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(101.1,103,100.7,102.4,100,102,100,101.8,100.8,101.6,101.9,101.4,102.7,101.3,103.1,101.4,103.5,101.7,103.9,102.4,104.4,103.1,105.2,103.8,106,104.4,107,105,108.2,105.7,109,106.4,109.1,107.1,109.3,107.9,110.1,108.8,110.7,109.6,110.8,110.3,110.7,110.8,110.9,111.2,111.3,111.7,111.6,112.3,111.8,112.8,112.1,113.1,112.3,113.1,112.5,113.1,113,113.2,113.6,113.1,114.4,112.8,114.9,112.5,115.2,112.3,116,112.5,117,112.9,118,113.5,119.4,114.1,121.1,114.6,123.1,114.9,125,115.4,126.3,115.7,127.4,116.1,129,116.5,131,117.1,133.3,117.5,135.9,117.7,138.4,117.7,140.3,117.7,141.7,117.6,143.1,117.5,144.5,117.6,146,117.9,147.7,118.2,149,118.5,149.7,118.7,150.2,118.8,150.5,118.9,150.7,119,150.9,119),dim=c(2,60),dimnames=list(c('Machines','Transportmiddelen'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Machines','Transportmiddelen'),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 = '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
Transportmiddelen Machines t
1 103.0 101.1 1
2 102.4 100.7 2
3 102.0 100.0 3
4 101.8 100.0 4
5 101.6 100.8 5
6 101.4 101.9 6
7 101.3 102.7 7
8 101.4 103.1 8
9 101.7 103.5 9
10 102.4 103.9 10
11 103.1 104.4 11
12 103.8 105.2 12
13 104.4 106.0 13
14 105.0 107.0 14
15 105.7 108.2 15
16 106.4 109.0 16
17 107.1 109.1 17
18 107.9 109.3 18
19 108.8 110.1 19
20 109.6 110.7 20
21 110.3 110.8 21
22 110.8 110.7 22
23 111.2 110.9 23
24 111.7 111.3 24
25 112.3 111.6 25
26 112.8 111.8 26
27 113.1 112.1 27
28 113.1 112.3 28
29 113.1 112.5 29
30 113.2 113.0 30
31 113.1 113.6 31
32 112.8 114.4 32
33 112.5 114.9 33
34 112.3 115.2 34
35 112.5 116.0 35
36 112.9 117.0 36
37 113.5 118.0 37
38 114.1 119.4 38
39 114.6 121.1 39
40 114.9 123.1 40
41 115.4 125.0 41
42 115.7 126.3 42
43 116.1 127.4 43
44 116.5 129.0 44
45 117.1 131.0 45
46 117.5 133.3 46
47 117.7 135.9 47
48 117.7 138.4 48
49 117.7 140.3 49
50 117.6 141.7 50
51 117.5 143.1 51
52 117.6 144.5 52
53 117.9 146.0 53
54 118.2 147.7 54
55 118.5 149.0 55
56 118.7 149.7 56
57 118.8 150.2 57
58 118.9 150.5 58
59 119.0 150.7 59
60 119.0 150.9 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Machines t
117.1732 -0.1698 0.4812
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.22558 -0.98474 0.01052 0.85577 2.51673
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 117.17323 3.15398 37.15 < 2e-16 ***
Machines -0.16984 0.03383 -5.02 5.39e-06 ***
t 0.48124 0.03162 15.22 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.255 on 57 degrees of freedom
Multiple R-squared: 0.9569, Adjusted R-squared: 0.9554
F-statistic: 633.1 on 2 and 57 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,] 7.919554e-04 1.583911e-03 9.992080e-01
[2,] 5.924439e-05 1.184888e-04 9.999408e-01
[3,] 6.944579e-05 1.388916e-04 9.999306e-01
[4,] 5.001057e-04 1.000211e-03 9.994999e-01
[5,] 9.102769e-03 1.820554e-02 9.908972e-01
[6,] 5.447442e-02 1.089488e-01 9.455256e-01
[7,] 1.424356e-01 2.848711e-01 8.575644e-01
[8,] 2.359382e-01 4.718764e-01 7.640618e-01
[9,] 2.987263e-01 5.974527e-01 7.012737e-01
[10,] 3.307271e-01 6.614543e-01 6.692729e-01
[11,] 3.863972e-01 7.727945e-01 6.136028e-01
[12,] 5.948463e-01 8.103075e-01 4.051537e-01
[13,] 8.732782e-01 2.534436e-01 1.267218e-01
[14,] 9.665937e-01 6.681252e-02 3.340626e-02
