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.
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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
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> x <- array(list(20366,1,22782,1,19169,1,13807,1,29743,1,25591,1,29096,1,26482,1,22405,1,27044,1,17970,1,18730,1,19684,1,19785,1,18479,1,10698,1,31956,1,29506,1,34506,1,27165,1,26736,1,23691,1,18157,1,17328,1,18205,1,20995,1,17382,1,9367,1,31124,1,26551,1,30651,1,25859,1,25100,1,25778,1,20418,1,18688,1,20424,1,24776,1,19814,1,12738,1,31566,1,30111,1,30019,1,31934,1,25826,1,26835,1,20205,1,17789,1,20520,1,22518,1,15572,0,11509,0,25447,0,24090,0,27786,0,26195,0,20516,0,22759,0,19028,0,16971,0),dim=c(2,60),dimnames=list(c('wagens','dummies'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('wagens','dummies'),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
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
wagens dummies M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 20366 1 1 0 0 0 0 0 0 0 0 0 0 1
2 22782 1 0 1 0 0 0 0 0 0 0 0 0 2
3 19169 1 0 0 1 0 0 0 0 0 0 0 0 3
4 13807 1 0 0 0 1 0 0 0 0 0 0 0 4
5 29743 1 0 0 0 0 1 0 0 0 0 0 0 5
6 25591 1 0 0 0 0 0 1 0 0 0 0 0 6
7 29096 1 0 0 0 0 0 0 1 0 0 0 0 7
8 26482 1 0 0 0 0 0 0 0 1 0 0 0 8
9 22405 1 0 0 0 0 0 0 0 0 1 0 0 9
10 27044 1 0 0 0 0 0 0 0 0 0 1 0 10
11 17970 1 0 0 0 0 0 0 0 0 0 0 1 11
12 18730 1 0 0 0 0 0 0 0 0 0 0 0 12
13 19684 1 1 0 0 0 0 0 0 0 0 0 0 13
14 19785 1 0 1 0 0 0 0 0 0 0 0 0 14
15 18479 1 0 0 1 0 0 0 0 0 0 0 0 15
16 10698 1 0 0 0 1 0 0 0 0 0 0 0 16
17 31956 1 0 0 0 0 1 0 0 0 0 0 0 17
18 29506 1 0 0 0 0 0 1 0 0 0 0 0 18
19 34506 1 0 0 0 0 0 0 1 0 0 0 0 19
20 27165 1 0 0 0 0 0 0 0 1 0 0 0 20
21 26736 1 0 0 0 0 0 0 0 0 1 0 0 21
22 23691 1 0 0 0 0 0 0 0 0 0 1 0 22
23 18157 1 0 0 0 0 0 0 0 0 0 0 1 23
24 17328 1 0 0 0 0 0 0 0 0 0 0 0 24
25 18205 1 1 0 0 0 0 0 0 0 0 0 0 25
26 20995 1 0 1 0 0 0 0 0 0 0 0 0 26
27 17382 1 0 0 1 0 0 0 0 0 0 0 0 27
28 9367 1 0 0 0 1 0 0 0 0 0 0 0 28
29 31124 1 0 0 0 0 1 0 0 0 0 0 0 29
30 26551 1 0 0 0 0 0 1 0 0 0 0 0 30
31 30651 1 0 0 0 0 0 0 1 0 0 0 0 31
32 25859 1 0 0 0 0 0 0 0 1 0 0 0 32
33 25100 1 0 0 0 0 0 0 0 0 1 0 0 33
34 25778 1 0 0 0 0 0 0 0 0 0 1 0 34
35 20418 1 0 0 0 0 0 0 0 0 0 0 1 35
36 18688 1 0 0 0 0 0 0 0 0 0 0 0 36
37 20424 1 1 0 0 0 0 0 0 0 0 0 0 37
38 24776 1 0 1 0 0 0 0 0 0 0 0 0 38
39 19814 1 0 0 1 0 0 0 0 0 0 0 0 39
40 12738 1 0 0 0 1 0 0 0 0 0 0 0 40
41 31566 1 0 0 0 0 1 0 0 0 0 0 0 41
42 30111 1 0 0 0 0 0 1 0 0 0 0 0 42
43 30019 1 0 0 0 0 0 0 1 0 0 0 0 43
44 31934 1 0 0 0 0 0 0 0 1 0 0 0 44
45 25826 1 0 0 0 0 0 0 0 0 1 0 0 45
46 26835 1 0 0 0 0 0 0 0 0 0 1 0 46
47 20205 1 0 0 0 0 0 0 0 0 0 0 1 47
48 17789 1 0 0 0 0 0 0 0 0 0 0 0 48
49 20520 1 1 0 0 0 0 0 0 0 0 0 0 49
50 22518 1 0 1 0 0 0 0 0 0 0 0 0 50
51 15572 0 0 0 1 0 0 0 0 0 0 0 0 51
52 11509 0 0 0 0 1 0 0 0 0 0 0 0 52
53 25447 0 0 0 0 0 1 0 0 0 0 0 0 53
54 24090 0 0 0 0 0 0 1 0 0 0 0 0 54
55 27786 0 0 0 0 0 0 0 1 0 0 0 0 55
56 26195 0 0 0 0 0 0 0 0 1 0 0 0 56
57 20516 0 0 0 0 0 0 0 0 0 1 0 0 57
58 22759 0 0 0 0 0 0 0 0 0 0 1 0 58
59 19028 0 0 0 0 0 0 0 0 0 0 0 1 59
60 16971 0 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) dummies M1 M2 M3 M4
13837.97 3629.44 1567.06 3866.24 471.92 -6019.70
M5 M6 M7 M8 M9 M10
12291.49 9461.88 12671.47 9754.65 6312.04 7384.63
