R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(104.3,0,119.8,0,116.8,0,118.2,0,107.4,0,110.8,0,94.8,0,96.5,0,113.4,0,109.8,0,118.7,0,117.2,0,110.3,0,111.6,0,128.1,0,121.3,0,107.3,0,120.5,0,98.5,0,97.7,0,113.2,0,114.6,0,118.3,0,123.9,0,113.6,0,117.5,0,130.1,0,124.7,0,114.2,0,127.3,0,105.9,0,101.5,0,120.2,0,117.1,0,131.1,0,130,0,120.6,0,123.1,0,135.3,0,134.1,0,123.7,0,134.6,0,108.3,1,110.4,1,127.8,1,126.6,1,131.4,1,141.1,1,127,1,127.3,1,143.6,1,149.4,1,126.6,1,136.5,1,116,1,118,1,131.4,1,140.7,1,144.9,1,143.9,1,127.1,1),dim=c(2,61),dimnames=list(c('x','y'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('x','y'),1:61))
> 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 = '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
x y
1 104.3 0
2 119.8 0
3 116.8 0
4 118.2 0
5 107.4 0
6 110.8 0
7 94.8 0
8 96.5 0
9 113.4 0
10 109.8 0
11 118.7 0
12 117.2 0
13 110.3 0
14 111.6 0
15 128.1 0
16 121.3 0
17 107.3 0
18 120.5 0
19 98.5 0
20 97.7 0
21 113.2 0
22 114.6 0
23 118.3 0
24 123.9 0
25 113.6 0
26 117.5 0
27 130.1 0
28 124.7 0
29 114.2 0
30 127.3 0
31 105.9 0
32 101.5 0
33 120.2 0
34 117.1 0
35 131.1 0
36 130.0 0
37 120.6 0
38 123.1 0
39 135.3 0
40 134.1 0
41 123.7 0
42 134.6 0
43 108.3 1
44 110.4 1
45 127.8 1
46 126.6 1
47 131.4 1
48 141.1 1
49 127.0 1
50 127.3 1
51 143.6 1
52 149.4 1
53 126.6 1
54 136.5 1
55 116.0 1
56 118.0 1
57 131.4 1
58 140.7 1
59 144.9 1
60 143.9 1
61 127.1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y
116.61 13.81
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-22.1211 -5.8095 0.5905 7.2905 18.9789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 116.610 1.683 69.296 < 2e-16 ***
y 13.812 3.015 4.581 2.45e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.91 on 59 degrees of freedom
Multiple R-squared: 0.2623, Adjusted R-squared: 0.2498
F-statistic: 20.98 on 1 and 59 DF, p-value: 2.448e-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,] 0.3279120 0.6558240 0.6720880
[2,] 0.1875472 0.3750944 0.8124528
[3,] 0.4483299 0.8966598 0.5516701
[4,] 0.5374904 0.9250191 0.4625096
[5,] 0.4365838 0.8731676 0.5634162
[6,] 0.3329822 0.6659644 0.6670178
[7,] 0.3041190 0.6082380 0.6958810
[8,] 0.2506684 0.5013369 0.7493316
[9,] 0.1821341 0.3642681 0.8178659
[10,] 0.1275936 0.2551872 0.8724064
[11,] 0.2256352 0.4512704 0.7743648
[12,] 0.2033062 0.4066124 0.7966938
[13,] 0.1699542 0.3399084 0.8300458
[14,] 0.1445960 0.2891920 0.8554040
[15,] 0.2250893 0.4501786 0.7749107
[16,] 0.3480984 0.6961968 0.6519016
[17,] 0.2915915 0.5831830 0.7084085
[18,] 0.2409255 0.4818509 0.7590745
[19,] 0.2038095 0.4076189 0.7961905
[20,] 0.2043516 0.4087031 0.7956484
[21,] 0.1660918 0.3321836 0.8339082
[22,] 0.1335341 0.2670683 0.8664659
[23,] 0.1857847 0.3715694 0.8142153
[24,] 0.1750303 0.3500605 0.8249697
[25,] 0.1415600 0.2831200 0.8584400
