R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(0.89,0,0.89,0,0.89,0,0.89,0,0.89,0,0.89,0,0.89,0,0.9,0,0.91,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,0.92,0,1.01,1,1.01,1,1.01,1,1.01,1,1.01,1,1.04,1,1.05,1,1.05,1,1.06,1,1.06,1,1.06,1,1.06,1,1.08,1,1.08,1,1.08,1,1.08,1,1.08,1,1.08,1,1.09,1,1.09,1,1.1,1,1.1,1,1.1,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0.89 0 1 0 0 0 0 0 0 0 0 0 0 1
2 0.89 0 0 1 0 0 0 0 0 0 0 0 0 2
3 0.89 0 0 0 1 0 0 0 0 0 0 0 0 3
4 0.89 0 0 0 0 1 0 0 0 0 0 0 0 4
5 0.89 0 0 0 0 0 1 0 0 0 0 0 0 5
6 0.89 0 0 0 0 0 0 1 0 0 0 0 0 6
7 0.89 0 0 0 0 0 0 0 1 0 0 0 0 7
8 0.90 0 0 0 0 0 0 0 0 1 0 0 0 8
9 0.91 0 0 0 0 0 0 0 0 0 1 0 0 9
10 0.92 0 0 0 0 0 0 0 0 0 0 1 0 10
11 0.92 0 0 0 0 0 0 0 0 0 0 0 1 11
12 0.92 0 0 0 0 0 0 0 0 0 0 0 0 12
13 0.92 0 1 0 0 0 0 0 0 0 0 0 0 13
14 0.92 0 0 1 0 0 0 0 0 0 0 0 0 14
15 0.92 0 0 0 1 0 0 0 0 0 0 0 0 15
16 0.92 0 0 0 0 1 0 0 0 0 0 0 0 16
17 0.92 0 0 0 0 0 1 0 0 0 0 0 0 17
18 0.92 0 0 0 0 0 0 1 0 0 0 0 0 18
19 0.92 0 0 0 0 0 0 0 1 0 0 0 0 19
20 0.92 0 0 0 0 0 0 0 0 1 0 0 0 20
21 0.92 0 0 0 0 0 0 0 0 0 1 0 0 21
22 0.92 0 0 0 0 0 0 0 0 0 0 1 0 22
23 0.92 0 0 0 0 0 0 0 0 0 0 0 1 23
24 0.92 0 0 0 0 0 0 0 0 0 0 0 0 24
25 0.92 0 1 0 0 0 0 0 0 0 0 0 0 25
26 0.92 0 0 1 0 0 0 0 0 0 0 0 0 26
27 0.92 0 0 0 1 0 0 0 0 0 0 0 0 27
28 0.92 0 0 0 0 1 0 0 0 0 0 0 0 28
29 0.92 0 0 0 0 0 1 0 0 0 0 0 0 29
30 0.92 0 0 0 0 0 0 1 0 0 0 0 0 30
31 0.92 0 0 0 0 0 0 0 1 0 0 0 0 31
32 0.92 0 0 0 0 0 0 0 0 1 0 0 0 32
33 0.92 0 0 0 0 0 0 0 0 0 1 0 0 33
34 0.92 0 0 0 0 0 0 0 0 0 0 1 0 34
35 0.92 0 0 0 0 0 0 0 0 0 0 0 1 35
36 0.92 0 0 0 0 0 0 0 0 0 0 0 0 36
37 0.92 0 1 0 0 0 0 0 0 0 0 0 0 37
38 0.92 0 0 1 0 0 0 0 0 0 0 0 0 38
39 1.01 1 0 0 1 0 0 0 0 0 0 0 0 39
40 1.01 1 0 0 0 1 0 0 0 0 0 0 0 40
41 1.01 1 0 0 0 0 1 0 0 0 0 0 0 41
42 1.01 1 0 0 0 0 0 1 0 0 0 0 0 42
43 1.01 1 0 0 0 0 0 0 1 0 0 0 0 43
44 1.04 1 0 0 0 0 0 0 0 1 0 0 0 44
45 1.05 1 0 0 0 0 0 0 0 0 1 0 0 45
46 1.05 1 0 0 0 0 0 0 0 0 0 1 0 46
47 1.06 1 0 0 0 0 0 0 0 0 0 0 1 47
48 1.06 1 0 0 0 0 0 0 0 0 0 0 0 48
49 1.06 1 1 0 0 0 0 0 0 0 0 0 0 49
50 1.06 1 0 1 0 0 0 0 0 0 0 0 0 50
51 1.08 1 0 0 1 0 0 0 0 0 0 0 0 51
52 1.08 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.08 1 0 0 0 0 1 0 0 0 0 0 0 53
54 1.08 1 0 0 0 0 0 1 0 0 0 0 0 54
55 1.08 1 0 0 0 0 0 0 1 0 0 0 0 55
56 1.08 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.09 1 0 0 0 0 0 0 0 0 1 0 0 57
58 1.09 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1.10 1 0 0 0 0 0 0 0 0 0 0 1 59
60 1.10 1 0 0 0 0 0 0 0 0 0 0 0 60
61 1.10 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
0.893237 0.105662 -0.001887 -0.007396 -0.007875 -0.009223
M5 M6 M7 M8 M9 M10
-0.010570 -0.011917 -0.013264 -0.006611 -0.001958 -0.001306
M11 t
0.001347 0.001347
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0335632 -0.0055692 0.0005968 0.0144308 0.0208103
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8932372 0.0098700 90.501 < 2e-16 ***
x 0.1056621 0.0087454 12.082 5.09e-16 ***
M1 -0.0018867 0.0105785 -0.178 0.859
M2 -0.0073959 0.0110977 -0.666 0.508
M3 -0.0078755 0.0112329 -0.701 0.487
M4 -0.0092227 0.0111883 -0.824 0.414
M5 -0.0105698 0.0111488 -0.948 0.348
M6 -0.0119170 0.0111144 -1.072 0.289
M7 -0.0132642 0.0110852 -1.197 0.237
