R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(23,25.7,19,24.7,18,24.2,19,23.6,19,24.4,22,22.5,23,19.4,20,18.1,14,18.1,14,20.7,14,19.1,15,18.3,11,16.9,17,17.9,16,20.2,20,21.2,24,23.8,23,24,20,26.6,21,25.3,19,27.6,23,24.7,23,26.6,23,24.4,23,24.6,27,26,26,24.8,17,24,24,22.7,26,23,24,24.1,27,24,27,22.7,26,22.6,24,23.1,23,24.4,23,23,24,22,17,21.3,21,21.5,19,21.3,22,23.2,22,21.8,18,23.3,16,21,14,22.4,12,20.4,14,19.9,16,21.3,8,18.9,3,15.6,0,12.5,5,7.8,1,5.5,1,4,3,3.3,6,3.7,7,3.1,8,5,14,6.3),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = 'Do not include Seasonal 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
Y X
1 23 25.7
2 19 24.7
3 18 24.2
4 19 23.6
5 19 24.4
6 22 22.5
7 23 19.4
8 20 18.1
9 14 18.1
10 14 20.7
11 14 19.1
12 15 18.3
13 11 16.9
14 17 17.9
15 16 20.2
16 20 21.2
17 24 23.8
18 23 24.0
19 20 26.6
20 21 25.3
21 19 27.6
22 23 24.7
23 23 26.6
24 23 24.4
25 23 24.6
26 27 26.0
27 26 24.8
28 17 24.0
29 24 22.7
30 26 23.0
31 24 24.1
32 27 24.0
33 27 22.7
34 26 22.6
35 24 23.1
36 23 24.4
37 23 23.0
38 24 22.0
39 17 21.3
40 21 21.5
41 19 21.3
42 22 23.2
43 22 21.8
44 18 23.3
45 16 21.0
46 14 22.4
47 12 20.4
48 14 19.9
49 16 21.3
50 8 18.9
51 3 15.6
52 0 12.5
53 5 7.8
54 1 5.5
55 1 4.0
56 3 3.3
57 6 3.7
58 7 3.1
59 8 5.0
60 14 6.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-0.3809 0.8983
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.8477 -2.7587 0.3557 3.0155 8.7217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3809 1.7463 -0.218 0.828
X 0.8983 0.0835 10.758 1.93e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.222 on 58 degrees of freedom
Multiple R-squared: 0.6661, Adjusted R-squared: 0.6604
F-statistic: 115.7 on 1 and 58 DF, p-value: 1.933e-15
> 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,] 0.029640796 0.059281592 0.9703592
[2,] 0.096657629 0.193315259 0.9033424
[3,] 0.057138901 0.114277803 0.9428611
[4,] 0.035237127 0.070474254 0.9647629
[5,] 0.129753664 0.259507329 0.8702463
[6,] 0.182690574 0.365381148 0.8173094
[7,] 0.172431504 0.344863008 0.8275685
[8,] 0.120715763 0.241431526 0.8792842
[9,] 0.129329052 0.258658104 0.8706709
[10,] 0.088845069 0.177690137 0.9111549
[11,] 0.058233362 0.116466725 0.9417666
[12,] 0.040809527 0.081619053 0.9591905
[13,] 0.041899251 0.083798502 0.9581007
[14,] 0.030672315 0.061344631 0.9693277
[15,] 0.024221041 0.048442083 0.9757790
[16,] 0.014342989 0.028685979 0.9856570
[17,] 0.016076163 0.032152326 0.9839238
[18,] 0.011506736 0.023013471 0.9884933
[19,] 0.006846884 0.013693768 0.9931531
[20,] 0.004718121 0.009436242 0.9952819
[21,] 0.003048582 0.006097163 0.9969514
[22,] 0.004184921 0.008369842 0.9958151
[23,] 0.004945184 0.009890368 0.9950548
[24,] 0.005135534 0.010271069 0.9948645
[25,] 0.005349728 0.010699456 0.9946503
[26,] 0.009437107 0.018874214 0.9905629
[27,] 0.007083617 0.014167233 0.9929164
[28,] 0.012046969 0.024093937 0.9879530
[29,] 0.028581595 0.057163190 0.9714184
[30,] 0.047831264 0.095662527 0.9521687
[31,] 0.047128298 0.094256595 0.9528717
[32,] 0.036056694 0.072113389 0.9639433
[33,] 0.033011658 0.066023315 0.9669883
[34,] 0.048917400 0.097834799 0.9510826
[35,] 0.035877388 0.071754775 0.9641226
[36,] 0.034275866 0.068551732 0.9657241
[37,] 0.027210615 0.054421230 0.9727894
[38,] 0.031534187 0.063068374 0.9684658
[39,] 0.060907701 0.121815403 0.9390923
[40,] 0.062010241 0.124020482 0.9379898
[41,] 0.061339594 0.122679188 0.9386604
[42,] 0.064358851 0.128717702 0.9356411
[43,] 0.061362676 0.122725353 0.9386373
[44,] 0.060718033 0.121436065 0.9392820
[45,] 0.168640168 0.337280337 0.8313598
[46,] 0.235623753 0.471247507 0.7643762
[47,] 0.221127318 0.442254636 0.7788727
[48,] 0.277069977 0.554139954 0.7229300
