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(2.7,0,2.3,0,1.9,0,2.0,0,2.3,0,2.8,0,2.4,0,2.3,0,2.7,0,2.7,0,2.9,0,3.0,0,2.2,0,2.3,0,2.8,0,2.8,0,2.8,0,2.2,0,2.6,0,2.8,0,2.5,0,2.4,0,2.3,0,1.9,0,1.7,0,2.0,0,2.1,0,1.7,0,1.8,0,1.8,0,1.8,0,1.3,0,1.3,0,1.3,1,1.2,1,1.4,1,2.2,1,2.9,1,3.1,1,3.5,1,3.6,1,4.4,1,4.1,1,5.1,1,5.8,1,5.9,1,5.4,1,5.5,1,4.8,1,3.2,1,2.7,1,2.1,1,1.9,1,0.6,1,0.7,1,-0.2,1,-1.0,1,-1.7,1,-0.7,1,-1.0,1),dim=c(2,60),dimnames=list(c('Inflatie','Kredietcrisis'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Inflatie','Kredietcrisis'),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
Inflatie Kredietcrisis t
1 2.7 0 1
2 2.3 0 2
3 1.9 0 3
4 2.0 0 4
5 2.3 0 5
6 2.8 0 6
7 2.4 0 7
8 2.3 0 8
9 2.7 0 9
10 2.7 0 10
11 2.9 0 11
12 3.0 0 12
13 2.2 0 13
14 2.3 0 14
15 2.8 0 15
16 2.8 0 16
17 2.8 0 17
18 2.2 0 18
19 2.6 0 19
20 2.8 0 20
21 2.5 0 21
22 2.4 0 22
23 2.3 0 23
24 1.9 0 24
25 1.7 0 25
26 2.0 0 26
27 2.1 0 27
28 1.7 0 28
29 1.8 0 29
30 1.8 0 30
31 1.8 0 31
32 1.3 0 32
33 1.3 0 33
34 1.3 1 34
35 1.2 1 35
36 1.4 1 36
37 2.2 1 37
38 2.9 1 38
39 3.1 1 39
40 3.5 1 40
41 3.6 1 41
42 4.4 1 42
43 4.1 1 43
44 5.1 1 44
45 5.8 1 45
46 5.9 1 46
47 5.4 1 47
48 5.5 1 48
49 4.8 1 49
50 3.2 1 50
51 2.7 1 51
52 2.1 1 52
53 1.9 1 53
54 0.6 1 54
55 0.7 1 55
56 -0.2 1 56
57 -1.0 1 57
58 -1.7 1 58
59 -0.7 1 59
60 -1.0 1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Kredietcrisis t
3.39673 2.17650 -0.06594
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.44874 -0.66485 0.08255 0.48473 3.35999
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.39673 0.44072 7.707 2.11e-10 ***
Kredietcrisis 2.17650 0.74044 2.939 0.00474 **
t -0.06594 0.02127 -3.100 0.00300 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.447 on 57 degrees of freedom
Multiple R-squared: 0.1478, Adjusted R-squared: 0.1179
F-statistic: 4.945 on 2 and 57 DF, p-value: 0.01047
> 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,] 3.973891e-02 7.947781e-02 0.96026109
[2,] 1.012746e-02 2.025493e-02 0.98987254
[3,] 2.425919e-03 4.851838e-03 0.99757408
[4,] 6.769092e-04 1.353818e-03 0.99932309
[5,] 1.525023e-04 3.050047e-04 0.99984750
[6,] 3.803331e-05 7.606663e-05 0.99996197
[7,] 8.714138e-06 1.742828e-05 0.99999129
[8,] 8.908429e-06 1.781686e-05 0.99999109
[9,] 3.475176e-06 6.950352e-06 0.99999652
[10,] 7.687895e-07 1.537579e-06 0.99999923
[11,] 1.551783e-07 3.103566e-07 0.99999984
[12,] 2.925784e-08 5.851567e-08 0.99999997
[13,] 2.221426e-08 4.442851e-08 0.99999998
[14,] 4.399501e-09 8.799002e-09 1.00000000
[15,] 8.307830e-10 1.661566e-09 1.00000000
[16,] 1.857523e-10 3.715047e-10 1.00000000
[17,] 4.871899e-11 9.743798e-11 1.00000000
[18,] 1.507808e-11 3.015617e-11 1.00000000
[19,] 2.062940e-11 4.125880e-11 1.00000000
[20,] 3.521605e-11 7.043210e-11 1.00000000
[21,] 1.070731e-11 2.141461e-11 1.00000000
[22,] 2.348133e-12 4.696266e-12 1.00000000
[23,] 1.245329e-12 2.490657e-12 1.00000000