M8 -0.0066113 0.0110613 -0.598 0.553
M9 -0.0019585 0.0110427 -0.177 0.860
M10 -0.0013057 0.0110293 -0.118 0.906
M11 0.0013472 0.0110213 0.122 0.903
t 0.0013472 0.0002426 5.552 1.27e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01742 on 47 degrees of freedom
Multiple R-squared: 0.9575, Adjusted R-squared: 0.9458
F-statistic: 81.52 on 13 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,] 1.326611e-42 2.653221e-42 1.000000e+00
[2,] 2.629753e-55 5.259505e-55 1.000000e+00
[3,] 2.263400e-72 4.526801e-72 1.000000e+00
[4,] 9.936476e-05 1.987295e-04 9.999006e-01
[5,] 2.781471e-03 5.562941e-03 9.972185e-01
[6,] 2.770105e-02 5.540210e-02 9.722990e-01
[7,] 5.980414e-02 1.196083e-01 9.401959e-01
[8,] 9.752153e-02 1.950431e-01 9.024785e-01
[9,] 1.377518e-01 2.755035e-01 8.622482e-01
[10,] 2.231813e-01 4.463626e-01 7.768187e-01
[11,] 2.075338e-01 4.150677e-01 7.924662e-01
[12,] 1.992069e-01 3.984137e-01 8.007931e-01
[13,] 2.064851e-01 4.129703e-01 7.935149e-01
[14,] 2.464254e-01 4.928507e-01 7.535746e-01
[15,] 3.713288e-01 7.426577e-01 6.286712e-01
[16,] 4.017709e-01 8.035418e-01 5.982291e-01
[17,] 3.956303e-01 7.912607e-01 6.043697e-01
[18,] 4.329484e-01 8.658967e-01 5.670516e-01
[19,] 4.065093e-01 8.130186e-01 5.934907e-01
[20,] 3.649877e-01 7.299755e-01 6.350123e-01
[21,] 2.960545e-01 5.921091e-01 7.039455e-01
[22,] 2.218448e-01 4.436897e-01 7.781552e-01
[23,] 2.079860e-01 4.159719e-01 7.920140e-01
[24,] 2.166962e-01 4.333924e-01 7.833038e-01
[25,] 2.709078e-01 5.418156e-01 7.290922e-01
[26,] 4.596233e-01 9.192466e-01 5.403767e-01
[27,] 1.000000e+00 6.769592e-54 3.384796e-54
[28,] 1.000000e+00 3.577547e-40 1.788773e-40
> postscript(file="/var/www/html/rcomp/tmp/16xv51229681757.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u28m1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mo031229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yvzd1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52c6u1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
-0.002697628 0.001464427 0.000596838 0.000596838 0.000596838 0.000596838
7 8 9 10 11 12
0.000596838 0.002596838 0.006596838 0.014596838 0.010596838 0.010596838
13 14 15 16 17 18
0.011136364 0.015298419 0.014430830 0.014430830 0.014430830 0.014430830
19 20 21 22 23 24
0.014430830 0.006430830 0.000430830 -0.001569170 -0.005569170 -0.005569170
25 26 27 28 29 30
-0.005029644 -0.000867589 -0.001735178 -0.001735178 -0.001735178 -0.001735178
31 32 33 34 35 36
-0.001735178 -0.009735178 -0.015735178 -0.017735178 -0.021735178 -0.021735178
37 38 39 40 41 42
-0.021195652 -0.017033597 -0.033563241 -0.033563241 -0.033563241 -0.033563241
43 44 45 46 47 48
-0.033563241 -0.011563241 -0.007563241 -0.009563241 -0.003563241 -0.003563241
49 50 51 52 53 54
-0.003023715 0.001138340 0.020270751 0.020270751 0.020270751 0.020270751
55 56 57 58 59 60
0.020270751 0.012270751 0.016270751 0.014270751 0.020270751 0.020270751
61
0.020810277
> postscript(file="/var/www/html/rcomp/tmp/63okk1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.002697628 NA
1 0.001464427 -0.002697628
2 0.000596838 0.001464427
3 0.000596838 0.000596838
4 0.000596838 0.000596838
5 0.000596838 0.000596838
6 0.000596838 0.000596838
7 0.002596838 0.000596838
8 0.006596838 0.002596838
