R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(37,30,47,35,30,43,82,40,47,19,52,136,80,42,54,66,81,63,137,72,107,58,36,52,79,77,54,84,48,96,83,66,61,53,30,74,69,59,42,65,70,100,63,105,82,81,75,102,121,98,76,77,63,37,35,23,40,29,37,51,20,28,13,22,25,13,16,13,16,17,9,17,25,14,8,7,10,7,10,3),dim=c(1,80),dimnames=list(c('casualties'),1:80))
> y <- array(NA,dim=c(1,80),dimnames=list(c('casualties'),1:80))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
casualties M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 37 1 0 0 0 0 0 0 0 0 0 0
2 30 0 1 0 0 0 0 0 0 0 0 0
3 47 0 0 1 0 0 0 0 0 0 0 0
4 35 0 0 0 1 0 0 0 0 0 0 0
5 30 0 0 0 0 1 0 0 0 0 0 0
6 43 0 0 0 0 0 1 0 0 0 0 0
7 82 0 0 0 0 0 0 1 0 0 0 0
8 40 0 0 0 0 0 0 0 1 0 0 0
9 47 0 0 0 0 0 0 0 0 1 0 0
10 19 0 0 0 0 0 0 0 0 0 1 0
11 52 0 0 0 0 0 0 0 0 0 0 1
12 136 0 0 0 0 0 0 0 0 0 0 0
13 80 1 0 0 0 0 0 0 0 0 0 0
14 42 0 1 0 0 0 0 0 0 0 0 0
15 54 0 0 1 0 0 0 0 0 0 0 0
16 66 0 0 0 1 0 0 0 0 0 0 0
17 81 0 0 0 0 1 0 0 0 0 0 0
18 63 0 0 0 0 0 1 0 0 0 0 0
19 137 0 0 0 0 0 0 1 0 0 0 0
20 72 0 0 0 0 0 0 0 1 0 0 0
21 107 0 0 0 0 0 0 0 0 1 0 0
22 58 0 0 0 0 0 0 0 0 0 1 0
23 36 0 0 0 0 0 0 0 0 0 0 1
24 52 0 0 0 0 0 0 0 0 0 0 0
25 79 1 0 0 0 0 0 0 0 0 0 0
26 77 0 1 0 0 0 0 0 0 0 0 0
27 54 0 0 1 0 0 0 0 0 0 0 0
28 84 0 0 0 1 0 0 0 0 0 0 0
29 48 0 0 0 0 1 0 0 0 0 0 0
30 96 0 0 0 0 0 1 0 0 0 0 0
31 83 0 0 0 0 0 0 1 0 0 0 0
32 66 0 0 0 0 0 0 0 1 0 0 0
33 61 0 0 0 0 0 0 0 0 1 0 0
34 53 0 0 0 0 0 0 0 0 0 1 0
35 30 0 0 0 0 0 0 0 0 0 0 1
36 74 0 0 0 0 0 0 0 0 0 0 0
37 69 1 0 0 0 0 0 0 0 0 0 0
38 59 0 1 0 0 0 0 0 0 0 0 0
39 42 0 0 1 0 0 0 0 0 0 0 0
40 65 0 0 0 1 0 0 0 0 0 0 0
41 70 0 0 0 0 1 0 0 0 0 0 0
42 100 0 0 0 0 0 1 0 0 0 0 0
43 63 0 0 0 0 0 0 1 0 0 0 0
44 105 0 0 0 0 0 0 0 1 0 0 0
45 82 0 0 0 0 0 0 0 0 1 0 0
46 81 0 0 0 0 0 0 0 0 0 1 0
47 75 0 0 0 0 0 0 0 0 0 0 1
48 102 0 0 0 0 0 0 0 0 0 0 0
49 121 1 0 0 0 0 0 0 0 0 0 0
50 98 0 1 0 0 0 0 0 0 0 0 0
51 76 0 0 1 0 0 0 0 0 0 0 0
52 77 0 0 0 1 0 0 0 0 0 0 0
53 63 0 0 0 0 1 0 0 0 0 0 0
54 37 0 0 0 0 0 1 0 0 0 0 0
55 35 0 0 0 0 0 0 1 0 0 0 0
56 23 0 0 0 0 0 0 0 1 0 0 0
57 40 0 0 0 0 0 0 0 0 1 0 0
58 29 0 0 0 0 0 0 0 0 0 1 0
59 37 0 0 0 0 0 0 0 0 0 0 1
60 51 0 0 0 0 0 0 0 0 0 0 0
61 20 1 0 0 0 0 0 0 0 0 0 0
62 28 0 1 0 0 0 0 0 0 0 0 0
63 13 0 0 1 0 0 0 0 0 0 0 0
64 22 0 0 0 1 0 0 0 0 0 0 0
65 25 0 0 0 0 1 0 0 0 0 0 0
66 13 0 0 0 0 0 1 0 0 0 0 0
67 16 0 0 0 0 0 0 1 0 0 0 0
68 13 0 0 0 0 0 0 0 1 0 0 0
69 16 0 0 0 0 0 0 0 0 1 0 0
70 17 0 0 0 0 0 0 0 0 0 1 0
71 9 0 0 0 0 0 0 0 0 0 0 1
72 17 0 0 0 0 0 0 0 0 0 0 0
73 25 1 0 0 0 0 0 0 0 0 0 0
74 14 0 1 0 0 0 0 0 0 0 0 0
75 8 0 0 1 0 0 0 0 0 0 0 0
76 7 0 0 0 1 0 0 0 0 0 0 0
77 10 0 0 0 0 1 0 0 0 0 0 0
78 7 0 0 0 0 0 1 0 0 0 0 0
79 10 0 0 0 0 0 0 1 0 0 0 0
80 3 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
72.00 -10.43 -22.29 -30.00 -21.14 -25.29
