R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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.
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(23
+ ,2497.84
+ ,21
+ ,25
+ ,19
+ ,21
+ ,23
+ ,2645.64
+ ,23
+ ,21
+ ,25
+ ,19
+ ,19
+ ,2756.76
+ ,23
+ ,23
+ ,21
+ ,25
+ ,18
+ ,2849.27
+ ,19
+ ,23
+ ,23
+ ,21
+ ,19
+ ,2921.44
+ ,18
+ ,19
+ ,23
+ ,23
+ ,19
+ ,2981.85
+ ,19
+ ,18
+ ,19
+ ,23
+ ,22
+ ,3080.58
+ ,19
+ ,19
+ ,18
+ ,19
+ ,23
+ ,3106.22
+ ,22
+ ,19
+ ,19
+ ,18
+ ,20
+ ,3119.31
+ ,23
+ ,22
+ ,19
+ ,19
+ ,14
+ ,3061.26
+ ,20
+ ,23
+ ,22
+ ,19
+ ,14
+ ,3097.31
+ ,14
+ ,20
+ ,23
+ ,22
+ ,14
+ ,3161.69
+ ,14
+ ,14
+ ,20
+ ,23
+ ,15
+ ,3257.16
+ ,14
+ ,14
+ ,14
+ ,20
+ ,11
+ ,3277.01
+ ,15
+ ,14
+ ,14
+ ,14
+ ,17
+ ,3295.32
+ ,11
+ ,15
+ ,14
+ ,14
+ ,16
+ ,3363.99
+ ,17
+ ,11
+ ,15
+ ,14
+ ,20
+ ,3494.17
+ ,16
+ ,17
+ ,11
+ ,15
+ ,24
+ ,3667.03
+ ,20
+ ,16
+ ,17
+ ,11
+ ,23
+ ,3813.06
+ ,24
+ ,20
+ ,16
+ ,17
+ ,20
+ ,3917.96
+ ,23
+ ,24
+ ,20
+ ,16
+ ,21
+ ,3895.51
+ ,20
+ ,23
+ ,24
+ ,20
+ ,19
+ ,3801.06
+ ,21
+ ,20
+ ,23
+ ,24
+ ,23
+ ,3570.12
+ ,19
+ ,21
+ ,20
+ ,23
+ ,23
+ ,3701.61
+ ,23
+ ,19
+ ,21
+ ,20
+ ,23
+ ,3862.27
+ ,23
+ ,23
+ ,19
+ ,21
+ ,23
+ ,3970.1
+ ,23
+ ,23
+ ,23
+ ,19
+ ,27
+ ,4138.52
+ ,23
+ ,23
+ ,23
+ ,23
+ ,26
+ ,4199.75
+ ,27
+ ,23
+ ,23
+ ,23
+ ,17
+ ,4290.89
+ ,26
+ ,27
+ ,23
+ ,23
+ ,24
+ ,4443.91
+ ,17
+ ,26
+ ,27
+ ,23
+ ,26
+ ,4502.64
+ ,24
+ ,17
+ ,26
+ ,27
+ ,24
+ ,4356.98
+ ,26
+ ,24
+ ,17
+ ,26
+ ,27
+ ,4591.27
+ ,24
+ ,26
+ ,24
+ ,17
+ ,27
+ ,4696.96
+ ,27
+ ,24
+ ,26
+ ,24
+ ,26
+ ,4621.4
+ ,27
+ ,27
+ ,24
+ ,26
+ ,24
+ ,4562.84
+ ,26
+ ,27
+ ,27
+ ,24
+ ,23
+ ,4202.52
+ ,24
+ ,26
+ ,27
+ ,27
+ ,23
+ ,4296.49
+ ,23
+ ,24
+ ,26
+ ,27
+ ,24
+ ,4435.23
+ ,23
+ ,23
+ ,24
+ ,26
+ ,17
+ ,4105.18
+ ,24
+ ,23
+ ,23
+ ,24
+ ,21
+ ,4116.68
+ ,17
+ ,24
+ ,23
+ ,23
+ ,19
+ ,3844.49
+ ,21
+ ,17
+ ,24
+ ,23
+ ,22
+ ,3720.98
+ ,19
+ ,21
+ ,17
+ ,24
+ ,22
+ ,3674.4
+ ,22
+ ,19
+ ,21
+ ,17
+ ,18
+ ,3857.62
+ ,22
+ ,22
+ ,19
+ ,21
+ ,16
+ ,3801.06
+ ,18
+ ,22
+ ,22
+ ,19
+ ,14
+ ,3504.37
+ ,16
+ ,18
+ ,22
+ ,22
+ ,12
+ ,3032.6
+ ,14
+ ,16
+ ,18
+ ,22
+ ,14
+ ,3047.03
+ ,12
+ ,14
+ ,16
+ ,18
+ ,16
+ ,2962.34
+ ,14
+ ,12
+ ,14
+ ,16
+ ,8
+ ,2197.82
+ ,16
+ ,14
+ ,12
+ ,14
+ ,3
+ ,2014.45
+ ,8
+ ,16
+ ,14
+ ,12
+ ,0
+ ,1862.83
+ ,3
+ ,8
+ ,16
+ ,14
+ ,5
+ ,1905.41
+ ,0
+ ,3
+ ,8
+ ,16
+ ,1
+ ,1810.99
+ ,5
+ ,0
+ ,3
+ ,8
+ ,1
+ ,1670.07
+ ,1
+ ,5
+ ,0
+ ,3
+ ,3
+ ,1864.44
+ ,1
+ ,1
+ ,5
+ ,0)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Consvertr'
+ ,'Aand'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Consvertr','Aand','Y1','Y2','Y3','Y4'),1:57))
> 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
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
Consvertr Aand Y1 Y2 Y3 Y4 t
1 23 2497.84 21 25 19 21 1