[26,] 0.1459198 0.2918396 0.8540802
[27,] 0.1717055 0.3434111 0.8282945
[28,] 0.3068849 0.6137698 0.6931151
[29,] 0.2728861 0.5457721 0.7271139
[30,] 0.2536188 0.5072377 0.7463812
[31,] 0.2807912 0.5615823 0.7192088
[32,] 0.2848970 0.5697940 0.7151030
[33,] 0.2546330 0.5092661 0.7453670
[34,] 0.2296118 0.4592235 0.7703882
[35,] 0.2640826 0.5281652 0.7359174
[36,] 0.2759670 0.5519339 0.7240330
[37,] 0.2417331 0.4834662 0.7582669
[38,] 0.2381149 0.4762297 0.7618851
[39,] 0.3623116 0.7246233 0.6376884
[40,] 0.5335050 0.9329899 0.4664950
[41,] 0.5069911 0.9860178 0.4930089
[42,] 0.4628865 0.9257730 0.5371135
[43,] 0.3998221 0.7996443 0.6001779
[44,] 0.3968093 0.7936186 0.6031907
[45,] 0.3304940 0.6609881 0.6695060
[46,] 0.2684875 0.5369749 0.7315125
[47,] 0.2679467 0.5358933 0.7320533
[48,] 0.3982250 0.7964501 0.6017750
[49,] 0.3075928 0.6151856 0.6924072
[50,] 0.2201829 0.4403658 0.7798171
[51,] 0.3218715 0.6437430 0.6781285
[52,] 0.5532234 0.8935533 0.4467766
> postscript(file="/var/www/html/rcomp/tmp/1gr321227790574.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20o2c1227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ad581227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vmf91227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x1y81227790574.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 = 61
Frequency = 1
1 2 3 4 5 6
-12.3095238 3.1904762 0.1904762 1.5904762 -9.2095238 -5.8095238
7 8 9 10 11 12
-21.8095238 -20.1095238 -3.2095238 -6.8095238 2.0904762 0.5904762
13 14 15 16 17 18
-6.3095238 -5.0095238 11.4904762 4.6904762 -9.3095238 3.8904762
19 20 21 22 23 24
-18.1095238 -18.9095238 -3.4095238 -2.0095238 1.6904762 7.2904762
25 26 27 28 29 30
-3.0095238 0.8904762 13.4904762 8.0904762 -2.4095238 10.6904762
31 32 33 34 35 36
-10.7095238 -15.1095238 3.5904762 0.4904762 14.4904762 13.3904762
37 38 39 40 41 42
3.9904762 6.4904762 18.6904762 17.4904762 7.0904762 17.9904762
43 44 45 46 47 48
-22.1210526 -20.0210526 -2.6210526 -3.8210526 0.9789474 10.6789474
49 50 51 52 53 54
-3.4210526 -3.1210526 13.1789474 18.9789474 -3.8210526 6.0789474
55 56 57 58 59 60
-14.4210526 -12.4210526 0.9789474 10.2789474 14.4789474 13.4789474
61
-3.3210526
> postscript(file="/var/www/html/rcomp/tmp/612oo1227790574.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -12.3095238 NA
1 3.1904762 -12.3095238
2 0.1904762 3.1904762
3 1.5904762 0.1904762
4 -9.2095238 1.5904762
5 -5.8095238 -9.2095238
6 -21.8095238 -5.8095238
7 -20.1095238 -21.8095238
8 -3.2095238 -20.1095238
9 -6.8095238 -3.2095238
10 2.0904762 -6.8095238
11 0.5904762 2.0904762
12 -6.3095238 0.5904762
13 -5.0095238 -6.3095238
14 11.4904762 -5.0095238
15 4.6904762 11.4904762
16 -9.3095238 4.6904762
17 3.8904762 -9.3095238
18 -18.1095238 3.8904762
19 -18.9095238 -18.1095238
20 -3.4095238 -18.9095238
21 -2.0095238 -3.4095238
