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(113,14.3,110,14.2,107,15.9,103,15.3,98,15.5,98,15.1,137,15,148,12.1,147,15.8,139,16.9,130,15.1,128,13.7,127,14.8,123,14.7,118,16,114,15.4,108,15,111,15.5,151,15.1,159,11.7,158,16.3,148,16.7,138,15,137,14.9,136,14.6,133,15.3,126,17.9,120,16.4,114,15.4,116,17.9,153,15.9,162,13.9,161,17.8,149,17.9,139,17.4,135,16.7,130,16,127,16.6,122,19.1,117,17.8,112,17.2,113,18.6,149,16.3,157,15.1,157,19.2,147,17.7,137,19.1,132,18,125,17.5,123,17.8,117,21.1,114,17.2,111,19.4,112,19.8,144,17.6,150,16.2,149,19.5,134,19.9,123,20,116,17.3),dim=c(2,60),dimnames=list(c('WK<25j','ExpBE'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WK<25j','ExpBE'),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
WK<25j ExpBE M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 113 14.3 1 0 0 0 0 0 0 0 0 0 0
2 110 14.2 0 1 0 0 0 0 0 0 0 0 0
3 107 15.9 0 0 1 0 0 0 0 0 0 0 0
4 103 15.3 0 0 0 1 0 0 0 0 0 0 0
5 98 15.5 0 0 0 0 1 0 0 0 0 0 0
6 98 15.1 0 0 0 0 0 1 0 0 0 0 0
7 137 15.0 0 0 0 0 0 0 1 0 0 0 0
8 148 12.1 0 0 0 0 0 0 0 1 0 0 0
9 147 15.8 0 0 0 0 0 0 0 0 1 0 0
10 139 16.9 0 0 0 0 0 0 0 0 0 1 0
11 130 15.1 0 0 0 0 0 0 0 0 0 0 1
12 128 13.7 0 0 0 0 0 0 0 0 0 0 0
13 127 14.8 1 0 0 0 0 0 0 0 0 0 0
14 123 14.7 0 1 0 0 0 0 0 0 0 0 0
15 118 16.0 0 0 1 0 0 0 0 0 0 0 0
16 114 15.4 0 0 0 1 0 0 0 0 0 0 0
17 108 15.0 0 0 0 0 1 0 0 0 0 0 0
18 111 15.5 0 0 0 0 0 1 0 0 0 0 0
19 151 15.1 0 0 0 0 0 0 1 0 0 0 0
20 159 11.7 0 0 0 0 0 0 0 1 0 0 0
21 158 16.3 0 0 0 0 0 0 0 0 1 0 0
22 148 16.7 0 0 0 0 0 0 0 0 0 1 0
23 138 15.0 0 0 0 0 0 0 0 0 0 0 1
24 137 14.9 0 0 0 0 0 0 0 0 0 0 0
25 136 14.6 1 0 0 0 0 0 0 0 0 0 0
26 133 15.3 0 1 0 0 0 0 0 0 0 0 0
27 126 17.9 0 0 1 0 0 0 0 0 0 0 0
28 120 16.4 0 0 0 1 0 0 0 0 0 0 0
29 114 15.4 0 0 0 0 1 0 0 0 0 0 0
30 116 17.9 0 0 0 0 0 1 0 0 0 0 0
31 153 15.9 0 0 0 0 0 0 1 0 0 0 0
32 162 13.9 0 0 0 0 0 0 0 1 0 0 0
33 161 17.8 0 0 0 0 0 0 0 0 1 0 0
34 149 17.9 0 0 0 0 0 0 0 0 0 1 0
35 139 17.4 0 0 0 0 0 0 0 0 0 0 1
36 135 16.7 0 0 0 0 0 0 0 0 0 0 0
37 130 16.0 1 0 0 0 0 0 0 0 0 0 0
38 127 16.6 0 1 0 0 0 0 0 0 0 0 0
39 122 19.1 0 0 1 0 0 0 0 0 0 0 0
40 117 17.8 0 0 0 1 0 0 0 0 0 0 0
41 112 17.2 0 0 0 0 1 0 0 0 0 0 0
42 113 18.6 0 0 0 0 0 1 0 0 0 0 0
43 149 16.3 0 0 0 0 0 0 1 0 0 0 0
44 157 15.1 0 0 0 0 0 0 0 1 0 0 0
45 157 19.2 0 0 0 0 0 0 0 0 1 0 0
46 147 17.7 0 0 0 0 0 0 0 0 0 1 0
47 137 19.1 0 0 0 0 0 0 0 0 0 0 1
48 132 18.0 0 0 0 0 0 0 0 0 0 0 0
49 125 17.5 1 0 0 0 0 0 0 0 0 0 0
50 123 17.8 0 1 0 0 0 0 0 0 0 0 0
51 117 21.1 0 0 1 0 0 0 0 0 0 0 0
52 114 17.2 0 0 0 1 0 0 0 0 0 0 0
53 111 19.4 0 0 0 0 1 0 0 0 0 0 0
54 112 19.8 0 0 0 0 0 1 0 0 0 0 0
55 144 17.6 0 0 0 0 0 0 1 0 0 0 0
56 150 16.2 0 0 0 0 0 0 0 1 0 0 0
57 149 19.5 0 0 0 0 0 0 0 0 1 0 0
58 134 19.9 0 0 0 0 0 0 0 0 0 1 0
59 123 20.0 0 0 0 0 0 0 0 0 0 0 1
60 116 17.3 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) ExpBE M1 M2 M3 M4
122.7900 0.4225 -3.1127 -6.2310 -12.3942 -16.1267
M5 M6 M7 M8 M9 M10
-21.1605 -20.1323 17.2591 26.5801 24.1241 13.0818
M11
3.2931
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.098 -3.616 1.428 4.784 10.155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122.7900 10.1760 12.067 5.32e-16 ***
