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.
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(100.34
+ ,105.02
+ ,100.39
+ ,100.36
+ ,100.35
+ ,100.35
+ ,100.34
+ ,104.43
+ ,100.34
+ ,100.39
+ ,100.36
+ ,100.35
+ ,100.35
+ ,104.63
+ ,100.34
+ ,100.34
+ ,100.39
+ ,100.36
+ ,100.43
+ ,104.93
+ ,100.35
+ ,100.34
+ ,100.34
+ ,100.39
+ ,100.47
+ ,105.87
+ ,100.43
+ ,100.35
+ ,100.34
+ ,100.34
+ ,100.67
+ ,105.66
+ ,100.47
+ ,100.43
+ ,100.35
+ ,100.34
+ ,100.75
+ ,106.76
+ ,100.67
+ ,100.47
+ ,100.43
+ ,100.35
+ ,100.78
+ ,106
+ ,100.75
+ ,100.67
+ ,100.47
+ ,100.43
+ ,100.79
+ ,107.22
+ ,100.78
+ ,100.75
+ ,100.67
+ ,100.47
+ ,100.67
+ ,107.33
+ ,100.79
+ ,100.78
+ ,100.75
+ ,100.67
+ ,100.64
+ ,107.11
+ ,100.67
+ ,100.79
+ ,100.78
+ ,100.75
+ ,100.64
+ ,108.86
+ ,100.64
+ ,100.67
+ ,100.79
+ ,100.78
+ ,100.76
+ ,107.72
+ ,100.64
+ ,100.64
+ ,100.67
+ ,100.79
+ ,100.79
+ ,107.88
+ ,100.76
+ ,100.64
+ ,100.64
+ ,100.67
+ ,100.79
+ ,108.38
+ ,100.79
+ ,100.76
+ ,100.64
+ ,100.64
+ ,100.9
+ ,107.72
+ ,100.79
+ ,100.79
+ ,100.76
+ ,100.64
+ ,100.98
+ ,108.41
+ ,100.9
+ ,100.79
+ ,100.79
+ ,100.76
+ ,101.11
+ ,109.9
+ ,100.98
+ ,100.9
+ ,100.79
+ ,100.79
+ ,101.18
+ ,111.45
+ ,101.11
+ ,100.98
+ ,100.9
+ ,100.79
+ ,101.22
+ ,112.18
+ ,101.18
+ ,101.11
+ ,100.98
+ ,100.9
+ ,101.23
+ ,113.34
+ ,101.22
+ ,101.18
+ ,101.11
+ ,100.98
+ ,101.09
+ ,113.46
+ ,101.23
+ ,101.22
+ ,101.18
+ ,101.11
+ ,101.26
+ ,114.06
+ ,101.09
+ ,101.23
+ ,101.22
+ ,101.18
+ ,101.28
+ ,115.54
+ ,101.26
+ ,101.09
+ ,101.23
+ ,101.22
+ ,101.43
+ ,116.39
+ ,101.28
+ ,101.26
+ ,101.09
+ ,101.23
+ ,101.53
+ ,115.94
+ ,101.43
+ ,101.28
+ ,101.26
+ ,101.09
+ ,101.54
+ ,116.97
+ ,101.53
+ ,101.43
+ ,101.28
+ ,101.26
+ ,101.54
+ ,115.94
+ ,101.54
+ ,101.53
+ ,101.43
+ ,101.28
+ ,101.79
+ ,115.91
+ ,101.54
+ ,101.54
+ ,101.53
+ ,101.43
+ ,102.18
+ ,116.43
+ ,101.79
+ ,101.54
+ ,101.54
+ ,101.53
+ ,102.37
+ ,116.26
+ ,102.18
+ ,101.79
+ ,101.54
+ ,101.54
+ ,102.46
+ ,116.35
+ ,102.37
+ ,102.18
+ ,101.79
+ ,101.54
+ ,102.46
+ ,117.9
+ ,102.46
+ ,102.37
+ ,102.18
+ ,101.79
+ ,102.03
+ ,117.7
+ ,102.46
+ ,102.46
+ ,102.37
+ ,102.18
+ ,102.26
+ ,117.53
+ ,102.03
+ ,102.46
+ ,102.46
+ ,102.37
+ ,102.33
+ ,117.86
+ ,102.26
+ ,102.03
+ ,102.46
+ ,102.46
+ ,102.44
+ ,117.65
+ ,102.33
+ ,102.26
+ ,102.03
+ ,102.46
+ ,102.5
+ ,116.51
+ ,102.44
+ ,102.33
+ ,102.26
+ ,102.03
+ ,102.52
+ ,115.93
+ ,102.5
+ ,102.44
+ ,102.33
+ ,102.26
+ ,102.66
+ ,115.31
+ ,102.52
+ ,102.5
+ ,102.44
+ ,102.33
+ ,102.72
+ ,115
+ ,102.66
+ ,102.52
+ ,102.5
+ ,102.44)
+ ,dim=c(6
+ ,41)
+ ,dimnames=list(c('y(t)'
+ ,'x(t)'
+ ,'y(t-1)'
+ ,'y(t-2)'
+ ,'y(t-3)'
+ ,'y(t-4)')
+ ,1:41))
> y <- array(NA,dim=c(6,41),dimnames=list(c('y(t)','x(t)','y(t-1)','y(t-2)','y(t-3)','y(t-4)'),1:41))
> 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(t) x(t) y(t-1) y(t-2) y(t-3) y(t-4)
1 100.34 105.02 100.39 100.36 100.35 100.35
2 100.34 104.43 100.34 100.39 100.36 100.35
3 100.35 104.63 100.34 100.34 100.39 100.36
4 100.43 104.93 100.35 100.34 100.34 100.39
5 100.47 105.87 100.43 100.35 100.34 100.34
6 100.67 105.66 100.47 100.43 100.35 100.34
7 100.75 106.76 100.67 100.47 100.43 100.35
