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(1.1608,0,1.1208,0,1.0883,0,1.0704,0,1.0628,0,1.0378,0,1.0353,0,1.0604,0,1.0501,0,1.0706,0,1.0338,0,1.011,0,1.0137,0,0.9834,0,0.9643,0,0.947,0,0.906,0,0.9492,0,0.9397,0,0.9041,0,0.8721,0,0.8552,0,0.8564,0,0.8973,0,0.9383,0,0.9217,0,0.9095,0,0.892,0,0.8742,0,0.8532,0,0.8607,0,0.9005,0,0.9111,0,0.9059,1,0.8883,1,0.8924,1,0.8833,1,0.87,1,0.8758,1,0.8858,1,0.917,1,0.9554,1,0.9922,1,0.9778,1,0.9808,1,0.9811,1,1.0014,1,1.0183,1),dim=c(2,48),dimnames=list(c('koers','dummy'),1:48))
> y <- array(NA,dim=c(2,48),dimnames=list(c('koers','dummy'),1:48))
> 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
koers dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.1608 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.1208 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0883 0 0 0 1 0 0 0 0 0 0 0 0 3
4 1.0704 0 0 0 0 1 0 0 0 0 0 0 0 4
5 1.0628 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1.0378 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1.0353 0 0 0 0 0 0 0 1 0 0 0 0 7
8 1.0604 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.0501 0 0 0 0 0 0 0 0 0 1 0 0 9
10 1.0706 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1.0338 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.0110 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1.0137 0 1 0 0 0 0 0 0 0 0 0 0 13
14 0.9834 0 0 1 0 0 0 0 0 0 0 0 0 14
15 0.9643 0 0 0 1 0 0 0 0 0 0 0 0 15
16 0.9470 0 0 0 0 1 0 0 0 0 0 0 0 16
17 0.9060 0 0 0 0 0 1 0 0 0 0 0 0 17
18 0.9492 0 0 0 0 0 0 1 0 0 0 0 0 18
19 0.9397 0 0 0 0 0 0 0 1 0 0 0 0 19
20 0.9041 0 0 0 0 0 0 0 0 1 0 0 0 20
21 0.8721 0 0 0 0 0 0 0 0 0 1 0 0 21
22 0.8552 0 0 0 0 0 0 0 0 0 0 1 0 22
23 0.8564 0 0 0 0 0 0 0 0 0 0 0 1 23
24 0.8973 0 0 0 0 0 0 0 0 0 0 0 0 24
25 0.9383 0 1 0 0 0 0 0 0 0 0 0 0 25
26 0.9217 0 0 1 0 0 0 0 0 0 0 0 0 26
27 0.9095 0 0 0 1 0 0 0 0 0 0 0 0 27
28 0.8920 0 0 0 0 1 0 0 0 0 0 0 0 28
29 0.8742 0 0 0 0 0 1 0 0 0 0 0 0 29
30 0.8532 0 0 0 0 0 0 1 0 0 0 0 0 30
31 0.8607 0 0 0 0 0 0 0 1 0 0 0 0 31
32 0.9005 0 0 0 0 0 0 0 0 1 0 0 0 32
33 0.9111 0 0 0 0 0 0 0 0 0 1 0 0 33
34 0.9059 1 0 0 0 0 0 0 0 0 0 1 0 34
35 0.8883 1 0 0 0 0 0 0 0 0 0 0 1 35
36 0.8924 1 0 0 0 0 0 0 0 0 0 0 0 36
37 0.8833 1 1 0 0 0 0 0 0 0 0 0 0 37
38 0.8700 1 0 1 0 0 0 0 0 0 0 0 0 38
39 0.8758 1 0 0 1 0 0 0 0 0 0 0 0 39
40 0.8858 1 0 0 0 1 0 0 0 0 0 0 0 40
41 0.9170 1 0 0 0 0 1 0 0 0 0 0 0 41
42 0.9554 1 0 0 0 0 0 1 0 0 0 0 0 42
43 0.9922 1 0 0 0 0 0 0 1 0 0 0 0 43
44 0.9778 1 0 0 0 0 0 0 0 1 0 0 0 44
45 0.9808 1 0 0 0 0 0 0 0 0 1 0 0 45
46 0.9811 1 0 0 0 0 0 0 0 0 0 1 0 46
47 1.0014 1 0 0 0 0 0 0 0 0 0 0 1 47
48 1.0183 1 0 0 0 0 0 0 0 0 0 0 0 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy M1 M2 M3 M4
1.0942237 0.1309692 0.0018659 -0.0163521 -0.0240202 -0.0278632
M5 M6 M7 M8 M9 M10
-0.0298313 -0.0140994 0.0008076 0.0113645 0.0110215 -0.0152139
M11 t
-0.0166069 -0.0068319
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.090977 -0.034281 0.004045 0.031343 0.121040
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0942237 0.0380537 28.755 < 2e-16 ***
dummy 0.1309692 0.0332954 3.934 0.000392 ***
M1 0.0018659 0.0441253 0.042 0.966517
M2 -0.0163521 0.0439975 -0.372 0.712452
M3 -0.0240202 0.0438979 -0.547 0.587827
M4 -0.0278632 0.0438266 -0.636 0.529189
M5 -0.0298313 0.0437838 -0.681 0.500276
M6 -0.0140994 0.0437695 -0.322 0.749328
M7 0.0008076 0.0437838 0.018 0.985392
M8 0.0113645 0.0438266 0.259 0.796962
M9 0.0110215 0.0438979 0.251 0.803271
M10 -0.0152139 0.0435491 -0.349 0.728981
M11 -0.0166069 0.0435060 -0.382 0.705047
t -0.0068319 0.0011185 -6.108 6.23e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06151 on 34 degrees of freedom
Multiple R-squared: 0.5581, Adjusted R-squared: 0.3891