2 23 2645.64 23 21 25 19 2
3 19 2756.76 23 23 21 25 3
4 18 2849.27 19 23 23 21 4
5 19 2921.44 18 19 23 23 5
6 19 2981.85 19 18 19 23 6
7 22 3080.58 19 19 18 19 7
8 23 3106.22 22 19 19 18 8
9 20 3119.31 23 22 19 19 9
10 14 3061.26 20 23 22 19 10
11 14 3097.31 14 20 23 22 11
12 14 3161.69 14 14 20 23 12
13 15 3257.16 14 14 14 20 13
14 11 3277.01 15 14 14 14 14
15 17 3295.32 11 15 14 14 15
16 16 3363.99 17 11 15 14 16
17 20 3494.17 16 17 11 15 17
18 24 3667.03 20 16 17 11 18
19 23 3813.06 24 20 16 17 19
20 20 3917.96 23 24 20 16 20
21 21 3895.51 20 23 24 20 21
22 19 3801.06 21 20 23 24 22
23 23 3570.12 19 21 20 23 23
24 23 3701.61 23 19 21 20 24
25 23 3862.27 23 23 19 21 25
26 23 3970.10 23 23 23 19 26
27 27 4138.52 23 23 23 23 27
28 26 4199.75 27 23 23 23 28
29 17 4290.89 26 27 23 23 29
30 24 4443.91 17 26 27 23 30
31 26 4502.64 24 17 26 27 31
32 24 4356.98 26 24 17 26 32
33 27 4591.27 24 26 24 17 33
34 27 4696.96 27 24 26 24 34
35 26 4621.40 27 27 24 26 35
36 24 4562.84 26 27 27 24 36
37 23 4202.52 24 26 27 27 37
38 23 4296.49 23 24 26 27 38
39 24 4435.23 23 23 24 26 39
40 17 4105.18 24 23 23 24 40
41 21 4116.68 17 24 23 23 41
42 19 3844.49 21 17 24 23 42
43 22 3720.98 19 21 17 24 43
44 22 3674.40 22 19 21 17 44
45 18 3857.62 22 22 19 21 45
46 16 3801.06 18 22 22 19 46
47 14 3504.37 16 18 22 22 47
48 12 3032.60 14 16 18 22 48
49 14 3047.03 12 14 16 18 49
50 16 2962.34 14 12 14 16 50
51 8 2197.82 16 14 12 14 51
52 3 2014.45 8 16 14 12 52
53 0 1862.83 3 8 16 14 53
54 5 1905.41 0 3 8 16 54
55 1 1810.99 5 0 3 8 55
56 1 1670.07 1 5 0 3 56
57 3 1864.44 1 1 5 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Aand Y1 Y2 Y3 Y4
-0.872484 0.004138 0.437428 -0.016178 -0.011072 0.007874
t
-0.101399
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8056 -1.7500 0.2428 2.0204 4.9012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.8724842 1.9510287 -0.447 0.656668
Aand 0.0041380 0.0009408 4.398 5.71e-05 ***
Y1 0.4374277 0.1385624 3.157 0.002702 **
Y2 -0.0161778 0.1453937 -0.111 0.911848
Y3 -0.0110721 0.1455645 -0.076 0.939672
Y4 0.0078737 0.1231817 0.064 0.949289
t -0.1013992 0.0283598 -3.575 0.000787 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.836 on 50 degrees of freedom
Multiple R-squared: 0.8541, Adjusted R-squared: 0.8366
F-statistic: 48.8 on 6 and 50 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.08722176 0.17444352 0.91277824
[2,] 0.12500664 0.25001329 0.87499336
[3,] 0.08831645 0.17663289 0.91168355
[4,] 0.10489420 0.20978841 0.89510580
[5,] 0.55588962 0.88822075 0.44411038
[6,] 0.65153280 0.69693439 0.34846720
[7,] 0.62059169 0.75881663 0.37940831
[8,] 0.69990431 0.60019138 0.30009569
[9,] 0.76828515 0.46342970 0.23171485
[10,] 0.69598658 0.60802684 0.30401342
[11,] 0.66395808 0.67208384 0.33604192
[12,] 0.69518792 0.60962416 0.30481208
