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(8.1,1.3,7.7,1.3,7.5,1.2,7.6,1.1,7.8,1.4,7.8,1.2,7.8,1.5,7.5,1.1,7.5,1.3,7.1,1.5,7.5,1.1,7.5,1.4,7.6,1.3,7.7,1.5,7.7,1.6,7.9,1.7,8.1,1.1,8.2,1.6,8.2,1.3,8.2,1.7,7.9,1.6,7.3,1.7,6.9,1.9,6.6,1.8,6.7,1.9,6.9,1.6,7.0,1.5,7.1,1.6,7.2,1.6,7.1,1.7,6.9,2.0,7.0,2.0,6.8,1.9,6.4,1.7,6.7,1.8,6.6,1.9,6.4,1.7,6.3,2.0,6.2,2.1,6.5,2.4,6.8,2.5,6.8,2.5,6.4,2.6,6.1,2.2,5.8,2.5,6.1,2.8,7.2,2.8,7.3,2.9,6.9,3.0,6.1,3.1,5.8,2.9,6.2,2.7,7.1,2.2,7.7,2.5,7.9,2.3,7.7,2.6,7.4,2.3,7.5,2.2,8.0,1.8,8.1,1.8),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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.1 1.3 1 0 0 0 0 0 0 0 0 0 0
2 7.7 1.3 0 1 0 0 0 0 0 0 0 0 0
3 7.5 1.2 0 0 1 0 0 0 0 0 0 0 0
4 7.6 1.1 0 0 0 1 0 0 0 0 0 0 0
5 7.8 1.4 0 0 0 0 1 0 0 0 0 0 0
6 7.8 1.2 0 0 0 0 0 1 0 0 0 0 0
7 7.8 1.5 0 0 0 0 0 0 1 0 0 0 0
8 7.5 1.1 0 0 0 0 0 0 0 1 0 0 0
9 7.5 1.3 0 0 0 0 0 0 0 0 1 0 0
10 7.1 1.5 0 0 0 0 0 0 0 0 0 1 0
11 7.5 1.1 0 0 0 0 0 0 0 0 0 0 1
12 7.5 1.4 0 0 0 0 0 0 0 0 0 0 0
13 7.6 1.3 1 0 0 0 0 0 0 0 0 0 0
14 7.7 1.5 0 1 0 0 0 0 0 0 0 0 0
15 7.7 1.6 0 0 1 0 0 0 0 0 0 0 0
16 7.9 1.7 0 0 0 1 0 0 0 0 0 0 0
17 8.1 1.1 0 0 0 0 1 0 0 0 0 0 0
18 8.2 1.6 0 0 0 0 0 1 0 0 0 0 0
19 8.2 1.3 0 0 0 0 0 0 1 0 0 0 0
20 8.2 1.7 0 0 0 0 0 0 0 1 0 0 0
21 7.9 1.6 0 0 0 0 0 0 0 0 1 0 0
22 7.3 1.7 0 0 0 0 0 0 0 0 0 1 0
23 6.9 1.9 0 0 0 0 0 0 0 0 0 0 1
24 6.6 1.8 0 0 0 0 0 0 0 0 0 0 0
25 6.7 1.9 1 0 0 0 0 0 0 0 0 0 0
26 6.9 1.6 0 1 0 0 0 0 0 0 0 0 0
27 7.0 1.5 0 0 1 0 0 0 0 0 0 0 0
28 7.1 1.6 0 0 0 1 0 0 0 0 0 0 0
29 7.2 1.6 0 0 0 0 1 0 0 0 0 0 0
30 7.1 1.7 0 0 0 0 0 1 0 0 0 0 0
31 6.9 2.0 0 0 0 0 0 0 1 0 0 0 0
32 7.0 2.0 0 0 0 0 0 0 0 1 0 0 0
33 6.8 1.9 0 0 0 0 0 0 0 0 1 0 0
34 6.4 1.7 0 0 0 0 0 0 0 0 0 1 0
35 6.7 1.8 0 0 0 0 0 0 0 0 0 0 1
36 6.6 1.9 0 0 0 0 0 0 0 0 0 0 0
37 6.4 1.7 1 0 0 0 0 0 0 0 0 0 0
38 6.3 2.0 0 1 0 0 0 0 0 0 0 0 0
39 6.2 2.1 0 0 1 0 0 0 0 0 0 0 0
40 6.5 2.4 0 0 0 1 0 0 0 0 0 0 0
41 6.8 2.5 0 0 0 0 1 0 0 0 0 0 0
42 6.8 2.5 0 0 0 0 0 1 0 0 0 0 0
43 6.4 2.6 0 0 0 0 0 0 1 0 0 0 0
44 6.1 2.2 0 0 0 0 0 0 0 1 0 0 0
45 5.8 2.5 0 0 0 0 0 0 0 0 1 0 0
46 6.1 2.8 0 0 0 0 0 0 0 0 0 1 0
47 7.2 2.8 0 0 0 0 0 0 0 0 0 0 1
48 7.3 2.9 0 0 0 0 0 0 0 0 0 0 0
49 6.9 3.0 1 0 0 0 0 0 0 0 0 0 0
50 6.1 3.1 0 1 0 0 0 0 0 0 0 0 0
51 5.8 2.9 0 0 1 0 0 0 0 0 0 0 0
52 6.2 2.7 0 0 0 1 0 0 0 0 0 0 0
53 7.1 2.2 0 0 0 0 1 0 0 0 0 0 0
54 7.7 2.5 0 0 0 0 0 1 0 0 0 0 0
55 7.9 2.3 0 0 0 0 0 0 1 0 0 0 0
56 7.7 2.6 0 0 0 0 0 0 0 1 0 0 0
57 7.4 2.3 0 0 0 0 0 0 0 0 1 0 0
58 7.5 2.2 0 0 0 0 0 0 0 0 0 1 0
59 8.0 1.8 0 0 0 0 0 0 0 0 0 0 1
60 8.1 1.8 0 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) X M1 M2 M3 M4
