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(1.43,0.51,1.43,0.51,1.43,0.51,1.43,0.51,1.43,0.52,1.43,0.52,1.44,0.52,1.48,0.53,1.48,0.53,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.52,1.48,0.53,1.48,0.53,1.48,0.53,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.54,1.48,0.53,1.48,0.53,1.48,0.53,1.48,0.53,1.48,0.53,1.57,0.54,1.58,0.55,1.58,0.55,1.58,0.55,1.58,0.55,1.59,0.55,1.6,0.55,1.6,0.55,1.61,0.55,1.61,0.56,1.61,0.56,1.62,0.56,1.63,0.56,1.63,0.56,1.64,0.55,1.64,0.56,1.64,0.55,1.64,0.55,1.64,0.56,1.65,0.55,1.65,0.55,1.65,0.55,1.65,0.55),dim=c(2,60),dimnames=list(c('Broodprijs','Bakmeelprijs'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Broodprijs','Bakmeelprijs'),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
Broodprijs Bakmeelprijs M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.43 0.51 1 0 0 0 0 0 0 0 0 0 0
2 1.43 0.51 0 1 0 0 0 0 0 0 0 0 0
3 1.43 0.51 0 0 1 0 0 0 0 0 0 0 0
4 1.43 0.51 0 0 0 1 0 0 0 0 0 0 0
5 1.43 0.52 0 0 0 0 1 0 0 0 0 0 0
6 1.43 0.52 0 0 0 0 0 1 0 0 0 0 0
7 1.44 0.52 0 0 0 0 0 0 1 0 0 0 0
8 1.48 0.53 0 0 0 0 0 0 0 1 0 0 0
9 1.48 0.53 0 0 0 0 0 0 0 0 1 0 0
10 1.48 0.52 0 0 0 0 0 0 0 0 0 1 0
11 1.48 0.52 0 0 0 0 0 0 0 0 0 0 1
12 1.48 0.52 0 0 0 0 0 0 0 0 0 0 0
13 1.48 0.52 1 0 0 0 0 0 0 0 0 0 0
14 1.48 0.52 0 1 0 0 0 0 0 0 0 0 0
15 1.48 0.52 0 0 1 0 0 0 0 0 0 0 0
16 1.48 0.52 0 0 0 1 0 0 0 0 0 0 0
17 1.48 0.52 0 0 0 0 1 0 0 0 0 0 0
18 1.48 0.52 0 0 0 0 0 1 0 0 0 0 0
19 1.48 0.52 0 0 0 0 0 0 1 0 0 0 0
20 1.48 0.53 0 0 0 0 0 0 0 1 0 0 0
21 1.48 0.53 0 0 0 0 0 0 0 0 1 0 0
22 1.48 0.53 0 0 0 0 0 0 0 0 0 1 0
23 1.48 0.54 0 0 0 0 0 0 0 0 0 0 1
24 1.48 0.54 0 0 0 0 0 0 0 0 0 0 0
25 1.48 0.54 1 0 0 0 0 0 0 0 0 0 0
26 1.48 0.54 0 1 0 0 0 0 0 0 0 0 0
27 1.48 0.54 0 0 1 0 0 0 0 0 0 0 0
28 1.48 0.54 0 0 0 1 0 0 0 0 0 0 0
29 1.48 0.54 0 0 0 0 1 0 0 0 0 0 0
30 1.48 0.54 0 0 0 0 0 1 0 0 0 0 0
31 1.48 0.54 0 0 0 0 0 0 1 0 0 0 0
32 1.48 0.54 0 0 0 0 0 0 0 1 0 0 0
33 1.48 0.53 0 0 0 0 0 0 0 0 1 0 0
34 1.48 0.53 0 0 0 0 0 0 0 0 0 1 0
35 1.48 0.53 0 0 0 0 0 0 0 0 0 0 1
36 1.48 0.53 0 0 0 0 0 0 0 0 0 0 0
37 1.48 0.53 1 0 0 0 0 0 0 0 0 0 0
38 1.57 0.54 0 1 0 0 0 0 0 0 0 0 0
39 1.58 0.55 0 0 1 0 0 0 0 0 0 0 0
40 1.58 0.55 0 0 0 1 0 0 0 0 0 0 0
41 1.58 0.55 0 0 0 0 1 0 0 0 0 0 0
42 1.58 0.55 0 0 0 0 0 1 0 0 0 0 0
43 1.59 0.55 0 0 0 0 0 0 1 0 0 0 0
44 1.60 0.55 0 0 0 0 0 0 0 1 0 0 0
45 1.60 0.55 0 0 0 0 0 0 0 0 1 0 0
46 1.61 0.55 0 0 0 0 0 0 0 0 0 1 0
47 1.61 0.56 0 0 0 0 0 0 0 0 0 0 1
48 1.61 0.56 0 0 0 0 0 0 0 0 0 0 0
49 1.62 0.56 1 0 0 0 0 0 0 0 0 0 0
50 1.63 0.56 0 1 0 0 0 0 0 0 0 0 0
51 1.63 0.56 0 0 1 0 0 0 0 0 0 0 0
52 1.64 0.55 0 0 0 1 0 0 0 0 0 0 0
53 1.64 0.56 0 0 0 0 1 0 0 0 0 0 0
54 1.64 0.55 0 0 0 0 0 1 0 0 0 0 0
55 1.64 0.55 0 0 0 0 0 0 1 0 0 0 0
