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 = '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
Inflatie Kredietcrisis
1 2.7 0
2 2.3 0
3 1.9 0
4 2.0 0
5 2.3 0
6 2.8 0
7 2.4 0
8 2.3 0
9 2.7 0
10 2.7 0
11 2.9 0
12 3.0 0
13 2.2 0
14 2.3 0
15 2.8 0
16 2.8 0
17 2.8 0
18 2.2 0
19 2.6 0
20 2.8 0
21 2.5 0
22 2.4 0
23 2.3 0
24 1.9 0
25 1.7 0
26 2.0 0
27 2.1 0
28 1.7 0
29 1.8 0
30 1.8 0
31 1.8 0
32 1.3 0
33 1.3 0
34 1.3 1
35 1.2 1
36 1.4 1
37 2.2 1
38 2.9 1
39 3.1 1
40 3.5 1
41 3.6 1
42 4.4 1
43 4.1 1
44 5.1 1
45 5.8 1
46 5.9 1
47 5.4 1
48 5.5 1
49 4.8 1
50 3.2 1
51 2.7 1
52 2.1 1
53 1.9 1
54 0.6 1
55 0.7 1
56 -0.2 1
57 -1.0 1
58 -1.7 1
59 -0.7 1
60 -1.0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Kredietcrisis
2.2758 0.1983
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.17407 -0.50034 0.02424 0.52424 3.42593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.2758 0.2700 8.429 1.18e-11 ***
Kredietcrisis 0.1983 0.4025 0.493 0.624
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.551 on 58 degrees of freedom
Multiple R-squared: 0.004168, Adjusted R-squared: -0.013
F-statistic: 0.2428 on 1 and 58 DF, p-value: 0.6241
> 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,] 1.484715e-02 2.969429e-02 0.9851529
[2,] 7.121763e-03 1.424353e-02 0.9928782
[3,] 1.489148e-03 2.978296e-03 0.9985109
[4,] 2.829807e-04 5.659615e-04 0.9997170
[5,] 8.620684e-05 1.724137e-04 0.9999138
[6,] 2.371421e-05 4.742841e-05 0.9999763
[7,] 1.112130e-05 2.224260e-05 0.9999889
[8,] 6.136210e-06 1.227242e-05 0.9999939
[9,] 1.635148e-06 3.270296e-06 0.9999984
[10,] 3.498936e-07 6.997873e-07 0.9999997
[11,] 1.006691e-07 2.013382e-07 0.9999999
[12,] 2.737708e-08 5.475416e-08 1.0000000
[13,] 7.091433e-09 1.418287e-08 1.0000000
[14,] 1.899347e-09 3.798694e-09 1.0000000
[15,] 3.486744e-10 6.973488e-10 1.0000000
[16,] 8.767082e-11 1.753416e-10 1.0000000
[17,] 1.480773e-11 2.961546e-11 1.0000000
[18,] 2.576825e-12 5.153650e-12 1.0000000
[19,] 5.082257e-13 1.016451e-12 1.0000000
[20,] 4.292315e-13 8.584631e-13 1.0000000
[21,] 7.816429e-13 1.563286e-12 1.0000000
[22,] 2.769583e-13 5.539166e-13 1.0000000
[23,] 7.057418e-14 1.411484e-13 1.0000000
[24,] 6.866978e-14 1.373396e-13 1.0000000
[25,] 3.598920e-14 7.197840e-14 1.0000000
[26,] 1.687163e-14 3.374326e-14 1.0000000
[27,] 7.246549e-15 1.449310e-14 1.0000000
[28,] 2.060598e-14 4.121196e-14 1.0000000
[29,] 3.599385e-14 7.198770e-14 1.0000000
[30,] 8.068243e-15 1.613649e-14 1.0000000
[31,] 1.866098e-15 3.732196e-15 1.0000000
[32,] 4.133575e-16 8.267150e-16 1.0000000
[33,] 2.949602e-16 5.899204e-16 1.0000000
[34,] 1.118088e-15 2.236175e-15 1.0000000
[35,] 2.469388e-15 4.938777e-15 1.0000000
[36,] 8.983752e-15 1.796750e-14 1.0000000
[37,] 1.809655e-14 3.619311e-14 1.0000000
[38,] 3.077402e-13 6.154804e-13 1.0000000
[39,] 6.494500e-13 1.298900e-12 1.0000000
[40,] 2.235312e-11 4.470623e-11 1.0000000
[41,] 3.244466e-09 6.488931e-09 1.0000000
[42,] 2.543090e-07 5.086180e-07 0.9999997
[43,] 4.592651e-06 9.185302e-06 0.9999954
[44,] 2.053379e-04 4.106759e-04 0.9997947
[45,] 4.939042e-03 9.878085e-03 0.9950610
[46,] 1.824373e-02 3.648745e-02 0.9817563
[47,] 6.278644e-02 1.255729e-01 0.9372136
[48,] 1.661869e-01 3.323738e-01 0.8338131
[49,] 4.818763e-01 9.637526e-01 0.5181237
[50,] 5.816172e-01 8.367657e-01 0.4183828
[51,] 8.178909e-01 3.642181e-01 0.1821091
> postscript(file="/var/www/html/rcomp/tmp/1k7lw1258744264.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c9dt1258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36x5v1258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j89y1258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5s1ar1258744264.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
