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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(172.69,104.31,172.98,103.88,172.98,103.88,172.89,103.86,173.38,103.89,173.20,103.98,173.24,103.98,172.86,104.29,172.86,104.29,172.74,104.24,172.28,103.98,171.05,103.54,171.07,103.44,171.07,103.32,171.07,103.30,171.11,103.26,170.72,103.14,170.49,103.11,170.48,102.91,170.48,103.23,170.48,103.23,170.57,103.14,170.39,102.91,170.04,102.42,169.67,102.10,169.57,102.07,169.57,102.06,169.53,101.98,169.24,101.83,169.29,101.75,169.21,101.56,168.58,101.66,168.58,101.65,168.55,101.61,168.46,101.52,167.39,101.31,167.16,101.19,167.16,101.11,167.16,101.10,167.17,101.07,166.52,100.98,166.35,100.93,166.19,100.92,166.19,101.02,166.19,101.01,166.07,100.97,166.64,100.89,166.26,100.62,166.44,100.53,166.27,100.48,166.27,100.48,166.30,100.47,165.97,100.52,164.58,100.49,164.28,100.47,163.93,100.44),dim=c(2,56),dimnames=list(c('Gemconsprijsblazers','consumptieindexkleding'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('Gemconsprijsblazers','consumptieindexkleding'),1:56))
> 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
Gemconsprijsblazers consumptieindexkleding
1 172.69 104.31
2 172.98 103.88
3 172.98 103.88
4 172.89 103.86
5 173.38 103.89
6 173.20 103.98
7 173.24 103.98
8 172.86 104.29
9 172.86 104.29
10 172.74 104.24
11 172.28 103.98
12 171.05 103.54
13 171.07 103.44
14 171.07 103.32
15 171.07 103.30
16 171.11 103.26
17 170.72 103.14
18 170.49 103.11
19 170.48 102.91
20 170.48 103.23
21 170.48 103.23
22 170.57 103.14
23 170.39 102.91
24 170.04 102.42
25 169.67 102.10
26 169.57 102.07
27 169.57 102.06
28 169.53 101.98
29 169.24 101.83
30 169.29 101.75
31 169.21 101.56
32 168.58 101.66
33 168.58 101.65
34 168.55 101.61
35 168.46 101.52
36 167.39 101.31
37 167.16 101.19
38 167.16 101.11
39 167.16 101.10
40 167.17 101.07
41 166.52 100.98
42 166.35 100.93
43 166.19 100.92
44 166.19 101.02
45 166.19 101.01
46 166.07 100.97
47 166.64 100.89
48 166.26 100.62
49 166.44 100.53
50 166.27 100.48
51 166.27 100.48
52 166.30 100.47
53 165.97 100.52
54 164.58 100.49
55 164.28 100.47
56 163.93 100.44
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) consumptieindexkleding
-31.517 1.964
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.785136 -0.439613 0.006655 0.513839 1.295541
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -31.51702 6.72097 -4.689 1.90e-05 ***
consumptieindexkleding 1.96368 0.06577 29.858 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6422 on 54 degrees of freedom
Multiple R-squared: 0.9429, Adjusted R-squared: 0.9418
F-statistic: 891.5 on 1 and 54 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,] 0.0555280255 0.1110560509 0.9444720
[2,] 0.0256417491 0.0512834982 0.9743583
[3,] 0.0126649377 0.0253298754 0.9873351
[4,] 0.0038205558 0.0076411115 0.9961794
[5,] 0.0010602948 0.0021205895 0.9989397
[6,] 0.0003382406 0.0006764811 0.9996618
[7,] 0.0066633920 0.0133267840 0.9933366
[8,] 0.3353517024 0.6707034048 0.6646483
[9,] 0.3827301347 0.7654602695 0.6172699
[10,] 0.3177634264 0.6355268528 0.6822366
[11,] 0.2474029722 0.4948059444 0.7525970
[12,] 0.1809413932 0.3618827864 0.8190586
[13,] 0.1335218086 0.2670436171 0.8664782
[14,] 0.1060708127 0.2121416254 0.8939292
[15,] 0.0737943299 0.1475886597 0.9262057
[16,] 0.0838875101 0.1677750202 0.9161125
[17,] 0.1104633802 0.2209267603 0.8895366
[18,] 0.1349673242 0.2699346484 0.8650327
[19,] 0.1752167878 0.3504335756 0.8247832
[20,] 0.2244569032 0.4489138064 0.7755431
[21,] 0.2538349778 0.5076699556 0.7461650
[22,] 0.2354776928 0.4709553856 0.7645223
[23,] 0.2049173011 0.4098346022 0.7950827
[24,] 0.1741773656 0.3483547312 0.8258226
[25,] 0.1386069547 0.2772139094 0.8613930
[26,] 0.1218326439 0.2436652878 0.8781674
[27,] 0.1570978105 0.3141956211 0.8429022
[28,] 0.1173772449 0.2347544898 0.8826228
[29,] 0.0852758355 0.1705516709 0.9147242
[30,] 0.0618642809 0.1237285617 0.9381357
[31,] 0.0497271409 0.0994542819 0.9502729
[32,] 0.0465194934 0.0930389867 0.9534805
