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(20.3,3016,20,2155,19.2,2172,21.8,2150,21.3,2533,21.5,2058,19.5,2160,19.5,2260,19.7,2498,18.7,2695,19.7,2799,20,2946,19.7,2930,19.2,2318,19.7,2540,22,2570,21.8,2669,22.8,2450,21,2842,25,3440,23.3,2678,25,2981,26.8,2260,25.3,2844,26.5,2546,27.8,2456,22,2295,22.3,2379,28,2479,25,2057,27.3,2280,25.8,2351,27.3,2276,23.5,2548,24.5,2311,18,2201,21.3,2725,21.8,2408,20.5,2139,22.3,1898,18.7,2537,22.3,2068,17.7,2063,19.7,2520,20.5,2434,18.5,2190,10,2794,14.2,2070,15.5,2615,16.5,2265,20.5,2139,15.7,2428,11.7,2137,7.5,1823,3.5,2063,4.5,1806,2.2,1758,5,2243,2.3,1993,6.1,1932,3.3,2465),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 20.3 3016 1 0 0 0 0 0 0 0 0 0 0
2 20.0 2155 0 1 0 0 0 0 0 0 0 0 0
3 19.2 2172 0 0 1 0 0 0 0 0 0 0 0
4 21.8 2150 0 0 0 1 0 0 0 0 0 0 0
5 21.3 2533 0 0 0 0 1 0 0 0 0 0 0
6 21.5 2058 0 0 0 0 0 1 0 0 0 0 0
7 19.5 2160 0 0 0 0 0 0 1 0 0 0 0
8 19.5 2260 0 0 0 0 0 0 0 1 0 0 0
9 19.7 2498 0 0 0 0 0 0 0 0 1 0 0
10 18.7 2695 0 0 0 0 0 0 0 0 0 1 0
11 19.7 2799 0 0 0 0 0 0 0 0 0 0 1
12 20.0 2946 0 0 0 0 0 0 0 0 0 0 0
13 19.7 2930 1 0 0 0 0 0 0 0 0 0 0
14 19.2 2318 0 1 0 0 0 0 0 0 0 0 0
15 19.7 2540 0 0 1 0 0 0 0 0 0 0 0
16 22.0 2570 0 0 0 1 0 0 0 0 0 0 0
17 21.8 2669 0 0 0 0 1 0 0 0 0 0 0
18 22.8 2450 0 0 0 0 0 1 0 0 0 0 0
19 21.0 2842 0 0 0 0 0 0 1 0 0 0 0
20 25.0 3440 0 0 0 0 0 0 0 1 0 0 0
21 23.3 2678 0 0 0 0 0 0 0 0 1 0 0
22 25.0 2981 0 0 0 0 0 0 0 0 0 1 0
23 26.8 2260 0 0 0 0 0 0 0 0 0 0 1
24 25.3 2844 0 0 0 0 0 0 0 0 0 0 0
25 26.5 2546 1 0 0 0 0 0 0 0 0 0 0
26 27.8 2456 0 1 0 0 0 0 0 0 0 0 0
27 22.0 2295 0 0 1 0 0 0 0 0 0 0 0
28 22.3 2379 0 0 0 1 0 0 0 0 0 0 0
29 28.0 2479 0 0 0 0 1 0 0 0 0 0 0
30 25.0 2057 0 0 0 0 0 1 0 0 0 0 0
31 27.3 2280 0 0 0 0 0 0 1 0 0 0 0
32 25.8 2351 0 0 0 0 0 0 0 1 0 0 0
33 27.3 2276 0 0 0 0 0 0 0 0 1 0 0
34 23.5 2548 0 0 0 0 0 0 0 0 0 1 0
35 24.5 2311 0 0 0 0 0 0 0 0 0 0 1
36 18.0 2201 0 0 0 0 0 0 0 0 0 0 0
37 21.3 2725 1 0 0 0 0 0 0 0 0 0 0
38 21.8 2408 0 1 0 0 0 0 0 0 0 0 0
39 20.5 2139 0 0 1 0 0 0 0 0 0 0 0
40 22.3 1898 0 0 0 1 0 0 0 0 0 0 0
41 18.7 2537 0 0 0 0 1 0 0 0 0 0 0
42 22.3 2068 0 0 0 0 0 1 0 0 0 0 0
43 17.7 2063 0 0 0 0 0 0 1 0 0 0 0
44 19.7 2520 0 0 0 0 0 0 0 1 0 0 0
45 20.5 2434 0 0 0 0 0 0 0 0 1 0 0
46 18.5 2190 0 0 0 0 0 0 0 0 0 1 0
47 10.0 2794 0 0 0 0 0 0 0 0 0 0 1
48 14.2 2070 0 0 0 0 0 0 0 0 0 0 0
49 15.5 2615 1 0 0 0 0 0 0 0 0 0 0
50 16.5 2265 0 1 0 0 0 0 0 0 0 0 0
51 20.5 2139 0 0 1 0 0 0 0 0 0 0 0
52 15.7 2428 0 0 0 1 0 0 0 0 0 0 0
53 11.7 2137 0 0 0 0 1 0 0 0 0 0 0
54 7.5 1823 0 0 0 0 0 1 0 0 0 0 0
55 3.5 2063 0 0 0 0 0 0 1 0 0 0 0
56 4.5 1806 0 0 0 0 0 0 0 1 0 0 0
57 2.2 1758 0 0 0 0 0 0 0 0 1 0 0
58 5.0 2243 0 0 0 0 0 0 0 0 0 1 0
59 2.3 1993 0 0 0 0 0 0 0 0 0 0 1
60 6.1 1932 0 0 0 0 0 0 0 0 0 0 0
61 3.3 2465 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
-11.79335 0.01189 -2.72841 5.26960 5.34327 5.45042
M5 M6 M7 M8 M9 M10
2.71935 6.75422 2.47084 1.26704 2.70975 -0.15866
M11
-0.44991
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.7014 -3.2272 0.2698 3.1606 12.1775
