R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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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
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> x <- array(list(161,0,149,0,139,0,135,0,130,0,127,0,122,0,117,0,112,0,113,0,149,0,157,0,157,0,147,0,137,0,132,0,125,0,123,0,117,0,114,0,111,0,112,0,144,0,150,0,149,0,134,0,123,0,116,0,117,0,111,0,105,0,102,0,95,0,93,0,124,0,130,0,124,0,115,0,106,0,105,0,105,1,101,1,95,1,93,1,84,1,87,1,116,1,120,1,117,1,109,1,105,1,107,1,109,1,109,1,108,1,107,1,99,1,103,1,131,1,137,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 161 0 1 0 0 0 0 0 0 0 0 0 0 1
2 149 0 0 1 0 0 0 0 0 0 0 0 0 2
3 139 0 0 0 1 0 0 0 0 0 0 0 0 3
4 135 0 0 0 0 1 0 0 0 0 0 0 0 4
5 130 0 0 0 0 0 1 0 0 0 0 0 0 5
6 127 0 0 0 0 0 0 1 0 0 0 0 0 6
7 122 0 0 0 0 0 0 0 1 0 0 0 0 7
8 117 0 0 0 0 0 0 0 0 1 0 0 0 8
9 112 0 0 0 0 0 0 0 0 0 1 0 0 9
10 113 0 0 0 0 0 0 0 0 0 0 1 0 10
11 149 0 0 0 0 0 0 0 0 0 0 0 1 11
12 157 0 0 0 0 0 0 0 0 0 0 0 0 12
13 157 0 1 0 0 0 0 0 0 0 0 0 0 13
14 147 0 0 1 0 0 0 0 0 0 0 0 0 14
15 137 0 0 0 1 0 0 0 0 0 0 0 0 15
16 132 0 0 0 0 1 0 0 0 0 0 0 0 16
17 125 0 0 0 0 0 1 0 0 0 0 0 0 17
18 123 0 0 0 0 0 0 1 0 0 0 0 0 18
19 117 0 0 0 0 0 0 0 1 0 0 0 0 19
20 114 0 0 0 0 0 0 0 0 1 0 0 0 20
21 111 0 0 0 0 0 0 0 0 0 1 0 0 21
22 112 0 0 0 0 0 0 0 0 0 0 1 0 22
23 144 0 0 0 0 0 0 0 0 0 0 0 1 23
24 150 0 0 0 0 0 0 0 0 0 0 0 0 24
25 149 0 1 0 0 0 0 0 0 0 0 0 0 25
26 134 0 0 1 0 0 0 0 0 0 0 0 0 26
27 123 0 0 0 1 0 0 0 0 0 0 0 0 27
28 116 0 0 0 0 1 0 0 0 0 0 0 0 28
29 117 0 0 0 0 0 1 0 0 0 0 0 0 29
30 111 0 0 0 0 0 0 1 0 0 0 0 0 30
31 105 0 0 0 0 0 0 0 1 0 0 0 0 31
32 102 0 0 0 0 0 0 0 0 1 0 0 0 32
33 95 0 0 0 0 0 0 0 0 0 1 0 0 33
34 93 0 0 0 0 0 0 0 0 0 0 1 0 34
35 124 0 0 0 0 0 0 0 0 0 0 0 1 35
36 130 0 0 0 0 0 0 0 0 0 0 0 0 36
37 124 0 1 0 0 0 0 0 0 0 0 0 0 37
38 115 0 0 1 0 0 0 0 0 0 0 0 0 38
39 106 0 0 0 1 0 0 0 0 0 0 0 0 39
40 105 0 0 0 0 1 0 0 0 0 0 0 0 40
41 105 1 0 0 0 0 1 0 0 0 0 0 0 41
42 101 1 0 0 0 0 0 1 0 0 0 0 0 42
43 95 1 0 0 0 0 0 0 1 0 0 0 0 43
44 93 1 0 0 0 0 0 0 0 1 0 0 0 44
45 84 1 0 0 0 0 0 0 0 0 1 0 0 45
46 87 1 0 0 0 0 0 0 0 0 0 1 0 46
47 116 1 0 0 0 0 0 0 0 0 0 0 1 47
48 120 1 0 0 0 0 0 0 0 0 0 0 0 48
49 117 1 1 0 0 0 0 0 0 0 0 0 0 49
50 109 1 0 1 0 0 0 0 0 0 0 0 0 50
51 105 1 0 0 1 0 0 0 0 0 0 0 0 51
52 107 1 0 0 0 1 0 0 0 0 0 0 0 52
53 109 1 0 0 0 0 1 0 0 0 0 0 0 53
54 109 1 0 0 0 0 0 1 0 0 0 0 0 54
55 108 1 0 0 0 0 0 0 1 0 0 0 0 55
56 107 1 0 0 0 0 0 0 0 1 0 0 0 56
57 99 1 0 0 0 0 0 0 0 0 1 0 0 57
58 103 1 0 0 0 0 0 0 0 0 0 1 0 58
59 131 1 0 0 0 0 0 0 0 0 0 0 1 59
60 137 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
161.7313 3.7031 -3.9187 -14.0406 -22.1625 -24.4844
M5 M6 M7 M8 M9 M10
-26.3469 -28.6687 -32.7906 -34.9125 -40.6344 -38.5562
M11 t
-6.6781 -0.6781
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.8844 -6.4438 0.6047 4.2438 15.4531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 161.7313 4.4967 35.967 < 2e-16 ***