[24,] 3.633784e-13 7.267568e-13 1.00000000
[25,] 9.013680e-14 1.802736e-13 1.00000000
[26,] 1.982149e-14 3.964298e-14 1.00000000
[27,] 1.596538e-14 3.193076e-14 1.00000000
[28,] 7.899658e-15 1.579932e-14 1.00000000
[29,] 6.023294e-15 1.204659e-14 1.00000000
[30,] 1.018808e-14 2.037616e-14 1.00000000
[31,] 4.262364e-14 8.524728e-14 1.00000000
[32,] 1.372828e-12 2.745657e-12 1.00000000
[33,] 2.790520e-10 5.581040e-10 1.00000000
[34,] 3.286735e-08 6.573470e-08 0.99999997
[35,] 4.404857e-06 8.809713e-06 0.99999560
[36,] 4.639226e-04 9.278453e-04 0.99953608
[37,] 1.934352e-02 3.868704e-02 0.98065648
[38,] 3.288437e-01 6.576873e-01 0.67115634
[39,] 7.623801e-01 4.752398e-01 0.23761989
[40,] 8.736124e-01 2.527752e-01 0.12638761
[41,] 8.940040e-01 2.119920e-01 0.10599601
[42,] 8.737583e-01 2.524834e-01 0.12624169
[43,] 9.074900e-01 1.850199e-01 0.09250997
[44,] 9.472706e-01 1.054587e-01 0.05272936
[45,] 9.091417e-01 1.817166e-01 0.09085828
[46,] 8.591940e-01 2.816119e-01 0.14080597
[47,] 7.934825e-01 4.130349e-01 0.20651746
[48,] 7.960436e-01 4.079129e-01 0.20395645
[49,] 6.904118e-01 6.191764e-01 0.30958822
> postscript(file="/var/www/html/rcomp/tmp/1cftv1259346923.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20zzq1259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38lma1259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qz471259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kpam1259346923.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
-0.63078997 -0.96485045 -1.29891092 -1.13297140 -0.76703187 -0.20109235
7 8 9 10 11 12
-0.53515282 -0.56921330 -0.10327377 -0.03733425 0.22860528 0.39454480
13 14 15 16 17 18
-0.33951568 -0.17357615 0.39236337 0.45830290 0.52424242 -0.00981805
19 20 21 22 23 24
0.45612147 0.72206100 0.48800052 0.45394005 0.41987957 0.08581910
25 26 27 28 29 30
-0.04824138 0.31769815 0.48363767 0.14957720 0.31551672 0.38145625
31 32 33 34 35 36
0.44739577 0.01333530 0.07927482 -2.03128790 -2.06534837 -1.79940885
37 38 39 40 41 42
-0.93346932 -0.16752980 0.09840973 0.56434925 0.73028878 1.59622830
43 44 45 46 47 48
1.36216783 2.42810735 3.19404688 3.35998640 2.92592593 3.09186545
49 50 51 52 53 54
2.45780498 0.92374450 0.48968403 -0.04437645 -0.17843693 -1.41249740
55 56 57 58 59 60
-1.24655788 -2.08061835 -2.81467883 -3.44873930 -2.38279978 -2.61686025
> postscript(file="/var/www/html/rcomp/tmp/65ak71259346923.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 -0.63078997 NA
1 -0.96485045 -0.63078997
2 -1.29891092 -0.96485045
3 -1.13297140 -1.29891092
4 -0.76703187 -1.13297140
5 -0.20109235 -0.76703187
6 -0.53515282 -0.20109235
7 -0.56921330 -0.53515282
8 -0.10327377 -0.56921330
9 -0.03733425 -0.10327377
10 0.22860528 -0.03733425
11 0.39454480 0.22860528
12 -0.33951568 0.39454480
13 -0.17357615 -0.33951568
14 0.39236337 -0.17357615
15 0.45830290 0.39236337
16 0.52424242 0.45830290
17 -0.00981805 0.52424242