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -11.793348 7.137969 -1.652 0.105
X 0.011887 0.002738 4.341 7.29e-05 ***
M1 -2.728406 3.881330 -0.703 0.485
M2 5.269602 3.956646 1.332 0.189
M3 5.343269 3.969830 1.346 0.185
M4 5.450420 3.963075 1.375 0.175
M5 2.719345 3.955818 0.687 0.495
M6 6.754216 4.039534 1.672 0.101
M7 2.470837 3.963817 0.623 0.536
M8 1.267040 3.956440 0.320 0.750
M9 2.709747 3.955467 0.685 0.497
M10 -0.158659 3.967549 -0.040 0.968
M11 -0.449910 3.951867 -0.114 0.910
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.247 on 48 degrees of freedom
Multiple R-squared: 0.3167, Adjusted R-squared: 0.1458
F-statistic: 1.854 on 12 and 48 DF, p-value: 0.06562
> 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,] 3.015528e-04 6.031056e-04 0.9996984
[2,] 1.881490e-05 3.762981e-05 0.9999812
[3,] 2.521424e-06 5.042849e-06 0.9999975
[4,] 1.588813e-07 3.177626e-07 0.9999998
[5,] 4.851454e-07 9.702908e-07 0.9999995
[6,] 1.018894e-06 2.037788e-06 0.9999990
[7,] 9.230957e-06 1.846191e-05 0.9999908
[8,] 4.191904e-04 8.383808e-04 0.9995808
[9,] 3.808165e-04 7.616330e-04 0.9996192
[10,] 1.378376e-03 2.756751e-03 0.9986216
[11,] 2.873487e-03 5.746973e-03 0.9971265
[12,] 1.404790e-03 2.809580e-03 0.9985952
[13,] 5.617168e-04 1.123434e-03 0.9994383
[14,] 8.119646e-04 1.623929e-03 0.9991880
[15,] 4.903207e-04 9.806413e-04 0.9995097
[16,] 9.945831e-04 1.989166e-03 0.9990054
[17,] 8.082504e-04 1.616501e-03 0.9991917
[18,] 1.271175e-03 2.542350e-03 0.9987288
[19,] 6.802227e-04 1.360445e-03 0.9993198
[20,] 3.580395e-03 7.160789e-03 0.9964196
[21,] 2.884119e-03 5.768239e-03 0.9971159
[22,] 2.509889e-03 5.019778e-03 0.9974901
[23,] 1.186035e-03 2.372069e-03 0.9988140
[24,] 4.906026e-04 9.812052e-04 0.9995094
[25,] 1.705136e-03 3.410271e-03 0.9982949
[26,] 1.098215e-03 2.196429e-03 0.9989018
[27,] 1.164968e-03 2.329935e-03 0.9988350
[28,] 6.742712e-03 1.348542e-02 0.9932573
[29,] 3.286628e-03 6.573256e-03 0.9967134
[30,] 2.976573e-03 5.953146e-03 0.9970234
> postscript(file="/var/www/html/rcomp/tmp/1gpg31258726847.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yi221258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3eccp1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4klas1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54cwl1258726847.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.0309342 0.9061918 -0.1695628 2.5848120 0.2629753 2.0746649
7 8 9 10 11 12
3.1455195 3.1605666 -0.9113643 -1.3847943 -1.3298435 -3.2272153
13 14 15 16 17 18
-0.6086096 -1.8314700 -4.0441614 -2.2079363 -0.8537242 -1.2852336
19 20 21 22 23 24
-3.4617527 -5.3666786 0.5488864 1.5153818 12.1775168 3.2853093
25 26 27 28 29 30
10.7561889 5.1280555 1.1682752 0.3625754 7.6049000 5.5865524
31 32 33 34 35 36
9.5190200 8.3788045 9.3276598 5.1626676 9.2712545 3.6289692
37 38 39 40 41 42
3.4283271 -0.3013446 1.5227245 6.0804609 -2.3845747 2.7557899
43 44 45 46 47 48
2.4986066 0.2698177 0.6494354 4.4183911 -10.9704060 1.3862312
49 50 51 52 53 54
-1.0640483 -3.9014327 1.5227245 -6.8199119 -4.6295763 -9.1317736
55 56 57 58 59 60
-11.7013934 -6.4425102 -9.6146173 -9.7116462 -9.1485218 -5.0732944
