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(0.7461,0.527,0.7775,0.472,0.7790,0,0.7744,0.052,0.7905,0.313,0.7719,0.364,0.7811,0.363,0.7557,-0.155,0.7637,0.052,0.7595,0.568,0.7471,0.668,0.7615,1.378,0.7487,0.252,0.7389,-0.402,0.7337,-0.05,0.7510,0.555,0.7382,0.05,0.7159,0.15,0.7542,0.45,0.7636,0.299,0.7433,0.199,0.7658,0.496,0.7627,0.444,0.7480,-0.393,0.7692,-0.444,0.7850,0.198,0.7913,0.494,0.7720,0.133,0.7880,0.388,0.8070,0.484,0.8268,0.278,0.8244,0.369,0.8487,0.165,0.8572,0.155,0.8214,0.087,0.8827,0.414,0.9216,0.36,0.8865,0.975,0.8816,0.27,0.8884,0.359,0.9466,0.169,0.9180,0.381,0.9337,0.154,0.9559,0.486,0.9626,0.925,0.9434,0.728,0.8639,-0.014,0.7996,0.046,0.6680,-0.819,0.6572,-1.674,0.6928,-0.788,0.6438,0.279,0.6454,0.396,0.6873,-0.141,0.7265,-0.019,0.7912,0.099,0.8114,0.742,0.8281,0.005,0.8393,0.448),dim=c(2,59),dimnames=list(c('Y','X'),1:59))
> y <- array(NA,dim=c(2,59),dimnames=list(c('Y','X'),1:59))
> 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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 0.7461 0.527 1 0 0 0 0 0 0 0 0 0 0
2 0.7775 0.472 0 1 0 0 0 0 0 0 0 0 0
3 0.7790 0.000 0 0 1 0 0 0 0 0 0 0 0
4 0.7744 0.052 0 0 0 1 0 0 0 0 0 0 0
5 0.7905 0.313 0 0 0 0 1 0 0 0 0 0 0
6 0.7719 0.364 0 0 0 0 0 1 0 0 0 0 0
7 0.7811 0.363 0 0 0 0 0 0 1 0 0 0 0
8 0.7557 -0.155 0 0 0 0 0 0 0 1 0 0 0
9 0.7637 0.052 0 0 0 0 0 0 0 0 1 0 0
10 0.7595 0.568 0 0 0 0 0 0 0 0 0 1 0
11 0.7471 0.668 0 0 0 0 0 0 0 0 0 0 1
12 0.7615 1.378 0 0 0 0 0 0 0 0 0 0 0
13 0.7487 0.252 1 0 0 0 0 0 0 0 0 0 0
14 0.7389 -0.402 0 1 0 0 0 0 0 0 0 0 0
15 0.7337 -0.050 0 0 1 0 0 0 0 0 0 0 0
16 0.7510 0.555 0 0 0 1 0 0 0 0 0 0 0
17 0.7382 0.050 0 0 0 0 1 0 0 0 0 0 0
18 0.7159 0.150 0 0 0 0 0 1 0 0 0 0 0
19 0.7542 0.450 0 0 0 0 0 0 1 0 0 0 0
20 0.7636 0.299 0 0 0 0 0 0 0 1 0 0 0
21 0.7433 0.199 0 0 0 0 0 0 0 0 1 0 0
22 0.7658 0.496 0 0 0 0 0 0 0 0 0 1 0
23 0.7627 0.444 0 0 0 0 0 0 0 0 0 0 1
24 0.7480 -0.393 0 0 0 0 0 0 0 0 0 0 0
25 0.7692 -0.444 1 0 0 0 0 0 0 0 0 0 0
26 0.7850 0.198 0 1 0 0 0 0 0 0 0 0 0
27 0.7913 0.494 0 0 1 0 0 0 0 0 0 0 0
28 0.7720 0.133 0 0 0 1 0 0 0 0 0 0 0
29 0.7880 0.388 0 0 0 0 1 0 0 0 0 0 0
30 0.8070 0.484 0 0 0 0 0 1 0 0 0 0 0
31 0.8268 0.278 0 0 0 0 0 0 1 0 0 0 0
32 0.8244 0.369 0 0 0 0 0 0 0 1 0 0 0
33 0.8487 0.165 0 0 0 0 0 0 0 0 1 0 0
34 0.8572 0.155 0 0 0 0 0 0 0 0 0 1 0
35 0.8214 0.087 0 0 0 0 0 0 0 0 0 0 1
36 0.8827 0.414 0 0 0 0 0 0 0 0 0 0 0
37 0.9216 0.360 1 0 0 0 0 0 0 0 0 0 0
38 0.8865 0.975 0 1 0 0 0 0 0 0 0 0 0
39 0.8816 0.270 0 0 1 0 0 0 0 0 0 0 0
40 0.8884 0.359 0 0 0 1 0 0 0 0 0 0 0
41 0.9466 0.169 0 0 0 0 1 0 0 0 0 0 0
42 0.9180 0.381 0 0 0 0 0 1 0 0 0 0 0
43 0.9337 0.154 0 0 0 0 0 0 1 0 0 0 0
44 0.9559 0.486 0 0 0 0 0 0 0 1 0 0 0
45 0.9626 0.925 0 0 0 0 0 0 0 0 1 0 0
46 0.9434 0.728 0 0 0 0 0 0 0 0 0 1 0
