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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(101.76,102.37,102.38,102.86,102.87,102.92,102.95,103.02,104.08,104.16,104.24,104.33,104.73,104.86,105.03,105.62,105.63,105.63,105.94,106.61,107.69,107.78,107.93,108.48,108.14,108.48,108.48,108.89,108.93,109.21,109.47,109.80,111.73,111.85,112.12,112.15,112.17,112.67,112.80,113.44,113.53,114.53,114.51,115.05,116.67,117.07,116.92,117.00,117.02,117.35,117.36,117.82,117.88,118.24,118.50,118.80,119.76,120.09),dim=c(1,58),dimnames=list(c('vrijetijdsbesteding'),1:58))
> y <- array(NA,dim=c(1,58),dimnames=list(c('vrijetijdsbesteding'),1:58))
> 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'
> 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
vrijetijdsbesteding M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 101.76 1 0 0 0 0 0 0 0 0 0 0
2 102.37 0 1 0 0 0 0 0 0 0 0 0
3 102.38 0 0 1 0 0 0 0 0 0 0 0
4 102.86 0 0 0 1 0 0 0 0 0 0 0
5 102.87 0 0 0 0 1 0 0 0 0 0 0
6 102.92 0 0 0 0 0 1 0 0 0 0 0
7 102.95 0 0 0 0 0 0 1 0 0 0 0
8 103.02 0 0 0 0 0 0 0 1 0 0 0
9 104.08 0 0 0 0 0 0 0 0 1 0 0
10 104.16 0 0 0 0 0 0 0 0 0 1 0
11 104.24 0 0 0 0 0 0 0 0 0 0 1
12 104.33 0 0 0 0 0 0 0 0 0 0 0
13 104.73 1 0 0 0 0 0 0 0 0 0 0
14 104.86 0 1 0 0 0 0 0 0 0 0 0
15 105.03 0 0 1 0 0 0 0 0 0 0 0
16 105.62 0 0 0 1 0 0 0 0 0 0 0
17 105.63 0 0 0 0 1 0 0 0 0 0 0
18 105.63 0 0 0 0 0 1 0 0 0 0 0
19 105.94 0 0 0 0 0 0 1 0 0 0 0
20 106.61 0 0 0 0 0 0 0 1 0 0 0
21 107.69 0 0 0 0 0 0 0 0 1 0 0
22 107.78 0 0 0 0 0 0 0 0 0 1 0
23 107.93 0 0 0 0 0 0 0 0 0 0 1
24 108.48 0 0 0 0 0 0 0 0 0 0 0
25 108.14 1 0 0 0 0 0 0 0 0 0 0
26 108.48 0 1 0 0 0 0 0 0 0 0 0
27 108.48 0 0 1 0 0 0 0 0 0 0 0
28 108.89 0 0 0 1 0 0 0 0 0 0 0
29 108.93 0 0 0 0 1 0 0 0 0 0 0
30 109.21 0 0 0 0 0 1 0 0 0 0 0
31 109.47 0 0 0 0 0 0 1 0 0 0 0
32 109.80 0 0 0 0 0 0 0 1 0 0 0
33 111.73 0 0 0 0 0 0 0 0 1 0 0
34 111.85 0 0 0 0 0 0 0 0 0 1 0
35 112.12 0 0 0 0 0 0 0 0 0 0 1
36 112.15 0 0 0 0 0 0 0 0 0 0 0
37 112.17 1 0 0 0 0 0 0 0 0 0 0
38 112.67 0 1 0 0 0 0 0 0 0 0 0
39 112.80 0 0 1 0 0 0 0 0 0 0 0
40 113.44 0 0 0 1 0 0 0 0 0 0 0
41 113.53 0 0 0 0 1 0 0 0 0 0 0
42 114.53 0 0 0 0 0 1 0 0 0 0 0
43 114.51 0 0 0 0 0 0 1 0 0 0 0
44 115.05 0 0 0 0 0 0 0 1 0 0 0
45 116.67 0 0 0 0 0 0 0 0 1 0 0
46 117.07 0 0 0 0 0 0 0 0 0 1 0
47 116.92 0 0 0 0 0 0 0 0 0 0 1
48 117.00 0 0 0 0 0 0 0 0 0 0 0
49 117.02 1 0 0 0 0 0 0 0 0 0 0
50 117.35 0 1 0 0 0 0 0 0 0 0 0
51 117.36 0 0 1 0 0 0 0 0 0 0 0
52 117.82 0 0 0 1 0 0 0 0 0 0 0
53 117.88 0 0 0 0 1 0 0 0 0 0 0
54 118.24 0 0 0 0 0 1 0 0 0 0 0
55 118.50 0 0 0 0 0 0 1 0 0 0 0
56 118.80 0 0 0 0 0 0 0 1 0 0 0
57 119.76 0 0 0 0 0 0 0 0 1 0 0
58 120.09 0 0 0 0 0 0 0 0 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
