R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(-999
+ ,-999
+ ,38.6
+ ,6.654
+ ,5.712
+ ,645
+ ,3
+ ,5
+ ,3
+ ,6.3
+ ,2
+ ,4.5
+ ,1
+ ,6.6
+ ,42
+ ,3
+ ,1
+ ,3
+ ,-999
+ ,-999
+ ,14
+ ,3.385
+ ,44.5
+ ,60
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,-999
+ ,-999
+ ,0.92
+ ,5.7
+ ,25
+ ,5
+ ,2
+ ,3
+ ,2.1
+ ,1.8
+ ,69
+ ,2547
+ ,4603
+ ,624
+ ,3
+ ,5
+ ,4
+ ,0.1
+ ,0.7
+ ,27
+ ,10.55
+ ,0.5
+ ,180
+ ,4
+ ,4
+ ,4
+ ,15.8
+ ,3.9
+ ,19
+ ,0.023
+ ,0.3
+ ,35
+ ,1
+ ,1
+ ,1
+ ,5.2
+ ,1
+ ,30.4
+ ,160
+ ,169
+ ,392
+ ,4
+ ,5
+ ,4
+ ,10.9
+ ,3.6
+ ,28
+ ,3.3
+ ,25.6
+ ,63
+ ,1
+ ,2
+ ,1
+ ,8.3
+ ,1.4
+ ,50
+ ,52.16
+ ,440
+ ,230
+ ,1
+ ,1
+ ,1
+ ,11
+ ,1.5
+ ,7
+ ,0.425
+ ,6.4
+ ,112
+ ,5
+ ,4
+ ,4
+ ,3.2
+ ,0.7
+ ,30
+ ,465
+ ,423
+ ,281
+ ,5
+ ,5
+ ,5
+ ,7.6
+ ,2.7
+ ,-999
+ ,0.55
+ ,2.4
+ ,-999
+ ,2
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,40
+ ,187.1
+ ,419
+ ,365
+ ,5
+ ,5
+ ,5
+ ,6.3
+ ,2.1
+ ,3.5
+ ,0.075
+ ,1.2
+ ,42
+ ,1
+ ,1
+ ,1
+ ,8.6
+ ,0
+ ,50
+ ,3
+ ,25
+ ,28
+ ,2
+ ,2
+ ,2
+ ,6.6
+ ,4.1
+ ,6
+ ,0.785
+ ,3.5
+ ,42
+ ,2
+ ,2
+ ,2
+ ,9.5
+ ,1.2
+ ,10.4
+ ,0.2
+ ,5
+ ,120
+ ,2
+ ,2
+ ,2
+ ,4.8
+ ,1.3
+ ,34
+ ,1.41
+ ,17.5
+ ,-999
+ ,1
+ ,2
+ ,1
+ ,12
+ ,6.1
+ ,7
+ ,60
+ ,81
+ ,-999
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,0.3
+ ,28
+ ,529
+ ,680
+ ,400
+ ,5
+ ,5
+ ,5
+ ,3.3
+ ,0.5
+ ,20
+ ,27.66
+ ,115
+ ,148
+ ,5
+ ,5
+ ,5
+ ,11
+ ,3.4
+ ,3.9
+ ,0.12
+ ,1
+ ,16
+ ,3
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,39.3
+ ,207
+ ,406
+ ,252
+ ,1
+ ,4
+ ,1
+ ,4.7
+ ,1.5
+ ,41
+ ,85
+ ,325
+ ,310
+ ,1
+ ,3
+ ,1
+ ,-999
+ ,-999
+ ,16.2
+ ,36.33
+ ,119.5
+ ,63
+ ,1
+ ,1
+ ,1
+ ,10.4
+ ,3.4
+ ,9
+ ,0.101
+ ,4
+ ,28
+ ,5
+ ,1
+ ,3
+ ,7.4
+ ,0.8
+ ,7.6
+ ,1.04
+ ,5.5
+ ,68
+ ,5
+ ,3
+ ,4
+ ,2.1
+ ,0.8
+ ,46
+ ,521
+ ,655
+ ,336
+ ,5
+ ,5
+ ,5
+ ,2.1
+ ,-999
+ ,22.4
+ ,100
+ ,157
+ ,100
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,-999
+ ,16.3
+ ,35
+ ,56
+ ,33
+ ,3
+ ,5
+ ,4
+ ,7.7
+ ,1.4
+ ,2.6
+ ,0.005
+ ,0.14
+ ,21.5
+ ,5
+ ,2
+ ,4
+ ,17.9
+ ,2
+ ,24
+ ,0.01
+ ,0.25
+ ,50
+ ,1
+ ,1
+ ,1
+ ,6.1
+ ,1.9
+ ,100
+ ,62
+ ,1320
+ ,267
+ ,1
+ ,1
+ ,1
+ ,8.2
+ ,2.4
+ ,-999
+ ,0.122
+ ,3
+ ,30
+ ,2
+ ,1
+ ,1
+ ,8.4
+ ,2.8
+ ,-999
+ ,1.35
+ ,8.1
+ ,45
+ ,3
+ ,1
+ ,3
+ ,11.9
+ ,1.3
+ ,3.2
+ ,0.23
+ ,0.4
+ ,19
+ ,4
+ ,1
+ ,3
+ ,10.8
+ ,2
+ ,2
+ ,0.048
+ ,0.33
+ ,30
+ ,4
+ ,1
+ ,3
+ ,13.8
+ ,5.6
+ ,5
+ ,1.7
+ ,6.3
+ ,12
+ ,2
+ ,1
+ ,1
+ ,14.3
+ ,3.1
+ ,6.5
+ ,3.5
+ ,10.8
+ ,120
+ ,2
+ ,1
+ ,1
+ ,-999
