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(105.4,102.7,105.4,102.5,105.6,102.2,105.7,102.9,105.8,103.1,105.8,103,105.8,102.8,105.9,102.5,106.1,101.9,106.4,101.9,106.4,101.8,106.3,102,106.2,102.6,106.2,102.5,106.3,102.5,106.4,101.6,106.5,101.4,106.6,100.8,106.6,101.1,106.6,101.3,106.8,101.2,107,101.3,107.2,101.1,107.3,101.3,107.5,101.2,107.6,101.6,107.6,101.7,107.7,101.5,107.7,100.9,107.7,101.5,107.7,101.4,107.6,101.6,107.7,101.7,107.9,101.4,107.9,101.8,107.9,101.7,107.8,101.4,107.6,101.2,107.4,101,107,101.7,107,102.4,107.2,102,107.5,102.1,107.8,102,107.8,101.8,107.7,102.7,107.6,102.3,107.6,101.9,107.5,102,107.5,102.3,107.6,102.8,107.6,102.4,107.9,102.3,107.6,102.7,107.5,102.7,107.5,102.9,107.6,103,107.7,102.2,107.8,102.3,107.9,102.8,107.9,102.8),dim=c(2,61),dimnames=list(c('Werkl','Inflatie'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werkl','Inflatie'),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 = 'No 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
Werkl Inflatie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 105.4 102.7 1 0 0 0 0 0 0 0 0 0 0
2 105.4 102.5 0 1 0 0 0 0 0 0 0 0 0
3 105.6 102.2 0 0 1 0 0 0 0 0 0 0 0
4 105.7 102.9 0 0 0 1 0 0 0 0 0 0 0
5 105.8 103.1 0 0 0 0 1 0 0 0 0 0 0
6 105.8 103.0 0 0 0 0 0 1 0 0 0 0 0
7 105.8 102.8 0 0 0 0 0 0 1 0 0 0 0
8 105.9 102.5 0 0 0 0 0 0 0 1 0 0 0
9 106.1 101.9 0 0 0 0 0 0 0 0 1 0 0
10 106.4 101.9 0 0 0 0 0 0 0 0 0 1 0
11 106.4 101.8 0 0 0 0 0 0 0 0 0 0 1
12 106.3 102.0 0 0 0 0 0 0 0 0 0 0 0
13 106.2 102.6 1 0 0 0 0 0 0 0 0 0 0
14 106.2 102.5 0 1 0 0 0 0 0 0 0 0 0
15 106.3 102.5 0 0 1 0 0 0 0 0 0 0 0
16 106.4 101.6 0 0 0 1 0 0 0 0 0 0 0
17 106.5 101.4 0 0 0 0 1 0 0 0 0 0 0
18 106.6 100.8 0 0 0 0 0 1 0 0 0 0 0
19 106.6 101.1 0 0 0 0 0 0 1 0 0 0 0
20 106.6 101.3 0 0 0 0 0 0 0 1 0 0 0
21 106.8 101.2 0 0 0 0 0 0 0 0 1 0 0
22 107.0 101.3 0 0 0 0 0 0 0 0 0 1 0
23 107.2 101.1 0 0 0 0 0 0 0 0 0 0 1
24 107.3 101.3 0 0 0 0 0 0 0 0 0 0 0
25 107.5 101.2 1 0 0 0 0 0 0 0 0 0 0
26 107.6 101.6 0 1 0 0 0 0 0 0 0 0 0
27 107.6 101.7 0 0 1 0 0 0 0 0 0 0 0
28 107.7 101.5 0 0 0 1 0 0 0 0 0 0 0
29 107.7 100.9 0 0 0 0 1 0 0 0 0 0 0
30 107.7 101.5 0 0 0 0 0 1 0 0 0 0 0
31 107.7 101.4 0 0 0 0 0 0 1 0 0 0 0
32 107.6 101.6 0 0 0 0 0 0 0 1 0 0 0
33 107.7 101.7 0 0 0 0 0 0 0 0 1 0 0
34 107.9 101.4 0 0 0 0 0 0 0 0 0 1 0
35 107.9 101.8 0 0 0 0 0 0 0 0 0 0 1
36 107.9 101.7 0 0 0 0 0 0 0 0 0 0 0
37 107.8 101.4 1 0 0 0 0 0 0 0 0 0 0
38 107.6 101.2 0 1 0 0 0 0 0 0 0 0 0
39 107.4 101.0 0 0 1 0 0 0 0 0 0 0 0
40 107.0 101.7 0 0 0 1 0 0 0 0 0 0 0
41 107.0 102.4 0 0 0 0 1 0 0 0 0 0 0
42 107.2 102.0 0 0 0 0 0 1 0 0 0 0 0
43 107.5 102.1 0 0 0 0 0 0 1 0 0 0 0
44 107.8 102.0 0 0 0 0 0 0 0 1 0 0 0
45 107.8 101.8 0 0 0 0 0 0 0 0 1 0 0
46 107.7 102.7 0 0 0 0 0 0 0 0 0 1 0
47 107.6 102.3 0 0 0 0 0 0 0 0 0 0 1
48 107.6 101.9 0 0 0 0 0 0 0 0 0 0 0
49 107.5 102.0 1 0 0 0 0 0 0 0 0 0 0
50 107.5 102.3 0 1 0 0 0 0 0 0 0 0 0
51 107.6 102.8 0 0 1 0 0 0 0 0 0 0 0
52 107.6 102.4 0 0 0 1 0 0 0 0 0 0 0
53 107.9 102.3 0 0 0 0 1 0 0 0 0 0 0
54 107.6 102.7 0 0 0 0 0 1 0 0 0 0 0
55 107.5 102.7 0 0 0 0 0 0 1 0 0 0 0
56 107.5 102.9 0 0 0 0 0 0 0 1 0 0 0
57 107.6 103.0 0 0 0 0 0 0 0 0 1 0 0
58 107.7 102.2 0 0 0 0 0 0 0 0 0 1 0
59 107.8 102.3 0 0 0 0 0 0 0 0 0 0 1
60 107.9 102.8 0 0 0 0 0 0 0 0 0 0 0
