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(13768040.14
+ ,14731798.37
+ ,17487530.67
+ ,16471559.62
+ ,16198106.13
+ ,15213975.95
+ ,17535166.38
+ ,17637387.4
+ ,16571771.60
+ ,17972385.83
+ ,16198892.67
+ ,16896235.55
+ ,16554237.93
+ ,16697955.94
+ ,19554176.37
+ ,19691579.52
+ ,15903762.33
+ ,15930700.75
+ ,18003781.65
+ ,17444615.98
+ ,18329610.38
+ ,17699369.88
+ ,16260733.42
+ ,15189796.81
+ ,14851949.20
+ ,15672722.75
+ ,18174068.44
+ ,17180794.3
+ ,18406552.23
+ ,17664893.45
+ ,18466459.42
+ ,17862884.98
+ ,16016524.60
+ ,16162288.88
+ ,17428458.32
+ ,17463628.82
+ ,17167191.42
+ ,16772112.17
+ ,19629987.60
+ ,19106861.48
+ ,17183629.01
+ ,16721314.25
+ ,18344657.85
+ ,18161267.85
+ ,19301440.71
+ ,18509941.2
+ ,18147463.68
+ ,17802737.97
+ ,16192909.22
+ ,16409869.75
+ ,18374420.60
+ ,17967742.04
+ ,20515191.95
+ ,20286602.27
+ ,18957217.20
+ ,19537280.81
+ ,16471529.53
+ ,18021889.62
+ ,18746813.27
+ ,20194317.23
+ ,19009453.59
+ ,19049596.62
+ ,19211178.55
+ ,20244720.94
+ ,20547653.75
+ ,21473302.24
+ ,19325754.03
+ ,19673603.19
+ ,20605542.58
+ ,21053177.29
+ ,20056915.06
+ ,20159479.84
+ ,16141449.72
+ ,18203628.31
+ ,20359793.22
+ ,21289464.94
+ ,19711553.27
+ ,20432335.71
+ ,15638580.70
+ ,17180395.07
+ ,14384486.00
+ ,15816786.32
+ ,13855616.12
+ ,15071819.75
+ ,14308336.46
+ ,14521120.61
+ ,15290621.44
+ ,15668789.39
+ ,14423755.53
+ ,14346884.11
+ ,13779681.49
+ ,13881008.13
+ ,15686348.94
+ ,15465943.69
+ ,14733828.17
+ ,14238232.92
+ ,12522497.94
+ ,13557713.21
+ ,16189383.57
+ ,16127590.29
+ ,16059123.25
+ ,16793894.2
+ ,16007123.26
+ ,16014007.43
+ ,15806842.33
+ ,16867867.15
+ ,15159951.13
+ ,16014583.21
+ ,15692144.17
+ ,15878594.85
+ ,18908869.11
+ ,18664899.14
+ ,16969881.42
+ ,17962530.06
+ ,16997477.78
+ ,17332692.2
+ ,19858875.65
+ ,19542066.35
+ ,17681170.13
+ ,17203555.19)
+ ,dim=c(2
+ ,60)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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 13768040 14731798
2 17487531 16471560
3 16198106 15213976
4 17535166 17637387
5 16571772 17972386
6 16198893 16896236
7 16554238 16697956
8 19554176 19691580
9 15903762 15930701
10 18003782 17444616
11 18329610 17699370
12 16260733 15189797
13 14851949 15672723
14 18174068 17180794
15 18406552 17664893
16 18466459 17862885
17 16016525 16162289
18 17428458 17463629
19 17167191 16772112
20 19629988 19106861
21 17183629 16721314
22 18344658 18161268
23 19301441 18509941
24 18147464 17802738
25 16192909 16409870
26 18374421 17967742
27 20515192 20286602
28 18957217 19537281
29 16471530 18021890
30 18746813 20194317
31 19009454 19049597
32 19211179 20244721
33 20547654 21473302
34 19325754 19673603
35 20605543 21053177
36 20056915 20159480
37 16141450 18203628
38 20359793 21289465
39 19711553 20432336
40 15638581 17180395
41 14384486 15816786
42 13855616 15071820
43 14308336 14521121
44 15290621 15668789
45 14423756 14346884
46 13779681 13881008
47 15686349 15465944
48 14733828 14238233
49 12522498 13557713
50 16189384 16127590
51 16059123 16793894
52 16007123 16014007
53 15806842 16867867
54 15159951 16014583
55 15692144 15878595
56 18908869 18664899
57 16969881 17962530
58 16997478 17332692
59 19858876 19542066
60 17681170 17203555
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
