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(8.9,1.9,9,1.6,9,1.7,9,2,9,2.5,9,2.4,9,2.3,9,2.3,9,2.1,9,2.4,9,2.2,9.1,2.4,9,1.9,9,2.1,9.1,2.1,9,2.1,9,2,9,2.1,9,2.2,8.9,2.2,8.9,2.6,8.9,2.5,8.9,2.3,8.8,2.2,8.8,2.4,8.7,2.3,8.7,2.2,8.5,2.5,8.5,2.5,8.4,2.5,8.2,2.4,8.2,2.3,8.1,1.7,8.1,1.6,8,1.9,7.9,1.9,7.8,1.8,7.7,1.8,7.6,1.9,7.5,1.9,7.5,1.9,7.5,1.9,7.5,1.8,7.5,1.7,7.4,2.1,7.4,2.6,7.3,3.1,7.3,3.1,7.3,3.2,7.2,3.3,7.2,3.6,7.3,3.3,7.4,3.7,7.4,4,7.5,4,7.6,3.8,7.7,3.6,7.9,3.2,8,2.1,8.2,1.6),dim=c(2,60),dimnames=list(c('werkl','infl'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werkl','infl'),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
werkl infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.9 1.9 1 0 0 0 0 0 0 0 0 0 0
2 9.0 1.6 0 1 0 0 0 0 0 0 0 0 0
3 9.0 1.7 0 0 1 0 0 0 0 0 0 0 0
4 9.0 2.0 0 0 0 1 0 0 0 0 0 0 0
5 9.0 2.5 0 0 0 0 1 0 0 0 0 0 0
6 9.0 2.4 0 0 0 0 0 1 0 0 0 0 0
7 9.0 2.3 0 0 0 0 0 0 1 0 0 0 0
8 9.0 2.3 0 0 0 0 0 0 0 1 0 0 0
9 9.0 2.1 0 0 0 0 0 0 0 0 1 0 0
10 9.0 2.4 0 0 0 0 0 0 0 0 0 1 0
11 9.0 2.2 0 0 0 0 0 0 0 0 0 0 1
12 9.1 2.4 0 0 0 0 0 0 0 0 0 0 0
13 9.0 1.9 1 0 0 0 0 0 0 0 0 0 0
14 9.0 2.1 0 1 0 0 0 0 0 0 0 0 0
15 9.1 2.1 0 0 1 0 0 0 0 0 0 0 0
16 9.0 2.1 0 0 0 1 0 0 0 0 0 0 0
17 9.0 2.0 0 0 0 0 1 0 0 0 0 0 0
18 9.0 2.1 0 0 0 0 0 1 0 0 0 0 0
19 9.0 2.2 0 0 0 0 0 0 1 0 0 0 0
20 8.9 2.2 0 0 0 0 0 0 0 1 0 0 0
21 8.9 2.6 0 0 0 0 0 0 0 0 1 0 0
22 8.9 2.5 0 0 0 0 0 0 0 0 0 1 0
23 8.9 2.3 0 0 0 0 0 0 0 0 0 0 1
24 8.8 2.2 0 0 0 0 0 0 0 0 0 0 0
25 8.8 2.4 1 0 0 0 0 0 0 0 0 0 0
26 8.7 2.3 0 1 0 0 0 0 0 0 0 0 0
27 8.7 2.2 0 0 1 0 0 0 0 0 0 0 0
28 8.5 2.5 0 0 0 1 0 0 0 0 0 0 0
29 8.5 2.5 0 0 0 0 1 0 0 0 0 0 0
30 8.4 2.5 0 0 0 0 0 1 0 0 0 0 0
31 8.2 2.4 0 0 0 0 0 0 1 0 0 0 0
32 8.2 2.3 0 0 0 0 0 0 0 1 0 0 0
33 8.1 1.7 0 0 0 0 0 0 0 0 1 0 0
34 8.1 1.6 0 0 0 0 0 0 0 0 0 1 0
35 8.0 1.9 0 0 0 0 0 0 0 0 0 0 1
36 7.9 1.9 0 0 0 0 0 0 0 0 0 0 0
37 7.8 1.8 1 0 0 0 0 0 0 0 0 0 0
38 7.7 1.8 0 1 0 0 0 0 0 0 0 0 0
39 7.6 1.9 0 0 1 0 0 0 0 0 0 0 0
40 7.5 1.9 0 0 0 1 0 0 0 0 0 0 0
41 7.5 1.9 0 0 0 0 1 0 0 0 0 0 0
42 7.5 1.9 0 0 0 0 0 1 0 0 0 0 0
43 7.5 1.8 0 0 0 0 0 0 1 0 0 0 0
44 7.5 1.7 0 0 0 0 0 0 0 1 0 0 0
45 7.4 2.1 0 0 0 0 0 0 0 0 1 0 0
46 7.4 2.6 0 0 0 0 0 0 0 0 0 1 0
47 7.3 3.1 0 0 0 0 0 0 0 0 0 0 1
48 7.3 3.1 0 0 0 0 0 0 0 0 0 0 0
49 7.3 3.2 1 0 0 0 0 0 0 0 0 0 0
50 7.2 3.3 0 1 0 0 0 0 0 0 0 0 0
51 7.2 3.6 0 0 1 0 0 0 0 0 0 0 0
52 7.3 3.3 0 0 0 1 0 0 0 0 0 0 0
53 7.4 3.7 0 0 0 0 1 0 0 0 0 0 0
54 7.4 4.0 0 0 0 0 0 1 0 0 0 0 0
55 7.5 4.0 0 0 0 0 0 0 1 0 0 0 0
56 7.6 3.8 0 0 0 0 0 0 0 1 0 0 0
57 7.7 3.6 0 0 0 0 0 0 0 0 1 0 0
58 7.9 3.2 0 0 0 0 0 0 0 0 0 1 0
59 8.0 2.1 0 0 0 0 0 0 0 0 0 0 1
60 8.2 1.6 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) infl M1 M2 M3 M4
9.34448 -0.48414 0.10000 0.05032 0.08905 0.05810
M5 M6 M7 M8 M9 M10
0.15556 0.16461 0.12524 0.08651 0.04715 0.10651
M11
0.01873
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.10795 -0.52937 0.07628 0.57313 0.91746
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.34448 0.45011 20.760 < 2e-16 ***
infl -0.48414 0.14597 -3.317 0.00176 **
M1 0.10000 0.43745 0.229 0.82017
M2 0.05032 0.43746 0.115 0.90892
M3 0.08905 0.43754 0.204 0.83961
M4 0.05810 0.43780 0.133 0.89500
M5 0.15556 0.43936 0.354 0.72488
