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(1.39,1.08,1.34,1.12,1.33,1.12,1.3,1.16,1.28,1.16,1.29,1.16,1.29,1.16,1.28,1.15,1.27,1.17,1.26,1.16,1.29,1.19,1.36,1.13,1.33,1.14,1.35,1.13,1.31,1.16,1.3,1.17,1.32,1.14,1.33,1.14,1.36,1.11,1.35,1.12,1.4,1.08,1.41,1.07,1.4,1.09,1.4,1.08,1.4,1.08,1.41,1.08,1.4,1.09,1.39,1.08,1.41,1.07,1.42,1.07,1.43,1.07,1.42,1.08,1.42,1.07,1.43,1.06,1.43,1.06,1.43,1.06,1.46,1.04,1.47,1.03,1.47,1.03,1.46,1.04,1.47,1.03,1.49,1.02,1.5,1.01,1.47,1.03,1.48,1.02,1.49,1.01,1.49,1.02,1.5,1.01,1.48,1.02,1.46,1.03,1.43,1.04,1.44,1.04,1.43,1.03),dim=c(2,53),dimnames=list(c('eu/us','us/ch'),1:53))
> y <- array(NA,dim=c(2,53),dimnames=list(c('eu/us','us/ch'),1:53))
> 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 = '2'
> #'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
us/ch eu/us
1 1.08 1.39
2 1.12 1.34
3 1.12 1.33
4 1.16 1.30
5 1.16 1.28
6 1.16 1.29
7 1.16 1.29
8 1.15 1.28
9 1.17 1.27
10 1.16 1.26
11 1.19 1.29
12 1.13 1.36
13 1.14 1.33
14 1.13 1.35
15 1.16 1.31
16 1.17 1.30
17 1.14 1.32
18 1.14 1.33
19 1.11 1.36
20 1.12 1.35
21 1.08 1.40
22 1.07 1.41
23 1.09 1.40
24 1.08 1.40
25 1.08 1.40
26 1.08 1.41
27 1.09 1.40
28 1.08 1.39
29 1.07 1.41
30 1.07 1.42
31 1.07 1.43
32 1.08 1.42
33 1.07 1.42
34 1.06 1.43
35 1.06 1.43
36 1.06 1.43
37 1.04 1.46
38 1.03 1.47
39 1.03 1.47
40 1.04 1.46
41 1.03 1.47
42 1.02 1.49
43 1.01 1.50
44 1.03 1.47
45 1.02 1.48
46 1.01 1.49
47 1.02 1.49
48 1.01 1.50
49 1.02 1.48
50 1.03 1.46
51 1.04 1.43
52 1.04 1.44
53 1.03 1.43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `eu/us`
2.1094 -0.7344
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0291829 -0.0038707 0.0001927 0.0048805 0.0280026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.10936 0.02818 74.85 <2e-16 ***
`eu/us` -0.73439 0.02018 -36.39 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01024 on 51 degrees of freedom
Multiple R-squared: 0.9629, Adjusted R-squared: 0.9622
F-statistic: 1324 on 1 and 51 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,] 0.3479733 0.695946648 0.652026676
[2,] 0.2011988 0.402397643 0.798801178
[3,] 0.1063817 0.212763498 0.893618251
[4,] 0.3090077 0.618015357 0.690992321
[5,] 0.2189502 0.437900376 0.781049812
[6,] 0.5948802 0.810239644 0.405119822
[7,] 0.9929230 0.014154083 0.007077042
[8,] 0.9980636 0.003872878 0.001936439
[9,] 0.9968087 0.006382561 0.003191280
[10,] 0.9960547 0.007890633 0.003945317
[11,] 0.9963188 0.007362368 0.003681184
[12,] 0.9979349 0.004130125 0.002065062
[13,] 0.9960638 0.007872339 0.003936169
[14,] 0.9940910 0.011817970 0.005908985
[15,] 0.9904035 0.019193098 0.009596549
[16,] 0.9840503 0.031899455 0.015949727
[17,] 0.9772479 0.045504254 0.022752127
[18,] 0.9696569 0.060686138 0.030343069
[19,] 0.9637546 0.072490854 0.036245427
[20,] 0.9466579 0.106684180 0.053342090
[21,] 0.9228794 0.154241287 0.077120643
[22,] 0.9026960 0.194608054 0.097304027
[23,] 0.9017254 0.196549186 0.098274593
