R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(19,2407.6,21,25,2454.62,19,21,2448.05,25,23,2497.84,21,23,2645.64,23,19,2756.76,23,18,2849.27,19,19,2921.44,18,19,2981.85,19,22,3080.58,19,23,3106.22,22,20,3119.31,23,14,3061.26,20,14,3097.31,14,14,3161.69,14,15,3257.16,14,11,3277.01,15,17,3295.32,11,16,3363.99,17,20,3494.17,16,24,3667.03,20,23,3813.06,24,20,3917.96,23,21,3895.51,20,19,3801.06,21,23,3570.12,19,23,3701.61,23,23,3862.27,23,23,3970.1,23,27,4138.52,23,26,4199.75,27,17,4290.89,26,24,4443.91,17,26,4502.64,24,24,4356.98,26,27,4591.27,24,27,4696.96,27,26,4621.4,27,24,4562.84,26,23,4202.52,24,23,4296.49,23,24,4435.23,23,17,4105.18,24,21,4116.68,17,19,3844.49,21,22,3720.98,19,22,3674.4,22,18,3857.62,22,16,3801.06,18,14,3504.37,16,12,3032.6,14,14,3047.03,12,16,2962.34,14,8,2197.82,16,3,2014.45,8,0,1862.83,3,5,1905.41,0,1,1810.99,5,1,1670.07,1,3,1864.44,1),dim=c(3,60),dimnames=list(c('Consvertr','Aand','Y1'),1:60))
> y <- array(NA,dim=c(3,60),dimnames=list(c('Consvertr','Aand','Y1'),1:60))
> 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 = 'Include Monthly 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 19 2407.60 21 1 0 0 0 0 0 0 0 0 0 0 1
2 25 2454.62 19 0 1 0 0 0 0 0 0 0 0 0 2
3 21 2448.05 25 0 0 1 0 0 0 0 0 0 0 0 3
4 23 2497.84 21 0 0 0 1 0 0 0 0 0 0 0 4
5 23 2645.64 23 0 0 0 0 1 0 0 0 0 0 0 5
6 19 2756.76 23 0 0 0 0 0 1 0 0 0 0 0 6
7 18 2849.27 19 0 0 0 0 0 0 1 0 0 0 0 7
8 19 2921.44 18 0 0 0 0 0 0 0 1 0 0 0 8
9 19 2981.85 19 0 0 0 0 0 0 0 0 1 0 0 9
10 22 3080.58 19 0 0 0 0 0 0 0 0 0 1 0 10
11 23 3106.22 22 0 0 0 0 0 0 0 0 0 0 1 11
12 20 3119.31 23 0 0 0 0 0 0 0 0 0 0 0 12
13 14 3061.26 20 1 0 0 0 0 0 0 0 0 0 0 13
14 14 3097.31 14 0 1 0 0 0 0 0 0 0 0 0 14
15 14 3161.69 14 0 0 1 0 0 0 0 0 0 0 0 15
16 15 3257.16 14 0 0 0 1 0 0 0 0 0 0 0 16
17 11 3277.01 15 0 0 0 0 1 0 0 0 0 0 0 17
18 17 3295.32 11 0 0 0 0 0 1 0 0 0 0 0 18
19 16 3363.99 17 0 0 0 0 0 0 1 0 0 0 0 19
20 20 3494.17 16 0 0 0 0 0 0 0 1 0 0 0 20
21 24 3667.03 20 0 0 0 0 0 0 0 0 1 0 0 21
22 23 3813.06 24 0 0 0 0 0 0 0 0 0 1 0 22
23 20 3917.96 23 0 0 0 0 0 0 0 0 0 0 1 23
24 21 3895.51 20 0 0 0 0 0 0 0 0 0 0 0 24
25 19 3801.06 21 1 0 0 0 0 0 0 0 0 0 0 25
26 23 3570.12 19 0 1 0 0 0 0 0 0 0 0 0 26
27 23 3701.61 23 0 0 1 0 0 0 0 0 0 0 0 27
28 23 3862.27 23 0 0 0 1 0 0 0 0 0 0 0 28
29 23 3970.10 23 0 0 0 0 1 0 0 0 0 0 0 29
30 27 4138.52 23 0 0 0 0 0 1 0 0 0 0 0 30
31 26 4199.75 27 0 0 0 0 0 0 1 0 0 0 0 31
32 17 4290.89 26 0 0 0 0 0 0 0 1 0 0 0 32
33 24 4443.91 17 0 0 0 0 0 0 0 0 1 0 0 33
34 26 4502.64 24 0 0 0 0 0 0 0 0 0 1 0 34
35 24 4356.98 26 0 0 0 0 0 0 0 0 0 0 1 35
