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(17823.2,0,17872,0,17420.4,0,16704.4,0,15991.2,0,16583.6,0,19123.5,0,17838.7,0,17209.4,0,18586.5,0,16258.1,0,15141.6,0,19202.1,0,17746.5,0,19090.1,0,18040.3,0,17515.5,0,17751.8,0,21072.4,0,17170,0,19439.5,0,19795.4,0,17574.9,0,16165.4,0,19464.6,0,19932.1,0,19961.2,0,17343.4,0,18924.2,0,18574.1,0,21350.6,0,18594.6,0,19823.1,0,20844.4,0,19640.2,0,17735.4,0,19813.6,0,22160,0,20664.3,0,17877.4,0,20906.5,0,21164.1,0,21374.4,0,22952.3,0,21343.5,0,23899.3,0,22392.9,0,18274.1,0,22786.7,0,22321.5,0,17842.2,1,16373.5,1,15993.8,1,16446.1,1,17729,1,16643,1,16196.7,1,18252.1,1,17570.4,1,15836.8,1),dim=c(2,60),dimnames=list(c('uitvoer','dummy'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('uitvoer','dummy'),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
uitvoer dummy
1 17823.2 0
2 17872.0 0
3 17420.4 0
4 16704.4 0
5 15991.2 0
6 16583.6 0
7 19123.5 0
8 17838.7 0
9 17209.4 0
10 18586.5 0
11 16258.1 0
12 15141.6 0
13 19202.1 0
14 17746.5 0
15 19090.1 0
16 18040.3 0
17 17515.5 0
18 17751.8 0
19 21072.4 0
20 17170.0 0
21 19439.5 0
22 19795.4 0
23 17574.9 0
24 16165.4 0
25 19464.6 0
26 19932.1 0
27 19961.2 0
28 17343.4 0
29 18924.2 0
30 18574.1 0
31 21350.6 0
32 18594.6 0
33 19823.1 0
34 20844.4 0
35 19640.2 0
36 17735.4 0
37 19813.6 0
38 22160.0 0
39 20664.3 0
40 17877.4 0
41 20906.5 0
42 21164.1 0
43 21374.4 0
44 22952.3 0
45 21343.5 0
46 23899.3 0
47 22392.9 0
48 18274.1 0
49 22786.7 0
50 22321.5 0
51 17842.2 1
52 16373.5 1
53 15993.8 1
54 16446.1 1
55 17729.0 1
56 16643.0 1
57 16196.7 1
58 18252.1 1
59 17570.4 1
60 15836.8 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy
19145 -2256
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4003.1 -1339.3 -232.9 1056.3 4754.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19144.7 269.7 70.979 < 2e-16 ***
dummy -2256.3 660.7 -3.415 0.00117 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1907 on 58 degrees of freedom
Multiple R-squared: 0.1674, Adjusted R-squared: 0.1531
F-statistic: 11.66 on 1 and 58 DF, p-value: 0.001170
> 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,] 0.13555261 0.271105225 0.864447387
[2,] 0.06750979 0.135019575 0.932490212
[3,] 0.13391624 0.267832472 0.866083764
[4,] 0.07499990 0.149999797 0.925000101
[5,] 0.03970393 0.079407870 0.960296065
[6,] 0.03158864 0.063177287 0.968411357
[7,] 0.03249439 0.064988770 0.967505615
[8,] 0.10000759 0.200015173 0.899992413
[9,] 0.12651489 0.253029780 0.873485110
[10,] 0.09184997 0.183699935 0.908150032
[11,] 0.09597204 0.191944078 0.904027961
[12,] 0.07072396 0.141447917 0.929276042
[13,] 0.05335099 0.106701984 0.946649008
[14,] 0.03974593 0.079491852 0.960254074
[15,] 0.16014007 0.320280139 0.839859931
[16,] 0.15395663 0.307913264 0.846043368
[17,] 0.15217069 0.304341377 0.847829311
[18,] 0.16101216 0.322024320 0.838987840
[19,] 0.15090181 0.301803623 0.849098188
[20,] 0.27090433 0.541808651 0.729095674
[21,] 0.26510092 0.530201848 0.734899076
[22,] 0.27618471 0.552369427 0.723815286
[23,] 0.27937676 0.558753518 0.720623241
[24,] 0.33808204 0.676164075 0.661917963
[25,] 0.32225543 0.644510866 0.677744567
[26,] 0.32355147 0.647102944 0.676448528
[27,] 0.43092249 0.861844971 0.569077515
[28,] 0.44063238 0.881264753 0.559367623
[29,] 0.42672736 0.853454724 0.573272638
[30,] 0.44714360 0.894287197 0.552856401
[31,] 0.42710938 0.854218761 0.572890619
[32,] 0.58874809 0.822503819 0.411251909
[33,] 0.59095997 0.818080062 0.409040031
