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(7.5,0,7.2,0,6.9,0,6.7,0,6.4,0,6.3,0,6.8,0,7.3,0,7.1,0,7.1,0,6.8,0,6.5,0,6.3,0,6.1,0,6.1,0,6.3,0,6.3,0,6,0,6.2,0,6.4,0,6.8,0,7.5,0,7.5,0,7.6,0,7.6,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,7.5,0,7.6,0,7.8,0,8.0,0,8.1,0,8.2,0,8.3,0,8.2,0,8.0,0,7.9,0,7.6,0,7.6,0,8.2,0,8.3,0,8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,1,8.2,1,8.3,1,8.5,1,8.6,1,8.6,1,8.2,1,8.1,1,8.0,1,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8.0,1,8.0,1,7.9,1,8.0,1,7.7,1,7.2,1,7.5,1,7.3,1,7.0,1,7.0,1,7.0,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1,6.8,1),dim=c(2,105),dimnames=list(c('w','d'),1:105))
> y <- array(NA,dim=c(2,105),dimnames=list(c('w','d'),1:105))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
w d
1 7.5 0
2 7.2 0
3 6.9 0
4 6.7 0
5 6.4 0
6 6.3 0
7 6.8 0
8 7.3 0
9 7.1 0
10 7.1 0
11 6.8 0
12 6.5 0
13 6.3 0
14 6.1 0
15 6.1 0
16 6.3 0
17 6.3 0
18 6.0 0
19 6.2 0
20 6.4 0
21 6.8 0
22 7.5 0
23 7.5 0
24 7.6 0
25 7.6 0
26 7.4 0
27 7.3 0
28 7.1 0
29 6.9 0
30 6.8 0
31 7.5 0
32 7.6 0
33 7.8 0
34 8.0 0
35 8.1 0
36 8.2 0
37 8.3 0
38 8.2 0
39 8.0 0
40 7.9 0
41 7.6 0
42 7.6 0
43 8.2 0
44 8.3 0
45 8.4 0
46 8.4 0
47 8.4 0
48 8.6 0
49 8.9 0
50 8.8 0
51 8.3 0
52 7.5 0
53 7.2 0
54 7.5 0
55 8.8 0
56 9.3 0
57 9.3 0
58 8.7 1
59 8.2 1
60 8.3 1
61 8.5 1
62 8.6 1
63 8.6 1
64 8.2 1
65 8.1 1
66 8.0 1
67 8.6 1
68 8.7 1
69 8.8 1
70 8.5 1
71 8.4 1
72 8.5 1
73 8.7 1
74 8.7 1
75 8.6 1
76 8.5 1
77 8.3 1
78 8.1 1
79 8.2 1
80 8.1 1
81 8.1 1
82 7.9 1
83 7.9 1
84 7.9 1
85 8.0 1
86 8.0 1
87 7.9 1
88 8.0 1
89 7.7 1
90 7.2 1
91 7.5 1
92 7.3 1
93 7.0 1
94 7.0 1
95 7.0 1
96 7.2 1
97 7.3 1
98 7.1 1
99 6.8 1
100 6.6 1
101 6.2 1
102 6.2 1
103 6.8 1
104 6.9 1
105 6.8 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d
7.4649 0.3726
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6375 -0.6649 0.0625 0.6625 1.8351
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.4649 0.1058 70.544 <2e-16 ***
d 0.3726 0.1565 2.381 0.0191 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7989 on 103 degrees of freedom
Multiple R-squared: 0.05215, Adjusted R-squared: 0.04295
F-statistic: 5.667 on 1 and 103 DF, p-value: 0.01912
> 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,] 0.229906374 0.45981275 0.770093626
[2,] 0.206031378 0.41206276 0.793968622
[3,] 0.109838201 0.21967640 0.890161799
[4,] 0.078884156 0.15776831 0.921115844
[5,] 0.042580971 0.08516194 0.957419029
[6,] 0.021845844 0.04369169 0.978154156
[7,] 0.010630992 0.02126198 0.989369008
[8,] 0.007831245 0.01566249 0.992168755
[9,] 0.008883286 0.01776657 0.991116714
[10,] 0.015153165 0.03030633 0.984846835
[11,] 0.021320111 0.04264022 0.978679889
[12,] 0.019123022 0.03824604 0.980876978
[13,] 0.017251644 0.03450329 0.982748356
[14,] 0.027639804 0.05527961 0.972360196
[15,] 0.030019931 0.06003986 0.969980069
[16,] 0.026424816 0.05284963 0.973575184
[17,] 0.020496812 0.04099362 0.979503188
[18,] 0.034625124 0.06925025 0.965374876
[19,] 0.048753572 0.09750714 0.951246428
