R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(9.3,96.8,9.3,114.1,8.7,110.3,8.2,103.9,8.3,101.6,8.5,94.6,8.6,95.9,8.5,104.7,8.2,102.8,8.1,98.1,7.9,113.9,8.6,80.9,8.7,95.7,8.7,113.2,8.5,105.9,8.4,108.8,8.5,102.3,8.7,99,8.7,100.7,8.6,115.5,8.5,100.7,8.3,109.9,8,114.6,8.2,85.4,8.1,100.5,8.1,114.8,8,116.5,7.9,112.9,7.9,102,8,106,8,105.3,7.9,118.8,8,106.1,7.7,109.3,7.2,117.2,7.5,92.5,7.3,104.2,7,112.5,7,122.4,7,113.3,7.2,100,7.3,110.7,7.1,112.8,6.8,109.8,6.4,117.3,6.1,109.1,6.5,115.9,7.7,96,7.9,99.8,7.5,116.8,6.9,115.7,6.6,99.4,6.9,94.3,7.7,91,8,93.2,8,103.1,7.7,94.1,7.3,91.8,7.4,102.7,8.1,82.6),dim=c(2,60),dimnames=list(c('werklh','ecogr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werklh','ecogr'),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 = '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
werklh ecogr t
1 9.3 96.8 1
2 9.3 114.1 2
3 8.7 110.3 3
4 8.2 103.9 4
5 8.3 101.6 5
6 8.5 94.6 6
7 8.6 95.9 7
8 8.5 104.7 8
9 8.2 102.8 9
10 8.1 98.1 10
11 7.9 113.9 11
12 8.6 80.9 12
13 8.7 95.7 13
14 8.7 113.2 14
15 8.5 105.9 15
16 8.4 108.8 16
17 8.5 102.3 17
18 8.7 99.0 18
19 8.7 100.7 19
20 8.6 115.5 20
21 8.5 100.7 21
22 8.3 109.9 22
23 8.0 114.6 23
24 8.2 85.4 24
25 8.1 100.5 25
26 8.1 114.8 26
27 8.0 116.5 27
28 7.9 112.9 28
29 7.9 102.0 29
30 8.0 106.0 30
31 8.0 105.3 31
32 7.9 118.8 32
33 8.0 106.1 33
34 7.7 109.3 34
35 7.2 117.2 35
36 7.5 92.5 36
37 7.3 104.2 37
38 7.0 112.5 38
39 7.0 122.4 39
40 7.0 113.3 40
41 7.2 100.0 41
42 7.3 110.7 42
43 7.1 112.8 43
44 6.8 109.8 44
45 6.4 117.3 45
46 6.1 109.1 46
47 6.5 115.9 47
48 7.7 96.0 48
49 7.9 99.8 49
50 7.5 116.8 50
51 6.9 115.7 51
52 6.6 99.4 52
53 6.9 94.3 53
54 7.7 91.0 54
55 8.0 93.2 55
56 8.0 103.1 56
57 7.7 94.1 57
58 7.3 91.8 58
59 7.4 102.7 59
60 8.1 82.6 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ecogr t
11.16034 -0.02264 -0.03048
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.18879 -0.36016 0.07606 0.30702 0.88017
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.160344 0.640388 17.427 < 2e-16 ***
ecogr -0.022636 0.005980 -3.785 0.000371 ***
t -0.030478 0.003241 -9.403 3.41e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4344 on 57 degrees of freedom
Multiple R-squared: 0.6364, Adjusted R-squared: 0.6237
F-statistic: 49.89 on 2 and 57 DF, p-value: 2.994e-13
> 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,] 0.410342298 0.82068460 0.5896577
[2,] 0.436673088 0.87334618 0.5633269
[3,] 0.369274745 0.73854949 0.6307253
[4,] 0.252672911 0.50534582 0.7473271
[5,] 0.174297513 0.34859503 0.8257025
[6,] 0.111581855 0.22316371 0.8884181
[7,] 0.164812152 0.32962430 0.8351878
[8,] 0.265324271 0.53064854 0.7346757
[9,] 0.382679857 0.76535971 0.6173201
[10,] 0.320755050 0.64151010 0.6792450
[11,] 0.248124663 0.49624933 0.7518753
[12,] 0.194404971 0.38880994 0.8055950
[13,] 0.175205126 0.35041025 0.8247949
[14,] 0.151567028 0.30313406 0.8484330
[15,] 0.144536313 0.28907263 0.8554637
[16,] 0.105050389 0.21010078 0.8949496
[17,] 0.079646050 0.15929210 0.9203539