+ ,1
+ ,23.6
+ ,250
+ ,490
+ ,440
+ ,5
+ ,5
+ ,5
+ ,15.2
+ ,1.8
+ ,12
+ ,0.48
+ ,15.5
+ ,140
+ ,2
+ ,2
+ ,2
+ ,10
+ ,0.9
+ ,20.2
+ ,10
+ ,115
+ ,170
+ ,4
+ ,4
+ ,4
+ ,11.9
+ ,1.8
+ ,13
+ ,1.62
+ ,11.4
+ ,17
+ ,2
+ ,1
+ ,2
+ ,6.5
+ ,1.9
+ ,27
+ ,192
+ ,180
+ ,115
+ ,4
+ ,4
+ ,4
+ ,7.5
+ ,0.9
+ ,18
+ ,2.5
+ ,12.1
+ ,31
+ ,5
+ ,5
+ ,5
+ ,-999
+ ,-999
+ ,13.7
+ ,4.288
+ ,39.2
+ ,63
+ ,2
+ ,2
+ ,2
+ ,10.6
+ ,2.6
+ ,4.7
+ ,0.28
+ ,1.9
+ ,21
+ ,3
+ ,1
+ ,3
+ ,7.4
+ ,2.4
+ ,9.8
+ ,4.235
+ ,50.4
+ ,52
+ ,1
+ ,1
+ ,1
+ ,8.4
+ ,1.2
+ ,29
+ ,6.8
+ ,179
+ ,164
+ ,2
+ ,3
+ ,2
+ ,5.7
+ ,0.9
+ ,7
+ ,0.75
+ ,12.3
+ ,225
+ ,2
+ ,2
+ ,2
+ ,4.9
+ ,0.5
+ ,6
+ ,3.6
+ ,21
+ ,150
+ ,3
+ ,2
+ ,3
+ ,-999
+ ,-999
+ ,17
+ ,14.83
+ ,98.2
+ ,151
+ ,5
+ ,5
+ ,5
+ ,3.2
+ ,0.6
+ ,20
+ ,55.5
+ ,175
+ ,150
+ ,5
+ ,5
+ ,5
+ ,-999
+ ,-999
+ ,12.7
+ ,1.4
+ ,12.5
+ ,90
+ ,2
+ ,2
+ ,2
+ ,8.1
+ ,2.2
+ ,3.5
+ ,0.06
+ ,1
+ ,-999
+ ,3
+ ,1
+ ,2
+ ,11
+ ,2.3
+ ,4.5
+ ,0.9
+ ,2.6
+ ,60
+ ,2
+ ,1
+ ,2
+ ,4.9
+ ,0.5
+ ,7.5
+ ,2
+ ,12.3
+ ,200
+ ,3
+ ,1
+ ,3
+ ,13.2
+ ,2.6
+ ,2.3
+ ,0.104
+ ,2.5
+ ,46
+ ,3
+ ,2
+ ,2
+ ,9.7
+ ,0.6
+ ,24
+ ,4.19
+ ,58
+ ,210
+ ,4
+ ,3
+ ,4
+ ,12.8
+ ,6.6
+ ,3
+ ,3.5
+ ,3.9
+ ,14
+ ,1
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,13
+ ,4.05
+ ,17
+ ,38
+ ,3
+ ,1
+ ,1)
+ ,dim=c(9
+ ,62)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'L'
+ ,'WB'
+ ,'WBR'
+ ,'TG'
+ ,'P'
+ ,'S'
+ ,'D')
+ ,1:62))
> y <- array(NA,dim=c(9,62),dimnames=list(c('SWS','PS','L','WB','WBR','TG','P','S','D'),1:62))
> 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
SWS PS L WB WBR TG P S D
1 -999.0 -999.0 38.6 6.654 5.712 645.0 3 5 3
2 6.3 2.0 4.5 1.000 6.600 42.0 3 1 3
3 -999.0 -999.0 14.0 3.385 44.500 60.0 1 1 1
4 -999.0 -999.0 -999.0 0.920 5.700 25.0 5 2 3
5 2.1 1.8 69.0 2547.000 4603.000 624.0 3 5 4
6 0.1 0.7 27.0 10.550 0.500 180.0 4 4 4
7 15.8 3.9 19.0 0.023 0.300 35.0 1 1 1
8 5.2 1.0 30.4 160.000 169.000 392.0 4 5 4
9 10.9 3.6 28.0 3.300 25.600 63.0 1 2 1
10 8.3 1.4 50.0 52.160 440.000 230.0 1 1 1
11 11.0 1.5 7.0 0.425 6.400 112.0 5 4 4
12 3.2 0.7 30.0 465.000 423.000 281.0 5 5 5
13 7.6 2.7 -999.0 0.550 2.400 -999.0 2 1 2
14 -999.0 -999.0 40.0 187.100 419.000 365.0 5 5 5
15 6.3 2.1 3.5 0.075 1.200 42.0 1 1 1
16 8.6 0.0 50.0 3.000 25.000 28.0 2 2 2
17 6.6 4.1 6.0 0.785 3.500 42.0 2 2 2
18 9.5 1.2 10.4 0.200 5.000 120.0 2 2 2
19 4.8 1.3 34.0 1.410 17.500 -999.0 1 2 1
20 12.0 6.1 7.0 60.000 81.000 -999.0 1 1 1
21 -999.0 0.3 28.0 529.000 680.000 400.0 5 5 5
