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(97.4,116.7,97,109,105.4,119.5,102.7,115.1,98.1,107.1,104.5,109.7,87.4,110.4,89.9,105,109.8,115.8,111.7,116.4,98.6,111.1,96.9,119.5,95.1,110.9,97,115.1,112.7,125.2,102.9,116,97.4,112.9,111.4,121.7,87.4,123.2,96.8,116.6,114.1,136.2,110.3,120.9,103.9,119.6,101.6,125.9,94.6,116.1,95.9,107.5,104.7,116.7,102.8,112.5,98.1,113,113.9,126.4,80.9,114.1,95.7,112.5,113.2,112.4,105.9,113.1,108.8,116.3,102.3,111.7,99,118.8,100.7,116.5,115.5,125.1,100.7,113.1,109.9,119.6,114.6,114.4,85.4,114,100.5,117.8,114.8,117,116.5,120.9,112.9,115,102,117.3,106,119.4,105.3,114.9,118.8,125.8,106.1,117.6,109.3,117.6,117.2,114.9,92.5,121.9,104.2,117,112.5,106.4,122.4,110.5,113.3,113.6,100,114.2),dim=c(2,60),dimnames=list(c('Tip','ipchn'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Tip','ipchn'),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 = '2'
> #'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
ipchn Tip M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 116.7 97.4 1 0 0 0 0 0 0 0 0 0 0
2 109.0 97.0 0 1 0 0 0 0 0 0 0 0 0
3 119.5 105.4 0 0 1 0 0 0 0 0 0 0 0
4 115.1 102.7 0 0 0 1 0 0 0 0 0 0 0
5 107.1 98.1 0 0 0 0 1 0 0 0 0 0 0
6 109.7 104.5 0 0 0 0 0 1 0 0 0 0 0
7 110.4 87.4 0 0 0 0 0 0 1 0 0 0 0
8 105.0 89.9 0 0 0 0 0 0 0 1 0 0 0
9 115.8 109.8 0 0 0 0 0 0 0 0 1 0 0
10 116.4 111.7 0 0 0 0 0 0 0 0 0 1 0
11 111.1 98.6 0 0 0 0 0 0 0 0 0 0 1
12 119.5 96.9 0 0 0 0 0 0 0 0 0 0 0
13 110.9 95.1 1 0 0 0 0 0 0 0 0 0 0
14 115.1 97.0 0 1 0 0 0 0 0 0 0 0 0
15 125.2 112.7 0 0 1 0 0 0 0 0 0 0 0
16 116.0 102.9 0 0 0 1 0 0 0 0 0 0 0
17 112.9 97.4 0 0 0 0 1 0 0 0 0 0 0
18 121.7 111.4 0 0 0 0 0 1 0 0 0 0 0
19 123.2 87.4 0 0 0 0 0 0 1 0 0 0 0
20 116.6 96.8 0 0 0 0 0 0 0 1 0 0 0
21 136.2 114.1 0 0 0 0 0 0 0 0 1 0 0
22 120.9 110.3 0 0 0 0 0 0 0 0 0 1 0
23 119.6 103.9 0 0 0 0 0 0 0 0 0 0 1
24 125.9 101.6 0 0 0 0 0 0 0 0 0 0 0
25 116.1 94.6 1 0 0 0 0 0 0 0 0 0 0
26 107.5 95.9 0 1 0 0 0 0 0 0 0 0 0
27 116.7 104.7 0 0 1 0 0 0 0 0 0 0 0
28 112.5 102.8 0 0 0 1 0 0 0 0 0 0 0
29 113.0 98.1 0 0 0 0 1 0 0 0 0 0 0
30 126.4 113.9 0 0 0 0 0 1 0 0 0 0 0
31 114.1 80.9 0 0 0 0 0 0 1 0 0 0 0
32 112.5 95.7 0 0 0 0 0 0 0 1 0 0 0
33 112.4 113.2 0 0 0 0 0 0 0 0 1 0 0
34 113.1 105.9 0 0 0 0 0 0 0 0 0 1 0
35 116.3 108.8 0 0 0 0 0 0 0 0 0 0 1
36 111.7 102.3 0 0 0 0 0 0 0 0 0 0 0
37 118.8 99.0 1 0 0 0 0 0 0 0 0 0 0
38 116.5 100.7 0 1 0 0 0 0 0 0 0 0 0
39 125.1 115.5 0 0 1 0 0 0 0 0 0 0 0
40 113.1 100.7 0 0 0 1 0 0 0 0 0 0 0
41 119.6 109.9 0 0 0 0 1 0 0 0 0 0 0
42 114.4 114.6 0 0 0 0 0 1 0 0 0 0 0
43 114.0 85.4 0 0 0 0 0 0 1 0 0 0 0
44 117.8 100.5 0 0 0 0 0 0 0 1 0 0 0
45 117.0 114.8 0 0 0 0 0 0 0 0 1 0 0
46 120.9 116.5 0 0 0 0 0 0 0 0 0 1 0
47 115.0 112.9 0 0 0 0 0 0 0 0 0 0 1
48 117.3 102.0 0 0 0 0 0 0 0 0 0 0 0
49 119.4 106.0 1 0 0 0 0 0 0 0 0 0 0
50 114.9 105.3 0 1 0 0 0 0 0 0 0 0 0
51 125.8 118.8 0 0 1 0 0 0 0 0 0 0 0
52 117.6 106.1 0 0 0 1 0 0 0 0 0 0 0
53 117.6 109.3 0 0 0 0 1 0 0 0 0 0 0
54 114.9 117.2 0 0 0 0 0 1 0 0 0 0 0
55 121.9 92.5 0 0 0 0 0 0 1 0 0 0 0
56 117.0 104.2 0 0 0 0 0 0 0 1 0 0 0
57 106.4 112.5 0 0 0 0 0 0 0 0 1 0 0
58 110.5 122.4 0 0 0 0 0 0 0 0 0 1 0
59 113.6 113.3 0 0 0 0 0 0 0 0 0 0 1
60 114.2 100.0 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) Tip M1 M2 M3 M4
