R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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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(2.47459765056973
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+ ,1)
+ ,dim=c(7
+ ,65)
+ ,dimnames=list(c('MRwaarden'
+ ,'Q1'
+ ,'Q2'
+ ,'Q4'
+ ,'Q5'
+ ,'Q6'
+ ,'Q8')
+ ,1:65))
> y <- array(NA,dim=c(7,65),dimnames=list(c('MRwaarden','Q1','Q2','Q4','Q5','Q6','Q8'),1:65))
> 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'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
MRwaarden Q1 Q2 Q4 Q5 Q6 Q8
1 2.474598 0 0 0 0 1 0
2 2.669374 0 0 1 0 0 0
3 2.532196 0 0 0 0 0 0
4 2.127298 0 0 0 1 0 0
5 2.492512 0 0 0 0 0 0
6 3.548146 0 0 0 1 0 0
7 2.562343 0 1 0 0 0 0
8 2.274289 0 1 0 0 0 0
9 2.472458 0 0 1 0 0 0
10 2.486729 0 0 1 0 0 0
11 3.079292 0 1 0 1 1 0
12 2.783518 0 0 0 1 1 0
13 2.987791 0 0 0 0 1 0
14 1.975832 0 0 0 1 0 0
15 2.043364 0 0 0 1 0 0
16 2.295497 0 0 1 1 1 0
17 2.831970 0 1 1 1 0 0
18 2.474450 0 0 0 0 0 0
19 2.783039 0 1 0 0 1 0
20 2.745933 0 0 0 1 1 1
21 3.498363 0 1 0 1 0 0
22 2.523131 0 0 0 0 0 0
23 3.148953 0 1 1 1 1 0
24 2.570188 0 1 1 1 0 0
25 2.440693 0 1 1 0 0 1
26 1.643876 0 0 1 0 0 1
27 2.228915 0 0 0 0 0 0
28 3.147182 0 0 1 1 1 0
29 2.157485 0 1 0 1 0 0
30 1.792312 0 0 1 0 0 1
31 3.197721 0 1 0 1 0 1
32 2.525658 0 1 0 1 1 0
33 2.414614 0 0 1 1 0 1
34 1.784152 0 0 0 1 1 0
35 2.596559 0 1 1 1 1 0
36 4.218933 0 0 1 1 1 1
37 3.046783 0 1 1 1 1 0
38 2.042199 0 1 0 1 0 0
39 2.368026 0 1 0 1 1 0
40 2.932328 1 0 0 0 1 1
41 2.947850 1 0 1 0 1 0
42 2.688951 0 1 1 1 0 1
43 1.626373 0 0 1 1 1 0
44 1.942363 0 1 0 0 1 0
45 2.502225 0 1 1 1 1 0
46 2.947074 1 0 1 1 1 0
47 3.387565 0 0 1 1 1 0
48 3.142151 0 1 1 0 1 1
49 3.092256 0 1 1 1 1 0
50 2.920949 1 1 1 1 0 1
51 2.528700 0 1 0 1 1 0
52 1.725358 0 1 0 0 1 0
53 2.480684 0 1 0 1 1 1
54 2.471232 0 1 0 0 1 1
55 2.560630 0 1 0 1 1 1
56 2.102176 0 1 1 0 1 0
57 2.934453 0 1 1 1 1 0
58 2.869739 1 0 0 1 1 1
59 2.800482 0 1 1 1 1 0
60 2.938677 0 1 1 1 1 1
61 2.368610 0 1 0 1 0 0
62 2.511573 0 1 1 1 1 0
63 2.572411 1 1 0 1 0 1
64 3.413693 1 1 0 1 0 1
65 2.605128 0 1 1 1 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q1 Q2 Q4 Q5 Q6
2.28501 0.31387 0.01887 0.10966 0.21882 0.12381
Q8
0.08738
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1109 -0.2540 -0.0308 0.2472 1.3942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.28501 0.12952 17.643 <2e-16 ***
Q1 0.31387 0.20163 1.557 0.125
Q2 0.01887 0.12344 0.153 0.879
Q4 0.10966 0.11954 0.917 0.363
Q5 0.21882 0.12812 1.708 0.093 .