22 3.3 0.5 20.0 27.660 115.000 148.0 5 5 5
23 11.0 3.4 3.9 0.120 1.000 16.0 3 1 2
24 -999.0 -999.0 39.3 207.000 406.000 252.0 1 4 1
25 4.7 1.5 41.0 85.000 325.000 310.0 1 3 1
26 -999.0 -999.0 16.2 36.330 119.500 63.0 1 1 1
27 10.4 3.4 9.0 0.101 4.000 28.0 5 1 3
28 7.4 0.8 7.6 1.040 5.500 68.0 5 3 4
29 2.1 0.8 46.0 521.000 655.000 336.0 5 5 5
30 2.1 -999.0 22.4 100.000 157.000 100.0 1 1 1
31 -999.0 -999.0 16.3 35.000 56.000 33.0 3 5 4
32 7.7 1.4 2.6 0.005 0.140 21.5 5 2 4
33 17.9 2.0 24.0 0.010 0.250 50.0 1 1 1
34 6.1 1.9 100.0 62.000 1320.000 267.0 1 1 1
35 8.2 2.4 -999.0 0.122 3.000 30.0 2 1 1
36 8.4 2.8 -999.0 1.350 8.100 45.0 3 1 3
37 11.9 1.3 3.2 0.230 0.400 19.0 4 1 3
38 10.8 2.0 2.0 0.048 0.330 30.0 4 1 3
39 13.8 5.6 5.0 1.700 6.300 12.0 2 1 1
40 14.3 3.1 6.5 3.500 10.800 120.0 2 1 1
41 -999.0 1.0 23.6 250.000 490.000 440.0 5 5 5
42 15.2 1.8 12.0 0.480 15.500 140.0 2 2 2
43 10.0 0.9 20.2 10.000 115.000 170.0 4 4 4
44 11.9 1.8 13.0 1.620 11.400 17.0 2 1 2
45 6.5 1.9 27.0 192.000 180.000 115.0 4 4 4
46 7.5 0.9 18.0 2.500 12.100 31.0 5 5 5
47 -999.0 -999.0 13.7 4.288 39.200 63.0 2 2 2
48 10.6 2.6 4.7 0.280 1.900 21.0 3 1 3
49 7.4 2.4 9.8 4.235 50.400 52.0 1 1 1
50 8.4 1.2 29.0 6.800 179.000 164.0 2 3 2
51 5.7 0.9 7.0 0.750 12.300 225.0 2 2 2
52 4.9 0.5 6.0 3.600 21.000 150.0 3 2 3
53 -999.0 -999.0 17.0 14.830 98.200 151.0 5 5 5
54 3.2 0.6 20.0 55.500 175.000 150.0 5 5 5
55 -999.0 -999.0 12.7 1.400 12.500 90.0 2 2 2
56 8.1 2.2 3.5 0.060 1.000 -999.0 3 1 2
57 11.0 2.3 4.5 0.900 2.600 60.0 2 1 2
58 4.9 0.5 7.5 2.000 12.300 200.0 3 1 3
59 13.2 2.6 2.3 0.104 2.500 46.0 3 2 2
60 9.7 0.6 24.0 4.190 58.000 210.0 4 3 4
61 12.8 6.6 3.0 3.500 3.900 14.0 1 1 2
62 -999.0 -999.0 13.0 4.050 17.000 38.0 3 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PS L WB WBR TG
101.747169 0.858986 0.048356 0.008978 -0.002296 -0.046586
P S D
-24.623378 -39.238612 11.529514
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-825.25 -34.52 9.05 53.31 813.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.747169 69.112230 1.472 0.147
PS 0.858986 0.075204 11.422 6.57e-16 ***
L 0.048356 0.118259 0.409 0.684
WB 0.008978 0.300033 0.030 0.976
WBR -0.002296 0.163977 -0.014 0.989
TG -0.046586 0.104604 -0.445 0.658
P -24.623378 51.141729 -0.481 0.632
S -39.238612 33.633703 -1.167 0.249
D 11.529514 67.319369 0.171 0.865
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 215.8 on 53 degrees of freedom
Multiple R-squared: 0.7633, Adjusted R-squared: 0.7276
F-statistic: 21.37 on 8 and 53 DF, p-value: 4.613e-14