X 3.7031 3.8942 0.951 0.34661
M1 -3.9187 5.1347 -0.763 0.44924
M2 -14.0406 5.1248 -2.740 0.00872 **
M3 -22.1625 5.1171 -4.331 7.96e-05 ***
M4 -24.4844 5.1116 -4.790 1.77e-05 ***
M5 -26.3469 5.1412 -5.125 5.78e-06 ***
M6 -28.6687 5.1270 -5.592 1.18e-06 ***
M7 -32.7906 5.1149 -6.411 7.00e-08 ***
M8 -34.9125 5.1050 -6.839 1.59e-08 ***
M9 -40.6344 5.0973 -7.972 3.26e-10 ***
M10 -38.5562 5.0918 -7.572 1.27e-09 ***
M11 -6.6781 5.0885 -1.312 0.19590
t -0.6781 0.1060 -6.398 7.31e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.044 on 46 degrees of freedom
Multiple R-squared: 0.8512, Adjusted R-squared: 0.8092
F-statistic: 20.24 on 13 and 46 DF, p-value: 8.795e-15
> 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,] 2.359998e-03 4.719995e-03 0.997640002
[2,] 2.649287e-04 5.298574e-04 0.999735071
[3,] 4.754985e-05 9.509970e-05 0.999952450
[4,] 4.829996e-06 9.659991e-06 0.999995170
[5,] 2.141774e-06 4.283548e-06 0.999997858
[6,] 6.711335e-07 1.342267e-06 0.999999329
[7,] 2.164648e-07 4.329296e-07 0.999999784
[8,] 4.197360e-07 8.394720e-07 0.999999580
[9,] 6.411187e-06 1.282237e-05 0.999993589
[10,] 7.020976e-04 1.404195e-03 0.999297902
[11,] 2.779057e-02 5.558114e-02 0.972209430
[12,] 3.883482e-01 7.766964e-01 0.611651816
[13,] 5.072734e-01 9.854533e-01 0.492726640
[14,] 5.729163e-01 8.541674e-01 0.427083695
[15,] 5.786359e-01 8.427282e-01 0.421364116
[16,] 5.281548e-01 9.436905e-01 0.471845236
[17,] 6.025878e-01 7.948245e-01 0.397412231
[18,] 6.456312e-01 7.087375e-01 0.354368765
[19,] 7.394456e-01 5.211088e-01 0.260554424
[20,] 8.250876e-01 3.498248e-01 0.174912381
[21,] 9.031966e-01 1.936069e-01 0.096803434
[22,] 9.191795e-01 1.616410e-01 0.080820486
[23,] 8.819767e-01 2.360467e-01 0.118023332
[24,] 7.989434e-01 4.021131e-01 0.201056574
[25,] 9.439093e-01 1.121814e-01 0.056090724
[26,] 9.932963e-01 1.340750e-02 0.006703748
[27,] 9.883084e-01 2.338313e-02 0.011691567
> postscript(file="/var/www/html/rcomp/tmp/1hcep1258710109.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hcbm1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gmmp1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a17f1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56ip11258710109.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 7
3.865625 2.665625 1.465625 0.465625 -1.993750 -1.993750 -2.193750
8 9 10 11 12 13 14
-4.393750 -2.993750 -3.393750 1.406250 3.406250 8.003125 8.803125
15 16 17 18 19 20 21
7.603125 5.603125 1.143750 2.143750 0.943750 0.743750 4.143750
22 23 24 25 26 27 28
3.743750 4.543750 4.543750 8.140625 3.940625 1.740625 -2.259375
29 30 31 32 33 34 35
1.281250 -1.718750 -2.918750 -3.118750 -3.718750 -7.118750 -7.318750
36 37 38 39 40 41 42
-7.318750 -8.721875 -6.921875 -7.121875 -5.121875 -6.284375 -7.284375
43 44 45 46 47 48 49
-8.484375 -7.684375 -10.284375 -8.684375 -10.884375 -12.884375 -11.287500
50 51 52 53 54 55 56