61 107.9 102.8 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Inflatie M1 M2 M3 M4
136.68335 -0.28726 -0.29925 -0.51702 -0.47127 -0.49702
M5 M6 M7 M8 M9 M10
-0.39702 -0.40276 -0.35702 -0.28553 -0.20575 -0.07149
M11
-0.04298
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.4824 -0.6843 0.3464 0.5764 1.0463
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 136.68335 17.19068 7.951 2.61e-10 ***
Inflatie -0.28726 0.16860 -1.704 0.0949 .
M1 -0.29925 0.48792 -0.613 0.5426
M2 -0.51702 0.50885 -1.016 0.3147
M3 -0.47127 0.50895 -0.926 0.3591
M4 -0.49702 0.50885 -0.977 0.3336
M5 -0.39702 0.50885 -0.780 0.4391
M6 -0.40276 0.50877 -0.792 0.4325
M7 -0.35702 0.50885 -0.702 0.4863
M8 -0.28553 0.50907 -0.561 0.5775
M9 -0.20575 0.50868 -0.404 0.6877
M10 -0.07149 0.50871 -0.141 0.8888
M11 -0.04298 0.50885 -0.084 0.9330
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8043 on 48 degrees of freedom
Multiple R-squared: 0.1134, Adjusted R-squared: -0.1082
F-statistic: 0.5118 on 12 and 48 DF, p-value: 0.8967
> 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,] 0.8946317 0.2107366842 0.1053683421
[2,] 0.8587072 0.2825855741 0.1412927871
[3,] 0.8007833 0.3984334233 0.1992167117
[4,] 0.7739988 0.4520024311 0.2260012156
[5,] 0.8043854 0.3912291498 0.1956145749
[6,] 0.8643026 0.2713947512 0.1356973756
[7,] 0.9079625 0.1840749900 0.0920374950
[8,] 0.9386690 0.1226620835 0.0613310417
[9,] 0.9721161 0.0557677182 0.0278838591
[10,] 0.9913577 0.0172846994 0.0086423497
[11,] 0.9984279 0.0031441657 0.0015720829
[12,] 0.9994349 0.0011301896 0.0005650948
[13,] 0.9997731 0.0004537059 0.0002268530
[14,] 0.9996084 0.0007831708 0.0003915854
[15,] 0.9997811 0.0004378312 0.0002189156
[16,] 0.9997760 0.0004479174 0.0002239587
[17,] 0.9997114 0.0005771590 0.0002885795
[18,] 0.9997107 0.0005785672 0.0002892836
[19,] 0.9995956 0.0008087129 0.0004043565
[20,] 0.9995621 0.0008758592 0.0004379296
[21,] 0.9992913 0.0014174790 0.0007087395
[22,] 0.9985421 0.0029157634 0.0014578817
[23,] 0.9966422 0.0067156797 0.0033578398
[24,] 0.9915919 0.0168162768 0.0084081384
[25,] 0.9891379 0.0217242905 0.0108621452
[26,] 0.9985539 0.0028922493 0.0014461246
[27,] 0.9978108 0.0043783455 0.0021891727
[28,] 0.9929279 0.0141442044 0.0070721022
[29,] 0.9891056 0.0217888869 0.0108944434
[30,] 0.9932980 0.0134039899 0.0067019949
> postscript(file="/var/www/html/rcomp/tmp/10hh71258722060.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dthh1258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jjpo1258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mahb1258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qg7t1258722060.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
-1.48243126 -1.32211487 -1.25403829 -0.92721059 -0.86975846 -0.89273931
7 8 9 10 11 12
-0.99593666 -1.05360530 -1.10574521 -0.94000000 -0.99723564 -1.08276436
13 14 15 16 17 18
-0.71115733 -0.52211487 -0.46786008 -0.60064949 -0.65810163 -0.72471283
19 20 21 22 23 24
-0.68427983 -0.69831812 -0.60682770 -0.51235641 -0.39831812 -0.28384684
25 26 27 28 29 30
0.18667770 0.61935051 0.60233137 0.67062444 0.39826803 0.57636966
31 32 33 34 35 36
0.50189837 0.38786008 0.43680265 0.41636966 0.50276436 0.43105743
37 38 39 40 41 42
0.54412984 0.50444623 0.20124888 0.02807658 0.12915906 0.22000000
43 44 45 46 47 48
0.50298086 0.70276436 0.56552872 0.58980855 0.34639470 0.18850957