[15,] 9.919353e-01 1.612938e-02 8.064689e-03
[16,] 9.988853e-01 2.229405e-03 1.114703e-03
[17,] 9.998747e-01 2.506298e-04 1.253149e-04
[18,] 9.999689e-01 6.212498e-05 3.106249e-05
[19,] 9.999826e-01 3.472024e-05 1.736012e-05
[20,] 9.999819e-01 3.614415e-05 1.807207e-05
[21,] 9.999732e-01 5.357927e-05 2.678964e-05
[22,] 9.999539e-01 9.229438e-05 4.614719e-05
[23,] 9.999095e-01 1.809798e-04 9.048991e-05
[24,] 9.998269e-01 3.461371e-04 1.730685e-04
[25,] 9.997071e-01 5.858537e-04 2.929268e-04
[26,] 9.995259e-01 9.482256e-04 4.741128e-04
[27,] 9.994022e-01 1.195509e-03 5.977547e-04
[28,] 9.996025e-01 7.950824e-04 3.975412e-04
[29,] 9.998966e-01 2.068385e-04 1.034192e-04
[30,] 9.999855e-01 2.891226e-05 1.445613e-05
[31,] 9.999986e-01 2.874414e-06 1.437207e-06
[32,] 9.999997e-01 5.591486e-07 2.795743e-07
[33,] 9.999999e-01 2.392185e-07 1.196093e-07
[34,] 9.999999e-01 1.690621e-07 8.453104e-08
[35,] 1.000000e+00 7.415906e-08 3.707953e-08
[36,] 1.000000e+00 6.023389e-08 3.011694e-08
[37,] 1.000000e+00 3.462362e-08 1.731181e-08
[38,] 1.000000e+00 2.144657e-08 1.072328e-08
[39,] 1.000000e+00 1.069362e-08 5.346808e-09
[40,] 1.000000e+00 4.031621e-08 2.015810e-08
[41,] 9.999999e-01 2.612685e-07 1.306342e-07
[42,] 9.999996e-01 7.095302e-07 3.547651e-07
[43,] 9.999996e-01 8.143825e-07 4.071912e-07
[44,] 9.999999e-01 2.035360e-07 1.017680e-07
[45,] 1.000000e+00 5.374182e-09 2.687091e-09
[46,] 1.000000e+00 1.266555e-09 6.332777e-10
[47,] 1.000000e+00 4.625544e-08 2.312772e-08
[48,] 9.999997e-01 5.955213e-07 2.977606e-07
[49,] 9.999846e-01 3.081565e-05 1.540782e-05
> postscript(file="/var/www/html/freestat/rcomp/tmp/1o33z1229979252.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/218mq1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/34grb1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4aofw1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5u0iq1229979252.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
2.516726139 1.367547581 0.367415927 -0.313825169 -0.859191342 -1.353604420
7 8 9 10 11 12
-1.798970593 -2.112274228 -2.225577863 -1.938881498 -1.635200767 -1.280566941
13 14 15 16 17 18
-1.025933114 -0.737330557 -0.314759269 0.039874557 0.275617826 0.628345461
19 20 21 22 23 24
1.182979287 1.603644383 1.839387652 1.841162191 1.793889825 1.880586190
25 26 27 28 29 30
2.050298190 2.103025824 1.972737824 1.525465459 1.078193093 0.781873823
31 32 33 34 35 36
0.302538919 -0.342827254 -1.039146524 -1.669434524 -1.814800697 -1.726198140
37 38 39 40 41 42
-1.437595583 -1.081055564 -0.773562450 -0.615116239 -0.273654394 -0.234098741
43 44 45 46 47 48
-0.128511818 0.061996931 0.520443142 0.829842448 0.990194851 0.933562888
49 50 51 52 53 54
0.775024733 0.431564752 0.088104771 -0.055355211 0.018169173 0.125662288
55 56 57 58 59 60
0.165217941 0.002867402 -0.293451868 -0.623739868 -0.971012233 -1.418284599
> postscript(file="/var/www/html/freestat/rcomp/tmp/60e6z1229979252.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 2.516726139 NA
1 1.367547581 2.516726139
2 0.367415927 1.367547581
3 -0.313825169 0.367415927
4 -0.859191342 -0.313825169
5 -1.353604420 -0.859191342
6 -1.798970593 -1.353604420
7 -2.112274228 -1.798970593
8 -2.225577863 -2.112274228
9 -1.938881498 -2.225577863