> 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,] 9.969739e-07 1.993948e-06 9.999990e-01
[2,] 8.492604e-08 1.698521e-07 9.999999e-01
[3,] 2.465453e-09 4.930905e-09 1.000000e+00
[4,] 4.779755e-11 9.559510e-11 1.000000e+00
[5,] 7.218356e-13 1.443671e-12 1.000000e+00
[6,] 1.795647e-14 3.591294e-14 1.000000e+00
[7,] 3.204559e-16 6.409118e-16 1.000000e+00
[8,] 9.980588e-18 1.996118e-17 1.000000e+00
[9,] 1.315423e-19 2.630846e-19 1.000000e+00
[10,] 7.058429e-01 5.883141e-01 2.941571e-01
[11,] 6.376337e-01 7.247325e-01 3.623663e-01
[12,] 5.489556e-01 9.020887e-01 4.510444e-01
[13,] 4.629125e-01 9.258250e-01 5.370875e-01
[14,] 3.799495e-01 7.598989e-01 6.200505e-01
[15,] 3.332577e-01 6.665155e-01 6.667423e-01
[16,] 2.583052e-01 5.166103e-01 7.416948e-01
[17,] 2.026857e-01 4.053714e-01 7.973143e-01
[18,] 2.288031e-01 4.576063e-01 7.711969e-01
[19,] 9.995761e-01 8.477425e-04 4.238712e-04
[20,] 9.990915e-01 1.816974e-03 9.084872e-04
[21,] 9.980950e-01 3.810050e-03 1.905025e-03
[22,] 9.962090e-01 7.581948e-03 3.790974e-03
[23,] 9.998839e-01 2.321254e-04 1.160627e-04
[24,] 9.997310e-01 5.380997e-04 2.690498e-04
[25,] 9.999535e-01 9.302141e-05 4.651070e-05
[26,] 9.998812e-01 2.376098e-04 1.188049e-04
[27,] 9.997551e-01 4.898515e-04 2.449257e-04
[28,] 9.993623e-01 1.275402e-03 6.377012e-04
[29,] 9.984184e-01 3.163269e-03 1.581635e-03
[30,] 1.000000e+00 3.931491e-22 1.965745e-22
[31,] 1.000000e+00 5.675535e-21 2.837768e-21
[32,] 1.000000e+00 3.073984e-19 1.536992e-19
[33,] 1.000000e+00 2.015601e-17 1.007801e-17
[34,] 1.000000e+00 9.701104e-16 4.850552e-16
[35,] 1.000000e+00 7.463335e-14 3.731667e-14
[36,] 1.000000e+00 7.990545e-12 3.995273e-12
[37,] 1.000000e+00 6.267885e-10 3.133943e-10
[38,] 1.000000e+00 6.300459e-08 3.150229e-08
[39,] 9.999970e-01 6.049843e-06 3.024922e-06
> postscript(file="/var/www/html/rcomp/tmp/19y4e1293043559.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/228lz1293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/328lz1293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/428lz1293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dzl21293043559.ps",horizontal=F,onefile=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 = 62
Frequency = 1
1 2 3 4 5 6
20.9885502 -16.8997716 -188.0982109 -26.1375987 136.1864883 114.0676567
7 8 9 10 11 12
-36.2525306 166.9054449 -0.7583678 -33.4784855 146.8075995 170.9503135
13 14 15 16 17 18
-29.2705213 33.3935050 -43.1291351 10.4348258 7.6633850 16.4840449
