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(7.55,42.97,7.55,42.98,7.59,43.01,7.59,43.09,7.59,43.14,7.57,43.39,7.57,43.46,7.59,43.54,7.6,43.62,7.64,44.01,7.64,44.5,7.76,44.73,7.76,44.89,7.76,45.09,7.77,45.17,7.83,45.24,7.94,45.42,7.94,45.67,7.94,45.68,8.09,46.56,8.18,46.72,8.26,47.01,8.28,47.26,8.28,47.49,8.28,47.51,8.29,47.52,8.3,47.66,8.3,47.71,8.31,47.87,8.33,48,8.33,48,8.34,48.05,8.48,48.25,8.59,48.72,8.67,48.94,8.67,49.16,8.67,49.18,8.71,49.25,8.72,49.34,8.72,49.49,8.72,49.57,8.74,49.63,8.74,49.67,8.74,49.7,8.74,49.8,8.79,50.09,8.85,50.49,8.86,50.73,8.87,51.12,8.92,51.15,8.96,51.41,8.97,51.61,8.99,52.06,8.98,52.17,8.98,52.18,9.01,52.19,9.01,52.74,9.03,53.05,9.05,53.38,9.05,53.78),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 = '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 7.55 42.97 1 0 0 0 0 0 0 0 0 0 0 1
2 7.55 42.98 0 1 0 0 0 0 0 0 0 0 0 2
3 7.59 43.01 0 0 1 0 0 0 0 0 0 0 0 3
4 7.59 43.09 0 0 0 1 0 0 0 0 0 0 0 4
5 7.59 43.14 0 0 0 0 1 0 0 0 0 0 0 5
6 7.57 43.39 0 0 0 0 0 1 0 0 0 0 0 6
7 7.57 43.46 0 0 0 0 0 0 1 0 0 0 0 7
8 7.59 43.54 0 0 0 0 0 0 0 1 0 0 0 8
9 7.60 43.62 0 0 0 0 0 0 0 0 1 0 0 9
10 7.64 44.01 0 0 0 0 0 0 0 0 0 1 0 10
11 7.64 44.50 0 0 0 0 0 0 0 0 0 0 1 11
12 7.76 44.73 0 0 0 0 0 0 0 0 0 0 0 12
13 7.76 44.89 1 0 0 0 0 0 0 0 0 0 0 13
14 7.76 45.09 0 1 0 0 0 0 0 0 0 0 0 14
15 7.77 45.17 0 0 1 0 0 0 0 0 0 0 0 15
16 7.83 45.24 0 0 0 1 0 0 0 0 0 0 0 16
17 7.94 45.42 0 0 0 0 1 0 0 0 0 0 0 17
18 7.94 45.67 0 0 0 0 0 1 0 0 0 0 0 18
19 7.94 45.68 0 0 0 0 0 0 1 0 0 0 0 19
20 8.09 46.56 0 0 0 0 0 0 0 1 0 0 0 20
21 8.18 46.72 0 0 0 0 0 0 0 0 1 0 0 21
22 8.26 47.01 0 0 0 0 0 0 0 0 0 1 0 22
23 8.28 47.26 0 0 0 0 0 0 0 0 0 0 1 23
24 8.28 47.49 0 0 0 0 0 0 0 0 0 0 0 24
25 8.28 47.51 1 0 0 0 0 0 0 0 0 0 0 25
26 8.29 47.52 0 1 0 0 0 0 0 0 0 0 0 26
27 8.30 47.66 0 0 1 0 0 0 0 0 0 0 0 27
28 8.30 47.71 0 0 0 1 0 0 0 0 0 0 0 28
29 8.31 47.87 0 0 0 0 1 0 0 0 0 0 0 29
30 8.33 48.00 0 0 0 0 0 1 0 0 0 0 0 30
31 8.33 48.00 0 0 0 0 0 0 1 0 0 0 0 31
32 8.34 48.05 0 0 0 0 0 0 0 1 0 0 0 32
33 8.48 48.25 0 0 0 0 0 0 0 0 1 0 0 33
34 8.59 48.72 0 0 0 0 0 0 0 0 0 1 0 34
35 8.67 48.94 0 0 0 0 0 0 0 0 0 0 1 35
36 8.67 49.16 0 0 0 0 0 0 0 0 0 0 0 36
37 8.67 49.18 1 0 0 0 0 0 0 0 0 0 0 37
38 8.71 49.25 0 1 0 0 0 0 0 0 0 0 0 38
39 8.72 49.34 0 0 1 0 0 0 0 0 0 0 0 39
40 8.72 49.49 0 0 0 1 0 0 0 0 0 0 0 40
41 8.72 49.57 0 0 0 0 1 0 0 0 0 0 0 41
42 8.74 49.63 0 0 0 0 0 1 0 0 0 0 0 42
43 8.74 49.67 0 0 0 0 0 0 1 0 0 0 0 43
44 8.74 49.70 0 0 0 0 0 0 0 1 0 0 0 44
45 8.74 49.80 0 0 0 0 0 0 0 0 1 0 0 45
46 8.79 50.09 0 0 0 0 0 0 0 0 0 1 0 46
47 8.85 50.49 0 0 0 0 0 0 0 0 0 0 1 47
48 8.86 50.73 0 0 0 0 0 0 0 0 0 0 0 48
49 8.87 51.12 1 0 0 0 0 0 0 0 0 0 0 49
50 8.92 51.15 0 1 0 0 0 0 0 0 0 0 0 50
