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(14.1,14.8,16.8,15.4,15.2,16.9,14.1,14.7,16.5,15.2,17.6,18,16.9,16.7,19.7,15.9,17.4,17.7,15.2,15.7,17.2,17.7,17.9,16.2,17.5,16.8,19.1,16.7,18.2,18.5,17.8,16.4,18,20.3,19.5,18,20.2,19,20.2,21.5,19.7,21.1,20.2,18.2,21.3,20.4,17.2,15.8,15.1,14.5,15.8,14.3,13.9,15.5,14.3,13.6,16.3,16.8,16,16.8,16),dim=c(1,61),dimnames=list(c('HPC'),1:61))
> y <- array(NA,dim=c(1,61),dimnames=list(c('HPC'),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 = 'Linear Trend'
> par2 = 'Include Monthly 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
HPC M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 14.1 1 0 0 0 0 0 0 0 0 0 0 1
2 14.8 0 1 0 0 0 0 0 0 0 0 0 2
3 16.8 0 0 1 0 0 0 0 0 0 0 0 3
4 15.4 0 0 0 1 0 0 0 0 0 0 0 4
5 15.2 0 0 0 0 1 0 0 0 0 0 0 5
6 16.9 0 0 0 0 0 1 0 0 0 0 0 6
7 14.1 0 0 0 0 0 0 1 0 0 0 0 7
8 14.7 0 0 0 0 0 0 0 1 0 0 0 8
9 16.5 0 0 0 0 0 0 0 0 1 0 0 9
10 15.2 0 0 0 0 0 0 0 0 0 1 0 10
11 17.6 0 0 0 0 0 0 0 0 0 0 1 11
12 18.0 0 0 0 0 0 0 0 0 0 0 0 12
13 16.9 1 0 0 0 0 0 0 0 0 0 0 13
14 16.7 0 1 0 0 0 0 0 0 0 0 0 14
15 19.7 0 0 1 0 0 0 0 0 0 0 0 15
16 15.9 0 0 0 1 0 0 0 0 0 0 0 16
17 17.4 0 0 0 0 1 0 0 0 0 0 0 17
18 17.7 0 0 0 0 0 1 0 0 0 0 0 18
19 15.2 0 0 0 0 0 0 1 0 0 0 0 19
20 15.7 0 0 0 0 0 0 0 1 0 0 0 20
21 17.2 0 0 0 0 0 0 0 0 1 0 0 21
22 17.7 0 0 0 0 0 0 0 0 0 1 0 22
23 17.9 0 0 0 0 0 0 0 0 0 0 1 23
24 16.2 0 0 0 0 0 0 0 0 0 0 0 24
25 17.5 1 0 0 0 0 0 0 0 0 0 0 25
26 16.8 0 1 0 0 0 0 0 0 0 0 0 26
27 19.1 0 0 1 0 0 0 0 0 0 0 0 27
28 16.7 0 0 0 1 0 0 0 0 0 0 0 28
29 18.2 0 0 0 0 1 0 0 0 0 0 0 29
30 18.5 0 0 0 0 0 1 0 0 0 0 0 30
31 17.8 0 0 0 0 0 0 1 0 0 0 0 31
32 16.4 0 0 0 0 0 0 0 1 0 0 0 32
33 18.0 0 0 0 0 0 0 0 0 1 0 0 33
34 20.3 0 0 0 0 0 0 0 0 0 1 0 34
35 19.5 0 0 0 0 0 0 0 0 0 0 1 35
36 18.0 0 0 0 0 0 0 0 0 0 0 0 36
37 20.2 1 0 0 0 0 0 0 0 0 0 0 37
38 19.0 0 1 0 0 0 0 0 0 0 0 0 38
39 20.2 0 0 1 0 0 0 0 0 0 0 0 39
40 21.5 0 0 0 1 0 0 0 0 0 0 0 40
41 19.7 0 0 0 0 1 0 0 0 0 0 0 41
42 21.1 0 0 0 0 0 1 0 0 0 0 0 42
43 20.2 0 0 0 0 0 0 1 0 0 0 0 43
44 18.2 0 0 0 0 0 0 0 1 0 0 0 44
45 21.3 0 0 0 0 0 0 0 0 1 0 0 45
46 20.4 0 0 0 0 0 0 0 0 0 1 0 46
47 17.2 0 0 0 0 0 0 0 0 0 0 1 47
48 15.8 0 0 0 0 0 0 0 0 0 0 0 48
49 15.1 1 0 0 0 0 0 0 0 0 0 0 49
50 14.5 0 1 0 0 0 0 0 0 0 0 0 50
51 15.8 0 0 1 0 0 0 0 0 0 0 0 51
52 14.3 0 0 0 1 0 0 0 0 0 0 0 52
53 13.9 0 0 0 0 1 0 0 0 0 0 0 53
54 15.5 0 0 0 0 0 1 0 0 0 0 0 54
55 14.3 0 0 0 0 0 0 1 0 0 0 0 55
56 13.6 0 0 0 0 0 0 0 1 0 0 0 56
57 16.3 0 0 0 0 0 0 0 0 1 0 0 57
58 16.8 0 0 0 0 0 0 0 0 0 1 0 58
59 16.0 0 0 0 0 0 0 0 0 0 0 1 59
60 16.8 0 0 0 0 0 0 0 0 0 0 0 60
61 16.0 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
16.585882 -0.274706 -0.496078 1.453529 -0.116863 -0.007255
M6 M7 M8 M9 M10 M11
1.042353 -0.588039 -1.198431 0.931176 1.140784 0.690392
t
0.010392
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.2294 -1.3106 -0.1153 1.2894 4.6153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.585882 1.077422 15.394 <2e-16 ***
M1 -0.274706 1.256527 -0.219 0.828
M2 -0.496078 1.318858 -0.376 0.708
M3 1.453529 1.317174 1.104 0.275