[34,] 0.68209090 0.635818205 0.317909103
[35,] 0.66245675 0.675086507 0.337543254
[36,] 0.87416455 0.251670890 0.125835445
[37,] 0.86872297 0.262554059 0.131277030
[38,] 0.85961930 0.280761395 0.140380697
[39,] 0.84653424 0.306931525 0.153465763
[40,] 0.87891861 0.242162789 0.121081395
[41,] 0.85271809 0.294563822 0.147281911
[42,] 0.93489143 0.130217148 0.065108574
[43,] 0.93046260 0.139074810 0.069537405
[44,] 0.99708962 0.005820759 0.002910380
[45,] 0.99482736 0.010345281 0.005172640
[46,] 0.98928715 0.021425692 0.010712846
[47,] 0.98397462 0.032050755 0.016025378
[48,] 0.96518644 0.069627125 0.034813562
[49,] 0.94303646 0.113927088 0.056963544
[50,] 0.88545074 0.229098522 0.114549261
[51,] 0.79740597 0.405188058 0.202594029
> postscript(file="/var/www/html/rcomp/tmp/1d3cd1258558520.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/209wi1258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/357sy1258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ncd81258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zrok1258558520.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 7 8
-1321.50 -1272.70 -1724.30 -2440.30 -3153.50 -2561.10 -21.20 -1306.00
9 10 11 12 13 14 15 16
-1935.30 -558.20 -2886.60 -4003.10 57.40 -1398.20 -54.60 -1104.40
17 18 19 20 21 22 23 24
-1629.20 -1392.90 1927.70 -1974.70 294.80 650.70 -1569.80 -2979.30
25 26 27 28 29 30 31 32
319.90 787.40 816.50 -1801.30 -220.50 -570.60 2205.90 -550.10
33 34 35 36 37 38 39 40
678.40 1699.70 495.50 -1409.30 668.90 3015.30 1519.60 -1267.30
41 42 43 44 45 46 47 48
1761.80 2019.40 2229.70 3807.60 2198.80 4754.60 3248.20 -870.60
49 50 51 52 53 54 55 56
3642.00 3176.80 953.84 -514.86 -894.56 -442.26 840.64 -245.36
57 58 59 60
-691.66 1363.74 682.04 -1051.56
> postscript(file="/var/www/html/rcomp/tmp/6uvyq1258558520.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 -1321.50 NA
1 -1272.70 -1321.50
2 -1724.30 -1272.70
3 -2440.30 -1724.30
4 -3153.50 -2440.30
5 -2561.10 -3153.50
6 -21.20 -2561.10
7 -1306.00 -21.20
8 -1935.30 -1306.00
9 -558.20 -1935.30
10 -2886.60 -558.20
11 -4003.10 -2886.60
12 57.40 -4003.10
13 -1398.20 57.40
14 -54.60 -1398.20
15 -1104.40 -54.60
16 -1629.20 -1104.40
17 -1392.90 -1629.20
18 1927.70 -1392.90
19 -1974.70 1927.70
20 294.80 -1974.70
21 650.70 294.80
22 -1569.80 650.70
23 -2979.30 -1569.80
24 319.90 -2979.30
25 787.40 319.90
26 816.50 787.40
27 -1801.30 816.50
28 -220.50 -1801.30
29 -570.60 -220.50
30 2205.90 -570.60
31 -550.10 2205.90
32 678.40 -550.10
33 1699.70 678.40
34 495.50 1699.70
35 -1409.30 495.50
36 668.90 -1409.30
37 3015.30 668.90
38 1519.60 3015.30
39 -1267.30 1519.60
40 1761.80 -1267.30
41 2019.40 1761.80
42 2229.70 2019.40
43 3807.60 2229.70
44 2198.80 3807.60
45 4754.60 2198.80
46 3248.20 4754.60
47 -870.60 3248.20
48 3642.00 -870.60
49 3176.80 3642.00
50 953.84 3176.80
51 -514.86 953.84
52 -894.56 -514.86
53 -442.26 -894.56
54 840.64 -442.26
55 -245.36 840.64
56 -691.66 -245.36
57 1363.74 -691.66
58 682.04 1363.74
59 -1051.56 682.04
60 NA -1051.56
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1272.70 -1321.50
[2,] -1724.30 -1272.70
[3,] -2440.30 -1724.30
[4,] -3153.50 -2440.30
[5,] -2561.10 -3153.50
[6,] -21.20 -2561.10
[7,] -1306.00 -21.20
[8,] -1935.30 -1306.00
[9,] -558.20 -1935.30
[10,] -2886.60 -558.20
[11,] -4003.10 -2886.60
[12,] 57.40 -4003.10
[13,] -1398.20 57.40
[14,] -54.60 -1398.20
[15,] -1104.40 -54.60
[16,] -1629.20 -1104.40
[17,] -1392.90 -1629.20
[18,] 1927.70 -1392.90
[19,] -1974.70 1927.70