51 8.96 51.41 0 0 1 0 0 0 0 0 0 0 0 51
52 8.97 51.61 0 0 0 1 0 0 0 0 0 0 0 52
53 8.99 52.06 0 0 0 0 1 0 0 0 0 0 0 53
54 8.98 52.17 0 0 0 0 0 1 0 0 0 0 0 54
55 8.98 52.18 0 0 0 0 0 0 1 0 0 0 0 55
56 9.01 52.19 0 0 0 0 0 0 0 1 0 0 0 56
57 9.01 52.74 0 0 0 0 0 0 0 0 1 0 0 57
58 9.03 53.05 0 0 0 0 0 0 0 0 0 1 0 58
59 9.05 53.38 0 0 0 0 0 0 0 0 0 0 1 59
60 9.05 53.78 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
4.221162 0.075855 0.031961 0.031205 0.028201 0.017956
M5 M6 M7 M8 M9 M10
0.016097 -0.009941 -0.027814 -0.017645 -0.002082 0.015467
M11 t
0.009927 0.015901
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.2047134 -0.0430035 0.0007787 0.0677946 0.1700278
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.221162 1.809593 2.333 0.0241 *
X 0.075855 0.042226 1.796 0.0790 .
M1 0.031961 0.059665 0.536 0.5948
M2 0.031205 0.060122 0.519 0.6062
M3 0.028201 0.060453 0.466 0.6431
M4 0.017956 0.060981 0.294 0.7697
M5 0.016097 0.060840 0.265 0.7925
M6 -0.009941 0.060947 -0.163 0.8712
M7 -0.027814 0.062740 -0.443 0.6596
M8 -0.017645 0.062236 -0.284 0.7781
M9 -0.002082 0.061671 -0.034 0.9732
M10 0.015467 0.059932 0.258 0.7975
M11 0.009927 0.059100 0.168 0.8673
t 0.015901 0.007468 2.129 0.0386 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.09325 on 46 degrees of freedom
Multiple R-squared: 0.9746, Adjusted R-squared: 0.9675
F-statistic: 135.9 on 13 and 46 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,] 0.2179804 0.4359608587 0.7820195707
[2,] 0.2151328 0.4302656475 0.7848671763
[3,] 0.2728138 0.5456275904 0.7271862048
[4,] 0.2321168 0.4642336139 0.7678831931
[5,] 0.1523806 0.3047612592 0.8476193704
[6,] 0.1542385 0.3084769501 0.8457615249
[7,] 0.3385580 0.6771160880 0.6614419560
[8,] 0.2478141 0.4956282334 0.7521858833
[9,] 0.2220255 0.4440509324 0.7779745338
[10,] 0.2541353 0.5082706655 0.7458646672
[11,] 0.2196071 0.4392141398 0.7803929301
[12,] 0.2037050 0.4074099520 0.7962950240
[13,] 0.2466891 0.4933781551 0.7533109224
[14,] 0.3050371 0.6100741018 0.6949629491
[15,] 0.5093243 0.9813513375 0.4906756687
[16,] 0.9854569 0.0290861091 0.0145430546
[17,] 0.9981857 0.0036285844 0.0018142922
[18,] 0.9981078 0.0037843662 0.0018921831
[19,] 0.9995133 0.0009733485 0.0004866743
[20,] 0.9997981 0.0004037220 0.0002018610
[21,] 0.9997310 0.0005379421 0.0002689710
[22,] 0.9997383 0.0005234973 0.0002617486
[23,] 0.9993307 0.0013385050 0.0006692525
[24,] 0.9981298 0.0037403856 0.0018701928
[25,] 0.9934651 0.0130698640 0.0065349320
[26,] 0.9781913 0.0436174879 0.0218087440
[27,] 0.9396164 0.1207671555 0.0603835777
> postscript(file="/var/www/html/rcomp/tmp/13xma1258559591.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2e49x1258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jztx1258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xjv31258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c62d1258559591.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
