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(3.9,4.2,3.6,4.5,3.3,4.6,3.2,4.9,3.4,4.9,3.4,4.5,3.5,4.6,3.2,4.7,3.3,4.7,3.3,4.3,3.4,4.2,3.7,4.4,3.9,4,4,3.8,3.7,3.6,3.9,3.6,4.2,3.3,4.4,3.4,4.3,3.4,4.2,3.3,4.3,3.3,4.3,3.2,4.3,3.1,4.5,3.1,5,2.4,5.2,2.4,5.2,2.4,5.4,2.1,5.5,2,5.4,2,5.5,2.1,5.4,2.1,5.7,2,5.7,2,6.1,2,6.5,1.7,6.9,1.3,6.8,1.2,6.7,1.1,6.6,1.4,6.5,1.5,6.4,1.4,6.1,1.1,6.2,1.1,6.3,1,6.4,1.4,6.5,1.3,6.7,1.2,7,1.5,7,1.6,6.8,1.8,6.7,1.5,6.7,1.3,6.5,1.6,6.4,1.6,6.1,1.8,6.2,1.8,6,1.6,6.1,1.8,6.1,2,6.2,1.3),dim=c(2,61),dimnames=list(c('Werkl','Infl'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werkl','Infl'),1:61))
> 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
Werkl Infl
1 3.9 4.2
2 3.6 4.5
3 3.3 4.6
4 3.2 4.9
5 3.4 4.9
6 3.4 4.5
7 3.5 4.6
8 3.2 4.7
9 3.3 4.7
10 3.3 4.3
11 3.4 4.2
12 3.7 4.4
13 3.9 4.0
14 4.0 3.8
15 3.7 3.6
16 3.9 3.6
17 4.2 3.3
18 4.4 3.4
19 4.3 3.4
20 4.2 3.3
21 4.3 3.3
22 4.3 3.2
23 4.3 3.1
24 4.5 3.1
25 5.0 2.4
26 5.2 2.4
27 5.2 2.4
28 5.4 2.1
29 5.5 2.0
30 5.4 2.0
31 5.5 2.1
32 5.4 2.1
33 5.7 2.0
34 5.7 2.0
35 6.1 2.0
36 6.5 1.7
37 6.9 1.3
38 6.8 1.2
39 6.7 1.1
40 6.6 1.4
41 6.5 1.5
42 6.4 1.4
43 6.1 1.1
44 6.2 1.1
45 6.3 1.0
46 6.4 1.4
47 6.5 1.3
48 6.7 1.2
49 7.0 1.5
50 7.0 1.6
51 6.8 1.8
52 6.7 1.5
53 6.7 1.3
54 6.5 1.6
55 6.4 1.6
56 6.1 1.8
57 6.2 1.8
58 6.0 1.6
59 6.1 1.8
60 6.1 2.0
61 6.2 1.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl
7.79 -0.99
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.600146 -0.231865 -0.002128 0.198863 0.794898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.78924 0.09594 81.19 <2e-16 ***
Infl -0.99009 0.03360 -29.46 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3244 on 59 degrees of freedom
Multiple R-squared: 0.9364, Adjusted R-squared: 0.9353
F-statistic: 868.1 on 1 and 59 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,] 0.1201445429 0.2402890859 0.87985546
[2,] 0.0778132196 0.1556264392 0.92218678
[3,] 0.0332432429 0.0664864858 0.96675676
[4,] 0.0262419436 0.0524838873 0.97375806
[5,] 0.0122498267 0.0244996534 0.98775017
[6,] 0.0331607112 0.0663214225 0.96683929
[7,] 0.0266538592 0.0533077184 0.97334614
[8,] 0.0269350141 0.0538700281 0.97306499
[9,] 0.0201344943 0.0402689886 0.97986551
[10,] 0.0118065059 0.0236130119 0.98819349
[11,] 0.0161120723 0.0322241445 0.98388793
[12,] 0.0084253286 0.0168506573 0.99157467
[13,] 0.0048166520 0.0096333039 0.99518335
[14,] 0.0067836125 0.0135672250 0.99321639
[15,] 0.0044967849 0.0089935698 0.99550322
[16,] 0.0022788983 0.0045577967 0.99772110
[17,] 0.0011717844 0.0023435687 0.99882822
[18,] 0.0005554054 0.0011108108 0.99944459
[19,] 0.0002899289 0.0005798577 0.99971007
[20,] 0.0001679274 0.0003358548 0.99983207
[21,] 0.0001352232 0.0002704464 0.99986478
[22,] 0.0002064791 0.0004129582 0.99979352
[23,] 0.0002206685 0.0004413371 0.99977933
[24,] 0.0002139452 0.0004278904 0.99978605
[25,] 0.0002162531 0.0004325063 0.99978375
[26,] 0.0002471514 0.0004943028 0.99975285
[27,] 0.0003475920 0.0006951840 0.99965241
[28,] 0.0006633226 0.0013266451 0.99933668
[29,] 0.0016370108 0.0032740217 0.99836299
[30,] 0.0046248198 0.0092496395 0.99537518
[31,] 0.0287141210 0.0574282419 0.97128588