0.42424242 0.02424242 -0.37575758 -0.27575758 0.02424242 0.52424242
7 8 9 10 11 12
0.12424242 0.02424242 0.42424242 0.42424242 0.62424242 0.72424242
13 14 15 16 17 18
-0.07575758 0.02424242 0.52424242 0.52424242 0.52424242 -0.07575758
19 20 21 22 23 24
0.32424242 0.52424242 0.22424242 0.12424242 0.02424242 -0.37575758
25 26 27 28 29 30
-0.57575758 -0.27575758 -0.17575758 -0.57575758 -0.47575758 -0.47575758
31 32 33 34 35 36
-0.47575758 -0.97575758 -0.97575758 -1.17407407 -1.27407407 -1.07407407
37 38 39 40 41 42
-0.27407407 0.42592593 0.62592593 1.02592593 1.12592593 1.92592593
43 44 45 46 47 48
1.62592593 2.62592593 3.32592593 3.42592593 2.92592593 3.02592593
49 50 51 52 53 54
2.32592593 0.72592593 0.22592593 -0.37407407 -0.57407407 -1.87407407
55 56 57 58 59 60
-1.77407407 -2.67407407 -3.47407407 -4.17407407 -3.17407407 -3.47407407
> postscript(file="/var/www/html/rcomp/tmp/6vvch1258744264.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 0.42424242 NA
1 0.02424242 0.42424242
2 -0.37575758 0.02424242
3 -0.27575758 -0.37575758
4 0.02424242 -0.27575758
5 0.52424242 0.02424242
6 0.12424242 0.52424242
7 0.02424242 0.12424242
8 0.42424242 0.02424242
9 0.42424242 0.42424242
10 0.62424242 0.42424242
11 0.72424242 0.62424242
12 -0.07575758 0.72424242
13 0.02424242 -0.07575758
14 0.52424242 0.02424242
15 0.52424242 0.52424242
16 0.52424242 0.52424242
17 -0.07575758 0.52424242
18 0.32424242 -0.07575758
19 0.52424242 0.32424242
20 0.22424242 0.52424242
21 0.12424242 0.22424242
22 0.02424242 0.12424242
23 -0.37575758 0.02424242
24 -0.57575758 -0.37575758
25 -0.27575758 -0.57575758
26 -0.17575758 -0.27575758
27 -0.57575758 -0.17575758
28 -0.47575758 -0.57575758
29 -0.47575758 -0.47575758
30 -0.47575758 -0.47575758
31 -0.97575758 -0.47575758
32 -0.97575758 -0.97575758
33 -1.17407407 -0.97575758
34 -1.27407407 -1.17407407
35 -1.07407407 -1.27407407
36 -0.27407407 -1.07407407
37 0.42592593 -0.27407407
38 0.62592593 0.42592593
39 1.02592593 0.62592593
40 1.12592593 1.02592593
41 1.92592593 1.12592593
42 1.62592593 1.92592593
43 2.62592593 1.62592593
44 3.32592593 2.62592593
45 3.42592593 3.32592593
46 2.92592593 3.42592593
47 3.02592593 2.92592593
48 2.32592593 3.02592593
49 0.72592593 2.32592593
50 0.22592593 0.72592593
51 -0.37407407 0.22592593
52 -0.57407407 -0.37407407
53 -1.87407407 -0.57407407
54 -1.77407407 -1.87407407
55 -2.67407407 -1.77407407
56 -3.47407407 -2.67407407
57 -4.17407407 -3.47407407
58 -3.17407407 -4.17407407
59 -3.47407407 -3.17407407
60 NA -3.47407407
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.02424242 0.42424242
[2,] -0.37575758 0.02424242
[3,] -0.27575758 -0.37575758
[4,] 0.02424242 -0.27575758
[5,] 0.52424242 0.02424242
[6,] 0.12424242 0.52424242
[7,] 0.02424242 0.12424242
[8,] 0.42424242 0.02424242
[9,] 0.42424242 0.42424242
[10,] 0.62424242 0.42424242
[11,] 0.72424242 0.62424242
[12,] -0.07575758 0.72424242
[13,] 0.02424242 -0.07575758
[14,] 0.52424242 0.02424242
[15,] 0.52424242 0.52424242
[16,] 0.52424242 0.52424242
[17,] -0.07575758 0.52424242
[18,] 0.32424242 -0.07575758
[19,] 0.52424242 0.32424242
[20,] 0.22424242 0.52424242
[21,] 0.12424242 0.22424242
[22,] 0.02424242 0.12424242
[23,] -0.37575758 0.02424242
[24,] -0.57575758 -0.37575758
[25,] -0.27575758 -0.57575758
[26,] -0.17575758 -0.27575758
[27,] -0.57575758 -0.17575758
[28,] -0.47575758 -0.57575758
[29,] -0.47575758 -0.47575758
[30,] -0.47575758 -0.47575758
[31,] -0.97575758 -0.47575758
[32,] -0.97575758 -0.97575758
[33,] -1.17407407 -0.97575758
[34,] -1.27407407 -1.17407407
[35,] -1.07407407 -1.27407407
[36,] -0.27407407 -1.07407407