[20,] 0.069557503 0.13911501 0.930442497
[21,] 0.088259576 0.17651915 0.911740424
[22,] 0.088185033 0.17637007 0.911814967
[23,] 0.081990408 0.16398082 0.918009592
[24,] 0.071971809 0.14394362 0.928028191
[25,] 0.065403911 0.13080782 0.934596089
[26,] 0.064186817 0.12837363 0.935813183
[27,] 0.070174815 0.14034963 0.929825185
[28,] 0.080541716 0.16108343 0.919458284
[29,] 0.104577043 0.20915409 0.895422957
[30,] 0.150023059 0.30004612 0.849976941
[31,] 0.209105408 0.41821082 0.790894592
[32,] 0.281678556 0.56335711 0.718321444
[33,] 0.365878203 0.73175641 0.634121797
[34,] 0.417814517 0.83562903 0.582185483
[35,] 0.430439774 0.86087955 0.569560226
[36,] 0.428194752 0.85638950 0.571805248
[37,] 0.410961520 0.82192304 0.589038480
[38,] 0.396725794 0.79345159 0.603274206
[39,] 0.421695667 0.84339133 0.578304333
[40,] 0.452833392 0.90566678 0.547166608
[41,] 0.489939735 0.97987947 0.510060265
[42,] 0.517971256 0.96405749 0.482028744
[43,] 0.538162573 0.92367485 0.461837427
[44,] 0.578949392 0.84210122 0.421050608
[45,] 0.660620625 0.67875875 0.339379375
[46,] 0.709905941 0.58018812 0.290094059
[47,] 0.694963927 0.61007215 0.305036073
[48,] 0.675667067 0.64866587 0.324332933
[49,] 0.710000973 0.57999805 0.289999027
[50,] 0.753616016 0.49276797 0.246383984
[51,] 0.782286303 0.43542739 0.217713697
[52,] 0.837483831 0.32503234 0.162516169
[53,] 0.873704524 0.25259095 0.126295476
[54,] 0.866174095 0.26765181 0.133825905
[55,] 0.840457141 0.31908572 0.159542859
[56,] 0.812926124 0.37414775 0.187073876
[57,] 0.793416421 0.41316716 0.206583579
[58,] 0.781586784 0.43682643 0.218413216
[59,] 0.771341891 0.45731622 0.228658109
[60,] 0.737479344 0.52504131 0.262520656
[61,] 0.697920994 0.60415801 0.302079006
[62,] 0.653350003 0.69329999 0.346649997
[63,] 0.647559027 0.70488195 0.352440973
[64,] 0.659165323 0.68166935 0.340834677
[65,] 0.692330214 0.61533957 0.307669786
[66,] 0.688689371 0.62262126 0.311310629
[67,] 0.677527802 0.64494440 0.322472198
[68,] 0.682737322 0.63452536 0.317262678
[69,] 0.726832728 0.54633454 0.273167272
[70,] 0.779457974 0.44108405 0.220542026
[71,] 0.822276652 0.35544670 0.177723348
[72,] 0.856181631 0.28763674 0.143818369
[73,] 0.870887792 0.25822442 0.129112208
[74,] 0.870814920 0.25837016 0.129185080
[75,] 0.884017443 0.23196511 0.115982557
[76,] 0.891864223 0.21627155 0.108135777
[77,] 0.903986946 0.19202611 0.096013054
[78,] 0.902776176 0.19444765 0.097223824
[79,] 0.904441995 0.19111601 0.095558005
[80,] 0.909741041 0.18051792 0.090258959
[81,] 0.929440153 0.14111969 0.070559847
[82,] 0.952616687 0.09476663 0.047383313
[83,] 0.968888307 0.06222339 0.031111693
[84,] 0.989428618 0.02114276 0.010571382
[85,] 0.994447789 0.01110442 0.005552211
[86,] 0.991986709 0.01602658 0.008013291
[87,] 0.994238868 0.01152226 0.005761132
[88,] 0.993743120 0.01251376 0.006256880
[89,] 0.989021159 0.02195768 0.010978841
[90,] 0.980892433 0.03821513 0.019107567
[91,] 0.967242091 0.06551582 0.032757909
[92,] 0.959328632 0.08134274 0.040671368
