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(1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1,0,1,0,1,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,1,1,1,0,1,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0),dim=c(2,86),dimnames=list(c('T40','CorrectAnalysis'),1:86))
> y <- array(NA,dim=c(2,86),dimnames=list(c('T40','CorrectAnalysis'),1:86))
> 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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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
CorrectAnalysis T40
1 0 1
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 1
9 0 0
10 0 0
11 0 1
12 0 0
13 0 0
14 0 1
15 0 0
16 0 1
17 1 1
18 0 1
19 0 0
20 1 1
21 1 0
22 1 0
23 1 0
24 1 0
25 0 1
26 1 0
27 0 0
28 0 0
29 0 0
30 1 0
31 0 0
32 0 0
33 1 0
34 0 1
35 0 0
36 0 0
37 1 1
38 0 0
39 1 0
40 1 1
41 1 0
42 0 0
43 1 0
44 0 1
45 1 0
46 1 0
47 0 0
48 0 0
49 1 0
50 0 0
51 0 1
52 1 1
53 0 0
54 0 0
55 0 0
56 0 1
57 1 0
58 0 0
59 0 0
60 1 1
61 0 1
62 1 0
63 0 0
64 0 1
65 0 0
66 0 0
67 1 1
68 0 0
69 0 0
70 0 0
71 0 0
72 0 0
73 0 0
74 0 0
75 0 0
76 1 1
77 0 0
78 1 0
79 0 1
80 1 1
81 0 0
82 0 0
83 0 0
84 0 0
85 1 0
86 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T40
0.2698 0.1215
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.3913 -0.2698 -0.2698 0.6087 0.7302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.26984 0.05814 4.641 1.27e-05 ***
T40 0.12146 0.11243 1.080 0.283
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4615 on 84 degrees of freedom
Multiple R-squared: 0.0137, Adjusted R-squared: 0.001962
F-statistic: 1.167 on 1 and 84 DF, p-value: 0.2831
> 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,] 0.0000000000 0.0000000000 1.00000000
[2,] 0.0000000000 0.0000000000 1.00000000
[3,] 0.0000000000 0.0000000000 1.00000000
[4,] 0.0000000000 0.0000000000 1.00000000
[5,] 0.0000000000 0.0000000000 1.00000000
[6,] 0.0000000000 0.0000000000 1.00000000
[7,] 0.0000000000 0.0000000000 1.00000000
[8,] 0.0000000000 0.0000000000 1.00000000
[9,] 0.0000000000 0.0000000000 1.00000000
[10,] 0.0000000000 0.0000000000 1.00000000
[11,] 0.0000000000 0.0000000000 1.00000000
[12,] 0.0000000000 0.0000000000 1.00000000
[13,] 0.0016743781 0.0033487562 0.99832562
[14,] 0.0009878773 0.0019757546 0.99901212
[15,] 0.0004610512 0.0009221024 0.99953895
[16,] 0.0083648463 0.0167296926 0.99163515
[17,] 0.0735232781 0.1470465561 0.92647672
[18,] 0.1937015295 0.3874030591 0.80629847
[19,] 0.3259965367 0.6519930734 0.67400346
[20,] 0.4458337205 0.8916674409 0.55416628
[21,] 0.4089761985 0.8179523970 0.59102380
[22,] 0.5114277425 0.9771445151 0.48857226
[23,] 0.4673889928 0.9347779857 0.53261101
[24,] 0.4227110002 0.8454220003 0.57728900
[25,] 0.3782973132 0.7565946265 0.62170269
[26,] 0.4768000410 0.9536000819 0.52319996
[27,] 0.4339750246 0.8679500493 0.56602498
[28,] 0.3913176782 0.7826353563 0.60868232
[29,] 0.4855507084 0.9711014169 0.51444929
[30,] 0.4598642340 0.9197284680 0.54013577
[31,] 0.4190030624 0.8380061249 0.58099694
[32,] 0.3785012983 0.7570025967 0.62149870
[33,] 0.4441555521 0.8883111042 0.55584445
[34,] 0.4024442367 0.8048884734 0.59755576
[35,] 0.4933012844 0.9866025689 0.50669872
[36,] 0.5415788651 0.9168422697 0.45842113
[37,] 0.6277490524 0.7445018952 0.37225095
[38,] 0.5877917285 0.8244165431 0.41220827
[39,] 0.6735571952 0.6528856096 0.32644280
[40,] 0.6611155513 0.6777688975 0.33888445
[41,] 0.7461542556 0.5076914887 0.25384574
[42,] 0.8250736768 0.3498526464 0.17492632
[43,] 0.7960230066 0.4079539868 0.20397699
[44,] 0.7636188264 0.4727623472 0.23638117
[45,] 0.8470977512 0.3058044976 0.15290225
[46,] 0.8184358456 0.3631283088 0.18156415
[47,] 0.8208754142 0.3582491716 0.17912459
[48,] 0.8419971027 0.3160057947 0.15800290
[49,] 0.8112118672 0.3775762655 0.18878813
[50,] 0.7764509504 0.4470980991 0.22354905
[51,] 0.7378132390 0.5243735220 0.26218676
[52,] 0.7445440936 0.5109118127 0.25545591
[53,] 0.8494576322 0.3010847356 0.15054237
[54,] 0.8158349250 0.3683301499 0.18416507
[55,] 0.7773528421 0.4452943158 0.22264716