36 27 4591.27 24 0 0 0 0 0 0 0 0 0 0 0 36
37 27 4696.96 27 1 0 0 0 0 0 0 0 0 0 0 37
38 26 4621.40 27 0 1 0 0 0 0 0 0 0 0 0 38
39 24 4562.84 26 0 0 1 0 0 0 0 0 0 0 0 39
40 23 4202.52 24 0 0 0 1 0 0 0 0 0 0 0 40
41 23 4296.49 23 0 0 0 0 1 0 0 0 0 0 0 41
42 24 4435.23 23 0 0 0 0 0 1 0 0 0 0 0 42
43 17 4105.18 24 0 0 0 0 0 0 1 0 0 0 0 43
44 21 4116.68 17 0 0 0 0 0 0 0 1 0 0 0 44
45 19 3844.49 21 0 0 0 0 0 0 0 0 1 0 0 45
46 22 3720.98 19 0 0 0 0 0 0 0 0 0 1 0 46
47 22 3674.40 22 0 0 0 0 0 0 0 0 0 0 1 47
48 18 3857.62 22 0 0 0 0 0 0 0 0 0 0 0 48
49 16 3801.06 18 1 0 0 0 0 0 0 0 0 0 0 49
50 14 3504.37 16 0 1 0 0 0 0 0 0 0 0 0 50
51 12 3032.60 14 0 0 1 0 0 0 0 0 0 0 0 51
52 14 3047.03 12 0 0 0 1 0 0 0 0 0 0 0 52
53 16 2962.34 14 0 0 0 0 1 0 0 0 0 0 0 53
54 8 2197.82 16 0 0 0 0 0 1 0 0 0 0 0 54
55 3 2014.45 8 0 0 0 0 0 0 1 0 0 0 0 55
56 0 1862.83 3 0 0 0 0 0 0 0 1 0 0 0 56
57 5 1905.41 0 0 0 0 0 0 0 0 0 1 0 0 57
58 1 1810.99 5 0 0 0 0 0 0 0 0 0 1 0 58
59 1 1670.07 1 0 0 0 0 0 0 0 0 0 0 1 59
60 3 1864.44 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Aand Y1 M1 M2 M3
0.743430 0.003348 0.512904 -1.992936 1.091268 -0.893483
M4 M5 M6 M7 M8 M9
0.858889 -0.037148 0.292557 -2.305040 -1.364062 1.743730
M10 M11 t
0.955268 0.088162 -0.104962
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7220 -1.7692 0.3363 1.7955 5.4120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.743430 2.197679 0.338 0.736726
Aand 0.003348 0.000835 4.010 0.000226 ***
Y1 0.512904 0.115951 4.423 6.1e-05 ***
M1 -1.992936 1.829644 -1.089 0.281844
M2 1.091268 1.822776 0.599 0.552385
M3 -0.893483 1.827577 -0.489 0.627294
M4 0.858889 1.817565 0.473 0.638819
M5 -0.037148 1.816997 -0.020 0.983779
M6 0.292557 1.816653 0.161 0.872781
M7 -2.305040 1.818369 -1.268 0.211446
M8 -1.364062 1.824681 -0.748 0.458613
M9 1.743730 1.833477 0.951 0.346659
M10 0.955268 1.810148 0.528 0.600280
M11 0.088162 1.816656 0.049 0.961509
t -0.104962 0.028622 -3.667 0.000646 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.86 on 45 degrees of freedom
Multiple R-squared: 0.869, Adjusted R-squared: 0.8282
F-statistic: 21.32 on 14 and 45 DF, p-value: 2.61e-15
> 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,] 0.8060609 0.38787819 0.19393910
[2,] 0.7334788 0.53304235 0.26652117
[3,] 0.7953852 0.40922966 0.20461483
[4,] 0.9392543 0.12149146 0.06074573
[5,] 0.8939618 0.21207646 0.10603823
[6,] 0.8689804 0.26203925 0.13101963
[7,] 0.8878824 0.22423529 0.11211765
[8,] 0.8869123 0.22617536 0.11308768
[9,] 0.8464804 0.30703928 0.15351964
[10,] 0.8149302 0.37013968 0.18506984
[11,] 0.7364571 0.52708588 0.26354294