M6 0.16461 0.44026 0.374 0.71017
M7 0.12524 0.43964 0.285 0.77699
M8 0.08651 0.43863 0.197 0.84450
M9 0.04715 0.43824 0.108 0.91479
M10 0.10651 0.43863 0.243 0.80920
M11 0.01873 0.43761 0.043 0.96604
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6917 on 47 degrees of freedom
Multiple R-squared: 0.1924, Adjusted R-squared: -0.01381
F-statistic: 0.933 on 12 and 47 DF, p-value: 0.5231
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 6.979900e-04 1.395980e-03 0.99930201
[2,] 5.785643e-05 1.157129e-04 0.99994214
[3,] 4.239151e-06 8.478302e-06 0.99999576
[4,] 3.081907e-07 6.163814e-07 0.99999969
[5,] 8.795204e-08 1.759041e-07 0.99999991
[6,] 4.273694e-08 8.547388e-08 0.99999996
[7,] 1.401847e-08 2.803694e-08 0.99999999
[8,] 5.149199e-09 1.029840e-08 0.99999999
[9,] 1.162469e-07 2.324937e-07 0.99999988
[10,] 1.231304e-07 2.462607e-07 0.99999988
[11,] 7.728380e-07 1.545676e-06 0.99999923
[12,] 7.198395e-06 1.439679e-05 0.99999280
[13,] 1.214038e-04 2.428077e-04 0.99987860
[14,] 1.586874e-03 3.173749e-03 0.99841313
[15,] 1.812160e-02 3.624320e-02 0.98187840
[16,] 1.448984e-01 2.897968e-01 0.85510159
[17,] 3.845416e-01 7.690833e-01 0.61545837
[18,] 7.208263e-01 5.583474e-01 0.27917370
[19,] 7.998586e-01 4.002828e-01 0.20014141
[20,] 8.437290e-01 3.125421e-01 0.15627104
[21,] 8.545084e-01 2.909832e-01 0.14549160
[22,] 8.831221e-01 2.337558e-01 0.11687789
[23,] 9.046815e-01 1.906370e-01 0.09531852
[24,] 9.139927e-01 1.720147e-01 0.08600734
[25,] 8.919796e-01 2.160408e-01 0.10802040
[26,] 8.438831e-01 3.122337e-01 0.15611685
[27,] 7.660153e-01 4.679693e-01 0.23398466
[28,] 6.542188e-01 6.915624e-01 0.34578118
[29,] 5.429558e-01 9.140883e-01 0.45704416
> postscript(file="/var/www/html/rcomp/tmp/10umx1259344485.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2aa3f1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3l1rn1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kh3m1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pxmd1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.375391056 0.379830750 0.389513629 0.565708177 0.710317121 0.652854089
7 8 9 10 11 12
0.643805452 0.682536968 0.625073936 0.710951363 0.701902726 0.917463032
13 14 15 16 17 18
0.475391056 0.621902726 0.683171210 0.614122573 0.468245145 0.507610903
19 20 21 22 23 24
0.595391056 0.534122573 0.767145911 0.659365758 0.650317121 0.520634242
25 26 27 28 29 30
0.517463032 0.418731516 0.331585605 0.307780153 0.210317121 0.101268484
31 32 33 34 35 36
-0.107780153 -0.117463032 -0.468583645 -0.576363798 -0.443340460 -0.524608944
37 38 39 40 41 42
-0.773023339 -0.823340460 -0.913657581 -0.982706218 -1.080169250 -1.089217887
43 44 45 46 47 48
-1.098266524 -1.107949403 -0.974926064 -0.792219847 -0.562367718 -0.543636202
49 50 51 52 53 54
-0.595221807 -0.597124532 -0.490612863 -0.504904686 -0.308710137 -0.172515589
55 56 57 58 59 60
-0.033149831 0.008752895 0.051289863 -0.001733476 -0.346511669 -0.369852129
> postscript(file="/var/www/html/rcomp/tmp/6ns871259344485.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 0.375391056 NA
1 0.379830750 0.375391056
2 0.389513629 0.379830750
3 0.565708177 0.389513629
4 0.710317121 0.565708177
5 0.652854089 0.710317121
6 0.643805452 0.652854089