M4 -0.116863 1.315665 -0.089 0.930
M5 -0.007255 1.314333 -0.006 0.996
M6 1.042353 1.313177 0.794 0.431
M7 -0.588039 1.312198 -0.448 0.656
M8 -1.198431 1.311396 -0.914 0.365
M9 0.931176 1.310772 0.710 0.481
M10 1.140784 1.310327 0.871 0.388
M11 0.690392 1.310059 0.527 0.601
t 0.010392 0.015286 0.680 0.500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.071 on 48 degrees of freedom
Multiple R-squared: 0.1613, Adjusted R-squared: -0.04833
F-statistic: 0.7695 on 12 and 48 DF, p-value: 0.6777
> 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,] 6.064083e-02 0.1212816597 0.9393592
[2,] 1.771742e-02 0.0354348407 0.9822826
[3,] 9.590647e-03 0.0191812934 0.9904094
[4,] 4.073683e-03 0.0081473665 0.9959263
[5,] 1.542864e-03 0.0030857277 0.9984571
[6,] 7.865850e-04 0.0015731701 0.9992134
[7,] 4.559585e-04 0.0009119169 0.9995440
[8,] 3.324387e-04 0.0006648774 0.9996676
[9,] 3.998828e-03 0.0079976563 0.9960012
[10,] 1.957546e-03 0.0039150920 0.9980425
[11,] 1.096302e-03 0.0021926045 0.9989037
[12,] 5.638542e-04 0.0011277083 0.9994361
[13,] 4.252640e-04 0.0008505281 0.9995747
[14,] 1.786357e-04 0.0003572714 0.9998214
[15,] 1.001243e-04 0.0002002487 0.9998999
[16,] 1.328829e-04 0.0002657657 0.9998671
[17,] 9.370317e-05 0.0001874063 0.9999063
[18,] 2.123104e-04 0.0004246207 0.9997877
[19,] 7.281577e-04 0.0014563155 0.9992718
[20,] 4.148292e-04 0.0008296583 0.9995852
[21,] 7.737585e-04 0.0015475170 0.9992262
[22,] 8.169341e-04 0.0016338683 0.9991831
[23,] 3.661268e-04 0.0007322536 0.9996339
[24,] 1.801621e-04 0.0003603243 0.9998198
[25,] 2.191287e-03 0.0043825742 0.9978087
[26,] 1.945538e-03 0.0038910757 0.9980545
[27,] 2.173966e-03 0.0043479324 0.9978260
[28,] 7.510176e-03 0.0150203512 0.9924898
[29,] 1.001553e-02 0.0200310536 0.9899845
[30,] 6.911075e-02 0.1382215059 0.9308892
> postscript(file="/var/www/html/rcomp/tmp/1vsqc1292589029.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vsqc1292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3n1pf1292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n1pf1292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n1pf1292589029.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 = 61
Frequency = 1
1 2 3 4 5 6 7
-2.2215686 -1.3105882 -1.2705882 -1.1105882 -1.4305882 -0.7905882 -1.9705882
8 9 10 11 12 13 14
-0.7705882 -1.1105882 -2.6305882 0.2094118 1.2894118 0.4537255 0.4647059
15 16 17 18 19 20 21
1.5047059 -0.7352941 0.6447059 -0.1152941 -0.9952941 0.1047059 -0.5352941
22 23 24 25 26 27 28
-0.2552941 0.3847059 -0.6352941 0.9290196 0.4400000 0.7800000 -0.0600000
29 30 31 32 33 34 35
1.3200000 0.5600000 1.4800000 0.6800000 0.1400000 2.2200000 1.8600000
36 37 38 39 40 41 42
1.0400000 3.5043137 2.5152941 1.7552941 4.6152941 2.6952941 3.0352941
43 44 45 46 47 48 49
3.7552941 2.3552941 3.3152941 2.1952941 -0.5647059 -1.2847059 -1.7203922
50 51 52 53 54 55 56
-2.1094118 -2.7694118 -2.7094118 -3.2294118 -2.6894118 -2.2694118 -2.3694118
57 58 59 60 61
-1.8094118 -1.5294118 -1.8894118 -0.4094118 -0.9450980
> postscript(file="/var/www/html/rcomp/tmp/6gsoi1292589029.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.2215686 NA
