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(-820.8,0,993.3,0,741.7,0,603.6,0,-145.8,0,-35.1,0,395.1,0,523.1,0,462.3,0,183.4,0,791.5,0,344.8,0,-217.0,0,406.7,0,228.6,0,-580.1,0,-1550.4,0,-1447.5,0,-40.1,0,-1033.5,0,-925.6,0,-347.8,0,-447.7,0,-102.6,0,-2062.2,0,-929.7,1,-720.7,1,-1541.8,1,-1432.3,1,-1216.2,1,-212.8,1,-378.2,1,76.9,1,-101.3,1,220.4,1,495.6,1,-1035.2,1,61.8,1,-734.8,1,-6.9,1,-1061.1,1,-854.6,1,-186.5,1,244.0,1,-992.6,1,-335.2,1,316.8,1,477.6,1,-572.1,1,1115.2,1),dim=c(2,50),dimnames=list(c('Totaal','Dummy'),1:50))
> y <- array(NA,dim=c(2,50),dimnames=list(c('Totaal','Dummy'),1:50))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Totaal Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 -820.8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 993.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 741.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 603.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 -145.8 0 0 0 0 0 1 0 0 0 0 0 0 5
6 -35.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 395.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 523.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 462.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 183.4 0 0 0 0 0 0 0 0 0 0 1 0 10
11 791.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 344.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 -217.0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 406.7 0 0 1 0 0 0 0 0 0 0 0 0 14
15 228.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 -580.1 0 0 0 0 1 0 0 0 0 0 0 0 16
17 -1550.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 -1447.5 0 0 0 0 0 0 1 0 0 0 0 0 18
19 -40.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 -1033.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 -925.6 0 0 0 0 0 0 0 0 0 1 0 0 21
22 -347.8 0 0 0 0 0 0 0 0 0 0 1 0 22
23 -447.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 -102.6 0 0 0 0 0 0 0 0 0 0 0 0 24
25 -2062.2 0 1 0 0 0 0 0 0 0 0 0 0 25
26 -929.7 1 0 1 0 0 0 0 0 0 0 0 0 26
27 -720.7 1 0 0 1 0 0 0 0 0 0 0 0 27
28 -1541.8 1 0 0 0 1 0 0 0 0 0 0 0 28
29 -1432.3 1 0 0 0 0 1 0 0 0 0 0 0 29
30 -1216.2 1 0 0 0 0 0 1 0 0 0 0 0 30
31 -212.8 1 0 0 0 0 0 0 1 0 0 0 0 31
32 -378.2 1 0 0 0 0 0 0 0 1 0 0 0 32
33 76.9 1 0 0 0 0 0 0 0 0 1 0 0 33
34 -101.3 1 0 0 0 0 0 0 0 0 0 1 0 34
35 220.4 1 0 0 0 0 0 0 0 0 0 0 1 35
36 495.6 1 0 0 0 0 0 0 0 0 0 0 0 36
37 -1035.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 61.8 1 0 1 0 0 0 0 0 0 0 0 0 38
39 -734.8 1 0 0 1 0 0 0 0 0 0 0 0 39
40 -6.9 1 0 0 0 1 0 0 0 0 0 0 0 40
41 -1061.1 1 0 0 0 0 1 0 0 0 0 0 0 41
42 -854.6 1 0 0 0 0 0 1 0 0 0 0 0 42
43 -186.5 1 0 0 0 0 0 0 1 0 0 0 0 43
44 244.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 -992.6 1 0 0 0 0 0 0 0 0 1 0 0 45
46 -335.2 1 0 0 0 0 0 0 0 0 0 1 0 46
47 316.8 1 0 0 0 0 0 0 0 0 0 0 1 47
48 477.6 1 0 0 0 0 0 0 0 0 0 0 0 48
49 -572.1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 1115.2 1 0 1 0 0 0 0 0 0 0 0 0 50
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
752.93 197.80 -1316.86 -67.23 -589.54 -831.28
M5 M6 M7 M8 M9 M10
-1479.11 -1301.80 -406.25 -538.06 -703.40 -490.61
M11 t
-101.87 -18.27
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1338.28 -359.94 27.88 388.76 1145.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 752.93 397.44 1.894 0.06622 .
Dummy 197.80 376.51 0.525 0.60256
M1 -1316.86 419.95 -3.136 0.00341 **
M2 -67.23 427.42 -0.157 0.87589
M3 -589.54 456.66 -1.291 0.20493
M4 -831.28 453.40 -1.833 0.07502 .