47 0.8639 -0.014 0 0 0 0 0 0 0 0 0 0 1
48 0.7996 0.046 0 0 0 0 0 0 0 0 0 0 0
49 0.6680 -0.819 1 0 0 0 0 0 0 0 0 0 0
50 0.6572 -1.674 0 1 0 0 0 0 0 0 0 0 0
51 0.6928 -0.788 0 0 1 0 0 0 0 0 0 0 0
52 0.6438 0.279 0 0 0 1 0 0 0 0 0 0 0
53 0.6454 0.396 0 0 0 0 1 0 0 0 0 0 0
54 0.6873 -0.141 0 0 0 0 0 1 0 0 0 0 0
55 0.7265 -0.019 0 0 0 0 0 0 1 0 0 0 0
56 0.7912 0.099 0 0 0 0 0 0 0 1 0 0 0
57 0.8114 0.742 0 0 0 0 0 0 0 0 1 0 0
58 0.8281 0.005 0 0 0 0 0 0 0 0 0 1 0
59 0.8393 0.448 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
0.7726477 0.0700411 -0.0001906 0.0024099 0.0040690 -0.0260310
M5 M6 M7 M8 M9 M10
-0.0093425 -0.0099698 0.0146383 0.0301313 0.0241132 0.0308083
M11
0.0113569
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.145641 -0.044250 -0.004224 0.029572 0.171458
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7726477 0.0391907 19.715 < 2e-16 ***
X 0.0700411 0.0238388 2.938 0.00515 **
M1 -0.0001906 0.0521138 -0.004 0.99710
M2 0.0024099 0.0523920 0.046 0.96351
M3 0.0040690 0.0520722 0.078 0.93806
M4 -0.0260310 0.0513353 -0.507 0.61452
M5 -0.0093425 0.0513480 -0.182 0.85643
M6 -0.0099698 0.0513662 -0.194 0.84696
M7 0.0146383 0.0513693 0.285 0.77695
M8 0.0301313 0.0514058 0.586 0.56064
M9 0.0241132 0.0513117 0.470 0.64062
M10 0.0308083 0.0512994 0.601 0.55108
M11 0.0113569 0.0513014 0.221 0.82578
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07647 on 46 degrees of freedom
Multiple R-squared: 0.229, Adjusted R-squared: 0.02786
F-statistic: 1.139 on 12 and 46 DF, p-value: 0.3543
> 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,] 3.359739e-02 6.719478e-02 0.9664026
[2,] 1.832221e-02 3.664442e-02 0.9816778
[3,] 1.109599e-02 2.219198e-02 0.9889040
[4,] 4.766798e-03 9.533597e-03 0.9952332
[5,] 1.514473e-03 3.028947e-03 0.9984855
[6,] 5.721827e-04 1.144365e-03 0.9994278
[7,] 2.060834e-04 4.121667e-04 0.9997939
[8,] 8.301917e-05 1.660383e-04 0.9999170
[9,] 2.567694e-05 5.135387e-05 0.9999743
[10,] 1.123587e-05 2.247173e-05 0.9999888
[11,] 4.211869e-06 8.423737e-06 0.9999958
[12,] 2.038874e-06 4.077748e-06 0.9999980
[13,] 5.164256e-07 1.032851e-06 0.9999995
[14,] 1.527542e-07 3.055084e-07 0.9999998
[15,] 2.925466e-07 5.850932e-07 0.9999997
[16,] 4.171416e-07 8.342832e-07 0.9999996
[17,] 4.945322e-07 9.890644e-07 0.9999995
[18,] 2.320093e-06 4.640186e-06 0.9999977
[19,] 6.700972e-06 1.340194e-05 0.9999933
[20,] 5.176948e-06 1.035390e-05 0.9999948
[21,] 2.110313e-05 4.220625e-05 0.9999789
[22,] 2.293669e-04 4.587337e-04 0.9997706
[23,] 3.576522e-04 7.153044e-04 0.9996423
[24,] 3.222880e-04 6.445760e-04 0.9996777
[25,] 1.343176e-03 2.686351e-03 0.9986568
[26,] 1.267939e-01 2.535879e-01 0.8732061
[27,] 1.534478e-01 3.068956e-01 0.8465522
[28,] 3.333803e-01 6.667605e-01 0.6666197
