R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(4.25,101.8,0,4.5,108.3,0,4.7,106.7,0,4.75,108.2,0,4.75,94.2,0,4.75,95.1,0,4.75,98.1,0,4.75,93.2,0,4.75,94,0,4.58,97.2,0,4.5,95,0,4.5,90.5,0,4.49,91.6,0,4.03,90.5,0,3.75,79.9,0,3.39,74.9,0,3.25,74.3,0,3.25,75.9,1,3.25,77.7,1,3.25,86.9,1,3.25,90.7,1,3.25,91,1,3.25,89.5,1,3.25,92.5,1,3.25,94.1,1,3.25,98.5,1,3.25,96.8,1,3.25,91.2,1,2.85,97.1,1,2.75,104.9,1,2.75,110.9,1,2.55,104.8,1,2.5,94.1,1,2.5,95.8,1,2.1,99.3,1,2,101.1,1,2,104,1,2,99,1,2,105.4,1,2,107.1,1,2,110.7,1,2,117.1,1,2,118.7,1,2,126.5,1,2,127.5,1,2,134.6,1,2,131.8,1,2,135.9,1,2,142.7,1,2,141.7,1,2,153.4,1,2,145,1,2,137.7,1,2,148.3,1,2,152.2,1,2,169.4,1,2,168.6,1,2,161.1,1,2,174.1,1,2,179,1,2,190.6,1),dim=c(3,61),dimnames=list(c('rentetarief','grondstofprijs','dummy'),1:61))
> y <- array(NA,dim=c(3,61),dimnames=list(c('rentetarief','grondstofprijs','dummy'),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 = '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
rentetarief grondstofprijs dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 4.25 101.8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 4.50 108.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 4.70 106.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 4.75 108.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 4.75 94.2 0 0 0 0 0 1 0 0 0 0 0 0 5
6 4.75 95.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 4.75 98.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 4.75 93.2 0 0 0 0 0 0 0 0 1 0 0 0 8
9 4.75 94.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 4.58 97.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 4.50 95.0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 4.50 90.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 4.49 91.6 0 1 0 0 0 0 0 0 0 0 0 0 13
14 4.03 90.5 0 0 1 0 0 0 0 0 0 0 0 0 14
15 3.75 79.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 3.39 74.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 3.25 74.3 0 0 0 0 0 1 0 0 0 0 0 0 17
18 3.25 75.9 1 0 0 0 0 0 1 0 0 0 0 0 18
19 3.25 77.7 1 0 0 0 0 0 0 1 0 0 0 0 19
20 3.25 86.9 1 0 0 0 0 0 0 0 1 0 0 0 20
21 3.25 90.7 1 0 0 0 0 0 0 0 0 1 0 0 21
22 3.25 91.0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 3.25 89.5 1 0 0 0 0 0 0 0 0 0 0 1 23
24 3.25 92.5 1 0 0 0 0 0 0 0 0 0 0 0 24
25 3.25 94.1 1 1 0 0 0 0 0 0 0 0 0 0 25
26 3.25 98.5 1 0 1 0 0 0 0 0 0 0 0 0 26
27 3.25 96.8 1 0 0 1 0 0 0 0 0 0 0 0 27
28 3.25 91.2 1 0 0 0 1 0 0 0 0 0 0 0 28
29 2.85 97.1 1 0 0 0 0 1 0 0 0 0 0 0 29
30 2.75 104.9 1 0 0 0 0 0 1 0 0 0 0 0 30
31 2.75 110.9 1 0 0 0 0 0 0 1 0 0 0 0 31
32 2.55 104.8 1 0 0 0 0 0 0 0 1 0 0 0 32
33 2.50 94.1 1 0 0 0 0 0 0 0 0 1 0 0 33
34 2.50 95.8 1 0 0 0 0 0 0 0 0 0 1 0 34
35 2.10 99.3 1 0 0 0 0 0 0 0 0 0 0 1 35
36 2.00 101.1 1 0 0 0 0 0 0 0 0 0 0 0 36
37 2.00 104.0 1 1 0 0 0 0 0 0 0 0 0 0 37
38 2.00 99.0 1 0 1 0 0 0 0 0 0 0 0 0 38
39 2.00 105.4 1 0 0 1 0 0 0 0 0 0 0 0 39
40 2.00 107.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 2.00 110.7 1 0 0 0 0 1 0 0 0 0 0 0 41
42 2.00 117.1 1 0 0 0 0 0 1 0 0 0 0 0 42
43 2.00 118.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 2.00 126.5 1 0 0 0 0 0 0 0 1 0 0 0 44
45 2.00 127.5 1 0 0 0 0 0 0 0 0 1 0 0 45
46 2.00 134.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 2.00 131.8 1 0 0 0 0 0 0 0 0 0 0 1 47
48 2.00 135.9 1 0 0 0 0 0 0 0 0 0 0 0 48
49 2.00 142.7 1 1 0 0 0 0 0 0 0 0 0 0 49
50 2.00 141.7 1 0 1 0 0 0 0 0 0 0 0 0 50
51 2.00 153.4 1 0 0 1 0 0 0 0 0 0 0 0 51
52 2.00 145.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 2.00 137.7 1 0 0 0 0 1 0 0 0 0 0 0 53