6.223e+05 9.516e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1803304 -527101 82091 585567 1202636
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.223e+05 8.754e+05 0.711 0.48
X 9.516e-01 5.006e-02 19.008 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 746300 on 58 degrees of freedom
Multiple R-squared: 0.8617, Adjusted R-squared: 0.8593
F-statistic: 361.3 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,] 0.9544378 0.09112436 0.04556218
[2,] 0.9202123 0.15957541 0.07978770
[3,] 0.8584024 0.28319522 0.14159761
[4,] 0.8071063 0.38578734 0.19289367
[5,] 0.7168160 0.56636801 0.28318400
[6,] 0.7043982 0.59120357 0.29560179
[7,] 0.6983461 0.60330777 0.30165389
[8,] 0.7428456 0.51430877 0.25715439
[9,] 0.7633824 0.47323521 0.23661760
[10,] 0.8186084 0.36278330 0.18139165
[11,] 0.8239569 0.35208613 0.17604306
[12,] 0.8103636 0.37927274 0.18963637
[13,] 0.7543627 0.49127461 0.24563730
[14,] 0.6894345 0.62113094 0.31056547
[15,] 0.6447663 0.71046730 0.35523365
[16,] 0.6260378 0.74792444 0.37396222
[17,] 0.5947874 0.81042522 0.40521261
[18,] 0.5383259 0.92334813 0.46167406
[19,] 0.5915607 0.81687861 0.40843931
[20,] 0.5618310 0.87633794 0.43816897
[21,] 0.5021690 0.99566199 0.49783099
[22,] 0.4903565 0.98071296 0.50964352
[23,] 0.4766062 0.95321239 0.52339381
[24,] 0.4643961 0.92879216 0.53560392
[25,] 0.6939767 0.61204656 0.30602328
[26,] 0.7851767 0.42964651 0.21482326
[27,] 0.7461565 0.50768696 0.25384348
[28,] 0.7282296 0.54354088 0.27177044
[29,] 0.6783532 0.64329355 0.32164678
[30,] 0.6102384 0.77952322 0.38976161
[31,] 0.5371615 0.92567704 0.46283852
[32,] 0.4919186 0.98383727 0.50808137
[33,] 0.8173993 0.36520139 0.18260070
[34,] 0.7781854 0.44362925 0.22181463
[35,] 0.7265471 0.54690588 0.27345294
[36,] 0.8668382 0.26632359 0.13316179
[37,] 0.9418531 0.11629382 0.05814691
[38,] 0.9677227 0.06455452 0.03227726
[39,] 0.9478497 0.10430065 0.05215033
[40,] 0.9202537 0.15949269 0.07974634
[41,] 0.8892880 0.22142391 0.11071195
[42,] 0.8432292 0.31354163 0.15677081
[43,] 0.8152995 0.36940108 0.18470054
[44,] 0.8994921 0.20101572 0.10050786
[45,] 0.8628473 0.27430530 0.13715265
[46,] 0.8324058 0.33518842 0.16759421
[47,] 0.7745256 0.45094879 0.22547439
[48,] 0.7284221 0.54315590 0.27157795
[49,] 0.7344249 0.53115019 0.26557510
[50,] 0.6670020 0.66599605 0.33299803
[51,] 0.4994438 0.99888755 0.50055623
> postscript(file="/var/www/html/rcomp/tmp/1gaft1291201631.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gaft1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38jee1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48jee1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58jee1291201631.ps",horizontal=F,onefile=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
-872943.72 1191001.55 1098285.29 129243.28 -1152933.78 -501755.25
7 8 9 10 11 12
42271.58 193497.68 121910.92 781298.56 864705.17 1183921.29
13 14 15 16 17 18
-684412.05 1202636.31 974454.56 845954.32 14295.46 187882.73
19 20 21 22 23 24
584658.49 825722.72 649435.04 440213.59 1065201.21 584194.08
25 26 27 28 29 30
-44916.23 654134.22 588293.56 -256631.86 -1300283.33 -1092267.29
31 32 33 34 35 36
259681.57 -675865.84 -508500.45 -17818.50 -50823.51 250985.53
37 38 39 40 41 42