ExpBE 0.4225 0.5999 0.704 0.484784
M1 -3.1127 4.4978 -0.692 0.492310
M2 -6.2310 4.4857 -1.389 0.171355
M3 -12.3942 4.6191 -2.683 0.010034 *
M4 -16.1267 4.4829 -3.597 0.000770 ***
M5 -21.1605 4.4851 -4.718 2.17e-05 ***
M6 -20.1323 4.5426 -4.432 5.57e-05 ***
M7 17.2591 4.4801 3.852 0.000353 ***
M8 26.5801 4.6905 5.667 8.57e-07 ***
M9 24.1241 4.5810 5.266 3.40e-06 ***
M10 13.0818 4.5939 2.848 0.006513 **
M11 3.2931 4.5368 0.726 0.471525
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.082 on 47 degrees of freedom
Multiple R-squared: 0.8657, Adjusted R-squared: 0.8314
F-statistic: 25.25 on 12 and 47 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,] 0.6046307 0.790738673 0.395369336
[2,] 0.9803908 0.039218339 0.019609169
[3,] 0.9831877 0.033624605 0.016812302
[4,] 0.9872364 0.025527121 0.012763561
[5,] 0.9964182 0.007163509 0.003581754
[6,] 0.9943457 0.011308582 0.005654291
[7,] 0.9938463 0.012307383 0.006153692
[8,] 0.9947845 0.010431083 0.005215541
[9,] 0.9899069 0.020186157 0.010093079
[10,] 0.9941165 0.011766942 0.005883471
[11,] 0.9907851 0.018429808 0.009214904
[12,] 0.9853085 0.029382948 0.014691474
[13,] 0.9734686 0.053062817 0.026531409
[14,] 0.9788227 0.042354551 0.021177276
[15,] 0.9724790 0.055041969 0.027520985
[16,] 0.9546687 0.090662533 0.045331267
[17,] 0.9309564 0.138087162 0.069043581
[18,] 0.8943251 0.211349808 0.105674904
[19,] 0.8606162 0.278767520 0.139383760
[20,] 0.8207467 0.358506639 0.179253320
[21,] 0.8356601 0.328679865 0.164339933
[22,] 0.7588094 0.482381160 0.241190580
[23,] 0.6665110 0.666977944 0.333488972
[24,] 0.5689020 0.862195993 0.431097996
[25,] 0.4785613 0.957122668 0.521438666
[26,] 0.4134713 0.826942616 0.586528692
[27,] 0.3170275 0.634054914 0.682972543
[28,] 0.2030036 0.406007126 0.796996437
[29,] 0.1208698 0.241739674 0.879130163
> postscript(file="/var/www/html/rcomp/tmp/1qseg1261156166.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2g2u61261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39hq31261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43w591261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5el3t1261156166.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
-12.71840202 -12.55786936 -10.11284583 -10.12685111 -10.17754563 -11.03680404
7 8 9 10 11 12
-9.38599472 -6.48182757 -6.58888761 -4.01134198 -2.46215130 -0.57766043
13 14 15 16 17 18
1.07037080 0.23090346 0.84490874 0.83090346 0.03368155 1.79421421
19 20 21 22 23 24
4.57175984 4.68715417 4.19988520 5.07314889 5.58009413 7.91539433
25 26 27 28 29 30
10.15486167 9.97743083 8.04224544 6.40844909 5.86469980 5.78032373
31 32 33 34 35 36
6.23379635 6.75775456 6.56620365 5.56620365 5.56620365 5.15497647
37 38 39 40 41 42
3.56342555 3.42824016 3.53530020 2.81701297 3.10428194 2.48460567
43 44 45 46 47 48
2.06481460 1.25080932 1.97476754 3.65069452 2.84803122 1.60578579
49 50 51 52 53 54
-2.07025600 -1.07870509 -2.30960854 0.07048559 1.17488233 0.97766043
55 56 57 58 59 60
-3.48437608 -6.21389048 -6.15196878 -10.27870509 -11.53217771 -14.09849615
> postscript(file="/var/www/html/rcomp/tmp/6zclj1261156166.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 -12.71840202 NA
1 -12.55786936 -12.71840202
2 -10.11284583 -12.55786936