-8.487500 -3.687500 1.312500 5.853125 8.853125 12.653125 14.453125
57 58 59 60
12.853125 15.453125 12.253125 12.253125
> postscript(file="/var/www/html/rcomp/tmp/6n9ix1258710109.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 3.865625 NA
1 2.665625 3.865625
2 1.465625 2.665625
3 0.465625 1.465625
4 -1.993750 0.465625
5 -1.993750 -1.993750
6 -2.193750 -1.993750
7 -4.393750 -2.193750
8 -2.993750 -4.393750
9 -3.393750 -2.993750
10 1.406250 -3.393750
11 3.406250 1.406250
12 8.003125 3.406250
13 8.803125 8.003125
14 7.603125 8.803125
15 5.603125 7.603125
16 1.143750 5.603125
17 2.143750 1.143750
18 0.943750 2.143750
19 0.743750 0.943750
20 4.143750 0.743750
21 3.743750 4.143750
22 4.543750 3.743750
23 4.543750 4.543750
24 8.140625 4.543750
25 3.940625 8.140625
26 1.740625 3.940625
27 -2.259375 1.740625
28 1.281250 -2.259375
29 -1.718750 1.281250
30 -2.918750 -1.718750
31 -3.118750 -2.918750
32 -3.718750 -3.118750
33 -7.118750 -3.718750
34 -7.318750 -7.118750
35 -7.318750 -7.318750
36 -8.721875 -7.318750
37 -6.921875 -8.721875
38 -7.121875 -6.921875
39 -5.121875 -7.121875
40 -6.284375 -5.121875
41 -7.284375 -6.284375
42 -8.484375 -7.284375
43 -7.684375 -8.484375
44 -10.284375 -7.684375
45 -8.684375 -10.284375
46 -10.884375 -8.684375
47 -12.884375 -10.884375
48 -11.287500 -12.884375
49 -8.487500 -11.287500
50 -3.687500 -8.487500
51 1.312500 -3.687500
52 5.853125 1.312500
53 8.853125 5.853125
54 12.653125 8.853125
55 14.453125 12.653125
56 12.853125 14.453125
57 15.453125 12.853125
58 12.253125 15.453125
59 12.253125 12.253125
60 NA 12.253125
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.665625 3.865625
[2,] 1.465625 2.665625
[3,] 0.465625 1.465625
[4,] -1.993750 0.465625
[5,] -1.993750 -1.993750
[6,] -2.193750 -1.993750
[7,] -4.393750 -2.193750
[8,] -2.993750 -4.393750
[9,] -3.393750 -2.993750
[10,] 1.406250 -3.393750
[11,] 3.406250 1.406250
[12,] 8.003125 3.406250
[13,] 8.803125 8.003125
[14,] 7.603125 8.803125
[15,] 5.603125 7.603125
[16,] 1.143750 5.603125
[17,] 2.143750 1.143750
[18,] 0.943750 2.143750
[19,] 0.743750 0.943750
[20,] 4.143750 0.743750
[21,] 3.743750 4.143750
[22,] 4.543750 3.743750
[23,] 4.543750 4.543750
[24,] 8.140625 4.543750
[25,] 3.940625 8.140625
[26,] 1.740625 3.940625
[27,] -2.259375 1.740625
[28,] 1.281250 -2.259375
[29,] -1.718750 1.281250
[30,] -2.918750 -1.718750
[31,] -3.118750 -2.918750
[32,] -3.718750 -3.118750
[33,] -7.118750 -3.718750
[34,] -7.318750 -7.118750
[35,] -7.318750 -7.318750
[36,] -8.721875 -7.318750
[37,] -6.921875 -8.721875
[38,] -7.121875 -6.921875
[39,] -5.121875 -7.121875
[40,] -6.284375 -5.121875
[41,] -7.284375 -6.284375
[42,] -8.484375 -7.284375
[43,] -7.684375 -8.484375
[44,] -10.284375 -7.684375
[45,] -8.684375 -10.284375
[46,] -10.884375 -8.684375
[47,] -12.884375 -10.884375
[48,] -11.287500 -12.884375
[49,] -8.487500 -11.287500
[50,] -3.687500 -8.487500
[51,] 1.312500 -3.687500
[52,] 5.853125 1.312500
