R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,0,8.2,0,8.3,0,8.5,0,8.6,0,8.6,0,8.2,0,8.1,0,8,1,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8,1,8,1,7.9,1,8,1,7.7,1,7.2,1,7.5,1,7.3,1,7,1,7,1,7,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1),dim=c(2,60),dimnames=list(c('Totaal%werkzoekenden','stockmarketcrashfollowedbyeconomicdepression'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Totaal%werkzoekenden','stockmarketcrashfollowedbyeconomicdepression'),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 = '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
Totaal%werkzoekenden stockmarketcrashfollowedbyeconomicdepression M1 M2 M3
1 8.4 0 1 0 0
2 8.4 0 0 1 0
3 8.4 0 0 0 1
4 8.6 0 0 0 0
5 8.9 0 0 0 0
6 8.8 0 0 0 0
7 8.3 0 0 0 0
8 7.5 0 0 0 0
9 7.2 0 0 0 0
10 7.5 0 0 0 0
11 8.8 0 0 0 0
12 9.3 0 0 0 0
13 9.3 0 1 0 0
14 8.7 0 0 1 0
15 8.2 0 0 0 1
16 8.3 0 0 0 0
17 8.5 0 0 0 0
18 8.6 0 0 0 0
19 8.6 0 0 0 0
20 8.2 0 0 0 0
21 8.1 0 0 0 0
22 8.0 1 0 0 0
23 8.6 1 0 0 0
24 8.7 1 0 0 0
25 8.8 1 1 0 0
26 8.5 1 0 1 0
27 8.4 1 0 0 1
28 8.5 1 0 0 0
29 8.7 1 0 0 0
30 8.7 1 0 0 0
31 8.6 1 0 0 0
32 8.5 1 0 0 0
33 8.3 1 0 0 0
34 8.1 1 0 0 0
35 8.2 1 0 0 0
36 8.1 1 0 0 0
37 8.1 1 1 0 0
38 7.9 1 0 1 0
39 7.9 1 0 0 1
40 7.9 1 0 0 0
41 8.0 1 0 0 0
42 8.0 1 0 0 0
43 7.9 1 0 0 0
44 8.0 1 0 0 0
45 7.7 1 0 0 0
46 7.2 1 0 0 0
47 7.5 1 0 0 0
48 7.3 1 0 0 0
49 7.0 1 1 0 0
50 7.0 1 0 1 0
51 7.0 1 0 0 1
52 7.2 1 0 0 0
53 7.3 1 0 0 0
54 7.1 1 0 0 0
55 6.8 1 0 0 0
56 6.6 1 0 0 0
57 6.2 1 0 0 0
58 6.2 1 0 0 0
59 6.8 1 0 0 0
60 6.9 1 0 0 0
M4 M5 M6 M7 M8 M9 M10 M11 t
1 0 0 0 0 0 0 0 0 1
2 0 0 0 0 0 0 0 0 2
3 0 0 0 0 0 0 0 0 3
4 1 0 0 0 0 0 0 0 4
5 0 1 0 0 0 0 0 0 5
6 0 0 1 0 0 0 0 0 6
7 0 0 0 1 0 0 0 0 7
8 0 0 0 0 1 0 0 0 8
9 0 0 0 0 0 1 0 0 9
10 0 0 0 0 0 0 1 0 10
11 0 0 0 0 0 0 0 1 11
12 0 0 0 0 0 0 0 0 12
13 0 0 0 0 0 0 0 0 13
14 0 0 0 0 0 0 0 0 14
15 0 0 0 0 0 0 0 0 15
16 1 0 0 0 0 0 0 0 16
17 0 1 0 0 0 0 0 0 17
18 0 0 1 0 0 0 0 0 18
19 0 0 0 1 0 0 0 0 19
20 0 0 0 0 1 0 0 0 20
21 0 0 0 0 0 1 0 0 21
22 0 0 0 0 0 0 1 0 22
23 0 0 0 0 0 0 0 1 23
24 0 0 0 0 0 0 0 0 24
25 0 0 0 0 0 0 0 0 25
26 0 0 0 0 0 0 0 0 26
27 0 0 0 0 0 0 0 0 27
28 1 0 0 0 0 0 0 0 28
29 0 1 0 0 0 0 0 0 29
30 0 0 1 0 0 0 0 0 30
31 0 0 0 1 0 0 0 0 31
32 0 0 0 0 1 0 0 0 32
33 0 0 0 0 0 1 0 0 33
34 0 0 0 0 0 0 1 0 34
35 0 0 0 0 0 0 0 1 35
36 0 0 0 0 0 0 0 0 36
37 0 0 0 0 0 0 0 0 37
38 0 0 0 0 0 0 0 0 38
39 0 0 0 0 0 0 0 0 39
40 1 0 0 0 0 0 0 0 40
41 0 1 0 0 0 0 0 0 41
42 0 0 1 0 0 0 0 0 42
43 0 0 0 1 0 0 0 0 43
44 0 0 0 0 1 0 0 0 44
45 0 0 0 0 0 1 0 0 45
46 0 0 0 0 0 0 1 0 46
47 0 0 0 0 0 0 0 1 47
48 0 0 0 0 0 0 0 0 48
49 0 0 0 0 0 0 0 0 49
50 0 0 0 0 0 0 0 0 50
51 0 0 0 0 0 0 0 0 51
52 1 0 0 0 0 0 0 0 52
53 0 1 0 0 0 0 0 0 53
54 0 0 1 0 0 0 0 0 54
55 0 0 0 1 0 0 0 0 55
56 0 0 0 0 1 0 0 0 56
57 0 0 0 0 0 1 0 0 57
58 0 0 0 0 0 0 1 0 58
59 0 0 0 0 0 0 0 1 59
60 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept)
9.19236
stockmarketcrashfollowedbyeconomicdepression
0.89455
M1
-0.12576
M2
-0.29442
M3
-0.36309
M4
-0.19176
M5
0.03958
M6
0.05091
M7
-0.09776
M8
-0.32642
M9
-0.53509
M10
-0.76267
M11
-0.13133
t
-0.05133
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.99527 -0.19500 0.02818 0.18418 0.90073
