R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(16198.9,16896.2,16554.2,16698,19554.2,19691.6,15903.8,15930.7,18003.8,17444.6,18329.6,17699.4,16260.7,15189.8,14851.9,15672.7,18174.1,17180.8,18406.6,17664.9,18466.5,17862.9,16016.5,16162.3,17428.5,17463.6,17167.2,16772.1,19630,19106.9,17183.6,16721.3,18344.7,18161.3,19301.4,18509.9,18147.5,17802.7,16192.9,16409.9,18374.4,17967.7,20515.2,20286.6,18957.2,19537.3,16471.5,18021.9,18746.8,20194.3,19009.5,19049.6,19211.2,20244.7,20547.7,21473.3,19325.8,19673.6,20605.5,21053.2,20056.9,20159.5,16141.4,18203.6,20359.8,21289.5,19711.6,20432.3,15638.6,17180.4,14384.5,15816.8,13855.6,15071.8,14308.3,14521.1,15290.6,15668.8,14423.8,14346.9,13779.7,13881,15686.3,15465.9,14733.8,14238.2,12522.5,13557.7,16189.4,16127.6,16059.1,16793.9,16007.1,16014,15806.8,16867.9,15160,16014.6,15692.1,15878.6,18908.9,18664.9,16969.9,17962.5,16997.5,17332.7,19858.9,19542.1,17681.2,17203.6),dim=c(2,55),dimnames=list(c('uitvoer','invoer'),1:55))
> y <- array(NA,dim=c(2,55),dimnames=list(c('uitvoer','invoer'),1:55))
> 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
> 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
uitvoer invoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 16198.9 16896.2 1 0 0 0 0 0 0 0 0 0 0
2 16554.2 16698.0 0 1 0 0 0 0 0 0 0 0 0
3 19554.2 19691.6 0 0 1 0 0 0 0 0 0 0 0
4 15903.8 15930.7 0 0 0 1 0 0 0 0 0 0 0
5 18003.8 17444.6 0 0 0 0 1 0 0 0 0 0 0
6 18329.6 17699.4 0 0 0 0 0 1 0 0 0 0 0
7 16260.7 15189.8 0 0 0 0 0 0 1 0 0 0 0
8 14851.9 15672.7 0 0 0 0 0 0 0 1 0 0 0
9 18174.1 17180.8 0 0 0 0 0 0 0 0 1 0 0
10 18406.6 17664.9 0 0 0 0 0 0 0 0 0 1 0
11 18466.5 17862.9 0 0 0 0 0 0 0 0 0 0 1
12 16016.5 16162.3 0 0 0 0 0 0 0 0 0 0 0
13 17428.5 17463.6 1 0 0 0 0 0 0 0 0 0 0
14 17167.2 16772.1 0 1 0 0 0 0 0 0 0 0 0
15 19630.0 19106.9 0 0 1 0 0 0 0 0 0 0 0
16 17183.6 16721.3 0 0 0 1 0 0 0 0 0 0 0
17 18344.7 18161.3 0 0 0 0 1 0 0 0 0 0 0
18 19301.4 18509.9 0 0 0 0 0 1 0 0 0 0 0
19 18147.5 17802.7 0 0 0 0 0 0 1 0 0 0 0
20 16192.9 16409.9 0 0 0 0 0 0 0 1 0 0 0
21 18374.4 17967.7 0 0 0 0 0 0 0 0 1 0 0
22 20515.2 20286.6 0 0 0 0 0 0 0 0 0 1 0
23 18957.2 19537.3 0 0 0 0 0 0 0 0 0 0 1
24 16471.5 18021.9 0 0 0 0 0 0 0 0 0 0 0
25 18746.8 20194.3 1 0 0 0 0 0 0 0 0 0 0
26 19009.5 19049.6 0 1 0 0 0 0 0 0 0 0 0
27 19211.2 20244.7 0 0 1 0 0 0 0 0 0 0 0
28 20547.7 21473.3 0 0 0 1 0 0 0 0 0 0 0
29 19325.8 19673.6 0 0 0 0 1 0 0 0 0 0 0
30 20605.5 21053.2 0 0 0 0 0 1 0 0 0 0 0
31 20056.9 20159.5 0 0 0 0 0 0 1 0 0 0 0
32 16141.4 18203.6 0 0 0 0 0 0 0 1 0 0 0
33 20359.8 21289.5 0 0 0 0 0 0 0 0 1 0 0
34 19711.6 20432.3 0 0 0 0 0 0 0 0 0 1 0
35 15638.6 17180.4 0 0 0 0 0 0 0 0 0 0 1
36 14384.5 15816.8 0 0 0 0 0 0 0 0 0 0 0
37 13855.6 15071.8 1 0 0 0 0 0 0 0 0 0 0
38 14308.3 14521.1 0 1 0 0 0 0 0 0 0 0 0
39 15290.6 15668.8 0 0 1 0 0 0 0 0 0 0 0
40 14423.8 14346.9 0 0 0 1 0 0 0 0 0 0 0
41 13779.7 13881.0 0 0 0 0 1 0 0 0 0 0 0
42 15686.3 15465.9 0 0 0 0 0 1 0 0 0 0 0
43 14733.8 14238.2 0 0 0 0 0 0 1 0 0 0 0
44 12522.5 13557.7 0 0 0 0 0 0 0 1 0 0 0
45 16189.4 16127.6 0 0 0 0 0 0 0 0 1 0 0
46 16059.1 16793.9 0 0 0 0 0 0 0 0 0 1 0
47 16007.1 16014.0 0 0 0 0 0 0 0 0 0 0 1
48 15806.8 16867.9 0 0 0 0 0 0 0 0 0 0 0
