R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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> x <- array(list(3440,2678,2981,2260,2844,2546,2456,2295,2379,2479,2057,2280,2351,2276,2548,2311,2201,2725,2408,2139,1898,2539,2070,2063,2565,2442,2194,2798,2074,2628,2289,2154,2467,2137,1850,2075,1791,1755,2232,1952,1822,2522,2074,2366,2173,2094,1833,1858,2040,2133,2921,3252,3318,3554,2308,1621,1315,1501,1418,1657),dim=c(1,60),dimnames=list(c('Bouwvergunningen'),1:60))
> y <- array(NA,dim=c(1,60),dimnames=list(c('Bouwvergunningen'),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 = '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
Bouwvergunningen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3440 1 0 0 0 0 0 0 0 0 0 0 1
2 2678 0 1 0 0 0 0 0 0 0 0 0 2
3 2981 0 0 1 0 0 0 0 0 0 0 0 3
4 2260 0 0 0 1 0 0 0 0 0 0 0 4
5 2844 0 0 0 0 1 0 0 0 0 0 0 5
6 2546 0 0 0 0 0 1 0 0 0 0 0 6
7 2456 0 0 0 0 0 0 1 0 0 0 0 7
8 2295 0 0 0 0 0 0 0 1 0 0 0 8
9 2379 0 0 0 0 0 0 0 0 1 0 0 9
10 2479 0 0 0 0 0 0 0 0 0 1 0 10
11 2057 0 0 0 0 0 0 0 0 0 0 1 11
12 2280 0 0 0 0 0 0 0 0 0 0 0 12
13 2351 1 0 0 0 0 0 0 0 0 0 0 13
14 2276 0 1 0 0 0 0 0 0 0 0 0 14
15 2548 0 0 1 0 0 0 0 0 0 0 0 15
16 2311 0 0 0 1 0 0 0 0 0 0 0 16
17 2201 0 0 0 0 1 0 0 0 0 0 0 17
18 2725 0 0 0 0 0 1 0 0 0 0 0 18
19 2408 0 0 0 0 0 0 1 0 0 0 0 19
20 2139 0 0 0 0 0 0 0 1 0 0 0 20
21 1898 0 0 0 0 0 0 0 0 1 0 0 21
22 2539 0 0 0 0 0 0 0 0 0 1 0 22
23 2070 0 0 0 0 0 0 0 0 0 0 1 23
24 2063 0 0 0 0 0 0 0 0 0 0 0 24
25 2565 1 0 0 0 0 0 0 0 0 0 0 25
26 2442 0 1 0 0 0 0 0 0 0 0 0 26
27 2194 0 0 1 0 0 0 0 0 0 0 0 27
28 2798 0 0 0 1 0 0 0 0 0 0 0 28
29 2074 0 0 0 0 1 0 0 0 0 0 0 29
30 2628 0 0 0 0 0 1 0 0 0 0 0 30
31 2289 0 0 0 0 0 0 1 0 0 0 0 31
32 2154 0 0 0 0 0 0 0 1 0 0 0 32
33 2467 0 0 0 0 0 0 0 0 1 0 0 33
34 2137 0 0 0 0 0 0 0 0 0 1 0 34
35 1850 0 0 0 0 0 0 0 0 0 0 1 35
36 2075 0 0 0 0 0 0 0 0 0 0 0 36
37 1791 1 0 0 0 0 0 0 0 0 0 0 37
38 1755 0 1 0 0 0 0 0 0 0 0 0 38
39 2232 0 0 1 0 0 0 0 0 0 0 0 39
40 1952 0 0 0 1 0 0 0 0 0 0 0 40
41 1822 0 0 0 0 1 0 0 0 0 0 0 41
42 2522 0 0 0 0 0 1 0 0 0 0 0 42
43 2074 0 0 0 0 0 0 1 0 0 0 0 43
44 2366 0 0 0 0 0 0 0 1 0 0 0 44
45 2173 0 0 0 0 0 0 0 0 1 0 0 45
46 2094 0 0 0 0 0 0 0 0 0 1 0 46
47 1833 0 0 0 0 0 0 0 0 0 0 1 47
48 1858 0 0 0 0 0 0 0 0 0 0 0 48
49 2040 1 0 0 0 0 0 0 0 0 0 0 49
50 2133 0 1 0 0 0 0 0 0 0 0 0 50
51 2921 0 0 1 0 0 0 0 0 0 0 0 51
52 3252 0 0 0 1 0 0 0 0 0 0 0 52
53 3318 0 0 0 0 1 0 0 0 0 0 0 53
54 3554 0 0 0 0 0 1 0 0 0 0 0 54
55 2308 0 0 0 0 0 0 1 0 0 0 0 55
56 1621 0 0 0 0 0 0 0 1 0 0 0 56
57 1315 0 0 0 0 0 0 0 0 1 0 0 57
58 1501 0 0 0 0 0 0 0 0 0 1 0 58
59 1418 0 0 0 0 0 0 0 0 0 0 1 59
60 1657 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
2245.875 371.577 198.179 523.781 470.383 414.785
M6 M7 M8 M9 M10 M11
765.188 284.390 99.592 38.194 148.996 -148.202
t
-7.202