56 1.64 0.56 0 0 0 0 0 0 0 1 0 0 0
57 1.65 0.55 0 0 0 0 0 0 0 0 1 0 0
58 1.65 0.55 0 0 0 0 0 0 0 0 0 1 0
59 1.65 0.55 0 0 0 0 0 0 0 0 0 0 1
60 1.65 0.55 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) Bakmeelprijs M1 M2 M3
-8.171e-01 4.365e+00 -7.080e-03 4.190e-03 -2.540e-03
M4 M5 M6 M7 M8
8.190e-03 -9.270e-03 -5.399e-04 3.460e-03 -1.273e-02
M9 M10 M11
6.730e-03 1.746e-02 3.072e-17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.068190 -0.022390 0.001334 0.026183 0.066350
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.171e-01 1.906e-01 -4.288 8.9e-05 ***
Bakmeelprijs 4.365e+00 3.514e-01 12.423 < 2e-16 ***
M1 -7.080e-03 2.553e-02 -0.277 0.783
M2 4.190e-03 2.546e-02 0.165 0.870
M3 -2.540e-03 2.541e-02 -0.100 0.921
M4 8.190e-03 2.546e-02 0.322 0.749
M5 -9.270e-03 2.539e-02 -0.365 0.717
M6 -5.399e-04 2.541e-02 -0.021 0.983
M7 3.460e-03 2.541e-02 0.136 0.892
M8 -1.273e-02 2.539e-02 -0.501 0.618
M9 6.730e-03 2.539e-02 0.265 0.792
M10 1.746e-02 2.541e-02 0.687 0.495
M11 3.072e-17 2.538e-02 1.21e-15 1.000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04012 on 47 degrees of freedom
Multiple R-squared: 0.7727, Adjusted R-squared: 0.7147
F-statistic: 13.32 on 12 and 47 DF, p-value: 2.398e-11
> 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,] 9.098302e-41 1.819660e-40 1.0000000
[2,] 5.297724e-02 1.059545e-01 0.9470228
[3,] 9.223388e-02 1.844678e-01 0.9077661
[4,] 9.992377e-02 1.998475e-01 0.9000762
[5,] 5.367832e-02 1.073566e-01 0.9463217
[6,] 2.527121e-02 5.054241e-02 0.9747288
[7,] 2.731949e-02 5.463898e-02 0.9726805
[8,] 5.143368e-02 1.028674e-01 0.9485663
[9,] 4.904418e-02 9.808837e-02 0.9509558
[10,] 3.025746e-02 6.051492e-02 0.9697425
[11,] 2.036490e-02 4.072981e-02 0.9796351
[12,] 1.125510e-02 2.251020e-02 0.9887449
[13,] 8.773460e-03 1.754692e-02 0.9912265
[14,] 5.028453e-03 1.005691e-02 0.9949715
[15,] 4.554289e-03 9.108578e-03 0.9954457
[16,] 5.541337e-03 1.108267e-02 0.9944587
[17,] 5.281069e-03 1.056214e-02 0.9947189
[18,] 3.570768e-03 7.141535e-03 0.9964292
[19,] 3.215055e-03 6.430110e-03 0.9967849
[20,] 2.163330e-03 4.326660e-03 0.9978367
[21,] 2.241433e-03 4.482866e-03 0.9977586
[22,] 2.169843e-03 4.339687e-03 0.9978302
[23,] 2.680794e-02 5.361589e-02 0.9731921
[24,] 6.297786e-02 1.259557e-01 0.9370221
[25,] 1.208467e-01 2.416934e-01 0.8791533
[26,] 2.138173e-01 4.276345e-01 0.7861827
[27,] 3.084918e-01 6.169836e-01 0.6915082
[28,] 3.620357e-01 7.240714e-01 0.6379643
[29,] 5.785659e-01 8.428681e-01 0.4214341
> postscript(file="/var/www/html/rcomp/tmp/1x8hk1258718935.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/218m71258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a4z81258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48snz1258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v8oq1258718935.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