8.55716 -0.68223 -0.16187 -0.32093 -0.44822 -0.20093
M5 M6 M7 M8 M9 M10
0.04355 0.25907 0.20636 0.05271 -0.16729 -0.32636
M11
-0.01458
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.00898 -0.33445 -0.09038 0.50318 0.86391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.55716 0.36560 23.406 < 2e-16 ***
X -0.68223 0.13520 -5.046 7.2e-06 ***
M1 -0.16187 0.35656 -0.454 0.652
M2 -0.32093 0.35629 -0.901 0.372
M3 -0.44822 0.35645 -1.257 0.215
M4 -0.20093 0.35629 -0.564 0.575
M5 0.04355 0.35722 0.122 0.903
M6 0.25907 0.35629 0.727 0.471
M7 0.20636 0.35621 0.579 0.565
M8 0.05271 0.35624 0.148 0.883
M9 -0.16729 0.35624 -0.470 0.641
M10 -0.32636 0.35621 -0.916 0.364
M11 -0.01458 0.35636 -0.041 0.968
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5632 on 47 degrees of freedom
Multiple R-squared: 0.4199, Adjusted R-squared: 0.2718
F-statistic: 2.835 on 12 and 47 DF, p-value: 0.005295
> 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,] 0.048514298 0.097028597 0.9514857
[2,] 0.033087344 0.066174687 0.9669127
[3,] 0.015234251 0.030468503 0.9847657
[4,] 0.013418802 0.026837603 0.9865812
[5,] 0.011764100 0.023528201 0.9882359
[6,] 0.006800123 0.013600247 0.9931999
[7,] 0.002705875 0.005411750 0.9972941
[8,] 0.011674151 0.023348302 0.9883258
[9,] 0.030905412 0.061810824 0.9690946
[10,] 0.076945313 0.153890627 0.9230547
[11,] 0.082181164 0.164362327 0.9178188
[12,] 0.079217672 0.158435344 0.9207823
[13,] 0.071315219 0.142630438 0.9286848
[14,] 0.056935464 0.113870927 0.9430645
[15,] 0.052465412 0.104930823 0.9475346
[16,] 0.044180117 0.088360235 0.9558199
[17,] 0.027584483 0.055168966 0.9724155
[18,] 0.018469497 0.036938993 0.9815305
[19,] 0.016940263 0.033880526 0.9830597
[20,] 0.015539400 0.031078800 0.9844606
[21,] 0.020323967 0.040647935 0.9796760
[22,] 0.038034447 0.076068893 0.9619656
[23,] 0.031094701 0.062189403 0.9689053
[24,] 0.019202576 0.038405151 0.9807974
[25,] 0.009542214 0.019084427 0.9904578
[26,] 0.004981480 0.009962960 0.9950185
[27,] 0.003631844 0.007263688 0.9963682
[28,] 0.004457728 0.008915455 0.9955423
[29,] 0.081652540 0.163305081 0.9183475
> postscript(file="/var/www/html/rcomp/tmp/16wye1259253364.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ke7y1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3l9pb1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4crh21259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h1ut1259253364.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
0.5915975104 0.3506639004 0.2097302905 -0.0057814661 0.1543983402
6 7 8 9 10
-0.1975587828 0.0598201936 -0.3594260028 -0.0029806362 -0.1074688797
11 12 13 14 15