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-559.98 -239.81 -11.51 163.27 1039.05
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2245.875 209.629 10.714 3.3e-14 ***
M1 371.577 255.025 1.457 0.15176
M2 198.179 254.644 0.778 0.44032
M3 523.781 254.299 2.060 0.04499 *
M4 470.383 253.990 1.852 0.07032 .
M5 414.785 253.717 1.635 0.10876
M6 765.188 253.480 3.019 0.00409 **
M7 284.390 253.279 1.123 0.26721
M8 99.592 253.115 0.393 0.69576
M9 38.194 252.987 0.151 0.88064
M10 148.996 252.895 0.589 0.55858
M11 -148.202 252.840 -0.586 0.56058
t -7.202 3.041 -2.368 0.02203 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 399.7 on 47 degrees of freedom
Multiple R-squared: 0.4031, Adjusted R-squared: 0.2507
F-statistic: 2.645 on 12 and 47 DF, p-value: 0.008631
> 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,] 0.3852963559 0.7705927118 0.6147036
[2,] 0.2380483528 0.4760967056 0.7619516
[3,] 0.2762643023 0.5525286046 0.7237357
[4,] 0.1969809643 0.3939619287 0.8030190
[5,] 0.1211477952 0.2422955905 0.8788522
[6,] 0.0712408091 0.1424816182 0.9287592
[7,] 0.0574617921 0.1149235841 0.9425382
[8,] 0.0382560682 0.0765121363 0.9617439
[9,] 0.0196789973 0.0393579946 0.9803210
[10,] 0.0118822464 0.0237644928 0.9881178
[11,] 0.0096884239 0.0193768478 0.9903116
[12,] 0.0060293568 0.0120587136 0.9939706
[13,] 0.0221230908 0.0442461816 0.9778769
[14,] 0.0148937258 0.0297874516 0.9851063
[15,] 0.0103591096 0.0207182192 0.9896409
[16,] 0.0052633550 0.0105267100 0.9947366
[17,] 0.0027021623 0.0054043247 0.9972978
[18,] 0.0043820433 0.0087640866 0.9956180
[19,] 0.0026961332 0.0053922663 0.9973039
[20,] 0.0014209621 0.0028419242 0.9985790
[21,] 0.0009286033 0.0018572065 0.9990714
[22,] 0.0015775876 0.0031551751 0.9984224
[23,] 0.0009425367 0.0018850734 0.9990575
[24,] 0.0004965422 0.0009930843 0.9995035
[25,] 0.0011656267 0.0023312533 0.9988344
[26,] 0.0230665540 0.0461331080 0.9769334
[27,] 0.3866043750 0.7732087500 0.6133956
[28,] 0.7495755617 0.5008488766 0.2504244
[29,] 0.6765729213 0.6468541573 0.3234271
> postscript(file="/var/wessaorg/rcomp/tmp/187y21322577658.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/277ar1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3d32w1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40fye1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5yn9y1322577658.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 = 60
Frequency = 1
1 2 3 4 5 6 7 8
829.750 248.350 232.950 -427.450 219.350 -421.850 -23.850 7.150
9 10 11 12 13 14 15 16
159.750 156.150 38.550 120.550 -172.825 -67.225 -113.625 -290.025
17 18 19 20 21 22 23 24
-337.225 -156.425 14.575 -62.425 -234.825 302.575 137.975 -10.025
25 26 27 28 29 30 31 32
127.600 185.200 -381.200 283.400 -377.800 -167.000 -18.000 39.000
33 34 35 36 37 38 39 40
420.600 -13.000 4.400 88.400 -559.975 -415.375 -256.775 -476.175
41 42 43 44 45 46 47 48