[13,] 0.72917737 0.54164527 0.27082263
[14,] 0.84219866 0.31560269 0.15780134
[15,] 0.79025407 0.41949185 0.20974593
[16,] 0.72514722 0.54970556 0.27485278
[17,] 0.65252170 0.69495661 0.34747830
[18,] 0.76322197 0.47355607 0.23677803
[19,] 0.76650028 0.46699945 0.23349972
[20,] 0.96780913 0.06438174 0.03219087
[21,] 0.96887392 0.06225217 0.03112608
[22,] 0.95392565 0.09214870 0.04607435
[23,] 0.93856878 0.12286245 0.06143122
[24,] 0.92057537 0.15884926 0.07942463
[25,] 0.88151107 0.23697787 0.11848893
[26,] 0.82853616 0.34292767 0.17146384
[27,] 0.77183555 0.45632890 0.22816445
[28,] 0.70252707 0.59494586 0.29747293
[29,] 0.61457471 0.77085057 0.38542529
[30,] 0.51796595 0.96406809 0.48203405
[31,] 0.76219562 0.47560877 0.23780438
[32,] 0.69257195 0.61485610 0.30742805
[33,] 0.68653670 0.62692661 0.31346330
[34,] 0.61394044 0.77211911 0.38605956
[35,] 0.90318490 0.19363020 0.09681510
[36,] 0.83368716 0.33262567 0.16631284
[37,] 0.71857699 0.56284603 0.28142301
[38,] 0.88413274 0.23173451 0.11586726
> postscript(file="/var/www/html/rcomp/tmp/1kijy1258648015.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uel91258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35t1y1258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44qyo1258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54oln1258648015.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 = 57
Frequency = 1
1 2 3 4 5 6
4.90121331 3.53362406 -0.88397077 -0.36203117 0.79769478 0.15122100
7 8 9 10 11 12
2.88067244 2.58263502 -0.76690062 -5.06361081 -2.54790396 -2.85106881
13 14 15 16 17 18
-2.18753967 -6.55846580 1.23305478 -2.62791073 1.41713125 3.13526830
19 20 21 22 23 24
-0.11092429 -2.88930378 -0.38610660 -2.42239810 3.50033016 1.31024553
25 26 27 28 29 30
0.78152098 0.49675167 3.86972765 0.96804386 -7.80555855 2.62759861
31 32 33 34 35 36
1.23580900 -0.91343137 2.27404687 0.56048609 -0.01480280 -1.18468844
37 38 39 40 41 42
0.24278491 0.34933266 0.84617217 -5.11942172 2.02043587 -0.60371619
43 44 45 46 47 48
3.86296017 2.91187501 -1.75000272 -1.61588120 -1.50024470 -0.64843227
49 50 51 52 53 54
2.24510555 3.78334720 -1.80053772 -2.37067713 -2.57775585 3.47451514
55 56 57
-2.26141519 0.25986875 1.57122818
> postscript(file="/var/www/html/rcomp/tmp/63je81258648015.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 4.90121331 NA
1 3.53362406 4.90121331
2 -0.88397077 3.53362406
3 -0.36203117 -0.88397077
4 0.79769478 -0.36203117
5 0.15122100 0.79769478
6 2.88067244 0.15122100
7 2.58263502 2.88067244
8 -0.76690062 2.58263502
9 -5.06361081 -0.76690062
10 -2.54790396 -5.06361081
11 -2.85106881 -2.54790396
12 -2.18753967 -2.85106881
13 -6.55846580 -2.18753967
14 1.23305478 -6.55846580
15 -2.62791073 1.23305478
16 1.41713125 -2.62791073
17 3.13526830 1.41713125
