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 '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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(108.2,108.5,108.8,112.3,110.2,116.6,109.5,115.5,109.5,120.1,116,132.9,111.2,128.1,112.1,129.3,114,132.5,119.1,131,114.1,124.9,115.1,120.8,115.4,122,110.8,122.1,116,127.4,119.2,135.2,126.5,137.3,127.8,135,131.3,136,140.3,138.4,137.3,134.7,143,138.4,134.5,133.9,139.9,133.6,159.3,141.2,170.4,151.8,175,155.4,175.8,156.6,180.9,161.6,180.3,160.7,169.6,156,172.3,159.5,184.8,168.7,177.7,169.9,184.6,169.9,211.4,185.9,215.3,190.8,215.9,195.8,244.7,211.9,259.3,227.1,289,251.3,310.9,256.7,321,251.9,315.1,251.2,333.2,270.3,314.1,267.2,284.7,243,273.9,229.9,216,187.2,196.4,178.2,190.9,175.2,206.4,192.4,196.3,187,199.5,184,198.9,194.1,214.4,212.7,214.2,217.5,187.6,200.5,180.6,205.9,172.2,196.5),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 = '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
Y X
1 108.2 108.5
2 108.8 112.3
3 110.2 116.6
4 109.5 115.5
5 109.5 120.1
6 116.0 132.9
7 111.2 128.1
8 112.1 129.3
9 114.0 132.5
10 119.1 131.0
11 114.1 124.9
12 115.1 120.8
13 115.4 122.0
14 110.8 122.1
15 116.0 127.4
16 119.2 135.2
17 126.5 137.3
18 127.8 135.0
19 131.3 136.0
20 140.3 138.4
21 137.3 134.7
22 143.0 138.4
23 134.5 133.9
24 139.9 133.6
25 159.3 141.2
26 170.4 151.8
27 175.0 155.4
28 175.8 156.6
29 180.9 161.6
30 180.3 160.7
31 169.6 156.0
32 172.3 159.5
33 184.8 168.7
34 177.7 169.9
35 184.6 169.9
36 211.4 185.9
37 215.3 190.8
38 215.9 195.8
39 244.7 211.9
40 259.3 227.1
41 289.0 251.3
42 310.9 256.7
43 321.0 251.9
44 315.1 251.2
45 333.2 270.3
46 314.1 267.2
47 284.7 243.0
48 273.9 229.9
49 216.0 187.2
50 196.4 178.2
51 190.9 175.2
52 206.4 192.4
53 196.3 187.0
54 199.5 184.0
55 198.9 194.1
56 214.4 212.7
57 214.2 217.5
58 187.6 200.5
59 180.6 205.9
60 172.2 196.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-54.695 1.381
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-49.109 -4.816 3.671 8.942 27.753
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -54.69470 7.54758 -7.247 1.13e-09 ***
X 1.38127 0.04303 32.097 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.77 on 58 degrees of freedom
Multiple R-squared: 0.9467, Adjusted R-squared: 0.9458
F-statistic: 1030 on 1 and 58 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,] 5.326334e-05 1.065267e-04 0.9999467
[2,] 3.675663e-05 7.351325e-05 0.9999632
[3,] 1.268192e-05 2.536384e-05 0.9999873
[4,] 1.295602e-06 2.591203e-06 0.9999987
[5,] 9.903335e-08 1.980667e-07 0.9999999
[6,] 1.371649e-06 2.743298e-06 0.9999986
[7,] 2.176832e-07 4.353663e-07 0.9999998
[8,] 1.063745e-07 2.127490e-07 0.9999999
[9,] 3.686449e-08 7.372898e-08 1.0000000
[10,] 6.227164e-09 1.245433e-08 1.0000000
[11,] 1.224419e-09 2.448837e-09 1.0000000
[12,] 3.013039e-10 6.026077e-10 1.0000000
[13,] 3.251359e-09 6.502718e-09 1.0000000
[14,] 1.644772e-08 3.289544e-08 1.0000000
[15,] 8.667463e-08 1.733493e-07 0.9999999