110.4900 -1.7260 -1.3440 -1.2800 -0.7640 -0.7220
M6 M7 M8 M9 M10 M11
-0.3840 -0.2160 0.1660 1.4960 1.7000 -0.1875
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.030 -4.324 -0.767 4.417 8.256
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.4900 3.0498 36.228 <2e-16 ***
M1 -1.7260 4.0918 -0.422 0.675
M2 -1.3440 4.0918 -0.328 0.744
M3 -1.2800 4.0918 -0.313 0.756
M4 -0.7640 4.0918 -0.187 0.853
M5 -0.7220 4.0918 -0.176 0.861
M6 -0.3840 4.0918 -0.094 0.926
M7 -0.2160 4.0918 -0.053 0.958
M8 0.1660 4.0918 0.041 0.968
M9 1.4960 4.0918 0.366 0.716
M10 1.7000 4.0918 0.415 0.680
M11 -0.1875 4.3131 -0.043 0.966
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.1 on 46 degrees of freedom
Multiple R-squared: 0.0339, Adjusted R-squared: -0.1971
F-statistic: 0.1467 on 11 and 46 DF, p-value: 0.9992
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.064589393 0.12917879 0.9354106
[2,] 0.035989326 0.07197865 0.9640107
[3,] 0.021387921 0.04277584 0.9786121
[4,] 0.013617039 0.02723408 0.9863830
[5,] 0.010106656 0.02021331 0.9898933
[6,] 0.009631596 0.01926319 0.9903684
[7,] 0.009964935 0.01992987 0.9900351
[8,] 0.011256687 0.02251337 0.9887433
[9,] 0.011086484 0.02217297 0.9889135
[10,] 0.012148852 0.02429770 0.9878511
[11,] 0.020937705 0.04187541 0.9790623
[12,] 0.032555241 0.06511048 0.9674448
[13,] 0.047168567 0.09433713 0.9528314
[14,] 0.065930662 0.13186132 0.9340693
[15,] 0.092336584 0.18467317 0.9076634
[16,] 0.140624189 0.28124838 0.8593758
[17,] 0.210200835 0.42040167 0.7897992
[18,] 0.312124005 0.62424801 0.6878760
[19,] 0.444300268 0.88860054 0.5556997
[20,] 0.613391200 0.77321760 0.3866088
[21,] 0.662892804 0.67421439 0.3371072
[22,] 0.701594230 0.59681154 0.2984058
[23,] 0.766452204 0.46709559 0.2335478
[24,] 0.810691126 0.37861775 0.1893089
[25,] 0.839769048 0.32046190 0.1602310
[26,] 0.856206313 0.28758737 0.1437937
[27,] 0.866150001 0.26770000 0.1338500
[28,] 0.853261402 0.29347720 0.1467386
[29,] 0.838073031 0.32385394 0.1619270
> postscript(file="/var/wessaorg/rcomp/tmp/19btu1322603551.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2izl41322603551.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3zq4d1322603551.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4twmr1322603551.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kng91322603551.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 58
Frequency = 1
1 2 3 4 5 6 7 8 9 10
-7.0040 -6.7760 -6.8300 -6.8660 -6.8980 -7.1860 -7.3240 -7.6360 -7.9060 -8.0300
11 12 13 14 15 16 17 18 19 20
-6.0625 -6.1600 -4.0340 -4.2860 -4.1800 -4.1060 -4.1380 -4.4760 -4.3340 -4.0460
21 22 23 24 25 26 27 28 29 30
-4.2960 -4.4100 -2.3725 -2.0100 -0.6240 -0.6660 -0.7300 -0.8360 -0.8380 -0.8960
