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(10414.9
+ ,10723.8
+ ,12476.8
+ ,13938.9
+ ,12384.6
+ ,13979.8
+ ,12266.7
+ ,13807.4
+ ,12919.9
+ ,12973.9
+ ,11497.3
+ ,12509.8
+ ,12142
+ ,12934.1
+ ,13919.4
+ ,14908.3
+ ,12656.8
+ ,13772.1
+ ,12034.1
+ ,13012.6
+ ,13199.7
+ ,14049.9
+ ,10881.3
+ ,11816.5
+ ,11301.2
+ ,11593.2
+ ,13643.9
+ ,14466.2
+ ,12517
+ ,13615.9
+ ,13981.1
+ ,14733.9
+ ,14275.7
+ ,13880.7
+ ,13435
+ ,13527.5
+ ,13565.7
+ ,13584
+ ,16216.3
+ ,16170.2
+ ,12970
+ ,13260.6
+ ,14079.9
+ ,14741.9
+ ,14235
+ ,15486.5
+ ,12213.4
+ ,13154.5
+ ,12581
+ ,12621.2
+ ,14130.4
+ ,15031.6
+ ,14210.8
+ ,15452.4
+ ,14378.5
+ ,15428
+ ,13142.8
+ ,13105.9
+ ,13714.7
+ ,14716.8
+ ,13621.9
+ ,14180
+ ,15379.8
+ ,16202.2
+ ,13306.3
+ ,14392.4
+ ,14391.2
+ ,15140.6
+ ,14909.9
+ ,15960.1
+ ,14025.4
+ ,14351.3
+ ,12951.2
+ ,13230.2
+ ,14344.3
+ ,15202.1
+ ,16093.4
+ ,17056
+ ,15413.6
+ ,16077.7
+ ,14705.7
+ ,13348.2
+ ,15972.8
+ ,16402.4
+ ,16241.4
+ ,16559.1
+ ,16626.4
+ ,16579
+ ,17136.2
+ ,17561.2
+ ,15622.9
+ ,16129.6
+ ,18003.9
+ ,18484.3
+ ,16136.1
+ ,16402.6
+ ,14423.7
+ ,14032.3
+ ,16789.4
+ ,17109.1
+ ,16782.2
+ ,17157.2
+ ,14133.8
+ ,13879.8
+ ,12607
+ ,12362.4
+ ,12004.5
+ ,12683.5
+ ,12175.4
+ ,12608.8
+ ,13268
+ ,13583.7
+ ,12299.3
+ ,12846.3
+ ,11800.6
+ ,12347.1
+ ,13873.3
+ ,13967
+ ,12269.6
+ ,13114.3)
+ ,dim=c(2
+ ,60)
+ ,dimnames=list(c('InIEU'
+ ,'UitIEU')
+ ,1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('InIEU','UitIEU'),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 = '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
InIEU UitIEU
1 10414.9 10723.8
2 12476.8 13938.9
3 12384.6 13979.8
4 12266.7 13807.4
5 12919.9 12973.9
6 11497.3 12509.8
7 12142.0 12934.1
8 13919.4 14908.3
9 12656.8 13772.1
10 12034.1 13012.6
11 13199.7 14049.9
12 10881.3 11816.5
13 11301.2 11593.2
14 13643.9 14466.2
15 12517.0 13615.9
16 13981.1 14733.9
17 14275.7 13880.7
18 13435.0 13527.5
19 13565.7 13584.0
20 16216.3 16170.2
21 12970.0 13260.6
22 14079.9 14741.9
23 14235.0 15486.5
24 12213.4 13154.5
25 12581.0 12621.2
26 14130.4 15031.6
27 14210.8 15452.4
28 14378.5 15428.0
29 13142.8 13105.9
30 13714.7 14716.8
31 13621.9 14180.0
32 15379.8 16202.2
33 13306.3 14392.4
34 14391.2 15140.6
35 14909.9 15960.1
36 14025.4 14351.3
37 12951.2 13230.2
38 14344.3 15202.1
39 16093.4 17056.0
40 15413.6 16077.7
41 14705.7 13348.2
42 15972.8 16402.4
43 16241.4 16559.1
44 16626.4 16579.0
45 17136.2 17561.2
46 15622.9 16129.6
47 18003.9 18484.3
48 16136.1 16402.6
49 14423.7 14032.3
50 16789.4 17109.1
51 16782.2 17157.2
52 14133.8 13879.8
53 12607.0 12362.4
54 12004.5 12683.5
55 12175.4 12608.8
56 13268.0 13583.7
57 12299.3 12846.3
58 11800.6 12347.1
59 13873.3 13967.0
60 12269.6 13114.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UitIEU
-113.7583 0.9693
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1052.81 -419.54 -46.77 307.49 1880.52
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -113.75834 618.45841 -0.184 0.855
UitIEU 0.96934 0.04278 22.659 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 537 on 58 degrees of freedom
Multiple R-squared: 0.8985, Adjusted R-squared: 0.8968
