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(3.58,98.2,3.52,98.71,3.45,98.54,3.36,98.2,3.27,96.92,3.21,99.06,3.19,99.65,3.16,99.82,3.12,99.99,3.06,100.33,3.01,99.31,2.98,101.1,2.97,101.1,3.02,100.93,3.07,100.85,3.18,100.93,3.29,99.6,3.43,101.88,3.61,101.81,3.74,102.38,3.87,102.74,3.88,102.82,4.09,101.72,4.19,103.47,4.2,102.98,4.29,102.68,4.37,102.9,4.47,103.03,4.61,101.29,4.65,103.69,4.69,103.68,4.82,104.2,4.86,104.08,4.87,104.16,5.01,103.05,5.03,104.66,5.13,104.46,5.18,104.95,5.21,105.85,5.26,106.23,5.25,104.86,5.2,107.44,5.16,108.23,5.19,108.45,5.39,109.39,5.58,110.15,5.76,109.13,5.89,110.28,5.98,110.17,6.02,109.99,5.62,109.26,4.87,109.11,4.24,107.06,4.02,109.53,3.74,108.92,3.45,109.24,3.34,109.12,3.21,109,3.12,107.23,3.04,109.49),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = '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 3.58 98.20 1 0 0 0 0 0 0 0 0 0 0
2 3.52 98.71 0 1 0 0 0 0 0 0 0 0 0
3 3.45 98.54 0 0 1 0 0 0 0 0 0 0 0
4 3.36 98.20 0 0 0 1 0 0 0 0 0 0 0
5 3.27 96.92 0 0 0 0 1 0 0 0 0 0 0
6 3.21 99.06 0 0 0 0 0 1 0 0 0 0 0
7 3.19 99.65 0 0 0 0 0 0 1 0 0 0 0
8 3.16 99.82 0 0 0 0 0 0 0 1 0 0 0
9 3.12 99.99 0 0 0 0 0 0 0 0 1 0 0
10 3.06 100.33 0 0 0 0 0 0 0 0 0 1 0
11 3.01 99.31 0 0 0 0 0 0 0 0 0 0 1
12 2.98 101.10 0 0 0 0 0 0 0 0 0 0 0
13 2.97 101.10 1 0 0 0 0 0 0 0 0 0 0
14 3.02 100.93 0 1 0 0 0 0 0 0 0 0 0
15 3.07 100.85 0 0 1 0 0 0 0 0 0 0 0
16 3.18 100.93 0 0 0 1 0 0 0 0 0 0 0
17 3.29 99.60 0 0 0 0 1 0 0 0 0 0 0
18 3.43 101.88 0 0 0 0 0 1 0 0 0 0 0
19 3.61 101.81 0 0 0 0 0 0 1 0 0 0 0
20 3.74 102.38 0 0 0 0 0 0 0 1 0 0 0
21 3.87 102.74 0 0 0 0 0 0 0 0 1 0 0
22 3.88 102.82 0 0 0 0 0 0 0 0 0 1 0
23 4.09 101.72 0 0 0 0 0 0 0 0 0 0 1
24 4.19 103.47 0 0 0 0 0 0 0 0 0 0 0
25 4.20 102.98 1 0 0 0 0 0 0 0 0 0 0
26 4.29 102.68 0 1 0 0 0 0 0 0 0 0 0
27 4.37 102.90 0 0 1 0 0 0 0 0 0 0 0
28 4.47 103.03 0 0 0 1 0 0 0 0 0 0 0
29 4.61 101.29 0 0 0 0 1 0 0 0 0 0 0
30 4.65 103.69 0 0 0 0 0 1 0 0 0 0 0
31 4.69 103.68 0 0 0 0 0 0 1 0 0 0 0
32 4.82 104.20 0 0 0 0 0 0 0 1 0 0 0
33 4.86 104.08 0 0 0 0 0 0 0 0 1 0 0
34 4.87 104.16 0 0 0 0 0 0 0 0 0 1 0
35 5.01 103.05 0 0 0 0 0 0 0 0 0 0 1
36 5.03 104.66 0 0 0 0 0 0 0 0 0 0 0
37 5.13 104.46 1 0 0 0 0 0 0 0 0 0 0
38 5.18 104.95 0 1 0 0 0 0 0 0 0 0 0
39 5.21 105.85 0 0 1 0 0 0 0 0 0 0 0
40 5.26 106.23 0 0 0 1 0 0 0 0 0 0 0
41 5.25 104.86 0 0 0 0 1 0 0 0 0 0 0
42 5.20 107.44 0 0 0 0 0 1 0 0 0 0 0
43 5.16 108.23 0 0 0 0 0 0 1 0 0 0 0
44 5.19 108.45 0 0 0 0 0 0 0 1 0 0 0
45 5.39 109.39 0 0 0 0 0 0 0 0 1 0 0
46 5.58 110.15 0 0 0 0 0 0 0 0 0 1 0
47 5.76 109.13 0 0 0 0 0 0 0 0 0 0 1
48 5.89 110.28 0 0 0 0 0 0 0 0 0 0 0
49 5.98 110.17 1 0 0 0 0 0 0 0 0 0 0
50 6.02 109.99 0 1 0 0 0 0 0 0 0 0 0
51 5.62 109.26 0 0 1 0 0 0 0 0 0 0 0