0.0280306748 0.0167607362 0.0234907975 0.0127607362 -0.0134294479
6 7 8 9 10
-0.0221595092 -0.0161595092 -0.0036196319 -0.0230797546 0.0098404908
11 12 13 14 15
0.0273006135 0.0273006135 0.0343803681 0.0231104294 0.0298404908
16 17 18 19 20
0.0191104294 0.0365705521 0.0278404908 0.0238404908 -0.0036196319
21 22 23 24 25
-0.0230797546 -0.0338098160 -0.0600000000 -0.0600000000 -0.0529202454
26 27 28 29 30
-0.0641901840 -0.0574601227 -0.0681901840 -0.0507300613 -0.0594601227
31 32 33 34 35
-0.0634601227 -0.0472699387 -0.0230797546 -0.0338098160 -0.0163496933
36 37 38 39 40
-0.0163496933 -0.0092699387 0.0258098160 -0.0011104294 -0.0118404908
41 42 43 44 45
0.0056196319 -0.0031104294 0.0028895706 0.0290797546 0.0096196319
46 47 48 49 50
0.0088895706 -0.0173006135 -0.0173006135 -0.0002208589 -0.0014907975
51 52 53 54 55
0.0052392638 0.0481595092 0.0219693252 0.0568895706 0.0528895706
56 57 58 59 60
0.0254294479 0.0596196319 0.0488895706 0.0663496933 0.0663496933
> postscript(file="/var/www/html/rcomp/tmp/6ivij1258718935.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 0.0280306748 NA
1 0.0167607362 0.0280306748
2 0.0234907975 0.0167607362
3 0.0127607362 0.0234907975
4 -0.0134294479 0.0127607362
5 -0.0221595092 -0.0134294479
6 -0.0161595092 -0.0221595092
7 -0.0036196319 -0.0161595092
8 -0.0230797546 -0.0036196319
9 0.0098404908 -0.0230797546
10 0.0273006135 0.0098404908
11 0.0273006135 0.0273006135
12 0.0343803681 0.0273006135
13 0.0231104294 0.0343803681
14 0.0298404908 0.0231104294
15 0.0191104294 0.0298404908
16 0.0365705521 0.0191104294
17 0.0278404908 0.0365705521
18 0.0238404908 0.0278404908
19 -0.0036196319 0.0238404908
20 -0.0230797546 -0.0036196319
21 -0.0338098160 -0.0230797546
22 -0.0600000000 -0.0338098160
23 -0.0600000000 -0.0600000000
24 -0.0529202454 -0.0600000000
25 -0.0641901840 -0.0529202454
26 -0.0574601227 -0.0641901840
27 -0.0681901840 -0.0574601227
28 -0.0507300613 -0.0681901840
29 -0.0594601227 -0.0507300613
30 -0.0634601227 -0.0594601227
31 -0.0472699387 -0.0634601227
32 -0.0230797546 -0.0472699387
33 -0.0338098160 -0.0230797546
34 -0.0163496933 -0.0338098160
35 -0.0163496933 -0.0163496933
36 -0.0092699387 -0.0163496933
37 0.0258098160 -0.0092699387
38 -0.0011104294 0.0258098160
39 -0.0118404908 -0.0011104294
40 0.0056196319 -0.0118404908
41 -0.0031104294 0.0056196319
42 0.0028895706 -0.0031104294
43 0.0290797546 0.0028895706
44 0.0096196319 0.0290797546
45 0.0088895706 0.0096196319
46 -0.0173006135 0.0088895706
47 -0.0173006135 -0.0173006135
48 -0.0002208589 -0.0173006135
49 -0.0014907975 -0.0002208589
50 0.0052392638 -0.0014907975
51 0.0481595092 0.0052392638