49 50 51 52 53 54
0.41648625 0.72043299 0.91831812 0.82915906 1.00043299 0.82108248
55 56 57 58 59 60
0.67533727 0.66129898 0.71024154 0.44617821 0.54639470 0.74704419
61
1.04629480
> postscript(file="/var/www/html/rcomp/tmp/60kft1258722060.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 -1.48243126 NA
1 -1.32211487 -1.48243126
2 -1.25403829 -1.32211487
3 -0.92721059 -1.25403829
4 -0.86975846 -0.92721059
5 -0.89273931 -0.86975846
6 -0.99593666 -0.89273931
7 -1.05360530 -0.99593666
8 -1.10574521 -1.05360530
9 -0.94000000 -1.10574521
10 -0.99723564 -0.94000000
11 -1.08276436 -0.99723564
12 -0.71115733 -1.08276436
13 -0.52211487 -0.71115733
14 -0.46786008 -0.52211487
15 -0.60064949 -0.46786008
16 -0.65810163 -0.60064949
17 -0.72471283 -0.65810163
18 -0.68427983 -0.72471283
19 -0.69831812 -0.68427983
20 -0.60682770 -0.69831812
21 -0.51235641 -0.60682770
22 -0.39831812 -0.51235641
23 -0.28384684 -0.39831812
24 0.18667770 -0.28384684
25 0.61935051 0.18667770
26 0.60233137 0.61935051
27 0.67062444 0.60233137
28 0.39826803 0.67062444
29 0.57636966 0.39826803
30 0.50189837 0.57636966
31 0.38786008 0.50189837
32 0.43680265 0.38786008
33 0.41636966 0.43680265
34 0.50276436 0.41636966
35 0.43105743 0.50276436
36 0.54412984 0.43105743
37 0.50444623 0.54412984
38 0.20124888 0.50444623
39 0.02807658 0.20124888
40 0.12915906 0.02807658
41 0.22000000 0.12915906
42 0.50298086 0.22000000
43 0.70276436 0.50298086
44 0.56552872 0.70276436
45 0.58980855 0.56552872
46 0.34639470 0.58980855
47 0.18850957 0.34639470
48 0.41648625 0.18850957
49 0.72043299 0.41648625
50 0.91831812 0.72043299
51 0.82915906 0.91831812
52 1.00043299 0.82915906
53 0.82108248 1.00043299
54 0.67533727 0.82108248
55 0.66129898 0.67533727
56 0.71024154 0.66129898
57 0.44617821 0.71024154
58 0.54639470 0.44617821
59 0.74704419 0.54639470
60 1.04629480 0.74704419
61 NA 1.04629480
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.32211487 -1.48243126
[2,] -1.25403829 -1.32211487
[3,] -0.92721059 -1.25403829
[4,] -0.86975846 -0.92721059
[5,] -0.89273931 -0.86975846
[6,] -0.99593666 -0.89273931
[7,] -1.05360530 -0.99593666
[8,] -1.10574521 -1.05360530
[9,] -0.94000000 -1.10574521
[10,] -0.99723564 -0.94000000
[11,] -1.08276436 -0.99723564
[12,] -0.71115733 -1.08276436
[13,] -0.52211487 -0.71115733
[14,] -0.46786008 -0.52211487
[15,] -0.60064949 -0.46786008
[16,] -0.65810163 -0.60064949
[17,] -0.72471283 -0.65810163
[18,] -0.68427983 -0.72471283
[19,] -0.69831812 -0.68427983
[20,] -0.60682770 -0.69831812
[21,] -0.51235641 -0.60682770
[22,] -0.39831812 -0.51235641
[23,] -0.28384684 -0.39831812
[24,] 0.18667770 -0.28384684
[25,] 0.61935051 0.18667770
[26,] 0.60233137 0.61935051
[27,] 0.67062444 0.60233137
[28,] 0.39826803 0.67062444
[29,] 0.57636966 0.39826803
[30,] 0.50189837 0.57636966
[31,] 0.38786008 0.50189837
[32,] 0.43680265 0.38786008
[33,] 0.41636966 0.43680265
[34,] 0.50276436 0.41636966
[35,] 0.43105743 0.50276436
[36,] 0.54412984 0.43105743
[37,] 0.50444623 0.54412984
[38,] 0.20124888 0.50444623
[39,] 0.02807658 0.20124888
[40,] 0.12915906 0.02807658
[41,] 0.22000000 0.12915906
[42,] 0.50298086 0.22000000
[43,] 0.70276436 0.50298086
[44,] 0.56552872 0.70276436
[45,] 0.58980855 0.56552872
[46,] 0.34639470 0.58980855
[47,] 0.18850957 0.34639470
[48,] 0.41648625 0.18850957
[49,] 0.72043299 0.41648625