[32,] 0.0974660882 0.1949321765 0.90253391
[33,] 0.2341368695 0.4682737391 0.76586313
[34,] 0.2705412217 0.5410824435 0.72945878
[35,] 0.2403165053 0.4806330105 0.75968349
[36,] 0.2260579730 0.4521159460 0.77394203
[37,] 0.1965121679 0.3930243358 0.80348783
[38,] 0.1477077678 0.2954155355 0.85229223
[39,] 0.1973944970 0.3947889940 0.80260550
[40,] 0.2179394042 0.4358788084 0.78206060
[41,] 0.2516479704 0.5032959408 0.74835203
[42,] 0.2057387280 0.4114774560 0.79426127
[43,] 0.1613189186 0.3226378371 0.83868108
[44,] 0.1211474594 0.2422949188 0.87885254
[45,] 0.2864904032 0.5729808064 0.71350960
[46,] 0.6289711605 0.7420576789 0.37102884
[47,] 0.8967294098 0.2065411805 0.10327059
[48,] 0.9286231475 0.1427537049 0.07137685
[49,] 0.9516760344 0.0966479313 0.04832397
[50,] 0.9770487623 0.0459024754 0.02295124
[51,] 0.9932219597 0.0135560805 0.00677804
[52,] 0.9703483912 0.0593032175 0.02965161
> postscript(file="/var/www/html/rcomp/tmp/1e5lh1260099803.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2i7kp1260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fvh41260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mpte1260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bwlk1260099803.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 = 61
Frequency = 1
1 2 3 4 5
0.2691262628 0.2661525794 0.0651613516 0.2621876682 0.4621876682
6 7 8 9 10
0.0661525794 0.2651613516 0.0641701238 0.1641701238 -0.2318649650
11 12 13 14 15
-0.2308737372 0.2671438072 0.0711087184 -0.0269088260 -0.5249263705
16 17 18 19 20
-0.3249263705 -0.3219526871 -0.0229439149 -0.1229439149 -0.3219526871
21 22 23 24 25
-0.2219526871 -0.3209614593 -0.4199702315 -0.2199702315 -0.4130316370
26 27 28 29 30
-0.2130316370 -0.2130316370 -0.3100579536 -0.3090667258 -0.4090667258
31 32 33 34 35
-0.2100579536 -0.3100579536 -0.1090667258 -0.1090667258 0.2909332742
36 37 38 39 40
0.3939069576 0.3978718688 0.1988630966 -0.0001456756 0.1968806410
41 42 43 44 45
0.1958894132 -0.0031193590 -0.6001456756 -0.5001456756 -0.4991544479
46 47 48 49 50
-0.0031193590 -0.0021281312 0.0988630966 0.6958894132 0.7948981854
51 52 53 54 55
0.7929157298 0.3958894132 0.1978718688 0.2948981854 0.1948981854
56 57 58 59 60
0.0929157298 0.1929157298 -0.2051018146 0.0929157298 0.2909332742
61
-0.3021281312
> postscript(file="/var/www/html/rcomp/tmp/6pcsi1260099803.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.2691262628 NA
1 0.2661525794 0.2691262628
2 0.0651613516 0.2661525794
3 0.2621876682 0.0651613516
4 0.4621876682 0.2621876682
5 0.0661525794 0.4621876682
6 0.2651613516 0.0661525794
7 0.0641701238 0.2651613516
8 0.1641701238 0.0641701238
9 -0.2318649650 0.1641701238
10 -0.2308737372 -0.2318649650
11 0.2671438072 -0.2308737372
12 0.0711087184 0.2671438072
13 -0.0269088260 0.0711087184
14 -0.5249263705 -0.0269088260
15 -0.3249263705 -0.5249263705
16 -0.3219526871 -0.3249263705
17 -0.0229439149 -0.3219526871
18 -0.1229439149 -0.0229439149
19 -0.3219526871 -0.1229439149
20 -0.2219526871 -0.3219526871
21 -0.3209614593 -0.2219526871
22 -0.4199702315 -0.3209614593
23 -0.2199702315 -0.4199702315
24 -0.4130316370 -0.2199702315
25 -0.2130316370 -0.4130316370