7 0.682536968 0.643805452
8 0.625073936 0.682536968
9 0.710951363 0.625073936
10 0.701902726 0.710951363
11 0.917463032 0.701902726
12 0.475391056 0.917463032
13 0.621902726 0.475391056
14 0.683171210 0.621902726
15 0.614122573 0.683171210
16 0.468245145 0.614122573
17 0.507610903 0.468245145
18 0.595391056 0.507610903
19 0.534122573 0.595391056
20 0.767145911 0.534122573
21 0.659365758 0.767145911
22 0.650317121 0.659365758
23 0.520634242 0.650317121
24 0.517463032 0.520634242
25 0.418731516 0.517463032
26 0.331585605 0.418731516
27 0.307780153 0.331585605
28 0.210317121 0.307780153
29 0.101268484 0.210317121
30 -0.107780153 0.101268484
31 -0.117463032 -0.107780153
32 -0.468583645 -0.117463032
33 -0.576363798 -0.468583645
34 -0.443340460 -0.576363798
35 -0.524608944 -0.443340460
36 -0.773023339 -0.524608944
37 -0.823340460 -0.773023339
38 -0.913657581 -0.823340460
39 -0.982706218 -0.913657581
40 -1.080169250 -0.982706218
41 -1.089217887 -1.080169250
42 -1.098266524 -1.089217887
43 -1.107949403 -1.098266524
44 -0.974926064 -1.107949403
45 -0.792219847 -0.974926064
46 -0.562367718 -0.792219847
47 -0.543636202 -0.562367718
48 -0.595221807 -0.543636202
49 -0.597124532 -0.595221807
50 -0.490612863 -0.597124532
51 -0.504904686 -0.490612863
52 -0.308710137 -0.504904686
53 -0.172515589 -0.308710137
54 -0.033149831 -0.172515589
55 0.008752895 -0.033149831
56 0.051289863 0.008752895
57 -0.001733476 0.051289863
58 -0.346511669 -0.001733476
59 -0.369852129 -0.346511669
60 NA -0.369852129
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.379830750 0.375391056
[2,] 0.389513629 0.379830750
[3,] 0.565708177 0.389513629
[4,] 0.710317121 0.565708177
[5,] 0.652854089 0.710317121
[6,] 0.643805452 0.652854089
[7,] 0.682536968 0.643805452
[8,] 0.625073936 0.682536968
[9,] 0.710951363 0.625073936
[10,] 0.701902726 0.710951363
[11,] 0.917463032 0.701902726
[12,] 0.475391056 0.917463032
[13,] 0.621902726 0.475391056
[14,] 0.683171210 0.621902726
[15,] 0.614122573 0.683171210
[16,] 0.468245145 0.614122573
[17,] 0.507610903 0.468245145
[18,] 0.595391056 0.507610903
[19,] 0.534122573 0.595391056
[20,] 0.767145911 0.534122573
[21,] 0.659365758 0.767145911
[22,] 0.650317121 0.659365758
[23,] 0.520634242 0.650317121
[24,] 0.517463032 0.520634242
[25,] 0.418731516 0.517463032
[26,] 0.331585605 0.418731516
[27,] 0.307780153 0.331585605
[28,] 0.210317121 0.307780153
[29,] 0.101268484 0.210317121
[30,] -0.107780153 0.101268484
[31,] -0.117463032 -0.107780153
[32,] -0.468583645 -0.117463032
[33,] -0.576363798 -0.468583645
[34,] -0.443340460 -0.576363798
[35,] -0.524608944 -0.443340460
[36,] -0.773023339 -0.524608944
[37,] -0.823340460 -0.773023339
[38,] -0.913657581 -0.823340460
[39,] -0.982706218 -0.913657581
[40,] -1.080169250 -0.982706218
[41,] -1.089217887 -1.080169250
[42,] -1.098266524 -1.089217887
[43,] -1.107949403 -1.098266524
[44,] -0.974926064 -1.107949403
[45,] -0.792219847 -0.974926064
[46,] -0.562367718 -0.792219847
[47,] -0.543636202 -0.562367718
[48,] -0.595221807 -0.543636202
[49,] -0.597124532 -0.595221807
[50,] -0.490612863 -0.597124532
[51,] -0.504904686 -0.490612863
[52,] -0.308710137 -0.504904686
[53,] -0.172515589 -0.308710137
[54,] -0.033149831 -0.172515589
[55,] 0.008752895 -0.033149831