18 -0.11092429 3.13526830
19 -2.88930378 -0.11092429
20 -0.38610660 -2.88930378
21 -2.42239810 -0.38610660
22 3.50033016 -2.42239810
23 1.31024553 3.50033016
24 0.78152098 1.31024553
25 0.49675167 0.78152098
26 3.86972765 0.49675167
27 0.96804386 3.86972765
28 -7.80555855 0.96804386
29 2.62759861 -7.80555855
30 1.23580900 2.62759861
31 -0.91343137 1.23580900
32 2.27404687 -0.91343137
33 0.56048609 2.27404687
34 -0.01480280 0.56048609
35 -1.18468844 -0.01480280
36 0.24278491 -1.18468844
37 0.34933266 0.24278491
38 0.84617217 0.34933266
39 -5.11942172 0.84617217
40 2.02043587 -5.11942172
41 -0.60371619 2.02043587
42 3.86296017 -0.60371619
43 2.91187501 3.86296017
44 -1.75000272 2.91187501
45 -1.61588120 -1.75000272
46 -1.50024470 -1.61588120
47 -0.64843227 -1.50024470
48 2.24510555 -0.64843227
49 3.78334720 2.24510555
50 -1.80053772 3.78334720
51 -2.37067713 -1.80053772
52 -2.57775585 -2.37067713
53 3.47451514 -2.57775585
54 -2.26141519 3.47451514
55 0.25986875 -2.26141519
56 1.57122818 0.25986875
57 NA 1.57122818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.53362406 4.90121331
[2,] -0.88397077 3.53362406
[3,] -0.36203117 -0.88397077
[4,] 0.79769478 -0.36203117
[5,] 0.15122100 0.79769478
[6,] 2.88067244 0.15122100
[7,] 2.58263502 2.88067244
[8,] -0.76690062 2.58263502
[9,] -5.06361081 -0.76690062
[10,] -2.54790396 -5.06361081
[11,] -2.85106881 -2.54790396
[12,] -2.18753967 -2.85106881
[13,] -6.55846580 -2.18753967
[14,] 1.23305478 -6.55846580
[15,] -2.62791073 1.23305478
[16,] 1.41713125 -2.62791073
[17,] 3.13526830 1.41713125
[18,] -0.11092429 3.13526830
[19,] -2.88930378 -0.11092429
[20,] -0.38610660 -2.88930378
[21,] -2.42239810 -0.38610660
[22,] 3.50033016 -2.42239810
[23,] 1.31024553 3.50033016
[24,] 0.78152098 1.31024553
[25,] 0.49675167 0.78152098
[26,] 3.86972765 0.49675167
[27,] 0.96804386 3.86972765
[28,] -7.80555855 0.96804386
[29,] 2.62759861 -7.80555855
[30,] 1.23580900 2.62759861
[31,] -0.91343137 1.23580900
[32,] 2.27404687 -0.91343137
[33,] 0.56048609 2.27404687
[34,] -0.01480280 0.56048609
[35,] -1.18468844 -0.01480280
[36,] 0.24278491 -1.18468844
[37,] 0.34933266 0.24278491
[38,] 0.84617217 0.34933266
[39,] -5.11942172 0.84617217
[40,] 2.02043587 -5.11942172
[41,] -0.60371619 2.02043587
[42,] 3.86296017 -0.60371619
[43,] 2.91187501 3.86296017
[44,] -1.75000272 2.91187501
[45,] -1.61588120 -1.75000272
[46,] -1.50024470 -1.61588120
[47,] -0.64843227 -1.50024470
[48,] 2.24510555 -0.64843227
[49,] 3.78334720 2.24510555
[50,] -1.80053772 3.78334720
[51,] -2.37067713 -1.80053772
[52,] -2.57775585 -2.37067713
[53,] 3.47451514 -2.57775585
[54,] -2.26141519 3.47451514
[55,] 0.25986875 -2.26141519
[56,] 1.57122818 0.25986875
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.53362406 4.90121331
2 -0.88397077 3.53362406
3 -0.36203117 -0.88397077