8 100.78 106.00 100.75 100.67 100.47 100.43
9 100.79 107.22 100.78 100.75 100.67 100.47
10 100.67 107.33 100.79 100.78 100.75 100.67
11 100.64 107.11 100.67 100.79 100.78 100.75
12 100.64 108.86 100.64 100.67 100.79 100.78
13 100.76 107.72 100.64 100.64 100.67 100.79
14 100.79 107.88 100.76 100.64 100.64 100.67
15 100.79 108.38 100.79 100.76 100.64 100.64
16 100.90 107.72 100.79 100.79 100.76 100.64
17 100.98 108.41 100.90 100.79 100.79 100.76
18 101.11 109.90 100.98 100.90 100.79 100.79
19 101.18 111.45 101.11 100.98 100.90 100.79
20 101.22 112.18 101.18 101.11 100.98 100.90
21 101.23 113.34 101.22 101.18 101.11 100.98
22 101.09 113.46 101.23 101.22 101.18 101.11
23 101.26 114.06 101.09 101.23 101.22 101.18
24 101.28 115.54 101.26 101.09 101.23 101.22
25 101.43 116.39 101.28 101.26 101.09 101.23
26 101.53 115.94 101.43 101.28 101.26 101.09
27 101.54 116.97 101.53 101.43 101.28 101.26
28 101.54 115.94 101.54 101.53 101.43 101.28
29 101.79 115.91 101.54 101.54 101.53 101.43
30 102.18 116.43 101.79 101.54 101.54 101.53
31 102.37 116.26 102.18 101.79 101.54 101.54
32 102.46 116.35 102.37 102.18 101.79 101.54
33 102.46 117.90 102.46 102.37 102.18 101.79
34 102.03 117.70 102.46 102.46 102.37 102.18
35 102.26 117.53 102.03 102.46 102.46 102.37
36 102.33 117.86 102.26 102.03 102.46 102.46
37 102.44 117.65 102.33 102.26 102.03 102.46
38 102.50 116.51 102.44 102.33 102.26 102.03
39 102.52 115.93 102.50 102.44 102.33 102.26
40 102.66 115.31 102.52 102.50 102.44 102.33
41 102.72 115.00 102.66 102.52 102.50 102.44
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `x(t)` `y(t-1)` `y(t-2)` `y(t-3)` `y(t-4)`
3.234843 0.009806 1.059674 -0.078236 -0.259322 0.235745
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.458752 -0.053187 0.002652 0.042169 0.279703
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.234843 5.350932 0.605 0.549
`x(t)` 0.009806 0.009350 1.049 0.301
`y(t-1)` 1.059674 0.166392 6.369 2.53e-07 ***
`y(t-2)` -0.078236 0.241364 -0.324 0.748
`y(t-3)` -0.259322 0.242875 -1.068 0.293
`y(t-4)` 0.235745 0.167281 1.409 0.168
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1263 on 35 degrees of freedom
Multiple R-squared: 0.976, Adjusted R-squared: 0.9726
F-statistic: 285.1 on 5 and 35 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,] 0.2395932392 0.4791864784 0.7604068
[2,] 0.1222813694 0.2445627388 0.8777186
[3,] 0.0634053601 0.1268107203 0.9365946
[4,] 0.0278108972 0.0556217944 0.9721891
[5,] 0.0220840983 0.0441681966 0.9779159
[6,] 0.0089896716 0.0179793432 0.9910103
[7,] 0.0116383329 0.0232766658 0.9883617
[8,] 0.0175189357 0.0350378714 0.9824811
[9,] 0.0100057060 0.0200114121 0.9899943
[10,] 0.0047166382 0.0094332764 0.9952834
[11,] 0.0020390689 0.0040781379 0.9979609
[12,] 0.0009154432 0.0018308863 0.9990846
[13,] 0.0003851341 0.0007702682 0.9996149
[14,] 0.0019585542 0.0039171084 0.9980414
[15,] 0.0032000276 0.0064000552 0.9968000
[16,] 0.0026284649 0.0052569298 0.9973715
[17,] 0.0012512233 0.0025024466 0.9987488
[18,] 0.0006748338 0.0013496676 0.9993252
[19,] 0.0006618786 0.0013237572 0.9993381
[20,] 0.0024725037 0.0049450075 0.9975275
[21,] 0.0293638156 0.0587276313 0.9706362