[24,] 0.8866882 0.226623659 0.113311829
[25,] 0.8499143 0.300171451 0.150085725
[26,] 0.8097430 0.380513993 0.190256997
[27,] 0.8361490 0.327701953 0.163850976
[28,] 0.9295584 0.140883297 0.070441648
[29,] 0.9422678 0.115464355 0.057732177
[30,] 0.9447829 0.110434114 0.055217057
[31,] 0.9601306 0.079738728 0.039869364
[32,] 0.9874209 0.025158298 0.012579149
[33,] 0.9909440 0.018111973 0.009055987
[34,] 0.9858872 0.028225682 0.014112841
[35,] 0.9787350 0.042530013 0.021265007
[36,] 0.9915763 0.016847465 0.008423733
[37,] 0.9895158 0.020968478 0.010484239
[38,] 0.9819892 0.036021534 0.018010767
[39,] 0.9652511 0.069497821 0.034748911
[40,] 0.9618382 0.076323608 0.038161804
[41,] 0.9230419 0.153916298 0.076958149
[42,] 0.9140906 0.171818741 0.085909370
[43,] 0.8489361 0.302127823 0.151063912
[44,] 0.7466072 0.506785667 0.253392833
> postscript(file="/var/www/html/rcomp/tmp/1czdg1290501388.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24qd11290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34qd11290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44qd11290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xzul1290501388.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 = 53
Frequency = 1
1 2 3 4 5
-8.558440e-03 -5.277900e-03 -1.262179e-02 5.346532e-03 -9.341252e-03
6 7 8 9 10
-1.997360e-03 -1.997360e-03 -1.934125e-02 -6.685144e-03 -2.402904e-02
11 12 13 14 15
2.800264e-02 1.940988e-02 7.378208e-03 1.206599e-02 1.269042e-02
16 17 18 19 20
1.534653e-02 3.431588e-05 7.378208e-03 -5.901159e-04 2.065992e-03
21 22 23 24 25
-1.214548e-03 -3.870656e-03 8.785452e-03 -1.214548e-03 -1.214548e-03
26 27 28 29 30
6.129344e-03 8.785452e-03 -8.558440e-03 -3.870656e-03 3.473237e-03
31 32 33 34 35
1.081713e-02 1.347324e-02 3.473237e-03 8.171286e-04 8.171286e-04
36 37 38 39 40
8.171286e-04 2.848805e-03 1.926969e-04 1.926969e-04 2.848805e-03
41 42 43 44 45
1.926969e-04 4.880481e-03 2.224373e-03 1.926969e-04 -2.463411e-03
46 47 48 49 50
-5.119519e-03 4.880481e-03 2.224373e-03 -2.463411e-03 -7.151195e-03
51 52 53
-1.918287e-02 -1.183898e-02 -2.918287e-02
> postscript(file="/var/www/html/rcomp/tmp/6xzul1290501388.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 = 53
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.558440e-03 NA
1 -5.277900e-03 -8.558440e-03
2 -1.262179e-02 -5.277900e-03
3 5.346532e-03 -1.262179e-02
4 -9.341252e-03 5.346532e-03
5 -1.997360e-03 -9.341252e-03
6 -1.997360e-03 -1.997360e-03
7 -1.934125e-02 -1.997360e-03
8 -6.685144e-03 -1.934125e-02
9 -2.402904e-02 -6.685144e-03
10 2.800264e-02 -2.402904e-02
11 1.940988e-02 2.800264e-02
12 7.378208e-03 1.940988e-02
13 1.206599e-02 7.378208e-03
14 1.269042e-02 1.206599e-02
15 1.534653e-02 1.269042e-02
16 3.431588e-05 1.534653e-02
17 7.378208e-03 3.431588e-05
18 -5.901159e-04 7.378208e-03
19 2.065992e-03 -5.901159e-04
20 -1.214548e-03 2.065992e-03
21 -3.870656e-03 -1.214548e-03
22 8.785452e-03 -3.870656e-03