2.148894e-02 5.585112e-03 3.041206e-02 1.868771e-02 8.522704e-04
6 7 8 9 10
-2.797468e-02 -3.131229e-02 -4.345115e-02 -7.098317e-02 -9.401736e-02
11 12 13 14 15
-1.415473e-01 -4.496825e-02 -1.049670e-01 -1.352833e-01 -1.442491e-01
16 17 18 19 20
-9.521490e-02 -1.291148e-02 -2.173843e-02 -2.052475e-02 3.665243e-02
21 22 23 24 25
8.305202e-02 1.076033e-01 9.827855e-02 7.485762e-02 2.547854e-02
26 27 28 29 30
1.957470e-02 6.057606e-03 -3.391097e-03 -1.957058e-02 7.050704e-04
31 32 33 34 35
2.677299e-03 -1.718591e-02 7.617948e-02 1.170769e-01 1.700278e-01
36 37 38 39 40
1.473654e-01 9.798631e-02 1.175312e-01 1.078068e-01 9.077263e-02
41 42 43 44 45
7.066154e-02 9.624704e-02 9.518507e-02 6.683896e-02 2.778984e-02
46 47 48 49 50
2.234114e-02 4.163813e-02 2.745865e-02 -3.998676e-02 -7.407694e-03
51 52 53 54 55
-2.738779e-05 -1.085433e-02 -3.903175e-02 -4.723900e-02 -4.602532e-02
56 57 58 59 60
-4.285433e-02 -1.160382e-01 -1.530040e-01 -1.683971e-01 -2.047134e-01
> postscript(file="/var/www/html/rcomp/tmp/6glh41258559591.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 2.148894e-02 NA
1 5.585112e-03 2.148894e-02
2 3.041206e-02 5.585112e-03
3 1.868771e-02 3.041206e-02
4 8.522704e-04 1.868771e-02
5 -2.797468e-02 8.522704e-04
6 -3.131229e-02 -2.797468e-02
7 -4.345115e-02 -3.131229e-02
8 -7.098317e-02 -4.345115e-02
9 -9.401736e-02 -7.098317e-02
10 -1.415473e-01 -9.401736e-02
11 -4.496825e-02 -1.415473e-01
12 -1.049670e-01 -4.496825e-02
13 -1.352833e-01 -1.049670e-01
14 -1.442491e-01 -1.352833e-01
15 -9.521490e-02 -1.442491e-01
16 -1.291148e-02 -9.521490e-02
17 -2.173843e-02 -1.291148e-02
18 -2.052475e-02 -2.173843e-02
19 3.665243e-02 -2.052475e-02
20 8.305202e-02 3.665243e-02
21 1.076033e-01 8.305202e-02
22 9.827855e-02 1.076033e-01
23 7.485762e-02 9.827855e-02
24 2.547854e-02 7.485762e-02
25 1.957470e-02 2.547854e-02
26 6.057606e-03 1.957470e-02
27 -3.391097e-03 6.057606e-03
28 -1.957058e-02 -3.391097e-03
29 7.050704e-04 -1.957058e-02
30 2.677299e-03 7.050704e-04
31 -1.718591e-02 2.677299e-03
32 7.617948e-02 -1.718591e-02
33 1.170769e-01 7.617948e-02
34 1.700278e-01 1.170769e-01
35 1.473654e-01 1.700278e-01
36 9.798631e-02 1.473654e-01
37 1.175312e-01 9.798631e-02
38 1.078068e-01 1.175312e-01
39 9.077263e-02 1.078068e-01
40 7.066154e-02 9.077263e-02
41 9.624704e-02 7.066154e-02
42 9.518507e-02 9.624704e-02
43 6.683896e-02 9.518507e-02
44 2.778984e-02 6.683896e-02
45 2.234114e-02 2.778984e-02
46 4.163813e-02 2.234114e-02
47 2.745865e-02 4.163813e-02
48 -3.998676e-02 2.745865e-02
49 -7.407694e-03 -3.998676e-02
50 -2.738779e-05 -7.407694e-03
