
32 1.003800176 0.902125518
33 1.025570726 1.003800176
34 1.014685451 1.025570726
35 0.733944014 1.014685451
36 0.109661478 0.733944014
37 -0.274429274 0.109661478
38 -0.592850508 -0.274429274
39 -0.491175851 -0.592850508
40 -0.279453247 -0.491175851
41 -0.189501193 -0.279453247
42 -0.096199824 -0.189501193
43 -0.007085099 -0.096199824
44 -0.104573112 -0.007085099
45 -0.295362495 -0.104573112
46 -0.196199824 -0.295362495
47 -0.407085099 -0.196199824
48 -0.807922428 -0.407085099
49 -0.503735783 -0.807922428
50 -0.592850508 -0.503735783
51 -0.804573112 -0.592850508
52 -0.912946401 -0.804573112
53 -1.018807703 -0.912946401
54 -0.717133045 -1.018807703
55 -0.418807703 -0.717133045
56 -0.402898455 -0.418807703
57 -0.814621058 -0.402898455
58 -1.102061126 -0.814621058
59 -1.421319689 -1.102061126
60 -1.109597085 -1.421319689
61 -0.026343662 -1.109597085
62 0.069469694 -0.026343662
63 -0.318807703 0.069469694
64 -1.138903595 -0.318807703
65 -1.466535447 -1.138903595
66 -1.092492641 -1.466535447
67 -0.231847096 -1.092492641
68 0.348894340 -0.231847096
69 0.536334408 0.348894340
70 0.330473106 0.536334408
71 0.033822421 0.330473106
72 0.128798448 0.033822421
73 NA 0.128798448
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.368632365 0.063608392
[2,] 0.271981680 0.368632365
[3,] 0.378680311 0.271981680
[4,] 0.670307022 0.378680311
[5,] 0.668632365 0.670307022
[6,] 0.878680311 0.668632365
[7,] 0.973656338 0.878680311
[8,] 0.778680311 0.973656338
[9,] 0.284541613 0.778680311
[10,] -0.403735783 0.284541613
[11,] -0.702061126 -0.403735783
[12,] -0.603735783 -0.702061126
[13,] 0.399613532 -0.603735783
[14,] 0.820546753 0.399613532
[15,] 0.918034766 0.820546753
[16,] 0.509661478 0.918034766
[17,] 0.305474834 0.509661478
[18,] 0.400450861 0.305474834
[19,] 0.607149492 0.400450861
[20,] 0.591240244 0.607149492
[21,] 0.565283049 0.591240244
[22,] 0.254397774 0.565283049
[23,] 0.254397774 0.254397774
[24,] -0.123831676 0.254397774
[25,] 0.262771063 -0.123831676
[26,] 0.256072432 0.262771063
[27,] 0.344349828 0.256072432
[28,] 0.452723117 0.344349828
[29,] 0.471981680 0.452723117
[30,] 0.680354969 0.471981680
[31,] 0.902125518 0.680354969
[32,] 1.003800176 0.902125518
[33,] 1.025570726 1.003800176
[34,] 1.014685451 1.025570726
[35,] 0.733944014 1.014685451
[36,] 0.109661478 0.733944014
[37,] -0.274429274 0.109661478
[38,] -0.592850508 -0.274429274
[39,] -0.491175851 -0.592850508
[40,] -0.279453247 -0.491175851
[41,] -0.189501193 -0.279453247
[42,] -0.096199824 -0.189501193
[43,] -0.007085099 -0.096199824
[44,] -0.104573112 -0.007085099
[45,] -0.295362495 -0.104573112
[46,] -0.196199824 -0.295362495
[47,] -0.407085099 -0.196199824
[48,] -0.807922428 -0.407085099
[49,] -0.503735783 -0.807922428
[50,] -0.592850508 -0.503735783
[51,] -0.804573112 -0.592850508
[52,] -0.912946401 -0.804573112
[53,] -1.018807703 -0.912946401
[54,] -0.717133045 -1.018807703
[55,] -0.418807703 -0.717133045
[56,] -0.402898455 -0.418807703
[57,] -0.814621058 -0.402898455
[58,] -1.102061126 -0.814621058
[59,] -1.421319689 -1.102061126
[60,] -1.109597085 -1.421319689
[61,] -0.026343662 -1.109597085
[62,] 0.069469694 -0.026343662
[63,] -0.318807703 0.069469694
[64,] -1.138903595 -0.318807703
[65,] -1.466535447 -1.138903595
[66,] -1.092492641 -1.466535447
[67,] -0.231847096 -1.092492641
[68,] 0.348894340 -0.231847096
[69,] 0.536334408 0.348894340
[70,] 0.330473106 0.536334408
[71,] 0.033822421 0.330473106
[72,] 0.128798448 0.033822421