23 -1.214548e-03 8.785452e-03
24 -1.214548e-03 -1.214548e-03
25 6.129344e-03 -1.214548e-03
26 8.785452e-03 6.129344e-03
27 -8.558440e-03 8.785452e-03
28 -3.870656e-03 -8.558440e-03
29 3.473237e-03 -3.870656e-03
30 1.081713e-02 3.473237e-03
31 1.347324e-02 1.081713e-02
32 3.473237e-03 1.347324e-02
33 8.171286e-04 3.473237e-03
34 8.171286e-04 8.171286e-04
35 8.171286e-04 8.171286e-04
36 2.848805e-03 8.171286e-04
37 1.926969e-04 2.848805e-03
38 1.926969e-04 1.926969e-04
39 2.848805e-03 1.926969e-04
40 1.926969e-04 2.848805e-03
41 4.880481e-03 1.926969e-04
42 2.224373e-03 4.880481e-03
43 1.926969e-04 2.224373e-03
44 -2.463411e-03 1.926969e-04
45 -5.119519e-03 -2.463411e-03
46 4.880481e-03 -5.119519e-03
47 2.224373e-03 4.880481e-03
48 -2.463411e-03 2.224373e-03
49 -7.151195e-03 -2.463411e-03
50 -1.918287e-02 -7.151195e-03
51 -1.183898e-02 -1.918287e-02
52 -2.918287e-02 -1.183898e-02
53 NA -2.918287e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.277900e-03 -8.558440e-03
[2,] -1.262179e-02 -5.277900e-03
[3,] 5.346532e-03 -1.262179e-02
[4,] -9.341252e-03 5.346532e-03
[5,] -1.997360e-03 -9.341252e-03
[6,] -1.997360e-03 -1.997360e-03
[7,] -1.934125e-02 -1.997360e-03
[8,] -6.685144e-03 -1.934125e-02
[9,] -2.402904e-02 -6.685144e-03
[10,] 2.800264e-02 -2.402904e-02
[11,] 1.940988e-02 2.800264e-02
[12,] 7.378208e-03 1.940988e-02
[13,] 1.206599e-02 7.378208e-03
[14,] 1.269042e-02 1.206599e-02
[15,] 1.534653e-02 1.269042e-02
[16,] 3.431588e-05 1.534653e-02
[17,] 7.378208e-03 3.431588e-05
[18,] -5.901159e-04 7.378208e-03
[19,] 2.065992e-03 -5.901159e-04
[20,] -1.214548e-03 2.065992e-03
[21,] -3.870656e-03 -1.214548e-03
[22,] 8.785452e-03 -3.870656e-03
[23,] -1.214548e-03 8.785452e-03
[24,] -1.214548e-03 -1.214548e-03
[25,] 6.129344e-03 -1.214548e-03
[26,] 8.785452e-03 6.129344e-03
[27,] -8.558440e-03 8.785452e-03
[28,] -3.870656e-03 -8.558440e-03
[29,] 3.473237e-03 -3.870656e-03
[30,] 1.081713e-02 3.473237e-03
[31,] 1.347324e-02 1.081713e-02
[32,] 3.473237e-03 1.347324e-02
[33,] 8.171286e-04 3.473237e-03
[34,] 8.171286e-04 8.171286e-04
[35,] 8.171286e-04 8.171286e-04
[36,] 2.848805e-03 8.171286e-04
[37,] 1.926969e-04 2.848805e-03
[38,] 1.926969e-04 1.926969e-04
[39,] 2.848805e-03 1.926969e-04
[40,] 1.926969e-04 2.848805e-03
[41,] 4.880481e-03 1.926969e-04
[42,] 2.224373e-03 4.880481e-03
[43,] 1.926969e-04 2.224373e-03
[44,] -2.463411e-03 1.926969e-04
[45,] -5.119519e-03 -2.463411e-03
[46,] 4.880481e-03 -5.119519e-03
[47,] 2.224373e-03 4.880481e-03
[48,] -2.463411e-03 2.224373e-03
[49,] -7.151195e-03 -2.463411e-03
[50,] -1.918287e-02 -7.151195e-03
[51,] -1.183898e-02 -1.918287e-02
[52,] -2.918287e-02 -1.183898e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.277900e-03 -8.558440e-03
2 -1.262179e-02 -5.277900e-03
3 5.346532e-03 -1.262179e-02
4 -9.341252e-03 5.346532e-03
5 -1.997360e-03 -9.341252e-03
6 -1.997360e-03 -1.997360e-03
7 -1.934125e-02 -1.997360e-03