[20,] 294.80 -1974.70
[21,] 650.70 294.80
[22,] -1569.80 650.70
[23,] -2979.30 -1569.80
[24,] 319.90 -2979.30
[25,] 787.40 319.90
[26,] 816.50 787.40
[27,] -1801.30 816.50
[28,] -220.50 -1801.30
[29,] -570.60 -220.50
[30,] 2205.90 -570.60
[31,] -550.10 2205.90
[32,] 678.40 -550.10
[33,] 1699.70 678.40
[34,] 495.50 1699.70
[35,] -1409.30 495.50
[36,] 668.90 -1409.30
[37,] 3015.30 668.90
[38,] 1519.60 3015.30
[39,] -1267.30 1519.60
[40,] 1761.80 -1267.30
[41,] 2019.40 1761.80
[42,] 2229.70 2019.40
[43,] 3807.60 2229.70
[44,] 2198.80 3807.60
[45,] 4754.60 2198.80
[46,] 3248.20 4754.60
[47,] -870.60 3248.20
[48,] 3642.00 -870.60
[49,] 3176.80 3642.00
[50,] 953.84 3176.80
[51,] -514.86 953.84
[52,] -894.56 -514.86
[53,] -442.26 -894.56
[54,] 840.64 -442.26
[55,] -245.36 840.64
[56,] -691.66 -245.36
[57,] 1363.74 -691.66
[58,] 682.04 1363.74
[59,] -1051.56 682.04
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1272.70 -1321.50
2 -1724.30 -1272.70
3 -2440.30 -1724.30
4 -3153.50 -2440.30
5 -2561.10 -3153.50
6 -21.20 -2561.10
7 -1306.00 -21.20
8 -1935.30 -1306.00
9 -558.20 -1935.30
10 -2886.60 -558.20
11 -4003.10 -2886.60
12 57.40 -4003.10
13 -1398.20 57.40
14 -54.60 -1398.20
15 -1104.40 -54.60
16 -1629.20 -1104.40
17 -1392.90 -1629.20
18 1927.70 -1392.90
19 -1974.70 1927.70
20 294.80 -1974.70
21 650.70 294.80
22 -1569.80 650.70
23 -2979.30 -1569.80
24 319.90 -2979.30
25 787.40 319.90
26 816.50 787.40
27 -1801.30 816.50
28 -220.50 -1801.30
29 -570.60 -220.50
30 2205.90 -570.60
31 -550.10 2205.90
32 678.40 -550.10
33 1699.70 678.40
34 495.50 1699.70
35 -1409.30 495.50
36 668.90 -1409.30
37 3015.30 668.90
38 1519.60 3015.30
39 -1267.30 1519.60
40 1761.80 -1267.30
41 2019.40 1761.80
42 2229.70 2019.40
43 3807.60 2229.70
44 2198.80 3807.60
45 4754.60 2198.80
46 3248.20 4754.60
47 -870.60 3248.20
48 3642.00 -870.60
49 3176.80 3642.00
50 953.84 3176.80
51 -514.86 953.84
52 -894.56 -514.86
53 -442.26 -894.56
54 840.64 -442.26
55 -245.36 840.64
56 -691.66 -245.36
57 1363.74 -691.66
58 682.04 1363.74
59 -1051.56 682.04
> 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/76a8k1258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8odcd1258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97xm91258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10viyh1258558520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11a3uv1258558520.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/12eaym1258558520.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/134xgl1258558520.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/14nvwn1258558521.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/15j9m71258558521.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/1612wh1258558521.tab")
+ }
>
> system("convert tmp/1d3cd1258558520.ps tmp/1d3cd1258558520.png")
> system("convert tmp/209wi1258558520.ps tmp/209wi1258558520.png")
> system("convert tmp/357sy1258558520.ps tmp/357sy1258558520.png")
> system("convert tmp/4ncd81258558520.ps tmp/4ncd81258558520.png")
> system("convert tmp/5zrok1258558520.ps tmp/5zrok1258558520.png")
> system("convert tmp/6uvyq1258558520.ps tmp/6uvyq1258558520.png")
> system("convert tmp/76a8k1258558520.ps tmp/76a8k1258558520.png")
> system("convert tmp/8odcd1258558520.ps tmp/8odcd1258558520.png")
> system("convert tmp/97xm91258558520.ps tmp/97xm91258558520.png")
> system("convert tmp/10viyh1258558520.ps tmp/10viyh1258558520.png")
>
>
> proc.time()
user system elapsed
2.529 1.606 5.192