9 0.014596838 0.006596838
10 0.010596838 0.014596838
11 0.010596838 0.010596838
12 0.011136364 0.010596838
13 0.015298419 0.011136364
14 0.014430830 0.015298419
15 0.014430830 0.014430830
16 0.014430830 0.014430830
17 0.014430830 0.014430830
18 0.014430830 0.014430830
19 0.006430830 0.014430830
20 0.000430830 0.006430830
21 -0.001569170 0.000430830
22 -0.005569170 -0.001569170
23 -0.005569170 -0.005569170
24 -0.005029644 -0.005569170
25 -0.000867589 -0.005029644
26 -0.001735178 -0.000867589
27 -0.001735178 -0.001735178
28 -0.001735178 -0.001735178
29 -0.001735178 -0.001735178
30 -0.001735178 -0.001735178
31 -0.009735178 -0.001735178
32 -0.015735178 -0.009735178
33 -0.017735178 -0.015735178
34 -0.021735178 -0.017735178
35 -0.021735178 -0.021735178
36 -0.021195652 -0.021735178
37 -0.017033597 -0.021195652
38 -0.033563241 -0.017033597
39 -0.033563241 -0.033563241
40 -0.033563241 -0.033563241
41 -0.033563241 -0.033563241
42 -0.033563241 -0.033563241
43 -0.011563241 -0.033563241
44 -0.007563241 -0.011563241
45 -0.009563241 -0.007563241
46 -0.003563241 -0.009563241
47 -0.003563241 -0.003563241
48 -0.003023715 -0.003563241
49 0.001138340 -0.003023715
50 0.020270751 0.001138340
51 0.020270751 0.020270751
52 0.020270751 0.020270751
53 0.020270751 0.020270751
54 0.020270751 0.020270751
55 0.012270751 0.020270751
56 0.016270751 0.012270751
57 0.014270751 0.016270751
58 0.020270751 0.014270751
59 0.020270751 0.020270751
60 0.020810277 0.020270751
61 NA 0.020810277
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.001464427 -0.002697628
[2,] 0.000596838 0.001464427
[3,] 0.000596838 0.000596838
[4,] 0.000596838 0.000596838
[5,] 0.000596838 0.000596838
[6,] 0.000596838 0.000596838
[7,] 0.002596838 0.000596838
[8,] 0.006596838 0.002596838
[9,] 0.014596838 0.006596838
[10,] 0.010596838 0.014596838
[11,] 0.010596838 0.010596838
[12,] 0.011136364 0.010596838
[13,] 0.015298419 0.011136364
[14,] 0.014430830 0.015298419
[15,] 0.014430830 0.014430830
[16,] 0.014430830 0.014430830
[17,] 0.014430830 0.014430830
[18,] 0.014430830 0.014430830
[19,] 0.006430830 0.014430830
[20,] 0.000430830 0.006430830
[21,] -0.001569170 0.000430830
[22,] -0.005569170 -0.001569170
[23,] -0.005569170 -0.005569170
[24,] -0.005029644 -0.005569170
[25,] -0.000867589 -0.005029644
[26,] -0.001735178 -0.000867589
[27,] -0.001735178 -0.001735178
[28,] -0.001735178 -0.001735178
[29,] -0.001735178 -0.001735178
[30,] -0.001735178 -0.001735178
[31,] -0.009735178 -0.001735178
[32,] -0.015735178 -0.009735178
[33,] -0.017735178 -0.015735178
[34,] -0.021735178 -0.017735178
[35,] -0.021735178 -0.021735178
[36,] -0.021195652 -0.021735178
[37,] -0.017033597 -0.021195652
[38,] -0.033563241 -0.017033597
[39,] -0.033563241 -0.033563241
[40,] -0.033563241 -0.033563241
[41,] -0.033563241 -0.033563241
[42,] -0.033563241 -0.033563241
[43,] -0.011563241 -0.033563241
[44,] -0.007563241 -0.011563241
[45,] -0.009563241 -0.007563241
[46,] -0.003563241 -0.009563241
[47,] -0.003563241 -0.003563241
[48,] -0.003023715 -0.003563241
[49,] 0.001138340 -0.003023715
[50,] 0.020270751 0.001138340
[51,] 0.020270751 0.020270751
[52,] 0.020270751 0.020270751
[53,] 0.020270751 0.020270751
[54,] 0.020270751 0.020270751
[55,] 0.012270751 0.020270751
[56,] 0.016270751 0.012270751