F-statistic: 513.4 on 1 and 58 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,] 0.7256198 0.54876041 0.27438020
[2,] 0.6112854 0.77742918 0.38871459
[3,] 0.4840616 0.96812315 0.51593842
[4,] 0.5143005 0.97139898 0.48569949
[5,] 0.4124873 0.82497463 0.58751268
[6,] 0.3200984 0.64019677 0.67990162
[7,] 0.2741132 0.54822638 0.72588681
[8,] 0.2429235 0.48584700 0.75707650
[9,] 0.1933617 0.38672332 0.80663834
[10,] 0.1795201 0.35904016 0.82047992
[11,] 0.1464831 0.29296618 0.85351691
[12,] 0.1474288 0.29485762 0.85257119
[13,] 0.6017236 0.79655287 0.39827644
[14,] 0.6469093 0.70618134 0.35309067
[15,] 0.6958797 0.60824065 0.30412033
[16,] 0.7991632 0.40167355 0.20083678
[17,] 0.7600205 0.47995893 0.23997946
[18,] 0.6979284 0.60414316 0.30207158
[19,] 0.7141334 0.57173329 0.28586665
[20,] 0.6952707 0.60945863 0.30472931
[21,] 0.6835062 0.63298755 0.31649378
[22,] 0.6373192 0.72536156 0.36268078
[23,] 0.6605757 0.67884864 0.33942432
[24,] 0.6418704 0.71625921 0.35812960
[25,] 0.6487062 0.70258765 0.35129383
[26,] 0.6361150 0.72776999 0.36388499
[27,] 0.5754991 0.84900179 0.42450089
[28,] 0.5236355 0.95272907 0.47636454
[29,] 0.5588056 0.88238875 0.44119437
[30,] 0.5127010 0.97459809 0.48729905
[31,] 0.5226033 0.95479336 0.47739668
[32,] 0.4712445 0.94248901 0.52875549
[33,] 0.4117940 0.82358791 0.58820604
[34,] 0.3965134 0.79302673 0.60348663
[35,] 0.3938142 0.78762840 0.60618580
[36,] 0.3564486 0.71289720 0.64355140
[37,] 0.9854520 0.02909604 0.01454802
[38,] 0.9778185 0.04436300 0.02218150
[39,] 0.9663516 0.06729673 0.03364836
[40,] 0.9671094 0.06578123 0.03289061
[41,] 0.9478060 0.10438795 0.05219397
[42,] 0.9224316 0.15513687 0.07756844
[43,] 0.8910513 0.21789737 0.10894869
[44,] 0.8389746 0.32205084 0.16102542
[45,] 0.9149195 0.17016098 0.08508049
[46,] 0.8621238 0.27575237 0.13787618
[47,] 0.8273620 0.34527609 0.17263804
[48,] 0.8486351 0.30272971 0.15136486
[49,] 0.9831659 0.03366815 0.01683407
[50,] 0.9554777 0.08904452 0.04452226
[51,] 0.9017766 0.19644673 0.09822336
> postscript(file="/var/www/html/rcomp/tmp/1wwhq1258756696.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lp8o1258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xo811258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ut881258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ll2m1258756696.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
133.658205 -920.964385 -1052.810360 -1003.596275 457.547981 -515.181678
7 8 9 10 11 12
-281.772317 -418.041844 -579.278600 -465.765447 -305.661041 -459.138783
13 14 15 16 17 18
177.214670 -264.996966 -567.667811 -187.289081 934.351159 436.021778
19 20 21 22 23 24
511.954111 655.648969 229.738421 -96.243795 -662.913793 -424.014685
25 26 27 28 29 30
460.533931 -326.561372 -654.059325 -462.707447 552.495202 -437.113380
31 32 33 34 35 36
-9.572076 -211.869886 -531.059730 -171.419350 -447.092857 227.880112
37 38 39 40 41 42
240.406334 -277.933713 -325.891729 -57.387151 1880.524304 187.068398
43 44 45 46 47 48
303.772939 669.483088 227.198087 101.604142 200.101034 350.174530
49 50 51 52 53 54
935.399330 318.636357 264.811140 793.323564 737.398926 -176.355904
55 56 57 58 59 60
66.953738 214.544913 -39.364332 -54.170183 448.297182 -328.847248