Q6 0.12381 0.12129 1.021 0.312
Q8 0.08738 0.13793 0.634 0.529
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4708 on 58 degrees of freedom
Multiple R-squared: 0.1608, Adjusted R-squared: 0.07401
F-statistic: 1.853 on 6 and 58 DF, p-value: 0.1047
> 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,] 0.7882498 0.42350041 0.21175020
[2,] 0.6746604 0.65067917 0.32533958
[3,] 0.5486984 0.90260314 0.45130157
[4,] 0.5224302 0.95513966 0.47756983
[5,] 0.6165754 0.76684915 0.38342457
[6,] 0.5907440 0.81851209 0.40925605
[7,] 0.5779849 0.84403012 0.42201506
[8,] 0.5032574 0.99348527 0.49674263
[9,] 0.4178463 0.83569255 0.58215373
[10,] 0.3524733 0.70494667 0.64752667
[11,] 0.2675890 0.53517810 0.73241095
[12,] 0.4878358 0.97567158 0.51216421
[13,] 0.4663512 0.93270240 0.53364880
[14,] 0.4064754 0.81295072 0.59352464
[15,] 0.3444910 0.68898200 0.65550900
[16,] 0.2784411 0.55688211 0.72155895
[17,] 0.3260288 0.65205756 0.67397122
[18,] 0.2976320 0.59526394 0.70236803
[19,] 0.2991997 0.59839936 0.70080032
[20,] 0.3165194 0.63303887 0.68348057
[21,] 0.3116728 0.62334558 0.68832721
[22,] 0.4374300 0.87486004 0.56256998
[23,] 0.4330299 0.86605977 0.56697011
[24,] 0.4009643 0.80192857 0.59903571
[25,] 0.5701936 0.85961274 0.42980637
[26,] 0.5064784 0.98704319 0.49352160
[27,] 0.9316845 0.13663108 0.06831554
[28,] 0.9163172 0.16736553 0.08368276
[29,] 0.8998608 0.20027833 0.10013917
[30,] 0.8729493 0.25410140 0.12705070
[31,] 0.8320233 0.33595339 0.16797670
[32,] 0.7877010 0.42459803 0.21229901
[33,] 0.7232495 0.55350099 0.27675049
[34,] 0.9776221 0.04475582 0.02237791
[35,] 0.9688171 0.06236588 0.03118294
[36,] 0.9529772 0.09404561 0.04702280
[37,] 0.9267469 0.14650620 0.07325310
[38,] 0.9223602 0.15527957 0.07763978
[39,] 0.9613486 0.07730272 0.03865136
[40,] 0.9604665 0.07906704 0.03953352
[41,] 0.9379464 0.12410720 0.06205360
[42,] 0.8944544 0.21109123 0.10554561
[43,] 0.8760141 0.24797172 0.12398586
[44,] 0.8062697 0.38746061 0.19373030
[45,] 0.7418860 0.51622795 0.25811398
[46,] 0.5763461 0.84730775 0.42365387
> postscript(file="/var/wessaorg/rcomp/tmp/102sv1324129382.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/wessaorg/rcomp/tmp/227pn1324129382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/31t031324129382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4rerx1324129382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59i5g1324129382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 65
Frequency = 1
1 2 3 4 5 6
0.06577793 0.27470355 0.24718284 -0.37654022 0.20749866 1.04430783
7 8 9 10 11 12
0.25845603 -0.02959815 0.07778735 0.09205886 0.43277429 0.15587300
13 14 15 16 17 18
0.57897148 -0.52800660 -0.46047472 -0.44180473 0.19960110 0.18943616
19 20 21 22 23 24
0.35534582 0.03090932 0.97565143 0.23811723 0.39277801 -0.06218008
25 26 27 28 29 30
-0.06022900 -0.83817308 -0.05609857 0.40988027 -0.36522681 -0.68973735
31 32 33 34 35 36
0.58763018 -0.12085987 -0.28626027 -0.84349280 -0.15961580 1.39425314
37 38 39 40 41 42
0.29060887 -0.48051270 -0.27849175 0.12225549 0.11550004 -0.03079652
43 44 45 46 47 48
-1.11092838 -0.48532997 -0.25394980 -0.10410134 0.65026346 0.51742291
49 50 51 52 53 54
0.33608099 -0.11267177 -0.11781768 -0.70233463 -0.25321285 -0.04383999
55 56 57 58 59 60
-0.17326647 -0.43517361 0.17827879 -0.15915855 0.04430696 0.09512403
61 62 63 64 65
-0.15410149 -0.24460123 -0.35155304 0.48972916 -0.23842535
> postscript(file="/var/wessaorg/rcomp/tmp/600w91324129382.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 0.06577793 NA