[49,] 0.273665465 0.547330931 0.7263345
[50,] 0.605421852 0.789156297 0.3945781
[51,] 0.825098682 0.349802637 0.1749013
> postscript(file="/var/www/html/rcomp/tmp/1613t1258563178.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26sq71258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r0dq1258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4y4dq1258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51uzi1258563178.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
0.2947835 -2.8069227 -3.3577758 -1.8187996 -2.5374346 2.1693236
7 8 9 10 11 12
5.9540342 4.1218161 -1.8781839 -4.2137476 -2.7764776 -1.0578426
13 14 15 16 17 18
-3.8002313 1.3014749 -1.7646008 1.3371055 3.0015417 1.8218829
19 20 21 22 23 24
-3.5136808 -1.3458990 -5.4119746 1.1930773 -0.5136808 1.4625654
25 26 27 28 29 30
1.2829067 4.0252954 4.1032479 -4.1781171 3.9896648 5.7201767
31 32 33 34 35 36
2.7320536 5.8218829 6.9896648 6.0794942 3.6303473 1.4625654
37 38 39 40 41 42
2.7201767 4.6184705 -1.7527239 2.0676173 0.2472761 1.5405179
43 44 45 46 47 48
2.7981292 -2.5493114 -2.4832358 -5.7408470 -5.9442595 -3.4951126
49 50 51 52 53 54
-2.7527239 -8.5968189 -10.6324495 -10.8477388 -1.6257581 -3.5596825
55 56 57 58 59 60
-2.2122418 0.4165638 3.0572463 4.5962226 3.8894644 8.7216825
> postscript(file="/var/www/html/rcomp/tmp/6xuz71258563178.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 0.2947835 NA
1 -2.8069227 0.2947835
2 -3.3577758 -2.8069227
3 -1.8187996 -3.3577758
4 -2.5374346 -1.8187996
5 2.1693236 -2.5374346
6 5.9540342 2.1693236
7 4.1218161 5.9540342
8 -1.8781839 4.1218161
9 -4.2137476 -1.8781839
10 -2.7764776 -4.2137476
11 -1.0578426 -2.7764776
12 -3.8002313 -1.0578426
13 1.3014749 -3.8002313
14 -1.7646008 1.3014749
15 1.3371055 -1.7646008
16 3.0015417 1.3371055
17 1.8218829 3.0015417
18 -3.5136808 1.8218829
19 -1.3458990 -3.5136808
20 -5.4119746 -1.3458990
21 1.1930773 -5.4119746
22 -0.5136808 1.1930773
23 1.4625654 -0.5136808
24 1.2829067 1.4625654
25 4.0252954 1.2829067
26 4.1032479 4.0252954
27 -4.1781171 4.1032479
28 3.9896648 -4.1781171
29 5.7201767 3.9896648
30 2.7320536 5.7201767
31 5.8218829 2.7320536
32 6.9896648 5.8218829
33 6.0794942 6.9896648
34 3.6303473 6.0794942
35 1.4625654 3.6303473
36 2.7201767 1.4625654
37 4.6184705 2.7201767
38 -1.7527239 4.6184705
39 2.0676173 -1.7527239
40 0.2472761 2.0676173
41 1.5405179 0.2472761
42 2.7981292 1.5405179
43 -2.5493114 2.7981292
44 -2.4832358 -2.5493114
45 -5.7408470 -2.4832358
46 -5.9442595 -5.7408470
47 -3.4951126 -5.9442595
48 -2.7527239 -3.4951126
49 -8.5968189 -2.7527239
50 -10.6324495 -8.5968189
51 -10.8477388 -10.6324495
52 -1.6257581 -10.8477388
53 -3.5596825 -1.6257581
54 -2.2122418 -3.5596825
55 0.4165638 -2.2122418
56 3.0572463 0.4165638
57 4.5962226 3.0572463
58 3.8894644 4.5962226
59 8.7216825 3.8894644
60 NA 8.7216825
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.8069227 0.2947835
[2,] -3.3577758 -2.8069227
[3,] -1.8187996 -3.3577758
[4,] -2.5374346 -1.8187996
[5,] 2.1693236 -2.5374346
[6,] 5.9540342 2.1693236
[7,] 4.1218161 5.9540342
[8,] -1.8781839 4.1218161
[9,] -4.2137476 -1.8781839
[10,] -2.7764776 -4.2137476
[11,] -1.0578426 -2.7764776
[12,] -3.8002313 -1.0578426
[13,] 1.3014749 -3.8002313
[14,] -1.7646008 1.3014749
[15,] 1.3371055 -1.7646008
[16,] 3.0015417 1.3371055
[17,] 1.8218829 3.0015417
[18,] -3.5136808 1.8218829
[19,] -1.3458990 -3.5136808
[20,] -5.4119746 -1.3458990
[21,] 1.1930773 -5.4119746
[22,] -0.5136808 1.1930773
[23,] 1.4625654 -0.5136808
[24,] 1.2829067 1.4625654
[25,] 4.0252954 1.2829067
[26,] 4.1032479 4.0252954
[27,] -4.1781171 4.1032479
[28,] 3.9896648 -4.1781171
[29,] 5.7201767 3.9896648
[30,] 2.7320536 5.7201767