31 32 33 34 35 36 37 38 39 40
-0.8040 -0.8560 -0.2560 -0.3400 1.8175 1.6600 3.4060 3.5240 3.5900 3.7140
41 42 43 44 45 46 47 48 49 50
3.7620 4.4240 4.2360 4.3940 4.6840 4.8800 6.6175 6.5100 8.2560 8.2040
51 52 53 54 55 56 57 58
8.1500 8.0940 8.1120 8.1340 8.2260 8.1440 7.7740 7.9000
> postscript(file="/var/wessaorg/rcomp/tmp/61lr01322603551.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -7.0040 NA
1 -6.7760 -7.0040
2 -6.8300 -6.7760
3 -6.8660 -6.8300
4 -6.8980 -6.8660
5 -7.1860 -6.8980
6 -7.3240 -7.1860
7 -7.6360 -7.3240
8 -7.9060 -7.6360
9 -8.0300 -7.9060
10 -6.0625 -8.0300
11 -6.1600 -6.0625
12 -4.0340 -6.1600
13 -4.2860 -4.0340
14 -4.1800 -4.2860
15 -4.1060 -4.1800
16 -4.1380 -4.1060
17 -4.4760 -4.1380
18 -4.3340 -4.4760
19 -4.0460 -4.3340
20 -4.2960 -4.0460
21 -4.4100 -4.2960
22 -2.3725 -4.4100
23 -2.0100 -2.3725
24 -0.6240 -2.0100
25 -0.6660 -0.6240
26 -0.7300 -0.6660
27 -0.8360 -0.7300
28 -0.8380 -0.8360
29 -0.8960 -0.8380
30 -0.8040 -0.8960
31 -0.8560 -0.8040
32 -0.2560 -0.8560
33 -0.3400 -0.2560
34 1.8175 -0.3400
35 1.6600 1.8175
36 3.4060 1.6600
37 3.5240 3.4060
38 3.5900 3.5240
39 3.7140 3.5900
40 3.7620 3.7140
41 4.4240 3.7620
42 4.2360 4.4240
43 4.3940 4.2360
44 4.6840 4.3940
45 4.8800 4.6840
46 6.6175 4.8800
47 6.5100 6.6175
48 8.2560 6.5100
49 8.2040 8.2560
50 8.1500 8.2040
51 8.0940 8.1500
52 8.1120 8.0940
53 8.1340 8.1120
54 8.2260 8.1340
55 8.1440 8.2260
56 7.7740 8.1440
57 7.9000 7.7740
58 NA 7.9000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.7760 -7.0040
[2,] -6.8300 -6.7760
[3,] -6.8660 -6.8300
[4,] -6.8980 -6.8660
[5,] -7.1860 -6.8980
[6,] -7.3240 -7.1860
[7,] -7.6360 -7.3240
[8,] -7.9060 -7.6360
[9,] -8.0300 -7.9060
[10,] -6.0625 -8.0300
[11,] -6.1600 -6.0625
[12,] -4.0340 -6.1600
[13,] -4.2860 -4.0340
[14,] -4.1800 -4.2860
[15,] -4.1060 -4.1800
[16,] -4.1380 -4.1060
[17,] -4.4760 -4.1380
[18,] -4.3340 -4.4760
[19,] -4.0460 -4.3340
[20,] -4.2960 -4.0460
[21,] -4.4100 -4.2960
[22,] -2.3725 -4.4100
[23,] -2.0100 -2.3725
[24,] -0.6240 -2.0100
[25,] -0.6660 -0.6240
[26,] -0.7300 -0.6660
[27,] -0.8360 -0.7300
[28,] -0.8380 -0.8360
[29,] -0.8960 -0.8380
[30,] -0.8040 -0.8960
[31,] -0.8560 -0.8040
[32,] -0.2560 -0.8560
[33,] -0.3400 -0.2560
[34,] 1.8175 -0.3400
[35,] 1.6600 1.8175
[36,] 3.4060 1.6600
[37,] 3.5240 3.4060
[38,] 3.5900 3.5240
[39,] 3.7140 3.5900
[40,] 3.7620 3.7140
[41,] 4.4240 3.7620
[42,] 4.2360 4.4240
[43,] 4.3940 4.2360
[44,] 4.6840 4.3940
[45,] 4.8800 4.6840
[46,] 6.6175 4.8800
[47,] 6.5100 6.6175
[48,] 8.2560 6.5100
[49,] 8.2040 8.2560
[50,] 8.1500 8.2040
[51,] 8.0940 8.1500
[52,] 8.1120 8.0940
[53,] 8.1340 8.1120
[54,] 8.2260 8.1340
[55,] 8.1440 8.2260
[56,] 7.7740 8.1440
[57,] 7.9000 7.7740
