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(103.91
+ ,89.00
+ ,103.88
+ ,103.77
+ ,103.66
+ ,103.64
+ ,103.63
+ ,103.91
+ ,86.40
+ ,103.91
+ ,103.88
+ ,103.77
+ ,103.66
+ ,103.64
+ ,103.92
+ ,84.50
+ ,103.91
+ ,103.91
+ ,103.88
+ ,103.77
+ ,103.66
+ ,104.05
+ ,82.70
+ ,103.92
+ ,103.91
+ ,103.91
+ ,103.88
+ ,103.77
+ ,104.23
+ ,80.80
+ ,104.05
+ ,103.92
+ ,103.91
+ ,103.91
+ ,103.88
+ ,104.30
+ ,81.80
+ ,104.23
+ ,104.05
+ ,103.92
+ ,103.91
+ ,103.91
+ ,104.31
+ ,81.80
+ ,104.30
+ ,104.23
+ ,104.05
+ ,103.92
+ ,103.91
+ ,104.31
+ ,82.90
+ ,104.31
+ ,104.30
+ ,104.23
+ ,104.05
+ ,103.92
+ ,104.34
+ ,83.80
+ ,104.31
+ ,104.31
+ ,104.30
+ ,104.23
+ ,104.05
+ ,104.55
+ ,86.20
+ ,104.34
+ ,104.31
+ ,104.31
+ ,104.30
+ ,104.23
+ ,104.65
+ ,86.10
+ ,104.55
+ ,104.34
+ ,104.31
+ ,104.31
+ ,104.30
+ ,104.73
+ ,86.20
+ ,104.65
+ ,104.55
+ ,104.34
+ ,104.31
+ ,104.31
+ ,104.75
+ ,88.80
+ ,104.73
+ ,104.65
+ ,104.55
+ ,104.34
+ ,104.31
+ ,104.75
+ ,89.60
+ ,104.75
+ ,104.73
+ ,104.65
+ ,104.55
+ ,104.34
+ ,104.76
+ ,87.80
+ ,104.75
+ ,104.75
+ ,104.73
+ ,104.65
+ ,104.55
+ ,104.94
+ ,88.30
+ ,104.76
+ ,104.75
+ ,104.75
+ ,104.73
+ ,104.65
+ ,105.29
+ ,88.60
+ ,104.94
+ ,104.76
+ ,104.75
+ ,104.75
+ ,104.73
+ ,105.38
+ ,91.00
+ ,105.29
+ ,104.94
+ ,104.76
+ ,104.75
+ ,104.75
+ ,105.43
+ ,91.50
+ ,105.38
+ ,105.29
+ ,104.94
+ ,104.76
+ ,104.75
+ ,105.43
+ ,95.40
+ ,105.43
+ ,105.38
+ ,105.29
+ ,104.94
+ ,104.76
+ ,105.42
+ ,98.70
+ ,105.43
+ ,105.43
+ ,105.38
+ ,105.29
+ ,104.94
+ ,105.52
+ ,99.90
+ ,105.42
+ ,105.43
+ ,105.43
+ ,105.38
+ ,105.29
+ ,105.69
+ ,98.60
+ ,105.52
+ ,105.42
+ ,105.43
+ ,105.43
+ ,105.38
+ ,105.72
+ ,100.30
+ ,105.69
+ ,105.52
+ ,105.42
+ ,105.43
+ ,105.43
+ ,105.74
+ ,100.20
+ ,105.72
+ ,105.69
+ ,105.52
+ ,105.42
+ ,105.43
+ ,105.74
+ ,100.40
+ ,105.74
+ ,105.72
+ ,105.69
+ ,105.52
+ ,105.42
+ ,105.74
+ ,101.40
+ ,105.74
+ ,105.74
+ ,105.72
+ ,105.69
+ ,105.52
+ ,105.95
+ ,103.00
+ ,105.74
+ ,105.74
+ ,105.74
+ ,105.72
+ ,105.69
+ ,106.17
+ ,109.10
+ ,105.95
+ ,105.74
+ ,105.74
+ ,105.74
+ ,105.72
+ ,106.34
+ ,111.40
+ ,106.17
+ ,105.95
+ ,105.74
+ ,105.74
+ ,105.74
+ ,106.37
+ ,114.10
+ ,106.34
+ ,106.17
+ ,105.95
+ ,105.74
+ ,105.74
+ ,106.37
+ ,121.80
+ ,106.37
+ ,106.34
+ ,106.17
+ ,105.95
+ ,105.74
+ ,106.36
+ ,127.60
+ ,106.37
+ ,106.37
+ ,106.34
+ ,106.17
+ ,105.95
+ ,106.44
+ ,129.90
+ ,106.36
+ ,106.37
+ ,106.37
+ ,106.34
+ ,106.17
+ ,106.29
+ ,128.00
+ ,106.44
+ ,106.36
+ ,106.37
+ ,106.37
+ ,106.34
+ ,106.23
+ ,123.50
