R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(126.51,0,131.02,0,136.51,0,138.04,0,132.92,0,129.61,0,122.96,0,124.04,0,121.29,0,124.56,0,118.53,0,113.14,0,114.15,0,122.17,0,129.23,0,131.19,0,129.12,0,128.28,0,126.83,0,138.13,0,140.52,0,146.83,0,135.14,0,131.84,0,125.7,0,128.98,0,133.25,0,136.76,0,133.24,0,128.54,0,121.08,0,120.23,0,119.08,0,125.75,0,126.89,0,126.6,0,121.89,0,123.44,0,126.46,0,129.49,0,127.78,0,125.29,0,119.02,0,119.96,0,122.86,0,131.89,0,132.73,0,135.01,0,136.71,1,142.73,1,144.43,1,144.93,1,138.75,1,130.22,1,122.19,1,128.4,1,140.43,1,153.5,1,149.33,1,142.97,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X t
1 126.51 0 1
2 131.02 0 2
3 136.51 0 3
4 138.04 0 4
5 132.92 0 5
6 129.61 0 6
7 122.96 0 7
8 124.04 0 8
9 121.29 0 9
10 124.56 0 10
11 118.53 0 11
12 113.14 0 12
13 114.15 0 13
14 122.17 0 14
15 129.23 0 15
16 131.19 0 16
17 129.12 0 17
18 128.28 0 18
19 126.83 0 19
20 138.13 0 20
21 140.52 0 21
22 146.83 0 22
23 135.14 0 23
24 131.84 0 24
25 125.70 0 25
26 128.98 0 26
27 133.25 0 27
28 136.76 0 28
29 133.24 0 29
30 128.54 0 30
31 121.08 0 31
32 120.23 0 32
33 119.08 0 33
34 125.75 0 34
35 126.89 0 35
36 126.60 0 36
37 121.89 0 37
38 123.44 0 38
39 126.46 0 39
40 129.49 0 40
41 127.78 0 41
42 125.29 0 42
43 119.02 0 43
44 119.96 0 44
45 122.86 0 45
46 131.89 0 46
47 132.73 0 47
48 135.01 0 48
49 136.71 1 49
50 142.73 1 50
51 144.43 1 51
52 144.93 1 52
53 138.75 1 53
54 130.22 1 54
55 122.19 1 55
56 128.40 1 56
57 140.43 1 57
58 153.50 1 58
59 149.33 1 59
60 142.97 1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X t
128.46112 12.55500 -0.02692
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.3457 -4.6206 0.3629 4.6969 18.9610
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 128.46112 2.14339 59.934 < 2e-16 ***
X 12.55500 3.29093 3.815 0.000337 ***
t -0.02692 0.07601 -0.354 0.724560
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.352 on 57 degrees of freedom
Multiple R-squared: 0.3018, Adjusted R-squared: 0.2773
F-statistic: 12.32 on 2 and 57 DF, p-value: 3.579e-05
> 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,] 0.32822178 0.65644355 0.6717782
[2,] 0.41154771 0.82309542 0.5884523
[3,] 0.30096075 0.60192150 0.6990393
[4,] 0.21838529 0.43677058 0.7816147
[5,] 0.13635905 0.27271810 0.8636410
[6,] 0.09636269 0.19272537 0.9036373
[7,] 0.10908374 0.21816748 0.8909163
[8,] 0.09701914 0.19403828 0.9029809
[9,] 0.12728972 0.25457945 0.8727103
[10,] 0.30707518 0.61415035 0.6929248
[11,] 0.45492207 0.90984413 0.5450779
[12,] 0.46920027 0.93840054 0.5307997
[13,] 0.44432849 0.88865697 0.5556715
[14,] 0.40022626 0.80045253 0.5997737
[15,] 0.54973765 0.90052471 0.4502624
[16,] 0.67951159 0.64097682 0.3204884
[17,] 0.89740733 0.20518534 0.1025927
[18,] 0.87897572 0.24204856 0.1210243
[19,] 0.84372976 0.31254049 0.1562702