49 15160.0 16014.6 1 0 0 0 0 0 0 0 0 0 0
50 15692.1 15878.6 0 1 0 0 0 0 0 0 0 0 0
51 18908.9 18664.9 0 0 1 0 0 0 0 0 0 0 0
52 16969.9 17962.5 0 0 0 1 0 0 0 0 0 0 0
53 16997.5 17332.7 0 0 0 0 1 0 0 0 0 0 0
54 19858.9 19542.1 0 0 0 0 0 1 0 0 0 0 0
55 17681.2 17203.6 0 0 0 0 0 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) invoer M1 M2 M3 M4
748.0722 0.8926 241.3890 995.4584 1101.3108 827.4088
M5 M6 M7 M8 M9 M10
1101.5054 1536.1848 1526.3054 -67.6232 1333.3850 1149.1965
M11
766.1374
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1210.791 -379.801 2.483 341.833 1007.911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 748.07215 789.35027 0.948 0.348700
invoer 0.89260 0.04399 20.291 < 2e-16 ***
M1 241.38901 385.22372 0.627 0.534299
M2 995.45841 384.84417 2.587 0.013247 *
M3 1101.31079 394.32318 2.793 0.007833 **
M4 827.40883 385.61475 2.146 0.037724 *
M5 1101.50544 385.64854 2.856 0.006637 **
M6 1536.18476 392.31175 3.916 0.000325 ***
M7 1526.30537 384.90158 3.965 0.000280 ***
M8 -67.62316 406.97625 -0.166 0.868828
M9 1333.38499 410.42413 3.249 0.002284 **
M10 1149.19652 415.77962 2.764 0.008443 **
M11 766.13740 407.67852 1.879 0.067158 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 573.6 on 42 degrees of freedom
Multiple R-squared: 0.9372, Adjusted R-squared: 0.9193
F-statistic: 52.25 on 12 and 42 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,] 0.5268496 0.9463008 0.4731504
[2,] 0.4500496 0.9000992 0.5499504
[3,] 0.3312893 0.6625787 0.6687107
[4,] 0.2780630 0.5561260 0.7219370
[5,] 0.4172149 0.8344298 0.5827851
[6,] 0.4087961 0.8175921 0.5912039
[7,] 0.4394497 0.8788995 0.5605503
[8,] 0.5244526 0.9510948 0.4755474
[9,] 0.5662495 0.8675010 0.4337505
[10,] 0.4590491 0.9180981 0.5409509
[11,] 0.4783914 0.9567828 0.5216086
[12,] 0.6253327 0.7493346 0.3746673
[13,] 0.5435883 0.9128234 0.4564117
[14,] 0.4644300 0.9288600 0.5355700
[15,] 0.3921140 0.7842279 0.6078860
[16,] 0.3019192 0.6038384 0.6980808
[17,] 0.3590038 0.7180077 0.6409962
[18,] 0.3455416 0.6910832 0.6544584
[19,] 0.3018622 0.6037244 0.6981378
[20,] 0.8821297 0.2357406 0.1178703
[21,] 0.8556455 0.2887091 0.1443545
[22,] 0.8388314 0.3223372 0.1611686
[23,] 0.7449621 0.5100758 0.2550379
[24,] 0.7889035 0.4221930 0.2110965
> postscript(file="/var/www/rcomp/tmp/1op7q1290246763.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2zyoa1290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3zyoa1290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4zyoa1290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5r75d1290246763.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 = 55
Frequency = 1
1 2 3 4 5 6
127.933355 -93.923224 128.144593 108.616401 583.216472 246.903315
7 8 9 10 11 12
427.945333 182.038538 757.104136 741.686165 1007.910991 841.999655
13 14 15 16 17 18
851.073547 452.935303 725.846337 682.728837 284.391861 495.253061
19 20 21 22 23 24
-17.522613 865.015678 255.019182 510.163361 4.045776 -362.874613
25 26 27 28 29 30
-268.042382 262.344550 -708.551072 -194.794371 -84.383303 -470.790101
31 32 33 34 35 36
-211.796346 -787.536416 -724.611115 -423.488092 -1210.791232 -481.607917
37 38 39 40 41 42
-586.911809 -396.727778 -544.714279 42.312285 -460.023161 -402.780221
