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.
You are welcome to redistribute it under certain conditions.
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(1.58,0.55,1.59,0.55,1.6,0.55,1.6,0.55,1.6,0.55,1.6,0.56,1.61,0.56,1.61,0.56,1.62,0.56,1.63,0.56,1.63,0.55,1.63,0.56,1.63,0.55,1.63,0.55,1.64,0.56,1.64,0.55,1.64,0.55,1.65,0.55,1.65,0.55,1.65,0.53,1.65,0.53,1.65,0.53,1.66,0.53,1.67,0.54,1.68,0.54,1.68,0.54,1.68,0.55,1.68,0.55,1.69,0.54,1.7,0.55,1.7,0.56,1.71,0.58,1.73,0.59,1.73,0.6,1.73,0.6,1.74,0.6,1.74,0.59,1.74,0.6,1.75,0.6,1.78,0.62,1.82,0.65,1.83,0.68,1.84,0.73,1.85,0.78,1.86,0.78,1.86,0.82,1.87,0.82,1.87,0.81,1.87,0.83,1.87,0.85,1.87,0.86,1.87,0.85,1.87,0.85,1.88,0.82,1.88,0.8,1.87,0.81,1.87,0.8,1.87,0.8,1.87,0.8,1.87,0.8,1.87,0.79),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X
1 1.58 0.55
2 1.59 0.55
3 1.60 0.55
4 1.60 0.55
5 1.60 0.55
6 1.60 0.56
7 1.61 0.56
8 1.61 0.56
9 1.62 0.56
10 1.63 0.56
11 1.63 0.55
12 1.63 0.56
13 1.63 0.55
14 1.63 0.55
15 1.64 0.56
16 1.64 0.55
17 1.64 0.55
18 1.65 0.55
19 1.65 0.55
20 1.65 0.53
21 1.65 0.53
22 1.65 0.53
23 1.66 0.53
24 1.67 0.54
25 1.68 0.54
26 1.68 0.54
27 1.68 0.55
28 1.68 0.55
29 1.69 0.54
30 1.70 0.55
31 1.70 0.56
32 1.71 0.58
33 1.73 0.59
34 1.73 0.60
35 1.73 0.60
36 1.74 0.60
37 1.74 0.59
38 1.74 0.60
39 1.75 0.60
40 1.78 0.62
41 1.82 0.65
42 1.83 0.68
43 1.84 0.73
44 1.85 0.78
45 1.86 0.78
46 1.86 0.82
47 1.87 0.82
48 1.87 0.81
49 1.87 0.83
50 1.87 0.85
51 1.87 0.86
52 1.87 0.85
53 1.87 0.85
54 1.88 0.82
55 1.88 0.80
56 1.87 0.81
57 1.87 0.80
58 1.87 0.80
59 1.87 0.80
60 1.87 0.80
61 1.87 0.79
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
1.2036 0.8239
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.076735 -0.026735 0.007288 0.031504 0.080874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.20358 0.02667 45.12 <2e-16 ***
X 0.82391 0.04090 20.14 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0377 on 59 degrees of freedom
Multiple R-squared: 0.873, Adjusted R-squared: 0.8709
F-statistic: 405.7 on 1 and 59 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,] 0.037309858 7.461972e-02 9.626901e-01
[2,] 0.010966706 2.193341e-02 9.890333e-01
[3,] 0.004364618 8.729237e-03 9.956354e-01
[4,] 0.001544712 3.089424e-03 9.984553e-01
[5,] 0.001259360 2.518719e-03 9.987406e-01
[6,] 0.002430071 4.860141e-03 9.975699e-01
[7,] 0.021588664 4.317733e-02 9.784113e-01
[8,] 0.020918481 4.183696e-02 9.790815e-01
[9,] 0.045240805 9.048161e-02 9.547592e-01
[10,] 0.070597978 1.411960e-01 9.294020e-01
[11,] 0.097637824 1.952756e-01 9.023622e-01
[12,] 0.178394979 3.567900e-01 8.216050e-01
[13,] 0.271448723 5.428974e-01 7.285513e-01
[14,] 0.418865231 8.377305e-01 5.811348e-01
[15,] 0.557884616 8.842308e-01 4.421154e-01
[16,] 0.580891527 8.382169e-01 4.191085e-01
[17,] 0.584340540 8.313189e-01 4.156595e-01
[18,] 0.600199654 7.996007e-01 3.998003e-01
[19,] 0.606994969 7.860101e-01 3.930050e-01
[20,] 0.700140330 5.997193e-01 2.998597e-01
[21,] 0.789775180 4.204496e-01 2.102248e-01
[22,] 0.844123732 3.117525e-01 1.558763e-01
[23,] 0.932347465 1.353051e-01 6.765254e-02
[24,] 0.973306167 5.338767e-02 2.669383e-02
[25,] 0.981025543 3.794891e-02 1.897446e-02
[26,] 0.993155647 1.368871e-02 6.844353e-03