Coefficients:
Estimate Std. Error t value
(Intercept) 9.192364 0.214208 42.913
stockmarketcrashfollowedbyeconomicdepression 0.894545 0.201066 4.449
M1 -0.125758 0.260735 -0.482
M2 -0.294424 0.260202 -1.132
M3 -0.363091 0.259786 -1.398
M4 -0.191758 0.259489 -0.739
M5 0.039576 0.259310 0.153
M6 0.050909 0.259250 0.196
M7 -0.097758 0.259310 -0.377
M8 -0.326424 0.259489 -1.258
M9 -0.535091 0.259786 -2.060
M10 -0.762667 0.258513 -2.950
M11 -0.131333 0.258334 -0.508
t -0.051333 0.005557 -9.237
Pr(>|t|)
(Intercept) < 2e-16 ***
stockmarketcrashfollowedbyeconomicdepression 5.44e-05 ***
M1 0.63187
M2 0.26370
M3 0.16892
M4 0.46367
M5 0.87937
M6 0.84519
M7 0.70791
M8 0.21476
M9 0.04511 *
M10 0.00498 **
M11 0.61361
t 4.77e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4084 on 46 degrees of freedom
Multiple R-squared: 0.7605, Adjusted R-squared: 0.6929
F-statistic: 11.24 on 13 and 46 DF, p-value: 2.767e-10
> 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,] 0.9819597 0.03608062 0.018040312
[2,] 0.9640857 0.07182852 0.035914260
[3,] 0.9431353 0.11372935 0.056864673
[4,] 0.9566996 0.08660077 0.043300385
[5,] 0.9721901 0.05561974 0.027809872
[6,] 0.9835001 0.03299985 0.016499923
[7,] 0.9907307 0.01853853 0.009269263
[8,] 0.9944442 0.01111165 0.005555825
[9,] 0.9887409 0.02251829 0.011259143
[10,] 0.9803529 0.03929427 0.019647133
[11,] 0.9734377 0.05312467 0.026562334
[12,] 0.9662533 0.06749343 0.033746715
[13,] 0.9511782 0.09764351 0.048821753
[14,] 0.9237620 0.15247605 0.076238023
[15,] 0.8829706 0.23405884 0.117029419
[16,] 0.8912624 0.21747519 0.108737597
[17,] 0.8795443 0.24091149 0.120455744
[18,] 0.8235557 0.35288852 0.176444262
[19,] 0.8591625 0.28167492 0.140837458
[20,] 0.9355983 0.12880350 0.064401748
[21,] 0.9316180 0.13676390 0.068381952
[22,] 0.9002642 0.19947159 0.099735796
[23,] 0.8379467 0.32410652 0.162053259
[24,] 0.7797629 0.44047420 0.220237102
[25,] 0.7167840 0.56643208 0.283216042
[26,] 0.5921784 0.81564310 0.407821552
[27,] 0.4287143 0.85742864 0.571285679
> postscript(file="/var/www/html/rcomp/tmp/1765a1227559328.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v6wx1227559328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lvri1227559328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ah4m1227559328.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u2xx1227559329.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
-0.61527273 -0.39527273 -0.27527273 -0.19527273 -0.07527273 -0.13527273
7 8 9 10 11 12
-0.43527273 -0.95527273 -0.99527273 -0.41636364 0.30363636 0.72363636
13 14 15 16 17 18
0.90072727 0.52072727 0.14072727 0.12072727 0.14072727 0.28072727
19 20 21 22 23 24
0.48072727 0.36072727 0.52072727 -0.19490909 -0.17490909 -0.15490909
25 26 27 28 29 30
0.12218182 0.04218182 0.06218182 0.04218182 0.06218182 0.10218182
31 32 33 34 35 36
0.20218182 0.38218182 0.44218182 0.52109091 0.04109091 -0.13890909
37 38 39 40 41 42
0.03818182 0.05818182 0.17818182 0.05818182 -0.02181818 0.01818182
43 44 45 46 47 48
0.11818182 0.49818182 0.45818182 0.23709091 -0.04290909 -0.32290909
49 50 51 52 53 54
-0.44581818 -0.22581818 -0.10581818 -0.02581818 -0.10581818 -0.26581818
55 56 57 58 59 60
-0.36581818 -0.28581818 -0.42581818 -0.14690909 -0.12690909 -0.10690909