[12,] 0.6846156 0.63076888 0.31538444
[13,] 0.6875001 0.62499990 0.31249995
[14,] 0.7991650 0.40166993 0.20083497
[15,] 0.9503530 0.09929404 0.04964702
[16,] 0.9330913 0.13381734 0.06690867
[17,] 0.8896179 0.22076426 0.11038213
[18,] 0.8583421 0.28331570 0.14165785
[19,] 0.8118778 0.37624450 0.18812225
[20,] 0.7899117 0.42017666 0.21008833
[21,] 0.7739154 0.45216930 0.22608465
[22,] 0.6600076 0.67998483 0.33999242
[23,] 0.5433284 0.91334316 0.45667158
[24,] 0.4643571 0.92871426 0.53564287
[25,] 0.4006628 0.80132553 0.59933724
> postscript(file="/var/www/html/rcomp/tmp/14r561258620290.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21r7f1258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kzx81258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a6441258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qccs1258620290.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 = 60
Frequency = 1
1 2 3 4 5 6 7
1.5228436 5.4119873 0.4462702 2.6837801 2.1641363 -2.4326363 1.0118173
8 9 10 11 12 13 14
1.4470802 -2.2709064 1.2919692 1.6394820 -1.7241244 -3.8931623 -3.9156736
15 16 17 18 19 20 21
-2.0415047 -3.0085487 -6.5869122 1.1786601 -0.4261138 2.8149319 1.1817502
22 23 24 25 26 27 28
-1.4653519 -3.3315837 -0.5245848 -0.6233732 2.1963791 1.7942466 -0.3910531
29 30 31 32 33 34 35
0.2489311 3.4603185 2.9062624 -6.7219862 1.3790131 0.4854779 -1.0805935
36 37 38 39 40 41 42
2.3539368 2.5592720 -1.1669958 -0.3683201 -0.7835727 0.4157191 0.7264749
43 44 45 46 47 48 49
-2.9788646 3.7369473 -2.4062093 2.9265334 2.5158384 -1.9044579 0.4344200
50 51 52 53 54 55 56
-2.5256970 0.1693080 1.4993944 3.7581258 -2.9328173 -0.5131013 -1.2769732
57 58 59 60
2.1163524 -3.2386286 0.2568568 1.7992303
> postscript(file="/var/www/html/rcomp/tmp/6iak01258620290.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 1.5228436 NA
1 5.4119873 1.5228436
2 0.4462702 5.4119873
3 2.6837801 0.4462702
4 2.1641363 2.6837801
5 -2.4326363 2.1641363
6 1.0118173 -2.4326363
7 1.4470802 1.0118173
8 -2.2709064 1.4470802
9 1.2919692 -2.2709064
10 1.6394820 1.2919692
11 -1.7241244 1.6394820
12 -3.8931623 -1.7241244
13 -3.9156736 -3.8931623
14 -2.0415047 -3.9156736
15 -3.0085487 -2.0415047
16 -6.5869122 -3.0085487
17 1.1786601 -6.5869122
18 -0.4261138 1.1786601
19 2.8149319 -0.4261138
20 1.1817502 2.8149319
21 -1.4653519 1.1817502
22 -3.3315837 -1.4653519
23 -0.5245848 -3.3315837
24 -0.6233732 -0.5245848
25 2.1963791 -0.6233732
26 1.7942466 2.1963791
27 -0.3910531 1.7942466
28 0.2489311 -0.3910531
29 3.4603185 0.2489311
30 2.9062624 3.4603185
31 -6.7219862 2.9062624