F-statistic: 3.303 on 13 and 34 DF, p-value: 0.002564
> 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,] 0.02089193 0.04178387 0.97910807
[2,] 0.05876985 0.11753971 0.94123015
[3,] 0.06063814 0.12127629 0.93936186
[4,] 0.05168008 0.10336016 0.94831992
[5,] 0.06421197 0.12842395 0.93578803
[6,] 0.14510443 0.29020886 0.85489557
[7,] 0.10027138 0.20054275 0.89972862
[8,] 0.06654007 0.13308015 0.93345993
[9,] 0.13756455 0.27512909 0.86243545
[10,] 0.32150839 0.64301678 0.67849161
[11,] 0.62421898 0.75156203 0.37578102
[12,] 0.87403719 0.25192563 0.12596281
[13,] 0.91689909 0.16620183 0.08310091
[14,] 0.83308301 0.33383399 0.16691699
[15,] 0.90118535 0.19762930 0.09881465
> postscript(file="/var/www/html/rcomp/tmp/12xkr1227520397.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lrls1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3386v1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t7511227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ideh1227520397.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 = 48
Frequency = 1
1 2 3 4 5 6
0.071542308 0.056592308 0.038592308 0.031367308 0.032567308 -0.001332692
7 8 9 10 11 12
-0.011907692 0.009467308 0.006342308 0.059909615 0.031334615 -0.001240385
13 14 15 16 17 18
0.006425641 0.001175641 -0.003424359 -0.010049359 -0.042249359 -0.007949359
19 20 21 22 23 24
-0.025524359 -0.064849359 -0.089674359 -0.073507051 -0.064082051 -0.032957051
25 26 27 28 29 30
0.013008974 0.021458974 0.023758974 0.016933974 0.007933974 -0.021966026
31 32 33 34 35 36
-0.022541026 0.013533974 0.031308974 -0.071792949 -0.081167949 -0.086842949
37 38 39 40 41 42
-0.090976923 -0.079226923 -0.058926923 -0.038251923 0.001748077 0.031248077
43 44 45 46 47 48
0.059973077 0.041848077 0.052023077 0.085390385 0.113915385 0.121040385
> postscript(file="/var/www/html/rcomp/tmp/6ag0g1227520397.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 = 48
Frequency = 1
lag(myerror, k = 1) myerror
0 0.071542308 NA
1 0.056592308 0.071542308
2 0.038592308 0.056592308
3 0.031367308 0.038592308
4 0.032567308 0.031367308
5 -0.001332692 0.032567308
6 -0.011907692 -0.001332692
7 0.009467308 -0.011907692
8 0.006342308 0.009467308
9 0.059909615 0.006342308
10 0.031334615 0.059909615
11 -0.001240385 0.031334615
12 0.006425641 -0.001240385
13 0.001175641 0.006425641
14 -0.003424359 0.001175641
15 -0.010049359 -0.003424359
16 -0.042249359 -0.010049359
17 -0.007949359 -0.042249359
18 -0.025524359 -0.007949359
19 -0.064849359 -0.025524359
20 -0.089674359 -0.064849359
21 -0.073507051 -0.089674359
22 -0.064082051 -0.073507051
23 -0.032957051 -0.064082051
24 0.013008974 -0.032957051
25 0.021458974 0.013008974
26 0.023758974 0.021458974
27 0.016933974 0.023758974
28 0.007933974 0.016933974
29 -0.021966026 0.007933974
30 -0.022541026 -0.021966026
31 0.013533974 -0.022541026
32 0.031308974 0.013533974
33 -0.071792949 0.031308974
34 -0.081167949 -0.071792949
35 -0.086842949 -0.081167949
36 -0.090976923 -0.086842949
37 -0.079226923 -0.090976923
38 -0.058926923 -0.079226923
39 -0.038251923 -0.058926923
40 0.001748077 -0.038251923
41 0.031248077 0.001748077
42 0.059973077 0.031248077
43 0.041848077 0.059973077
44 0.052023077 0.041848077
45 0.085390385 0.052023077
46 0.113915385 0.085390385
47 0.121040385 0.113915385
48 NA 0.121040385
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.056592308 0.071542308
[2,] 0.038592308 0.056592308
[3,] 0.031367308 0.038592308
[4,] 0.032567308 0.031367308
[5,] -0.001332692 0.032567308
[6,] -0.011907692 -0.001332692
[7,] 0.009467308 -0.011907692
[8,] 0.006342308 0.009467308
[9,] 0.059909615 0.006342308
[10,] 0.031334615 0.059909615
[11,] -0.001240385 0.031334615
[12,] 0.006425641 -0.001240385
[13,] 0.001175641 0.006425641
[14,] -0.003424359 0.001175641
[15,] -0.010049359 -0.003424359
