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(95.1,93.8,97,93.8,112.7,107.6,102.9,101,97.4,95.4,111.4,96.5,87.4,89.2,96.8,87.1,114.1,110.5,110.3,110.8,103.9,104.2,101.6,88.9,94.6,89.8,95.9,90,104.7,93.9,102.8,91.3,98.1,87.8,113.9,99.7,80.9,73.5,95.7,79.2,113.2,96.9,105.9,95.2,108.8,95.6,102.3,89.7,99,92.8,100.7,88,115.5,101.1,100.7,92.7,109.9,95.8,114.6,103.8,85.4,81.8,100.5,87.1,114.8,105.9,116.5,108.1,112.9,102.6,102,93.7,106,103.5,105.3,100.6,118.8,113.3,106.1,102.4,109.3,102.1,117.2,106.9,92.5,87.3,104.2,93.1,112.5,109.1,122.4,120.3,113.3,104.9,100,92.6,110.7,109.8,112.8,111.4,109.8,117.9,117.3,121.6,109.1,117.8,115.9,124.2,96,106.8,99.8,102.7,116.8,116.8,115.7,113.6,99.4,96.1,94.3,85),dim=c(2,60),dimnames=list(c('TIA','IAidM'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TIA','IAidM'),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
TIA IAidM M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 95.1 93.8 1 0 0 0 0 0 0 0 0 0 0
2 97.0 93.8 0 1 0 0 0 0 0 0 0 0 0
3 112.7 107.6 0 0 1 0 0 0 0 0 0 0 0
4 102.9 101.0 0 0 0 1 0 0 0 0 0 0 0
5 97.4 95.4 0 0 0 0 1 0 0 0 0 0 0
6 111.4 96.5 0 0 0 0 0 1 0 0 0 0 0
7 87.4 89.2 0 0 0 0 0 0 1 0 0 0 0
8 96.8 87.1 0 0 0 0 0 0 0 1 0 0 0
9 114.1 110.5 0 0 0 0 0 0 0 0 1 0 0
10 110.3 110.8 0 0 0 0 0 0 0 0 0 1 0
11 103.9 104.2 0 0 0 0 0 0 0 0 0 0 1
12 101.6 88.9 0 0 0 0 0 0 0 0 0 0 0
13 94.6 89.8 1 0 0 0 0 0 0 0 0 0 0
14 95.9 90.0 0 1 0 0 0 0 0 0 0 0 0
15 104.7 93.9 0 0 1 0 0 0 0 0 0 0 0
16 102.8 91.3 0 0 0 1 0 0 0 0 0 0 0
17 98.1 87.8 0 0 0 0 1 0 0 0 0 0 0
18 113.9 99.7 0 0 0 0 0 1 0 0 0 0 0
19 80.9 73.5 0 0 0 0 0 0 1 0 0 0 0
20 95.7 79.2 0 0 0 0 0 0 0 1 0 0 0
21 113.2 96.9 0 0 0 0 0 0 0 0 1 0 0
22 105.9 95.2 0 0 0 0 0 0 0 0 0 1 0
23 108.8 95.6 0 0 0 0 0 0 0 0 0 0 1
24 102.3 89.7 0 0 0 0 0 0 0 0 0 0 0
25 99.0 92.8 1 0 0 0 0 0 0 0 0 0 0
26 100.7 88.0 0 1 0 0 0 0 0 0 0 0 0
27 115.5 101.1 0 0 1 0 0 0 0 0 0 0 0
28 100.7 92.7 0 0 0 1 0 0 0 0 0 0 0
29 109.9 95.8 0 0 0 0 1 0 0 0 0 0 0
30 114.6 103.8 0 0 0 0 0 1 0 0 0 0 0
31 85.4 81.8 0 0 0 0 0 0 1 0 0 0 0
32 100.5 87.1 0 0 0 0 0 0 0 1 0 0 0
33 114.8 105.9 0 0 0 0 0 0 0 0 1 0 0
34 116.5 108.1 0 0 0 0 0 0 0 0 0 1 0
35 112.9 102.6 0 0 0 0 0 0 0 0 0 0 1
36 102.0 93.7 0 0 0 0 0 0 0 0 0 0 0
37 106.0 103.5 1 0 0 0 0 0 0 0 0 0 0
38 105.3 100.6 0 1 0 0 0 0 0 0 0 0 0
39 118.8 113.3 0 0 1 0 0 0 0 0 0 0 0
40 106.1 102.4 0 0 0 1 0 0 0 0 0 0 0
41 109.3 102.1 0 0 0 0 1 0 0 0 0 0 0
42 117.2 106.9 0 0 0 0 0 1 0 0 0 0 0
43 92.5 87.3 0 0 0 0 0 0 1 0 0 0 0
44 104.2 93.1 0 0 0 0 0 0 0 1 0 0 0
45 112.5 109.1 0 0 0 0 0 0 0 0 1 0 0
46 122.4 120.3 0 0 0 0 0 0 0 0 0 1 0
47 113.3 104.9 0 0 0 0 0 0 0 0 0 0 1
48 100.0 92.6 0 0 0 0 0 0 0 0 0 0 0
49 110.7 109.8 1 0 0 0 0 0 0 0 0 0 0
50 112.8 111.4 0 1 0 0 0 0 0 0 0 0 0
51 109.8 117.9 0 0 1 0 0 0 0 0 0 0 0
52 117.3 121.6 0 0 0 1 0 0 0 0 0 0 0
53 109.1 117.8 0 0 0 0 1 0 0 0 0 0 0
54 115.9 124.2 0 0 0 0 0 1 0 0 0 0 0
55 96.0 106.8 0 0 0 0 0 0 1 0 0 0 0
56 99.8 102.7 0 0 0 0 0 0 0 1 0 0 0
57 116.8 116.8 0 0 0 0 0 0 0 0 1 0 0
58 115.7 113.6 0 0 0 0 0 0 0 0 0 1 0
59 99.4 96.1 0 0 0 0 0 0 0 0 0 0 1
60 94.3 85.0 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) IAidM M1 M2 M3 M4
61.8576 0.4243 -2.3378 -0.5770 5.1395 0.9043
M5 M6 M7 M8 M9 M10