52 4.87 109.11 0 0 0 1 0 0 0 0 0 0 0
53 4.24 107.06 0 0 0 0 1 0 0 0 0 0 0
54 4.02 109.53 0 0 0 0 0 1 0 0 0 0 0
55 3.74 108.92 0 0 0 0 0 0 1 0 0 0 0
56 3.45 109.24 0 0 0 0 0 0 0 1 0 0 0
57 3.34 109.12 0 0 0 0 0 0 0 0 1 0 0
58 3.21 109.00 0 0 0 0 0 0 0 0 0 1 0
59 3.12 107.23 0 0 0 0 0 0 0 0 0 0 1
60 3.04 109.49 0 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
-13.106564 0.163824 0.542126 0.564658 0.498071 0.378795
M5 M6 M7 M8 M9 M10
0.537377 0.118459 0.071852 0.006875 0.010574 -0.022777
M11
0.252466
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.79051 -0.30910 0.06217 0.56932 0.99076
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.106564 2.947952 -4.446 5.32e-05 ***
X 0.163824 0.027660 5.923 3.52e-07 ***
M1 0.542126 0.507807 1.068 0.291
M2 0.564658 0.507556 1.113 0.272
M3 0.498071 0.507457 0.982 0.331
M4 0.378795 0.507388 0.747 0.459
M5 0.537377 0.514547 1.044 0.302
M6 0.118459 0.505045 0.235 0.816
M7 0.071852 0.504750 0.142 0.887
M8 0.006875 0.504116 0.014 0.989
M9 0.010574 0.503795 0.021 0.983
M10 -0.022777 0.503580 -0.045 0.964
M11 0.252466 0.505606 0.499 0.620
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7959 on 47 degrees of freedom
Multiple R-squared: 0.4357, Adjusted R-squared: 0.2916
F-statistic: 3.024 on 12 and 47 DF, p-value: 0.003259
> 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,] 5.275277e-03 1.055055e-02 0.9947247
[2,] 4.201860e-03 8.403720e-03 0.9957981
[3,] 4.047622e-03 8.095245e-03 0.9959524
[4,] 3.635679e-03 7.271358e-03 0.9963643
[5,] 3.834407e-03 7.668813e-03 0.9961656
[6,] 4.171266e-03 8.342532e-03 0.9958287
[7,] 3.816705e-03 7.633409e-03 0.9961833
[8,] 4.781246e-03 9.562491e-03 0.9952188
[9,] 5.700760e-03 1.140152e-02 0.9942992
[10,] 4.202697e-03 8.405394e-03 0.9957973
[11,] 3.578869e-03 7.157738e-03 0.9964211
[12,] 2.750938e-03 5.501876e-03 0.9972491
[13,] 1.806725e-03 3.613449e-03 0.9981933
[14,] 1.350840e-03 2.701680e-03 0.9986492
[15,] 8.381479e-04 1.676296e-03 0.9991619
[16,] 4.925262e-04 9.850524e-04 0.9995075
[17,] 2.939728e-04 5.879455e-04 0.9997060
[18,] 1.874836e-04 3.749672e-04 0.9998125
[19,] 1.256262e-04 2.512523e-04 0.9998744
[20,] 9.597830e-05 1.919566e-04 0.9999040
[21,] 8.443774e-05 1.688755e-04 0.9999156
[22,] 3.480814e-05 6.961629e-05 0.9999652
[23,] 1.223933e-05 2.447865e-05 0.9999878
[24,] 3.726992e-06 7.453985e-06 0.9999963
[25,] 1.321686e-06 2.643372e-06 0.9999987
[26,] 1.033514e-06 2.067028e-06 0.9999990
[27,] 8.392300e-06 1.678460e-05 0.9999916
[28,] 5.153386e-05 1.030677e-04 0.9999485
[29,] 1.085949e-02 2.171898e-02 0.9891405
> postscript(file="/var/www/html/rcomp/tmp/1tj5n1258559651.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m7yx1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z6yv1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xoiy1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lely1258559651.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
