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(2.085,0,2.053,0,2.077,0,2.058,0,2.057,0,2.076,0,2.07,0,2.062,0,2.073,0,2.061,0,2.094,0,2.067,0,2.086,0,2.276,0,2.326,0,2.349,0,2.52,0,2.628,0,2.577,0,2.698,0,2.814,0,2.968,0,3.041,0,3.278,0,3.328,0,3.5,0,3.563,0,3.569,0,3.69,0,3.819,0,3.79,0,3.956,0,4.063,0,4.047,0,4.029,0,3.941,0,4.022,0,3.879,0,4.022,0,4.028,0,4.091,0,3.987,0,4.01,0,4.007,0,4.191,0,4.299,0,4.273,0,3.82,0,3.15,1,2.486,1,1.812,1,1.257,1,1.062,1,0.842,1,0.782,1,0.698,1,0.358,1,0.347,1,0.363,1,0.359,1,0.355,1),dim=c(2,61),dimnames=list(c('intb','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('intb','x'),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
intb x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2.085 0 1 0 0 0 0 0 0 0 0 0 0 1
2 2.053 0 0 1 0 0 0 0 0 0 0 0 0 2
3 2.077 0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.058 0 0 0 0 1 0 0 0 0 0 0 0 4
5 2.057 0 0 0 0 0 1 0 0 0 0 0 0 5
6 2.076 0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.070 0 0 0 0 0 0 0 1 0 0 0 0 7
8 2.062 0 0 0 0 0 0 0 0 1 0 0 0 8
9 2.073 0 0 0 0 0 0 0 0 0 1 0 0 9
10 2.061 0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.094 0 0 0 0 0 0 0 0 0 0 0 1 11
12 2.067 0 0 0 0 0 0 0 0 0 0 0 0 12
13 2.086 0 1 0 0 0 0 0 0 0 0 0 0 13
14 2.276 0 0 1 0 0 0 0 0 0 0 0 0 14
15 2.326 0 0 0 1 0 0 0 0 0 0 0 0 15
16 2.349 0 0 0 0 1 0 0 0 0 0 0 0 16
17 2.520 0 0 0 0 0 1 0 0 0 0 0 0 17
18 2.628 0 0 0 0 0 0 1 0 0 0 0 0 18
19 2.577 0 0 0 0 0 0 0 1 0 0 0 0 19
20 2.698 0 0 0 0 0 0 0 0 1 0 0 0 20
21 2.814 0 0 0 0 0 0 0 0 0 1 0 0 21
22 2.968 0 0 0 0 0 0 0 0 0 0 1 0 22
23 3.041 0 0 0 0 0 0 0 0 0 0 0 1 23
24 3.278 0 0 0 0 0 0 0 0 0 0 0 0 24
25 3.328 0 1 0 0 0 0 0 0 0 0 0 0 25
26 3.500 0 0 1 0 0 0 0 0 0 0 0 0 26
27 3.563 0 0 0 1 0 0 0 0 0 0 0 0 27
28 3.569 0 0 0 0 1 0 0 0 0 0 0 0 28
29 3.690 0 0 0 0 0 1 0 0 0 0 0 0 29
30 3.819 0 0 0 0 0 0 1 0 0 0 0 0 30
31 3.790 0 0 0 0 0 0 0 1 0 0 0 0 31
32 3.956 0 0 0 0 0 0 0 0 1 0 0 0 32
33 4.063 0 0 0 0 0 0 0 0 0 1 0 0 33
34 4.047 0 0 0 0 0 0 0 0 0 0 1 0 34
35 4.029 0 0 0 0 0 0 0 0 0 0 0 1 35
36 3.941 0 0 0 0 0 0 0 0 0 0 0 0 36
37 4.022 0 1 0 0 0 0 0 0 0 0 0 0 37
38 3.879 0 0 1 0 0 0 0 0 0 0 0 0 38
39 4.022 0 0 0 1 0 0 0 0 0 0 0 0 39
40 4.028 0 0 0 0 1 0 0 0 0 0 0 0 40
41 4.091 0 0 0 0 0 1 0 0 0 0 0 0 41
42 3.987 0 0 0 0 0 0 1 0 0 0 0 0 42
43 4.010 0 0 0 0 0 0 0 1 0 0 0 0 43
44 4.007 0 0 0 0 0 0 0 0 1 0 0 0 44
45 4.191 0 0 0 0 0 0 0 0 0 1 0 0 45
46 4.299 0 0 0 0 0 0 0 0 0 0 1 0 46
47 4.273 0 0 0 0 0 0 0 0 0 0 0 1 47
48 3.820 0 0 0 0 0 0 0 0 0 0 0 0 48
49 3.150 1 1 0 0 0 0 0 0 0 0 0 0 49
50 2.486 1 0 1 0 0 0 0 0 0 0 0 0 50
51 1.812 1 0 0 1 0 0 0 0 0 0 0 0 51
52 1.257 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.062 1 0 0 0 0 1 0 0 0 0 0 0 53
54 0.842 1 0 0 0 0 0 1 0 0 0 0 0 54
55 0.782 1 0 0 0 0 0 0 1 0 0 0 0 55
56 0.698 1 0 0 0 0 0 0 0 1 0 0 0 56
57 0.358 1 0 0 0 0 0 0 0 0 1 0 0 57
58 0.347 1 0 0 0 0 0 0 0 0 0 1 0 58
59 0.363 1 0 0 0 0 0 0 0 0 0 0 1 59
60 0.359 1 0 0 0 0 0 0 0 0 0 0 0 60
61 0.355 1 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) x M1 M2 M3 M4
1.39724 -3.83335 0.60890 0.71870 0.58261 0.41752
M5 M6 M7 M8 M9 M10
0.39203 0.32114 0.23925 0.22036 0.17867 0.16598
M11 t
0.12429 0.05729
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.31245 -0.17207 -0.04715 0.14441 2.17002
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.397243 0.309584 4.513 4.27e-05 ***
x -3.833352 0.254746 -15.048 < 2e-16 ***
M1 0.608895 0.344213 1.769 0.0834 .