72.0310 0.4543 -0.3677 -4.4930 -0.1942 -3.9868
M5 M6 M7 M8 M9 M10
-4.5887 -5.6431 5.2881 -2.5134 -5.7575 -7.1756
M11
-5.7532
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.98735 -2.63384 0.07685 2.17510 18.08570
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.0310 15.5434 4.634 2.86e-05 ***
Tip 0.4543 0.1529 2.972 0.00466 **
M1 -0.3677 3.2369 -0.114 0.91004
M2 -4.4930 3.2272 -1.392 0.17041
M3 -0.1942 3.6232 -0.054 0.95748
M4 -3.9868 3.2426 -1.230 0.22500
M5 -4.5887 3.2348 -1.419 0.16263
M6 -5.6431 3.6883 -1.530 0.13272
M7 5.2881 3.8534 1.372 0.17647
M8 -2.5134 3.2559 -0.772 0.44402
M9 -5.7575 3.7308 -1.543 0.12948
M10 -7.1756 3.7684 -1.904 0.06302 .
M11 -5.7532 3.3907 -1.697 0.09635 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.092 on 47 degrees of freedom
Multiple R-squared: 0.325, Adjusted R-squared: 0.1527
F-statistic: 1.886 on 12 and 47 DF, p-value: 0.06105
> 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,] 0.1544879 0.3089758 0.8455121
[2,] 0.1603380 0.3206759 0.8396620
[3,] 0.1135572 0.2271144 0.8864428
[4,] 0.3336434 0.6672869 0.6663566
[5,] 0.2313054 0.4626108 0.7686946
[6,] 0.7895519 0.4208963 0.2104481
[7,] 0.8060948 0.3878104 0.1939052
[8,] 0.7794020 0.4411959 0.2205980
[9,] 0.8463348 0.3073303 0.1536652
[10,] 0.8120650 0.3758700 0.1879350
[11,] 0.7867734 0.4264531 0.2132266
[12,] 0.7446281 0.5107438 0.2553719
[13,] 0.6861135 0.6277731 0.3138865
[14,] 0.6194258 0.7611484 0.3805742
[15,] 0.8229223 0.3541553 0.1770777
[16,] 0.8228816 0.3542369 0.1771184
[17,] 0.7836817 0.4326365 0.2163183
[18,] 0.8997094 0.2005811 0.1002906
[19,] 0.8476193 0.3047614 0.1523807
[20,] 0.8353335 0.3293331 0.1646665
[21,] 0.8914748 0.2170505 0.1085252
[22,] 0.8308379 0.3383242 0.1691621
[23,] 0.7635884 0.4728233 0.2364116
[24,] 0.6694388 0.6611224 0.3305612
[25,] 0.5696317 0.8607366 0.4303683
[26,] 0.4524142 0.9048285 0.5475858
[27,] 0.3672292 0.7344585 0.6327708
[28,] 0.3922696 0.7845393 0.6077304
[29,] 0.2487961 0.4975922 0.7512039
> postscript(file="/var/www/html/rcomp/tmp/1fijw1259062491.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2k3j81259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bmkl1259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41mwm1259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hd701259062491.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
0.78343251 -2.60952659 -0.22483949 0.39447750 -4.91361863 -4.16701741
7 8 9 10 11 12
-6.62895501 -5.36332109 -0.36061555 0.79421369 0.02367583 3.44290489
13 14 15 16 17 18
-3.97157261 3.49047341 2.15843763 1.20360838 1.20442329 4.69799795
19 20 21 22 23 24
6.17104499 3.10169427 18.08569837 5.93029753 6.11564416 7.70748058
25 26 27 28 29 30
1.45560019 -3.60974643 -2.70679757 -2.25095706 0.98638137 8.26213395
31 32 33 34 35 36
0.02429139 -0.49852557 -5.30539059 0.12941817 0.58935072 -6.81056134
37 38 39 40 41 42
2.15647955 3.20939469 0.78626996 -0.69683130 2.22510330 -4.05590797
43 44 45 46 47 48
-2.12026381 2.62061555 -1.43234355 3.11335482 -2.57346624 -1.07425766
49 50 51 52 53 54
-0.42393964 -0.48059507 -0.01307052 1.34970247 0.49771066 -4.73720652
55 56 57 58 59 60
2.55388244 0.13953684 -10.98734867 -9.96728422 -4.15520448 -3.26556646