22 1.6904762 -2.0095238
23 7.2904762 1.6904762
24 -3.0095238 7.2904762
25 0.8904762 -3.0095238
26 13.4904762 0.8904762
27 8.0904762 13.4904762
28 -2.4095238 8.0904762
29 10.6904762 -2.4095238
30 -10.7095238 10.6904762
31 -15.1095238 -10.7095238
32 3.5904762 -15.1095238
33 0.4904762 3.5904762
34 14.4904762 0.4904762
35 13.3904762 14.4904762
36 3.9904762 13.3904762
37 6.4904762 3.9904762
38 18.6904762 6.4904762
39 17.4904762 18.6904762
40 7.0904762 17.4904762
41 17.9904762 7.0904762
42 -22.1210526 17.9904762
43 -20.0210526 -22.1210526
44 -2.6210526 -20.0210526
45 -3.8210526 -2.6210526
46 0.9789474 -3.8210526
47 10.6789474 0.9789474
48 -3.4210526 10.6789474
49 -3.1210526 -3.4210526
50 13.1789474 -3.1210526
51 18.9789474 13.1789474
52 -3.8210526 18.9789474
53 6.0789474 -3.8210526
54 -14.4210526 6.0789474
55 -12.4210526 -14.4210526
56 0.9789474 -12.4210526
57 10.2789474 0.9789474
58 14.4789474 10.2789474
59 13.4789474 14.4789474
60 -3.3210526 13.4789474
61 NA -3.3210526
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.1904762 -12.3095238
[2,] 0.1904762 3.1904762
[3,] 1.5904762 0.1904762
[4,] -9.2095238 1.5904762
[5,] -5.8095238 -9.2095238
[6,] -21.8095238 -5.8095238
[7,] -20.1095238 -21.8095238
[8,] -3.2095238 -20.1095238
[9,] -6.8095238 -3.2095238
[10,] 2.0904762 -6.8095238
[11,] 0.5904762 2.0904762
[12,] -6.3095238 0.5904762
[13,] -5.0095238 -6.3095238
[14,] 11.4904762 -5.0095238
[15,] 4.6904762 11.4904762
[16,] -9.3095238 4.6904762
[17,] 3.8904762 -9.3095238
[18,] -18.1095238 3.8904762
[19,] -18.9095238 -18.1095238
[20,] -3.4095238 -18.9095238
[21,] -2.0095238 -3.4095238
[22,] 1.6904762 -2.0095238
[23,] 7.2904762 1.6904762
[24,] -3.0095238 7.2904762
[25,] 0.8904762 -3.0095238
[26,] 13.4904762 0.8904762
[27,] 8.0904762 13.4904762
[28,] -2.4095238 8.0904762
[29,] 10.6904762 -2.4095238
[30,] -10.7095238 10.6904762
[31,] -15.1095238 -10.7095238
[32,] 3.5904762 -15.1095238
[33,] 0.4904762 3.5904762
[34,] 14.4904762 0.4904762
[35,] 13.3904762 14.4904762
[36,] 3.9904762 13.3904762
[37,] 6.4904762 3.9904762
[38,] 18.6904762 6.4904762
[39,] 17.4904762 18.6904762
[40,] 7.0904762 17.4904762
[41,] 17.9904762 7.0904762
[42,] -22.1210526 17.9904762
[43,] -20.0210526 -22.1210526
[44,] -2.6210526 -20.0210526
[45,] -3.8210526 -2.6210526
[46,] 0.9789474 -3.8210526
[47,] 10.6789474 0.9789474
[48,] -3.4210526 10.6789474
[49,] -3.1210526 -3.4210526
[50,] 13.1789474 -3.1210526
[51,] 18.9789474 13.1789474
[52,] -3.8210526 18.9789474
[53,] 6.0789474 -3.8210526
[54,] -14.4210526 6.0789474
[55,] -12.4210526 -14.4210526
[56,] 0.9789474 -12.4210526
[57,] 10.2789474 0.9789474
[58,] 14.4789474 10.2789474
[59,] 13.4789474 14.4789474
[60,] -3.3210526 13.4789474
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.1904762 -12.3095238
2 0.1904762 3.1904762
3 1.5904762 0.1904762