M2 0.718696 0.359160 2.001 0.0512 .
M3 0.582607 0.358211 1.626 0.1105
M4 0.417517 0.357360 1.168 0.2486
M5 0.392028 0.356608 1.099 0.2772
M6 0.321138 0.355954 0.902 0.3716
M7 0.239248 0.355401 0.673 0.5041
M8 0.220359 0.354947 0.621 0.5377
M9 0.178669 0.354593 0.504 0.6167
M10 0.165979 0.354341 0.468 0.6417
M11 0.124290 0.354189 0.351 0.7272
t 0.057290 0.005985 9.573 1.29e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5599 on 47 degrees of freedom
Multiple R-squared: 0.8311, Adjusted R-squared: 0.7844
F-statistic: 17.79 on 13 and 47 DF, p-value: 6.496e-14
> 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,] 1.380904e-02 2.761808e-02 0.9861910
[2,] 6.974252e-03 1.394850e-02 0.9930257
[3,] 2.336235e-03 4.672470e-03 0.9976638
[4,] 1.257733e-03 2.515466e-03 0.9987423
[5,] 9.107893e-04 1.821579e-03 0.9990892
[6,] 1.096516e-03 2.193032e-03 0.9989035
[7,] 1.049171e-03 2.098341e-03 0.9989508
[8,] 1.971547e-03 3.943095e-03 0.9980285
[9,] 1.772404e-03 3.544809e-03 0.9982276
[10,] 1.946939e-03 3.893879e-03 0.9980531
[11,] 1.747081e-03 3.494162e-03 0.9982529
[12,] 1.284910e-03 2.569820e-03 0.9987151
[13,] 8.938284e-04 1.787657e-03 0.9991062
[14,] 5.824159e-04 1.164832e-03 0.9994176
[15,] 3.690538e-04 7.381075e-04 0.9996309
[16,] 2.574717e-04 5.149433e-04 0.9997425
[17,] 1.705603e-04 3.411206e-04 0.9998294
[18,] 9.410671e-05 1.882134e-04 0.9999059
[19,] 5.420815e-05 1.084163e-04 0.9999458
[20,] 4.444909e-05 8.889819e-05 0.9999556
[21,] 1.159587e-03 2.319175e-03 0.9988404
[22,] 5.535178e-02 1.107036e-01 0.9446482
[23,] 2.774965e-01 5.549930e-01 0.7225035
[24,] 4.303590e-01 8.607180e-01 0.5696410
[25,] 4.992857e-01 9.985713e-01 0.5007143
[26,] 5.763500e-01 8.472999e-01 0.4236500
[27,] 6.494921e-01 7.010158e-01 0.3505079
[28,] 7.797688e-01 4.404623e-01 0.2202312
> postscript(file="/var/www/html/rcomp/tmp/1sybt1258616486.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zrtd1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ckgk1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aokh1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57bu11258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
0.02157197 -0.17751894 -0.07471894 0.01408106 -0.01871894 0.01388106
7 8 9 10 11 12
0.03248106 -0.01391894 -0.01851894 -0.07511894 -0.05771894 -0.01771894
13 14 15 16 17 18
-0.66490379 -0.64199470 -0.51319470 -0.38239470 -0.24319470 -0.12159470
19 20 21 22 23 24
-0.14799470 -0.06539470 0.03500530 0.14440530 0.20180530 0.50580530
25 26 27 28 29 30
-0.11037955 -0.10547045 0.03632955 0.15012955 0.23932955 0.38192955
31 32 33 34 35 36
0.37752955 0.50512955 0.59652955 0.53592955 0.50232955 0.48132955
37 38 39 40 41 42
-0.10385530 -0.41394621 -0.19214621 -0.07834621 -0.04714621 -0.13754621
43 44 45 46 47 48
-0.08994621 -0.13134621 0.03705379 0.10045379 0.05885379 -0.32714621
49 50 51 52 53 54
2.17002121 1.33893030 0.74373030 0.29653030 0.06973030 -0.13666970
55 56 57 58 59 60
-0.17206970 -0.29446970 -0.65006970 -0.70566970 -0.70526970 -0.64226970
61
-1.31245455
> postscript(file="/var/www/html/rcomp/tmp/6kg471258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.02157197 NA
1 -0.17751894 0.02157197
2 -0.07471894 -0.17751894