+ ,106.29
+ ,106.44
+ ,106.36
+ ,106.37
+ ,106.37
+ ,106.23
+ ,124.00
+ ,106.23
+ ,106.29
+ ,106.44
+ ,106.36
+ ,106.37
+ ,106.23
+ ,127.40
+ ,106.23
+ ,106.23
+ ,106.29
+ ,106.44
+ ,106.36
+ ,106.23
+ ,127.60
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.29
+ ,106.44
+ ,106.34
+ ,128.40
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.29
+ ,106.44
+ ,131.40
+ ,106.34
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.44
+ ,135.10
+ ,106.44
+ ,106.34
+ ,106.23
+ ,106.23
+ ,106.23
+ ,106.48
+ ,134.00
+ ,106.44
+ ,106.44
+ ,106.34
+ ,106.23
+ ,106.23
+ ,106.50
+ ,144.50
+ ,106.48
+ ,106.44
+ ,106.44
+ ,106.34
+ ,106.23
+ ,106.57
+ ,147.30
+ ,106.50
+ ,106.48
+ ,106.44
+ ,106.44
+ ,106.34
+ ,106.40
+ ,150.90
+ ,106.57
+ ,106.50
+ ,106.48
+ ,106.44
+ ,106.44
+ ,106.37
+ ,148.70
+ ,106.40
+ ,106.57
+ ,106.50
+ ,106.48
+ ,106.44
+ ,106.25
+ ,141.40
+ ,106.37
+ ,106.40
+ ,106.57
+ ,106.50
+ ,106.48
+ ,106.21
+ ,138.90
+ ,106.25
+ ,106.37
+ ,106.40
+ ,106.57
+ ,106.50
+ ,106.21
+ ,139.80
+ ,106.21
+ ,106.25
+ ,106.37
+ ,106.40
+ ,106.57
+ ,106.24
+ ,145.60
+ ,106.21
+ ,106.21
+ ,106.25
+ ,106.37
+ ,106.40
+ ,106.19
+ ,147.90
+ ,106.24
+ ,106.21
+ ,106.21
+ ,106.25
+ ,106.37
+ ,106.08
+ ,148.50
+ ,106.19
+ ,106.24
+ ,106.21
+ ,106.21
+ ,106.25
+ ,106.13
+ ,151.10
+ ,106.08
+ ,106.19
+ ,106.24
+ ,106.21
+ ,106.21
+ ,106.09
+ ,157.50
+ ,106.13
+ ,106.08
+ ,106.19
+ ,106.24
+ ,106.21)
+ ,dim=c(7
+ ,55)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5')
+ ,1:55))
> y <- array(NA,dim=c(7,55),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5'),1:55))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 Y5 t
1 103.91 89.0 103.88 103.77 103.66 103.64 103.63 1
2 103.91 86.4 103.91 103.88 103.77 103.66 103.64 2
3 103.92 84.5 103.91 103.91 103.88 103.77 103.66 3
4 104.05 82.7 103.92 103.91 103.91 103.88 103.77 4
5 104.23 80.8 104.05 103.92 103.91 103.91 103.88 5
6 104.30 81.8 104.23 104.05 103.92 103.91 103.91 6
7 104.31 81.8 104.30 104.23 104.05 103.92 103.91 7
8 104.31 82.9 104.31 104.30 104.23 104.05 103.92 8
9 104.34 83.8 104.31 104.31 104.30 104.23 104.05 9
10 104.55 86.2 104.34 104.31 104.31 104.30 104.23 10
11 104.65 86.1 104.55 104.34 104.31 104.31 104.30 11
12 104.73 86.2 104.65 104.55 104.34 104.31 104.31 12
13 104.75 88.8 104.73 104.65 104.55 104.34 104.31 13
14 104.75 89.6 104.75 104.73 104.65 104.55 104.34 14
15 104.76 87.8 104.75 104.75 104.73 104.65 104.55 15
16 104.94 88.3 104.76 104.75 104.75 104.73 104.65 16