1 0.27470355 0.06577793
2 0.24718284 0.27470355
3 -0.37654022 0.24718284
4 0.20749866 -0.37654022
5 1.04430783 0.20749866
6 0.25845603 1.04430783
7 -0.02959815 0.25845603
8 0.07778735 -0.02959815
9 0.09205886 0.07778735
10 0.43277429 0.09205886
11 0.15587300 0.43277429
12 0.57897148 0.15587300
13 -0.52800660 0.57897148
14 -0.46047472 -0.52800660
15 -0.44180473 -0.46047472
16 0.19960110 -0.44180473
17 0.18943616 0.19960110
18 0.35534582 0.18943616
19 0.03090932 0.35534582
20 0.97565143 0.03090932
21 0.23811723 0.97565143
22 0.39277801 0.23811723
23 -0.06218008 0.39277801
24 -0.06022900 -0.06218008
25 -0.83817308 -0.06022900
26 -0.05609857 -0.83817308
27 0.40988027 -0.05609857
28 -0.36522681 0.40988027
29 -0.68973735 -0.36522681
30 0.58763018 -0.68973735
31 -0.12085987 0.58763018
32 -0.28626027 -0.12085987
33 -0.84349280 -0.28626027
34 -0.15961580 -0.84349280
35 1.39425314 -0.15961580
36 0.29060887 1.39425314
37 -0.48051270 0.29060887
38 -0.27849175 -0.48051270
39 0.12225549 -0.27849175
40 0.11550004 0.12225549
41 -0.03079652 0.11550004
42 -1.11092838 -0.03079652
43 -0.48532997 -1.11092838
44 -0.25394980 -0.48532997
45 -0.10410134 -0.25394980
46 0.65026346 -0.10410134
47 0.51742291 0.65026346
48 0.33608099 0.51742291
49 -0.11267177 0.33608099
50 -0.11781768 -0.11267177
51 -0.70233463 -0.11781768
52 -0.25321285 -0.70233463
53 -0.04383999 -0.25321285
54 -0.17326647 -0.04383999
55 -0.43517361 -0.17326647
56 0.17827879 -0.43517361
57 -0.15915855 0.17827879
58 0.04430696 -0.15915855
59 0.09512403 0.04430696
60 -0.15410149 0.09512403
61 -0.24460123 -0.15410149
62 -0.35155304 -0.24460123
63 0.48972916 -0.35155304
64 -0.23842535 0.48972916
65 NA -0.23842535
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.27470355 0.06577793
[2,] 0.24718284 0.27470355
[3,] -0.37654022 0.24718284
[4,] 0.20749866 -0.37654022
[5,] 1.04430783 0.20749866
[6,] 0.25845603 1.04430783
[7,] -0.02959815 0.25845603
[8,] 0.07778735 -0.02959815
[9,] 0.09205886 0.07778735
[10,] 0.43277429 0.09205886
[11,] 0.15587300 0.43277429
[12,] 0.57897148 0.15587300
[13,] -0.52800660 0.57897148
[14,] -0.46047472 -0.52800660
[15,] -0.44180473 -0.46047472
[16,] 0.19960110 -0.44180473
[17,] 0.18943616 0.19960110
[18,] 0.35534582 0.18943616
[19,] 0.03090932 0.35534582
[20,] 0.97565143 0.03090932
[21,] 0.23811723 0.97565143
[22,] 0.39277801 0.23811723
[23,] -0.06218008 0.39277801
[24,] -0.06022900 -0.06218008
[25,] -0.83817308 -0.06022900
[26,] -0.05609857 -0.83817308
[27,] 0.40988027 -0.05609857
[28,] -0.36522681 0.40988027
[29,] -0.68973735 -0.36522681
[30,] 0.58763018 -0.68973735
[31,] -0.12085987 0.58763018
[32,] -0.28626027 -0.12085987
[33,] -0.84349280 -0.28626027
[34,] -0.15961580 -0.84349280
[35,] 1.39425314 -0.15961580
[36,] 0.29060887 1.39425314
[37,] -0.48051270 0.29060887
[38,] -0.27849175 -0.48051270
[39,] 0.12225549 -0.27849175
[40,] 0.11550004 0.12225549
[41,] -0.03079652 0.11550004
[42,] -1.11092838 -0.03079652
[43,] -0.48532997 -1.11092838
[44,] -0.25394980 -0.48532997
[45,] -0.10410134 -0.25394980
[46,] 0.65026346 -0.10410134
[47,] 0.51742291 0.65026346
[48,] 0.33608099 0.51742291
[49,] -0.11267177 0.33608099
[50,] -0.11781768 -0.11267177
[51,] -0.70233463 -0.11781768
[52,] -0.25321285 -0.70233463
[53,] -0.04383999 -0.25321285
[54,] -0.17326647 -0.04383999
[55,] -0.43517361 -0.17326647
[56,] 0.17827879 -0.43517361
[57,] -0.15915855 0.17827879
[58,] 0.04430696 -0.15915855
[59,] 0.09512403 0.04430696
[60,] -0.15410149 0.09512403
[61,] -0.24460123 -0.15410149