0.5614 7.6687 -10.6410 -0.5806 6.6612 5.7944
M11
3.0795
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.22719 -2.19076 0.07234 2.65499 6.82889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.85757 5.49052 11.266 5.95e-15 ***
IAidM 0.42434 0.05811 7.303 2.85e-09 ***
M1 -2.33777 2.41431 -0.968 0.33785
M2 -0.57705 2.40212 -0.240 0.81120
M3 5.13952 2.56236 2.006 0.05066 .
M4 0.90426 2.46712 0.367 0.71562
M5 0.56143 2.43706 0.230 0.81880
M6 7.66866 2.55059 3.007 0.00423 **
M7 -10.64098 2.37322 -4.484 4.70e-05 ***
M8 -0.58059 2.36960 -0.245 0.80751
M9 6.66122 2.58689 2.575 0.01323 *
M10 5.79438 2.62959 2.204 0.03249 *
M11 3.07952 2.44980 1.257 0.21495
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.747 on 47 degrees of freedom
Multiple R-squared: 0.8619, Adjusted R-squared: 0.8267
F-statistic: 24.45 on 12 and 47 DF, p-value: 3.292e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.146336957 0.29267391 0.8536630
[2,] 0.119266633 0.23853327 0.8807334
[3,] 0.060754308 0.12150862 0.9392457
[4,] 0.029536183 0.05907237 0.9704638
[5,] 0.014133879 0.02826776 0.9858661
[6,] 0.012468043 0.02493609 0.9875320
[7,] 0.006276414 0.01255283 0.9937236
[8,] 0.034770776 0.06954155 0.9652292
[9,] 0.019649810 0.03929962 0.9803502
[10,] 0.024302578 0.04860516 0.9756974
[11,] 0.037047755 0.07409551 0.9629522
[12,] 0.091080223 0.18216045 0.9089198
[13,] 0.072231647 0.14446329 0.9277684
[14,] 0.311138090 0.62227618 0.6888619
[15,] 0.234283772 0.46856754 0.7657162
[16,] 0.189745109 0.37949022 0.8102549
[17,] 0.148182910 0.29636582 0.8518171
[18,] 0.103105110 0.20621022 0.8968949
[19,] 0.111556083 0.22311217 0.8884439
[20,] 0.135306828 0.27061366 0.8646932
[21,] 0.099953350 0.19990670 0.9000466
[22,] 0.092418470 0.18483694 0.9075815
[23,] 0.075139330 0.15027866 0.9248607
[24,] 0.126912817 0.25382563 0.8730872
[25,] 0.123233847 0.24646769 0.8767662
[26,] 0.092680249 0.18536050 0.9073198
[27,] 0.091600877 0.18320175 0.9083991
[28,] 0.094274617 0.18854923 0.9057254
[29,] 0.583864317 0.83227137 0.4161357
> postscript(file="/var/www/html/rcomp/tmp/172cs1258744170.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hntn1258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3amu61258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fxru1258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vtef1258744170.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
-4.22321768 -4.08394308 0.04355141 -2.72052516 -5.50137523 0.92461935
7 8 9 10 11 12
-1.66802846 -1.43729866 -1.30875386 -4.36921227 -5.25368931 2.01829104
13 14 15 16 17 18
-3.02584346 -3.57143757 -2.14294188 1.29560733 -1.57636421 2.06671998
19 20 21 22 23 24
-1.50583465 0.81501542 3.56231849 -2.14945281 3.29566526 2.37881620
25 26 27 28 29 30
0.10112587 2.07724954 5.60178452 -1.39847365 6.82888735 1.02691140
31 32 33 34 35 36
-0.52788616 2.26270134 1.34322650 2.97651533 4.42526037 0.38144198
37 38 39 40 41 42
2.56064983 1.33052075 3.72479315 -0.11460613 3.55552295 2.31144638
43 44 45 46 47 48
4.23822429 3.41664001 -2.31467288 3.69952396 3.84927020 -1.15178011
49 50 51 52 53 54
4.58728544 4.24761036 -7.22718720 2.93799761 -3.30667086 -6.32969712
55 56 57 58 59 60
-0.53647503 -5.05705812 -1.28211825 -0.15737422 -6.31650652 -3.62676910