[33,] 0.0407365340 0.0814730679 0.9592635
[34,] 0.0320681775 0.0641363551 0.9679318
[35,] 0.0249059357 0.0498118714 0.9750941
[36,] 0.0204113069 0.0408226138 0.9795887
[37,] 0.0176706840 0.0353413680 0.9823293
[38,] 0.0147000832 0.0294001664 0.9852999
[39,] 0.0124852300 0.0249704601 0.9875148
[40,] 0.0118211543 0.0236423086 0.9881788
[41,] 0.0104141331 0.0208282662 0.9895859
[42,] 0.0127932510 0.0255865020 0.9872067
[43,] 0.0147217131 0.0294434263 0.9852783
[44,] 0.0198125215 0.0396250431 0.9801875
[45,] 0.0099187974 0.0198375949 0.9900812
[46,] 0.0117479240 0.0234958480 0.9882521
[47,] 0.0228626590 0.0457253180 0.9771373
> postscript(file="/var/www/html/rcomp/tmp/1lr6g1293184310.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lr6g1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3eini1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4eini1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5eini1293184310.ps",horizontal=F,onefile=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 = 56
Frequency = 1
1 2 3 4 5 6
-0.62458272 0.50980028 0.50980028 0.45907391 0.89016347 0.53343214
7 8 9 10 11 12
0.57343214 -0.41530909 -0.41530909 -0.43712502 -0.38656786 -0.75254805
13 14 15 16 17 18
-0.53617991 -0.30053814 -0.26126452 -0.14271726 -0.29707549 -0.46816505
19 20 21 22 23 24
-0.08542878 -0.71380682 -0.71380682 -0.44707549 -0.17542878 0.43677510
25 26 27 28 29 30
0.69515315 0.65406359 0.67370040 0.79079491 0.79534712 1.00244163
31 32 33 34 35 36
1.29554109 0.46917295 0.48880977 0.53735702 0.62408835 -0.03353856
37 38 39 40 41 42
-0.02789679 0.12919772 0.14883453 0.21774497 -0.25552370 -0.32733963
43 44 45 46 47 48
-0.46770282 -0.66407096 -0.64443415 -0.68588689 0.04120762 0.19140159
49 50 51 52 53 54
0.54813292 0.47631699 0.47631699 0.52595380 0.09776973 -1.23331983
55 56
-1.49404620 -1.78513576
> postscript(file="/var/www/html/rcomp/tmp/6ps5l1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.62458272 NA
1 0.50980028 -0.62458272
2 0.50980028 0.50980028
3 0.45907391 0.50980028
4 0.89016347 0.45907391
5 0.53343214 0.89016347
6 0.57343214 0.53343214
7 -0.41530909 0.57343214
8 -0.41530909 -0.41530909
9 -0.43712502 -0.41530909
10 -0.38656786 -0.43712502
11 -0.75254805 -0.38656786
12 -0.53617991 -0.75254805
13 -0.30053814 -0.53617991
14 -0.26126452 -0.30053814
15 -0.14271726 -0.26126452
16 -0.29707549 -0.14271726
17 -0.46816505 -0.29707549
18 -0.08542878 -0.46816505
19 -0.71380682 -0.08542878
20 -0.71380682 -0.71380682
21 -0.44707549 -0.71380682
22 -0.17542878 -0.44707549
23 0.43677510 -0.17542878
24 0.69515315 0.43677510
25 0.65406359 0.69515315
26 0.67370040 0.65406359
27 0.79079491 0.67370040
28 0.79534712 0.79079491
29 1.00244163 0.79534712
30 1.29554109 1.00244163
31 0.46917295 1.29554109
32 0.48880977 0.46917295
33 0.53735702 0.48880977
34 0.62408835 0.53735702
35 -0.03353856 0.62408835
36 -0.02789679 -0.03353856
37 0.12919772 -0.02789679
38 0.14883453 0.12919772
39 0.21774497 0.14883453
40 -0.25552370 0.21774497
41 -0.32733963 -0.25552370
42 -0.46770282 -0.32733963
43 -0.66407096 -0.46770282
44 -0.64443415 -0.66407096
45 -0.68588689 -0.64443415
46 0.04120762 -0.68588689
47 0.19140159 0.04120762
48 0.54813292 0.19140159
49 0.47631699 0.54813292
50 0.47631699 0.47631699
51 0.52595380 0.47631699
52 0.09776973 0.52595380
53 -1.23331983 0.09776973
54 -1.49404620 -1.23331983
55 -1.78513576 -1.49404620
56 NA -1.78513576
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.50980028 -0.62458272
[2,] 0.50980028 0.50980028
[3,] 0.45907391 0.50980028
[4,] 0.89016347 0.45907391
[5,] 0.53343214 0.89016347
[6,] 0.57343214 0.53343214
[7,] -0.41530909 0.57343214
[8,] -0.41530909 -0.41530909
[9,] -0.43712502 -0.41530909
[10,] -0.38656786 -0.43712502
[11,] -0.75254805 -0.38656786
[12,] -0.53617991 -0.75254805
[13,] -0.30053814 -0.53617991
[14,] -0.26126452 -0.30053814
[15,] -0.14271726 -0.26126452
[16,] -0.29707549 -0.14271726
[17,] -0.46816505 -0.29707549
[18,] -0.08542878 -0.46816505
[19,] -0.71380682 -0.08542878
[20,] -0.71380682 -0.71380682
[21,] -0.44707549 -0.71380682