[18,] 0.070464087 0.14092817 0.9295359
[19,] 0.054076918 0.10815384 0.9459231
[20,] 0.037237218 0.07447444 0.9627628
[21,] 0.028989911 0.05797982 0.9710101
[22,] 0.024265357 0.04853071 0.9757346
[23,] 0.019148610 0.03829722 0.9808514
[24,] 0.012789749 0.02557950 0.9872103
[25,] 0.008763741 0.01752748 0.9912363
[26,] 0.006302349 0.01260470 0.9936977
[27,] 0.009119894 0.01823979 0.9908801
[28,] 0.010456888 0.02091378 0.9895431
[29,] 0.012040744 0.02408149 0.9879593
[30,] 0.022314358 0.04462872 0.9776856
[31,] 0.017452595 0.03490519 0.9825474
[32,] 0.015910244 0.03182049 0.9840898
[33,] 0.019680284 0.03936057 0.9803197
[34,] 0.022582537 0.04516507 0.9774175
[35,] 0.019820318 0.03964064 0.9801797
[36,] 0.013033309 0.02606662 0.9869667
[37,] 0.012292049 0.02458410 0.9877080
[38,] 0.010717827 0.02143565 0.9892822
[39,] 0.008096624 0.01619325 0.9919034
[40,] 0.009848285 0.01969657 0.9901517
[41,] 0.057856348 0.11571270 0.9421437
[42,] 0.070899308 0.14179862 0.9291007
[43,] 0.073899218 0.14779844 0.9261008
[44,] 0.179029540 0.35805908 0.8209705
[45,] 0.258766903 0.51753381 0.7412331
[46,] 0.177641637 0.35528327 0.8223584
[47,] 0.238285940 0.47657188 0.7617141
[48,] 0.499218528 0.99843706 0.5007815
[49,] 0.441614037 0.88322807 0.5583860
> postscript(file="/var/www/html/rcomp/tmp/1gsko1261074836.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qqxe1261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ogur1261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rzw21261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/576o91261074836.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
0.36127618 0.78335294 0.12781508 -0.48657576 -0.40815998 -0.33613228
7 8 9 10 11 12
-0.17622774 -0.04655497 -0.35908488 -0.53499492 -0.34687181 -0.36337398
13 14 15 16 17 18
0.10211338 0.52871729 0.19395426 0.19007602 0.17342160 0.32920162
19 20 21 22 23 24
0.39816047 0.66364782 0.25911656 0.29784363 0.13470976 -0.29577650
25 26 27 28 29 30
-0.02349842 0.33067105 0.29962990 0.14861919 -0.06763259 0.15338851
31 32 33 34 35 36
0.16802152 0.40408238 0.24708622 0.04999871 -0.24070071 -0.46932604
37 38 39 40 41 42
-0.37400955 -0.45565467 -0.20108256 -0.37658997 -0.44716758 -0.07448686
43 44 45 46 47 48
-0.19647371 -0.53390296 -0.73365669 -1.18879191 -0.60439066 0.17563568
49 50 51 52 53 54
0.49212962 0.50741566 -0.08700564 -0.72549055 -0.51045490 0.24532513
55 56 57 58 59 60
0.62560185 0.88017396 0.40693013 -0.01465408 0.36255379 0.63805298
> postscript(file="/var/www/html/rcomp/tmp/611ow1261074836.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 0.36127618 NA
1 0.78335294 0.36127618
2 0.12781508 0.78335294
3 -0.48657576 0.12781508
4 -0.40815998 -0.48657576
5 -0.33613228 -0.40815998
6 -0.17622774 -0.33613228
7 -0.04655497 -0.17622774
8 -0.35908488 -0.04655497
9 -0.53499492 -0.35908488
10 -0.34687181 -0.53499492
11 -0.36337398 -0.34687181
12 0.10211338 -0.36337398
13 0.52871729 0.10211338
14 0.19395426 0.52871729
15 0.19007602 0.19395426
16 0.17342160 0.19007602