M11 t
1286.61 32.21
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2982.69 -1234.77 -77.98 940.28 3755.07
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13837.97 1334.39 10.370 1.26e-13 ***
dummies 3629.44 773.85 4.690 2.47e-05 ***
M1 1567.06 1067.13 1.468 0.148779
M2 3866.24 1065.96 3.627 0.000716 ***
M3 471.92 1067.85 0.442 0.660610
M4 -6019.70 1065.65 -5.649 9.70e-07 ***
M5 12291.49 1063.69 11.555 3.37e-15 ***
M6 9461.88 1062.00 8.910 1.40e-11 ***
M7 12671.47 1060.56 11.948 1.06e-15 ***
M8 9754.65 1059.38 9.208 5.25e-12 ***
M9 6312.04 1058.47 5.963 3.29e-07 ***
M10 7384.63 1057.81 6.981 9.73e-09 ***
M11 1286.61 1057.42 1.217 0.229906
t 32.21 16.65 1.935 0.059198 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1672 on 46 degrees of freedom
Multiple R-squared: 0.9343, Adjusted R-squared: 0.9157
F-statistic: 50.28 on 13 and 46 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,] 0.6003290 0.79934202 0.39967101
[2,] 0.8049677 0.39006461 0.19503230
[3,] 0.9662648 0.06747046 0.03373523
[4,] 0.9347401 0.13051989 0.06525995
[5,] 0.9687353 0.06252947 0.03126473
[6,] 0.9745324 0.05093519 0.02546760
[7,] 0.9548810 0.09023792 0.04511896
[8,] 0.9328824 0.13423520 0.06711760
[9,] 0.9113452 0.17730964 0.08865482
[10,] 0.8658953 0.26820943 0.13410471
[11,] 0.8199313 0.36013737 0.18006869
[12,] 0.8803153 0.23936944 0.11968472
[13,] 0.8463100 0.30737991 0.15368995
[14,] 0.8006427 0.39871460 0.19935730
[15,] 0.7304057 0.53918853 0.26959426
[16,] 0.8834687 0.23306251 0.11653125
[17,] 0.8299201 0.34015984 0.17007992
[18,] 0.7669318 0.46613645 0.23306822
[19,] 0.7484859 0.50302820 0.25151410
[20,] 0.6855336 0.62893287 0.31446644
[21,] 0.6308356 0.73832888 0.36916444
[22,] 0.5991905 0.80161903 0.40080952
[23,] 0.4898640 0.97972809 0.51013595
[24,] 0.4736552 0.94731036 0.52634482
[25,] 0.4349162 0.86983234 0.56508383
[26,] 0.4496745 0.89934896 0.55032552
[27,] 0.3269124 0.65382482 0.67308759
> postscript(file="/var/www/html/rcomp/tmp/1lplp1261770226.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/255o41261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39gug1261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48gnd1261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57dfe1261770226.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
1299.314286 1383.914286 1133.025714 2230.425714 -176.974286 -1531.574286
7 8 9 10 11 12
-1268.374286 -997.774286 -1664.374286 1869.825714 -1138.374286 876.025714
13 14 15 16 17 18
230.757143 -1999.642857 56.468571 -1265.131429 1649.468571 1996.868571
19 20 21 22 23 24
3755.068571 -701.331429 2280.068571 -1869.731429 -1337.931429 -912.531429
25 26 27 28 29 30
-1634.800000 -1176.200000 -1427.088571 -2982.688571 430.911429 -1344.688571
31 32 33 34 35 36
-486.488571 -2393.888571 257.511429 -169.288571 536.511429 60.911429
37 38 39 40 41 42
197.642857 2218.242857 618.354286 1.754286 486.354286 1828.754286
43 44 45 46 47 48
-1505.045714 3294.554286 596.954286 501.154286 -63.045714 -1224.645714
49 50 51 52 53 54
-92.914286 -426.314286 -380.760000 2015.640000 -2389.760000 -949.360000
55 56 57 58 59 60
-495.160000 798.440000 -1470.160000 -331.960000 2002.840000 1200.240000
> postscript(file="/var/www/html/rcomp/tmp/6l21h1261770226.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 1299.314286 NA