43 44 45 46 47 48
-249.558909 -259.517800 -287.512202 -828.361434 198.834465 2.482875
49 50 51 52 53 54
-124.052710 -224.628852 399.274422 -638.863151 -323.201870 131.413946
55
50.932535
> postscript(file="/var/www/rcomp/tmp/6r75d1290246763.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 = 55
Frequency = 1
lag(myerror, k = 1) myerror
0 127.933355 NA
1 -93.923224 127.933355
2 128.144593 -93.923224
3 108.616401 128.144593
4 583.216472 108.616401
5 246.903315 583.216472
6 427.945333 246.903315
7 182.038538 427.945333
8 757.104136 182.038538
9 741.686165 757.104136
10 1007.910991 741.686165
11 841.999655 1007.910991
12 851.073547 841.999655
13 452.935303 851.073547
14 725.846337 452.935303
15 682.728837 725.846337
16 284.391861 682.728837
17 495.253061 284.391861
18 -17.522613 495.253061
19 865.015678 -17.522613
20 255.019182 865.015678
21 510.163361 255.019182
22 4.045776 510.163361
23 -362.874613 4.045776
24 -268.042382 -362.874613
25 262.344550 -268.042382
26 -708.551072 262.344550
27 -194.794371 -708.551072
28 -84.383303 -194.794371
29 -470.790101 -84.383303
30 -211.796346 -470.790101
31 -787.536416 -211.796346
32 -724.611115 -787.536416
33 -423.488092 -724.611115
34 -1210.791232 -423.488092
35 -481.607917 -1210.791232
36 -586.911809 -481.607917
37 -396.727778 -586.911809
38 -544.714279 -396.727778
39 42.312285 -544.714279
40 -460.023161 42.312285
41 -402.780221 -460.023161
42 -249.558909 -402.780221
43 -259.517800 -249.558909
44 -287.512202 -259.517800
45 -828.361434 -287.512202
46 198.834465 -828.361434
47 2.482875 198.834465
48 -124.052710 2.482875
49 -224.628852 -124.052710
50 399.274422 -224.628852
51 -638.863151 399.274422
52 -323.201870 -638.863151
53 131.413946 -323.201870
54 50.932535 131.413946
55 NA 50.932535
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -93.923224 127.933355
[2,] 128.144593 -93.923224
[3,] 108.616401 128.144593
[4,] 583.216472 108.616401
[5,] 246.903315 583.216472
[6,] 427.945333 246.903315
[7,] 182.038538 427.945333
[8,] 757.104136 182.038538
[9,] 741.686165 757.104136
[10,] 1007.910991 741.686165
[11,] 841.999655 1007.910991
[12,] 851.073547 841.999655
[13,] 452.935303 851.073547
[14,] 725.846337 452.935303
[15,] 682.728837 725.846337
[16,] 284.391861 682.728837
[17,] 495.253061 284.391861
[18,] -17.522613 495.253061
[19,] 865.015678 -17.522613
[20,] 255.019182 865.015678
[21,] 510.163361 255.019182
[22,] 4.045776 510.163361
[23,] -362.874613 4.045776
[24,] -268.042382 -362.874613
[25,] 262.344550 -268.042382
[26,] -708.551072 262.344550
[27,] -194.794371 -708.551072
[28,] -84.383303 -194.794371
[29,] -470.790101 -84.383303
[30,] -211.796346 -470.790101
[31,] -787.536416 -211.796346
[32,] -724.611115 -787.536416
[33,] -423.488092 -724.611115
[34,] -1210.791232 -423.488092
[35,] -481.607917 -1210.791232
[36,] -586.911809 -481.607917
[37,] -396.727778 -586.911809
[38,] -544.714279 -396.727778
[39,] 42.312285 -544.714279
[40,] -460.023161 42.312285
[41,] -402.780221 -460.023161
[42,] -249.558909 -402.780221
[43,] -259.517800 -249.558909
[44,] -287.512202 -259.517800
[45,] -828.361434 -287.512202
[46,] 198.834465 -828.361434
[47,] 2.482875 198.834465
[48,] -124.052710 2.482875
[49,] -224.628852 -124.052710