M6 M7 M8 M9 M10 M11
-20.71 -11.14 -26.00 -13.17 -29.17 -32.17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-55.000 -24.018 0.643 21.393 76.143
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.00 13.47 5.345 1.14e-06 ***
M1 -10.43 18.36 -0.568 0.5718
M2 -22.29 18.36 -1.214 0.2289
M3 -30.00 18.36 -1.634 0.1068
M4 -21.14 18.36 -1.152 0.2534
M5 -25.29 18.36 -1.377 0.1729
M6 -20.71 18.36 -1.128 0.2631
M7 -11.14 18.36 -0.607 0.5459
M8 -26.00 18.36 -1.416 0.1612
M9 -13.17 19.05 -0.691 0.4918
M10 -29.17 19.05 -1.531 0.1304
M11 -32.17 19.05 -1.689 0.0959 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 33 on 68 degrees of freedom
Multiple R-squared: 0.08022, Adjusted R-squared: -0.06857
F-statistic: 0.5392 on 11 and 68 DF, p-value: 0.8697
> 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,] 0.158475562 0.3169511233 0.8415244384
[2,] 0.123993256 0.2479865115 0.8760067442
[3,] 0.187887423 0.3757748464 0.8121125768
[4,] 0.120393022 0.2407860442 0.8796069779
[5,] 0.219515918 0.4390318363 0.7804840818
[6,] 0.184623973 0.3692479468 0.8153760266
[7,] 0.275707867 0.5514157334 0.7242921333
[8,] 0.250432132 0.5008642630 0.7495678685
[9,] 0.183105851 0.3662117020 0.8168941490
[10,] 0.339723828 0.6794476558 0.6602761721
[11,] 0.278289347 0.5565786938 0.7217106531
[12,] 0.274465690 0.5489313798 0.7255343101
[13,] 0.208286392 0.4165727838 0.7917136081
[14,] 0.197896909 0.3957938172 0.8021030914
[15,] 0.144595597 0.2891911938 0.8554044031
[16,] 0.170231826 0.3404636512 0.8297681744
[17,] 0.163399393 0.3267987862 0.8366006069
[18,] 0.127304042 0.2546080841 0.8726959579
[19,] 0.095349182 0.1906983642 0.9046508179
[20,] 0.068746065 0.1374921300 0.9312539350
[21,] 0.048255663 0.0965113259 0.9517443371
[22,] 0.035270648 0.0705412970 0.9647293515
[23,] 0.022876726 0.0457534518 0.9771232741
[24,] 0.014674879 0.0293497580 0.9853251210
[25,] 0.009121178 0.0182423557 0.9908788222
[26,] 0.005912627 0.0118252532 0.9940873734
[27,] 0.004429385 0.0088587694 0.9955706153
[28,] 0.008795546 0.0175910929 0.9912044536
[29,] 0.010269542 0.0205390841 0.9897304579
[30,] 0.037224671 0.0744493424 0.9627753288
[31,] 0.037100425 0.0742008500 0.9628995750
[32,] 0.053031221 0.1060624418 0.9469687791
[33,] 0.067202204 0.1344044078 0.9327977961
[34,] 0.096783717 0.1935674347 0.9032162826
[35,] 0.406877946 0.8137558911 0.5931220545
[36,] 0.714201260 0.5715974797 0.2857987399
[37,] 0.888107396 0.2237852079 0.1118926039
[38,] 0.979427497 0.0411450061 0.0205725030
[39,] 0.995200177 0.0095996454 0.0047998227
[40,] 0.997141513 0.0057169744 0.0028584872
[41,] 0.998151540 0.0036969203 0.0018484601
[42,] 0.997801094 0.0043978119 0.0021989059
[43,] 0.997934999 0.0041300020 0.0020650010
[44,] 0.996077341 0.0078453173 0.0039226587
[45,] 0.997256499 0.0054870021 0.0027435011