[22,] 0.0386633552 0.0773267104 0.9613366
[23,] 0.0388013029 0.0776026059 0.9611987
[24,] 0.1064795986 0.2129591972 0.8935204
> postscript(file="/var/www/html/rcomp/tmp/1uuj81258763045.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26may1258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38dx11258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lgvz1258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jsx01258763045.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 = 41
Frequency = 1
1 2 3 4 5 6
-0.087597209 -0.023887520 -0.014338369 0.032084542 -0.009337597 0.159186892
7 8 9 10 11 12
0.037983050 -0.002177578 0.012762175 -0.142969441 -0.053948796 -0.053186994
13 14 15 16 17 18
0.042168880 -0.036051211 -0.056283816 0.093654077 0.029813875 0.061962317
19 20 21 22 23 24
0.013789424 -0.022561767 -0.045995125 -0.207133487 0.099990201 -0.092457273
25 26 27 28 29 30
0.002651549 0.026767157 -0.102455459 -0.060944686 0.180702299 0.279703271
31 32 33 34 35 36
0.075299159 0.058421351 0.004915344 -0.458751574 0.207122654 -0.024697132
37 38 39 40 41
-0.080329252 0.040776933 -0.024578749 0.117025110 0.022902778
> postscript(file="/var/www/html/rcomp/tmp/6ee5e1258763045.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 = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.087597209 NA
1 -0.023887520 -0.087597209
2 -0.014338369 -0.023887520
3 0.032084542 -0.014338369
4 -0.009337597 0.032084542
5 0.159186892 -0.009337597
6 0.037983050 0.159186892
7 -0.002177578 0.037983050
8 0.012762175 -0.002177578
9 -0.142969441 0.012762175
10 -0.053948796 -0.142969441
11 -0.053186994 -0.053948796
12 0.042168880 -0.053186994
13 -0.036051211 0.042168880
14 -0.056283816 -0.036051211
15 0.093654077 -0.056283816
16 0.029813875 0.093654077
17 0.061962317 0.029813875
18 0.013789424 0.061962317
19 -0.022561767 0.013789424
20 -0.045995125 -0.022561767
21 -0.207133487 -0.045995125
22 0.099990201 -0.207133487
23 -0.092457273 0.099990201
24 0.002651549 -0.092457273
25 0.026767157 0.002651549
26 -0.102455459 0.026767157
27 -0.060944686 -0.102455459
28 0.180702299 -0.060944686
29 0.279703271 0.180702299
30 0.075299159 0.279703271
31 0.058421351 0.075299159
32 0.004915344 0.058421351
33 -0.458751574 0.004915344
34 0.207122654 -0.458751574
35 -0.024697132 0.207122654
36 -0.080329252 -0.024697132
37 0.040776933 -0.080329252
38 -0.024578749 0.040776933
39 0.117025110 -0.024578749
40 0.022902778 0.117025110
41 NA 0.022902778
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.023887520 -0.087597209
[2,] -0.014338369 -0.023887520
[3,] 0.032084542 -0.014338369
[4,] -0.009337597 0.032084542
[5,] 0.159186892 -0.009337597
[6,] 0.037983050 0.159186892
[7,] -0.002177578 0.037983050
[8,] 0.012762175 -0.002177578
[9,] -0.142969441 0.012762175
[10,] -0.053948796 -0.142969441
[11,] -0.053186994 -0.053948796
[12,] 0.042168880 -0.053186994
[13,] -0.036051211 0.042168880
[14,] -0.056283816 -0.036051211
[15,] 0.093654077 -0.056283816
[16,] 0.029813875 0.093654077
[17,] 0.061962317 0.029813875
[18,] 0.013789424 0.061962317
[19,] -0.022561767 0.013789424
[20,] -0.045995125 -0.022561767
[21,] -0.207133487 -0.045995125