52 0.0219693252 0.0481595092
53 0.0568895706 0.0219693252
54 0.0528895706 0.0568895706
55 0.0254294479 0.0528895706
56 0.0596196319 0.0254294479
57 0.0488895706 0.0596196319
58 0.0663496933 0.0488895706
59 0.0663496933 0.0663496933
60 NA 0.0663496933
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0167607362 0.0280306748
[2,] 0.0234907975 0.0167607362
[3,] 0.0127607362 0.0234907975
[4,] -0.0134294479 0.0127607362
[5,] -0.0221595092 -0.0134294479
[6,] -0.0161595092 -0.0221595092
[7,] -0.0036196319 -0.0161595092
[8,] -0.0230797546 -0.0036196319
[9,] 0.0098404908 -0.0230797546
[10,] 0.0273006135 0.0098404908
[11,] 0.0273006135 0.0273006135
[12,] 0.0343803681 0.0273006135
[13,] 0.0231104294 0.0343803681
[14,] 0.0298404908 0.0231104294
[15,] 0.0191104294 0.0298404908
[16,] 0.0365705521 0.0191104294
[17,] 0.0278404908 0.0365705521
[18,] 0.0238404908 0.0278404908
[19,] -0.0036196319 0.0238404908
[20,] -0.0230797546 -0.0036196319
[21,] -0.0338098160 -0.0230797546
[22,] -0.0600000000 -0.0338098160
[23,] -0.0600000000 -0.0600000000
[24,] -0.0529202454 -0.0600000000
[25,] -0.0641901840 -0.0529202454
[26,] -0.0574601227 -0.0641901840
[27,] -0.0681901840 -0.0574601227
[28,] -0.0507300613 -0.0681901840
[29,] -0.0594601227 -0.0507300613
[30,] -0.0634601227 -0.0594601227
[31,] -0.0472699387 -0.0634601227
[32,] -0.0230797546 -0.0472699387
[33,] -0.0338098160 -0.0230797546
[34,] -0.0163496933 -0.0338098160
[35,] -0.0163496933 -0.0163496933
[36,] -0.0092699387 -0.0163496933
[37,] 0.0258098160 -0.0092699387
[38,] -0.0011104294 0.0258098160
[39,] -0.0118404908 -0.0011104294
[40,] 0.0056196319 -0.0118404908
[41,] -0.0031104294 0.0056196319
[42,] 0.0028895706 -0.0031104294
[43,] 0.0290797546 0.0028895706
[44,] 0.0096196319 0.0290797546
[45,] 0.0088895706 0.0096196319
[46,] -0.0173006135 0.0088895706
[47,] -0.0173006135 -0.0173006135
[48,] -0.0002208589 -0.0173006135
[49,] -0.0014907975 -0.0002208589
[50,] 0.0052392638 -0.0014907975
[51,] 0.0481595092 0.0052392638
[52,] 0.0219693252 0.0481595092
[53,] 0.0568895706 0.0219693252
[54,] 0.0528895706 0.0568895706
[55,] 0.0254294479 0.0528895706
[56,] 0.0596196319 0.0254294479
[57,] 0.0488895706 0.0596196319
[58,] 0.0663496933 0.0488895706
[59,] 0.0663496933 0.0663496933
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0167607362 0.0280306748
2 0.0234907975 0.0167607362
3 0.0127607362 0.0234907975
4 -0.0134294479 0.0127607362
5 -0.0221595092 -0.0134294479
6 -0.0161595092 -0.0221595092
7 -0.0036196319 -0.0161595092
8 -0.0230797546 -0.0036196319
9 0.0098404908 -0.0230797546
10 0.0273006135 0.0098404908
11 0.0273006135 0.0273006135
12 0.0343803681 0.0273006135
13 0.0231104294 0.0343803681
14 0.0298404908 0.0231104294
15 0.0191104294 0.0298404908