17 105.29 88.6 104.94 104.76 104.75 104.75 104.73 17
18 105.38 91.0 105.29 104.94 104.76 104.75 104.75 18
19 105.43 91.5 105.38 105.29 104.94 104.76 104.75 19
20 105.43 95.4 105.43 105.38 105.29 104.94 104.76 20
21 105.42 98.7 105.43 105.43 105.38 105.29 104.94 21
22 105.52 99.9 105.42 105.43 105.43 105.38 105.29 22
23 105.69 98.6 105.52 105.42 105.43 105.43 105.38 23
24 105.72 100.3 105.69 105.52 105.42 105.43 105.43 24
25 105.74 100.2 105.72 105.69 105.52 105.42 105.43 25
26 105.74 100.4 105.74 105.72 105.69 105.52 105.42 26
27 105.74 101.4 105.74 105.74 105.72 105.69 105.52 27
28 105.95 103.0 105.74 105.74 105.74 105.72 105.69 28
29 106.17 109.1 105.95 105.74 105.74 105.74 105.72 29
30 106.34 111.4 106.17 105.95 105.74 105.74 105.74 30
31 106.37 114.1 106.34 106.17 105.95 105.74 105.74 31
32 106.37 121.8 106.37 106.34 106.17 105.95 105.74 32
33 106.36 127.6 106.37 106.37 106.34 106.17 105.95 33
34 106.44 129.9 106.36 106.37 106.37 106.34 106.17 34
35 106.29 128.0 106.44 106.36 106.37 106.37 106.34 35
36 106.23 123.5 106.29 106.44 106.36 106.37 106.37 36
37 106.23 124.0 106.23 106.29 106.44 106.36 106.37 37
38 106.23 127.4 106.23 106.23 106.29 106.44 106.36 38
39 106.23 127.6 106.23 106.23 106.23 106.29 106.44 39
40 106.34 128.4 106.23 106.23 106.23 106.23 106.29 40
41 106.44 131.4 106.34 106.23 106.23 106.23 106.23 41
42 106.44 135.1 106.44 106.34 106.23 106.23 106.23 42
43 106.48 134.0 106.44 106.44 106.34 106.23 106.23 43
44 106.50 144.5 106.48 106.44 106.44 106.34 106.23 44
45 106.57 147.3 106.50 106.48 106.44 106.44 106.34 45
46 106.40 150.9 106.57 106.50 106.48 106.44 106.44 46
47 106.37 148.7 106.40 106.57 106.50 106.48 106.44 47
48 106.25 141.4 106.37 106.40 106.57 106.50 106.48 48
49 106.21 138.9 106.25 106.37 106.40 106.57 106.50 49
50 106.21 139.8 106.21 106.25 106.37 106.40 106.57 50
51 106.24 145.6 106.21 106.21 106.25 106.37 106.40 51
52 106.19 147.9 106.24 106.21 106.21 106.25 106.37 52
53 106.08 148.5 106.19 106.24 106.21 106.21 106.25 53
54 106.13 151.1 106.08 106.19 106.24 106.21 106.21 54
55 106.09 157.5 106.13 106.08 106.19 106.24 106.21 55
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-0.882022 -0.004521 1.287768 -0.416249 -0.111638 0.271637
Y5 t
-0.018862 0.003530
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.18390 -0.04620 -0.01479 0.05863 0.19599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.882022 4.049460 -0.218 0.8285
X -0.004521 0.001859 -2.432 0.0189 *
Y1 1.287768 0.138486 9.299 3.17e-12 ***
Y2 -0.416249 0.229841 -1.811 0.0765 .