1 -1.3105882 -2.2215686
2 -1.2705882 -1.3105882
3 -1.1105882 -1.2705882
4 -1.4305882 -1.1105882
5 -0.7905882 -1.4305882
6 -1.9705882 -0.7905882
7 -0.7705882 -1.9705882
8 -1.1105882 -0.7705882
9 -2.6305882 -1.1105882
10 0.2094118 -2.6305882
11 1.2894118 0.2094118
12 0.4537255 1.2894118
13 0.4647059 0.4537255
14 1.5047059 0.4647059
15 -0.7352941 1.5047059
16 0.6447059 -0.7352941
17 -0.1152941 0.6447059
18 -0.9952941 -0.1152941
19 0.1047059 -0.9952941
20 -0.5352941 0.1047059
21 -0.2552941 -0.5352941
22 0.3847059 -0.2552941
23 -0.6352941 0.3847059
24 0.9290196 -0.6352941
25 0.4400000 0.9290196
26 0.7800000 0.4400000
27 -0.0600000 0.7800000
28 1.3200000 -0.0600000
29 0.5600000 1.3200000
30 1.4800000 0.5600000
31 0.6800000 1.4800000
32 0.1400000 0.6800000
33 2.2200000 0.1400000
34 1.8600000 2.2200000
35 1.0400000 1.8600000
36 3.5043137 1.0400000
37 2.5152941 3.5043137
38 1.7552941 2.5152941
39 4.6152941 1.7552941
40 2.6952941 4.6152941
41 3.0352941 2.6952941
42 3.7552941 3.0352941
43 2.3552941 3.7552941
44 3.3152941 2.3552941
45 2.1952941 3.3152941
46 -0.5647059 2.1952941
47 -1.2847059 -0.5647059
48 -1.7203922 -1.2847059
49 -2.1094118 -1.7203922
50 -2.7694118 -2.1094118
51 -2.7094118 -2.7694118
52 -3.2294118 -2.7094118
53 -2.6894118 -3.2294118
54 -2.2694118 -2.6894118
55 -2.3694118 -2.2694118
56 -1.8094118 -2.3694118
57 -1.5294118 -1.8094118
58 -1.8894118 -1.5294118
59 -0.4094118 -1.8894118
60 -0.9450980 -0.4094118
61 NA -0.9450980
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.3105882 -2.2215686
[2,] -1.2705882 -1.3105882
[3,] -1.1105882 -1.2705882
[4,] -1.4305882 -1.1105882
[5,] -0.7905882 -1.4305882
[6,] -1.9705882 -0.7905882
[7,] -0.7705882 -1.9705882
[8,] -1.1105882 -0.7705882
[9,] -2.6305882 -1.1105882
[10,] 0.2094118 -2.6305882
[11,] 1.2894118 0.2094118
[12,] 0.4537255 1.2894118
[13,] 0.4647059 0.4537255
[14,] 1.5047059 0.4647059
[15,] -0.7352941 1.5047059
[16,] 0.6447059 -0.7352941
[17,] -0.1152941 0.6447059
[18,] -0.9952941 -0.1152941
[19,] 0.1047059 -0.9952941
[20,] -0.5352941 0.1047059
[21,] -0.2552941 -0.5352941
[22,] 0.3847059 -0.2552941
[23,] -0.6352941 0.3847059
[24,] 0.9290196 -0.6352941
[25,] 0.4400000 0.9290196
[26,] 0.7800000 0.4400000
[27,] -0.0600000 0.7800000
[28,] 1.3200000 -0.0600000
[29,] 0.5600000 1.3200000
[30,] 1.4800000 0.5600000
[31,] 0.6800000 1.4800000
[32,] 0.1400000 0.6800000
[33,] 2.2200000 0.1400000
[34,] 1.8600000 2.2200000
[35,] 1.0400000 1.8600000
[36,] 3.5043137 1.0400000
[37,] 2.5152941 3.5043137
[38,] 1.7552941 2.5152941
[39,] 4.6152941 1.7552941
[40,] 2.6952941 4.6152941
[41,] 3.0352941 2.6952941
[42,] 3.7552941 3.0352941
[43,] 2.3552941 3.7552941
[44,] 3.3152941 2.3552941
[45,] 2.1952941 3.3152941
[46,] -0.5647059 2.1952941
[47,] -1.2847059 -0.5647059
[48,] -1.7203922 -1.2847059
[49,] -2.1094118 -1.7203922
[50,] -2.7694118 -2.1094118
[51,] -2.7094118 -2.7694118
[52,] -3.2294118 -2.7094118
[53,] -2.6894118 -3.2294118
[54,] -2.2694118 -2.6894118
[55,] -2.3694118 -2.2694118
[56,] -1.8094118 -2.3694118
[57,] -1.5294118 -1.8094118
[58,] -1.8894118 -1.5294118
[59,] -0.4094118 -1.8894118