0.0569352437 -0.1091472548 -0.0847101305 0.0002664592 -0.0386212785
6 7 8 9 10
-0.0302864952 -0.1003348800 -0.0932083466 -0.1647577332 -0.2471060055
11 12 13 14 15
-0.4052495958 -0.4760278569 -1.0281539510 -0.9728362245 -0.8431432476
16 17 18 19 20
-0.6269726792 -0.4576692239 -0.2722697810 -0.0341944181 0.0674025712
21 22 23 24 25
0.1347266512 0.1649725825 0.2799349010 0.3457095943 -0.1061428082
26 27 28 29 30
0.0104720201 0.1210178389 0.3189972143 0.5854684523 0.6512090319
31 32 33 34 35
0.7394549633 0.8492431456 0.9052026785 0.9354486098 0.9820491669
36 37 38 39 40
0.9907592007 0.5813978787 0.5285918574 0.4777374513 0.5847608616
41 42 43 44 45
0.6406172713 0.5868695561 0.4640563992 0.5229917396 0.5652979806
46 47 48 49 50
0.6641436870 0.7360000967 0.9300691062 0.4959636367 0.5429196018
51 52 53 54 55
0.3290980879 -0.2770518559 -0.7297952212 -0.9355223118 -1.0689820644
56 57 58 59 60
-1.3464291099 -1.4404695771 -1.5174588737 -1.5927345688 -1.7905100443
> postscript(file="/var/www/html/rcomp/tmp/6tlyw1258559651.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 0.0569352437 NA
1 -0.1091472548 0.0569352437
2 -0.0847101305 -0.1091472548
3 0.0002664592 -0.0847101305
4 -0.0386212785 0.0002664592
5 -0.0302864952 -0.0386212785
6 -0.1003348800 -0.0302864952
7 -0.0932083466 -0.1003348800
8 -0.1647577332 -0.0932083466
9 -0.2471060055 -0.1647577332
10 -0.4052495958 -0.2471060055
11 -0.4760278569 -0.4052495958
12 -1.0281539510 -0.4760278569
13 -0.9728362245 -1.0281539510
14 -0.8431432476 -0.9728362245
15 -0.6269726792 -0.8431432476
16 -0.4576692239 -0.6269726792
17 -0.2722697810 -0.4576692239
18 -0.0341944181 -0.2722697810
19 0.0674025712 -0.0341944181
20 0.1347266512 0.0674025712
21 0.1649725825 0.1347266512
22 0.2799349010 0.1649725825
23 0.3457095943 0.2799349010
24 -0.1061428082 0.3457095943
25 0.0104720201 -0.1061428082
26 0.1210178389 0.0104720201
27 0.3189972143 0.1210178389
28 0.5854684523 0.3189972143
29 0.6512090319 0.5854684523
30 0.7394549633 0.6512090319
31 0.8492431456 0.7394549633
32 0.9052026785 0.8492431456
33 0.9354486098 0.9052026785
34 0.9820491669 0.9354486098
35 0.9907592007 0.9820491669
36 0.5813978787 0.9907592007
37 0.5285918574 0.5813978787
38 0.4777374513 0.5285918574
39 0.5847608616 0.4777374513
40 0.6406172713 0.5847608616
41 0.5868695561 0.6406172713
42 0.4640563992 0.5868695561
43 0.5229917396 0.4640563992
44 0.5652979806 0.5229917396
45 0.6641436870 0.5652979806
46 0.7360000967 0.6641436870
47 0.9300691062 0.7360000967
48 0.4959636367 0.9300691062
49 0.5429196018 0.4959636367
50 0.3290980879 0.5429196018