54 2.00 148.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 2.00 152.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 2.00 169.4 1 0 0 0 0 0 0 0 1 0 0 0 56
57 2.00 168.6 1 0 0 0 0 0 0 0 0 1 0 0 57
58 2.00 161.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 2.00 174.1 1 0 0 0 0 0 0 0 0 0 0 1 59
60 2.00 179.0 1 0 0 0 0 0 0 0 0 0 0 0 60
61 2.00 190.6 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) grondstofprijs dummy M1 M2
3.69690 0.01446 -0.32712 -0.14546 -0.15449
M3 M4 M5 M6 M7
-0.11549 -0.06466 -0.06966 -0.03603 -0.01602
M8 M9 M10 M11 t
-0.05595 0.01825 0.03752 -0.02025 -0.06714
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.706051 -0.159472 0.004256 0.120861 0.506405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.696905 0.250192 14.776 < 2e-16 ***
grondstofprijs 0.014457 0.002741 5.274 3.48e-06 ***
dummy -0.327115 0.156742 -2.087 0.0425 *
M1 -0.145461 0.160720 -0.905 0.3702
M2 -0.154487 0.167684 -0.921 0.3617
M3 -0.115487 0.167488 -0.690 0.4940
M4 -0.064658 0.168063 -0.385 0.7022
M5 -0.069659 0.169563 -0.411 0.6831
M6 -0.036029 0.167570 -0.215 0.8307
M7 -0.016015 0.167322 -0.096 0.9242
M8 -0.055953 0.167431 -0.334 0.7398
M9 0.018251 0.166831 0.109 0.9134
M10 0.037516 0.166657 0.225 0.8229
M11 -0.020254 0.166571 -0.122 0.9038
t -0.067144 0.006309 -10.642 5.42e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2633 on 46 degrees of freedom
Multiple R-squared: 0.9504, Adjusted R-squared: 0.9353
F-statistic: 63 on 14 and 46 DF, p-value: < 2.2e-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,] 0.9843274 3.134513e-02 1.567256e-02
[2,] 0.9629848 7.403035e-02 3.701518e-02
[3,] 0.9783990 4.320192e-02 2.160096e-02
[4,] 0.9768926 4.621473e-02 2.310737e-02
[5,] 0.9601159 7.976821e-02 3.988410e-02
[6,] 0.9304438 1.391124e-01 6.955619e-02
[7,] 0.9040646 1.918709e-01 9.593544e-02
[8,] 0.8995912 2.008176e-01 1.004088e-01
[9,] 0.8614135 2.771730e-01 1.385865e-01
[10,] 0.9058360 1.883279e-01 9.416396e-02
[11,] 0.9904108 1.917833e-02 9.589163e-03
[12,] 0.9943301 1.133979e-02 5.669896e-03
[13,] 0.9989923 2.015469e-03 1.007734e-03
[14,] 0.9990255 1.948974e-03 9.744868e-04
[15,] 0.9994657 1.068608e-03 5.343038e-04
[16,] 0.9999380 1.240628e-04 6.203140e-05
[17,] 1.0000000 4.393811e-12 2.196906e-12
[18,] 1.0000000 1.767760e-183 8.838802e-184
[19,] 1.0000000 6.249100e-153 3.124550e-153
[20,] 1.0000000 2.585799e-132 1.292900e-132
[21,] 1.0000000 9.720987e-120 4.860493e-120
[22,] 1.0000000 1.253765e-102 6.268824e-103
[23,] 1.0000000 3.577920e-102 1.788960e-102
[24,] 1.0000000 1.211227e-78 6.056133e-79
[25,] 1.0000000 4.072004e-59 2.036002e-59
[26,] 1.0000000 3.945861e-44 1.972930e-44
> postscript(file="/var/www/html/rcomp/tmp/1jspj1227781806.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gpnh1227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3capr1227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pbn81227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59fcr1227781806.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
-0.706051480 -0.473853057 -0.222577278 -0.177948234 0.096599710 0.117101898
7 8 9 10 11 12
0.120860793 0.298783309 0.280157881 0.111773558 0.188494163 0.300442507
13 14 15 16 17 18
0.487144764 0.119218567 0.020609928 -0.250788665 -0.309968364 0.027528921
19 20 21 22 23 24
0.048636560 0.022711332 -0.039285956 0.004255853 0.150856357 0.154375050
25 26 27 28 29 30
0.343848664 0.356407389 0.409128897 0.506404677 0.093252614 -0.086000477
31 32 33 34 35 36
-0.125613443 -0.130342182 -0.032708812 -0.009407204 -0.335093135 -0.414225697
37 38 39 40 41 42