16 0.0365705521 0.0191104294
17 0.0278404908 0.0365705521
18 0.0238404908 0.0278404908
19 -0.0036196319 0.0238404908
20 -0.0230797546 -0.0036196319
21 -0.0338098160 -0.0230797546
22 -0.0600000000 -0.0338098160
23 -0.0600000000 -0.0600000000
24 -0.0529202454 -0.0600000000
25 -0.0641901840 -0.0529202454
26 -0.0574601227 -0.0641901840
27 -0.0681901840 -0.0574601227
28 -0.0507300613 -0.0681901840
29 -0.0594601227 -0.0507300613
30 -0.0634601227 -0.0594601227
31 -0.0472699387 -0.0634601227
32 -0.0230797546 -0.0472699387
33 -0.0338098160 -0.0230797546
34 -0.0163496933 -0.0338098160
35 -0.0163496933 -0.0163496933
36 -0.0092699387 -0.0163496933
37 0.0258098160 -0.0092699387
38 -0.0011104294 0.0258098160
39 -0.0118404908 -0.0011104294
40 0.0056196319 -0.0118404908
41 -0.0031104294 0.0056196319
42 0.0028895706 -0.0031104294
43 0.0290797546 0.0028895706
44 0.0096196319 0.0290797546
45 0.0088895706 0.0096196319
46 -0.0173006135 0.0088895706
47 -0.0173006135 -0.0173006135
48 -0.0002208589 -0.0173006135
49 -0.0014907975 -0.0002208589
50 0.0052392638 -0.0014907975
51 0.0481595092 0.0052392638
52 0.0219693252 0.0481595092
53 0.0568895706 0.0219693252
54 0.0528895706 0.0568895706
55 0.0254294479 0.0528895706
56 0.0596196319 0.0254294479
57 0.0488895706 0.0596196319
58 0.0663496933 0.0488895706
59 0.0663496933 0.0663496933
> 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/7j8pz1258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sm3i1258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oqds1258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cmvq1258718935.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wfsz1258718935.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/12cj5f1258718935.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/130auy1258718935.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/146buz1258718935.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/15jn1y1258718935.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/16e6qm1258718935.tab")
+ }
>
> system("convert tmp/1x8hk1258718935.ps tmp/1x8hk1258718935.png")
> system("convert tmp/218m71258718935.ps tmp/218m71258718935.png")
> system("convert tmp/3a4z81258718935.ps tmp/3a4z81258718935.png")
> system("convert tmp/48snz1258718935.ps tmp/48snz1258718935.png")
> system("convert tmp/5v8oq1258718935.ps tmp/5v8oq1258718935.png")
> system("convert tmp/6ivij1258718935.ps tmp/6ivij1258718935.png")
> system("convert tmp/7j8pz1258718935.ps tmp/7j8pz1258718935.png")
> system("convert tmp/8sm3i1258718935.ps tmp/8sm3i1258718935.png")
> system("convert tmp/9oqds1258718935.ps tmp/9oqds1258718935.png")
> system("convert tmp/10cmvq1258718935.ps tmp/10cmvq1258718935.png")
>
>
> proc.time()
user system elapsed
2.391 1.626 2.928