M5 -1479.11 450.50 -3.283 0.00229 **
M6 -1301.80 447.98 -2.906 0.00623 **
M7 -406.25 445.84 -0.911 0.36824
M8 -538.06 444.07 -1.212 0.23354
M9 -703.40 442.70 -1.589 0.12083
M10 -490.61 441.71 -1.111 0.27406
M11 -101.87 441.12 -0.231 0.81868
t -18.27 13.21 -1.383 0.17512
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 623.6 on 36 degrees of freedom
Multiple R-squared: 0.4698, Adjusted R-squared: 0.2783
F-statistic: 2.454 on 13 and 36 DF, p-value: 0.01678
> 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,] 0.8451414 0.3097172 0.1548586
[2,] 0.8052505 0.3894990 0.1947495
[3,] 0.7493778 0.5012444 0.2506222
[4,] 0.7323751 0.5352499 0.2676249
[5,] 0.6579720 0.6840560 0.3420280
[6,] 0.5956409 0.8087182 0.4043591
[7,] 0.4937429 0.9874859 0.5062571
[8,] 0.4234572 0.8469143 0.5765428
[9,] 0.3167062 0.6334124 0.6832938
[10,] 0.3499879 0.6999758 0.6500121
[11,] 0.2650270 0.5300540 0.7349730
[12,] 0.4180177 0.8360354 0.5819823
[13,] 0.3923393 0.7846786 0.6076607
[14,] 0.3365938 0.6731876 0.6634062
[15,] 0.2577672 0.5155345 0.7422328
[16,] 0.2353073 0.4706147 0.7646927
[17,] 0.5529791 0.8940419 0.4470209
> postscript(file="/var/www/html/rcomp/tmp/1qhth1291328577.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qhth1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qhth1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j8ak1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j8ak1291328577.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 = 50
Frequency = 1
1 2 3 4 5 6
-238.602500 344.137292 633.112917 755.012917 671.712917 623.362917
7 8 9 10 11 12
176.287917 454.362917 577.162917 103.737917 341.362917 -188.937083
13 14 15 16 17 18
584.388542 -23.271667 339.203958 -209.496042 -513.696042 -569.846042
19 20 21 22 23 24
-39.721042 -883.046042 -591.546042 -208.271042 -678.646042 -417.146042
25 26 27 28 29 30
-1041.620417 -1338.279583 -588.703958 -1149.803958 -374.203958 -317.153958
31 32 33 34 35 36
-191.028958 -206.353958 432.346042 59.621042 10.846042 202.446042
37 38 39 40 41 42
6.771667 -127.588542 -383.612917 604.287083 216.187083 263.637083
43 44 45 46 47 48
54.462083 635.037083 -417.962917 44.912083 326.437083 403.637083
49 50
689.062708 1145.002500
> postscript(file="/var/www/html/rcomp/tmp/6j8ak1291328577.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 = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 -238.602500 NA
1 344.137292 -238.602500
2 633.112917 344.137292
3 755.012917 633.112917
4 671.712917 755.012917
5 623.362917 671.712917
6 176.287917 623.362917
7 454.362917 176.287917
8 577.162917 454.362917
9 103.737917 577.162917
10 341.362917 103.737917
11 -188.937083 341.362917
12 584.388542 -188.937083
13 -23.271667 584.388542
14 339.203958 -23.271667
15 -209.496042 339.203958
16 -513.696042 -209.496042
17 -569.846042 -513.696042
18 -39.721042 -569.846042
19 -883.046042 -39.721042
20 -591.546042 -883.046042
21 -208.271042 -591.546042
22 -678.646042 -208.271042
23 -417.146042 -678.646042
24 -1041.620417 -417.146042
25 -1338.279583 -1041.620417
26 -588.703958 -1338.279583
27 -1149.803958 -588.703958
28 -374.203958 -1149.803958
29 -317.153958 -374.203958
30 -191.028958 -317.153958
31 -206.353958 -191.028958
32 432.346042 -206.353958
33 59.621042 432.346042
34 10.846042 59.621042
35 202.446042 10.846042
36 6.771667 202.446042
37 -127.588542 6.771667
38 -383.612917 -127.588542
39 604.287083 -383.612917
40 216.187083 604.287083
41 263.637083 216.187083
42 54.462083 263.637083
43 635.037083 54.462083
44 -417.962917 635.037083
45 44.912083 -417.962917
46 326.437083 44.912083
47 403.637083 326.437083
48 689.062708 403.637083
49 1145.002500 689.062708
50 NA 1145.002500
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 344.137292 -238.602500
[2,] 633.112917 344.137292
[3,] 755.012917 633.112917
[4,] 671.712917 755.012917
[5,] 623.362917 671.712917
[6,] 176.287917 623.362917
[7,] 454.362917 176.287917
[8,] 577.162917 454.362917
[9,] 103.737917 577.162917
[10,] 341.362917 103.737917