> postscript(file="/var/www/html/rcomp/tmp/17jgr1258649626.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d2la1258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yrsl1258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41yje1258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5glv81258649626.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 = 59
Frequency = 1
1 2 3 4 5
-0.0632686814 -0.0306169444 0.0022833917 0.0241411909 0.0052719530
6 7 8 9 10
-0.0162727845 -0.0316108421 -0.0362226023 -0.0367030134 -0.0837393001
11 12 13 14 15
-0.0836920330 -0.1076642928 -0.0414073777 -0.0080010193 -0.0395145531
16 17 18 19 20
-0.0344894845 -0.0286072366 -0.0572839882 -0.0646044182 -0.0601212637
21 22 23 24 25
-0.0673990557 -0.0723963406 -0.0524028256 0.0028785029 0.0278412309
26 27 28 29 30
-0.0039256818 -0.0200169139 0.0160678615 -0.0024811298 0.0104222829
31 32 33 34 35
0.0200426518 -0.0042241410 0.0403823418 0.0428876760 0.0313018486
36 37 38 39 40
0.0810553317 0.1239281831 0.0431523801 0.0859722935 0.1166385719
41 42 43 44 45
0.1714578720 0.1286365167 0.1356277487 0.1190810498 0.1010511026
46 47 48 49 50
0.0889541232 0.0808760001 0.0237304581 -0.0470933550 -0.0006087346
51 52 53 54 55
-0.0287242182 -0.1223581398 -0.1456414587 -0.0655020269 -0.0594551402
56 57 58 59
-0.0185130428 -0.0373313753 0.0242938416 0.0239170099
> postscript(file="/var/www/html/rcomp/tmp/6w3jn1258649626.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0632686814 NA
1 -0.0306169444 -0.0632686814
2 0.0022833917 -0.0306169444
3 0.0241411909 0.0022833917
4 0.0052719530 0.0241411909
5 -0.0162727845 0.0052719530
6 -0.0316108421 -0.0162727845
7 -0.0362226023 -0.0316108421
8 -0.0367030134 -0.0362226023
9 -0.0837393001 -0.0367030134
10 -0.0836920330 -0.0837393001
11 -0.1076642928 -0.0836920330
12 -0.0414073777 -0.1076642928
13 -0.0080010193 -0.0414073777
14 -0.0395145531 -0.0080010193
15 -0.0344894845 -0.0395145531
16 -0.0286072366 -0.0344894845
17 -0.0572839882 -0.0286072366
18 -0.0646044182 -0.0572839882
19 -0.0601212637 -0.0646044182
20 -0.0673990557 -0.0601212637
21 -0.0723963406 -0.0673990557
22 -0.0524028256 -0.0723963406
23 0.0028785029 -0.0524028256
24 0.0278412309 0.0028785029
25 -0.0039256818 0.0278412309
26 -0.0200169139 -0.0039256818
27 0.0160678615 -0.0200169139
28 -0.0024811298 0.0160678615
29 0.0104222829 -0.0024811298
30 0.0200426518 0.0104222829
31 -0.0042241410 0.0200426518
32 0.0403823418 -0.0042241410
33 0.0428876760 0.0403823418
34 0.0313018486 0.0428876760
35 0.0810553317 0.0313018486
36 0.1239281831 0.0810553317
37 0.0431523801 0.1239281831
38 0.0859722935 0.0431523801
39 0.1166385719 0.0859722935
40 0.1714578720 0.1166385719
41 0.1286365167 0.1714578720
42 0.1356277487 0.1286365167
43 0.1190810498 0.1356277487
44 0.1010511026 0.1190810498
45 0.0889541232 0.1010511026