[20,] 0.81249612 0.37500777 0.1875039
[21,] 0.76160977 0.47678047 0.2383902
[22,] 0.72993822 0.54012356 0.2700618
[23,] 0.76958029 0.46083943 0.2304197
[24,] 0.78118538 0.43762924 0.2188146
[25,] 0.76650504 0.46698992 0.2334950
[26,] 0.76793434 0.46413131 0.2320657
[27,] 0.76237010 0.47525979 0.2376299
[28,] 0.75991436 0.48017128 0.2400856
[29,] 0.70323484 0.59353033 0.2967652
[30,] 0.64407027 0.71185946 0.3559297
[31,] 0.58077532 0.83844935 0.4192247
[32,] 0.52189671 0.95620658 0.4781033
[33,] 0.44919822 0.89839644 0.5508018
[34,] 0.37329589 0.74659178 0.6267041
[35,] 0.32243941 0.64487882 0.6775606
[36,] 0.26198887 0.52397774 0.7380111
[37,] 0.19960358 0.39920717 0.8003964
[38,] 0.18804406 0.37608812 0.8119559
[39,] 0.18367448 0.36734896 0.8163255
[40,] 0.17061189 0.34122378 0.8293881
[41,] 0.12785771 0.25571541 0.8721423
[42,] 0.09303855 0.18607711 0.9069614
[43,] 0.06746558 0.13493117 0.9325344
[44,] 0.04035995 0.08071989 0.9596401
[45,] 0.02920409 0.05840818 0.9707959
[46,] 0.03132946 0.06265892 0.9686705
[47,] 0.07805532 0.15611065 0.9219447
[48,] 0.16155794 0.32311588 0.8384421
[49,] 0.13605869 0.27211738 0.8639413
> postscript(file="/var/www/html/rcomp/tmp/15fdb1258718236.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jnhb1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z2qz1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4llbe1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hr5b1258718236.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
-1.9242073 2.6127093 8.1296260 9.6865426 4.5934592 1.3103758
7 8 9 10 11 12
-5.3127076 -4.2057909 -6.9288743 -3.6319577 -9.6350411 -14.9981244
13 14 15 16 17 18
-13.9612078 -5.9142912 1.1726254 3.1595420 1.1164587 0.3033753
19 20 21 22 23 24
-1.1197081 10.2072085 12.6241252 18.9610418 7.2979584 4.0248750
25 26 27 28 29 30
-2.0882084 1.2187083 5.5156249 9.0525415 5.5594581 0.8863748
31 32 33 34 35 36
-6.5467086 -7.3697920 -8.4928754 -1.7959588 -0.6290421 -0.8921255
37 38 39 40 41 42
-5.5752089 -3.9982923 -0.9513756 2.1055410 0.4224576 -2.0406258
43 44 45 46 47 48
-8.2837092 -7.3167925 -4.3898759 4.6670407 5.5339573 7.8408740
49 50 51 52 53 54
-2.9872081 3.0597085 4.7866252 5.3135418 -0.8395416 -9.3426250
55 56 57 58 59 60
-17.3457084 -11.1087917 0.9481249 14.0450415 9.9019581 3.5688748
> postscript(file="/var/www/html/rcomp/tmp/634941258718236.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 -1.9242073 NA
1 2.6127093 -1.9242073
2 8.1296260 2.6127093
3 9.6865426 8.1296260
4 4.5934592 9.6865426
5 1.3103758 4.5934592
6 -5.3127076 1.3103758
7 -4.2057909 -5.3127076
8 -6.9288743 -4.2057909
9 -3.6319577 -6.9288743
10 -9.6350411 -3.6319577
11 -14.9981244 -9.6350411
12 -13.9612078 -14.9981244
13 -5.9142912 -13.9612078
14 1.1726254 -5.9142912
15 3.1595420 1.1726254