[93,] 0.968225666 0.06354867 0.031774334
[94,] 0.965079474 0.06984105 0.034920526
[95,] 0.928169197 0.14366161 0.071830803
[96,] 0.837917284 0.32416543 0.162082716
> postscript(file="/var/www/html/rcomp/tmp/1r0yi1227789725.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ce0r1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xrly1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yjqc1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58fzo1227789725.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 = 105
Frequency = 1
1 2 3 4 5 6
0.03508772 -0.26491228 -0.56491228 -0.76491228 -1.06491228 -1.16491228
7 8 9 10 11 12
-0.66491228 -0.16491228 -0.36491228 -0.36491228 -0.66491228 -0.96491228
13 14 15 16 17 18
-1.16491228 -1.36491228 -1.36491228 -1.16491228 -1.16491228 -1.46491228
19 20 21 22 23 24
-1.26491228 -1.06491228 -0.66491228 0.03508772 0.03508772 0.13508772
25 26 27 28 29 30
0.13508772 -0.06491228 -0.16491228 -0.36491228 -0.56491228 -0.66491228
31 32 33 34 35 36
0.03508772 0.13508772 0.33508772 0.53508772 0.63508772 0.73508772
37 38 39 40 41 42
0.83508772 0.73508772 0.53508772 0.43508772 0.13508772 0.13508772
43 44 45 46 47 48
0.73508772 0.83508772 0.93508772 0.93508772 0.93508772 1.13508772
49 50 51 52 53 54
1.43508772 1.33508772 0.83508772 0.03508772 -0.26491228 0.03508772
55 56 57 58 59 60
1.33508772 1.83508772 1.83508772 0.86250000 0.36250000 0.46250000
61 62 63 64 65 66
0.66250000 0.76250000 0.76250000 0.36250000 0.26250000 0.16250000
67 68 69 70 71 72
0.76250000 0.86250000 0.96250000 0.66250000 0.56250000 0.66250000
73 74 75 76 77 78
0.86250000 0.86250000 0.76250000 0.66250000 0.46250000 0.26250000
79 80 81 82 83 84
0.36250000 0.26250000 0.26250000 0.06250000 0.06250000 0.06250000
85 86 87 88 89 90
0.16250000 0.16250000 0.06250000 0.16250000 -0.13750000 -0.63750000
91 92 93 94 95 96
-0.33750000 -0.53750000 -0.83750000 -0.83750000 -0.83750000 -0.63750000
97 98 99 100 101 102
-0.53750000 -0.73750000 -1.03750000 -1.23750000 -1.63750000 -1.63750000
103 104 105
-1.03750000 -0.93750000 -1.03750000
> postscript(file="/var/www/html/rcomp/tmp/6ox7w1227789725.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 0.03508772 NA
1 -0.26491228 0.03508772
2 -0.56491228 -0.26491228
3 -0.76491228 -0.56491228
4 -1.06491228 -0.76491228
5 -1.16491228 -1.06491228
6 -0.66491228 -1.16491228
7 -0.16491228 -0.66491228
8 -0.36491228 -0.16491228
9 -0.36491228 -0.36491228
10 -0.66491228 -0.36491228
11 -0.96491228 -0.66491228
12 -1.16491228 -0.96491228
13 -1.36491228 -1.16491228
14 -1.36491228 -1.36491228
15 -1.16491228 -1.36491228
16 -1.16491228 -1.16491228
17 -1.46491228 -1.16491228
18 -1.26491228 -1.46491228
19 -1.06491228 -1.26491228
20 -0.66491228 -1.06491228
21 0.03508772 -0.66491228
22 0.03508772 0.03508772
23 0.13508772 0.03508772
24 0.13508772 0.13508772
25 -0.06491228 0.13508772
26 -0.16491228 -0.06491228
27 -0.36491228 -0.16491228
28 -0.56491228 -0.36491228
29 -0.66491228 -0.56491228
30 0.03508772 -0.66491228
31 0.13508772 0.03508772
32 0.33508772 0.13508772