> postscript(file="/var/www/html/rcomp/tmp/6g0n11258744170.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 -4.22321768 NA
1 -4.08394308 -4.22321768
2 0.04355141 -4.08394308
3 -2.72052516 0.04355141
4 -5.50137523 -2.72052516
5 0.92461935 -5.50137523
6 -1.66802846 0.92461935
7 -1.43729866 -1.66802846
8 -1.30875386 -1.43729866
9 -4.36921227 -1.30875386
10 -5.25368931 -4.36921227
11 2.01829104 -5.25368931
12 -3.02584346 2.01829104
13 -3.57143757 -3.02584346
14 -2.14294188 -3.57143757
15 1.29560733 -2.14294188
16 -1.57636421 1.29560733
17 2.06671998 -1.57636421
18 -1.50583465 2.06671998
19 0.81501542 -1.50583465
20 3.56231849 0.81501542
21 -2.14945281 3.56231849
22 3.29566526 -2.14945281
23 2.37881620 3.29566526
24 0.10112587 2.37881620
25 2.07724954 0.10112587
26 5.60178452 2.07724954
27 -1.39847365 5.60178452
28 6.82888735 -1.39847365
29 1.02691140 6.82888735
30 -0.52788616 1.02691140
31 2.26270134 -0.52788616
32 1.34322650 2.26270134
33 2.97651533 1.34322650
34 4.42526037 2.97651533
35 0.38144198 4.42526037
36 2.56064983 0.38144198
37 1.33052075 2.56064983
38 3.72479315 1.33052075
39 -0.11460613 3.72479315
40 3.55552295 -0.11460613
41 2.31144638 3.55552295
42 4.23822429 2.31144638
43 3.41664001 4.23822429
44 -2.31467288 3.41664001
45 3.69952396 -2.31467288
46 3.84927020 3.69952396
47 -1.15178011 3.84927020
48 4.58728544 -1.15178011
49 4.24761036 4.58728544
50 -7.22718720 4.24761036
51 2.93799761 -7.22718720
52 -3.30667086 2.93799761
53 -6.32969712 -3.30667086
54 -0.53647503 -6.32969712
55 -5.05705812 -0.53647503
56 -1.28211825 -5.05705812
57 -0.15737422 -1.28211825
58 -6.31650652 -0.15737422
59 -3.62676910 -6.31650652
60 NA -3.62676910
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.08394308 -4.22321768
[2,] 0.04355141 -4.08394308
[3,] -2.72052516 0.04355141
[4,] -5.50137523 -2.72052516
[5,] 0.92461935 -5.50137523
[6,] -1.66802846 0.92461935
[7,] -1.43729866 -1.66802846
[8,] -1.30875386 -1.43729866
[9,] -4.36921227 -1.30875386
[10,] -5.25368931 -4.36921227
[11,] 2.01829104 -5.25368931
[12,] -3.02584346 2.01829104
[13,] -3.57143757 -3.02584346
[14,] -2.14294188 -3.57143757
[15,] 1.29560733 -2.14294188
[16,] -1.57636421 1.29560733
[17,] 2.06671998 -1.57636421
[18,] -1.50583465 2.06671998
[19,] 0.81501542 -1.50583465
[20,] 3.56231849 0.81501542
[21,] -2.14945281 3.56231849
[22,] 3.29566526 -2.14945281
[23,] 2.37881620 3.29566526
[24,] 0.10112587 2.37881620
[25,] 2.07724954 0.10112587
[26,] 5.60178452 2.07724954
[27,] -1.39847365 5.60178452
[28,] 6.82888735 -1.39847365
[29,] 1.02691140 6.82888735
[30,] -0.52788616 1.02691140
[31,] 2.26270134 -0.52788616
[32,] 1.34322650 2.26270134
[33,] 2.97651533 1.34322650
[34,] 4.42526037 2.97651533
[35,] 0.38144198 4.42526037
[36,] 2.56064983 0.38144198
[37,] 1.33052075 2.56064983
[38,] 3.72479315 1.33052075
[39,] -0.11460613 3.72479315
[40,] 3.55552295 -0.11460613
[41,] 2.31144638 3.55552295
[42,] 4.23822429 2.31144638
[43,] 3.41664001 4.23822429
[44,] -2.31467288 3.41664001
[45,] 3.69952396 -2.31467288
[46,] 3.84927020 3.69952396
[47,] -1.15178011 3.84927020
[48,] 4.58728544 -1.15178011
[49,] 4.24761036 4.58728544
[50,] -7.22718720 4.24761036
[51,] 2.93799761 -7.22718720
[52,] -3.30667086 2.93799761