19 20 21 22 23 24
-54.6483361 -89.8846703 -825.2502206 168.7289592 -3.0600049 -63.6593843
25 26 27 28 29 30
44.9160338 -188.1884149 34.3767045 102.4839115 171.5828204 813.8499284
31 32 33 34 35 36
-18.1120072 61.1024473 -32.0634543 -34.8700660 31.0584528 33.1786764
37 38 39 40 41 42
12.9089696 11.7796217 -15.4851876 -7.8848457 -821.7067659 22.5445881
43 44 45 46 47 48
123.9266467 -25.7920520 115.1919255 167.2211962 -135.6317470 -14.1074579
49 50 51 52 53 54
-42.0500083 56.1132297 18.0094587 27.1957439 25.3464492 168.5240475
55 56 57 58 59 60
-134.3609537 -52.1936877 -24.7210768 -9.7917343 40.5443197 86.2466926
61 62
-53.3288513 -139.8970890
> postscript(file="/var/www/html/rcomp/tmp/6dzl21293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 20.9885502 NA
1 -16.8997716 20.9885502
2 -188.0982109 -16.8997716
3 -26.1375987 -188.0982109
4 136.1864883 -26.1375987
5 114.0676567 136.1864883
6 -36.2525306 114.0676567
7 166.9054449 -36.2525306
8 -0.7583678 166.9054449
9 -33.4784855 -0.7583678
10 146.8075995 -33.4784855
11 170.9503135 146.8075995
12 -29.2705213 170.9503135
13 33.3935050 -29.2705213
14 -43.1291351 33.3935050
15 10.4348258 -43.1291351
16 7.6633850 10.4348258
17 16.4840449 7.6633850
18 -54.6483361 16.4840449
19 -89.8846703 -54.6483361
20 -825.2502206 -89.8846703
21 168.7289592 -825.2502206
22 -3.0600049 168.7289592
23 -63.6593843 -3.0600049
24 44.9160338 -63.6593843
25 -188.1884149 44.9160338
26 34.3767045 -188.1884149
27 102.4839115 34.3767045
28 171.5828204 102.4839115
29 813.8499284 171.5828204
30 -18.1120072 813.8499284
31 61.1024473 -18.1120072
32 -32.0634543 61.1024473
33 -34.8700660 -32.0634543
34 31.0584528 -34.8700660
35 33.1786764 31.0584528
36 12.9089696 33.1786764
37 11.7796217 12.9089696
38 -15.4851876 11.7796217
39 -7.8848457 -15.4851876
40 -821.7067659 -7.8848457
41 22.5445881 -821.7067659
42 123.9266467 22.5445881
43 -25.7920520 123.9266467
44 115.1919255 -25.7920520
45 167.2211962 115.1919255
46 -135.6317470 167.2211962
47 -14.1074579 -135.6317470
48 -42.0500083 -14.1074579
49 56.1132297 -42.0500083
50 18.0094587 56.1132297
51 27.1957439 18.0094587
52 25.3464492 27.1957439
53 168.5240475 25.3464492
54 -134.3609537 168.5240475
55 -52.1936877 -134.3609537
56 -24.7210768 -52.1936877
57 -9.7917343 -24.7210768
58 40.5443197 -9.7917343
59 86.2466926 40.5443197
60 -53.3288513 86.2466926
61 -139.8970890 -53.3288513
62 NA -139.8970890
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -16.8997716 20.9885502