Y3 -0.111638 0.243676 -0.458 0.6490
Y4 0.271637 0.236105 1.150 0.2558
Y5 -0.018862 0.149062 -0.127 0.8998
t 0.003530 0.003991 0.884 0.3809
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07895 on 47 degrees of freedom
Multiple R-squared: 0.9925, Adjusted R-squared: 0.9914
F-statistic: 887.4 on 7 and 47 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,] 0.0712659509 0.142531902 0.92873405
[2,] 0.0218894858 0.043778972 0.97811051
[3,] 0.0081467026 0.016293405 0.99185330
[4,] 0.0049330873 0.009866175 0.99506691
[5,] 0.0021666780 0.004333356 0.99783332
[6,] 0.0006359126 0.001271825 0.99936409
[7,] 0.1066322338 0.213264468 0.89336777
[8,] 0.0938331916 0.187666383 0.90616681
[9,] 0.2384143542 0.476828708 0.76158565
[10,] 0.1979306142 0.395861228 0.80206939
[11,] 0.1602051979 0.320410396 0.83979480
[12,] 0.1501414249 0.300282850 0.84985858
[13,] 0.1081637036 0.216327407 0.89183630
[14,] 0.1671819474 0.334363895 0.83281805
[15,] 0.1517987723 0.303597545 0.84820123
[16,] 0.1737674083 0.347534817 0.82623259
[17,] 0.3392357503 0.678471501 0.66076425
[18,] 0.2942318666 0.588463733 0.70576813
[19,] 0.2386372801 0.477274560 0.76136272
[20,] 0.2002142963 0.400428593 0.79978570
[21,] 0.1474724255 0.294944851 0.85252757
[22,] 0.1330162570 0.266032514 0.86698374
[23,] 0.1244495980 0.248899196 0.87555040
[24,] 0.0920529696 0.184105939 0.90794703
[25,] 0.5563518782 0.887296244 0.44364812
[26,] 0.6270494476 0.745901105 0.37295055
[27,] 0.5922743211 0.815451358 0.40772568
[28,] 0.6481576408 0.703684718 0.35184236
[29,] 0.6864296928 0.627140614 0.31357031
[30,] 0.6961571432 0.607685714 0.30384286
[31,] 0.6487543133 0.702491373 0.35124569
[32,] 0.8513944709 0.297211058 0.14860553
[33,] 0.7558498711 0.488300258 0.24415013
[34,] 0.9557873353 0.088425329 0.04421266
> postscript(file="/var/www/html/rcomp/tmp/1ufxy1259337308.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qqup1259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r0gd1259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kv2n1259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vlna1259337308.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 = 55
Frequency = 1
1 2 3 4 5 6
-0.013694877 -0.014789755 -0.021645341 0.059352541 0.057910471 -0.047101747
7 8 9 10 11 12
-0.044053985 -0.041380046 -0.045306305 0.118878754 -0.044443133 -0.005347643
13 14 15 16 17 18
-0.023223766 -0.060906214 -0.068521218 0.079719789 0.195986725 -0.080992361
19 20 21 22 23 24
0.014904951 -0.007550535 -0.066978172 0.035531302 0.051300093 -0.092012536
25 26 27 28 29 30
-0.029985085 -0.054252462 -0.085879109 0.125115194 0.093867105 0.075216797
31 32 33 34 35 36
0.009992702 0.040922757 0.029283160 0.090350311 -0.183896647 -0.041857885
37 38 39 40 41 42
-0.016651005 -0.068448762 -0.035518202 0.088037905 0.055285741 -0.014504732
43 44 45 46 47 48
0.070897024 0.064613851 0.109549257 -0.123171028 0.072777418 -0.112750952
49 50 51 52 53 54
-0.063155353 -0.016905774 0.010683965 -0.043514899 -0.068854084 0.112808086
55
-0.025692286
> postscript(file="/var/www/html/rcomp/tmp/6wihf1259337308.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 = 55