26 -0.2130316370 -0.2130316370
27 -0.3100579536 -0.2130316370
28 -0.3090667258 -0.3100579536
29 -0.4090667258 -0.3090667258
30 -0.2100579536 -0.4090667258
31 -0.3100579536 -0.2100579536
32 -0.1090667258 -0.3100579536
33 -0.1090667258 -0.1090667258
34 0.2909332742 -0.1090667258
35 0.3939069576 0.2909332742
36 0.3978718688 0.3939069576
37 0.1988630966 0.3978718688
38 -0.0001456756 0.1988630966
39 0.1968806410 -0.0001456756
40 0.1958894132 0.1968806410
41 -0.0031193590 0.1958894132
42 -0.6001456756 -0.0031193590
43 -0.5001456756 -0.6001456756
44 -0.4991544479 -0.5001456756
45 -0.0031193590 -0.4991544479
46 -0.0021281312 -0.0031193590
47 0.0988630966 -0.0021281312
48 0.6958894132 0.0988630966
49 0.7948981854 0.6958894132
50 0.7929157298 0.7948981854
51 0.3958894132 0.7929157298
52 0.1978718688 0.3958894132
53 0.2948981854 0.1978718688
54 0.1948981854 0.2948981854
55 0.0929157298 0.1948981854
56 0.1929157298 0.0929157298
57 -0.2051018146 0.1929157298
58 0.0929157298 -0.2051018146
59 0.2909332742 0.0929157298
60 -0.3021281312 0.2909332742
61 NA -0.3021281312
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.2661525794 0.2691262628
[2,] 0.0651613516 0.2661525794
[3,] 0.2621876682 0.0651613516
[4,] 0.4621876682 0.2621876682
[5,] 0.0661525794 0.4621876682
[6,] 0.2651613516 0.0661525794
[7,] 0.0641701238 0.2651613516
[8,] 0.1641701238 0.0641701238
[9,] -0.2318649650 0.1641701238
[10,] -0.2308737372 -0.2318649650
[11,] 0.2671438072 -0.2308737372
[12,] 0.0711087184 0.2671438072
[13,] -0.0269088260 0.0711087184
[14,] -0.5249263705 -0.0269088260
[15,] -0.3249263705 -0.5249263705
[16,] -0.3219526871 -0.3249263705
[17,] -0.0229439149 -0.3219526871
[18,] -0.1229439149 -0.0229439149
[19,] -0.3219526871 -0.1229439149
[20,] -0.2219526871 -0.3219526871
[21,] -0.3209614593 -0.2219526871
[22,] -0.4199702315 -0.3209614593
[23,] -0.2199702315 -0.4199702315
[24,] -0.4130316370 -0.2199702315
[25,] -0.2130316370 -0.4130316370
[26,] -0.2130316370 -0.2130316370
[27,] -0.3100579536 -0.2130316370
[28,] -0.3090667258 -0.3100579536
[29,] -0.4090667258 -0.3090667258
[30,] -0.2100579536 -0.4090667258
[31,] -0.3100579536 -0.2100579536
[32,] -0.1090667258 -0.3100579536
[33,] -0.1090667258 -0.1090667258
[34,] 0.2909332742 -0.1090667258
[35,] 0.3939069576 0.2909332742
[36,] 0.3978718688 0.3939069576
[37,] 0.1988630966 0.3978718688
[38,] -0.0001456756 0.1988630966
[39,] 0.1968806410 -0.0001456756
[40,] 0.1958894132 0.1968806410
[41,] -0.0031193590 0.1958894132
[42,] -0.6001456756 -0.0031193590
[43,] -0.5001456756 -0.6001456756
[44,] -0.4991544479 -0.5001456756
[45,] -0.0031193590 -0.4991544479
[46,] -0.0021281312 -0.0031193590
[47,] 0.0988630966 -0.0021281312
[48,] 0.6958894132 0.0988630966
[49,] 0.7948981854 0.6958894132
[50,] 0.7929157298 0.7948981854
[51,] 0.3958894132 0.7929157298
[52,] 0.1978718688 0.3958894132
[53,] 0.2948981854 0.1978718688
[54,] 0.1948981854 0.2948981854
[55,] 0.0929157298 0.1948981854
[56,] 0.1929157298 0.0929157298
[57,] -0.2051018146 0.1929157298
[58,] 0.0929157298 -0.2051018146
[59,] 0.2909332742 0.0929157298
[60,] -0.3021281312 0.2909332742
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.2661525794 0.2691262628