[46,] 0.999689476 0.0006210488 0.0003105244
[47,] 0.999149479 0.0017010411 0.0008505206
[48,] 0.998573344 0.0028533125 0.0014266562
[49,] 0.995214088 0.0095718247 0.0047859124
[50,] 0.992750923 0.0144981542 0.0072490771
[51,] 0.990914548 0.0181709049 0.0090854525
> postscript(file="/var/wessaorg/rcomp/tmp/12ve41352473662.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2afkw1352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3al8l1352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4kjvn1352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5jqc61352473662.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 = 80
Frequency = 1
1 2 3 4 5
-2.457143e+01 -1.971429e+01 5.000000e+00 -1.585714e+01 -1.671429e+01
6 7 8 9 10
-8.285714e+00 2.114286e+01 -6.000000e+00 -1.183333e+01 -2.383333e+01
11 12 13 14 15
1.216667e+01 6.400000e+01 1.842857e+01 -7.714286e+00 1.200000e+01
16 17 18 19 20
1.514286e+01 3.428571e+01 1.171429e+01 7.614286e+01 2.600000e+01
21 22 23 24 25
4.816667e+01 1.516667e+01 -3.833333e+00 -2.000000e+01 1.742857e+01
26 27 28 29 30
2.728571e+01 1.200000e+01 3.314286e+01 1.285714e+00 4.471429e+01
31 32 33 34 35
2.214286e+01 2.000000e+01 2.166667e+00 1.016667e+01 -9.833333e+00
36 37 38 39 40
2.000000e+00 7.428571e+00 9.285714e+00 5.149093e-15 1.414286e+01
41 42 43 44 45
2.328571e+01 4.871429e+01 2.142857e+00 5.900000e+01 2.316667e+01
46 47 48 49 50
3.816667e+01 3.516667e+01 3.000000e+01 5.942857e+01 4.828571e+01
51 52 53 54 55
3.400000e+01 2.614286e+01 1.628571e+01 -1.428571e+01 -2.585714e+01
56 57 58 59 60
-2.300000e+01 -1.883333e+01 -1.383333e+01 -2.833333e+00 -2.100000e+01
61 62 63 64 65
-4.157143e+01 -2.171429e+01 -2.900000e+01 -2.885714e+01 -2.171429e+01
66 67 68 69 70
-3.828571e+01 -4.485714e+01 -3.300000e+01 -4.283333e+01 -2.583333e+01
71 72 73 74 75
-3.083333e+01 -5.500000e+01 -3.657143e+01 -3.571429e+01 -3.400000e+01
76 77 78 79 80
-4.385714e+01 -3.671429e+01 -4.428571e+01 -5.085714e+01 -4.300000e+01
> postscript(file="/var/wessaorg/rcomp/tmp/6lxd91352473662.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 = 80
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.457143e+01 NA
1 -1.971429e+01 -2.457143e+01
2 5.000000e+00 -1.971429e+01
3 -1.585714e+01 5.000000e+00
4 -1.671429e+01 -1.585714e+01
5 -8.285714e+00 -1.671429e+01
6 2.114286e+01 -8.285714e+00
7 -6.000000e+00 2.114286e+01
8 -1.183333e+01 -6.000000e+00
9 -2.383333e+01 -1.183333e+01
10 1.216667e+01 -2.383333e+01
11 6.400000e+01 1.216667e+01
12 1.842857e+01 6.400000e+01
13 -7.714286e+00 1.842857e+01
14 1.200000e+01 -7.714286e+00
15 1.514286e+01 1.200000e+01
16 3.428571e+01 1.514286e+01
17 1.171429e+01 3.428571e+01
18 7.614286e+01 1.171429e+01
19 2.600000e+01 7.614286e+01
20 4.816667e+01 2.600000e+01
21 1.516667e+01 4.816667e+01
22 -3.833333e+00 1.516667e+01
23 -2.000000e+01 -3.833333e+00