17 0.32920162 0.17342160
18 0.39816047 0.32920162
19 0.66364782 0.39816047
20 0.25911656 0.66364782
21 0.29784363 0.25911656
22 0.13470976 0.29784363
23 -0.29577650 0.13470976
24 -0.02349842 -0.29577650
25 0.33067105 -0.02349842
26 0.29962990 0.33067105
27 0.14861919 0.29962990
28 -0.06763259 0.14861919
29 0.15338851 -0.06763259
30 0.16802152 0.15338851
31 0.40408238 0.16802152
32 0.24708622 0.40408238
33 0.04999871 0.24708622
34 -0.24070071 0.04999871
35 -0.46932604 -0.24070071
36 -0.37400955 -0.46932604
37 -0.45565467 -0.37400955
38 -0.20108256 -0.45565467
39 -0.37658997 -0.20108256
40 -0.44716758 -0.37658997
41 -0.07448686 -0.44716758
42 -0.19647371 -0.07448686
43 -0.53390296 -0.19647371
44 -0.73365669 -0.53390296
45 -1.18879191 -0.73365669
46 -0.60439066 -1.18879191
47 0.17563568 -0.60439066
48 0.49212962 0.17563568
49 0.50741566 0.49212962
50 -0.08700564 0.50741566
51 -0.72549055 -0.08700564
52 -0.51045490 -0.72549055
53 0.24532513 -0.51045490
54 0.62560185 0.24532513
55 0.88017396 0.62560185
56 0.40693013 0.88017396
57 -0.01465408 0.40693013
58 0.36255379 -0.01465408
59 0.63805298 0.36255379
60 NA 0.63805298
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.78335294 0.36127618
[2,] 0.12781508 0.78335294
[3,] -0.48657576 0.12781508
[4,] -0.40815998 -0.48657576
[5,] -0.33613228 -0.40815998
[6,] -0.17622774 -0.33613228
[7,] -0.04655497 -0.17622774
[8,] -0.35908488 -0.04655497
[9,] -0.53499492 -0.35908488
[10,] -0.34687181 -0.53499492
[11,] -0.36337398 -0.34687181
[12,] 0.10211338 -0.36337398
[13,] 0.52871729 0.10211338
[14,] 0.19395426 0.52871729
[15,] 0.19007602 0.19395426
[16,] 0.17342160 0.19007602
[17,] 0.32920162 0.17342160
[18,] 0.39816047 0.32920162
[19,] 0.66364782 0.39816047
[20,] 0.25911656 0.66364782
[21,] 0.29784363 0.25911656
[22,] 0.13470976 0.29784363
[23,] -0.29577650 0.13470976
[24,] -0.02349842 -0.29577650
[25,] 0.33067105 -0.02349842
[26,] 0.29962990 0.33067105
[27,] 0.14861919 0.29962990
[28,] -0.06763259 0.14861919
[29,] 0.15338851 -0.06763259
[30,] 0.16802152 0.15338851
[31,] 0.40408238 0.16802152
[32,] 0.24708622 0.40408238
[33,] 0.04999871 0.24708622
[34,] -0.24070071 0.04999871
[35,] -0.46932604 -0.24070071
[36,] -0.37400955 -0.46932604
[37,] -0.45565467 -0.37400955
[38,] -0.20108256 -0.45565467
[39,] -0.37658997 -0.20108256
[40,] -0.44716758 -0.37658997
[41,] -0.07448686 -0.44716758
[42,] -0.19647371 -0.07448686
[43,] -0.53390296 -0.19647371
[44,] -0.73365669 -0.53390296
[45,] -1.18879191 -0.73365669
[46,] -0.60439066 -1.18879191
[47,] 0.17563568 -0.60439066
[48,] 0.49212962 0.17563568
[49,] 0.50741566 0.49212962
[50,] -0.08700564 0.50741566
[51,] -0.72549055 -0.08700564
[52,] -0.51045490 -0.72549055
[53,] 0.24532513 -0.51045490
[54,] 0.62560185 0.24532513
[55,] 0.88017396 0.62560185
[56,] 0.40693013 0.88017396
[57,] -0.01465408 0.40693013
[58,] 0.36255379 -0.01465408
[59,] 0.63805298 0.36255379
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.78335294 0.36127618
2 0.12781508 0.78335294
3 -0.48657576 0.12781508
4 -0.40815998 -0.48657576