46 0.0808760001 0.0889541232
47 0.0237304581 0.0808760001
48 -0.0470933550 0.0237304581
49 -0.0006087346 -0.0470933550
50 -0.0287242182 -0.0006087346
51 -0.1223581398 -0.0287242182
52 -0.1456414587 -0.1223581398
53 -0.0655020269 -0.1456414587
54 -0.0594551402 -0.0655020269
55 -0.0185130428 -0.0594551402
56 -0.0373313753 -0.0185130428
57 0.0242938416 -0.0373313753
58 0.0239170099 0.0242938416
59 NA 0.0239170099
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0306169444 -0.0632686814
[2,] 0.0022833917 -0.0306169444
[3,] 0.0241411909 0.0022833917
[4,] 0.0052719530 0.0241411909
[5,] -0.0162727845 0.0052719530
[6,] -0.0316108421 -0.0162727845
[7,] -0.0362226023 -0.0316108421
[8,] -0.0367030134 -0.0362226023
[9,] -0.0837393001 -0.0367030134
[10,] -0.0836920330 -0.0837393001
[11,] -0.1076642928 -0.0836920330
[12,] -0.0414073777 -0.1076642928
[13,] -0.0080010193 -0.0414073777
[14,] -0.0395145531 -0.0080010193
[15,] -0.0344894845 -0.0395145531
[16,] -0.0286072366 -0.0344894845
[17,] -0.0572839882 -0.0286072366
[18,] -0.0646044182 -0.0572839882
[19,] -0.0601212637 -0.0646044182
[20,] -0.0673990557 -0.0601212637
[21,] -0.0723963406 -0.0673990557
[22,] -0.0524028256 -0.0723963406
[23,] 0.0028785029 -0.0524028256
[24,] 0.0278412309 0.0028785029
[25,] -0.0039256818 0.0278412309
[26,] -0.0200169139 -0.0039256818
[27,] 0.0160678615 -0.0200169139
[28,] -0.0024811298 0.0160678615
[29,] 0.0104222829 -0.0024811298
[30,] 0.0200426518 0.0104222829
[31,] -0.0042241410 0.0200426518
[32,] 0.0403823418 -0.0042241410
[33,] 0.0428876760 0.0403823418
[34,] 0.0313018486 0.0428876760
[35,] 0.0810553317 0.0313018486
[36,] 0.1239281831 0.0810553317
[37,] 0.0431523801 0.1239281831
[38,] 0.0859722935 0.0431523801
[39,] 0.1166385719 0.0859722935
[40,] 0.1714578720 0.1166385719
[41,] 0.1286365167 0.1714578720
[42,] 0.1356277487 0.1286365167
[43,] 0.1190810498 0.1356277487
[44,] 0.1010511026 0.1190810498
[45,] 0.0889541232 0.1010511026
[46,] 0.0808760001 0.0889541232
[47,] 0.0237304581 0.0808760001
[48,] -0.0470933550 0.0237304581
[49,] -0.0006087346 -0.0470933550
[50,] -0.0287242182 -0.0006087346
[51,] -0.1223581398 -0.0287242182
[52,] -0.1456414587 -0.1223581398
[53,] -0.0655020269 -0.1456414587
[54,] -0.0594551402 -0.0655020269
[55,] -0.0185130428 -0.0594551402
[56,] -0.0373313753 -0.0185130428
[57,] 0.0242938416 -0.0373313753
[58,] 0.0239170099 0.0242938416
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0306169444 -0.0632686814
2 0.0022833917 -0.0306169444
3 0.0241411909 0.0022833917
4 0.0052719530 0.0241411909
5 -0.0162727845 0.0052719530
6 -0.0316108421 -0.0162727845
7 -0.0362226023 -0.0316108421
8 -0.0367030134 -0.0362226023
9 -0.0837393001 -0.0367030134
10 -0.0836920330 -0.0837393001
11 -0.1076642928 -0.0836920330
12 -0.0414073777 -0.1076642928
13 -0.0080010193 -0.0414073777
