R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(100.00,0,100.83,0,101.51,0,102.16,0,102.39,0,102.54,0,102.85,0,103.47,0,103.57,0,103.69,0,103.50,0,103.47,0,103.45,0,103.48,0,103.93,0,103.89,0,104.40,0,104.79,0,104.77,0,105.13,0,105.26,0,104.96,0,104.75,0,105.01,0,105.15,0,105.20,0,105.77,0,105.78,0,106.26,0,106.13,0,106.12,0,106.57,0,106.44,0,106.54,0,107.10,0,108.10,0,108.40,0,108.84,0,109.62,0,110.42,0,110.67,0,111.66,0,112.28,0,112.87,1,112.18,1,112.36,1,112.16,1,111.49,1,111.25,1,111.36,1,111.74,1,111.10,1,111.33,1,111.25,1,111.04,1,110.97,1,111.31,1,111.02,1,111.07,1,111.36,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.00 0 1 0 0 0 0 0 0 0 0 0 0 1
2 100.83 0 0 1 0 0 0 0 0 0 0 0 0 2
3 101.51 0 0 0 1 0 0 0 0 0 0 0 0 3
4 102.16 0 0 0 0 1 0 0 0 0 0 0 0 4
5 102.39 0 0 0 0 0 1 0 0 0 0 0 0 5
6 102.54 0 0 0 0 0 0 1 0 0 0 0 0 6
7 102.85 0 0 0 0 0 0 0 1 0 0 0 0 7
8 103.47 0 0 0 0 0 0 0 0 1 0 0 0 8
9 103.57 0 0 0 0 0 0 0 0 0 1 0 0 9
10 103.69 0 0 0 0 0 0 0 0 0 0 1 0 10
11 103.50 0 0 0 0 0 0 0 0 0 0 0 1 11
12 103.47 0 0 0 0 0 0 0 0 0 0 0 0 12
13 103.45 0 1 0 0 0 0 0 0 0 0 0 0 13
14 103.48 0 0 1 0 0 0 0 0 0 0 0 0 14
15 103.93 0 0 0 1 0 0 0 0 0 0 0 0 15
16 103.89 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.40 0 0 0 0 0 1 0 0 0 0 0 0 17
18 104.79 0 0 0 0 0 0 1 0 0 0 0 0 18
19 104.77 0 0 0 0 0 0 0 1 0 0 0 0 19
20 105.13 0 0 0 0 0 0 0 0 1 0 0 0 20
21 105.26 0 0 0 0 0 0 0 0 0 1 0 0 21
22 104.96 0 0 0 0 0 0 0 0 0 0 1 0 22
23 104.75 0 0 0 0 0 0 0 0 0 0 0 1 23
24 105.01 0 0 0 0 0 0 0 0 0 0 0 0 24
25 105.15 0 1 0 0 0 0 0 0 0 0 0 0 25
26 105.20 0 0 1 0 0 0 0 0 0 0 0 0 26
27 105.77 0 0 0 1 0 0 0 0 0 0 0 0 27
28 105.78 0 0 0 0 1 0 0 0 0 0 0 0 28
29 106.26 0 0 0 0 0 1 0 0 0 0 0 0 29
30 106.13 0 0 0 0 0 0 1 0 0 0 0 0 30
31 106.12 0 0 0 0 0 0 0 1 0 0 0 0 31
32 106.57 0 0 0 0 0 0 0 0 1 0 0 0 32
33 106.44 0 0 0 0 0 0 0 0 0 1 0 0 33
34 106.54 0 0 0 0 0 0 0 0 0 0 1 0 34
35 107.10 0 0 0 0 0 0 0 0 0 0 0 1 35
36 108.10 0 0 0 0 0 0 0 0 0 0 0 0 36
37 108.40 0 1 0 0 0 0 0 0 0 0 0 0 37
38 108.84 0 0 1 0 0 0 0 0 0 0 0 0 38
39 109.62 0 0 0 1 0 0 0 0 0 0 0 0 39
40 110.42 0 0 0 0 1 0 0 0 0 0 0 0 40
41 110.67 0 0 0 0 0 1 0 0 0 0 0 0 41
42 111.66 0 0 0 0 0 0 1 0 0 0 0 0 42
43 112.28 0 0 0 0 0 0 0 1 0 0 0 0 43
44 112.87 1 0 0 0 0 0 0 0 1 0 0 0 44
45 112.18 1 0 0 0 0 0 0 0 0 1 0 0 45
46 112.36 1 0 0 0 0 0 0 0 0 0 1 0 46
47 112.16 1 0 0 0 0 0 0 0 0 0 0 1 47
48 111.49 1 0 0 0 0 0 0 0 0 0 0 0 48
49 111.25 1 1 0 0 0 0 0 0 0 0 0 0 49
50 111.36 1 0 1 0 0 0 0 0 0 0 0 0 50
51 111.74 1 0 0 1 0 0 0 0 0 0 0 0 51
52 111.10 1 0 0 0 1 0 0 0 0 0 0 0 52
53 111.33 1 0 0 0 0 1 0 0 0 0 0 0 53
54 111.25 1 0 0 0 0 0 1 0 0 0 0 0 54
55 111.04 1 0 0 0 0 0 0 1 0 0 0 0 55
56 110.97 1 0 0 0 0 0 0 0 1 0 0 0 56
57 111.31 1 0 0 0 0 0 0 0 0 1 0 0 57
58 111.02 1 0 0 0 0 0 0 0 0 0 1 0 58
59 111.07 1 0 0 0 0 0 0 0 0 0 0 1 59
60 111.36 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
100.79854 0.40550 -0.03885 0.06078 0.44042 0.40405
M5 M6 M7 M8 M9 M10
0.55168 0.62331 0.56894 0.68547 0.44311 0.21274
M11 t
0.02237 0.19237
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6921 -0.6644 -0.1979 0.5328 2.6407