> postscript(file="/var/www/html/rcomp/tmp/64aaa1259062491.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 0.78343251 NA
1 -2.60952659 0.78343251
2 -0.22483949 -2.60952659
3 0.39447750 -0.22483949
4 -4.91361863 0.39447750
5 -4.16701741 -4.91361863
6 -6.62895501 -4.16701741
7 -5.36332109 -6.62895501
8 -0.36061555 -5.36332109
9 0.79421369 -0.36061555
10 0.02367583 0.79421369
11 3.44290489 0.02367583
12 -3.97157261 3.44290489
13 3.49047341 -3.97157261
14 2.15843763 3.49047341
15 1.20360838 2.15843763
16 1.20442329 1.20360838
17 4.69799795 1.20442329
18 6.17104499 4.69799795
19 3.10169427 6.17104499
20 18.08569837 3.10169427
21 5.93029753 18.08569837
22 6.11564416 5.93029753
23 7.70748058 6.11564416
24 1.45560019 7.70748058
25 -3.60974643 1.45560019
26 -2.70679757 -3.60974643
27 -2.25095706 -2.70679757
28 0.98638137 -2.25095706
29 8.26213395 0.98638137
30 0.02429139 8.26213395
31 -0.49852557 0.02429139
32 -5.30539059 -0.49852557
33 0.12941817 -5.30539059
34 0.58935072 0.12941817
35 -6.81056134 0.58935072
36 2.15647955 -6.81056134
37 3.20939469 2.15647955
38 0.78626996 3.20939469
39 -0.69683130 0.78626996
40 2.22510330 -0.69683130
41 -4.05590797 2.22510330
42 -2.12026381 -4.05590797
43 2.62061555 -2.12026381
44 -1.43234355 2.62061555
45 3.11335482 -1.43234355
46 -2.57346624 3.11335482
47 -1.07425766 -2.57346624
48 -0.42393964 -1.07425766
49 -0.48059507 -0.42393964
50 -0.01307052 -0.48059507
51 1.34970247 -0.01307052
52 0.49771066 1.34970247
53 -4.73720652 0.49771066
54 2.55388244 -4.73720652
55 0.13953684 2.55388244
56 -10.98734867 0.13953684
57 -9.96728422 -10.98734867
58 -4.15520448 -9.96728422
59 -3.26556646 -4.15520448
60 NA -3.26556646
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.60952659 0.78343251
[2,] -0.22483949 -2.60952659
[3,] 0.39447750 -0.22483949
[4,] -4.91361863 0.39447750
[5,] -4.16701741 -4.91361863
[6,] -6.62895501 -4.16701741
[7,] -5.36332109 -6.62895501
[8,] -0.36061555 -5.36332109
[9,] 0.79421369 -0.36061555
[10,] 0.02367583 0.79421369
[11,] 3.44290489 0.02367583
[12,] -3.97157261 3.44290489
[13,] 3.49047341 -3.97157261
[14,] 2.15843763 3.49047341
[15,] 1.20360838 2.15843763
[16,] 1.20442329 1.20360838
[17,] 4.69799795 1.20442329
[18,] 6.17104499 4.69799795
[19,] 3.10169427 6.17104499
[20,] 18.08569837 3.10169427
[21,] 5.93029753 18.08569837
[22,] 6.11564416 5.93029753
[23,] 7.70748058 6.11564416
[24,] 1.45560019 7.70748058
[25,] -3.60974643 1.45560019
[26,] -2.70679757 -3.60974643
[27,] -2.25095706 -2.70679757
[28,] 0.98638137 -2.25095706
[29,] 8.26213395 0.98638137
[30,] 0.02429139 8.26213395
[31,] -0.49852557 0.02429139
[32,] -5.30539059 -0.49852557
[33,] 0.12941817 -5.30539059
[34,] 0.58935072 0.12941817
[35,] -6.81056134 0.58935072
[36,] 2.15647955 -6.81056134
[37,] 3.20939469 2.15647955
[38,] 0.78626996 3.20939469
[39,] -0.69683130 0.78626996
[40,] 2.22510330 -0.69683130
[41,] -4.05590797 2.22510330
[42,] -2.12026381 -4.05590797
[43,] 2.62061555 -2.12026381
[44,] -1.43234355 2.62061555
[45,] 3.11335482 -1.43234355
[46,] -2.57346624 3.11335482
[47,] -1.07425766 -2.57346624
[48,] -0.42393964 -1.07425766
[49,] -0.48059507 -0.42393964
[50,] -0.01307052 -0.48059507
[51,] 1.34970247 -0.01307052