[16,] 2.380281e-06 4.760562e-06 0.9999976
[17,] 7.546370e-06 1.509274e-05 0.9999925
[18,] 2.079461e-05 4.158922e-05 0.9999792
[19,] 1.535640e-05 3.071280e-05 0.9999846
[20,] 2.647649e-05 5.295299e-05 0.9999735
[21,] 3.043150e-04 6.086299e-04 0.9996957
[22,] 6.170576e-04 1.234115e-03 0.9993829
[23,] 6.800998e-04 1.360200e-03 0.9993199
[24,] 5.582545e-04 1.116509e-03 0.9994417
[25,] 3.636423e-04 7.272846e-04 0.9996364
[26,] 2.517144e-04 5.034287e-04 0.9997483
[27,] 1.518207e-04 3.036414e-04 0.9998482
[28,] 8.994729e-05 1.798946e-04 0.9999101
[29,] 5.599214e-05 1.119843e-04 0.9999440
[30,] 4.561504e-05 9.123008e-05 0.9999544
[31,] 2.868144e-05 5.736287e-05 0.9999713
[32,] 2.003988e-05 4.007977e-05 0.9999800
[33,] 1.340935e-05 2.681871e-05 0.9999866
[34,] 9.678182e-06 1.935636e-05 0.9999903
[35,] 5.396368e-06 1.079274e-05 0.9999946
[36,] 3.262357e-06 6.524714e-06 0.9999967
[37,] 2.512783e-06 5.025567e-06 0.9999975
[38,] 1.065426e-06 2.130853e-06 0.9999989
[39,] 4.707469e-06 9.414937e-06 0.9999953
[40,] 8.705101e-06 1.741020e-05 0.9999913
[41,] 7.748239e-06 1.549648e-05 0.9999923
[42,] 8.718328e-06 1.743666e-05 0.9999913
[43,] 2.353130e-05 4.706261e-05 0.9999765
[44,] 1.591116e-02 3.182233e-02 0.9840888
[45,] 3.644762e-02 7.289524e-02 0.9635524
[46,] 2.302442e-02 4.604884e-02 0.9769756
[47,] 1.252011e-02 2.504022e-02 0.9874799
[48,] 1.470547e-02 2.941094e-02 0.9852945
[49,] 1.152537e-02 2.305073e-02 0.9884746
[50,] 4.351331e-02 8.702661e-02 0.9564867
[51,] 3.969943e-01 7.939886e-01 0.6030057
> postscript(file="/var/www/html/rcomp/tmp/18vyn1258725719.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2trxe1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3c5xy1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fuvw1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wb7s1258725719.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
13.0270040 8.3781813 3.8387240 4.6581201 -1.6957179 -12.8759627
7 8 9 10 11 12
-11.0458709 -11.8033939 -14.3234551 -7.1515514 -3.7258097 2.9373937
13 14 15 16 17 18
1.5798708 -3.1582562 -5.2789825 -12.8528817 -8.4535469 -3.9766279
19 20 21 22 23 24
-1.8578970 3.8270571 5.9377528 6.5270571 4.2427681 10.0571489
25 26 27 28 29 30
18.9595035 15.4180507 15.0454819 14.1879589 12.3816133 13.0247555
31 32 33 34 35 36
8.8167204 6.6822784 6.4746025 -2.2829205 4.6170795 9.3167735
37 38 39 40 41 42
6.4485547 0.1422091 6.7037761 0.3084854 -3.4182275 11.0229192
43 44 45 46 47 48
27.7530110 22.8198994 14.5376590 -0.2804067 3.7463062 11.0409318
49 50 51 52 53 54
12.1211236 4.9525457 3.5963531 -4.6614759 -7.3026226 0.0411848
55 56 57 58 59 60
-14.5096334 -24.7012392 -31.5313310 -34.6497558 -49.1086091 -44.5246793
> postscript(file="/var/www/html/rcomp/tmp/6daat1258725719.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 13.0270040 NA
1 8.3781813 13.0270040
2 3.8387240 8.3781813
3 4.6581201 3.8387240
4 -1.6957179 4.6581201
5 -12.8759627 -1.6957179
6 -11.0458709 -12.8759627