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.013694877 NA
1 -0.014789755 -0.013694877
2 -0.021645341 -0.014789755
3 0.059352541 -0.021645341
4 0.057910471 0.059352541
5 -0.047101747 0.057910471
6 -0.044053985 -0.047101747
7 -0.041380046 -0.044053985
8 -0.045306305 -0.041380046
9 0.118878754 -0.045306305
10 -0.044443133 0.118878754
11 -0.005347643 -0.044443133
12 -0.023223766 -0.005347643
13 -0.060906214 -0.023223766
14 -0.068521218 -0.060906214
15 0.079719789 -0.068521218
16 0.195986725 0.079719789
17 -0.080992361 0.195986725
18 0.014904951 -0.080992361
19 -0.007550535 0.014904951
20 -0.066978172 -0.007550535
21 0.035531302 -0.066978172
22 0.051300093 0.035531302
23 -0.092012536 0.051300093
24 -0.029985085 -0.092012536
25 -0.054252462 -0.029985085
26 -0.085879109 -0.054252462
27 0.125115194 -0.085879109
28 0.093867105 0.125115194
29 0.075216797 0.093867105
30 0.009992702 0.075216797
31 0.040922757 0.009992702
32 0.029283160 0.040922757
33 0.090350311 0.029283160
34 -0.183896647 0.090350311
35 -0.041857885 -0.183896647
36 -0.016651005 -0.041857885
37 -0.068448762 -0.016651005
38 -0.035518202 -0.068448762
39 0.088037905 -0.035518202
40 0.055285741 0.088037905
41 -0.014504732 0.055285741
42 0.070897024 -0.014504732
43 0.064613851 0.070897024
44 0.109549257 0.064613851
45 -0.123171028 0.109549257
46 0.072777418 -0.123171028
47 -0.112750952 0.072777418
48 -0.063155353 -0.112750952
49 -0.016905774 -0.063155353
50 0.010683965 -0.016905774
51 -0.043514899 0.010683965
52 -0.068854084 -0.043514899
53 0.112808086 -0.068854084
54 -0.025692286 0.112808086
55 NA -0.025692286
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.014789755 -0.013694877
[2,] -0.021645341 -0.014789755
[3,] 0.059352541 -0.021645341
[4,] 0.057910471 0.059352541
[5,] -0.047101747 0.057910471
[6,] -0.044053985 -0.047101747
[7,] -0.041380046 -0.044053985
[8,] -0.045306305 -0.041380046
[9,] 0.118878754 -0.045306305
[10,] -0.044443133 0.118878754
[11,] -0.005347643 -0.044443133
[12,] -0.023223766 -0.005347643
[13,] -0.060906214 -0.023223766
[14,] -0.068521218 -0.060906214
[15,] 0.079719789 -0.068521218
[16,] 0.195986725 0.079719789
[17,] -0.080992361 0.195986725
[18,] 0.014904951 -0.080992361
[19,] -0.007550535 0.014904951
[20,] -0.066978172 -0.007550535
[21,] 0.035531302 -0.066978172
[22,] 0.051300093 0.035531302
[23,] -0.092012536 0.051300093
[24,] -0.029985085 -0.092012536
[25,] -0.054252462 -0.029985085
[26,] -0.085879109 -0.054252462
[27,] 0.125115194 -0.085879109
[28,] 0.093867105 0.125115194
[29,] 0.075216797 0.093867105
[30,] 0.009992702 0.075216797
[31,] 0.040922757 0.009992702
[32,] 0.029283160 0.040922757
[33,] 0.090350311 0.029283160
[34,] -0.183896647 0.090350311
[35,] -0.041857885 -0.183896647
[36,] -0.016651005 -0.041857885
[37,] -0.068448762 -0.016651005
[38,] -0.035518202 -0.068448762
[39,] 0.088037905 -0.035518202
[40,] 0.055285741 0.088037905
[41,] -0.014504732 0.055285741
[42,] 0.070897024 -0.014504732
[43,] 0.064613851 0.070897024
[44,] 0.109549257 0.064613851
[45,] -0.123171028 0.109549257
[46,] 0.072777418 -0.123171028
[47,] -0.112750952 0.072777418
[48,] -0.063155353 -0.112750952
[49,] -0.016905774 -0.063155353