[60,] -0.9450980 -0.4094118
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.3105882 -2.2215686
2 -1.2705882 -1.3105882
3 -1.1105882 -1.2705882
4 -1.4305882 -1.1105882
5 -0.7905882 -1.4305882
6 -1.9705882 -0.7905882
7 -0.7705882 -1.9705882
8 -1.1105882 -0.7705882
9 -2.6305882 -1.1105882
10 0.2094118 -2.6305882
11 1.2894118 0.2094118
12 0.4537255 1.2894118
13 0.4647059 0.4537255
14 1.5047059 0.4647059
15 -0.7352941 1.5047059
16 0.6447059 -0.7352941
17 -0.1152941 0.6447059
18 -0.9952941 -0.1152941
19 0.1047059 -0.9952941
20 -0.5352941 0.1047059
21 -0.2552941 -0.5352941
22 0.3847059 -0.2552941
23 -0.6352941 0.3847059
24 0.9290196 -0.6352941
25 0.4400000 0.9290196
26 0.7800000 0.4400000
27 -0.0600000 0.7800000
28 1.3200000 -0.0600000
29 0.5600000 1.3200000
30 1.4800000 0.5600000
31 0.6800000 1.4800000
32 0.1400000 0.6800000
33 2.2200000 0.1400000
34 1.8600000 2.2200000
35 1.0400000 1.8600000
36 3.5043137 1.0400000
37 2.5152941 3.5043137
38 1.7552941 2.5152941
39 4.6152941 1.7552941
40 2.6952941 4.6152941
41 3.0352941 2.6952941
42 3.7552941 3.0352941
43 2.3552941 3.7552941
44 3.3152941 2.3552941
45 2.1952941 3.3152941
46 -0.5647059 2.1952941
47 -1.2847059 -0.5647059
48 -1.7203922 -1.2847059
49 -2.1094118 -1.7203922
50 -2.7694118 -2.1094118
51 -2.7094118 -2.7694118
52 -3.2294118 -2.7094118
53 -2.6894118 -3.2294118
54 -2.2694118 -2.6894118
55 -2.3694118 -2.2694118
56 -1.8094118 -2.3694118
57 -1.5294118 -1.8094118
58 -1.8894118 -1.5294118
59 -0.4094118 -1.8894118
60 -0.9450980 -0.4094118
> 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/7rk631292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rk631292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rk631292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jt5n1292589029.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115bmt1292589029.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/12qc2h1292589029.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/13xvhb1292589029.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/14q4ge1292589029.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/15b5f21292589029.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/16pfdb1292589029.tab")
+ }
> try(system("convert tmp/1vsqc1292589029.ps tmp/1vsqc1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vsqc1292589029.ps tmp/2vsqc1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n1pf1292589029.ps tmp/3n1pf1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n1pf1292589029.ps tmp/4n1pf1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n1pf1292589029.ps tmp/5n1pf1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gsoi1292589029.ps tmp/6gsoi1292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rk631292589029.ps tmp/7rk631292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rk631292589029.ps tmp/8rk631292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rk631292589029.ps tmp/9rk631292589029.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jt5n1292589029.ps tmp/10jt5n1292589029.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.513 1.656 13.536