-1803304.47 -521422.63 -354024.09 -1332471.53 -1288965.19 -1108929.83
43 44 45 46 47 48
-132167.84 -241996.86 149053.53 -51696.02 346757.27 562517.92
49 50 51 52 53 54
-1001234.27 220173.38 -544137.32 145997.77 -866810.40 -701722.27
55 56 57 58 59 60
-40123.61 525172.66 -745445.28 -118499.39 640472.70 688078.88
> postscript(file="/var/www/html/rcomp/tmp/6jtdz1291201631.ps",horizontal=F,onefile=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 -872943.72 NA
1 1191001.55 -872943.72
2 1098285.29 1191001.55
3 129243.28 1098285.29
4 -1152933.78 129243.28
5 -501755.25 -1152933.78
6 42271.58 -501755.25
7 193497.68 42271.58
8 121910.92 193497.68
9 781298.56 121910.92
10 864705.17 781298.56
11 1183921.29 864705.17
12 -684412.05 1183921.29
13 1202636.31 -684412.05
14 974454.56 1202636.31
15 845954.32 974454.56
16 14295.46 845954.32
17 187882.73 14295.46
18 584658.49 187882.73
19 825722.72 584658.49
20 649435.04 825722.72
21 440213.59 649435.04
22 1065201.21 440213.59
23 584194.08 1065201.21
24 -44916.23 584194.08
25 654134.22 -44916.23
26 588293.56 654134.22
27 -256631.86 588293.56
28 -1300283.33 -256631.86
29 -1092267.29 -1300283.33
30 259681.57 -1092267.29
31 -675865.84 259681.57
32 -508500.45 -675865.84
33 -17818.50 -508500.45
34 -50823.51 -17818.50
35 250985.53 -50823.51
36 -1803304.47 250985.53
37 -521422.63 -1803304.47
38 -354024.09 -521422.63
39 -1332471.53 -354024.09
40 -1288965.19 -1332471.53
41 -1108929.83 -1288965.19
42 -132167.84 -1108929.83
43 -241996.86 -132167.84
44 149053.53 -241996.86
45 -51696.02 149053.53
46 346757.27 -51696.02
47 562517.92 346757.27
48 -1001234.27 562517.92
49 220173.38 -1001234.27
50 -544137.32 220173.38
51 145997.77 -544137.32
52 -866810.40 145997.77
53 -701722.27 -866810.40
54 -40123.61 -701722.27
55 525172.66 -40123.61
56 -745445.28 525172.66
57 -118499.39 -745445.28
58 640472.70 -118499.39
59 688078.88 640472.70
60 NA 688078.88
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1191001.55 -872943.72
[2,] 1098285.29 1191001.55
[3,] 129243.28 1098285.29
[4,] -1152933.78 129243.28
[5,] -501755.25 -1152933.78
[6,] 42271.58 -501755.25
[7,] 193497.68 42271.58
[8,] 121910.92 193497.68
[9,] 781298.56 121910.92
[10,] 864705.17 781298.56
[11,] 1183921.29 864705.17
[12,] -684412.05 1183921.29
[13,] 1202636.31 -684412.05
[14,] 974454.56 1202636.31
[15,] 845954.32 974454.56
[16,] 14295.46 845954.32
[17,] 187882.73 14295.46
[18,] 584658.49 187882.73
[19,] 825722.72 584658.49
[20,] 649435.04 825722.72
[21,] 440213.59 649435.04
[22,] 1065201.21 440213.59
[23,] 584194.08 1065201.21
[24,] -44916.23 584194.08
[25,] 654134.22 -44916.23
[26,] 588293.56 654134.22
[27,] -256631.86 588293.56
[28,] -1300283.33 -256631.86
[29,] -1092267.29 -1300283.33
[30,] 259681.57 -1092267.29
[31,] -675865.84 259681.57
[32,] -508500.45 -675865.84
[33,] -17818.50 -508500.45
[34,] -50823.51 -17818.50
[35,] 250985.53 -50823.51
[36,] -1803304.47 250985.53
[37,] -521422.63 -1803304.47
[38,] -354024.09 -521422.63
[39,] -1332471.53 -354024.09
[40,] -1288965.19 -1332471.53
[41,] -1108929.83 -1288965.19
[42,] -132167.84 -1108929.83
[43,] -241996.86 -132167.84
[44,] 149053.53 -241996.86
[45,] -51696.02 149053.53
[46,] 346757.27 -51696.02
[47,] 562517.92 346757.27
[48,] -1001234.27 562517.92
[49,] 220173.38 -1001234.27
[50,] -544137.32 220173.38