[56,] 0.051289863 0.008752895
[57,] -0.001733476 0.051289863
[58,] -0.346511669 -0.001733476
[59,] -0.369852129 -0.346511669
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.379830750 0.375391056
2 0.389513629 0.379830750
3 0.565708177 0.389513629
4 0.710317121 0.565708177
5 0.652854089 0.710317121
6 0.643805452 0.652854089
7 0.682536968 0.643805452
8 0.625073936 0.682536968
9 0.710951363 0.625073936
10 0.701902726 0.710951363
11 0.917463032 0.701902726
12 0.475391056 0.917463032
13 0.621902726 0.475391056
14 0.683171210 0.621902726
15 0.614122573 0.683171210
16 0.468245145 0.614122573
17 0.507610903 0.468245145
18 0.595391056 0.507610903
19 0.534122573 0.595391056
20 0.767145911 0.534122573
21 0.659365758 0.767145911
22 0.650317121 0.659365758
23 0.520634242 0.650317121
24 0.517463032 0.520634242
25 0.418731516 0.517463032
26 0.331585605 0.418731516
27 0.307780153 0.331585605
28 0.210317121 0.307780153
29 0.101268484 0.210317121
30 -0.107780153 0.101268484
31 -0.117463032 -0.107780153
32 -0.468583645 -0.117463032
33 -0.576363798 -0.468583645
34 -0.443340460 -0.576363798
35 -0.524608944 -0.443340460
36 -0.773023339 -0.524608944
37 -0.823340460 -0.773023339
38 -0.913657581 -0.823340460
39 -0.982706218 -0.913657581
40 -1.080169250 -0.982706218
41 -1.089217887 -1.080169250
42 -1.098266524 -1.089217887
43 -1.107949403 -1.098266524
44 -0.974926064 -1.107949403
45 -0.792219847 -0.974926064
46 -0.562367718 -0.792219847
47 -0.543636202 -0.562367718
48 -0.595221807 -0.543636202
49 -0.597124532 -0.595221807
50 -0.490612863 -0.597124532
51 -0.504904686 -0.490612863
52 -0.308710137 -0.504904686
53 -0.172515589 -0.308710137
54 -0.033149831 -0.172515589
55 0.008752895 -0.033149831
56 0.051289863 0.008752895
57 -0.001733476 0.051289863
58 -0.346511669 -0.001733476
59 -0.369852129 -0.346511669
> 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/77nj21259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8904o1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fcyy1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10on7n1259344485.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jv951259344485.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/12oist1259344485.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/13zeo31259344485.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/14hlta1259344485.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/1546cb1259344485.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/16hi001259344485.tab")
+ }
>
> system("convert tmp/10umx1259344485.ps tmp/10umx1259344485.png")
> system("convert tmp/2aa3f1259344485.ps tmp/2aa3f1259344485.png")
> system("convert tmp/3l1rn1259344485.ps tmp/3l1rn1259344485.png")
> system("convert tmp/4kh3m1259344485.ps tmp/4kh3m1259344485.png")
> system("convert tmp/5pxmd1259344485.ps tmp/5pxmd1259344485.png")
> system("convert tmp/6ns871259344485.ps tmp/6ns871259344485.png")
> system("convert tmp/77nj21259344485.ps tmp/77nj21259344485.png")
> system("convert tmp/8904o1259344485.ps tmp/8904o1259344485.png")
> system("convert tmp/9fcyy1259344485.ps tmp/9fcyy1259344485.png")
> system("convert tmp/10on7n1259344485.ps tmp/10on7n1259344485.png")
>
>
> proc.time()
user system elapsed
2.421 1.596 4.024