> postscript(file="/var/www/html/rcomp/tmp/6ify31258756696.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
0 133.658205 NA
1 -920.964385 133.658205
2 -1052.810360 -920.964385
3 -1003.596275 -1052.810360
4 457.547981 -1003.596275
5 -515.181678 457.547981
6 -281.772317 -515.181678
7 -418.041844 -281.772317
8 -579.278600 -418.041844
9 -465.765447 -579.278600
10 -305.661041 -465.765447
11 -459.138783 -305.661041
12 177.214670 -459.138783
13 -264.996966 177.214670
14 -567.667811 -264.996966
15 -187.289081 -567.667811
16 934.351159 -187.289081
17 436.021778 934.351159
18 511.954111 436.021778
19 655.648969 511.954111
20 229.738421 655.648969
21 -96.243795 229.738421
22 -662.913793 -96.243795
23 -424.014685 -662.913793
24 460.533931 -424.014685
25 -326.561372 460.533931
26 -654.059325 -326.561372
27 -462.707447 -654.059325
28 552.495202 -462.707447
29 -437.113380 552.495202
30 -9.572076 -437.113380
31 -211.869886 -9.572076
32 -531.059730 -211.869886
33 -171.419350 -531.059730
34 -447.092857 -171.419350
35 227.880112 -447.092857
36 240.406334 227.880112
37 -277.933713 240.406334
38 -325.891729 -277.933713
39 -57.387151 -325.891729
40 1880.524304 -57.387151
41 187.068398 1880.524304
42 303.772939 187.068398
43 669.483088 303.772939
44 227.198087 669.483088
45 101.604142 227.198087
46 200.101034 101.604142
47 350.174530 200.101034
48 935.399330 350.174530
49 318.636357 935.399330
50 264.811140 318.636357
51 793.323564 264.811140
52 737.398926 793.323564
53 -176.355904 737.398926
54 66.953738 -176.355904
55 214.544913 66.953738
56 -39.364332 214.544913
57 -54.170183 -39.364332
58 448.297182 -54.170183
59 -328.847248 448.297182
60 NA -328.847248
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -920.964385 133.658205
[2,] -1052.810360 -920.964385
[3,] -1003.596275 -1052.810360
[4,] 457.547981 -1003.596275
[5,] -515.181678 457.547981
[6,] -281.772317 -515.181678
[7,] -418.041844 -281.772317
[8,] -579.278600 -418.041844
[9,] -465.765447 -579.278600
[10,] -305.661041 -465.765447
[11,] -459.138783 -305.661041
[12,] 177.214670 -459.138783
[13,] -264.996966 177.214670
[14,] -567.667811 -264.996966
[15,] -187.289081 -567.667811
[16,] 934.351159 -187.289081
[17,] 436.021778 934.351159
[18,] 511.954111 436.021778
[19,] 655.648969 511.954111
[20,] 229.738421 655.648969
[21,] -96.243795 229.738421
[22,] -662.913793 -96.243795
[23,] -424.014685 -662.913793
[24,] 460.533931 -424.014685
[25,] -326.561372 460.533931
[26,] -654.059325 -326.561372
[27,] -462.707447 -654.059325
[28,] 552.495202 -462.707447
[29,] -437.113380 552.495202
[30,] -9.572076 -437.113380
[31,] -211.869886 -9.572076
[32,] -531.059730 -211.869886
[33,] -171.419350 -531.059730
[34,] -447.092857 -171.419350
[35,] 227.880112 -447.092857
[36,] 240.406334 227.880112
[37,] -277.933713 240.406334
[38,] -325.891729 -277.933713
[39,] -57.387151 -325.891729
[40,] 1880.524304 -57.387151
[41,] 187.068398 1880.524304
[42,] 303.772939 187.068398
[43,] 669.483088 303.772939
[44,] 227.198087 669.483088
[45,] 101.604142 227.198087
[46,] 200.101034 101.604142
[47,] 350.174530 200.101034
[48,] 935.399330 350.174530
[49,] 318.636357 935.399330
[50,] 264.811140 318.636357
[51,] 793.323564 264.811140
[52,] 737.398926 793.323564