24 1.742857e+01 -2.000000e+01
25 2.728571e+01 1.742857e+01
26 1.200000e+01 2.728571e+01
27 3.314286e+01 1.200000e+01
28 1.285714e+00 3.314286e+01
29 4.471429e+01 1.285714e+00
30 2.214286e+01 4.471429e+01
31 2.000000e+01 2.214286e+01
32 2.166667e+00 2.000000e+01
33 1.016667e+01 2.166667e+00
34 -9.833333e+00 1.016667e+01
35 2.000000e+00 -9.833333e+00
36 7.428571e+00 2.000000e+00
37 9.285714e+00 7.428571e+00
38 5.149093e-15 9.285714e+00
39 1.414286e+01 5.149093e-15
40 2.328571e+01 1.414286e+01
41 4.871429e+01 2.328571e+01
42 2.142857e+00 4.871429e+01
43 5.900000e+01 2.142857e+00
44 2.316667e+01 5.900000e+01
45 3.816667e+01 2.316667e+01
46 3.516667e+01 3.816667e+01
47 3.000000e+01 3.516667e+01
48 5.942857e+01 3.000000e+01
49 4.828571e+01 5.942857e+01
50 3.400000e+01 4.828571e+01
51 2.614286e+01 3.400000e+01
52 1.628571e+01 2.614286e+01
53 -1.428571e+01 1.628571e+01
54 -2.585714e+01 -1.428571e+01
55 -2.300000e+01 -2.585714e+01
56 -1.883333e+01 -2.300000e+01
57 -1.383333e+01 -1.883333e+01
58 -2.833333e+00 -1.383333e+01
59 -2.100000e+01 -2.833333e+00
60 -4.157143e+01 -2.100000e+01
61 -2.171429e+01 -4.157143e+01
62 -2.900000e+01 -2.171429e+01
63 -2.885714e+01 -2.900000e+01
64 -2.171429e+01 -2.885714e+01
65 -3.828571e+01 -2.171429e+01
66 -4.485714e+01 -3.828571e+01
67 -3.300000e+01 -4.485714e+01
68 -4.283333e+01 -3.300000e+01
69 -2.583333e+01 -4.283333e+01
70 -3.083333e+01 -2.583333e+01
71 -5.500000e+01 -3.083333e+01
72 -3.657143e+01 -5.500000e+01
73 -3.571429e+01 -3.657143e+01
74 -3.400000e+01 -3.571429e+01
75 -4.385714e+01 -3.400000e+01
76 -3.671429e+01 -4.385714e+01
77 -4.428571e+01 -3.671429e+01
78 -5.085714e+01 -4.428571e+01
79 -4.300000e+01 -5.085714e+01
80 NA -4.300000e+01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.971429e+01 -2.457143e+01
[2,] 5.000000e+00 -1.971429e+01
[3,] -1.585714e+01 5.000000e+00
[4,] -1.671429e+01 -1.585714e+01
[5,] -8.285714e+00 -1.671429e+01
[6,] 2.114286e+01 -8.285714e+00
[7,] -6.000000e+00 2.114286e+01
[8,] -1.183333e+01 -6.000000e+00
[9,] -2.383333e+01 -1.183333e+01
[10,] 1.216667e+01 -2.383333e+01
[11,] 6.400000e+01 1.216667e+01
[12,] 1.842857e+01 6.400000e+01
[13,] -7.714286e+00 1.842857e+01
[14,] 1.200000e+01 -7.714286e+00
[15,] 1.514286e+01 1.200000e+01
[16,] 3.428571e+01 1.514286e+01
[17,] 1.171429e+01 3.428571e+01
[18,] 7.614286e+01 1.171429e+01
[19,] 2.600000e+01 7.614286e+01
[20,] 4.816667e+01 2.600000e+01
[21,] 1.516667e+01 4.816667e+01
[22,] -3.833333e+00 1.516667e+01
[23,] -2.000000e+01 -3.833333e+00
[24,] 1.742857e+01 -2.000000e+01
[25,] 2.728571e+01 1.742857e+01
[26,] 1.200000e+01 2.728571e+01
[27,] 3.314286e+01 1.200000e+01
[28,] 1.285714e+00 3.314286e+01
[29,] 4.471429e+01 1.285714e+00
[30,] 2.214286e+01 4.471429e+01
[31,] 2.000000e+01 2.214286e+01
[32,] 2.166667e+00 2.000000e+01
[33,] 1.016667e+01 2.166667e+00