4 0.79769478 -0.36203117
5 0.15122100 0.79769478
6 2.88067244 0.15122100
7 2.58263502 2.88067244
8 -0.76690062 2.58263502
9 -5.06361081 -0.76690062
10 -2.54790396 -5.06361081
11 -2.85106881 -2.54790396
12 -2.18753967 -2.85106881
13 -6.55846580 -2.18753967
14 1.23305478 -6.55846580
15 -2.62791073 1.23305478
16 1.41713125 -2.62791073
17 3.13526830 1.41713125
18 -0.11092429 3.13526830
19 -2.88930378 -0.11092429
20 -0.38610660 -2.88930378
21 -2.42239810 -0.38610660
22 3.50033016 -2.42239810
23 1.31024553 3.50033016
24 0.78152098 1.31024553
25 0.49675167 0.78152098
26 3.86972765 0.49675167
27 0.96804386 3.86972765
28 -7.80555855 0.96804386
29 2.62759861 -7.80555855
30 1.23580900 2.62759861
31 -0.91343137 1.23580900
32 2.27404687 -0.91343137
33 0.56048609 2.27404687
34 -0.01480280 0.56048609
35 -1.18468844 -0.01480280
36 0.24278491 -1.18468844
37 0.34933266 0.24278491
38 0.84617217 0.34933266
39 -5.11942172 0.84617217
40 2.02043587 -5.11942172
41 -0.60371619 2.02043587
42 3.86296017 -0.60371619
43 2.91187501 3.86296017
44 -1.75000272 2.91187501
45 -1.61588120 -1.75000272
46 -1.50024470 -1.61588120
47 -0.64843227 -1.50024470
48 2.24510555 -0.64843227
49 3.78334720 2.24510555
50 -1.80053772 3.78334720
51 -2.37067713 -1.80053772
52 -2.57775585 -2.37067713
53 3.47451514 -2.57775585
54 -2.26141519 3.47451514
55 0.25986875 -2.26141519
56 1.57122818 0.25986875
> 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/73bip1258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sc6s1258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z6021258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ajbx1258648015.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117td21258648015.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/12jt361258648015.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/1393nn1258648015.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/141q6y1258648015.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/15vs5g1258648015.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/16gdmv1258648015.tab")
+ }
>
> system("convert tmp/1kijy1258648015.ps tmp/1kijy1258648015.png")
> system("convert tmp/2uel91258648015.ps tmp/2uel91258648015.png")
> system("convert tmp/35t1y1258648015.ps tmp/35t1y1258648015.png")
> system("convert tmp/44qyo1258648015.ps tmp/44qyo1258648015.png")
> system("convert tmp/54oln1258648015.ps tmp/54oln1258648015.png")
> system("convert tmp/63je81258648015.ps tmp/63je81258648015.png")
> system("convert tmp/73bip1258648015.ps tmp/73bip1258648015.png")
> system("convert tmp/8sc6s1258648015.ps tmp/8sc6s1258648015.png")
> system("convert tmp/9z6021258648015.ps tmp/9z6021258648015.png")
> system("convert tmp/10ajbx1258648015.ps tmp/10ajbx1258648015.png")
>
>
> proc.time()
user system elapsed
2.415 1.544 2.845