32 1.3790131 -6.7219862
33 0.4854779 1.3790131
34 -1.0805935 0.4854779
35 2.3539368 -1.0805935
36 2.5592720 2.3539368
37 -1.1669958 2.5592720
38 -0.3683201 -1.1669958
39 -0.7835727 -0.3683201
40 0.4157191 -0.7835727
41 0.7264749 0.4157191
42 -2.9788646 0.7264749
43 3.7369473 -2.9788646
44 -2.4062093 3.7369473
45 2.9265334 -2.4062093
46 2.5158384 2.9265334
47 -1.9044579 2.5158384
48 0.4344200 -1.9044579
49 -2.5256970 0.4344200
50 0.1693080 -2.5256970
51 1.4993944 0.1693080
52 3.7581258 1.4993944
53 -2.9328173 3.7581258
54 -0.5131013 -2.9328173
55 -1.2769732 -0.5131013
56 2.1163524 -1.2769732
57 -3.2386286 2.1163524
58 0.2568568 -3.2386286
59 1.7992303 0.2568568
60 NA 1.7992303
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.4119873 1.5228436
[2,] 0.4462702 5.4119873
[3,] 2.6837801 0.4462702
[4,] 2.1641363 2.6837801
[5,] -2.4326363 2.1641363
[6,] 1.0118173 -2.4326363
[7,] 1.4470802 1.0118173
[8,] -2.2709064 1.4470802
[9,] 1.2919692 -2.2709064
[10,] 1.6394820 1.2919692
[11,] -1.7241244 1.6394820
[12,] -3.8931623 -1.7241244
[13,] -3.9156736 -3.8931623
[14,] -2.0415047 -3.9156736
[15,] -3.0085487 -2.0415047
[16,] -6.5869122 -3.0085487
[17,] 1.1786601 -6.5869122
[18,] -0.4261138 1.1786601
[19,] 2.8149319 -0.4261138
[20,] 1.1817502 2.8149319
[21,] -1.4653519 1.1817502
[22,] -3.3315837 -1.4653519
[23,] -0.5245848 -3.3315837
[24,] -0.6233732 -0.5245848
[25,] 2.1963791 -0.6233732
[26,] 1.7942466 2.1963791
[27,] -0.3910531 1.7942466
[28,] 0.2489311 -0.3910531
[29,] 3.4603185 0.2489311
[30,] 2.9062624 3.4603185
[31,] -6.7219862 2.9062624
[32,] 1.3790131 -6.7219862
[33,] 0.4854779 1.3790131
[34,] -1.0805935 0.4854779
[35,] 2.3539368 -1.0805935
[36,] 2.5592720 2.3539368
[37,] -1.1669958 2.5592720
[38,] -0.3683201 -1.1669958
[39,] -0.7835727 -0.3683201
[40,] 0.4157191 -0.7835727
[41,] 0.7264749 0.4157191
[42,] -2.9788646 0.7264749
[43,] 3.7369473 -2.9788646
[44,] -2.4062093 3.7369473
[45,] 2.9265334 -2.4062093
[46,] 2.5158384 2.9265334
[47,] -1.9044579 2.5158384
[48,] 0.4344200 -1.9044579
[49,] -2.5256970 0.4344200
[50,] 0.1693080 -2.5256970
[51,] 1.4993944 0.1693080
[52,] 3.7581258 1.4993944
[53,] -2.9328173 3.7581258
[54,] -0.5131013 -2.9328173
[55,] -1.2769732 -0.5131013
[56,] 2.1163524 -1.2769732
[57,] -3.2386286 2.1163524
[58,] 0.2568568 -3.2386286
[59,] 1.7992303 0.2568568
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.4119873 1.5228436
2 0.4462702 5.4119873
3 2.6837801 0.4462702
4 2.1641363 2.6837801
5 -2.4326363 2.1641363
6 1.0118173 -2.4326363
7 1.4470802 1.0118173
8 -2.2709064 1.4470802
9 1.2919692 -2.2709064
10 1.6394820 1.2919692