4 -9.2095238 1.5904762
5 -5.8095238 -9.2095238
6 -21.8095238 -5.8095238
7 -20.1095238 -21.8095238
8 -3.2095238 -20.1095238
9 -6.8095238 -3.2095238
10 2.0904762 -6.8095238
11 0.5904762 2.0904762
12 -6.3095238 0.5904762
13 -5.0095238 -6.3095238
14 11.4904762 -5.0095238
15 4.6904762 11.4904762
16 -9.3095238 4.6904762
17 3.8904762 -9.3095238
18 -18.1095238 3.8904762
19 -18.9095238 -18.1095238
20 -3.4095238 -18.9095238
21 -2.0095238 -3.4095238
22 1.6904762 -2.0095238
23 7.2904762 1.6904762
24 -3.0095238 7.2904762
25 0.8904762 -3.0095238
26 13.4904762 0.8904762
27 8.0904762 13.4904762
28 -2.4095238 8.0904762
29 10.6904762 -2.4095238
30 -10.7095238 10.6904762
31 -15.1095238 -10.7095238
32 3.5904762 -15.1095238
33 0.4904762 3.5904762
34 14.4904762 0.4904762
35 13.3904762 14.4904762
36 3.9904762 13.3904762
37 6.4904762 3.9904762
38 18.6904762 6.4904762
39 17.4904762 18.6904762
40 7.0904762 17.4904762
41 17.9904762 7.0904762
42 -22.1210526 17.9904762
43 -20.0210526 -22.1210526
44 -2.6210526 -20.0210526
45 -3.8210526 -2.6210526
46 0.9789474 -3.8210526
47 10.6789474 0.9789474
48 -3.4210526 10.6789474
49 -3.1210526 -3.4210526
50 13.1789474 -3.1210526
51 18.9789474 13.1789474
52 -3.8210526 18.9789474
53 6.0789474 -3.8210526
54 -14.4210526 6.0789474
55 -12.4210526 -14.4210526
56 0.9789474 -12.4210526
57 10.2789474 0.9789474
58 14.4789474 10.2789474
59 13.4789474 14.4789474
60 -3.3210526 13.4789474
> 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/7kuzo1227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8y2701227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91l2h1227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10x2771227790574.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mjzu1227790574.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/12x9oe1227790574.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/139muq1227790574.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/14v1j91227790574.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/15pctl1227790574.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/16q39c1227790574.tab")
+ }
>
> system("convert tmp/1gr321227790574.ps tmp/1gr321227790574.png")
> system("convert tmp/20o2c1227790574.ps tmp/20o2c1227790574.png")
> system("convert tmp/3ad581227790574.ps tmp/3ad581227790574.png")
> system("convert tmp/4vmf91227790574.ps tmp/4vmf91227790574.png")
> system("convert tmp/5x1y81227790574.ps tmp/5x1y81227790574.png")
> system("convert tmp/612oo1227790574.ps tmp/612oo1227790574.png")
> system("convert tmp/7kuzo1227790574.ps tmp/7kuzo1227790574.png")
> system("convert tmp/8y2701227790574.ps tmp/8y2701227790574.png")
> system("convert tmp/91l2h1227790574.ps tmp/91l2h1227790574.png")
> system("convert tmp/10x2771227790574.ps tmp/10x2771227790574.png")
>
>
> proc.time()
user system elapsed
2.739 1.734 3.352