[53,] -176.355904 737.398926
[54,] 66.953738 -176.355904
[55,] 214.544913 66.953738
[56,] -39.364332 214.544913
[57,] -54.170183 -39.364332
[58,] 448.297182 -54.170183
[59,] -328.847248 448.297182
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -920.964385 133.658205
2 -1052.810360 -920.964385
3 -1003.596275 -1052.810360
4 457.547981 -1003.596275
5 -515.181678 457.547981
6 -281.772317 -515.181678
7 -418.041844 -281.772317
8 -579.278600 -418.041844
9 -465.765447 -579.278600
10 -305.661041 -465.765447
11 -459.138783 -305.661041
12 177.214670 -459.138783
13 -264.996966 177.214670
14 -567.667811 -264.996966
15 -187.289081 -567.667811
16 934.351159 -187.289081
17 436.021778 934.351159
18 511.954111 436.021778
19 655.648969 511.954111
20 229.738421 655.648969
21 -96.243795 229.738421
22 -662.913793 -96.243795
23 -424.014685 -662.913793
24 460.533931 -424.014685
25 -326.561372 460.533931
26 -654.059325 -326.561372
27 -462.707447 -654.059325
28 552.495202 -462.707447
29 -437.113380 552.495202
30 -9.572076 -437.113380
31 -211.869886 -9.572076
32 -531.059730 -211.869886
33 -171.419350 -531.059730
34 -447.092857 -171.419350
35 227.880112 -447.092857
36 240.406334 227.880112
37 -277.933713 240.406334
38 -325.891729 -277.933713
39 -57.387151 -325.891729
40 1880.524304 -57.387151
41 187.068398 1880.524304
42 303.772939 187.068398
43 669.483088 303.772939
44 227.198087 669.483088
45 101.604142 227.198087
46 200.101034 101.604142
47 350.174530 200.101034
48 935.399330 350.174530
49 318.636357 935.399330
50 264.811140 318.636357
51 793.323564 264.811140
52 737.398926 793.323564
53 -176.355904 737.398926
54 66.953738 -176.355904
55 214.544913 66.953738
56 -39.364332 214.544913
57 -54.170183 -39.364332
58 448.297182 -54.170183
59 -328.847248 448.297182
> 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/7qeep1258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lxd71258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/915cd1258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ezi01258756696.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wp9h1258756696.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/12c6z61258756696.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/13iscq1258756696.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/14v3yy1258756696.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/1532e01258756696.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/16nir61258756697.tab")
+ }
>
> system("convert tmp/1wwhq1258756696.ps tmp/1wwhq1258756696.png")
> system("convert tmp/2lp8o1258756696.ps tmp/2lp8o1258756696.png")
> system("convert tmp/3xo811258756696.ps tmp/3xo811258756696.png")
> system("convert tmp/4ut881258756696.ps tmp/4ut881258756696.png")
> system("convert tmp/5ll2m1258756696.ps tmp/5ll2m1258756696.png")
> system("convert tmp/6ify31258756696.ps tmp/6ify31258756696.png")
> system("convert tmp/7qeep1258756696.ps tmp/7qeep1258756696.png")
> system("convert tmp/8lxd71258756696.ps tmp/8lxd71258756696.png")
> system("convert tmp/915cd1258756696.ps tmp/915cd1258756696.png")
> system("convert tmp/10ezi01258756696.ps tmp/10ezi01258756696.png")
>
>
> proc.time()
user system elapsed
2.498 1.589 2.905