33 0.53508772 0.33508772
34 0.63508772 0.53508772
35 0.73508772 0.63508772
36 0.83508772 0.73508772
37 0.73508772 0.83508772
38 0.53508772 0.73508772
39 0.43508772 0.53508772
40 0.13508772 0.43508772
41 0.13508772 0.13508772
42 0.73508772 0.13508772
43 0.83508772 0.73508772
44 0.93508772 0.83508772
45 0.93508772 0.93508772
46 0.93508772 0.93508772
47 1.13508772 0.93508772
48 1.43508772 1.13508772
49 1.33508772 1.43508772
50 0.83508772 1.33508772
51 0.03508772 0.83508772
52 -0.26491228 0.03508772
53 0.03508772 -0.26491228
54 1.33508772 0.03508772
55 1.83508772 1.33508772
56 1.83508772 1.83508772
57 0.86250000 1.83508772
58 0.36250000 0.86250000
59 0.46250000 0.36250000
60 0.66250000 0.46250000
61 0.76250000 0.66250000
62 0.76250000 0.76250000
63 0.36250000 0.76250000
64 0.26250000 0.36250000
65 0.16250000 0.26250000
66 0.76250000 0.16250000
67 0.86250000 0.76250000
68 0.96250000 0.86250000
69 0.66250000 0.96250000
70 0.56250000 0.66250000
71 0.66250000 0.56250000
72 0.86250000 0.66250000
73 0.86250000 0.86250000
74 0.76250000 0.86250000
75 0.66250000 0.76250000
76 0.46250000 0.66250000
77 0.26250000 0.46250000
78 0.36250000 0.26250000
79 0.26250000 0.36250000
80 0.26250000 0.26250000
81 0.06250000 0.26250000
82 0.06250000 0.06250000
83 0.06250000 0.06250000
84 0.16250000 0.06250000
85 0.16250000 0.16250000
86 0.06250000 0.16250000
87 0.16250000 0.06250000
88 -0.13750000 0.16250000
89 -0.63750000 -0.13750000
90 -0.33750000 -0.63750000
91 -0.53750000 -0.33750000
92 -0.83750000 -0.53750000
93 -0.83750000 -0.83750000
94 -0.83750000 -0.83750000
95 -0.63750000 -0.83750000
96 -0.53750000 -0.63750000
97 -0.73750000 -0.53750000
98 -1.03750000 -0.73750000
99 -1.23750000 -1.03750000
100 -1.63750000 -1.23750000
101 -1.63750000 -1.63750000
102 -1.03750000 -1.63750000
103 -0.93750000 -1.03750000
104 -1.03750000 -0.93750000
105 NA -1.03750000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.26491228 0.03508772
[2,] -0.56491228 -0.26491228
[3,] -0.76491228 -0.56491228
[4,] -1.06491228 -0.76491228
[5,] -1.16491228 -1.06491228
[6,] -0.66491228 -1.16491228
[7,] -0.16491228 -0.66491228
[8,] -0.36491228 -0.16491228
[9,] -0.36491228 -0.36491228
[10,] -0.66491228 -0.36491228
[11,] -0.96491228 -0.66491228
[12,] -1.16491228 -0.96491228
[13,] -1.36491228 -1.16491228
[14,] -1.36491228 -1.36491228
[15,] -1.16491228 -1.36491228
[16,] -1.16491228 -1.16491228
[17,] -1.46491228 -1.16491228
[18,] -1.26491228 -1.46491228
[19,] -1.06491228 -1.26491228
[20,] -0.66491228 -1.06491228
[21,] 0.03508772 -0.66491228
[22,] 0.03508772 0.03508772
[23,] 0.13508772 0.03508772
[24,] 0.13508772 0.13508772
[25,] -0.06491228 0.13508772
[26,] -0.16491228 -0.06491228
[27,] -0.36491228 -0.16491228
[28,] -0.56491228 -0.36491228
[29,] -0.66491228 -0.56491228
[30,] 0.03508772 -0.66491228
[31,] 0.13508772 0.03508772
[32,] 0.33508772 0.13508772
[33,] 0.53508772 0.33508772
[34,] 0.63508772 0.53508772
[35,] 0.73508772 0.63508772
[36,] 0.83508772 0.73508772
[37,] 0.73508772 0.83508772
[38,] 0.53508772 0.73508772