8 -6.685144e-03 -1.934125e-02
9 -2.402904e-02 -6.685144e-03
10 2.800264e-02 -2.402904e-02
11 1.940988e-02 2.800264e-02
12 7.378208e-03 1.940988e-02
13 1.206599e-02 7.378208e-03
14 1.269042e-02 1.206599e-02
15 1.534653e-02 1.269042e-02
16 3.431588e-05 1.534653e-02
17 7.378208e-03 3.431588e-05
18 -5.901159e-04 7.378208e-03
19 2.065992e-03 -5.901159e-04
20 -1.214548e-03 2.065992e-03
21 -3.870656e-03 -1.214548e-03
22 8.785452e-03 -3.870656e-03
23 -1.214548e-03 8.785452e-03
24 -1.214548e-03 -1.214548e-03
25 6.129344e-03 -1.214548e-03
26 8.785452e-03 6.129344e-03
27 -8.558440e-03 8.785452e-03
28 -3.870656e-03 -8.558440e-03
29 3.473237e-03 -3.870656e-03
30 1.081713e-02 3.473237e-03
31 1.347324e-02 1.081713e-02
32 3.473237e-03 1.347324e-02
33 8.171286e-04 3.473237e-03
34 8.171286e-04 8.171286e-04
35 8.171286e-04 8.171286e-04
36 2.848805e-03 8.171286e-04
37 1.926969e-04 2.848805e-03
38 1.926969e-04 1.926969e-04
39 2.848805e-03 1.926969e-04
40 1.926969e-04 2.848805e-03
41 4.880481e-03 1.926969e-04
42 2.224373e-03 4.880481e-03
43 1.926969e-04 2.224373e-03
44 -2.463411e-03 1.926969e-04
45 -5.119519e-03 -2.463411e-03
46 4.880481e-03 -5.119519e-03
47 2.224373e-03 4.880481e-03
48 -2.463411e-03 2.224373e-03
49 -7.151195e-03 -2.463411e-03
50 -1.918287e-02 -7.151195e-03
51 -1.183898e-02 -1.918287e-02
52 -2.918287e-02 -1.183898e-02
> 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/789tp1290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/889tp1290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9j0br1290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10j0br1290501388.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1141rx1290501388.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/12717l1290501388.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/13e24x1290501388.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/147tm01290501388.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/15ack61290501388.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/1664ie1290501388.tab")
+ }
>
> try(system("convert tmp/1czdg1290501388.ps tmp/1czdg1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/24qd11290501388.ps tmp/24qd11290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/34qd11290501388.ps tmp/34qd11290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/44qd11290501388.ps tmp/44qd11290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xzul1290501388.ps tmp/5xzul1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xzul1290501388.ps tmp/6xzul1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/789tp1290501388.ps tmp/789tp1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/889tp1290501388.ps tmp/889tp1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j0br1290501388.ps tmp/9j0br1290501388.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j0br1290501388.ps tmp/10j0br1290501388.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.392 1.622 10.038