> postscript(file="/var/www/html/rcomp/tmp/601951227559329.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 -0.61527273 NA
1 -0.39527273 -0.61527273
2 -0.27527273 -0.39527273
3 -0.19527273 -0.27527273
4 -0.07527273 -0.19527273
5 -0.13527273 -0.07527273
6 -0.43527273 -0.13527273
7 -0.95527273 -0.43527273
8 -0.99527273 -0.95527273
9 -0.41636364 -0.99527273
10 0.30363636 -0.41636364
11 0.72363636 0.30363636
12 0.90072727 0.72363636
13 0.52072727 0.90072727
14 0.14072727 0.52072727
15 0.12072727 0.14072727
16 0.14072727 0.12072727
17 0.28072727 0.14072727
18 0.48072727 0.28072727
19 0.36072727 0.48072727
20 0.52072727 0.36072727
21 -0.19490909 0.52072727
22 -0.17490909 -0.19490909
23 -0.15490909 -0.17490909
24 0.12218182 -0.15490909
25 0.04218182 0.12218182
26 0.06218182 0.04218182
27 0.04218182 0.06218182
28 0.06218182 0.04218182
29 0.10218182 0.06218182
30 0.20218182 0.10218182
31 0.38218182 0.20218182
32 0.44218182 0.38218182
33 0.52109091 0.44218182
34 0.04109091 0.52109091
35 -0.13890909 0.04109091
36 0.03818182 -0.13890909
37 0.05818182 0.03818182
38 0.17818182 0.05818182
39 0.05818182 0.17818182
40 -0.02181818 0.05818182
41 0.01818182 -0.02181818
42 0.11818182 0.01818182
43 0.49818182 0.11818182
44 0.45818182 0.49818182
45 0.23709091 0.45818182
46 -0.04290909 0.23709091
47 -0.32290909 -0.04290909
48 -0.44581818 -0.32290909
49 -0.22581818 -0.44581818
50 -0.10581818 -0.22581818
51 -0.02581818 -0.10581818
52 -0.10581818 -0.02581818
53 -0.26581818 -0.10581818
54 -0.36581818 -0.26581818
55 -0.28581818 -0.36581818
56 -0.42581818 -0.28581818
57 -0.14690909 -0.42581818
58 -0.12690909 -0.14690909
59 -0.10690909 -0.12690909
60 NA -0.10690909
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.39527273 -0.61527273
[2,] -0.27527273 -0.39527273
[3,] -0.19527273 -0.27527273
[4,] -0.07527273 -0.19527273
[5,] -0.13527273 -0.07527273
[6,] -0.43527273 -0.13527273
[7,] -0.95527273 -0.43527273
[8,] -0.99527273 -0.95527273
[9,] -0.41636364 -0.99527273
[10,] 0.30363636 -0.41636364
[11,] 0.72363636 0.30363636
[12,] 0.90072727 0.72363636
[13,] 0.52072727 0.90072727
[14,] 0.14072727 0.52072727
[15,] 0.12072727 0.14072727
[16,] 0.14072727 0.12072727
[17,] 0.28072727 0.14072727
[18,] 0.48072727 0.28072727
[19,] 0.36072727 0.48072727
[20,] 0.52072727 0.36072727
[21,] -0.19490909 0.52072727
[22,] -0.17490909 -0.19490909
[23,] -0.15490909 -0.17490909
[24,] 0.12218182 -0.15490909
[25,] 0.04218182 0.12218182
[26,] 0.06218182 0.04218182
[27,] 0.04218182 0.06218182
[28,] 0.06218182 0.04218182
[29,] 0.10218182 0.06218182
[30,] 0.20218182 0.10218182
[31,] 0.38218182 0.20218182
[32,] 0.44218182 0.38218182
[33,] 0.52109091 0.44218182
[34,] 0.04109091 0.52109091
[35,] -0.13890909 0.04109091
[36,] 0.03818182 -0.13890909
[37,] 0.05818182 0.03818182
[38,] 0.17818182 0.05818182
[39,] 0.05818182 0.17818182
[40,] -0.02181818 0.05818182
[41,] 0.01818182 -0.02181818
[42,] 0.11818182 0.01818182
[43,] 0.49818182 0.11818182
[44,] 0.45818182 0.49818182
[45,] 0.23709091 0.45818182
[46,] -0.04290909 0.23709091
[47,] -0.32290909 -0.04290909
[48,] -0.44581818 -0.32290909
[49,] -0.22581818 -0.44581818
[50,] -0.10581818 -0.22581818
[51,] -0.02581818 -0.10581818
[52,] -0.10581818 -0.02581818