3 0.01408106 -0.07471894
4 -0.01871894 0.01408106
5 0.01388106 -0.01871894
6 0.03248106 0.01388106
7 -0.01391894 0.03248106
8 -0.01851894 -0.01391894
9 -0.07511894 -0.01851894
10 -0.05771894 -0.07511894
11 -0.01771894 -0.05771894
12 -0.66490379 -0.01771894
13 -0.64199470 -0.66490379
14 -0.51319470 -0.64199470
15 -0.38239470 -0.51319470
16 -0.24319470 -0.38239470
17 -0.12159470 -0.24319470
18 -0.14799470 -0.12159470
19 -0.06539470 -0.14799470
20 0.03500530 -0.06539470
21 0.14440530 0.03500530
22 0.20180530 0.14440530
23 0.50580530 0.20180530
24 -0.11037955 0.50580530
25 -0.10547045 -0.11037955
26 0.03632955 -0.10547045
27 0.15012955 0.03632955
28 0.23932955 0.15012955
29 0.38192955 0.23932955
30 0.37752955 0.38192955
31 0.50512955 0.37752955
32 0.59652955 0.50512955
33 0.53592955 0.59652955
34 0.50232955 0.53592955
35 0.48132955 0.50232955
36 -0.10385530 0.48132955
37 -0.41394621 -0.10385530
38 -0.19214621 -0.41394621
39 -0.07834621 -0.19214621
40 -0.04714621 -0.07834621
41 -0.13754621 -0.04714621
42 -0.08994621 -0.13754621
43 -0.13134621 -0.08994621
44 0.03705379 -0.13134621
45 0.10045379 0.03705379
46 0.05885379 0.10045379
47 -0.32714621 0.05885379
48 2.17002121 -0.32714621
49 1.33893030 2.17002121
50 0.74373030 1.33893030
51 0.29653030 0.74373030
52 0.06973030 0.29653030
53 -0.13666970 0.06973030
54 -0.17206970 -0.13666970
55 -0.29446970 -0.17206970
56 -0.65006970 -0.29446970
57 -0.70566970 -0.65006970
58 -0.70526970 -0.70566970
59 -0.64226970 -0.70526970
60 -1.31245455 -0.64226970
61 NA -1.31245455
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.17751894 0.02157197
[2,] -0.07471894 -0.17751894
[3,] 0.01408106 -0.07471894
[4,] -0.01871894 0.01408106
[5,] 0.01388106 -0.01871894
[6,] 0.03248106 0.01388106
[7,] -0.01391894 0.03248106
[8,] -0.01851894 -0.01391894
[9,] -0.07511894 -0.01851894
[10,] -0.05771894 -0.07511894
[11,] -0.01771894 -0.05771894
[12,] -0.66490379 -0.01771894
[13,] -0.64199470 -0.66490379
[14,] -0.51319470 -0.64199470
[15,] -0.38239470 -0.51319470
[16,] -0.24319470 -0.38239470
[17,] -0.12159470 -0.24319470
[18,] -0.14799470 -0.12159470
[19,] -0.06539470 -0.14799470
[20,] 0.03500530 -0.06539470
[21,] 0.14440530 0.03500530
[22,] 0.20180530 0.14440530
[23,] 0.50580530 0.20180530
[24,] -0.11037955 0.50580530
[25,] -0.10547045 -0.11037955
[26,] 0.03632955 -0.10547045
[27,] 0.15012955 0.03632955
[28,] 0.23932955 0.15012955
[29,] 0.38192955 0.23932955
[30,] 0.37752955 0.38192955
[31,] 0.50512955 0.37752955
[32,] 0.59652955 0.50512955
[33,] 0.53592955 0.59652955
[34,] 0.50232955 0.53592955
[35,] 0.48132955 0.50232955
[36,] -0.10385530 0.48132955
[37,] -0.41394621 -0.10385530
[38,] -0.19214621 -0.41394621
[39,] -0.07834621 -0.19214621
[40,] -0.04714621 -0.07834621
[41,] -0.13754621 -0.04714621
[42,] -0.08994621 -0.13754621
[43,] -0.13134621 -0.08994621
[44,] 0.03705379 -0.13134621
[45,] 0.10045379 0.03705379
[46,] 0.05885379 0.10045379
[47,] -0.32714621 0.05885379
[48,] 2.17002121 -0.32714621
[49,] 1.33893030 2.17002121
[50,] 0.74373030 1.33893030
[51,] 0.29653030 0.74373030
[52,] 0.06973030 0.29653030
[53,] -0.13666970 0.06973030
[54,] -0.17206970 -0.13666970
[55,] -0.29446970 -0.17206970