7 -11.8033939 -11.0458709
8 -14.3234551 -11.8033939
9 -7.1515514 -14.3234551
10 -3.7258097 -7.1515514
11 2.9373937 -3.7258097
12 1.5798708 2.9373937
13 -3.1582562 1.5798708
14 -5.2789825 -3.1582562
15 -12.8528817 -5.2789825
16 -8.4535469 -12.8528817
17 -3.9766279 -8.4535469
18 -1.8578970 -3.9766279
19 3.8270571 -1.8578970
20 5.9377528 3.8270571
21 6.5270571 5.9377528
22 4.2427681 6.5270571
23 10.0571489 4.2427681
24 18.9595035 10.0571489
25 15.4180507 18.9595035
26 15.0454819 15.4180507
27 14.1879589 15.0454819
28 12.3816133 14.1879589
29 13.0247555 12.3816133
30 8.8167204 13.0247555
31 6.6822784 8.8167204
32 6.4746025 6.6822784
33 -2.2829205 6.4746025
34 4.6170795 -2.2829205
35 9.3167735 4.6170795
36 6.4485547 9.3167735
37 0.1422091 6.4485547
38 6.7037761 0.1422091
39 0.3084854 6.7037761
40 -3.4182275 0.3084854
41 11.0229192 -3.4182275
42 27.7530110 11.0229192
43 22.8198994 27.7530110
44 14.5376590 22.8198994
45 -0.2804067 14.5376590
46 3.7463062 -0.2804067
47 11.0409318 3.7463062
48 12.1211236 11.0409318
49 4.9525457 12.1211236
50 3.5963531 4.9525457
51 -4.6614759 3.5963531
52 -7.3026226 -4.6614759
53 0.0411848 -7.3026226
54 -14.5096334 0.0411848
55 -24.7012392 -14.5096334
56 -31.5313310 -24.7012392
57 -34.6497558 -31.5313310
58 -49.1086091 -34.6497558
59 -44.5246793 -49.1086091
60 NA -44.5246793
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.3781813 13.0270040
[2,] 3.8387240 8.3781813
[3,] 4.6581201 3.8387240
[4,] -1.6957179 4.6581201
[5,] -12.8759627 -1.6957179
[6,] -11.0458709 -12.8759627
[7,] -11.8033939 -11.0458709
[8,] -14.3234551 -11.8033939
[9,] -7.1515514 -14.3234551
[10,] -3.7258097 -7.1515514
[11,] 2.9373937 -3.7258097
[12,] 1.5798708 2.9373937
[13,] -3.1582562 1.5798708
[14,] -5.2789825 -3.1582562
[15,] -12.8528817 -5.2789825
[16,] -8.4535469 -12.8528817
[17,] -3.9766279 -8.4535469
[18,] -1.8578970 -3.9766279
[19,] 3.8270571 -1.8578970
[20,] 5.9377528 3.8270571
[21,] 6.5270571 5.9377528
[22,] 4.2427681 6.5270571
[23,] 10.0571489 4.2427681
[24,] 18.9595035 10.0571489
[25,] 15.4180507 18.9595035
[26,] 15.0454819 15.4180507
[27,] 14.1879589 15.0454819
[28,] 12.3816133 14.1879589
[29,] 13.0247555 12.3816133
[30,] 8.8167204 13.0247555
[31,] 6.6822784 8.8167204
[32,] 6.4746025 6.6822784
[33,] -2.2829205 6.4746025
[34,] 4.6170795 -2.2829205
[35,] 9.3167735 4.6170795
[36,] 6.4485547 9.3167735
[37,] 0.1422091 6.4485547
[38,] 6.7037761 0.1422091
[39,] 0.3084854 6.7037761
[40,] -3.4182275 0.3084854
[41,] 11.0229192 -3.4182275
[42,] 27.7530110 11.0229192
[43,] 22.8198994 27.7530110
[44,] 14.5376590 22.8198994
[45,] -0.2804067 14.5376590
[46,] 3.7463062 -0.2804067
[47,] 11.0409318 3.7463062
[48,] 12.1211236 11.0409318
[49,] 4.9525457 12.1211236
[50,] 3.5963531 4.9525457
[51,] -4.6614759 3.5963531
[52,] -7.3026226 -4.6614759
[53,] 0.0411848 -7.3026226
[54,] -14.5096334 0.0411848
[55,] -24.7012392 -14.5096334
[56,] -31.5313310 -24.7012392
[57,] -34.6497558 -31.5313310
[58,] -49.1086091 -34.6497558