2 0.0651613516 0.2661525794
3 0.2621876682 0.0651613516
4 0.4621876682 0.2621876682
5 0.0661525794 0.4621876682
6 0.2651613516 0.0661525794
7 0.0641701238 0.2651613516
8 0.1641701238 0.0641701238
9 -0.2318649650 0.1641701238
10 -0.2308737372 -0.2318649650
11 0.2671438072 -0.2308737372
12 0.0711087184 0.2671438072
13 -0.0269088260 0.0711087184
14 -0.5249263705 -0.0269088260
15 -0.3249263705 -0.5249263705
16 -0.3219526871 -0.3249263705
17 -0.0229439149 -0.3219526871
18 -0.1229439149 -0.0229439149
19 -0.3219526871 -0.1229439149
20 -0.2219526871 -0.3219526871
21 -0.3209614593 -0.2219526871
22 -0.4199702315 -0.3209614593
23 -0.2199702315 -0.4199702315
24 -0.4130316370 -0.2199702315
25 -0.2130316370 -0.4130316370
26 -0.2130316370 -0.2130316370
27 -0.3100579536 -0.2130316370
28 -0.3090667258 -0.3100579536
29 -0.4090667258 -0.3090667258
30 -0.2100579536 -0.4090667258
31 -0.3100579536 -0.2100579536
32 -0.1090667258 -0.3100579536
33 -0.1090667258 -0.1090667258
34 0.2909332742 -0.1090667258
35 0.3939069576 0.2909332742
36 0.3978718688 0.3939069576
37 0.1988630966 0.3978718688
38 -0.0001456756 0.1988630966
39 0.1968806410 -0.0001456756
40 0.1958894132 0.1968806410
41 -0.0031193590 0.1958894132
42 -0.6001456756 -0.0031193590
43 -0.5001456756 -0.6001456756
44 -0.4991544479 -0.5001456756
45 -0.0031193590 -0.4991544479
46 -0.0021281312 -0.0031193590
47 0.0988630966 -0.0021281312
48 0.6958894132 0.0988630966
49 0.7948981854 0.6958894132
50 0.7929157298 0.7948981854
51 0.3958894132 0.7929157298
52 0.1978718688 0.3958894132
53 0.2948981854 0.1978718688
54 0.1948981854 0.2948981854
55 0.0929157298 0.1948981854
56 0.1929157298 0.0929157298
57 -0.2051018146 0.1929157298
58 0.0929157298 -0.2051018146
59 0.2909332742 0.0929157298
60 -0.3021281312 0.2909332742
> 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/7nfe71260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gecy1260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9al3w1260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107eqa1260099803.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ojdv1260099803.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/12t1ad1260099803.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/13wax41260099803.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/14rztq1260099803.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/153g001260099803.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/16lwbx1260099803.tab")
+ }
>
> system("convert tmp/1e5lh1260099803.ps tmp/1e5lh1260099803.png")
> system("convert tmp/2i7kp1260099803.ps tmp/2i7kp1260099803.png")
> system("convert tmp/3fvh41260099803.ps tmp/3fvh41260099803.png")
> system("convert tmp/4mpte1260099803.ps tmp/4mpte1260099803.png")
> system("convert tmp/5bwlk1260099803.ps tmp/5bwlk1260099803.png")
> system("convert tmp/6pcsi1260099803.ps tmp/6pcsi1260099803.png")
> system("convert tmp/7nfe71260099803.ps tmp/7nfe71260099803.png")
> system("convert tmp/8gecy1260099803.ps tmp/8gecy1260099803.png")
> system("convert tmp/9al3w1260099803.ps tmp/9al3w1260099803.png")
> system("convert tmp/107eqa1260099803.ps tmp/107eqa1260099803.png")
>
>
> proc.time()
user system elapsed
2.445 1.546 3.491