[56,] -0.65006970 -0.29446970
[57,] -0.70566970 -0.65006970
[58,] -0.70526970 -0.70566970
[59,] -0.64226970 -0.70526970
[60,] -1.31245455 -0.64226970
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.17751894 0.02157197
2 -0.07471894 -0.17751894
3 0.01408106 -0.07471894
4 -0.01871894 0.01408106
5 0.01388106 -0.01871894
6 0.03248106 0.01388106
7 -0.01391894 0.03248106
8 -0.01851894 -0.01391894
9 -0.07511894 -0.01851894
10 -0.05771894 -0.07511894
11 -0.01771894 -0.05771894
12 -0.66490379 -0.01771894
13 -0.64199470 -0.66490379
14 -0.51319470 -0.64199470
15 -0.38239470 -0.51319470
16 -0.24319470 -0.38239470
17 -0.12159470 -0.24319470
18 -0.14799470 -0.12159470
19 -0.06539470 -0.14799470
20 0.03500530 -0.06539470
21 0.14440530 0.03500530
22 0.20180530 0.14440530
23 0.50580530 0.20180530
24 -0.11037955 0.50580530
25 -0.10547045 -0.11037955
26 0.03632955 -0.10547045
27 0.15012955 0.03632955
28 0.23932955 0.15012955
29 0.38192955 0.23932955
30 0.37752955 0.38192955
31 0.50512955 0.37752955
32 0.59652955 0.50512955
33 0.53592955 0.59652955
34 0.50232955 0.53592955
35 0.48132955 0.50232955
36 -0.10385530 0.48132955
37 -0.41394621 -0.10385530
38 -0.19214621 -0.41394621
39 -0.07834621 -0.19214621
40 -0.04714621 -0.07834621
41 -0.13754621 -0.04714621
42 -0.08994621 -0.13754621
43 -0.13134621 -0.08994621
44 0.03705379 -0.13134621
45 0.10045379 0.03705379
46 0.05885379 0.10045379
47 -0.32714621 0.05885379
48 2.17002121 -0.32714621
49 1.33893030 2.17002121
50 0.74373030 1.33893030
51 0.29653030 0.74373030
52 0.06973030 0.29653030
53 -0.13666970 0.06973030
54 -0.17206970 -0.13666970
55 -0.29446970 -0.17206970
56 -0.65006970 -0.29446970
57 -0.70566970 -0.65006970
58 -0.70526970 -0.70566970
59 -0.64226970 -0.70526970
60 -1.31245455 -0.64226970
> 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/77isn1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cp6i1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g4v51258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ykmi1258616486.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c0cn1258616486.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/12271z1258616486.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/13m8l81258616486.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/142yfl1258616486.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/15307e1258616486.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/16o57k1258616486.tab")
+ }
>
> system("convert tmp/1sybt1258616486.ps tmp/1sybt1258616486.png")
> system("convert tmp/2zrtd1258616486.ps tmp/2zrtd1258616486.png")
> system("convert tmp/3ckgk1258616486.ps tmp/3ckgk1258616486.png")
> system("convert tmp/4aokh1258616486.ps tmp/4aokh1258616486.png")
> system("convert tmp/57bu11258616486.ps tmp/57bu11258616486.png")
> system("convert tmp/6kg471258616486.ps tmp/6kg471258616486.png")
> system("convert tmp/77isn1258616486.ps tmp/77isn1258616486.png")
> system("convert tmp/8cp6i1258616486.ps tmp/8cp6i1258616486.png")
> system("convert tmp/9g4v51258616486.ps tmp/9g4v51258616486.png")
> system("convert tmp/10ykmi1258616486.ps tmp/10ykmi1258616486.png")
>
>
> proc.time()
user system elapsed
2.370 1.539 3.143