1 1383.914286 1299.314286
2 1133.025714 1383.914286
3 2230.425714 1133.025714
4 -176.974286 2230.425714
5 -1531.574286 -176.974286
6 -1268.374286 -1531.574286
7 -997.774286 -1268.374286
8 -1664.374286 -997.774286
9 1869.825714 -1664.374286
10 -1138.374286 1869.825714
11 876.025714 -1138.374286
12 230.757143 876.025714
13 -1999.642857 230.757143
14 56.468571 -1999.642857
15 -1265.131429 56.468571
16 1649.468571 -1265.131429
17 1996.868571 1649.468571
18 3755.068571 1996.868571
19 -701.331429 3755.068571
20 2280.068571 -701.331429
21 -1869.731429 2280.068571
22 -1337.931429 -1869.731429
23 -912.531429 -1337.931429
24 -1634.800000 -912.531429
25 -1176.200000 -1634.800000
26 -1427.088571 -1176.200000
27 -2982.688571 -1427.088571
28 430.911429 -2982.688571
29 -1344.688571 430.911429
30 -486.488571 -1344.688571
31 -2393.888571 -486.488571
32 257.511429 -2393.888571
33 -169.288571 257.511429
34 536.511429 -169.288571
35 60.911429 536.511429
36 197.642857 60.911429
37 2218.242857 197.642857
38 618.354286 2218.242857
39 1.754286 618.354286
40 486.354286 1.754286
41 1828.754286 486.354286
42 -1505.045714 1828.754286
43 3294.554286 -1505.045714
44 596.954286 3294.554286
45 501.154286 596.954286
46 -63.045714 501.154286
47 -1224.645714 -63.045714
48 -92.914286 -1224.645714
49 -426.314286 -92.914286
50 -380.760000 -426.314286
51 2015.640000 -380.760000
52 -2389.760000 2015.640000
53 -949.360000 -2389.760000
54 -495.160000 -949.360000
55 798.440000 -495.160000
56 -1470.160000 798.440000
57 -331.960000 -1470.160000
58 2002.840000 -331.960000
59 1200.240000 2002.840000
60 NA 1200.240000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1383.914286 1299.314286
[2,] 1133.025714 1383.914286
[3,] 2230.425714 1133.025714
[4,] -176.974286 2230.425714
[5,] -1531.574286 -176.974286
[6,] -1268.374286 -1531.574286
[7,] -997.774286 -1268.374286
[8,] -1664.374286 -997.774286
[9,] 1869.825714 -1664.374286
[10,] -1138.374286 1869.825714
[11,] 876.025714 -1138.374286
[12,] 230.757143 876.025714
[13,] -1999.642857 230.757143
[14,] 56.468571 -1999.642857
[15,] -1265.131429 56.468571
[16,] 1649.468571 -1265.131429
[17,] 1996.868571 1649.468571
[18,] 3755.068571 1996.868571
[19,] -701.331429 3755.068571
[20,] 2280.068571 -701.331429
[21,] -1869.731429 2280.068571
[22,] -1337.931429 -1869.731429
[23,] -912.531429 -1337.931429
[24,] -1634.800000 -912.531429
[25,] -1176.200000 -1634.800000
[26,] -1427.088571 -1176.200000
[27,] -2982.688571 -1427.088571
[28,] 430.911429 -2982.688571
[29,] -1344.688571 430.911429
[30,] -486.488571 -1344.688571
[31,] -2393.888571 -486.488571
[32,] 257.511429 -2393.888571
[33,] -169.288571 257.511429
[34,] 536.511429 -169.288571
[35,] 60.911429 536.511429
[36,] 197.642857 60.911429
[37,] 2218.242857 197.642857
[38,] 618.354286 2218.242857
[39,] 1.754286 618.354286
[40,] 486.354286 1.754286
[41,] 1828.754286 486.354286
[42,] -1505.045714 1828.754286
[43,] 3294.554286 -1505.045714
[44,] 596.954286 3294.554286
[45,] 501.154286 596.954286
[46,] -63.045714 501.154286
[47,] -1224.645714 -63.045714
[48,] -92.914286 -1224.645714
[49,] -426.314286 -92.914286
[50,] -380.760000 -426.314286
[51,] 2015.640000 -380.760000
[52,] -2389.760000 2015.640000
[53,] -949.360000 -2389.760000
[54,] -495.160000 -949.360000
[55,] 798.440000 -495.160000
[56,] -1470.160000 798.440000