[62,] -0.35155304 -0.24460123
[63,] 0.48972916 -0.35155304
[64,] -0.23842535 0.48972916
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.27470355 0.06577793
2 0.24718284 0.27470355
3 -0.37654022 0.24718284
4 0.20749866 -0.37654022
5 1.04430783 0.20749866
6 0.25845603 1.04430783
7 -0.02959815 0.25845603
8 0.07778735 -0.02959815
9 0.09205886 0.07778735
10 0.43277429 0.09205886
11 0.15587300 0.43277429
12 0.57897148 0.15587300
13 -0.52800660 0.57897148
14 -0.46047472 -0.52800660
15 -0.44180473 -0.46047472
16 0.19960110 -0.44180473
17 0.18943616 0.19960110
18 0.35534582 0.18943616
19 0.03090932 0.35534582
20 0.97565143 0.03090932
21 0.23811723 0.97565143
22 0.39277801 0.23811723
23 -0.06218008 0.39277801
24 -0.06022900 -0.06218008
25 -0.83817308 -0.06022900
26 -0.05609857 -0.83817308
27 0.40988027 -0.05609857
28 -0.36522681 0.40988027
29 -0.68973735 -0.36522681
30 0.58763018 -0.68973735
31 -0.12085987 0.58763018
32 -0.28626027 -0.12085987
33 -0.84349280 -0.28626027
34 -0.15961580 -0.84349280
35 1.39425314 -0.15961580
36 0.29060887 1.39425314
37 -0.48051270 0.29060887
38 -0.27849175 -0.48051270
39 0.12225549 -0.27849175
40 0.11550004 0.12225549
41 -0.03079652 0.11550004
42 -1.11092838 -0.03079652
43 -0.48532997 -1.11092838
44 -0.25394980 -0.48532997
45 -0.10410134 -0.25394980
46 0.65026346 -0.10410134
47 0.51742291 0.65026346
48 0.33608099 0.51742291
49 -0.11267177 0.33608099
50 -0.11781768 -0.11267177
51 -0.70233463 -0.11781768
52 -0.25321285 -0.70233463
53 -0.04383999 -0.25321285
54 -0.17326647 -0.04383999
55 -0.43517361 -0.17326647
56 0.17827879 -0.43517361
57 -0.15915855 0.17827879
58 0.04430696 -0.15915855
59 0.09512403 0.04430696
60 -0.15410149 0.09512403
61 -0.24460123 -0.15410149
62 -0.35155304 -0.24460123
63 0.48972916 -0.35155304
64 -0.23842535 0.48972916
> 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/wessaorg/rcomp/tmp/7a58c1324129382.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/wessaorg/rcomp/tmp/8ivfi1324129382.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/wessaorg/rcomp/tmp/9x8d01324129382.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/wessaorg/rcomp/tmp/10g3oi1324129382.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11woln1324129382.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/123dkc1324129382.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13vqtw1324129382.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14rp511324129382.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/wessaorg/rcomp/tmp/15vdee1324129382.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/wessaorg/rcomp/tmp/168ux61324129382.tab")
+ }
>
> try(system("convert tmp/102sv1324129382.ps tmp/102sv1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/227pn1324129382.ps tmp/227pn1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/31t031324129382.ps tmp/31t031324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rerx1324129382.ps tmp/4rerx1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/59i5g1324129382.ps tmp/59i5g1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/600w91324129382.ps tmp/600w91324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a58c1324129382.ps tmp/7a58c1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ivfi1324129382.ps tmp/8ivfi1324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/9x8d01324129382.ps tmp/9x8d01324129382.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g3oi1324129382.ps tmp/10g3oi1324129382.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.262 0.568 3.917