[2,] -188.0982109 -16.8997716
[3,] -26.1375987 -188.0982109
[4,] 136.1864883 -26.1375987
[5,] 114.0676567 136.1864883
[6,] -36.2525306 114.0676567
[7,] 166.9054449 -36.2525306
[8,] -0.7583678 166.9054449
[9,] -33.4784855 -0.7583678
[10,] 146.8075995 -33.4784855
[11,] 170.9503135 146.8075995
[12,] -29.2705213 170.9503135
[13,] 33.3935050 -29.2705213
[14,] -43.1291351 33.3935050
[15,] 10.4348258 -43.1291351
[16,] 7.6633850 10.4348258
[17,] 16.4840449 7.6633850
[18,] -54.6483361 16.4840449
[19,] -89.8846703 -54.6483361
[20,] -825.2502206 -89.8846703
[21,] 168.7289592 -825.2502206
[22,] -3.0600049 168.7289592
[23,] -63.6593843 -3.0600049
[24,] 44.9160338 -63.6593843
[25,] -188.1884149 44.9160338
[26,] 34.3767045 -188.1884149
[27,] 102.4839115 34.3767045
[28,] 171.5828204 102.4839115
[29,] 813.8499284 171.5828204
[30,] -18.1120072 813.8499284
[31,] 61.1024473 -18.1120072
[32,] -32.0634543 61.1024473
[33,] -34.8700660 -32.0634543
[34,] 31.0584528 -34.8700660
[35,] 33.1786764 31.0584528
[36,] 12.9089696 33.1786764
[37,] 11.7796217 12.9089696
[38,] -15.4851876 11.7796217
[39,] -7.8848457 -15.4851876
[40,] -821.7067659 -7.8848457
[41,] 22.5445881 -821.7067659
[42,] 123.9266467 22.5445881
[43,] -25.7920520 123.9266467
[44,] 115.1919255 -25.7920520
[45,] 167.2211962 115.1919255
[46,] -135.6317470 167.2211962
[47,] -14.1074579 -135.6317470
[48,] -42.0500083 -14.1074579
[49,] 56.1132297 -42.0500083
[50,] 18.0094587 56.1132297
[51,] 27.1957439 18.0094587
[52,] 25.3464492 27.1957439
[53,] 168.5240475 25.3464492
[54,] -134.3609537 168.5240475
[55,] -52.1936877 -134.3609537
[56,] -24.7210768 -52.1936877
[57,] -9.7917343 -24.7210768
[58,] 40.5443197 -9.7917343
[59,] 86.2466926 40.5443197
[60,] -53.3288513 86.2466926
[61,] -139.8970890 -53.3288513
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -16.8997716 20.9885502
2 -188.0982109 -16.8997716
3 -26.1375987 -188.0982109
4 136.1864883 -26.1375987
5 114.0676567 136.1864883
6 -36.2525306 114.0676567
7 166.9054449 -36.2525306
8 -0.7583678 166.9054449
9 -33.4784855 -0.7583678
10 146.8075995 -33.4784855
11 170.9503135 146.8075995
12 -29.2705213 170.9503135
13 33.3935050 -29.2705213
14 -43.1291351 33.3935050
15 10.4348258 -43.1291351
16 7.6633850 10.4348258
17 16.4840449 7.6633850
18 -54.6483361 16.4840449
19 -89.8846703 -54.6483361
20 -825.2502206 -89.8846703
21 168.7289592 -825.2502206
22 -3.0600049 168.7289592
23 -63.6593843 -3.0600049
24 44.9160338 -63.6593843
25 -188.1884149 44.9160338
26 34.3767045 -188.1884149