16 1.1164587 3.1595420
17 0.3033753 1.1164587
18 -1.1197081 0.3033753
19 10.2072085 -1.1197081
20 12.6241252 10.2072085
21 18.9610418 12.6241252
22 7.2979584 18.9610418
23 4.0248750 7.2979584
24 -2.0882084 4.0248750
25 1.2187083 -2.0882084
26 5.5156249 1.2187083
27 9.0525415 5.5156249
28 5.5594581 9.0525415
29 0.8863748 5.5594581
30 -6.5467086 0.8863748
31 -7.3697920 -6.5467086
32 -8.4928754 -7.3697920
33 -1.7959588 -8.4928754
34 -0.6290421 -1.7959588
35 -0.8921255 -0.6290421
36 -5.5752089 -0.8921255
37 -3.9982923 -5.5752089
38 -0.9513756 -3.9982923
39 2.1055410 -0.9513756
40 0.4224576 2.1055410
41 -2.0406258 0.4224576
42 -8.2837092 -2.0406258
43 -7.3167925 -8.2837092
44 -4.3898759 -7.3167925
45 4.6670407 -4.3898759
46 5.5339573 4.6670407
47 7.8408740 5.5339573
48 -2.9872081 7.8408740
49 3.0597085 -2.9872081
50 4.7866252 3.0597085
51 5.3135418 4.7866252
52 -0.8395416 5.3135418
53 -9.3426250 -0.8395416
54 -17.3457084 -9.3426250
55 -11.1087917 -17.3457084
56 0.9481249 -11.1087917
57 14.0450415 0.9481249
58 9.9019581 14.0450415
59 3.5688748 9.9019581
60 NA 3.5688748
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.6127093 -1.9242073
[2,] 8.1296260 2.6127093
[3,] 9.6865426 8.1296260
[4,] 4.5934592 9.6865426
[5,] 1.3103758 4.5934592
[6,] -5.3127076 1.3103758
[7,] -4.2057909 -5.3127076
[8,] -6.9288743 -4.2057909
[9,] -3.6319577 -6.9288743
[10,] -9.6350411 -3.6319577
[11,] -14.9981244 -9.6350411
[12,] -13.9612078 -14.9981244
[13,] -5.9142912 -13.9612078
[14,] 1.1726254 -5.9142912
[15,] 3.1595420 1.1726254
[16,] 1.1164587 3.1595420
[17,] 0.3033753 1.1164587
[18,] -1.1197081 0.3033753
[19,] 10.2072085 -1.1197081
[20,] 12.6241252 10.2072085
[21,] 18.9610418 12.6241252
[22,] 7.2979584 18.9610418
[23,] 4.0248750 7.2979584
[24,] -2.0882084 4.0248750
[25,] 1.2187083 -2.0882084
[26,] 5.5156249 1.2187083
[27,] 9.0525415 5.5156249
[28,] 5.5594581 9.0525415
[29,] 0.8863748 5.5594581
[30,] -6.5467086 0.8863748
[31,] -7.3697920 -6.5467086
[32,] -8.4928754 -7.3697920
[33,] -1.7959588 -8.4928754
[34,] -0.6290421 -1.7959588
[35,] -0.8921255 -0.6290421
[36,] -5.5752089 -0.8921255
[37,] -3.9982923 -5.5752089
[38,] -0.9513756 -3.9982923
[39,] 2.1055410 -0.9513756
[40,] 0.4224576 2.1055410
[41,] -2.0406258 0.4224576
[42,] -8.2837092 -2.0406258
[43,] -7.3167925 -8.2837092
[44,] -4.3898759 -7.3167925
[45,] 4.6670407 -4.3898759
[46,] 5.5339573 4.6670407
[47,] 7.8408740 5.5339573
[48,] -2.9872081 7.8408740
[49,] 3.0597085 -2.9872081
[50,] 4.7866252 3.0597085
[51,] 5.3135418 4.7866252
[52,] -0.8395416 5.3135418
[53,] -9.3426250 -0.8395416
[54,] -17.3457084 -9.3426250
[55,] -11.1087917 -17.3457084
[56,] 0.9481249 -11.1087917
[57,] 14.0450415 0.9481249
[58,] 9.9019581 14.0450415
[59,] 3.5688748 9.9019581
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.6127093 -1.9242073