[11,] -188.937083 341.362917
[12,] 584.388542 -188.937083
[13,] -23.271667 584.388542
[14,] 339.203958 -23.271667
[15,] -209.496042 339.203958
[16,] -513.696042 -209.496042
[17,] -569.846042 -513.696042
[18,] -39.721042 -569.846042
[19,] -883.046042 -39.721042
[20,] -591.546042 -883.046042
[21,] -208.271042 -591.546042
[22,] -678.646042 -208.271042
[23,] -417.146042 -678.646042
[24,] -1041.620417 -417.146042
[25,] -1338.279583 -1041.620417
[26,] -588.703958 -1338.279583
[27,] -1149.803958 -588.703958
[28,] -374.203958 -1149.803958
[29,] -317.153958 -374.203958
[30,] -191.028958 -317.153958
[31,] -206.353958 -191.028958
[32,] 432.346042 -206.353958
[33,] 59.621042 432.346042
[34,] 10.846042 59.621042
[35,] 202.446042 10.846042
[36,] 6.771667 202.446042
[37,] -127.588542 6.771667
[38,] -383.612917 -127.588542
[39,] 604.287083 -383.612917
[40,] 216.187083 604.287083
[41,] 263.637083 216.187083
[42,] 54.462083 263.637083
[43,] 635.037083 54.462083
[44,] -417.962917 635.037083
[45,] 44.912083 -417.962917
[46,] 326.437083 44.912083
[47,] 403.637083 326.437083
[48,] 689.062708 403.637083
[49,] 1145.002500 689.062708
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 344.137292 -238.602500
2 633.112917 344.137292
3 755.012917 633.112917
4 671.712917 755.012917
5 623.362917 671.712917
6 176.287917 623.362917
7 454.362917 176.287917
8 577.162917 454.362917
9 103.737917 577.162917
10 341.362917 103.737917
11 -188.937083 341.362917
12 584.388542 -188.937083
13 -23.271667 584.388542
14 339.203958 -23.271667
15 -209.496042 339.203958
16 -513.696042 -209.496042
17 -569.846042 -513.696042
18 -39.721042 -569.846042
19 -883.046042 -39.721042
20 -591.546042 -883.046042
21 -208.271042 -591.546042
22 -678.646042 -208.271042
23 -417.146042 -678.646042
24 -1041.620417 -417.146042
25 -1338.279583 -1041.620417
26 -588.703958 -1338.279583
27 -1149.803958 -588.703958
28 -374.203958 -1149.803958
29 -317.153958 -374.203958
30 -191.028958 -317.153958
31 -206.353958 -191.028958
32 432.346042 -206.353958
33 59.621042 432.346042
34 10.846042 59.621042
35 202.446042 10.846042
36 6.771667 202.446042
37 -127.588542 6.771667
38 -383.612917 -127.588542
39 604.287083 -383.612917
40 216.187083 604.287083
41 263.637083 216.187083
42 54.462083 263.637083
43 635.037083 54.462083
44 -417.962917 635.037083
45 44.912083 -417.962917
46 326.437083 44.912083
47 403.637083 326.437083
48 689.062708 403.637083
49 1145.002500 689.062708
> 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/7uzr51291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mrrq1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mrrq1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mrrq1291328577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11897w1291328577.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/12bso11291328577.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/130blv1291328577.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/14ak2g1291328577.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/15w20m1291328577.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/16scgd1291328577.tab")
+ }
>
> try(system("convert tmp/1qhth1291328577.ps tmp/1qhth1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qhth1291328577.ps tmp/2qhth1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qhth1291328577.ps tmp/3qhth1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j8ak1291328577.ps tmp/4j8ak1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j8ak1291328577.ps tmp/5j8ak1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j8ak1291328577.ps tmp/6j8ak1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uzr51291328577.ps tmp/7uzr51291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mrrq1291328577.ps tmp/8mrrq1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mrrq1291328577.ps tmp/9mrrq1291328577.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mrrq1291328577.ps tmp/10mrrq1291328577.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.326 1.589 10.639