[52,] 0.49771066 1.34970247
[53,] -4.73720652 0.49771066
[54,] 2.55388244 -4.73720652
[55,] 0.13953684 2.55388244
[56,] -10.98734867 0.13953684
[57,] -9.96728422 -10.98734867
[58,] -4.15520448 -9.96728422
[59,] -3.26556646 -4.15520448
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.60952659 0.78343251
2 -0.22483949 -2.60952659
3 0.39447750 -0.22483949
4 -4.91361863 0.39447750
5 -4.16701741 -4.91361863
6 -6.62895501 -4.16701741
7 -5.36332109 -6.62895501
8 -0.36061555 -5.36332109
9 0.79421369 -0.36061555
10 0.02367583 0.79421369
11 3.44290489 0.02367583
12 -3.97157261 3.44290489
13 3.49047341 -3.97157261
14 2.15843763 3.49047341
15 1.20360838 2.15843763
16 1.20442329 1.20360838
17 4.69799795 1.20442329
18 6.17104499 4.69799795
19 3.10169427 6.17104499
20 18.08569837 3.10169427
21 5.93029753 18.08569837
22 6.11564416 5.93029753
23 7.70748058 6.11564416
24 1.45560019 7.70748058
25 -3.60974643 1.45560019
26 -2.70679757 -3.60974643
27 -2.25095706 -2.70679757
28 0.98638137 -2.25095706
29 8.26213395 0.98638137
30 0.02429139 8.26213395
31 -0.49852557 0.02429139
32 -5.30539059 -0.49852557
33 0.12941817 -5.30539059
34 0.58935072 0.12941817
35 -6.81056134 0.58935072
36 2.15647955 -6.81056134
37 3.20939469 2.15647955
38 0.78626996 3.20939469
39 -0.69683130 0.78626996
40 2.22510330 -0.69683130
41 -4.05590797 2.22510330
42 -2.12026381 -4.05590797
43 2.62061555 -2.12026381
44 -1.43234355 2.62061555
45 3.11335482 -1.43234355
46 -2.57346624 3.11335482
47 -1.07425766 -2.57346624
48 -0.42393964 -1.07425766
49 -0.48059507 -0.42393964
50 -0.01307052 -0.48059507
51 1.34970247 -0.01307052
52 0.49771066 1.34970247
53 -4.73720652 0.49771066
54 2.55388244 -4.73720652
55 0.13953684 2.55388244
56 -10.98734867 0.13953684
57 -9.96728422 -10.98734867
58 -4.15520448 -9.96728422
59 -3.26556646 -4.15520448
> 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/7ykik1259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o3t71259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9df881259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tq3y1259062491.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u5vf1259062491.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/12s3zv1259062491.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/1304vh1259062491.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/14uxyn1259062491.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/15h5v41259062491.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/1674kh1259062491.tab")
+ }
>
> system("convert tmp/1fijw1259062491.ps tmp/1fijw1259062491.png")
> system("convert tmp/2k3j81259062491.ps tmp/2k3j81259062491.png")
> system("convert tmp/3bmkl1259062491.ps tmp/3bmkl1259062491.png")
> system("convert tmp/41mwm1259062491.ps tmp/41mwm1259062491.png")
> system("convert tmp/5hd701259062491.ps tmp/5hd701259062491.png")
> system("convert tmp/64aaa1259062491.ps tmp/64aaa1259062491.png")
> system("convert tmp/7ykik1259062491.ps tmp/7ykik1259062491.png")
> system("convert tmp/8o3t71259062491.ps tmp/8o3t71259062491.png")
> system("convert tmp/9df881259062491.ps tmp/9df881259062491.png")
> system("convert tmp/10tq3y1259062491.ps tmp/10tq3y1259062491.png")
>
>
> proc.time()
user system elapsed
2.435 1.583 4.384