[57,] -331.960000 -1470.160000
[58,] 2002.840000 -331.960000
[59,] 1200.240000 2002.840000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1383.914286 1299.314286
2 1133.025714 1383.914286
3 2230.425714 1133.025714
4 -176.974286 2230.425714
5 -1531.574286 -176.974286
6 -1268.374286 -1531.574286
7 -997.774286 -1268.374286
8 -1664.374286 -997.774286
9 1869.825714 -1664.374286
10 -1138.374286 1869.825714
11 876.025714 -1138.374286
12 230.757143 876.025714
13 -1999.642857 230.757143
14 56.468571 -1999.642857
15 -1265.131429 56.468571
16 1649.468571 -1265.131429
17 1996.868571 1649.468571
18 3755.068571 1996.868571
19 -701.331429 3755.068571
20 2280.068571 -701.331429
21 -1869.731429 2280.068571
22 -1337.931429 -1869.731429
23 -912.531429 -1337.931429
24 -1634.800000 -912.531429
25 -1176.200000 -1634.800000
26 -1427.088571 -1176.200000
27 -2982.688571 -1427.088571
28 430.911429 -2982.688571
29 -1344.688571 430.911429
30 -486.488571 -1344.688571
31 -2393.888571 -486.488571
32 257.511429 -2393.888571
33 -169.288571 257.511429
34 536.511429 -169.288571
35 60.911429 536.511429
36 197.642857 60.911429
37 2218.242857 197.642857
38 618.354286 2218.242857
39 1.754286 618.354286
40 486.354286 1.754286
41 1828.754286 486.354286
42 -1505.045714 1828.754286
43 3294.554286 -1505.045714
44 596.954286 3294.554286
45 501.154286 596.954286
46 -63.045714 501.154286
47 -1224.645714 -63.045714
48 -92.914286 -1224.645714
49 -426.314286 -92.914286
50 -380.760000 -426.314286
51 2015.640000 -380.760000
52 -2389.760000 2015.640000
53 -949.360000 -2389.760000
54 -495.160000 -949.360000
55 798.440000 -495.160000
56 -1470.160000 798.440000
57 -331.960000 -1470.160000
58 2002.840000 -331.960000
59 1200.240000 2002.840000
> 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/7ioyh1261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88j1t1261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9msf91261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10osqd1261770226.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ok7h1261770226.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/128vfr1261770226.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/13fbhf1261770226.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/14dx0d1261770227.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/156wh61261770227.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/16xem01261770227.tab")
+ }
>
> try(system("convert tmp/1lplp1261770226.ps tmp/1lplp1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/255o41261770226.ps tmp/255o41261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/39gug1261770226.ps tmp/39gug1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/48gnd1261770226.ps tmp/48gnd1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/57dfe1261770226.ps tmp/57dfe1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l21h1261770226.ps tmp/6l21h1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ioyh1261770226.ps tmp/7ioyh1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/88j1t1261770226.ps tmp/88j1t1261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/9msf91261770226.ps tmp/9msf91261770226.png",intern=TRUE))
character(0)
> try(system("convert tmp/10osqd1261770226.ps tmp/10osqd1261770226.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.426 1.590 4.736