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.79854 0.64347 156.647 <2e-16 ***
X 0.40550 0.55009 0.737 0.465
M1 -0.03885 0.75209 -0.052 0.959
M2 0.06078 0.75088 0.081 0.936
M3 0.44042 0.74993 0.587 0.560
M4 0.40405 0.74925 0.539 0.592
M5 0.55168 0.74885 0.737 0.465
M6 0.62331 0.74871 0.833 0.409
M7 0.56894 0.74885 0.760 0.451
M8 0.68547 0.74768 0.917 0.364
M9 0.44311 0.74673 0.593 0.556
M10 0.21274 0.74605 0.285 0.777
M11 0.02237 0.74564 0.030 0.976
t 0.19237 0.01425 13.497 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.179 on 46 degrees of freedom
Multiple R-squared: 0.9193, Adjusted R-squared: 0.8964
F-statistic: 40.28 on 13 and 46 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,] 6.553611e-02 1.310722e-01 0.9344639
[2,] 1.991623e-02 3.983246e-02 0.9800838
[3,] 6.853304e-03 1.370661e-02 0.9931467
[4,] 2.927157e-03 5.854313e-03 0.9970728
[5,] 1.075402e-03 2.150803e-03 0.9989246
[6,] 6.737223e-04 1.347445e-03 0.9993263
[7,] 3.629101e-04 7.258203e-04 0.9996371
[8,] 1.226974e-04 2.453947e-04 0.9998773
[9,] 4.247354e-05 8.494707e-05 0.9999575
[10,] 1.198123e-05 2.396245e-05 0.9999880
[11,] 3.389972e-06 6.779945e-06 0.9999966
[12,] 1.180720e-06 2.361440e-06 0.9999988
[13,] 3.723062e-07 7.446123e-07 0.9999996
[14,] 3.055556e-07 6.111112e-07 0.9999997
[15,] 7.078834e-07 1.415767e-06 0.9999993
[16,] 9.302440e-07 1.860488e-06 0.9999991
[17,] 3.221448e-06 6.442896e-06 0.9999968
[18,] 1.696091e-05 3.392181e-05 0.9999830
[19,] 1.505455e-04 3.010909e-04 0.9998495
[20,] 3.459496e-03 6.918991e-03 0.9965405
[21,] 4.697152e-02 9.394304e-02 0.9530285
[22,] 2.319520e-01 4.639041e-01 0.7680480
[23,] 6.558416e-01 6.883169e-01 0.3441584
[24,] 7.673864e-01 4.652272e-01 0.2326136
[25,] 8.853303e-01 2.293394e-01 0.1146697
[26,] 8.858213e-01 2.283574e-01 0.1141787
[27,] 8.211342e-01 3.577316e-01 0.1788658
> postscript(file="/var/www/html/rcomp/tmp/1l0jx1260061077.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2oqut1260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d29d1260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w4301260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xwz21260061077.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
-0.95205808 -0.41405808 -0.30605808 0.18794192 0.07794192 -0.03605808
7 8 9 10 11 12
0.13594192 0.44704174 0.59704174 0.75504174 0.56304174 0.36304174
13 14 15 16 17 18
0.18952087 -0.07247913 -0.19447913 -0.39047913 -0.22047913 -0.09447913
19 20 21 22 23 24
-0.25247913 -0.20137931 -0.02137931 -0.28337931 -0.49537931 -0.40537931
25 26 27 28 29 30
-0.41890018 -0.66090018 -0.66290018 -0.80890018 -0.66890018 -1.06290018
31 32 33 34 35 36
-1.21090018 -1.06980036 -1.14980036 -1.01180036 -0.45380036 0.37619964
37 38 39 40 41 42
0.52267877 0.67067877 0.87867877 1.52267877 1.43267877 2.15867877
43 44 45 46 47 48
2.64067877 2.51627949 1.87627949 2.09427949 1.89227949 1.05227949
49 50 51 52 53 54
0.65875862 0.47675862 0.28475862 -0.51124138 -0.62124138 -0.96524138
55 56 57 58 59 60
-1.31324138 -1.69214156 -1.30214156 -1.55414156 -1.50614156 -1.38614156
> postscript(file="/var/www/html/rcomp/tmp/64nrs1260061077.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 -0.95205808 NA