-543.375 -186.575 -146.575 337.425 213.025 30.425 73.825 -42.175
49 50 51 52 53 54 55 56
-224.550 49.050 518.650 910.250 1039.050 931.850 173.850 -321.150
57 58 59 60
-558.550 -476.150 -254.750 -156.750
> postscript(file="/var/wessaorg/rcomp/tmp/6m2ab1322577658.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 829.750 NA
1 248.350 829.750
2 232.950 248.350
3 -427.450 232.950
4 219.350 -427.450
5 -421.850 219.350
6 -23.850 -421.850
7 7.150 -23.850
8 159.750 7.150
9 156.150 159.750
10 38.550 156.150
11 120.550 38.550
12 -172.825 120.550
13 -67.225 -172.825
14 -113.625 -67.225
15 -290.025 -113.625
16 -337.225 -290.025
17 -156.425 -337.225
18 14.575 -156.425
19 -62.425 14.575
20 -234.825 -62.425
21 302.575 -234.825
22 137.975 302.575
23 -10.025 137.975
24 127.600 -10.025
25 185.200 127.600
26 -381.200 185.200
27 283.400 -381.200
28 -377.800 283.400
29 -167.000 -377.800
30 -18.000 -167.000
31 39.000 -18.000
32 420.600 39.000
33 -13.000 420.600
34 4.400 -13.000
35 88.400 4.400
36 -559.975 88.400
37 -415.375 -559.975
38 -256.775 -415.375
39 -476.175 -256.775
40 -543.375 -476.175
41 -186.575 -543.375
42 -146.575 -186.575
43 337.425 -146.575
44 213.025 337.425
45 30.425 213.025
46 73.825 30.425
47 -42.175 73.825
48 -224.550 -42.175
49 49.050 -224.550
50 518.650 49.050
51 910.250 518.650
52 1039.050 910.250
53 931.850 1039.050
54 173.850 931.850
55 -321.150 173.850
56 -558.550 -321.150
57 -476.150 -558.550
58 -254.750 -476.150
59 -156.750 -254.750
60 NA -156.750
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 248.350 829.750
[2,] 232.950 248.350
[3,] -427.450 232.950
[4,] 219.350 -427.450
[5,] -421.850 219.350
[6,] -23.850 -421.850
[7,] 7.150 -23.850
[8,] 159.750 7.150
[9,] 156.150 159.750
[10,] 38.550 156.150
[11,] 120.550 38.550
[12,] -172.825 120.550
[13,] -67.225 -172.825
[14,] -113.625 -67.225
[15,] -290.025 -113.625
[16,] -337.225 -290.025
[17,] -156.425 -337.225
[18,] 14.575 -156.425
[19,] -62.425 14.575
[20,] -234.825 -62.425
[21,] 302.575 -234.825
[22,] 137.975 302.575
[23,] -10.025 137.975
[24,] 127.600 -10.025
[25,] 185.200 127.600
[26,] -381.200 185.200
[27,] 283.400 -381.200
[28,] -377.800 283.400
[29,] -167.000 -377.800
[30,] -18.000 -167.000
[31,] 39.000 -18.000
[32,] 420.600 39.000
[33,] -13.000 420.600
[34,] 4.400 -13.000
[35,] 88.400 4.400
[36,] -559.975 88.400
[37,] -415.375 -559.975
[38,] -256.775 -415.375
[39,] -476.175 -256.775
[40,] -543.375 -476.175
[41,] -186.575 -543.375
[42,] -146.575 -186.575
[43,] 337.425 -146.575
[44,] 213.025 337.425
[45,] 30.425 213.025
[46,] 73.825 30.425
[47,] -42.175 73.825
[48,] -224.550 -42.175
[49,] 49.050 -224.550
[50,] 518.650 49.050
[51,] 910.250 518.650
[52,] 1039.050 910.250
[53,] 931.850 1039.050
[54,] 173.850 931.850
[55,] -321.150 173.850
[56,] -558.550 -321.150
[57,] -476.150 -558.550
[58,] -254.750 -476.150
[59,] -156.750 -254.750