3 -10.12685111 -10.11284583
4 -10.17754563 -10.12685111
5 -11.03680404 -10.17754563
6 -9.38599472 -11.03680404
7 -6.48182757 -9.38599472
8 -6.58888761 -6.48182757
9 -4.01134198 -6.58888761
10 -2.46215130 -4.01134198
11 -0.57766043 -2.46215130
12 1.07037080 -0.57766043
13 0.23090346 1.07037080
14 0.84490874 0.23090346
15 0.83090346 0.84490874
16 0.03368155 0.83090346
17 1.79421421 0.03368155
18 4.57175984 1.79421421
19 4.68715417 4.57175984
20 4.19988520 4.68715417
21 5.07314889 4.19988520
22 5.58009413 5.07314889
23 7.91539433 5.58009413
24 10.15486167 7.91539433
25 9.97743083 10.15486167
26 8.04224544 9.97743083
27 6.40844909 8.04224544
28 5.86469980 6.40844909
29 5.78032373 5.86469980
30 6.23379635 5.78032373
31 6.75775456 6.23379635
32 6.56620365 6.75775456
33 5.56620365 6.56620365
34 5.56620365 5.56620365
35 5.15497647 5.56620365
36 3.56342555 5.15497647
37 3.42824016 3.56342555
38 3.53530020 3.42824016
39 2.81701297 3.53530020
40 3.10428194 2.81701297
41 2.48460567 3.10428194
42 2.06481460 2.48460567
43 1.25080932 2.06481460
44 1.97476754 1.25080932
45 3.65069452 1.97476754
46 2.84803122 3.65069452
47 1.60578579 2.84803122
48 -2.07025600 1.60578579
49 -1.07870509 -2.07025600
50 -2.30960854 -1.07870509
51 0.07048559 -2.30960854
52 1.17488233 0.07048559
53 0.97766043 1.17488233
54 -3.48437608 0.97766043
55 -6.21389048 -3.48437608
56 -6.15196878 -6.21389048
57 -10.27870509 -6.15196878
58 -11.53217771 -10.27870509
59 -14.09849615 -11.53217771
60 NA -14.09849615
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -12.55786936 -12.71840202
[2,] -10.11284583 -12.55786936
[3,] -10.12685111 -10.11284583
[4,] -10.17754563 -10.12685111
[5,] -11.03680404 -10.17754563
[6,] -9.38599472 -11.03680404
[7,] -6.48182757 -9.38599472
[8,] -6.58888761 -6.48182757
[9,] -4.01134198 -6.58888761
[10,] -2.46215130 -4.01134198
[11,] -0.57766043 -2.46215130
[12,] 1.07037080 -0.57766043
[13,] 0.23090346 1.07037080
[14,] 0.84490874 0.23090346
[15,] 0.83090346 0.84490874
[16,] 0.03368155 0.83090346
[17,] 1.79421421 0.03368155
[18,] 4.57175984 1.79421421
[19,] 4.68715417 4.57175984
[20,] 4.19988520 4.68715417
[21,] 5.07314889 4.19988520
[22,] 5.58009413 5.07314889
[23,] 7.91539433 5.58009413
[24,] 10.15486167 7.91539433
[25,] 9.97743083 10.15486167
[26,] 8.04224544 9.97743083
[27,] 6.40844909 8.04224544
[28,] 5.86469980 6.40844909
[29,] 5.78032373 5.86469980
[30,] 6.23379635 5.78032373
[31,] 6.75775456 6.23379635
[32,] 6.56620365 6.75775456
[33,] 5.56620365 6.56620365
[34,] 5.56620365 5.56620365
[35,] 5.15497647 5.56620365
[36,] 3.56342555 5.15497647
[37,] 3.42824016 3.56342555
[38,] 3.53530020 3.42824016
[39,] 2.81701297 3.53530020
[40,] 3.10428194 2.81701297
[41,] 2.48460567 3.10428194
[42,] 2.06481460 2.48460567
[43,] 1.25080932 2.06481460
[44,] 1.97476754 1.25080932
[45,] 3.65069452 1.97476754
[46,] 2.84803122 3.65069452
[47,] 1.60578579 2.84803122
[48,] -2.07025600 1.60578579
[49,] -1.07870509 -2.07025600
[50,] -2.30960854 -1.07870509
[51,] 0.07048559 -2.30960854
[52,] 1.17488233 0.07048559
[53,] 0.97766043 1.17488233
[54,] -3.48437608 0.97766043
[55,] -6.21389048 -3.48437608
[56,] -6.15196878 -6.21389048
[57,] -10.27870509 -6.15196878
[58,] -11.53217771 -10.27870509