[39,] 0.43508772 0.53508772
[40,] 0.13508772 0.43508772
[41,] 0.13508772 0.13508772
[42,] 0.73508772 0.13508772
[43,] 0.83508772 0.73508772
[44,] 0.93508772 0.83508772
[45,] 0.93508772 0.93508772
[46,] 0.93508772 0.93508772
[47,] 1.13508772 0.93508772
[48,] 1.43508772 1.13508772
[49,] 1.33508772 1.43508772
[50,] 0.83508772 1.33508772
[51,] 0.03508772 0.83508772
[52,] -0.26491228 0.03508772
[53,] 0.03508772 -0.26491228
[54,] 1.33508772 0.03508772
[55,] 1.83508772 1.33508772
[56,] 1.83508772 1.83508772
[57,] 0.86250000 1.83508772
[58,] 0.36250000 0.86250000
[59,] 0.46250000 0.36250000
[60,] 0.66250000 0.46250000
[61,] 0.76250000 0.66250000
[62,] 0.76250000 0.76250000
[63,] 0.36250000 0.76250000
[64,] 0.26250000 0.36250000
[65,] 0.16250000 0.26250000
[66,] 0.76250000 0.16250000
[67,] 0.86250000 0.76250000
[68,] 0.96250000 0.86250000
[69,] 0.66250000 0.96250000
[70,] 0.56250000 0.66250000
[71,] 0.66250000 0.56250000
[72,] 0.86250000 0.66250000
[73,] 0.86250000 0.86250000
[74,] 0.76250000 0.86250000
[75,] 0.66250000 0.76250000
[76,] 0.46250000 0.66250000
[77,] 0.26250000 0.46250000
[78,] 0.36250000 0.26250000
[79,] 0.26250000 0.36250000
[80,] 0.26250000 0.26250000
[81,] 0.06250000 0.26250000
[82,] 0.06250000 0.06250000
[83,] 0.06250000 0.06250000
[84,] 0.16250000 0.06250000
[85,] 0.16250000 0.16250000
[86,] 0.06250000 0.16250000
[87,] 0.16250000 0.06250000
[88,] -0.13750000 0.16250000
[89,] -0.63750000 -0.13750000
[90,] -0.33750000 -0.63750000
[91,] -0.53750000 -0.33750000
[92,] -0.83750000 -0.53750000
[93,] -0.83750000 -0.83750000
[94,] -0.83750000 -0.83750000
[95,] -0.63750000 -0.83750000
[96,] -0.53750000 -0.63750000
[97,] -0.73750000 -0.53750000
[98,] -1.03750000 -0.73750000
[99,] -1.23750000 -1.03750000
[100,] -1.63750000 -1.23750000
[101,] -1.63750000 -1.63750000
[102,] -1.03750000 -1.63750000
[103,] -0.93750000 -1.03750000
[104,] -1.03750000 -0.93750000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.26491228 0.03508772
2 -0.56491228 -0.26491228
3 -0.76491228 -0.56491228
4 -1.06491228 -0.76491228
5 -1.16491228 -1.06491228
6 -0.66491228 -1.16491228
7 -0.16491228 -0.66491228
8 -0.36491228 -0.16491228
9 -0.36491228 -0.36491228
10 -0.66491228 -0.36491228
11 -0.96491228 -0.66491228
12 -1.16491228 -0.96491228
13 -1.36491228 -1.16491228
14 -1.36491228 -1.36491228
15 -1.16491228 -1.36491228
16 -1.16491228 -1.16491228
17 -1.46491228 -1.16491228
18 -1.26491228 -1.46491228
19 -1.06491228 -1.26491228
20 -0.66491228 -1.06491228
21 0.03508772 -0.66491228
22 0.03508772 0.03508772
23 0.13508772 0.03508772
24 0.13508772 0.13508772
25 -0.06491228 0.13508772
26 -0.16491228 -0.06491228
27 -0.36491228 -0.16491228
28 -0.56491228 -0.36491228
29 -0.66491228 -0.56491228
30 0.03508772 -0.66491228
31 0.13508772 0.03508772
32 0.33508772 0.13508772
33 0.53508772 0.33508772
34 0.63508772 0.53508772
35 0.73508772 0.63508772
36 0.83508772 0.73508772
37 0.73508772 0.83508772
38 0.53508772 0.73508772
39 0.43508772 0.53508772
40 0.13508772 0.43508772
41 0.13508772 0.13508772
42 0.73508772 0.13508772