10 -1.635200767 -1.938881498
11 -1.280566941 -1.635200767
12 -1.025933114 -1.280566941
13 -0.737330557 -1.025933114
14 -0.314759269 -0.737330557
15 0.039874557 -0.314759269
16 0.275617826 0.039874557
17 0.628345461 0.275617826
18 1.182979287 0.628345461
19 1.603644383 1.182979287
20 1.839387652 1.603644383
21 1.841162191 1.839387652
22 1.793889825 1.841162191
23 1.880586190 1.793889825
24 2.050298190 1.880586190
25 2.103025824 2.050298190
26 1.972737824 2.103025824
27 1.525465459 1.972737824
28 1.078193093 1.525465459
29 0.781873823 1.078193093
30 0.302538919 0.781873823
31 -0.342827254 0.302538919
32 -1.039146524 -0.342827254
33 -1.669434524 -1.039146524
34 -1.814800697 -1.669434524
35 -1.726198140 -1.814800697
36 -1.437595583 -1.726198140
37 -1.081055564 -1.437595583
38 -0.773562450 -1.081055564
39 -0.615116239 -0.773562450
40 -0.273654394 -0.615116239
41 -0.234098741 -0.273654394
42 -0.128511818 -0.234098741
43 0.061996931 -0.128511818
44 0.520443142 0.061996931
45 0.829842448 0.520443142
46 0.990194851 0.829842448
47 0.933562888 0.990194851
48 0.775024733 0.933562888
49 0.431564752 0.775024733
50 0.088104771 0.431564752
51 -0.055355211 0.088104771
52 0.018169173 -0.055355211
53 0.125662288 0.018169173
54 0.165217941 0.125662288
55 0.002867402 0.165217941
56 -0.293451868 0.002867402
57 -0.623739868 -0.293451868
58 -0.971012233 -0.623739868
59 -1.418284599 -0.971012233
60 NA -1.418284599
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.367547581 2.516726139
[2,] 0.367415927 1.367547581
[3,] -0.313825169 0.367415927
[4,] -0.859191342 -0.313825169
[5,] -1.353604420 -0.859191342
[6,] -1.798970593 -1.353604420
[7,] -2.112274228 -1.798970593
[8,] -2.225577863 -2.112274228
[9,] -1.938881498 -2.225577863
[10,] -1.635200767 -1.938881498
[11,] -1.280566941 -1.635200767
[12,] -1.025933114 -1.280566941
[13,] -0.737330557 -1.025933114
[14,] -0.314759269 -0.737330557
[15,] 0.039874557 -0.314759269
[16,] 0.275617826 0.039874557
[17,] 0.628345461 0.275617826
[18,] 1.182979287 0.628345461
[19,] 1.603644383 1.182979287
[20,] 1.839387652 1.603644383
[21,] 1.841162191 1.839387652
[22,] 1.793889825 1.841162191
[23,] 1.880586190 1.793889825
[24,] 2.050298190 1.880586190
[25,] 2.103025824 2.050298190
[26,] 1.972737824 2.103025824
[27,] 1.525465459 1.972737824
[28,] 1.078193093 1.525465459
[29,] 0.781873823 1.078193093
[30,] 0.302538919 0.781873823
[31,] -0.342827254 0.302538919
[32,] -1.039146524 -0.342827254
[33,] -1.669434524 -1.039146524
[34,] -1.814800697 -1.669434524
[35,] -1.726198140 -1.814800697
[36,] -1.437595583 -1.726198140
[37,] -1.081055564 -1.437595583
[38,] -0.773562450 -1.081055564
[39,] -0.615116239 -0.773562450
[40,] -0.273654394 -0.615116239
[41,] -0.234098741 -0.273654394
[42,] -0.128511818 -0.234098741
[43,] 0.061996931 -0.128511818
[44,] 0.520443142 0.061996931
[45,] 0.829842448 0.520443142
[46,] 0.990194851 0.829842448
[47,] 0.933562888 0.990194851
[48,] 0.775024733 0.933562888
[49,] 0.431564752 0.775024733
[50,] 0.088104771 0.431564752
[51,] -0.055355211 0.088104771
[52,] 0.018169173 -0.055355211
[53,] 0.125662288 0.018169173
[54,] 0.165217941 0.125662288
[55,] 0.002867402 0.165217941
[56,] -0.293451868 0.002867402
[57,] -0.623739868 -0.293451868
[58,] -0.971012233 -0.623739868