51 -1.085433e-02 -2.738779e-05
52 -3.903175e-02 -1.085433e-02
53 -4.723900e-02 -3.903175e-02
54 -4.602532e-02 -4.723900e-02
55 -4.285433e-02 -4.602532e-02
56 -1.160382e-01 -4.285433e-02
57 -1.530040e-01 -1.160382e-01
58 -1.683971e-01 -1.530040e-01
59 -2.047134e-01 -1.683971e-01
60 NA -2.047134e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.585112e-03 2.148894e-02
[2,] 3.041206e-02 5.585112e-03
[3,] 1.868771e-02 3.041206e-02
[4,] 8.522704e-04 1.868771e-02
[5,] -2.797468e-02 8.522704e-04
[6,] -3.131229e-02 -2.797468e-02
[7,] -4.345115e-02 -3.131229e-02
[8,] -7.098317e-02 -4.345115e-02
[9,] -9.401736e-02 -7.098317e-02
[10,] -1.415473e-01 -9.401736e-02
[11,] -4.496825e-02 -1.415473e-01
[12,] -1.049670e-01 -4.496825e-02
[13,] -1.352833e-01 -1.049670e-01
[14,] -1.442491e-01 -1.352833e-01
[15,] -9.521490e-02 -1.442491e-01
[16,] -1.291148e-02 -9.521490e-02
[17,] -2.173843e-02 -1.291148e-02
[18,] -2.052475e-02 -2.173843e-02
[19,] 3.665243e-02 -2.052475e-02
[20,] 8.305202e-02 3.665243e-02
[21,] 1.076033e-01 8.305202e-02
[22,] 9.827855e-02 1.076033e-01
[23,] 7.485762e-02 9.827855e-02
[24,] 2.547854e-02 7.485762e-02
[25,] 1.957470e-02 2.547854e-02
[26,] 6.057606e-03 1.957470e-02
[27,] -3.391097e-03 6.057606e-03
[28,] -1.957058e-02 -3.391097e-03
[29,] 7.050704e-04 -1.957058e-02
[30,] 2.677299e-03 7.050704e-04
[31,] -1.718591e-02 2.677299e-03
[32,] 7.617948e-02 -1.718591e-02
[33,] 1.170769e-01 7.617948e-02
[34,] 1.700278e-01 1.170769e-01
[35,] 1.473654e-01 1.700278e-01
[36,] 9.798631e-02 1.473654e-01
[37,] 1.175312e-01 9.798631e-02
[38,] 1.078068e-01 1.175312e-01
[39,] 9.077263e-02 1.078068e-01
[40,] 7.066154e-02 9.077263e-02
[41,] 9.624704e-02 7.066154e-02
[42,] 9.518507e-02 9.624704e-02
[43,] 6.683896e-02 9.518507e-02
[44,] 2.778984e-02 6.683896e-02
[45,] 2.234114e-02 2.778984e-02
[46,] 4.163813e-02 2.234114e-02
[47,] 2.745865e-02 4.163813e-02
[48,] -3.998676e-02 2.745865e-02
[49,] -7.407694e-03 -3.998676e-02
[50,] -2.738779e-05 -7.407694e-03
[51,] -1.085433e-02 -2.738779e-05
[52,] -3.903175e-02 -1.085433e-02
[53,] -4.723900e-02 -3.903175e-02
[54,] -4.602532e-02 -4.723900e-02
[55,] -4.285433e-02 -4.602532e-02
[56,] -1.160382e-01 -4.285433e-02
[57,] -1.530040e-01 -1.160382e-01
[58,] -1.683971e-01 -1.530040e-01
[59,] -2.047134e-01 -1.683971e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.585112e-03 2.148894e-02
2 3.041206e-02 5.585112e-03
3 1.868771e-02 3.041206e-02
4 8.522704e-04 1.868771e-02
5 -2.797468e-02 8.522704e-04
6 -3.131229e-02 -2.797468e-02
7 -4.345115e-02 -3.131229e-02
8 -7.098317e-02 -4.345115e-02
9 -9.401736e-02 -7.098317e-02
10 -1.415473e-01 -9.401736e-02
11 -4.496825e-02 -1.415473e-01
12 -1.049670e-01 -4.496825e-02
13 -1.352833e-01 -1.049670e-01
14 -1.442491e-01 -1.352833e-01