2 8.1296260 2.6127093
3 9.6865426 8.1296260
4 4.5934592 9.6865426
5 1.3103758 4.5934592
6 -5.3127076 1.3103758
7 -4.2057909 -5.3127076
8 -6.9288743 -4.2057909
9 -3.6319577 -6.9288743
10 -9.6350411 -3.6319577
11 -14.9981244 -9.6350411
12 -13.9612078 -14.9981244
13 -5.9142912 -13.9612078
14 1.1726254 -5.9142912
15 3.1595420 1.1726254
16 1.1164587 3.1595420
17 0.3033753 1.1164587
18 -1.1197081 0.3033753
19 10.2072085 -1.1197081
20 12.6241252 10.2072085
21 18.9610418 12.6241252
22 7.2979584 18.9610418
23 4.0248750 7.2979584
24 -2.0882084 4.0248750
25 1.2187083 -2.0882084
26 5.5156249 1.2187083
27 9.0525415 5.5156249
28 5.5594581 9.0525415
29 0.8863748 5.5594581
30 -6.5467086 0.8863748
31 -7.3697920 -6.5467086
32 -8.4928754 -7.3697920
33 -1.7959588 -8.4928754
34 -0.6290421 -1.7959588
35 -0.8921255 -0.6290421
36 -5.5752089 -0.8921255
37 -3.9982923 -5.5752089
38 -0.9513756 -3.9982923
39 2.1055410 -0.9513756
40 0.4224576 2.1055410
41 -2.0406258 0.4224576
42 -8.2837092 -2.0406258
43 -7.3167925 -8.2837092
44 -4.3898759 -7.3167925
45 4.6670407 -4.3898759
46 5.5339573 4.6670407
47 7.8408740 5.5339573
48 -2.9872081 7.8408740
49 3.0597085 -2.9872081
50 4.7866252 3.0597085
51 5.3135418 4.7866252
52 -0.8395416 5.3135418
53 -9.3426250 -0.8395416
54 -17.3457084 -9.3426250
55 -11.1087917 -17.3457084
56 0.9481249 -11.1087917
57 14.0450415 0.9481249
58 9.9019581 14.0450415
59 3.5688748 9.9019581
> 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/7iszj1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8aria1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mzwz1258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10a3f01258718236.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bz7d1258718236.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/12ne3t1258718236.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/13z5ak1258718236.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/14m5d11258718236.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/15vwl01258718236.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/1607c01258718236.tab")
+ }
>
> system("convert tmp/15fdb1258718236.ps tmp/15fdb1258718236.png")
> system("convert tmp/2jnhb1258718236.ps tmp/2jnhb1258718236.png")
> system("convert tmp/3z2qz1258718236.ps tmp/3z2qz1258718236.png")
> system("convert tmp/4llbe1258718236.ps tmp/4llbe1258718236.png")
> system("convert tmp/5hr5b1258718236.ps tmp/5hr5b1258718236.png")
> system("convert tmp/634941258718236.ps tmp/634941258718236.png")
> system("convert tmp/7iszj1258718236.ps tmp/7iszj1258718236.png")
> system("convert tmp/8aria1258718236.ps tmp/8aria1258718236.png")
> system("convert tmp/9mzwz1258718236.ps tmp/9mzwz1258718236.png")
> system("convert tmp/10a3f01258718236.ps tmp/10a3f01258718236.png")
>
>
> proc.time()
user system elapsed
2.466 1.536 3.083