14 -0.0395145531 -0.0080010193
15 -0.0344894845 -0.0395145531
16 -0.0286072366 -0.0344894845
17 -0.0572839882 -0.0286072366
18 -0.0646044182 -0.0572839882
19 -0.0601212637 -0.0646044182
20 -0.0673990557 -0.0601212637
21 -0.0723963406 -0.0673990557
22 -0.0524028256 -0.0723963406
23 0.0028785029 -0.0524028256
24 0.0278412309 0.0028785029
25 -0.0039256818 0.0278412309
26 -0.0200169139 -0.0039256818
27 0.0160678615 -0.0200169139
28 -0.0024811298 0.0160678615
29 0.0104222829 -0.0024811298
30 0.0200426518 0.0104222829
31 -0.0042241410 0.0200426518
32 0.0403823418 -0.0042241410
33 0.0428876760 0.0403823418
34 0.0313018486 0.0428876760
35 0.0810553317 0.0313018486
36 0.1239281831 0.0810553317
37 0.0431523801 0.1239281831
38 0.0859722935 0.0431523801
39 0.1166385719 0.0859722935
40 0.1714578720 0.1166385719
41 0.1286365167 0.1714578720
42 0.1356277487 0.1286365167
43 0.1190810498 0.1356277487
44 0.1010511026 0.1190810498
45 0.0889541232 0.1010511026
46 0.0808760001 0.0889541232
47 0.0237304581 0.0808760001
48 -0.0470933550 0.0237304581
49 -0.0006087346 -0.0470933550
50 -0.0287242182 -0.0006087346
51 -0.1223581398 -0.0287242182
52 -0.1456414587 -0.1223581398
53 -0.0655020269 -0.1456414587
54 -0.0594551402 -0.0655020269
55 -0.0185130428 -0.0594551402
56 -0.0373313753 -0.0185130428
57 0.0242938416 -0.0373313753
58 0.0239170099 0.0242938416
> 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/7td841258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rz991258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bqb31258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101j4m1258649626.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118py21258649626.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/12u6aa1258649626.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/1333vy1258649626.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/148fvq1258649626.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/154wcl1258649626.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/164pqb1258649626.tab")
+ }
>
> system("convert tmp/17jgr1258649626.ps tmp/17jgr1258649626.png")
> system("convert tmp/2d2la1258649626.ps tmp/2d2la1258649626.png")
> system("convert tmp/3yrsl1258649626.ps tmp/3yrsl1258649626.png")
> system("convert tmp/41yje1258649626.ps tmp/41yje1258649626.png")
> system("convert tmp/5glv81258649626.ps tmp/5glv81258649626.png")
> system("convert tmp/6w3jn1258649626.ps tmp/6w3jn1258649626.png")
> system("convert tmp/7td841258649626.ps tmp/7td841258649626.png")
> system("convert tmp/8rz991258649626.ps tmp/8rz991258649626.png")
> system("convert tmp/9bqb31258649626.ps tmp/9bqb31258649626.png")
> system("convert tmp/101j4m1258649626.ps tmp/101j4m1258649626.png")
>
>
> proc.time()
user system elapsed
2.393 1.566 5.583