-0.2921369295 -0.1020470263 0.0915975104 0.4871092669 0.6826210235
16 17 18 19 20
0.7035546335 0.2497302905 0.4753319502 0.3233748271 0.7499100968
21 22 23 24 25
0.6016874136 0.2289764869 -0.3463554633 -0.7291562932 -0.3990663900
26 27 28 29 30
-0.2446680498 -0.0856016598 -0.1646680498 -0.3091562932 -0.5564453665
31 32 33 34 35
-0.4990663900 -0.2454218534 -0.2936445367 -0.6710235131 -0.6145781466
36 37 38 39 40
-0.6609336100 -0.8355117566 -0.5717773167 -0.4762655602 -0.2188865837
41 42 43 44 45
-0.0951521438 -0.3106639004 -0.5897302905 -1.0089764869 -0.8843084371
46 47 48 49 50
-0.2205739972 0.5676486860 0.7212932227 0.5513831259 -0.0213278008
51 52 53 54 55
-0.3304840941 -0.3142185339 0.0001798064 0.5893360996 0.7056016598
56 57 58 59 60
0.8639142462 0.5792461964 0.7700899032 0.6854218534 0.7708437068
> postscript(file="/var/www/html/rcomp/tmp/6f4sz1259253364.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 0.5915975104 NA
1 0.3506639004 0.5915975104
2 0.2097302905 0.3506639004
3 -0.0057814661 0.2097302905
4 0.1543983402 -0.0057814661
5 -0.1975587828 0.1543983402
6 0.0598201936 -0.1975587828
7 -0.3594260028 0.0598201936
8 -0.0029806362 -0.3594260028
9 -0.1074688797 -0.0029806362
10 -0.2921369295 -0.1074688797
11 -0.1020470263 -0.2921369295
12 0.0915975104 -0.1020470263
13 0.4871092669 0.0915975104
14 0.6826210235 0.4871092669
15 0.7035546335 0.6826210235
16 0.2497302905 0.7035546335
17 0.4753319502 0.2497302905
18 0.3233748271 0.4753319502
19 0.7499100968 0.3233748271
20 0.6016874136 0.7499100968
21 0.2289764869 0.6016874136
22 -0.3463554633 0.2289764869
23 -0.7291562932 -0.3463554633
24 -0.3990663900 -0.7291562932
25 -0.2446680498 -0.3990663900
26 -0.0856016598 -0.2446680498
27 -0.1646680498 -0.0856016598
28 -0.3091562932 -0.1646680498
29 -0.5564453665 -0.3091562932
30 -0.4990663900 -0.5564453665
31 -0.2454218534 -0.4990663900
32 -0.2936445367 -0.2454218534
33 -0.6710235131 -0.2936445367
34 -0.6145781466 -0.6710235131
35 -0.6609336100 -0.6145781466
36 -0.8355117566 -0.6609336100
37 -0.5717773167 -0.8355117566
38 -0.4762655602 -0.5717773167
39 -0.2188865837 -0.4762655602
40 -0.0951521438 -0.2188865837
41 -0.3106639004 -0.0951521438
42 -0.5897302905 -0.3106639004
43 -1.0089764869 -0.5897302905
44 -0.8843084371 -1.0089764869
45 -0.2205739972 -0.8843084371
46 0.5676486860 -0.2205739972
47 0.7212932227 0.5676486860
48 0.5513831259 0.7212932227
49 -0.0213278008 0.5513831259
50 -0.3304840941 -0.0213278008
51 -0.3142185339 -0.3304840941
52 0.0001798064 -0.3142185339
53 0.5893360996 0.0001798064
54 0.7056016598 0.5893360996
55 0.8639142462 0.7056016598
56 0.5792461964 0.8639142462
57 0.7700899032 0.5792461964