18 0.45612147 -0.00981805
19 0.72206100 0.45612147
20 0.48800052 0.72206100
21 0.45394005 0.48800052
22 0.41987957 0.45394005
23 0.08581910 0.41987957
24 -0.04824138 0.08581910
25 0.31769815 -0.04824138
26 0.48363767 0.31769815
27 0.14957720 0.48363767
28 0.31551672 0.14957720
29 0.38145625 0.31551672
30 0.44739577 0.38145625
31 0.01333530 0.44739577
32 0.07927482 0.01333530
33 -2.03128790 0.07927482
34 -2.06534837 -2.03128790
35 -1.79940885 -2.06534837
36 -0.93346932 -1.79940885
37 -0.16752980 -0.93346932
38 0.09840973 -0.16752980
39 0.56434925 0.09840973
40 0.73028878 0.56434925
41 1.59622830 0.73028878
42 1.36216783 1.59622830
43 2.42810735 1.36216783
44 3.19404688 2.42810735
45 3.35998640 3.19404688
46 2.92592593 3.35998640
47 3.09186545 2.92592593
48 2.45780498 3.09186545
49 0.92374450 2.45780498
50 0.48968403 0.92374450
51 -0.04437645 0.48968403
52 -0.17843693 -0.04437645
53 -1.41249740 -0.17843693
54 -1.24655788 -1.41249740
55 -2.08061835 -1.24655788
56 -2.81467883 -2.08061835
57 -3.44873930 -2.81467883
58 -2.38279978 -3.44873930
59 -2.61686025 -2.38279978
60 NA -2.61686025
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.96485045 -0.63078997
[2,] -1.29891092 -0.96485045
[3,] -1.13297140 -1.29891092
[4,] -0.76703187 -1.13297140
[5,] -0.20109235 -0.76703187
[6,] -0.53515282 -0.20109235
[7,] -0.56921330 -0.53515282
[8,] -0.10327377 -0.56921330
[9,] -0.03733425 -0.10327377
[10,] 0.22860528 -0.03733425
[11,] 0.39454480 0.22860528
[12,] -0.33951568 0.39454480
[13,] -0.17357615 -0.33951568
[14,] 0.39236337 -0.17357615
[15,] 0.45830290 0.39236337
[16,] 0.52424242 0.45830290
[17,] -0.00981805 0.52424242
[18,] 0.45612147 -0.00981805
[19,] 0.72206100 0.45612147
[20,] 0.48800052 0.72206100
[21,] 0.45394005 0.48800052
[22,] 0.41987957 0.45394005
[23,] 0.08581910 0.41987957
[24,] -0.04824138 0.08581910
[25,] 0.31769815 -0.04824138
[26,] 0.48363767 0.31769815
[27,] 0.14957720 0.48363767
[28,] 0.31551672 0.14957720
[29,] 0.38145625 0.31551672
[30,] 0.44739577 0.38145625
[31,] 0.01333530 0.44739577
[32,] 0.07927482 0.01333530
[33,] -2.03128790 0.07927482
[34,] -2.06534837 -2.03128790
[35,] -1.79940885 -2.06534837
[36,] -0.93346932 -1.79940885
[37,] -0.16752980 -0.93346932
[38,] 0.09840973 -0.16752980
[39,] 0.56434925 0.09840973
[40,] 0.73028878 0.56434925
[41,] 1.59622830 0.73028878
[42,] 1.36216783 1.59622830
[43,] 2.42810735 1.36216783
[44,] 3.19404688 2.42810735
[45,] 3.35998640 3.19404688
[46,] 2.92592593 3.35998640
[47,] 3.09186545 2.92592593
[48,] 2.45780498 3.09186545
[49,] 0.92374450 2.45780498
[50,] 0.48968403 0.92374450
[51,] -0.04437645 0.48968403
[52,] -0.17843693 -0.04437645
[53,] -1.41249740 -0.17843693
[54,] -1.24655788 -1.41249740
[55,] -2.08061835 -1.24655788
[56,] -2.81467883 -2.08061835
[57,] -3.44873930 -2.81467883
[58,] -2.38279978 -3.44873930
[59,] -2.61686025 -2.38279978
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.96485045 -0.63078997