[22,] 0.099990201 -0.207133487
[23,] -0.092457273 0.099990201
[24,] 0.002651549 -0.092457273
[25,] 0.026767157 0.002651549
[26,] -0.102455459 0.026767157
[27,] -0.060944686 -0.102455459
[28,] 0.180702299 -0.060944686
[29,] 0.279703271 0.180702299
[30,] 0.075299159 0.279703271
[31,] 0.058421351 0.075299159
[32,] 0.004915344 0.058421351
[33,] -0.458751574 0.004915344
[34,] 0.207122654 -0.458751574
[35,] -0.024697132 0.207122654
[36,] -0.080329252 -0.024697132
[37,] 0.040776933 -0.080329252
[38,] -0.024578749 0.040776933
[39,] 0.117025110 -0.024578749
[40,] 0.022902778 0.117025110
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.023887520 -0.087597209
2 -0.014338369 -0.023887520
3 0.032084542 -0.014338369
4 -0.009337597 0.032084542
5 0.159186892 -0.009337597
6 0.037983050 0.159186892
7 -0.002177578 0.037983050
8 0.012762175 -0.002177578
9 -0.142969441 0.012762175
10 -0.053948796 -0.142969441
11 -0.053186994 -0.053948796
12 0.042168880 -0.053186994
13 -0.036051211 0.042168880
14 -0.056283816 -0.036051211
15 0.093654077 -0.056283816
16 0.029813875 0.093654077
17 0.061962317 0.029813875
18 0.013789424 0.061962317
19 -0.022561767 0.013789424
20 -0.045995125 -0.022561767
21 -0.207133487 -0.045995125
22 0.099990201 -0.207133487
23 -0.092457273 0.099990201
24 0.002651549 -0.092457273
25 0.026767157 0.002651549
26 -0.102455459 0.026767157
27 -0.060944686 -0.102455459
28 0.180702299 -0.060944686
29 0.279703271 0.180702299
30 0.075299159 0.279703271
31 0.058421351 0.075299159
32 0.004915344 0.058421351
33 -0.458751574 0.004915344
34 0.207122654 -0.458751574
35 -0.024697132 0.207122654
36 -0.080329252 -0.024697132
37 0.040776933 -0.080329252
38 -0.024578749 0.040776933
39 0.117025110 -0.024578749
40 0.022902778 0.117025110
> 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/7664u1258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8makp1258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y3a61258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10d7611258763045.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oqno1258763045.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/12afak1258763045.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/13kcfm1258763046.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/14u30t1258763046.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/155oi21258763046.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/16tvn91258763046.tab")
+ }
>
> system("convert tmp/1uuj81258763045.ps tmp/1uuj81258763045.png")
> system("convert tmp/26may1258763045.ps tmp/26may1258763045.png")
> system("convert tmp/38dx11258763045.ps tmp/38dx11258763045.png")
> system("convert tmp/4lgvz1258763045.ps tmp/4lgvz1258763045.png")
> system("convert tmp/5jsx01258763045.ps tmp/5jsx01258763045.png")
> system("convert tmp/6ee5e1258763045.ps tmp/6ee5e1258763045.png")
> system("convert tmp/7664u1258763045.ps tmp/7664u1258763045.png")
> system("convert tmp/8makp1258763045.ps tmp/8makp1258763045.png")
> system("convert tmp/9y3a61258763045.ps tmp/9y3a61258763045.png")
> system("convert tmp/10d7611258763045.ps tmp/10d7611258763045.png")
>
>
> proc.time()
user system elapsed
2.233 1.505 2.652