[50,] 0.91831812 0.72043299
[51,] 0.82915906 0.91831812
[52,] 1.00043299 0.82915906
[53,] 0.82108248 1.00043299
[54,] 0.67533727 0.82108248
[55,] 0.66129898 0.67533727
[56,] 0.71024154 0.66129898
[57,] 0.44617821 0.71024154
[58,] 0.54639470 0.44617821
[59,] 0.74704419 0.54639470
[60,] 1.04629480 0.74704419
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.32211487 -1.48243126
2 -1.25403829 -1.32211487
3 -0.92721059 -1.25403829
4 -0.86975846 -0.92721059
5 -0.89273931 -0.86975846
6 -0.99593666 -0.89273931
7 -1.05360530 -0.99593666
8 -1.10574521 -1.05360530
9 -0.94000000 -1.10574521
10 -0.99723564 -0.94000000
11 -1.08276436 -0.99723564
12 -0.71115733 -1.08276436
13 -0.52211487 -0.71115733
14 -0.46786008 -0.52211487
15 -0.60064949 -0.46786008
16 -0.65810163 -0.60064949
17 -0.72471283 -0.65810163
18 -0.68427983 -0.72471283
19 -0.69831812 -0.68427983
20 -0.60682770 -0.69831812
21 -0.51235641 -0.60682770
22 -0.39831812 -0.51235641
23 -0.28384684 -0.39831812
24 0.18667770 -0.28384684
25 0.61935051 0.18667770
26 0.60233137 0.61935051
27 0.67062444 0.60233137
28 0.39826803 0.67062444
29 0.57636966 0.39826803
30 0.50189837 0.57636966
31 0.38786008 0.50189837
32 0.43680265 0.38786008
33 0.41636966 0.43680265
34 0.50276436 0.41636966
35 0.43105743 0.50276436
36 0.54412984 0.43105743
37 0.50444623 0.54412984
38 0.20124888 0.50444623
39 0.02807658 0.20124888
40 0.12915906 0.02807658
41 0.22000000 0.12915906
42 0.50298086 0.22000000
43 0.70276436 0.50298086
44 0.56552872 0.70276436
45 0.58980855 0.56552872
46 0.34639470 0.58980855
47 0.18850957 0.34639470
48 0.41648625 0.18850957
49 0.72043299 0.41648625
50 0.91831812 0.72043299
51 0.82915906 0.91831812
52 1.00043299 0.82915906
53 0.82108248 1.00043299
54 0.67533727 0.82108248
55 0.66129898 0.67533727
56 0.71024154 0.66129898
57 0.44617821 0.71024154
58 0.54639470 0.44617821
59 0.74704419 0.54639470
60 1.04629480 0.74704419
> 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/7zn4s1258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8al731258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93g6b1258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1041721258722060.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11drlo1258722060.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/12cf8n1258722060.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/13sci41258722060.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/14wlfw1258722060.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/15bafj1258722060.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/16afcj1258722060.tab")
+ }
>
> system("convert tmp/10hh71258722060.ps tmp/10hh71258722060.png")
> system("convert tmp/2dthh1258722060.ps tmp/2dthh1258722060.png")
> system("convert tmp/3jjpo1258722060.ps tmp/3jjpo1258722060.png")
> system("convert tmp/4mahb1258722060.ps tmp/4mahb1258722060.png")
> system("convert tmp/5qg7t1258722060.ps tmp/5qg7t1258722060.png")
> system("convert tmp/60kft1258722060.ps tmp/60kft1258722060.png")
> system("convert tmp/7zn4s1258722060.ps tmp/7zn4s1258722060.png")
> system("convert tmp/8al731258722060.ps tmp/8al731258722060.png")
> system("convert tmp/93g6b1258722060.ps tmp/93g6b1258722060.png")
> system("convert tmp/1041721258722060.ps tmp/1041721258722060.png")
>
>
> proc.time()
user system elapsed
2.415 1.554 3.388