[59,] -1.418284599 -0.971012233
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.367547581 2.516726139
2 0.367415927 1.367547581
3 -0.313825169 0.367415927
4 -0.859191342 -0.313825169
5 -1.353604420 -0.859191342
6 -1.798970593 -1.353604420
7 -2.112274228 -1.798970593
8 -2.225577863 -2.112274228
9 -1.938881498 -2.225577863
10 -1.635200767 -1.938881498
11 -1.280566941 -1.635200767
12 -1.025933114 -1.280566941
13 -0.737330557 -1.025933114
14 -0.314759269 -0.737330557
15 0.039874557 -0.314759269
16 0.275617826 0.039874557
17 0.628345461 0.275617826
18 1.182979287 0.628345461
19 1.603644383 1.182979287
20 1.839387652 1.603644383
21 1.841162191 1.839387652
22 1.793889825 1.841162191
23 1.880586190 1.793889825
24 2.050298190 1.880586190
25 2.103025824 2.050298190
26 1.972737824 2.103025824
27 1.525465459 1.972737824
28 1.078193093 1.525465459
29 0.781873823 1.078193093
30 0.302538919 0.781873823
31 -0.342827254 0.302538919
32 -1.039146524 -0.342827254
33 -1.669434524 -1.039146524
34 -1.814800697 -1.669434524
35 -1.726198140 -1.814800697
36 -1.437595583 -1.726198140
37 -1.081055564 -1.437595583
38 -0.773562450 -1.081055564
39 -0.615116239 -0.773562450
40 -0.273654394 -0.615116239
41 -0.234098741 -0.273654394
42 -0.128511818 -0.234098741
43 0.061996931 -0.128511818
44 0.520443142 0.061996931
45 0.829842448 0.520443142
46 0.990194851 0.829842448
47 0.933562888 0.990194851
48 0.775024733 0.933562888
49 0.431564752 0.775024733
50 0.088104771 0.431564752
51 -0.055355211 0.088104771
52 0.018169173 -0.055355211
53 0.125662288 0.018169173
54 0.165217941 0.125662288
55 0.002867402 0.165217941
56 -0.293451868 0.002867402
57 -0.623739868 -0.293451868
58 -0.971012233 -0.623739868
59 -1.418284599 -0.971012233
> 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/freestat/rcomp/tmp/7kgeb1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/80udk1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9scb41229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10c1di1229979252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11yg0g1229979252.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/freestat/rcomp/tmp/128i8w1229979252.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/freestat/rcomp/tmp/13zdx11229979252.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/freestat/rcomp/tmp/14u8tr1229979253.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/freestat/rcomp/tmp/15fisi1229979253.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/freestat/rcomp/tmp/16oqa81229979253.tab")
+ }
>
> system("convert tmp/1o33z1229979252.ps tmp/1o33z1229979252.png")
> system("convert tmp/218mq1229979252.ps tmp/218mq1229979252.png")
> system("convert tmp/34grb1229979252.ps tmp/34grb1229979252.png")
> system("convert tmp/4aofw1229979252.ps tmp/4aofw1229979252.png")
> system("convert tmp/5u0iq1229979252.ps tmp/5u0iq1229979252.png")
> system("convert tmp/60e6z1229979252.ps tmp/60e6z1229979252.png")
> system("convert tmp/7kgeb1229979252.ps tmp/7kgeb1229979252.png")
> system("convert tmp/80udk1229979252.ps tmp/80udk1229979252.png")
> system("convert tmp/9scb41229979252.ps tmp/9scb41229979252.png")
> system("convert tmp/10c1di1229979252.ps tmp/10c1di1229979252.png")
>
>
> proc.time()
user system elapsed
3.606 2.432 4.065