[27,] 0.998611526 2.776947e-03 1.388474e-03
[28,] 0.999863721 2.725577e-04 1.362789e-04
[29,] 0.999955068 8.986405e-05 4.493202e-05
[30,] 0.999974455 5.108964e-05 2.554482e-05
[31,] 0.999986581 2.683722e-05 1.341861e-05
[32,] 0.999989640 2.072075e-05 1.036037e-05
[33,] 0.999992914 1.417190e-05 7.085950e-06
[34,] 0.999998990 2.020213e-06 1.010106e-06
[35,] 0.999999982 3.669357e-08 1.834678e-08
[36,] 1.000000000 7.184909e-10 3.592454e-10
[37,] 0.999999999 2.424083e-09 1.212041e-09
[38,] 0.999999997 6.045896e-09 3.022948e-09
[39,] 0.999999999 1.548107e-09 7.740533e-10
[40,] 1.000000000 6.510816e-11 3.255408e-11
[41,] 1.000000000 4.776241e-11 2.388121e-11
[42,] 1.000000000 8.831264e-12 4.415632e-12
[43,] 1.000000000 8.269297e-11 4.134649e-11
[44,] 1.000000000 7.992988e-10 3.996494e-10
[45,] 0.999999996 7.416987e-09 3.708494e-09
[46,] 0.999999967 6.521388e-08 3.260694e-08
[47,] 0.999999723 5.539793e-07 2.769896e-07
[48,] 0.999997763 4.473873e-06 2.236937e-06
[49,] 0.999993115 1.376982e-05 6.884908e-06
[50,] 0.999962475 7.505003e-05 3.752501e-05
[51,] 1.000000000 8.278323e-56 4.139161e-56
[52,] 1.000000000 0.000000e+00 0.000000e+00
> postscript(file="/var/www/html/rcomp/tmp/1b30j1258716442.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2utiu1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3g5vy1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4sfv11258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5shci1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5
-0.0767347849 -0.0667347849 -0.0567347849 -0.0567347849 -0.0567347849
6 7 8 9 10
-0.0649738838 -0.0549738838 -0.0549738838 -0.0449738838 -0.0349738838
11 12 13 14 15
-0.0267347849 -0.0349738838 -0.0267347849 -0.0267347849 -0.0249738838
16 17 18 19 20
-0.0167347849 -0.0167347849 -0.0067347849 -0.0067347849 0.0097434131
21 22 23 24 25
0.0097434131 0.0097434131 0.0197434131 0.0215043141 0.0315043141
26 27 28 29 30
0.0315043141 0.0232652151 0.0232652151 0.0415043141 0.0432652151
31 32 33 34 35
0.0350261162 0.0285479182 0.0403088193 0.0320697203 0.0320697203
36 37 38 39 40
0.0420697203 0.0503088193 0.0420697203 0.0520697203 0.0655915224
41 42 43 44 45
0.0808742255 0.0661569286 0.0349614338 0.0037659390 0.0137659390
46 47 48 49 50
-0.0191904569 -0.0091904569 -0.0009513579 -0.0174295559 -0.0339077538
51 52 53 54 55
-0.0421468527 -0.0339077538 -0.0339077538 0.0008095431 0.0172877410
56 57 58 59 60
-0.0009513579 0.0072877410 0.0072877410 0.0072877410 0.0072877410
61
0.0155268400
> postscript(file="/var/www/html/rcomp/tmp/64o7r1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0767347849 NA
1 -0.0667347849 -0.0767347849
2 -0.0567347849 -0.0667347849
3 -0.0567347849 -0.0567347849
4 -0.0567347849 -0.0567347849
5 -0.0649738838 -0.0567347849
6 -0.0549738838 -0.0649738838
7 -0.0549738838 -0.0549738838
8 -0.0449738838 -0.0549738838
9 -0.0349738838 -0.0449738838
10 -0.0267347849 -0.0349738838
11 -0.0349738838 -0.0267347849
12 -0.0267347849 -0.0349738838
13 -0.0267347849 -0.0267347849
14 -0.0249738838 -0.0267347849
15 -0.0167347849 -0.0249738838
16 -0.0167347849 -0.0167347849
17 -0.0067347849 -0.0167347849
18 -0.0067347849 -0.0067347849
19 0.0097434131 -0.0067347849
20 0.0097434131 0.0097434131