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.7760 -7.0040
2 -6.8300 -6.7760
3 -6.8660 -6.8300
4 -6.8980 -6.8660
5 -7.1860 -6.8980
6 -7.3240 -7.1860
7 -7.6360 -7.3240
8 -7.9060 -7.6360
9 -8.0300 -7.9060
10 -6.0625 -8.0300
11 -6.1600 -6.0625
12 -4.0340 -6.1600
13 -4.2860 -4.0340
14 -4.1800 -4.2860
15 -4.1060 -4.1800
16 -4.1380 -4.1060
17 -4.4760 -4.1380
18 -4.3340 -4.4760
19 -4.0460 -4.3340
20 -4.2960 -4.0460
21 -4.4100 -4.2960
22 -2.3725 -4.4100
23 -2.0100 -2.3725
24 -0.6240 -2.0100
25 -0.6660 -0.6240
26 -0.7300 -0.6660
27 -0.8360 -0.7300
28 -0.8380 -0.8360
29 -0.8960 -0.8380
30 -0.8040 -0.8960
31 -0.8560 -0.8040
32 -0.2560 -0.8560
33 -0.3400 -0.2560
34 1.8175 -0.3400
35 1.6600 1.8175
36 3.4060 1.6600
37 3.5240 3.4060
38 3.5900 3.5240
39 3.7140 3.5900
40 3.7620 3.7140
41 4.4240 3.7620
42 4.2360 4.4240
43 4.3940 4.2360
44 4.6840 4.3940
45 4.8800 4.6840
46 6.6175 4.8800
47 6.5100 6.6175
48 8.2560 6.5100
49 8.2040 8.2560
50 8.1500 8.2040
51 8.0940 8.1500
52 8.1120 8.0940
53 8.1340 8.1120
54 8.2260 8.1340
55 8.1440 8.2260
56 7.7740 8.1440
57 7.9000 7.7740
> 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/7th6z1322603551.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/8xfa71322603551.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/9r41a1322603551.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/104ne31322603551.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11eih71322603551.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12e3zy1322603551.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13rf8s1322603551.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/141ztu1322603551.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/15hzuf1322603551.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/16f5m61322603551.tab")
+ }
>
> try(system("convert tmp/19btu1322603551.ps tmp/19btu1322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/2izl41322603551.ps tmp/2izl41322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zq4d1322603551.ps tmp/3zq4d1322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/4twmr1322603551.ps tmp/4twmr1322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kng91322603551.ps tmp/5kng91322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/61lr01322603551.ps tmp/61lr01322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/7th6z1322603551.ps tmp/7th6z1322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xfa71322603551.ps tmp/8xfa71322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/9r41a1322603551.ps tmp/9r41a1322603551.png",intern=TRUE))
character(0)
> try(system("convert tmp/104ne31322603551.ps tmp/104ne31322603551.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.035 0.509 3.567