61
-11.4809239
> postscript(file="/var/www/html/rcomp/tmp/6cha21258726847.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.0309342 NA
1 0.9061918 -1.0309342
2 -0.1695628 0.9061918
3 2.5848120 -0.1695628
4 0.2629753 2.5848120
5 2.0746649 0.2629753
6 3.1455195 2.0746649
7 3.1605666 3.1455195
8 -0.9113643 3.1605666
9 -1.3847943 -0.9113643
10 -1.3298435 -1.3847943
11 -3.2272153 -1.3298435
12 -0.6086096 -3.2272153
13 -1.8314700 -0.6086096
14 -4.0441614 -1.8314700
15 -2.2079363 -4.0441614
16 -0.8537242 -2.2079363
17 -1.2852336 -0.8537242
18 -3.4617527 -1.2852336
19 -5.3666786 -3.4617527
20 0.5488864 -5.3666786
21 1.5153818 0.5488864
22 12.1775168 1.5153818
23 3.2853093 12.1775168
24 10.7561889 3.2853093
25 5.1280555 10.7561889
26 1.1682752 5.1280555
27 0.3625754 1.1682752
28 7.6049000 0.3625754
29 5.5865524 7.6049000
30 9.5190200 5.5865524
31 8.3788045 9.5190200
32 9.3276598 8.3788045
33 5.1626676 9.3276598
34 9.2712545 5.1626676
35 3.6289692 9.2712545
36 3.4283271 3.6289692
37 -0.3013446 3.4283271
38 1.5227245 -0.3013446
39 6.0804609 1.5227245
40 -2.3845747 6.0804609
41 2.7557899 -2.3845747
42 2.4986066 2.7557899
43 0.2698177 2.4986066
44 0.6494354 0.2698177
45 4.4183911 0.6494354
46 -10.9704060 4.4183911
47 1.3862312 -10.9704060
48 -1.0640483 1.3862312
49 -3.9014327 -1.0640483
50 1.5227245 -3.9014327
51 -6.8199119 1.5227245
52 -4.6295763 -6.8199119
53 -9.1317736 -4.6295763
54 -11.7013934 -9.1317736
55 -6.4425102 -11.7013934
56 -9.6146173 -6.4425102
57 -9.7116462 -9.6146173
58 -9.1485218 -9.7116462
59 -5.0732944 -9.1485218
60 -11.4809239 -5.0732944
61 NA -11.4809239
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.9061918 -1.0309342
[2,] -0.1695628 0.9061918
[3,] 2.5848120 -0.1695628
[4,] 0.2629753 2.5848120
[5,] 2.0746649 0.2629753
[6,] 3.1455195 2.0746649
[7,] 3.1605666 3.1455195
[8,] -0.9113643 3.1605666
[9,] -1.3847943 -0.9113643
[10,] -1.3298435 -1.3847943
[11,] -3.2272153 -1.3298435
[12,] -0.6086096 -3.2272153
[13,] -1.8314700 -0.6086096
[14,] -4.0441614 -1.8314700
[15,] -2.2079363 -4.0441614
[16,] -0.8537242 -2.2079363
[17,] -1.2852336 -0.8537242
[18,] -3.4617527 -1.2852336
[19,] -5.3666786 -3.4617527
[20,] 0.5488864 -5.3666786
[21,] 1.5153818 0.5488864
[22,] 12.1775168 1.5153818
[23,] 3.2853093 12.1775168
[24,] 10.7561889 3.2853093
[25,] 5.1280555 10.7561889
[26,] 1.1682752 5.1280555
[27,] 0.3625754 1.1682752
[28,] 7.6049000 0.3625754
[29,] 5.5865524 7.6049000
[30,] 9.5190200 5.5865524
[31,] 8.3788045 9.5190200
[32,] 9.3276598 8.3788045
[33,] 5.1626676 9.3276598
[34,] 9.2712545 5.1626676
[35,] 3.6289692 9.2712545
[36,] 3.4283271 3.6289692
[37,] -0.3013446 3.4283271
[38,] 1.5227245 -0.3013446
[39,] 6.0804609 1.5227245
[40,] -2.3845747 6.0804609
[41,] 2.7557899 -2.3845747
[42,] 2.4986066 2.7557899
[43,] 0.2698177 2.4986066
[44,] 0.6494354 0.2698177
[45,] 4.4183911 0.6494354
[46,] -10.9704060 4.4183911
[47,] 1.3862312 -10.9704060
[48,] -1.0640483 1.3862312
[49,] -3.9014327 -1.0640483
[50,] 1.5227245 -3.9014327
[51,] -6.8199119 1.5227245
[52,] -4.6295763 -6.8199119
[53,] -9.1317736 -4.6295763