-0.243546556 -0.095089335 -0.159471850 -0.167734264 -0.147634568 -0.206647457
43 44 45 46 47 48
-0.182648361 -0.188333387 -0.209850272 -0.264618013 -0.099223036 -0.111607359
49 50 51 52 53 54
0.002688364 0.093316437 -0.047689697 0.090066485 0.267750608 0.148017114
55 56 57 58 59 60
0.138764451 -0.002819072 0.001687159 0.157995806 0.094965651 0.071015499
61
0.115916245
> postscript(file="/var/www/html/rcomp/tmp/6mnn21227781806.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 -0.706051480 NA
1 -0.473853057 -0.706051480
2 -0.222577278 -0.473853057
3 -0.177948234 -0.222577278
4 0.096599710 -0.177948234
5 0.117101898 0.096599710
6 0.120860793 0.117101898
7 0.298783309 0.120860793
8 0.280157881 0.298783309
9 0.111773558 0.280157881
10 0.188494163 0.111773558
11 0.300442507 0.188494163
12 0.487144764 0.300442507
13 0.119218567 0.487144764
14 0.020609928 0.119218567
15 -0.250788665 0.020609928
16 -0.309968364 -0.250788665
17 0.027528921 -0.309968364
18 0.048636560 0.027528921
19 0.022711332 0.048636560
20 -0.039285956 0.022711332
21 0.004255853 -0.039285956
22 0.150856357 0.004255853
23 0.154375050 0.150856357
24 0.343848664 0.154375050
25 0.356407389 0.343848664
26 0.409128897 0.356407389
27 0.506404677 0.409128897
28 0.093252614 0.506404677
29 -0.086000477 0.093252614
30 -0.125613443 -0.086000477
31 -0.130342182 -0.125613443
32 -0.032708812 -0.130342182
33 -0.009407204 -0.032708812
34 -0.335093135 -0.009407204
35 -0.414225697 -0.335093135
36 -0.243546556 -0.414225697
37 -0.095089335 -0.243546556
38 -0.159471850 -0.095089335
39 -0.167734264 -0.159471850
40 -0.147634568 -0.167734264
41 -0.206647457 -0.147634568
42 -0.182648361 -0.206647457
43 -0.188333387 -0.182648361
44 -0.209850272 -0.188333387
45 -0.264618013 -0.209850272
46 -0.099223036 -0.264618013
47 -0.111607359 -0.099223036
48 0.002688364 -0.111607359
49 0.093316437 0.002688364
50 -0.047689697 0.093316437
51 0.090066485 -0.047689697
52 0.267750608 0.090066485
53 0.148017114 0.267750608
54 0.138764451 0.148017114
55 -0.002819072 0.138764451
56 0.001687159 -0.002819072
57 0.157995806 0.001687159
58 0.094965651 0.157995806
59 0.071015499 0.094965651
60 0.115916245 0.071015499
61 NA 0.115916245
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.473853057 -0.706051480
[2,] -0.222577278 -0.473853057
[3,] -0.177948234 -0.222577278
[4,] 0.096599710 -0.177948234
[5,] 0.117101898 0.096599710
[6,] 0.120860793 0.117101898
[7,] 0.298783309 0.120860793
[8,] 0.280157881 0.298783309
[9,] 0.111773558 0.280157881
[10,] 0.188494163 0.111773558
[11,] 0.300442507 0.188494163
[12,] 0.487144764 0.300442507
[13,] 0.119218567 0.487144764
[14,] 0.020609928 0.119218567
[15,] -0.250788665 0.020609928
[16,] -0.309968364 -0.250788665
[17,] 0.027528921 -0.309968364
[18,] 0.048636560 0.027528921
[19,] 0.022711332 0.048636560
[20,] -0.039285956 0.022711332
[21,] 0.004255853 -0.039285956
[22,] 0.150856357 0.004255853
[23,] 0.154375050 0.150856357
[24,] 0.343848664 0.154375050
[25,] 0.356407389 0.343848664
[26,] 0.409128897 0.356407389
[27,] 0.506404677 0.409128897
[28,] 0.093252614 0.506404677
[29,] -0.086000477 0.093252614
[30,] -0.125613443 -0.086000477
[31,] -0.130342182 -0.125613443
[32,] -0.032708812 -0.130342182
[33,] -0.009407204 -0.032708812
[34,] -0.335093135 -0.009407204
[35,] -0.414225697 -0.335093135
[36,] -0.243546556 -0.414225697
[37,] -0.095089335 -0.243546556
[38,] -0.159471850 -0.095089335
[39,] -0.167734264 -0.159471850
[40,] -0.147634568 -0.167734264
[41,] -0.206647457 -0.147634568
[42,] -0.182648361 -0.206647457
[43,] -0.188333387 -0.182648361
[44,] -0.209850272 -0.188333387
[45,] -0.264618013 -0.209850272