[53,] -0.26581818 -0.10581818
[54,] -0.36581818 -0.26581818
[55,] -0.28581818 -0.36581818
[56,] -0.42581818 -0.28581818
[57,] -0.14690909 -0.42581818
[58,] -0.12690909 -0.14690909
[59,] -0.10690909 -0.12690909
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.39527273 -0.61527273
2 -0.27527273 -0.39527273
3 -0.19527273 -0.27527273
4 -0.07527273 -0.19527273
5 -0.13527273 -0.07527273
6 -0.43527273 -0.13527273
7 -0.95527273 -0.43527273
8 -0.99527273 -0.95527273
9 -0.41636364 -0.99527273
10 0.30363636 -0.41636364
11 0.72363636 0.30363636
12 0.90072727 0.72363636
13 0.52072727 0.90072727
14 0.14072727 0.52072727
15 0.12072727 0.14072727
16 0.14072727 0.12072727
17 0.28072727 0.14072727
18 0.48072727 0.28072727
19 0.36072727 0.48072727
20 0.52072727 0.36072727
21 -0.19490909 0.52072727
22 -0.17490909 -0.19490909
23 -0.15490909 -0.17490909
24 0.12218182 -0.15490909
25 0.04218182 0.12218182
26 0.06218182 0.04218182
27 0.04218182 0.06218182
28 0.06218182 0.04218182
29 0.10218182 0.06218182
30 0.20218182 0.10218182
31 0.38218182 0.20218182
32 0.44218182 0.38218182
33 0.52109091 0.44218182
34 0.04109091 0.52109091
35 -0.13890909 0.04109091
36 0.03818182 -0.13890909
37 0.05818182 0.03818182
38 0.17818182 0.05818182
39 0.05818182 0.17818182
40 -0.02181818 0.05818182
41 0.01818182 -0.02181818
42 0.11818182 0.01818182
43 0.49818182 0.11818182
44 0.45818182 0.49818182
45 0.23709091 0.45818182
46 -0.04290909 0.23709091
47 -0.32290909 -0.04290909
48 -0.44581818 -0.32290909
49 -0.22581818 -0.44581818
50 -0.10581818 -0.22581818
51 -0.02581818 -0.10581818
52 -0.10581818 -0.02581818
53 -0.26581818 -0.10581818
54 -0.36581818 -0.26581818
55 -0.28581818 -0.36581818
56 -0.42581818 -0.28581818
57 -0.14690909 -0.42581818
58 -0.12690909 -0.14690909
59 -0.10690909 -0.12690909
> 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/7zzdq1227559329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jqey1227559329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9on2h1227559329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10r0kg1227559329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114i921227559329.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/12p6081227559329.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/132us11227559329.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/1436401227559329.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/152vz31227559329.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/16ji5x1227559329.tab")
+ }
>
> system("convert tmp/1765a1227559328.ps tmp/1765a1227559328.png")
> system("convert tmp/2v6wx1227559328.ps tmp/2v6wx1227559328.png")
> system("convert tmp/3lvri1227559328.ps tmp/3lvri1227559328.png")
> system("convert tmp/4ah4m1227559328.ps tmp/4ah4m1227559328.png")
> system("convert tmp/5u2xx1227559329.ps tmp/5u2xx1227559329.png")
> system("convert tmp/601951227559329.ps tmp/601951227559329.png")
> system("convert tmp/7zzdq1227559329.ps tmp/7zzdq1227559329.png")
> system("convert tmp/8jqey1227559329.ps tmp/8jqey1227559329.png")
> system("convert tmp/9on2h1227559329.ps tmp/9on2h1227559329.png")
> system("convert tmp/10r0kg1227559329.ps tmp/10r0kg1227559329.png")
>
>
> proc.time()
user system elapsed
2.385 1.562 2.785