51 -0.2770518559 0.3290980879
52 -0.7297952212 -0.2770518559
53 -0.9355223118 -0.7297952212
54 -1.0689820644 -0.9355223118
55 -1.3464291099 -1.0689820644
56 -1.4404695771 -1.3464291099
57 -1.5174588737 -1.4404695771
58 -1.5927345688 -1.5174588737
59 -1.7905100443 -1.5927345688
60 NA -1.7905100443
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1091472548 0.0569352437
[2,] -0.0847101305 -0.1091472548
[3,] 0.0002664592 -0.0847101305
[4,] -0.0386212785 0.0002664592
[5,] -0.0302864952 -0.0386212785
[6,] -0.1003348800 -0.0302864952
[7,] -0.0932083466 -0.1003348800
[8,] -0.1647577332 -0.0932083466
[9,] -0.2471060055 -0.1647577332
[10,] -0.4052495958 -0.2471060055
[11,] -0.4760278569 -0.4052495958
[12,] -1.0281539510 -0.4760278569
[13,] -0.9728362245 -1.0281539510
[14,] -0.8431432476 -0.9728362245
[15,] -0.6269726792 -0.8431432476
[16,] -0.4576692239 -0.6269726792
[17,] -0.2722697810 -0.4576692239
[18,] -0.0341944181 -0.2722697810
[19,] 0.0674025712 -0.0341944181
[20,] 0.1347266512 0.0674025712
[21,] 0.1649725825 0.1347266512
[22,] 0.2799349010 0.1649725825
[23,] 0.3457095943 0.2799349010
[24,] -0.1061428082 0.3457095943
[25,] 0.0104720201 -0.1061428082
[26,] 0.1210178389 0.0104720201
[27,] 0.3189972143 0.1210178389
[28,] 0.5854684523 0.3189972143
[29,] 0.6512090319 0.5854684523
[30,] 0.7394549633 0.6512090319
[31,] 0.8492431456 0.7394549633
[32,] 0.9052026785 0.8492431456
[33,] 0.9354486098 0.9052026785
[34,] 0.9820491669 0.9354486098
[35,] 0.9907592007 0.9820491669
[36,] 0.5813978787 0.9907592007
[37,] 0.5285918574 0.5813978787
[38,] 0.4777374513 0.5285918574
[39,] 0.5847608616 0.4777374513
[40,] 0.6406172713 0.5847608616
[41,] 0.5868695561 0.6406172713
[42,] 0.4640563992 0.5868695561
[43,] 0.5229917396 0.4640563992
[44,] 0.5652979806 0.5229917396
[45,] 0.6641436870 0.5652979806
[46,] 0.7360000967 0.6641436870
[47,] 0.9300691062 0.7360000967
[48,] 0.4959636367 0.9300691062
[49,] 0.5429196018 0.4959636367
[50,] 0.3290980879 0.5429196018
[51,] -0.2770518559 0.3290980879
[52,] -0.7297952212 -0.2770518559
[53,] -0.9355223118 -0.7297952212
[54,] -1.0689820644 -0.9355223118
[55,] -1.3464291099 -1.0689820644
[56,] -1.4404695771 -1.3464291099
[57,] -1.5174588737 -1.4404695771
[58,] -1.5927345688 -1.5174588737
[59,] -1.7905100443 -1.5927345688
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1091472548 0.0569352437
2 -0.0847101305 -0.1091472548
3 0.0002664592 -0.0847101305
4 -0.0386212785 0.0002664592
5 -0.0302864952 -0.0386212785
6 -0.1003348800 -0.0302864952
7 -0.0932083466 -0.1003348800
8 -0.1647577332 -0.0932083466
9 -0.2471060055 -0.1647577332
10 -0.4052495958 -0.2471060055
11 -0.4760278569 -0.4052495958
12 -1.0281539510 -0.4760278569
13 -0.9728362245 -1.0281539510
14 -0.8431432476 -0.9728362245