1 -0.41405808 -0.95205808
2 -0.30605808 -0.41405808
3 0.18794192 -0.30605808
4 0.07794192 0.18794192
5 -0.03605808 0.07794192
6 0.13594192 -0.03605808
7 0.44704174 0.13594192
8 0.59704174 0.44704174
9 0.75504174 0.59704174
10 0.56304174 0.75504174
11 0.36304174 0.56304174
12 0.18952087 0.36304174
13 -0.07247913 0.18952087
14 -0.19447913 -0.07247913
15 -0.39047913 -0.19447913
16 -0.22047913 -0.39047913
17 -0.09447913 -0.22047913
18 -0.25247913 -0.09447913
19 -0.20137931 -0.25247913
20 -0.02137931 -0.20137931
21 -0.28337931 -0.02137931
22 -0.49537931 -0.28337931
23 -0.40537931 -0.49537931
24 -0.41890018 -0.40537931
25 -0.66090018 -0.41890018
26 -0.66290018 -0.66090018
27 -0.80890018 -0.66290018
28 -0.66890018 -0.80890018
29 -1.06290018 -0.66890018
30 -1.21090018 -1.06290018
31 -1.06980036 -1.21090018
32 -1.14980036 -1.06980036
33 -1.01180036 -1.14980036
34 -0.45380036 -1.01180036
35 0.37619964 -0.45380036
36 0.52267877 0.37619964
37 0.67067877 0.52267877
38 0.87867877 0.67067877
39 1.52267877 0.87867877
40 1.43267877 1.52267877
41 2.15867877 1.43267877
42 2.64067877 2.15867877
43 2.51627949 2.64067877
44 1.87627949 2.51627949
45 2.09427949 1.87627949
46 1.89227949 2.09427949
47 1.05227949 1.89227949
48 0.65875862 1.05227949
49 0.47675862 0.65875862
50 0.28475862 0.47675862
51 -0.51124138 0.28475862
52 -0.62124138 -0.51124138
53 -0.96524138 -0.62124138
54 -1.31324138 -0.96524138
55 -1.69214156 -1.31324138
56 -1.30214156 -1.69214156
57 -1.55414156 -1.30214156
58 -1.50614156 -1.55414156
59 -1.38614156 -1.50614156
60 NA -1.38614156
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.41405808 -0.95205808
[2,] -0.30605808 -0.41405808
[3,] 0.18794192 -0.30605808
[4,] 0.07794192 0.18794192
[5,] -0.03605808 0.07794192
[6,] 0.13594192 -0.03605808
[7,] 0.44704174 0.13594192
[8,] 0.59704174 0.44704174
[9,] 0.75504174 0.59704174
[10,] 0.56304174 0.75504174
[11,] 0.36304174 0.56304174
[12,] 0.18952087 0.36304174
[13,] -0.07247913 0.18952087
[14,] -0.19447913 -0.07247913
[15,] -0.39047913 -0.19447913
[16,] -0.22047913 -0.39047913
[17,] -0.09447913 -0.22047913
[18,] -0.25247913 -0.09447913
[19,] -0.20137931 -0.25247913
[20,] -0.02137931 -0.20137931
[21,] -0.28337931 -0.02137931
[22,] -0.49537931 -0.28337931
[23,] -0.40537931 -0.49537931
[24,] -0.41890018 -0.40537931
[25,] -0.66090018 -0.41890018
[26,] -0.66290018 -0.66090018
[27,] -0.80890018 -0.66290018
[28,] -0.66890018 -0.80890018
[29,] -1.06290018 -0.66890018
[30,] -1.21090018 -1.06290018
[31,] -1.06980036 -1.21090018
[32,] -1.14980036 -1.06980036
[33,] -1.01180036 -1.14980036
[34,] -0.45380036 -1.01180036
[35,] 0.37619964 -0.45380036
[36,] 0.52267877 0.37619964
[37,] 0.67067877 0.52267877
[38,] 0.87867877 0.67067877
[39,] 1.52267877 0.87867877
[40,] 1.43267877 1.52267877
[41,] 2.15867877 1.43267877
[42,] 2.64067877 2.15867877
[43,] 2.51627949 2.64067877
[44,] 1.87627949 2.51627949
[45,] 2.09427949 1.87627949
[46,] 1.89227949 2.09427949
[47,] 1.05227949 1.89227949
[48,] 0.65875862 1.05227949
[49,] 0.47675862 0.65875862
[50,] 0.28475862 0.47675862
[51,] -0.51124138 0.28475862
[52,] -0.62124138 -0.51124138
[53,] -0.96524138 -0.62124138
[54,] -1.31324138 -0.96524138