11 -1.7241244 1.6394820
12 -3.8931623 -1.7241244
13 -3.9156736 -3.8931623
14 -2.0415047 -3.9156736
15 -3.0085487 -2.0415047
16 -6.5869122 -3.0085487
17 1.1786601 -6.5869122
18 -0.4261138 1.1786601
19 2.8149319 -0.4261138
20 1.1817502 2.8149319
21 -1.4653519 1.1817502
22 -3.3315837 -1.4653519
23 -0.5245848 -3.3315837
24 -0.6233732 -0.5245848
25 2.1963791 -0.6233732
26 1.7942466 2.1963791
27 -0.3910531 1.7942466
28 0.2489311 -0.3910531
29 3.4603185 0.2489311
30 2.9062624 3.4603185
31 -6.7219862 2.9062624
32 1.3790131 -6.7219862
33 0.4854779 1.3790131
34 -1.0805935 0.4854779
35 2.3539368 -1.0805935
36 2.5592720 2.3539368
37 -1.1669958 2.5592720
38 -0.3683201 -1.1669958
39 -0.7835727 -0.3683201
40 0.4157191 -0.7835727
41 0.7264749 0.4157191
42 -2.9788646 0.7264749
43 3.7369473 -2.9788646
44 -2.4062093 3.7369473
45 2.9265334 -2.4062093
46 2.5158384 2.9265334
47 -1.9044579 2.5158384
48 0.4344200 -1.9044579
49 -2.5256970 0.4344200
50 0.1693080 -2.5256970
51 1.4993944 0.1693080
52 3.7581258 1.4993944
53 -2.9328173 3.7581258
54 -0.5131013 -2.9328173
55 -1.2769732 -0.5131013
56 2.1163524 -1.2769732
57 -3.2386286 2.1163524
58 0.2568568 -3.2386286
59 1.7992303 0.2568568
> 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/7f6u81258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gi8h1258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nmpy1258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rufi1258620290.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u4b61258620290.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/12zqyr1258620290.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/13dn9u1258620290.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/142ci51258620290.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/15r29e1258620290.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/1604lu1258620290.tab")
+ }
>
> system("convert tmp/14r561258620290.ps tmp/14r561258620290.png")
> system("convert tmp/21r7f1258620290.ps tmp/21r7f1258620290.png")
> system("convert tmp/3kzx81258620290.ps tmp/3kzx81258620290.png")
> system("convert tmp/4a6441258620290.ps tmp/4a6441258620290.png")
> system("convert tmp/5qccs1258620290.ps tmp/5qccs1258620290.png")
> system("convert tmp/6iak01258620290.ps tmp/6iak01258620290.png")
> system("convert tmp/7f6u81258620290.ps tmp/7f6u81258620290.png")
> system("convert tmp/8gi8h1258620290.ps tmp/8gi8h1258620290.png")
> system("convert tmp/9nmpy1258620290.ps tmp/9nmpy1258620290.png")
> system("convert tmp/10rufi1258620290.ps tmp/10rufi1258620290.png")
>
>
> proc.time()
user system elapsed
2.288 1.689 3.354