[37,] 0.42592593 -0.27407407
[38,] 0.62592593 0.42592593
[39,] 1.02592593 0.62592593
[40,] 1.12592593 1.02592593
[41,] 1.92592593 1.12592593
[42,] 1.62592593 1.92592593
[43,] 2.62592593 1.62592593
[44,] 3.32592593 2.62592593
[45,] 3.42592593 3.32592593
[46,] 2.92592593 3.42592593
[47,] 3.02592593 2.92592593
[48,] 2.32592593 3.02592593
[49,] 0.72592593 2.32592593
[50,] 0.22592593 0.72592593
[51,] -0.37407407 0.22592593
[52,] -0.57407407 -0.37407407
[53,] -1.87407407 -0.57407407
[54,] -1.77407407 -1.87407407
[55,] -2.67407407 -1.77407407
[56,] -3.47407407 -2.67407407
[57,] -4.17407407 -3.47407407
[58,] -3.17407407 -4.17407407
[59,] -3.47407407 -3.17407407
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.02424242 0.42424242
2 -0.37575758 0.02424242
3 -0.27575758 -0.37575758
4 0.02424242 -0.27575758
5 0.52424242 0.02424242
6 0.12424242 0.52424242
7 0.02424242 0.12424242
8 0.42424242 0.02424242
9 0.42424242 0.42424242
10 0.62424242 0.42424242
11 0.72424242 0.62424242
12 -0.07575758 0.72424242
13 0.02424242 -0.07575758
14 0.52424242 0.02424242
15 0.52424242 0.52424242
16 0.52424242 0.52424242
17 -0.07575758 0.52424242
18 0.32424242 -0.07575758
19 0.52424242 0.32424242
20 0.22424242 0.52424242
21 0.12424242 0.22424242
22 0.02424242 0.12424242
23 -0.37575758 0.02424242
24 -0.57575758 -0.37575758
25 -0.27575758 -0.57575758
26 -0.17575758 -0.27575758
27 -0.57575758 -0.17575758
28 -0.47575758 -0.57575758
29 -0.47575758 -0.47575758
30 -0.47575758 -0.47575758
31 -0.97575758 -0.47575758
32 -0.97575758 -0.97575758
33 -1.17407407 -0.97575758
34 -1.27407407 -1.17407407
35 -1.07407407 -1.27407407
36 -0.27407407 -1.07407407
37 0.42592593 -0.27407407
38 0.62592593 0.42592593
39 1.02592593 0.62592593
40 1.12592593 1.02592593
41 1.92592593 1.12592593
42 1.62592593 1.92592593
43 2.62592593 1.62592593
44 3.32592593 2.62592593
45 3.42592593 3.32592593
46 2.92592593 3.42592593
47 3.02592593 2.92592593
48 2.32592593 3.02592593
49 0.72592593 2.32592593
50 0.22592593 0.72592593
51 -0.37407407 0.22592593
52 -0.57407407 -0.37407407
53 -1.87407407 -0.57407407
54 -1.77407407 -1.87407407
55 -2.67407407 -1.77407407
56 -3.47407407 -2.67407407
57 -4.17407407 -3.47407407
58 -3.17407407 -4.17407407
59 -3.47407407 -3.17407407
> 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/7uq0t1258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k0vo1258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sba61258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107c471258744264.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112hv01258744264.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/12bhkk1258744264.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/13iadv1258744265.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/14vrfh1258744265.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/154xsf1258744265.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/16kfss1258744265.tab")
+ }
>
> system("convert tmp/1k7lw1258744264.ps tmp/1k7lw1258744264.png")
> system("convert tmp/2c9dt1258744264.ps tmp/2c9dt1258744264.png")
> system("convert tmp/36x5v1258744264.ps tmp/36x5v1258744264.png")
> system("convert tmp/4j89y1258744264.ps tmp/4j89y1258744264.png")
> system("convert tmp/5s1ar1258744264.ps tmp/5s1ar1258744264.png")
> system("convert tmp/6vvch1258744264.ps tmp/6vvch1258744264.png")
> system("convert tmp/7uq0t1258744264.ps tmp/7uq0t1258744264.png")
> system("convert tmp/8k0vo1258744264.ps tmp/8k0vo1258744264.png")
> system("convert tmp/9sba61258744264.ps tmp/9sba61258744264.png")
> system("convert tmp/107c471258744264.ps tmp/107c471258744264.png")
>
>
> proc.time()
user system elapsed
2.489 1.545 2.855