15 -0.6269726792 -0.8431432476
16 -0.4576692239 -0.6269726792
17 -0.2722697810 -0.4576692239
18 -0.0341944181 -0.2722697810
19 0.0674025712 -0.0341944181
20 0.1347266512 0.0674025712
21 0.1649725825 0.1347266512
22 0.2799349010 0.1649725825
23 0.3457095943 0.2799349010
24 -0.1061428082 0.3457095943
25 0.0104720201 -0.1061428082
26 0.1210178389 0.0104720201
27 0.3189972143 0.1210178389
28 0.5854684523 0.3189972143
29 0.6512090319 0.5854684523
30 0.7394549633 0.6512090319
31 0.8492431456 0.7394549633
32 0.9052026785 0.8492431456
33 0.9354486098 0.9052026785
34 0.9820491669 0.9354486098
35 0.9907592007 0.9820491669
36 0.5813978787 0.9907592007
37 0.5285918574 0.5813978787
38 0.4777374513 0.5285918574
39 0.5847608616 0.4777374513
40 0.6406172713 0.5847608616
41 0.5868695561 0.6406172713
42 0.4640563992 0.5868695561
43 0.5229917396 0.4640563992
44 0.5652979806 0.5229917396
45 0.6641436870 0.5652979806
46 0.7360000967 0.6641436870
47 0.9300691062 0.7360000967
48 0.4959636367 0.9300691062
49 0.5429196018 0.4959636367
50 0.3290980879 0.5429196018
51 -0.2770518559 0.3290980879
52 -0.7297952212 -0.2770518559
53 -0.9355223118 -0.7297952212
54 -1.0689820644 -0.9355223118
55 -1.3464291099 -1.0689820644
56 -1.4404695771 -1.3464291099
57 -1.5174588737 -1.4404695771
58 -1.5927345688 -1.5174588737
59 -1.7905100443 -1.5927345688
> 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/7s3941258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pxhz1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cjoj1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10byrc1258559651.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112dwb1258559651.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/129vdz1258559651.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/13hxab1258559651.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/14ddwo1258559651.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/156cg21258559651.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/16vgxe1258559651.tab")
+ }
>
> system("convert tmp/1tj5n1258559651.ps tmp/1tj5n1258559651.png")
> system("convert tmp/2m7yx1258559651.ps tmp/2m7yx1258559651.png")
> system("convert tmp/3z6yv1258559651.ps tmp/3z6yv1258559651.png")
> system("convert tmp/4xoiy1258559651.ps tmp/4xoiy1258559651.png")
> system("convert tmp/5lely1258559651.ps tmp/5lely1258559651.png")
> system("convert tmp/6tlyw1258559651.ps tmp/6tlyw1258559651.png")
> system("convert tmp/7s3941258559651.ps tmp/7s3941258559651.png")
> system("convert tmp/8pxhz1258559651.ps tmp/8pxhz1258559651.png")
> system("convert tmp/9cjoj1258559651.ps tmp/9cjoj1258559651.png")
> system("convert tmp/10byrc1258559651.ps tmp/10byrc1258559651.png")
>
>
> proc.time()
user system elapsed
2.434 1.568 2.954