43 0.83508772 0.73508772
44 0.93508772 0.83508772
45 0.93508772 0.93508772
46 0.93508772 0.93508772
47 1.13508772 0.93508772
48 1.43508772 1.13508772
49 1.33508772 1.43508772
50 0.83508772 1.33508772
51 0.03508772 0.83508772
52 -0.26491228 0.03508772
53 0.03508772 -0.26491228
54 1.33508772 0.03508772
55 1.83508772 1.33508772
56 1.83508772 1.83508772
57 0.86250000 1.83508772
58 0.36250000 0.86250000
59 0.46250000 0.36250000
60 0.66250000 0.46250000
61 0.76250000 0.66250000
62 0.76250000 0.76250000
63 0.36250000 0.76250000
64 0.26250000 0.36250000
65 0.16250000 0.26250000
66 0.76250000 0.16250000
67 0.86250000 0.76250000
68 0.96250000 0.86250000
69 0.66250000 0.96250000
70 0.56250000 0.66250000
71 0.66250000 0.56250000
72 0.86250000 0.66250000
73 0.86250000 0.86250000
74 0.76250000 0.86250000
75 0.66250000 0.76250000
76 0.46250000 0.66250000
77 0.26250000 0.46250000
78 0.36250000 0.26250000
79 0.26250000 0.36250000
80 0.26250000 0.26250000
81 0.06250000 0.26250000
82 0.06250000 0.06250000
83 0.06250000 0.06250000
84 0.16250000 0.06250000
85 0.16250000 0.16250000
86 0.06250000 0.16250000
87 0.16250000 0.06250000
88 -0.13750000 0.16250000
89 -0.63750000 -0.13750000
90 -0.33750000 -0.63750000
91 -0.53750000 -0.33750000
92 -0.83750000 -0.53750000
93 -0.83750000 -0.83750000
94 -0.83750000 -0.83750000
95 -0.63750000 -0.83750000
96 -0.53750000 -0.63750000
97 -0.73750000 -0.53750000
98 -1.03750000 -0.73750000
99 -1.23750000 -1.03750000
100 -1.63750000 -1.23750000
101 -1.63750000 -1.63750000
102 -1.03750000 -1.63750000
103 -0.93750000 -1.03750000
104 -1.03750000 -0.93750000
> 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/7jopc1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86fs61227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95w3r1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bdyn1227789725.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11673r1227789725.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/12gsk71227789725.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/133mu71227789726.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/1433j01227789726.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/15p8d61227789726.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/166d5t1227789726.tab")
+ }
>
> system("convert tmp/1r0yi1227789725.ps tmp/1r0yi1227789725.png")
> system("convert tmp/2ce0r1227789725.ps tmp/2ce0r1227789725.png")
> system("convert tmp/3xrly1227789725.ps tmp/3xrly1227789725.png")
> system("convert tmp/4yjqc1227789725.ps tmp/4yjqc1227789725.png")
> system("convert tmp/58fzo1227789725.ps tmp/58fzo1227789725.png")
> system("convert tmp/6ox7w1227789725.ps tmp/6ox7w1227789725.png")
> system("convert tmp/7jopc1227789725.ps tmp/7jopc1227789725.png")
> system("convert tmp/86fs61227789725.ps tmp/86fs61227789725.png")
> system("convert tmp/95w3r1227789725.ps tmp/95w3r1227789725.png")
> system("convert tmp/10bdyn1227789725.ps tmp/10bdyn1227789725.png")
>
>
> proc.time()
user system elapsed
2.933 1.587 3.322