[31,] 5.8218829 2.7320536
[32,] 6.9896648 5.8218829
[33,] 6.0794942 6.9896648
[34,] 3.6303473 6.0794942
[35,] 1.4625654 3.6303473
[36,] 2.7201767 1.4625654
[37,] 4.6184705 2.7201767
[38,] -1.7527239 4.6184705
[39,] 2.0676173 -1.7527239
[40,] 0.2472761 2.0676173
[41,] 1.5405179 0.2472761
[42,] 2.7981292 1.5405179
[43,] -2.5493114 2.7981292
[44,] -2.4832358 -2.5493114
[45,] -5.7408470 -2.4832358
[46,] -5.9442595 -5.7408470
[47,] -3.4951126 -5.9442595
[48,] -2.7527239 -3.4951126
[49,] -8.5968189 -2.7527239
[50,] -10.6324495 -8.5968189
[51,] -10.8477388 -10.6324495
[52,] -1.6257581 -10.8477388
[53,] -3.5596825 -1.6257581
[54,] -2.2122418 -3.5596825
[55,] 0.4165638 -2.2122418
[56,] 3.0572463 0.4165638
[57,] 4.5962226 3.0572463
[58,] 3.8894644 4.5962226
[59,] 8.7216825 3.8894644
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.8069227 0.2947835
2 -3.3577758 -2.8069227
3 -1.8187996 -3.3577758
4 -2.5374346 -1.8187996
5 2.1693236 -2.5374346
6 5.9540342 2.1693236
7 4.1218161 5.9540342
8 -1.8781839 4.1218161
9 -4.2137476 -1.8781839
10 -2.7764776 -4.2137476
11 -1.0578426 -2.7764776
12 -3.8002313 -1.0578426
13 1.3014749 -3.8002313
14 -1.7646008 1.3014749
15 1.3371055 -1.7646008
16 3.0015417 1.3371055
17 1.8218829 3.0015417
18 -3.5136808 1.8218829
19 -1.3458990 -3.5136808
20 -5.4119746 -1.3458990
21 1.1930773 -5.4119746
22 -0.5136808 1.1930773
23 1.4625654 -0.5136808
24 1.2829067 1.4625654
25 4.0252954 1.2829067
26 4.1032479 4.0252954
27 -4.1781171 4.1032479
28 3.9896648 -4.1781171
29 5.7201767 3.9896648
30 2.7320536 5.7201767
31 5.8218829 2.7320536
32 6.9896648 5.8218829
33 6.0794942 6.9896648
34 3.6303473 6.0794942
35 1.4625654 3.6303473
36 2.7201767 1.4625654
37 4.6184705 2.7201767
38 -1.7527239 4.6184705
39 2.0676173 -1.7527239
40 0.2472761 2.0676173
41 1.5405179 0.2472761
42 2.7981292 1.5405179
43 -2.5493114 2.7981292
44 -2.4832358 -2.5493114
45 -5.7408470 -2.4832358
46 -5.9442595 -5.7408470
47 -3.4951126 -5.9442595
48 -2.7527239 -3.4951126
49 -8.5968189 -2.7527239
50 -10.6324495 -8.5968189
51 -10.8477388 -10.6324495
52 -1.6257581 -10.8477388
53 -3.5596825 -1.6257581
54 -2.2122418 -3.5596825
55 0.4165638 -2.2122418
56 3.0572463 0.4165638
57 4.5962226 3.0572463
58 3.8894644 4.5962226
59 8.7216825 3.8894644
> 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/77f4u1258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8eokg1258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/965h71258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108thz1258563178.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11crz61258563178.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/12qx4b1258563178.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/13si9c1258563178.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/14cbox1258563178.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/15d0ow1258563178.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/162tz91258563178.tab")
+ }
>
> system("convert tmp/1613t1258563178.ps tmp/1613t1258563178.png")
> system("convert tmp/26sq71258563178.ps tmp/26sq71258563178.png")
> system("convert tmp/3r0dq1258563178.ps tmp/3r0dq1258563178.png")
> system("convert tmp/4y4dq1258563178.ps tmp/4y4dq1258563178.png")
> system("convert tmp/51uzi1258563178.ps tmp/51uzi1258563178.png")
> system("convert tmp/6xuz71258563178.ps tmp/6xuz71258563178.png")
> system("convert tmp/77f4u1258563178.ps tmp/77f4u1258563178.png")
> system("convert tmp/8eokg1258563178.ps tmp/8eokg1258563178.png")
> system("convert tmp/965h71258563178.ps tmp/965h71258563178.png")
> system("convert tmp/108thz1258563178.ps tmp/108thz1258563178.png")
>
>
> proc.time()
user system elapsed
2.458 1.558 2.875