[34,] -9.833333e+00 1.016667e+01
[35,] 2.000000e+00 -9.833333e+00
[36,] 7.428571e+00 2.000000e+00
[37,] 9.285714e+00 7.428571e+00
[38,] 5.149093e-15 9.285714e+00
[39,] 1.414286e+01 5.149093e-15
[40,] 2.328571e+01 1.414286e+01
[41,] 4.871429e+01 2.328571e+01
[42,] 2.142857e+00 4.871429e+01
[43,] 5.900000e+01 2.142857e+00
[44,] 2.316667e+01 5.900000e+01
[45,] 3.816667e+01 2.316667e+01
[46,] 3.516667e+01 3.816667e+01
[47,] 3.000000e+01 3.516667e+01
[48,] 5.942857e+01 3.000000e+01
[49,] 4.828571e+01 5.942857e+01
[50,] 3.400000e+01 4.828571e+01
[51,] 2.614286e+01 3.400000e+01
[52,] 1.628571e+01 2.614286e+01
[53,] -1.428571e+01 1.628571e+01
[54,] -2.585714e+01 -1.428571e+01
[55,] -2.300000e+01 -2.585714e+01
[56,] -1.883333e+01 -2.300000e+01
[57,] -1.383333e+01 -1.883333e+01
[58,] -2.833333e+00 -1.383333e+01
[59,] -2.100000e+01 -2.833333e+00
[60,] -4.157143e+01 -2.100000e+01
[61,] -2.171429e+01 -4.157143e+01
[62,] -2.900000e+01 -2.171429e+01
[63,] -2.885714e+01 -2.900000e+01
[64,] -2.171429e+01 -2.885714e+01
[65,] -3.828571e+01 -2.171429e+01
[66,] -4.485714e+01 -3.828571e+01
[67,] -3.300000e+01 -4.485714e+01
[68,] -4.283333e+01 -3.300000e+01
[69,] -2.583333e+01 -4.283333e+01
[70,] -3.083333e+01 -2.583333e+01
[71,] -5.500000e+01 -3.083333e+01
[72,] -3.657143e+01 -5.500000e+01
[73,] -3.571429e+01 -3.657143e+01
[74,] -3.400000e+01 -3.571429e+01
[75,] -4.385714e+01 -3.400000e+01
[76,] -3.671429e+01 -4.385714e+01
[77,] -4.428571e+01 -3.671429e+01
[78,] -5.085714e+01 -4.428571e+01
[79,] -4.300000e+01 -5.085714e+01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.971429e+01 -2.457143e+01
2 5.000000e+00 -1.971429e+01
3 -1.585714e+01 5.000000e+00
4 -1.671429e+01 -1.585714e+01
5 -8.285714e+00 -1.671429e+01
6 2.114286e+01 -8.285714e+00
7 -6.000000e+00 2.114286e+01
8 -1.183333e+01 -6.000000e+00
9 -2.383333e+01 -1.183333e+01
10 1.216667e+01 -2.383333e+01
11 6.400000e+01 1.216667e+01
12 1.842857e+01 6.400000e+01
13 -7.714286e+00 1.842857e+01
14 1.200000e+01 -7.714286e+00
15 1.514286e+01 1.200000e+01
16 3.428571e+01 1.514286e+01
17 1.171429e+01 3.428571e+01
18 7.614286e+01 1.171429e+01
19 2.600000e+01 7.614286e+01
20 4.816667e+01 2.600000e+01
21 1.516667e+01 4.816667e+01
22 -3.833333e+00 1.516667e+01
23 -2.000000e+01 -3.833333e+00
24 1.742857e+01 -2.000000e+01
25 2.728571e+01 1.742857e+01
26 1.200000e+01 2.728571e+01
27 3.314286e+01 1.200000e+01
28 1.285714e+00 3.314286e+01
29 4.471429e+01 1.285714e+00
30 2.214286e+01 4.471429e+01
31 2.000000e+01 2.214286e+01
32 2.166667e+00 2.000000e+01
33 1.016667e+01 2.166667e+00
34 -9.833333e+00 1.016667e+01
35 2.000000e+00 -9.833333e+00
36 7.428571e+00 2.000000e+00
37 9.285714e+00 7.428571e+00
38 5.149093e-15 9.285714e+00
39 1.414286e+01 5.149093e-15
40 2.328571e+01 1.414286e+01
41 4.871429e+01 2.328571e+01
42 2.142857e+00 4.871429e+01
43 5.900000e+01 2.142857e+00