[50,] 399.274422 -224.628852
[51,] -638.863151 399.274422
[52,] -323.201870 -638.863151
[53,] 131.413946 -323.201870
[54,] 50.932535 131.413946
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -93.923224 127.933355
2 128.144593 -93.923224
3 108.616401 128.144593
4 583.216472 108.616401
5 246.903315 583.216472
6 427.945333 246.903315
7 182.038538 427.945333
8 757.104136 182.038538
9 741.686165 757.104136
10 1007.910991 741.686165
11 841.999655 1007.910991
12 851.073547 841.999655
13 452.935303 851.073547
14 725.846337 452.935303
15 682.728837 725.846337
16 284.391861 682.728837
17 495.253061 284.391861
18 -17.522613 495.253061
19 865.015678 -17.522613
20 255.019182 865.015678
21 510.163361 255.019182
22 4.045776 510.163361
23 -362.874613 4.045776
24 -268.042382 -362.874613
25 262.344550 -268.042382
26 -708.551072 262.344550
27 -194.794371 -708.551072
28 -84.383303 -194.794371
29 -470.790101 -84.383303
30 -211.796346 -470.790101
31 -787.536416 -211.796346
32 -724.611115 -787.536416
33 -423.488092 -724.611115
34 -1210.791232 -423.488092
35 -481.607917 -1210.791232
36 -586.911809 -481.607917
37 -396.727778 -586.911809
38 -544.714279 -396.727778
39 42.312285 -544.714279
40 -460.023161 42.312285
41 -402.780221 -460.023161
42 -249.558909 -402.780221
43 -259.517800 -249.558909
44 -287.512202 -259.517800
45 -828.361434 -287.512202
46 198.834465 -828.361434
47 2.482875 198.834465
48 -124.052710 2.482875
49 -224.628852 -124.052710
50 399.274422 -224.628852
51 -638.863151 399.274422
52 -323.201870 -638.863151
53 131.413946 -323.201870
54 50.932535 131.413946
> 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/rcomp/tmp/7kg4y1290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8kg4y1290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9v8411290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10v8411290246763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11gq2p1290246763.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/rcomp/tmp/122qid1290246763.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/rcomp/tmp/13y0g41290246763.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/rcomp/tmp/1411fs1290246763.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/rcomp/tmp/155jvf1290246763.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/rcomp/tmp/16qkc31290246763.tab")
+ }
>
> try(system("convert tmp/1op7q1290246763.ps tmp/1op7q1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zyoa1290246763.ps tmp/2zyoa1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zyoa1290246763.ps tmp/3zyoa1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zyoa1290246763.ps tmp/4zyoa1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r75d1290246763.ps tmp/5r75d1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r75d1290246763.ps tmp/6r75d1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kg4y1290246763.ps tmp/7kg4y1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kg4y1290246763.ps tmp/8kg4y1290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v8411290246763.ps tmp/9v8411290246763.png",intern=TRUE))
character(0)
> try(system("convert tmp/10v8411290246763.ps tmp/10v8411290246763.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.720 1.850 5.572