[51,] 145997.77 -544137.32
[52,] -866810.40 145997.77
[53,] -701722.27 -866810.40
[54,] -40123.61 -701722.27
[55,] 525172.66 -40123.61
[56,] -745445.28 525172.66
[57,] -118499.39 -745445.28
[58,] 640472.70 -118499.39
[59,] 688078.88 640472.70
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1191001.55 -872943.72
2 1098285.29 1191001.55
3 129243.28 1098285.29
4 -1152933.78 129243.28
5 -501755.25 -1152933.78
6 42271.58 -501755.25
7 193497.68 42271.58
8 121910.92 193497.68
9 781298.56 121910.92
10 864705.17 781298.56
11 1183921.29 864705.17
12 -684412.05 1183921.29
13 1202636.31 -684412.05
14 974454.56 1202636.31
15 845954.32 974454.56
16 14295.46 845954.32
17 187882.73 14295.46
18 584658.49 187882.73
19 825722.72 584658.49
20 649435.04 825722.72
21 440213.59 649435.04
22 1065201.21 440213.59
23 584194.08 1065201.21
24 -44916.23 584194.08
25 654134.22 -44916.23
26 588293.56 654134.22
27 -256631.86 588293.56
28 -1300283.33 -256631.86
29 -1092267.29 -1300283.33
30 259681.57 -1092267.29
31 -675865.84 259681.57
32 -508500.45 -675865.84
33 -17818.50 -508500.45
34 -50823.51 -17818.50
35 250985.53 -50823.51
36 -1803304.47 250985.53
37 -521422.63 -1803304.47
38 -354024.09 -521422.63
39 -1332471.53 -354024.09
40 -1288965.19 -1332471.53
41 -1108929.83 -1288965.19
42 -132167.84 -1108929.83
43 -241996.86 -132167.84
44 149053.53 -241996.86
45 -51696.02 149053.53
46 346757.27 -51696.02
47 562517.92 346757.27
48 -1001234.27 562517.92
49 220173.38 -1001234.27
50 -544137.32 220173.38
51 145997.77 -544137.32
52 -866810.40 145997.77
53 -701722.27 -866810.40
54 -40123.61 -701722.27
55 525172.66 -40123.61
56 -745445.28 525172.66
57 -118499.39 -745445.28
58 640472.70 -118499.39
59 688078.88 640472.70
> 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/7u2uj1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8u2uj1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u2uj1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mtum1291201631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qcss1291201631.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/12tu9g1291201631.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/13p46p1291201631.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/14t4nd1291201631.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/15enmj1291201631.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/1606271291201631.tab")
+ }
>
> try(system("convert tmp/1gaft1291201631.ps tmp/1gaft1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gaft1291201631.ps tmp/2gaft1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/38jee1291201631.ps tmp/38jee1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/48jee1291201631.ps tmp/48jee1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/58jee1291201631.ps tmp/58jee1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jtdz1291201631.ps tmp/6jtdz1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u2uj1291201631.ps tmp/7u2uj1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u2uj1291201631.ps tmp/8u2uj1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u2uj1291201631.ps tmp/9u2uj1291201631.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mtum1291201631.ps tmp/10mtum1291201631.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.563 1.670 5.826