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.368632365 0.063608392
2 0.271981680 0.368632365
3 0.378680311 0.271981680
4 0.670307022 0.378680311
5 0.668632365 0.670307022
6 0.878680311 0.668632365
7 0.973656338 0.878680311
8 0.778680311 0.973656338
9 0.284541613 0.778680311
10 -0.403735783 0.284541613
11 -0.702061126 -0.403735783
12 -0.603735783 -0.702061126
13 0.399613532 -0.603735783
14 0.820546753 0.399613532
15 0.918034766 0.820546753
16 0.509661478 0.918034766
17 0.305474834 0.509661478
18 0.400450861 0.305474834
19 0.607149492 0.400450861
20 0.591240244 0.607149492
21 0.565283049 0.591240244
22 0.254397774 0.565283049
23 0.254397774 0.254397774
24 -0.123831676 0.254397774
25 0.262771063 -0.123831676
26 0.256072432 0.262771063
27 0.344349828 0.256072432
28 0.452723117 0.344349828
29 0.471981680 0.452723117
30 0.680354969 0.471981680
31 0.902125518 0.680354969
32 1.003800176 0.902125518
33 1.025570726 1.003800176
34 1.014685451 1.025570726
35 0.733944014 1.014685451
36 0.109661478 0.733944014
37 -0.274429274 0.109661478
38 -0.592850508 -0.274429274
39 -0.491175851 -0.592850508
40 -0.279453247 -0.491175851
41 -0.189501193 -0.279453247
42 -0.096199824 -0.189501193
43 -0.007085099 -0.096199824
44 -0.104573112 -0.007085099
45 -0.295362495 -0.104573112
46 -0.196199824 -0.295362495
47 -0.407085099 -0.196199824
48 -0.807922428 -0.407085099
49 -0.503735783 -0.807922428
50 -0.592850508 -0.503735783
51 -0.804573112 -0.592850508
52 -0.912946401 -0.804573112
53 -1.018807703 -0.912946401
54 -0.717133045 -1.018807703
55 -0.418807703 -0.717133045
56 -0.402898455 -0.418807703
57 -0.814621058 -0.402898455
58 -1.102061126 -0.814621058
59 -1.421319689 -1.102061126
60 -1.109597085 -1.421319689
61 -0.026343662 -1.109597085
62 0.069469694 -0.026343662
63 -0.318807703 0.069469694
64 -1.138903595 -0.318807703
65 -1.466535447 -1.138903595
66 -1.092492641 -1.466535447
67 -0.231847096 -1.092492641
68 0.348894340 -0.231847096
69 0.536334408 0.348894340
70 0.330473106 0.536334408
71 0.033822421 0.330473106
72 0.128798448 0.033822421
> 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/7e8rs1258730056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8apph1258730056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/951yy1258730056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10o0os1258730056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/118pr11258730056.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/126rqa1258730056.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/13cigz1258730056.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/14bwza1258730056.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/15uzs91258730056.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/1632l41258730056.tab")
+ }
>
> system("convert tmp/1r2391258730055.ps tmp/1r2391258730055.png")
> system("convert tmp/2vlc71258730055.ps tmp/2vlc71258730055.png")
> system("convert tmp/3pkqf1258730055.ps tmp/3pkqf1258730055.png")
> system("convert tmp/4vzv61258730055.ps tmp/4vzv61258730055.png")
> system("convert tmp/5z5a01258730055.ps tmp/5z5a01258730055.png")
> system("convert tmp/6d86v1258730055.ps tmp/6d86v1258730055.png")
> system("convert tmp/7e8rs1258730056.ps tmp/7e8rs1258730056.png")
> system("convert tmp/8apph1258730056.ps tmp/8apph1258730056.png")
> system("convert tmp/951yy1258730056.ps tmp/951yy1258730056.png")
> system("convert tmp/10o0os1258730056.ps tmp/10o0os1258730056.png")
>
>
> proc.time()
user system elapsed
2.581 1.547 3.008