[50,] 0.010683965 -0.016905774
[51,] -0.043514899 0.010683965
[52,] -0.068854084 -0.043514899
[53,] 0.112808086 -0.068854084
[54,] -0.025692286 0.112808086
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.014789755 -0.013694877
2 -0.021645341 -0.014789755
3 0.059352541 -0.021645341
4 0.057910471 0.059352541
5 -0.047101747 0.057910471
6 -0.044053985 -0.047101747
7 -0.041380046 -0.044053985
8 -0.045306305 -0.041380046
9 0.118878754 -0.045306305
10 -0.044443133 0.118878754
11 -0.005347643 -0.044443133
12 -0.023223766 -0.005347643
13 -0.060906214 -0.023223766
14 -0.068521218 -0.060906214
15 0.079719789 -0.068521218
16 0.195986725 0.079719789
17 -0.080992361 0.195986725
18 0.014904951 -0.080992361
19 -0.007550535 0.014904951
20 -0.066978172 -0.007550535
21 0.035531302 -0.066978172
22 0.051300093 0.035531302
23 -0.092012536 0.051300093
24 -0.029985085 -0.092012536
25 -0.054252462 -0.029985085
26 -0.085879109 -0.054252462
27 0.125115194 -0.085879109
28 0.093867105 0.125115194
29 0.075216797 0.093867105
30 0.009992702 0.075216797
31 0.040922757 0.009992702
32 0.029283160 0.040922757
33 0.090350311 0.029283160
34 -0.183896647 0.090350311
35 -0.041857885 -0.183896647
36 -0.016651005 -0.041857885
37 -0.068448762 -0.016651005
38 -0.035518202 -0.068448762
39 0.088037905 -0.035518202
40 0.055285741 0.088037905
41 -0.014504732 0.055285741
42 0.070897024 -0.014504732
43 0.064613851 0.070897024
44 0.109549257 0.064613851
45 -0.123171028 0.109549257
46 0.072777418 -0.123171028
47 -0.112750952 0.072777418
48 -0.063155353 -0.112750952
49 -0.016905774 -0.063155353
50 0.010683965 -0.016905774
51 -0.043514899 0.010683965
52 -0.068854084 -0.043514899
53 0.112808086 -0.068854084
54 -0.025692286 0.112808086
> 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/76ww91259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/883pg1259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hh681259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lbrk1259337308.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rjli1259337308.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/12jul31259337308.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/13qhoq1259337308.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/144t6g1259337308.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/15u00e1259337308.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/16rltd1259337308.tab")
+ }
>
> system("convert tmp/1ufxy1259337308.ps tmp/1ufxy1259337308.png")
> system("convert tmp/2qqup1259337308.ps tmp/2qqup1259337308.png")
> system("convert tmp/3r0gd1259337308.ps tmp/3r0gd1259337308.png")
> system("convert tmp/4kv2n1259337308.ps tmp/4kv2n1259337308.png")
> system("convert tmp/5vlna1259337308.ps tmp/5vlna1259337308.png")
> system("convert tmp/6wihf1259337308.ps tmp/6wihf1259337308.png")
> system("convert tmp/76ww91259337308.ps tmp/76ww91259337308.png")
> system("convert tmp/883pg1259337308.ps tmp/883pg1259337308.png")
> system("convert tmp/9hh681259337308.ps tmp/9hh681259337308.png")
> system("convert tmp/10lbrk1259337308.ps tmp/10lbrk1259337308.png")
>
>
> proc.time()
user system elapsed
2.413 1.552 3.846