[46,] -0.099223036 -0.264618013
[47,] -0.111607359 -0.099223036
[48,] 0.002688364 -0.111607359
[49,] 0.093316437 0.002688364
[50,] -0.047689697 0.093316437
[51,] 0.090066485 -0.047689697
[52,] 0.267750608 0.090066485
[53,] 0.148017114 0.267750608
[54,] 0.138764451 0.148017114
[55,] -0.002819072 0.138764451
[56,] 0.001687159 -0.002819072
[57,] 0.157995806 0.001687159
[58,] 0.094965651 0.157995806
[59,] 0.071015499 0.094965651
[60,] 0.115916245 0.071015499
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.473853057 -0.706051480
2 -0.222577278 -0.473853057
3 -0.177948234 -0.222577278
4 0.096599710 -0.177948234
5 0.117101898 0.096599710
6 0.120860793 0.117101898
7 0.298783309 0.120860793
8 0.280157881 0.298783309
9 0.111773558 0.280157881
10 0.188494163 0.111773558
11 0.300442507 0.188494163
12 0.487144764 0.300442507
13 0.119218567 0.487144764
14 0.020609928 0.119218567
15 -0.250788665 0.020609928
16 -0.309968364 -0.250788665
17 0.027528921 -0.309968364
18 0.048636560 0.027528921
19 0.022711332 0.048636560
20 -0.039285956 0.022711332
21 0.004255853 -0.039285956
22 0.150856357 0.004255853
23 0.154375050 0.150856357
24 0.343848664 0.154375050
25 0.356407389 0.343848664
26 0.409128897 0.356407389
27 0.506404677 0.409128897
28 0.093252614 0.506404677
29 -0.086000477 0.093252614
30 -0.125613443 -0.086000477
31 -0.130342182 -0.125613443
32 -0.032708812 -0.130342182
33 -0.009407204 -0.032708812
34 -0.335093135 -0.009407204
35 -0.414225697 -0.335093135
36 -0.243546556 -0.414225697
37 -0.095089335 -0.243546556
38 -0.159471850 -0.095089335
39 -0.167734264 -0.159471850
40 -0.147634568 -0.167734264
41 -0.206647457 -0.147634568
42 -0.182648361 -0.206647457
43 -0.188333387 -0.182648361
44 -0.209850272 -0.188333387
45 -0.264618013 -0.209850272
46 -0.099223036 -0.264618013
47 -0.111607359 -0.099223036
48 0.002688364 -0.111607359
49 0.093316437 0.002688364
50 -0.047689697 0.093316437
51 0.090066485 -0.047689697
52 0.267750608 0.090066485
53 0.148017114 0.267750608
54 0.138764451 0.148017114
55 -0.002819072 0.138764451
56 0.001687159 -0.002819072
57 0.157995806 0.001687159
58 0.094965651 0.157995806
59 0.071015499 0.094965651
60 0.115916245 0.071015499
> 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/7y5481227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f1rg1227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99wfg1227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i0je1227781806.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xclk1227781806.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/12lrn21227781806.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/131jw81227781806.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/14ftkf1227781806.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/15z90i1227781806.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/16lmq11227781806.tab")
+ }
>
> system("convert tmp/1jspj1227781806.ps tmp/1jspj1227781806.png")
> system("convert tmp/2gpnh1227781806.ps tmp/2gpnh1227781806.png")
> system("convert tmp/3capr1227781806.ps tmp/3capr1227781806.png")
> system("convert tmp/4pbn81227781806.ps tmp/4pbn81227781806.png")
> system("convert tmp/59fcr1227781806.ps tmp/59fcr1227781806.png")
> system("convert tmp/6mnn21227781806.ps tmp/6mnn21227781806.png")
> system("convert tmp/7y5481227781806.ps tmp/7y5481227781806.png")
> system("convert tmp/8f1rg1227781806.ps tmp/8f1rg1227781806.png")
> system("convert tmp/99wfg1227781806.ps tmp/99wfg1227781806.png")
> system("convert tmp/10i0je1227781806.ps tmp/10i0je1227781806.png")
>
>
> proc.time()
user system elapsed
4.959 2.741 5.324