5 -0.33613228 -0.40815998
6 -0.17622774 -0.33613228
7 -0.04655497 -0.17622774
8 -0.35908488 -0.04655497
9 -0.53499492 -0.35908488
10 -0.34687181 -0.53499492
11 -0.36337398 -0.34687181
12 0.10211338 -0.36337398
13 0.52871729 0.10211338
14 0.19395426 0.52871729
15 0.19007602 0.19395426
16 0.17342160 0.19007602
17 0.32920162 0.17342160
18 0.39816047 0.32920162
19 0.66364782 0.39816047
20 0.25911656 0.66364782
21 0.29784363 0.25911656
22 0.13470976 0.29784363
23 -0.29577650 0.13470976
24 -0.02349842 -0.29577650
25 0.33067105 -0.02349842
26 0.29962990 0.33067105
27 0.14861919 0.29962990
28 -0.06763259 0.14861919
29 0.15338851 -0.06763259
30 0.16802152 0.15338851
31 0.40408238 0.16802152
32 0.24708622 0.40408238
33 0.04999871 0.24708622
34 -0.24070071 0.04999871
35 -0.46932604 -0.24070071
36 -0.37400955 -0.46932604
37 -0.45565467 -0.37400955
38 -0.20108256 -0.45565467
39 -0.37658997 -0.20108256
40 -0.44716758 -0.37658997
41 -0.07448686 -0.44716758
42 -0.19647371 -0.07448686
43 -0.53390296 -0.19647371
44 -0.73365669 -0.53390296
45 -1.18879191 -0.73365669
46 -0.60439066 -1.18879191
47 0.17563568 -0.60439066
48 0.49212962 0.17563568
49 0.50741566 0.49212962
50 -0.08700564 0.50741566
51 -0.72549055 -0.08700564
52 -0.51045490 -0.72549055
53 0.24532513 -0.51045490
54 0.62560185 0.24532513
55 0.88017396 0.62560185
56 0.40693013 0.88017396
57 -0.01465408 0.40693013
58 0.36255379 -0.01465408
59 0.63805298 0.36255379
> 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/71yx71261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k4ku1261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nnlz1261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qs7y1261074836.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hsry1261074836.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/12w08g1261074836.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/13yswu1261074836.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/14jg411261074837.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/15jfey1261074837.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/16g08j1261074837.tab")
+ }
>
> try(system("convert tmp/1gsko1261074836.ps tmp/1gsko1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qqxe1261074836.ps tmp/2qqxe1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ogur1261074836.ps tmp/3ogur1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rzw21261074836.ps tmp/4rzw21261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/576o91261074836.ps tmp/576o91261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/611ow1261074836.ps tmp/611ow1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/71yx71261074836.ps tmp/71yx71261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k4ku1261074836.ps tmp/8k4ku1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nnlz1261074836.ps tmp/9nnlz1261074836.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qs7y1261074836.ps tmp/10qs7y1261074836.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.588 1.640 9.923