21 0.0097434131 0.0097434131
22 0.0197434131 0.0097434131
23 0.0215043141 0.0197434131
24 0.0315043141 0.0215043141
25 0.0315043141 0.0315043141
26 0.0232652151 0.0315043141
27 0.0232652151 0.0232652151
28 0.0415043141 0.0232652151
29 0.0432652151 0.0415043141
30 0.0350261162 0.0432652151
31 0.0285479182 0.0350261162
32 0.0403088193 0.0285479182
33 0.0320697203 0.0403088193
34 0.0320697203 0.0320697203
35 0.0420697203 0.0320697203
36 0.0503088193 0.0420697203
37 0.0420697203 0.0503088193
38 0.0520697203 0.0420697203
39 0.0655915224 0.0520697203
40 0.0808742255 0.0655915224
41 0.0661569286 0.0808742255
42 0.0349614338 0.0661569286
43 0.0037659390 0.0349614338
44 0.0137659390 0.0037659390
45 -0.0191904569 0.0137659390
46 -0.0091904569 -0.0191904569
47 -0.0009513579 -0.0091904569
48 -0.0174295559 -0.0009513579
49 -0.0339077538 -0.0174295559
50 -0.0421468527 -0.0339077538
51 -0.0339077538 -0.0421468527
52 -0.0339077538 -0.0339077538
53 0.0008095431 -0.0339077538
54 0.0172877410 0.0008095431
55 -0.0009513579 0.0172877410
56 0.0072877410 -0.0009513579
57 0.0072877410 0.0072877410
58 0.0072877410 0.0072877410
59 0.0072877410 0.0072877410
60 0.0155268400 0.0072877410
61 NA 0.0155268400
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0667347849 -0.0767347849
[2,] -0.0567347849 -0.0667347849
[3,] -0.0567347849 -0.0567347849
[4,] -0.0567347849 -0.0567347849
[5,] -0.0649738838 -0.0567347849
[6,] -0.0549738838 -0.0649738838
[7,] -0.0549738838 -0.0549738838
[8,] -0.0449738838 -0.0549738838
[9,] -0.0349738838 -0.0449738838
[10,] -0.0267347849 -0.0349738838
[11,] -0.0349738838 -0.0267347849
[12,] -0.0267347849 -0.0349738838
[13,] -0.0267347849 -0.0267347849
[14,] -0.0249738838 -0.0267347849
[15,] -0.0167347849 -0.0249738838
[16,] -0.0167347849 -0.0167347849
[17,] -0.0067347849 -0.0167347849
[18,] -0.0067347849 -0.0067347849
[19,] 0.0097434131 -0.0067347849
[20,] 0.0097434131 0.0097434131
[21,] 0.0097434131 0.0097434131
[22,] 0.0197434131 0.0097434131
[23,] 0.0215043141 0.0197434131
[24,] 0.0315043141 0.0215043141
[25,] 0.0315043141 0.0315043141
[26,] 0.0232652151 0.0315043141
[27,] 0.0232652151 0.0232652151
[28,] 0.0415043141 0.0232652151
[29,] 0.0432652151 0.0415043141
[30,] 0.0350261162 0.0432652151
[31,] 0.0285479182 0.0350261162
[32,] 0.0403088193 0.0285479182
[33,] 0.0320697203 0.0403088193
[34,] 0.0320697203 0.0320697203
[35,] 0.0420697203 0.0320697203
[36,] 0.0503088193 0.0420697203
[37,] 0.0420697203 0.0503088193
[38,] 0.0520697203 0.0420697203
[39,] 0.0655915224 0.0520697203
[40,] 0.0808742255 0.0655915224
[41,] 0.0661569286 0.0808742255
[42,] 0.0349614338 0.0661569286
[43,] 0.0037659390 0.0349614338
[44,] 0.0137659390 0.0037659390
[45,] -0.0191904569 0.0137659390
[46,] -0.0091904569 -0.0191904569
[47,] -0.0009513579 -0.0091904569
[48,] -0.0174295559 -0.0009513579
[49,] -0.0339077538 -0.0174295559
[50,] -0.0421468527 -0.0339077538
[51,] -0.0339077538 -0.0421468527
[52,] -0.0339077538 -0.0339077538
[53,] 0.0008095431 -0.0339077538
[54,] 0.0172877410 0.0008095431
[55,] -0.0009513579 0.0172877410
[56,] 0.0072877410 -0.0009513579
[57,] 0.0072877410 0.0072877410
[58,] 0.0072877410 0.0072877410
[59,] 0.0072877410 0.0072877410
[60,] 0.0155268400 0.0072877410