[53,] 8.853125 5.853125
[54,] 12.653125 8.853125
[55,] 14.453125 12.653125
[56,] 12.853125 14.453125
[57,] 15.453125 12.853125
[58,] 12.253125 15.453125
[59,] 12.253125 12.253125
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.665625 3.865625
2 1.465625 2.665625
3 0.465625 1.465625
4 -1.993750 0.465625
5 -1.993750 -1.993750
6 -2.193750 -1.993750
7 -4.393750 -2.193750
8 -2.993750 -4.393750
9 -3.393750 -2.993750
10 1.406250 -3.393750
11 3.406250 1.406250
12 8.003125 3.406250
13 8.803125 8.003125
14 7.603125 8.803125
15 5.603125 7.603125
16 1.143750 5.603125
17 2.143750 1.143750
18 0.943750 2.143750
19 0.743750 0.943750
20 4.143750 0.743750
21 3.743750 4.143750
22 4.543750 3.743750
23 4.543750 4.543750
24 8.140625 4.543750
25 3.940625 8.140625
26 1.740625 3.940625
27 -2.259375 1.740625
28 1.281250 -2.259375
29 -1.718750 1.281250
30 -2.918750 -1.718750
31 -3.118750 -2.918750
32 -3.718750 -3.118750
33 -7.118750 -3.718750
34 -7.318750 -7.118750
35 -7.318750 -7.318750
36 -8.721875 -7.318750
37 -6.921875 -8.721875
38 -7.121875 -6.921875
39 -5.121875 -7.121875
40 -6.284375 -5.121875
41 -7.284375 -6.284375
42 -8.484375 -7.284375
43 -7.684375 -8.484375
44 -10.284375 -7.684375
45 -8.684375 -10.284375
46 -10.884375 -8.684375
47 -12.884375 -10.884375
48 -11.287500 -12.884375
49 -8.487500 -11.287500
50 -3.687500 -8.487500
51 1.312500 -3.687500
52 5.853125 1.312500
53 8.853125 5.853125
54 12.653125 8.853125
55 14.453125 12.653125
56 12.853125 14.453125
57 15.453125 12.853125
58 12.253125 15.453125
59 12.253125 12.253125
> 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/7b31a1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8n0zz1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tedl1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jxgl1258710109.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pro61258710110.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/12fmbr1258710110.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/13r4jb1258710110.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/14mf7s1258710110.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/153ufg1258710110.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/160wcr1258710110.tab")
+ }
>
> system("convert tmp/1hcep1258710109.ps tmp/1hcep1258710109.png")
> system("convert tmp/2hcbm1258710109.ps tmp/2hcbm1258710109.png")
> system("convert tmp/3gmmp1258710109.ps tmp/3gmmp1258710109.png")
> system("convert tmp/4a17f1258710109.ps tmp/4a17f1258710109.png")
> system("convert tmp/56ip11258710109.ps tmp/56ip11258710109.png")
> system("convert tmp/6n9ix1258710109.ps tmp/6n9ix1258710109.png")
> system("convert tmp/7b31a1258710109.ps tmp/7b31a1258710109.png")
> system("convert tmp/8n0zz1258710109.ps tmp/8n0zz1258710109.png")
> system("convert tmp/9tedl1258710109.ps tmp/9tedl1258710109.png")
> system("convert tmp/10jxgl1258710109.ps tmp/10jxgl1258710109.png")
>
>
> proc.time()
user system elapsed
2.384 1.551 3.525