27 102.4839115 34.3767045
28 171.5828204 102.4839115
29 813.8499284 171.5828204
30 -18.1120072 813.8499284
31 61.1024473 -18.1120072
32 -32.0634543 61.1024473
33 -34.8700660 -32.0634543
34 31.0584528 -34.8700660
35 33.1786764 31.0584528
36 12.9089696 33.1786764
37 11.7796217 12.9089696
38 -15.4851876 11.7796217
39 -7.8848457 -15.4851876
40 -821.7067659 -7.8848457
41 22.5445881 -821.7067659
42 123.9266467 22.5445881
43 -25.7920520 123.9266467
44 115.1919255 -25.7920520
45 167.2211962 115.1919255
46 -135.6317470 167.2211962
47 -14.1074579 -135.6317470
48 -42.0500083 -14.1074579
49 56.1132297 -42.0500083
50 18.0094587 56.1132297
51 27.1957439 18.0094587
52 25.3464492 27.1957439
53 168.5240475 25.3464492
54 -134.3609537 168.5240475
55 -52.1936877 -134.3609537
56 -24.7210768 -52.1936877
57 -9.7917343 -24.7210768
58 40.5443197 -9.7917343
59 86.2466926 40.5443197
60 -53.3288513 86.2466926
61 -139.8970890 -53.3288513
> 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/7oqkn1293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oqkn1293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yh181293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yh181293043559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ki0w1293043559.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/12n1g21293043559.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/131swa1293043559.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/144tvg1293043559.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/1503eh1293043560.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/164mcn1293043560.tab")
+ }
> try(system("convert tmp/19y4e1293043559.ps tmp/19y4e1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/228lz1293043559.ps tmp/228lz1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/328lz1293043559.ps tmp/328lz1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/428lz1293043559.ps tmp/428lz1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dzl21293043559.ps tmp/5dzl21293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dzl21293043559.ps tmp/6dzl21293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oqkn1293043559.ps tmp/7oqkn1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oqkn1293043559.ps tmp/8oqkn1293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yh181293043559.ps tmp/9yh181293043559.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yh181293043559.ps tmp/10yh181293043559.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.569 1.659 6.011