58 0.6854218534 0.7700899032
59 0.7708437068 0.6854218534
60 NA 0.7708437068
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.3506639004 0.5915975104
[2,] 0.2097302905 0.3506639004
[3,] -0.0057814661 0.2097302905
[4,] 0.1543983402 -0.0057814661
[5,] -0.1975587828 0.1543983402
[6,] 0.0598201936 -0.1975587828
[7,] -0.3594260028 0.0598201936
[8,] -0.0029806362 -0.3594260028
[9,] -0.1074688797 -0.0029806362
[10,] -0.2921369295 -0.1074688797
[11,] -0.1020470263 -0.2921369295
[12,] 0.0915975104 -0.1020470263
[13,] 0.4871092669 0.0915975104
[14,] 0.6826210235 0.4871092669
[15,] 0.7035546335 0.6826210235
[16,] 0.2497302905 0.7035546335
[17,] 0.4753319502 0.2497302905
[18,] 0.3233748271 0.4753319502
[19,] 0.7499100968 0.3233748271
[20,] 0.6016874136 0.7499100968
[21,] 0.2289764869 0.6016874136
[22,] -0.3463554633 0.2289764869
[23,] -0.7291562932 -0.3463554633
[24,] -0.3990663900 -0.7291562932
[25,] -0.2446680498 -0.3990663900
[26,] -0.0856016598 -0.2446680498
[27,] -0.1646680498 -0.0856016598
[28,] -0.3091562932 -0.1646680498
[29,] -0.5564453665 -0.3091562932
[30,] -0.4990663900 -0.5564453665
[31,] -0.2454218534 -0.4990663900
[32,] -0.2936445367 -0.2454218534
[33,] -0.6710235131 -0.2936445367
[34,] -0.6145781466 -0.6710235131
[35,] -0.6609336100 -0.6145781466
[36,] -0.8355117566 -0.6609336100
[37,] -0.5717773167 -0.8355117566
[38,] -0.4762655602 -0.5717773167
[39,] -0.2188865837 -0.4762655602
[40,] -0.0951521438 -0.2188865837
[41,] -0.3106639004 -0.0951521438
[42,] -0.5897302905 -0.3106639004
[43,] -1.0089764869 -0.5897302905
[44,] -0.8843084371 -1.0089764869
[45,] -0.2205739972 -0.8843084371
[46,] 0.5676486860 -0.2205739972
[47,] 0.7212932227 0.5676486860
[48,] 0.5513831259 0.7212932227
[49,] -0.0213278008 0.5513831259
[50,] -0.3304840941 -0.0213278008
[51,] -0.3142185339 -0.3304840941
[52,] 0.0001798064 -0.3142185339
[53,] 0.5893360996 0.0001798064
[54,] 0.7056016598 0.5893360996
[55,] 0.8639142462 0.7056016598
[56,] 0.5792461964 0.8639142462
[57,] 0.7700899032 0.5792461964
[58,] 0.6854218534 0.7700899032
[59,] 0.7708437068 0.6854218534
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.3506639004 0.5915975104
2 0.2097302905 0.3506639004
3 -0.0057814661 0.2097302905
4 0.1543983402 -0.0057814661
5 -0.1975587828 0.1543983402
6 0.0598201936 -0.1975587828
7 -0.3594260028 0.0598201936
8 -0.0029806362 -0.3594260028
9 -0.1074688797 -0.0029806362
10 -0.2921369295 -0.1074688797
11 -0.1020470263 -0.2921369295
12 0.0915975104 -0.1020470263
13 0.4871092669 0.0915975104
14 0.6826210235 0.4871092669
15 0.7035546335 0.6826210235
16 0.2497302905 0.7035546335
17 0.4753319502 0.2497302905
18 0.3233748271 0.4753319502