[53,] -6.32969712 -3.30667086
[54,] -0.53647503 -6.32969712
[55,] -5.05705812 -0.53647503
[56,] -1.28211825 -5.05705812
[57,] -0.15737422 -1.28211825
[58,] -6.31650652 -0.15737422
[59,] -3.62676910 -6.31650652
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.08394308 -4.22321768
2 0.04355141 -4.08394308
3 -2.72052516 0.04355141
4 -5.50137523 -2.72052516
5 0.92461935 -5.50137523
6 -1.66802846 0.92461935
7 -1.43729866 -1.66802846
8 -1.30875386 -1.43729866
9 -4.36921227 -1.30875386
10 -5.25368931 -4.36921227
11 2.01829104 -5.25368931
12 -3.02584346 2.01829104
13 -3.57143757 -3.02584346
14 -2.14294188 -3.57143757
15 1.29560733 -2.14294188
16 -1.57636421 1.29560733
17 2.06671998 -1.57636421
18 -1.50583465 2.06671998
19 0.81501542 -1.50583465
20 3.56231849 0.81501542
21 -2.14945281 3.56231849
22 3.29566526 -2.14945281
23 2.37881620 3.29566526
24 0.10112587 2.37881620
25 2.07724954 0.10112587
26 5.60178452 2.07724954
27 -1.39847365 5.60178452
28 6.82888735 -1.39847365
29 1.02691140 6.82888735
30 -0.52788616 1.02691140
31 2.26270134 -0.52788616
32 1.34322650 2.26270134
33 2.97651533 1.34322650
34 4.42526037 2.97651533
35 0.38144198 4.42526037
36 2.56064983 0.38144198
37 1.33052075 2.56064983
38 3.72479315 1.33052075
39 -0.11460613 3.72479315
40 3.55552295 -0.11460613
41 2.31144638 3.55552295
42 4.23822429 2.31144638
43 3.41664001 4.23822429
44 -2.31467288 3.41664001
45 3.69952396 -2.31467288
46 3.84927020 3.69952396
47 -1.15178011 3.84927020
48 4.58728544 -1.15178011
49 4.24761036 4.58728544
50 -7.22718720 4.24761036
51 2.93799761 -7.22718720
52 -3.30667086 2.93799761
53 -6.32969712 -3.30667086
54 -0.53647503 -6.32969712
55 -5.05705812 -0.53647503
56 -1.28211825 -5.05705812
57 -0.15737422 -1.28211825
58 -6.31650652 -0.15737422
59 -3.62676910 -6.31650652
> 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/7f5oj1258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8w3l91258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91s1w1258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xmrm1258744170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z5kf1258744170.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/12kz1r1258744170.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/13ihsk1258744170.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/14ctuk1258744170.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/15dzc31258744170.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/16mvuz1258744170.tab")
+ }
>
> system("convert tmp/172cs1258744170.ps tmp/172cs1258744170.png")
> system("convert tmp/2hntn1258744170.ps tmp/2hntn1258744170.png")
> system("convert tmp/3amu61258744170.ps tmp/3amu61258744170.png")
> system("convert tmp/4fxru1258744170.ps tmp/4fxru1258744170.png")
> system("convert tmp/5vtef1258744170.ps tmp/5vtef1258744170.png")
> system("convert tmp/6g0n11258744170.ps tmp/6g0n11258744170.png")
> system("convert tmp/7f5oj1258744170.ps tmp/7f5oj1258744170.png")
> system("convert tmp/8w3l91258744170.ps tmp/8w3l91258744170.png")
> system("convert tmp/91s1w1258744170.ps tmp/91s1w1258744170.png")
> system("convert tmp/10xmrm1258744170.ps tmp/10xmrm1258744170.png")
>
>
> proc.time()
user system elapsed
2.409 1.554 2.769