44 2.316667e+01 5.900000e+01
45 3.816667e+01 2.316667e+01
46 3.516667e+01 3.816667e+01
47 3.000000e+01 3.516667e+01
48 5.942857e+01 3.000000e+01
49 4.828571e+01 5.942857e+01
50 3.400000e+01 4.828571e+01
51 2.614286e+01 3.400000e+01
52 1.628571e+01 2.614286e+01
53 -1.428571e+01 1.628571e+01
54 -2.585714e+01 -1.428571e+01
55 -2.300000e+01 -2.585714e+01
56 -1.883333e+01 -2.300000e+01
57 -1.383333e+01 -1.883333e+01
58 -2.833333e+00 -1.383333e+01
59 -2.100000e+01 -2.833333e+00
60 -4.157143e+01 -2.100000e+01
61 -2.171429e+01 -4.157143e+01
62 -2.900000e+01 -2.171429e+01
63 -2.885714e+01 -2.900000e+01
64 -2.171429e+01 -2.885714e+01
65 -3.828571e+01 -2.171429e+01
66 -4.485714e+01 -3.828571e+01
67 -3.300000e+01 -4.485714e+01
68 -4.283333e+01 -3.300000e+01
69 -2.583333e+01 -4.283333e+01
70 -3.083333e+01 -2.583333e+01
71 -5.500000e+01 -3.083333e+01
72 -3.657143e+01 -5.500000e+01
73 -3.571429e+01 -3.657143e+01
74 -3.400000e+01 -3.571429e+01
75 -4.385714e+01 -3.400000e+01
76 -3.671429e+01 -4.385714e+01
77 -4.428571e+01 -3.671429e+01
78 -5.085714e+01 -4.428571e+01
79 -4.300000e+01 -5.085714e+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/wessaorg/rcomp/tmp/707p61352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8g2sg1352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/93n6u1352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10nik11352473662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/113bmr1352473662.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/wessaorg/rcomp/tmp/12qxko1352473662.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/wessaorg/rcomp/tmp/131zvw1352473662.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/wessaorg/rcomp/tmp/14f7df1352473662.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/wessaorg/rcomp/tmp/15akzh1352473662.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/wessaorg/rcomp/tmp/16mnma1352473663.tab")
+ }
>
> try(system("convert tmp/12ve41352473662.ps tmp/12ve41352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/2afkw1352473662.ps tmp/2afkw1352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/3al8l1352473662.ps tmp/3al8l1352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kjvn1352473662.ps tmp/4kjvn1352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jqc61352473662.ps tmp/5jqc61352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lxd91352473662.ps tmp/6lxd91352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/707p61352473662.ps tmp/707p61352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g2sg1352473662.ps tmp/8g2sg1352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/93n6u1352473662.ps tmp/93n6u1352473662.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nik11352473662.ps tmp/10nik11352473662.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.868 1.475 9.498