[22,] -0.17542878 -0.44707549
[23,] 0.43677510 -0.17542878
[24,] 0.69515315 0.43677510
[25,] 0.65406359 0.69515315
[26,] 0.67370040 0.65406359
[27,] 0.79079491 0.67370040
[28,] 0.79534712 0.79079491
[29,] 1.00244163 0.79534712
[30,] 1.29554109 1.00244163
[31,] 0.46917295 1.29554109
[32,] 0.48880977 0.46917295
[33,] 0.53735702 0.48880977
[34,] 0.62408835 0.53735702
[35,] -0.03353856 0.62408835
[36,] -0.02789679 -0.03353856
[37,] 0.12919772 -0.02789679
[38,] 0.14883453 0.12919772
[39,] 0.21774497 0.14883453
[40,] -0.25552370 0.21774497
[41,] -0.32733963 -0.25552370
[42,] -0.46770282 -0.32733963
[43,] -0.66407096 -0.46770282
[44,] -0.64443415 -0.66407096
[45,] -0.68588689 -0.64443415
[46,] 0.04120762 -0.68588689
[47,] 0.19140159 0.04120762
[48,] 0.54813292 0.19140159
[49,] 0.47631699 0.54813292
[50,] 0.47631699 0.47631699
[51,] 0.52595380 0.47631699
[52,] 0.09776973 0.52595380
[53,] -1.23331983 0.09776973
[54,] -1.49404620 -1.23331983
[55,] -1.78513576 -1.49404620
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.50980028 -0.62458272
2 0.50980028 0.50980028
3 0.45907391 0.50980028
4 0.89016347 0.45907391
5 0.53343214 0.89016347
6 0.57343214 0.53343214
7 -0.41530909 0.57343214
8 -0.41530909 -0.41530909
9 -0.43712502 -0.41530909
10 -0.38656786 -0.43712502
11 -0.75254805 -0.38656786
12 -0.53617991 -0.75254805
13 -0.30053814 -0.53617991
14 -0.26126452 -0.30053814
15 -0.14271726 -0.26126452
16 -0.29707549 -0.14271726
17 -0.46816505 -0.29707549
18 -0.08542878 -0.46816505
19 -0.71380682 -0.08542878
20 -0.71380682 -0.71380682
21 -0.44707549 -0.71380682
22 -0.17542878 -0.44707549
23 0.43677510 -0.17542878
24 0.69515315 0.43677510
25 0.65406359 0.69515315
26 0.67370040 0.65406359
27 0.79079491 0.67370040
28 0.79534712 0.79079491
29 1.00244163 0.79534712
30 1.29554109 1.00244163
31 0.46917295 1.29554109
32 0.48880977 0.46917295
33 0.53735702 0.48880977
34 0.62408835 0.53735702
35 -0.03353856 0.62408835
36 -0.02789679 -0.03353856
37 0.12919772 -0.02789679
38 0.14883453 0.12919772
39 0.21774497 0.14883453
40 -0.25552370 0.21774497
41 -0.32733963 -0.25552370
42 -0.46770282 -0.32733963
43 -0.66407096 -0.46770282
44 -0.64443415 -0.66407096
45 -0.68588689 -0.64443415
46 0.04120762 -0.68588689
47 0.19140159 0.04120762
48 0.54813292 0.19140159
49 0.47631699 0.54813292
50 0.47631699 0.47631699
51 0.52595380 0.47631699
52 0.09776973 0.52595380
53 -1.23331983 0.09776973
54 -1.49404620 -1.23331983
55 -1.78513576 -1.49404620
> 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/7ps5l1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hjmo1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hjmo1293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ss391293184310.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vb1x1293184310.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/12ok101293184310.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/13vlyu1293184310.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/146cfx1293184310.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/15rve21293184310.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/1655tt1293184310.tab")
+ }
>
> try(system("convert tmp/1lr6g1293184310.ps tmp/1lr6g1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lr6g1293184310.ps tmp/2lr6g1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/3eini1293184310.ps tmp/3eini1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/4eini1293184310.ps tmp/4eini1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/5eini1293184310.ps tmp/5eini1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ps5l1293184310.ps tmp/6ps5l1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ps5l1293184310.ps tmp/7ps5l1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hjmo1293184310.ps tmp/8hjmo1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hjmo1293184310.ps tmp/9hjmo1293184310.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ss391293184310.ps tmp/10ss391293184310.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.423 1.615 6.700