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0667347849 -0.0767347849
2 -0.0567347849 -0.0667347849
3 -0.0567347849 -0.0567347849
4 -0.0567347849 -0.0567347849
5 -0.0649738838 -0.0567347849
6 -0.0549738838 -0.0649738838
7 -0.0549738838 -0.0549738838
8 -0.0449738838 -0.0549738838
9 -0.0349738838 -0.0449738838
10 -0.0267347849 -0.0349738838
11 -0.0349738838 -0.0267347849
12 -0.0267347849 -0.0349738838
13 -0.0267347849 -0.0267347849
14 -0.0249738838 -0.0267347849
15 -0.0167347849 -0.0249738838
16 -0.0167347849 -0.0167347849
17 -0.0067347849 -0.0167347849
18 -0.0067347849 -0.0067347849
19 0.0097434131 -0.0067347849
20 0.0097434131 0.0097434131
21 0.0097434131 0.0097434131
22 0.0197434131 0.0097434131
23 0.0215043141 0.0197434131
24 0.0315043141 0.0215043141
25 0.0315043141 0.0315043141
26 0.0232652151 0.0315043141
27 0.0232652151 0.0232652151
28 0.0415043141 0.0232652151
29 0.0432652151 0.0415043141
30 0.0350261162 0.0432652151
31 0.0285479182 0.0350261162
32 0.0403088193 0.0285479182
33 0.0320697203 0.0403088193
34 0.0320697203 0.0320697203
35 0.0420697203 0.0320697203
36 0.0503088193 0.0420697203
37 0.0420697203 0.0503088193
38 0.0520697203 0.0420697203
39 0.0655915224 0.0520697203
40 0.0808742255 0.0655915224
41 0.0661569286 0.0808742255
42 0.0349614338 0.0661569286
43 0.0037659390 0.0349614338
44 0.0137659390 0.0037659390
45 -0.0191904569 0.0137659390
46 -0.0091904569 -0.0191904569
47 -0.0009513579 -0.0091904569
48 -0.0174295559 -0.0009513579
49 -0.0339077538 -0.0174295559
50 -0.0421468527 -0.0339077538
51 -0.0339077538 -0.0421468527
52 -0.0339077538 -0.0339077538
53 0.0008095431 -0.0339077538
54 0.0172877410 0.0008095431
55 -0.0009513579 0.0172877410
56 0.0072877410 -0.0009513579
57 0.0072877410 0.0072877410
58 0.0072877410 0.0072877410
59 0.0072877410 0.0072877410
60 0.0155268400 0.0072877410
> 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/7ycky1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e2hu1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92e8u1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10crtd1258716442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ijl61258716442.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/12noni1258716442.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/130irf1258716442.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/14j49g1258716442.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/15ogos1258716442.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/16fslq1258716442.tab")
+ }
>
> system("convert tmp/1b30j1258716442.ps tmp/1b30j1258716442.png")
> system("convert tmp/2utiu1258716442.ps tmp/2utiu1258716442.png")
> system("convert tmp/3g5vy1258716442.ps tmp/3g5vy1258716442.png")
> system("convert tmp/4sfv11258716442.ps tmp/4sfv11258716442.png")
> system("convert tmp/5shci1258716442.ps tmp/5shci1258716442.png")
> system("convert tmp/64o7r1258716442.ps tmp/64o7r1258716442.png")
> system("convert tmp/7ycky1258716442.ps tmp/7ycky1258716442.png")
> system("convert tmp/8e2hu1258716442.ps tmp/8e2hu1258716442.png")
> system("convert tmp/92e8u1258716442.ps tmp/92e8u1258716442.png")
> system("convert tmp/10crtd1258716442.ps tmp/10crtd1258716442.png")
>
>
> proc.time()
user system elapsed
2.443 1.526 2.811