[57,] 0.014270751 0.016270751
[58,] 0.020270751 0.014270751
[59,] 0.020270751 0.020270751
[60,] 0.020810277 0.020270751
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.001464427 -0.002697628
2 0.000596838 0.001464427
3 0.000596838 0.000596838
4 0.000596838 0.000596838
5 0.000596838 0.000596838
6 0.000596838 0.000596838
7 0.002596838 0.000596838
8 0.006596838 0.002596838
9 0.014596838 0.006596838
10 0.010596838 0.014596838
11 0.010596838 0.010596838
12 0.011136364 0.010596838
13 0.015298419 0.011136364
14 0.014430830 0.015298419
15 0.014430830 0.014430830
16 0.014430830 0.014430830
17 0.014430830 0.014430830
18 0.014430830 0.014430830
19 0.006430830 0.014430830
20 0.000430830 0.006430830
21 -0.001569170 0.000430830
22 -0.005569170 -0.001569170
23 -0.005569170 -0.005569170
24 -0.005029644 -0.005569170
25 -0.000867589 -0.005029644
26 -0.001735178 -0.000867589
27 -0.001735178 -0.001735178
28 -0.001735178 -0.001735178
29 -0.001735178 -0.001735178
30 -0.001735178 -0.001735178
31 -0.009735178 -0.001735178
32 -0.015735178 -0.009735178
33 -0.017735178 -0.015735178
34 -0.021735178 -0.017735178
35 -0.021735178 -0.021735178
36 -0.021195652 -0.021735178
37 -0.017033597 -0.021195652
38 -0.033563241 -0.017033597
39 -0.033563241 -0.033563241
40 -0.033563241 -0.033563241
41 -0.033563241 -0.033563241
42 -0.033563241 -0.033563241
43 -0.011563241 -0.033563241
44 -0.007563241 -0.011563241
45 -0.009563241 -0.007563241
46 -0.003563241 -0.009563241
47 -0.003563241 -0.003563241
48 -0.003023715 -0.003563241
49 0.001138340 -0.003023715
50 0.020270751 0.001138340
51 0.020270751 0.020270751
52 0.020270751 0.020270751
53 0.020270751 0.020270751
54 0.020270751 0.020270751
55 0.012270751 0.020270751
56 0.016270751 0.012270751
57 0.014270751 0.016270751
58 0.020270751 0.014270751
59 0.020270751 0.020270751
60 0.020810277 0.020270751
> 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/7vin41229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v4ab1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96z7f1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108a8s1229681757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ddht1229681757.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/12e49x1229681757.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/135cp31229681757.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/142qqf1229681757.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/15hfer1229681758.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/165q6v1229681758.tab")
+ }
>
> system("convert tmp/16xv51229681757.ps tmp/16xv51229681757.png")
> system("convert tmp/2u28m1229681757.ps tmp/2u28m1229681757.png")
> system("convert tmp/3mo031229681757.ps tmp/3mo031229681757.png")
> system("convert tmp/4yvzd1229681757.ps tmp/4yvzd1229681757.png")
> system("convert tmp/52c6u1229681757.ps tmp/52c6u1229681757.png")
> system("convert tmp/63okk1229681757.ps tmp/63okk1229681757.png")
> system("convert tmp/7vin41229681757.ps tmp/7vin41229681757.png")
> system("convert tmp/8v4ab1229681757.ps tmp/8v4ab1229681757.png")
> system("convert tmp/96z7f1229681757.ps tmp/96z7f1229681757.png")
> system("convert tmp/108a8s1229681757.ps tmp/108a8s1229681757.png")
>
>
> proc.time()
user system elapsed
2.467 1.618 5.356