[54,] -11.7013934 -9.1317736
[55,] -6.4425102 -11.7013934
[56,] -9.6146173 -6.4425102
[57,] -9.7116462 -9.6146173
[58,] -9.1485218 -9.7116462
[59,] -5.0732944 -9.1485218
[60,] -11.4809239 -5.0732944
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.9061918 -1.0309342
2 -0.1695628 0.9061918
3 2.5848120 -0.1695628
4 0.2629753 2.5848120
5 2.0746649 0.2629753
6 3.1455195 2.0746649
7 3.1605666 3.1455195
8 -0.9113643 3.1605666
9 -1.3847943 -0.9113643
10 -1.3298435 -1.3847943
11 -3.2272153 -1.3298435
12 -0.6086096 -3.2272153
13 -1.8314700 -0.6086096
14 -4.0441614 -1.8314700
15 -2.2079363 -4.0441614
16 -0.8537242 -2.2079363
17 -1.2852336 -0.8537242
18 -3.4617527 -1.2852336
19 -5.3666786 -3.4617527
20 0.5488864 -5.3666786
21 1.5153818 0.5488864
22 12.1775168 1.5153818
23 3.2853093 12.1775168
24 10.7561889 3.2853093
25 5.1280555 10.7561889
26 1.1682752 5.1280555
27 0.3625754 1.1682752
28 7.6049000 0.3625754
29 5.5865524 7.6049000
30 9.5190200 5.5865524
31 8.3788045 9.5190200
32 9.3276598 8.3788045
33 5.1626676 9.3276598
34 9.2712545 5.1626676
35 3.6289692 9.2712545
36 3.4283271 3.6289692
37 -0.3013446 3.4283271
38 1.5227245 -0.3013446
39 6.0804609 1.5227245
40 -2.3845747 6.0804609
41 2.7557899 -2.3845747
42 2.4986066 2.7557899
43 0.2698177 2.4986066
44 0.6494354 0.2698177
45 4.4183911 0.6494354
46 -10.9704060 4.4183911
47 1.3862312 -10.9704060
48 -1.0640483 1.3862312
49 -3.9014327 -1.0640483
50 1.5227245 -3.9014327
51 -6.8199119 1.5227245
52 -4.6295763 -6.8199119
53 -9.1317736 -4.6295763
54 -11.7013934 -9.1317736
55 -6.4425102 -11.7013934
56 -9.6146173 -6.4425102
57 -9.7116462 -9.6146173
58 -9.1485218 -9.7116462
59 -5.0732944 -9.1485218
60 -11.4809239 -5.0732944
> 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/7gseb1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sf6a1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ectn1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k3yu1258726847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lnc51258726847.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/12unjj1258726847.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/13ipvo1258726847.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/14uf531258726847.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/159adn1258726847.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/16hmn71258726847.tab")
+ }
>
> system("convert tmp/1gpg31258726847.ps tmp/1gpg31258726847.png")
> system("convert tmp/2yi221258726847.ps tmp/2yi221258726847.png")
> system("convert tmp/3eccp1258726847.ps tmp/3eccp1258726847.png")
> system("convert tmp/4klas1258726847.ps tmp/4klas1258726847.png")
> system("convert tmp/54cwl1258726847.ps tmp/54cwl1258726847.png")
> system("convert tmp/6cha21258726847.ps tmp/6cha21258726847.png")
> system("convert tmp/7gseb1258726847.ps tmp/7gseb1258726847.png")
> system("convert tmp/8sf6a1258726847.ps tmp/8sf6a1258726847.png")
> system("convert tmp/9ectn1258726847.ps tmp/9ectn1258726847.png")
> system("convert tmp/10k3yu1258726847.ps tmp/10k3yu1258726847.png")
>
>
> proc.time()
user system elapsed
2.421 1.542 2.828