[55,] -1.69214156 -1.31324138
[56,] -1.30214156 -1.69214156
[57,] -1.55414156 -1.30214156
[58,] -1.50614156 -1.55414156
[59,] -1.38614156 -1.50614156
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.41405808 -0.95205808
2 -0.30605808 -0.41405808
3 0.18794192 -0.30605808
4 0.07794192 0.18794192
5 -0.03605808 0.07794192
6 0.13594192 -0.03605808
7 0.44704174 0.13594192
8 0.59704174 0.44704174
9 0.75504174 0.59704174
10 0.56304174 0.75504174
11 0.36304174 0.56304174
12 0.18952087 0.36304174
13 -0.07247913 0.18952087
14 -0.19447913 -0.07247913
15 -0.39047913 -0.19447913
16 -0.22047913 -0.39047913
17 -0.09447913 -0.22047913
18 -0.25247913 -0.09447913
19 -0.20137931 -0.25247913
20 -0.02137931 -0.20137931
21 -0.28337931 -0.02137931
22 -0.49537931 -0.28337931
23 -0.40537931 -0.49537931
24 -0.41890018 -0.40537931
25 -0.66090018 -0.41890018
26 -0.66290018 -0.66090018
27 -0.80890018 -0.66290018
28 -0.66890018 -0.80890018
29 -1.06290018 -0.66890018
30 -1.21090018 -1.06290018
31 -1.06980036 -1.21090018
32 -1.14980036 -1.06980036
33 -1.01180036 -1.14980036
34 -0.45380036 -1.01180036
35 0.37619964 -0.45380036
36 0.52267877 0.37619964
37 0.67067877 0.52267877
38 0.87867877 0.67067877
39 1.52267877 0.87867877
40 1.43267877 1.52267877
41 2.15867877 1.43267877
42 2.64067877 2.15867877
43 2.51627949 2.64067877
44 1.87627949 2.51627949
45 2.09427949 1.87627949
46 1.89227949 2.09427949
47 1.05227949 1.89227949
48 0.65875862 1.05227949
49 0.47675862 0.65875862
50 0.28475862 0.47675862
51 -0.51124138 0.28475862
52 -0.62124138 -0.51124138
53 -0.96524138 -0.62124138
54 -1.31324138 -0.96524138
55 -1.69214156 -1.31324138
56 -1.30214156 -1.69214156
57 -1.55414156 -1.30214156
58 -1.50614156 -1.55414156
59 -1.38614156 -1.50614156
> 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/72wy51260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vyux1260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kgjf1260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yvk31260061077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1116md1260061077.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/129s6x1260061077.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/13ok9t1260061077.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/14ppne1260061077.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/15jkh21260061077.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/16rlcv1260061077.tab")
+ }
> system("convert tmp/1l0jx1260061077.ps tmp/1l0jx1260061077.png")
> system("convert tmp/2oqut1260061077.ps tmp/2oqut1260061077.png")
> system("convert tmp/3d29d1260061077.ps tmp/3d29d1260061077.png")
> system("convert tmp/4w4301260061077.ps tmp/4w4301260061077.png")
> system("convert tmp/5xwz21260061077.ps tmp/5xwz21260061077.png")
> system("convert tmp/64nrs1260061077.ps tmp/64nrs1260061077.png")
> system("convert tmp/72wy51260061077.ps tmp/72wy51260061077.png")
> system("convert tmp/8vyux1260061077.ps tmp/8vyux1260061077.png")
> system("convert tmp/9kgjf1260061077.ps tmp/9kgjf1260061077.png")
> system("convert tmp/10yvk31260061077.ps tmp/10yvk31260061077.png")
>
>
> proc.time()
user system elapsed
2.423 1.557 3.766