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 248.350 829.750
2 232.950 248.350
3 -427.450 232.950
4 219.350 -427.450
5 -421.850 219.350
6 -23.850 -421.850
7 7.150 -23.850
8 159.750 7.150
9 156.150 159.750
10 38.550 156.150
11 120.550 38.550
12 -172.825 120.550
13 -67.225 -172.825
14 -113.625 -67.225
15 -290.025 -113.625
16 -337.225 -290.025
17 -156.425 -337.225
18 14.575 -156.425
19 -62.425 14.575
20 -234.825 -62.425
21 302.575 -234.825
22 137.975 302.575
23 -10.025 137.975
24 127.600 -10.025
25 185.200 127.600
26 -381.200 185.200
27 283.400 -381.200
28 -377.800 283.400
29 -167.000 -377.800
30 -18.000 -167.000
31 39.000 -18.000
32 420.600 39.000
33 -13.000 420.600
34 4.400 -13.000
35 88.400 4.400
36 -559.975 88.400
37 -415.375 -559.975
38 -256.775 -415.375
39 -476.175 -256.775
40 -543.375 -476.175
41 -186.575 -543.375
42 -146.575 -186.575
43 337.425 -146.575
44 213.025 337.425
45 30.425 213.025
46 73.825 30.425
47 -42.175 73.825
48 -224.550 -42.175
49 49.050 -224.550
50 518.650 49.050
51 910.250 518.650
52 1039.050 910.250
53 931.850 1039.050
54 173.850 931.850
55 -321.150 173.850
56 -558.550 -321.150
57 -476.150 -558.550
58 -254.750 -476.150
59 -156.750 -254.750
> 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/wessaorg/rcomp/tmp/7hrxk1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8x5161322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9dz8l1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107icl1322577658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11leil1322577658.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/wessaorg/rcomp/tmp/12uh0t1322577658.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/wessaorg/rcomp/tmp/134arq1322577658.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/wessaorg/rcomp/tmp/14d12v1322577658.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/wessaorg/rcomp/tmp/15t0gr1322577658.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/wessaorg/rcomp/tmp/166hxj1322577658.tab")
+ }
>
> try(system("convert tmp/187y21322577658.ps tmp/187y21322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/277ar1322577658.ps tmp/277ar1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d32w1322577658.ps tmp/3d32w1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/40fye1322577658.ps tmp/40fye1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yn9y1322577658.ps tmp/5yn9y1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m2ab1322577658.ps tmp/6m2ab1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hrxk1322577658.ps tmp/7hrxk1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/8x5161322577658.ps tmp/8x5161322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dz8l1322577658.ps tmp/9dz8l1322577658.png",intern=TRUE))
character(0)
> try(system("convert tmp/107icl1322577658.ps tmp/107icl1322577658.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.135 0.492 3.688