15 -9.521490e-02 -1.442491e-01
16 -1.291148e-02 -9.521490e-02
17 -2.173843e-02 -1.291148e-02
18 -2.052475e-02 -2.173843e-02
19 3.665243e-02 -2.052475e-02
20 8.305202e-02 3.665243e-02
21 1.076033e-01 8.305202e-02
22 9.827855e-02 1.076033e-01
23 7.485762e-02 9.827855e-02
24 2.547854e-02 7.485762e-02
25 1.957470e-02 2.547854e-02
26 6.057606e-03 1.957470e-02
27 -3.391097e-03 6.057606e-03
28 -1.957058e-02 -3.391097e-03
29 7.050704e-04 -1.957058e-02
30 2.677299e-03 7.050704e-04
31 -1.718591e-02 2.677299e-03
32 7.617948e-02 -1.718591e-02
33 1.170769e-01 7.617948e-02
34 1.700278e-01 1.170769e-01
35 1.473654e-01 1.700278e-01
36 9.798631e-02 1.473654e-01
37 1.175312e-01 9.798631e-02
38 1.078068e-01 1.175312e-01
39 9.077263e-02 1.078068e-01
40 7.066154e-02 9.077263e-02
41 9.624704e-02 7.066154e-02
42 9.518507e-02 9.624704e-02
43 6.683896e-02 9.518507e-02
44 2.778984e-02 6.683896e-02
45 2.234114e-02 2.778984e-02
46 4.163813e-02 2.234114e-02
47 2.745865e-02 4.163813e-02
48 -3.998676e-02 2.745865e-02
49 -7.407694e-03 -3.998676e-02
50 -2.738779e-05 -7.407694e-03
51 -1.085433e-02 -2.738779e-05
52 -3.903175e-02 -1.085433e-02
53 -4.723900e-02 -3.903175e-02
54 -4.602532e-02 -4.723900e-02
55 -4.285433e-02 -4.602532e-02
56 -1.160382e-01 -4.285433e-02
57 -1.530040e-01 -1.160382e-01
58 -1.683971e-01 -1.530040e-01
59 -2.047134e-01 -1.683971e-01
> 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/7gyzc1258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sj991258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w5pk1258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w49c1258559591.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ebys1258559591.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/12828a1258559591.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/131jdp1258559591.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/140zfd1258559591.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/15ee631258559591.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/16odzq1258559591.tab")
+ }
>
> system("convert tmp/13xma1258559591.ps tmp/13xma1258559591.png")
> system("convert tmp/2e49x1258559591.ps tmp/2e49x1258559591.png")
> system("convert tmp/3jztx1258559591.ps tmp/3jztx1258559591.png")
> system("convert tmp/4xjv31258559591.ps tmp/4xjv31258559591.png")
> system("convert tmp/5c62d1258559591.ps tmp/5c62d1258559591.png")
> system("convert tmp/6glh41258559591.ps tmp/6glh41258559591.png")
> system("convert tmp/7gyzc1258559591.ps tmp/7gyzc1258559591.png")
> system("convert tmp/8sj991258559591.ps tmp/8sj991258559591.png")
> system("convert tmp/9w5pk1258559591.ps tmp/9w5pk1258559591.png")
> system("convert tmp/10w49c1258559591.ps tmp/10w49c1258559591.png")
>
>
> proc.time()
user system elapsed
2.414 1.578 5.765