[59,] -44.5246793 -49.1086091
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.3781813 13.0270040
2 3.8387240 8.3781813
3 4.6581201 3.8387240
4 -1.6957179 4.6581201
5 -12.8759627 -1.6957179
6 -11.0458709 -12.8759627
7 -11.8033939 -11.0458709
8 -14.3234551 -11.8033939
9 -7.1515514 -14.3234551
10 -3.7258097 -7.1515514
11 2.9373937 -3.7258097
12 1.5798708 2.9373937
13 -3.1582562 1.5798708
14 -5.2789825 -3.1582562
15 -12.8528817 -5.2789825
16 -8.4535469 -12.8528817
17 -3.9766279 -8.4535469
18 -1.8578970 -3.9766279
19 3.8270571 -1.8578970
20 5.9377528 3.8270571
21 6.5270571 5.9377528
22 4.2427681 6.5270571
23 10.0571489 4.2427681
24 18.9595035 10.0571489
25 15.4180507 18.9595035
26 15.0454819 15.4180507
27 14.1879589 15.0454819
28 12.3816133 14.1879589
29 13.0247555 12.3816133
30 8.8167204 13.0247555
31 6.6822784 8.8167204
32 6.4746025 6.6822784
33 -2.2829205 6.4746025
34 4.6170795 -2.2829205
35 9.3167735 4.6170795
36 6.4485547 9.3167735
37 0.1422091 6.4485547
38 6.7037761 0.1422091
39 0.3084854 6.7037761
40 -3.4182275 0.3084854
41 11.0229192 -3.4182275
42 27.7530110 11.0229192
43 22.8198994 27.7530110
44 14.5376590 22.8198994
45 -0.2804067 14.5376590
46 3.7463062 -0.2804067
47 11.0409318 3.7463062
48 12.1211236 11.0409318
49 4.9525457 12.1211236
50 3.5963531 4.9525457
51 -4.6614759 3.5963531
52 -7.3026226 -4.6614759
53 0.0411848 -7.3026226
54 -14.5096334 0.0411848
55 -24.7012392 -14.5096334
56 -31.5313310 -24.7012392
57 -34.6497558 -31.5313310
58 -49.1086091 -34.6497558
59 -44.5246793 -49.1086091
> 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/7hchq1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88p8m1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kvro1258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bza01258725719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112t871258725719.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/12puf21258725720.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/138fhk1258725720.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/145ls01258725720.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/15v53s1258725720.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/16ejpw1258725720.tab")
+ }
>
> system("convert tmp/18vyn1258725719.ps tmp/18vyn1258725719.png")
> system("convert tmp/2trxe1258725719.ps tmp/2trxe1258725719.png")
> system("convert tmp/3c5xy1258725719.ps tmp/3c5xy1258725719.png")
> system("convert tmp/4fuvw1258725719.ps tmp/4fuvw1258725719.png")
> system("convert tmp/5wb7s1258725719.ps tmp/5wb7s1258725719.png")
> system("convert tmp/6daat1258725719.ps tmp/6daat1258725719.png")
> system("convert tmp/7hchq1258725719.ps tmp/7hchq1258725719.png")
> system("convert tmp/88p8m1258725719.ps tmp/88p8m1258725719.png")
> system("convert tmp/9kvro1258725719.ps tmp/9kvro1258725719.png")
> system("convert tmp/10bza01258725719.ps tmp/10bza01258725719.png")
>
>
> proc.time()
user system elapsed
2.496 1.555 3.120