19 0.7499100968 0.3233748271
20 0.6016874136 0.7499100968
21 0.2289764869 0.6016874136
22 -0.3463554633 0.2289764869
23 -0.7291562932 -0.3463554633
24 -0.3990663900 -0.7291562932
25 -0.2446680498 -0.3990663900
26 -0.0856016598 -0.2446680498
27 -0.1646680498 -0.0856016598
28 -0.3091562932 -0.1646680498
29 -0.5564453665 -0.3091562932
30 -0.4990663900 -0.5564453665
31 -0.2454218534 -0.4990663900
32 -0.2936445367 -0.2454218534
33 -0.6710235131 -0.2936445367
34 -0.6145781466 -0.6710235131
35 -0.6609336100 -0.6145781466
36 -0.8355117566 -0.6609336100
37 -0.5717773167 -0.8355117566
38 -0.4762655602 -0.5717773167
39 -0.2188865837 -0.4762655602
40 -0.0951521438 -0.2188865837
41 -0.3106639004 -0.0951521438
42 -0.5897302905 -0.3106639004
43 -1.0089764869 -0.5897302905
44 -0.8843084371 -1.0089764869
45 -0.2205739972 -0.8843084371
46 0.5676486860 -0.2205739972
47 0.7212932227 0.5676486860
48 0.5513831259 0.7212932227
49 -0.0213278008 0.5513831259
50 -0.3304840941 -0.0213278008
51 -0.3142185339 -0.3304840941
52 0.0001798064 -0.3142185339
53 0.5893360996 0.0001798064
54 0.7056016598 0.5893360996
55 0.8639142462 0.7056016598
56 0.5792461964 0.8639142462
57 0.7700899032 0.5792461964
58 0.6854218534 0.7700899032
59 0.7708437068 0.6854218534
> 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/7md3t1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84dhi1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dsgs1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103v9h1259253364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116p3i1259253364.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/12xpra1259253364.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/13c2a71259253365.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/14maj41259253365.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/15mrcj1259253365.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/162vyg1259253365.tab")
+ }
>
> system("convert tmp/16wye1259253364.ps tmp/16wye1259253364.png")
> system("convert tmp/2ke7y1259253364.ps tmp/2ke7y1259253364.png")
> system("convert tmp/3l9pb1259253364.ps tmp/3l9pb1259253364.png")
> system("convert tmp/4crh21259253364.ps tmp/4crh21259253364.png")
> system("convert tmp/5h1ut1259253364.ps tmp/5h1ut1259253364.png")
> system("convert tmp/6f4sz1259253364.ps tmp/6f4sz1259253364.png")
> system("convert tmp/7md3t1259253364.ps tmp/7md3t1259253364.png")
> system("convert tmp/84dhi1259253364.ps tmp/84dhi1259253364.png")
> system("convert tmp/9dsgs1259253364.ps tmp/9dsgs1259253364.png")
> system("convert tmp/103v9h1259253364.ps tmp/103v9h1259253364.png")
>
>
> proc.time()
user system elapsed
2.407 1.551 2.802