[59,] -14.09849615 -11.53217771
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -12.55786936 -12.71840202
2 -10.11284583 -12.55786936
3 -10.12685111 -10.11284583
4 -10.17754563 -10.12685111
5 -11.03680404 -10.17754563
6 -9.38599472 -11.03680404
7 -6.48182757 -9.38599472
8 -6.58888761 -6.48182757
9 -4.01134198 -6.58888761
10 -2.46215130 -4.01134198
11 -0.57766043 -2.46215130
12 1.07037080 -0.57766043
13 0.23090346 1.07037080
14 0.84490874 0.23090346
15 0.83090346 0.84490874
16 0.03368155 0.83090346
17 1.79421421 0.03368155
18 4.57175984 1.79421421
19 4.68715417 4.57175984
20 4.19988520 4.68715417
21 5.07314889 4.19988520
22 5.58009413 5.07314889
23 7.91539433 5.58009413
24 10.15486167 7.91539433
25 9.97743083 10.15486167
26 8.04224544 9.97743083
27 6.40844909 8.04224544
28 5.86469980 6.40844909
29 5.78032373 5.86469980
30 6.23379635 5.78032373
31 6.75775456 6.23379635
32 6.56620365 6.75775456
33 5.56620365 6.56620365
34 5.56620365 5.56620365
35 5.15497647 5.56620365
36 3.56342555 5.15497647
37 3.42824016 3.56342555
38 3.53530020 3.42824016
39 2.81701297 3.53530020
40 3.10428194 2.81701297
41 2.48460567 3.10428194
42 2.06481460 2.48460567
43 1.25080932 2.06481460
44 1.97476754 1.25080932
45 3.65069452 1.97476754
46 2.84803122 3.65069452
47 1.60578579 2.84803122
48 -2.07025600 1.60578579
49 -1.07870509 -2.07025600
50 -2.30960854 -1.07870509
51 0.07048559 -2.30960854
52 1.17488233 0.07048559
53 0.97766043 1.17488233
54 -3.48437608 0.97766043
55 -6.21389048 -3.48437608
56 -6.15196878 -6.21389048
57 -10.27870509 -6.15196878
58 -11.53217771 -10.27870509
59 -14.09849615 -11.53217771
> 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/7sw871261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tm5e1261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ribq1261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gnkn1261156166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hyfs1261156166.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/12zndk1261156166.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/13cgy91261156166.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/14v2t81261156166.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/15mtrc1261156167.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/16s2q01261156167.tab")
+ }
>
> try(system("convert tmp/1qseg1261156166.ps tmp/1qseg1261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g2u61261156166.ps tmp/2g2u61261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/39hq31261156166.ps tmp/39hq31261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/43w591261156166.ps tmp/43w591261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/5el3t1261156166.ps tmp/5el3t1261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zclj1261156166.ps tmp/6zclj1261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sw871261156166.ps tmp/7sw871261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tm5e1261156166.ps tmp/8tm5e1261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ribq1261156166.ps tmp/9ribq1261156166.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gnkn1261156166.ps tmp/10gnkn1261156166.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.433 1.572 3.134