[16,] -0.042249359 -0.010049359
[17,] -0.007949359 -0.042249359
[18,] -0.025524359 -0.007949359
[19,] -0.064849359 -0.025524359
[20,] -0.089674359 -0.064849359
[21,] -0.073507051 -0.089674359
[22,] -0.064082051 -0.073507051
[23,] -0.032957051 -0.064082051
[24,] 0.013008974 -0.032957051
[25,] 0.021458974 0.013008974
[26,] 0.023758974 0.021458974
[27,] 0.016933974 0.023758974
[28,] 0.007933974 0.016933974
[29,] -0.021966026 0.007933974
[30,] -0.022541026 -0.021966026
[31,] 0.013533974 -0.022541026
[32,] 0.031308974 0.013533974
[33,] -0.071792949 0.031308974
[34,] -0.081167949 -0.071792949
[35,] -0.086842949 -0.081167949
[36,] -0.090976923 -0.086842949
[37,] -0.079226923 -0.090976923
[38,] -0.058926923 -0.079226923
[39,] -0.038251923 -0.058926923
[40,] 0.001748077 -0.038251923
[41,] 0.031248077 0.001748077
[42,] 0.059973077 0.031248077
[43,] 0.041848077 0.059973077
[44,] 0.052023077 0.041848077
[45,] 0.085390385 0.052023077
[46,] 0.113915385 0.085390385
[47,] 0.121040385 0.113915385
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.056592308 0.071542308
2 0.038592308 0.056592308
3 0.031367308 0.038592308
4 0.032567308 0.031367308
5 -0.001332692 0.032567308
6 -0.011907692 -0.001332692
7 0.009467308 -0.011907692
8 0.006342308 0.009467308
9 0.059909615 0.006342308
10 0.031334615 0.059909615
11 -0.001240385 0.031334615
12 0.006425641 -0.001240385
13 0.001175641 0.006425641
14 -0.003424359 0.001175641
15 -0.010049359 -0.003424359
16 -0.042249359 -0.010049359
17 -0.007949359 -0.042249359
18 -0.025524359 -0.007949359
19 -0.064849359 -0.025524359
20 -0.089674359 -0.064849359
21 -0.073507051 -0.089674359
22 -0.064082051 -0.073507051
23 -0.032957051 -0.064082051
24 0.013008974 -0.032957051
25 0.021458974 0.013008974
26 0.023758974 0.021458974
27 0.016933974 0.023758974
28 0.007933974 0.016933974
29 -0.021966026 0.007933974
30 -0.022541026 -0.021966026
31 0.013533974 -0.022541026
32 0.031308974 0.013533974
33 -0.071792949 0.031308974
34 -0.081167949 -0.071792949
35 -0.086842949 -0.081167949
36 -0.090976923 -0.086842949
37 -0.079226923 -0.090976923
38 -0.058926923 -0.079226923
39 -0.038251923 -0.058926923
40 0.001748077 -0.038251923
41 0.031248077 0.001748077
42 0.059973077 0.031248077
43 0.041848077 0.059973077
44 0.052023077 0.041848077
45 0.085390385 0.052023077
46 0.113915385 0.085390385
47 0.121040385 0.113915385
> 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/7jews1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cvri1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tehc1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108uba1227520397.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11myad1227520397.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/123rkx1227520397.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/13rujc1227520397.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/14racg1227520397.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/150njw1227520397.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/16mkub1227520397.tab")
+ }
>
> system("convert tmp/12xkr1227520397.ps tmp/12xkr1227520397.png")
> system("convert tmp/2lrls1227520397.ps tmp/2lrls1227520397.png")
> system("convert tmp/3386v1227520397.ps tmp/3386v1227520397.png")
> system("convert tmp/4t7511227520397.ps tmp/4t7511227520397.png")
> system("convert tmp/5ideh1227520397.ps tmp/5ideh1227520397.png")
> system("convert tmp/6ag0g1227520397.ps tmp/6ag0g1227520397.png")
> system("convert tmp/7jews1227520397.ps tmp/7jews1227520397.png")
> system("convert tmp/8cvri1227520397.ps tmp/8cvri1227520397.png")
> system("convert tmp/9tehc1227520397.ps tmp/9tehc1227520397.png")
> system("convert tmp/108uba1227520397.ps tmp/108uba1227520397.png")
>
>
> proc.time()
user system elapsed
2.271 1.543 5.910