2 -1.29891092 -0.96485045
3 -1.13297140 -1.29891092
4 -0.76703187 -1.13297140
5 -0.20109235 -0.76703187
6 -0.53515282 -0.20109235
7 -0.56921330 -0.53515282
8 -0.10327377 -0.56921330
9 -0.03733425 -0.10327377
10 0.22860528 -0.03733425
11 0.39454480 0.22860528
12 -0.33951568 0.39454480
13 -0.17357615 -0.33951568
14 0.39236337 -0.17357615
15 0.45830290 0.39236337
16 0.52424242 0.45830290
17 -0.00981805 0.52424242
18 0.45612147 -0.00981805
19 0.72206100 0.45612147
20 0.48800052 0.72206100
21 0.45394005 0.48800052
22 0.41987957 0.45394005
23 0.08581910 0.41987957
24 -0.04824138 0.08581910
25 0.31769815 -0.04824138
26 0.48363767 0.31769815
27 0.14957720 0.48363767
28 0.31551672 0.14957720
29 0.38145625 0.31551672
30 0.44739577 0.38145625
31 0.01333530 0.44739577
32 0.07927482 0.01333530
33 -2.03128790 0.07927482
34 -2.06534837 -2.03128790
35 -1.79940885 -2.06534837
36 -0.93346932 -1.79940885
37 -0.16752980 -0.93346932
38 0.09840973 -0.16752980
39 0.56434925 0.09840973
40 0.73028878 0.56434925
41 1.59622830 0.73028878
42 1.36216783 1.59622830
43 2.42810735 1.36216783
44 3.19404688 2.42810735
45 3.35998640 3.19404688
46 2.92592593 3.35998640
47 3.09186545 2.92592593
48 2.45780498 3.09186545
49 0.92374450 2.45780498
50 0.48968403 0.92374450
51 -0.04437645 0.48968403
52 -0.17843693 -0.04437645
53 -1.41249740 -0.17843693
54 -1.24655788 -1.41249740
55 -2.08061835 -1.24655788
56 -2.81467883 -2.08061835
57 -3.44873930 -2.81467883
58 -2.38279978 -3.44873930
59 -2.61686025 -2.38279978
> 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/7mezl1259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ajiw1259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ai7v1259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105mv61259346923.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11x6tm1259346923.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/12gy3n1259346923.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/13zog01259346924.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/1496cx1259346924.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/15by181259346924.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/164t5c1259346924.tab")
+ }
>
> system("convert tmp/1cftv1259346923.ps tmp/1cftv1259346923.png")
> system("convert tmp/20zzq1259346923.ps tmp/20zzq1259346923.png")
> system("convert tmp/38lma1259346923.ps tmp/38lma1259346923.png")
> system("convert tmp/4qz471259346923.ps tmp/4qz471259346923.png")
> system("convert tmp/5kpam1259346923.ps tmp/5kpam1259346923.png")
> system("convert tmp/65ak71259346923.ps tmp/65ak71259346923.png")
> system("convert tmp/7mezl1259346923.ps tmp/7mezl1259346923.png")
> system("convert tmp/8ajiw1259346923.ps tmp/8ajiw1259346923.png")
> system("convert tmp/9ai7v1259346923.ps tmp/9ai7v1259346923.png")
> system("convert tmp/105mv61259346923.ps tmp/105mv61259346923.png")
>
>
> proc.time()
user system elapsed
2.461 1.571 2.852