[56,] 0.7953731186 0.4092537628 0.20462688
[57,] 0.8106990127 0.3786019746 0.18930099
[58,] 0.9163383402 0.1673233196 0.08366166
[59,] 0.8894669617 0.2210660767 0.11053304
[60,] 0.9277527984 0.1444944033 0.07224720
[61,] 0.9022202335 0.1955595331 0.09777977
[62,] 0.8700576483 0.2598847035 0.12994235
[63,] 0.8590446396 0.2819107208 0.14095536
[64,] 0.8160352299 0.3679295403 0.18396477
[65,] 0.7648174210 0.4703651580 0.23518258
[66,] 0.7056619934 0.5886760132 0.29433801
[67,] 0.6395271554 0.7209456893 0.36047284
[68,] 0.5681131740 0.8637736520 0.43188683
[69,] 0.4938160519 0.9876321039 0.50618395
[70,] 0.4195679910 0.8391359820 0.58043201
[71,] 0.3485835164 0.6971670329 0.65141648
[72,] 0.3309043072 0.6618086145 0.66909569
[73,] 0.2636948210 0.5273896420 0.73630518
[74,] 0.4150935261 0.8301870521 0.58490647
[75,] 0.5720489065 0.8559021870 0.42795109
[76,] 0.4361766101 0.8723532203 0.56382339
[77,] 0.3027933165 0.6055866331 0.69720668
> postscript(file="/var/wessaorg/rcomp/tmp/1ongt1354718140.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2lu5c1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nx861354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tyow1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/56e2j1354718140.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 = 86
Frequency = 1
1 2 3 4 5 6 7
-0.3913043 -0.2698413 -0.2698413 -0.2698413 -0.2698413 -0.2698413 -0.2698413
8 9 10 11 12 13 14
-0.3913043 -0.2698413 -0.2698413 -0.3913043 -0.2698413 -0.2698413 -0.3913043
15 16 17 18 19 20 21
-0.2698413 -0.3913043 0.6086957 -0.3913043 -0.2698413 0.6086957 0.7301587
22 23 24 25 26 27 28
0.7301587 0.7301587 0.7301587 -0.3913043 0.7301587 -0.2698413 -0.2698413
29 30 31 32 33 34 35
-0.2698413 0.7301587 -0.2698413 -0.2698413 0.7301587 -0.3913043 -0.2698413
36 37 38 39 40 41 42
-0.2698413 0.6086957 -0.2698413 0.7301587 0.6086957 0.7301587 -0.2698413
43 44 45 46 47 48 49
0.7301587 -0.3913043 0.7301587 0.7301587 -0.2698413 -0.2698413 0.7301587
50 51 52 53 54 55 56
-0.2698413 -0.3913043 0.6086957 -0.2698413 -0.2698413 -0.2698413 -0.3913043
57 58 59 60 61 62 63
0.7301587 -0.2698413 -0.2698413 0.6086957 -0.3913043 0.7301587 -0.2698413
64 65 66 67 68 69 70
-0.3913043 -0.2698413 -0.2698413 0.6086957 -0.2698413 -0.2698413 -0.2698413
71 72 73 74 75 76 77
-0.2698413 -0.2698413 -0.2698413 -0.2698413 -0.2698413 0.6086957 -0.2698413
78 79 80 81 82 83 84
0.7301587 -0.3913043 0.6086957 -0.2698413 -0.2698413 -0.2698413 -0.2698413
85 86
0.7301587 -0.2698413
> postscript(file="/var/wessaorg/rcomp/tmp/6p3j51354718140.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 = 86
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.3913043 NA
1 -0.2698413 -0.3913043
2 -0.2698413 -0.2698413
3 -0.2698413 -0.2698413
4 -0.2698413 -0.2698413
5 -0.2698413 -0.2698413
6 -0.2698413 -0.2698413
7 -0.3913043 -0.2698413
8 -0.2698413 -0.3913043
9 -0.2698413 -0.2698413
10 -0.3913043 -0.2698413
11 -0.2698413 -0.3913043
12 -0.2698413 -0.2698413
13 -0.3913043 -0.2698413
14 -0.2698413 -0.3913043
15 -0.3913043 -0.2698413
16 0.6086957 -0.3913043
17 -0.3913043 0.6086957
18 -0.2698413 -0.3913043
19 0.6086957 -0.2698413
20 0.7301587 0.6086957
21 0.7301587 0.7301587
22 0.7301587 0.7301587
23 0.7301587 0.7301587
24 -0.3913043 0.7301587
25 0.7301587 -0.3913043
26 -0.2698413 0.7301587
27 -0.2698413 -0.2698413
28 -0.2698413 -0.2698413
29 0.7301587 -0.2698413
30 -0.2698413 0.7301587
31 -0.2698413 -0.2698413
32 0.7301587 -0.2698413
33 -0.3913043 0.7301587
34 -0.2698413 -0.3913043
35 -0.2698413 -0.2698413
36 0.6086957 -0.2698413
37 -0.2698413 0.6086957
38 0.7301587 -0.2698413
39 0.6086957 0.7301587
40 0.7301587 0.6086957
41 -0.2698413 0.7301587
42 0.7301587 -0.2698413
43 -0.3913043 0.7301587
44 0.7301587 -0.3913043
45 0.7301587 0.7301587
46 -0.2698413 0.7301587
47 -0.2698413 -0.2698413
48 0.7301587 -0.2698413
49 -0.2698413 0.7301587
50 -0.3913043 -0.2698413
51 0.6086957 -0.3913043
52 -0.2698413 0.6086957
53 -0.2698413 -0.2698413
54 -0.2698413 -0.2698413
55 -0.3913043 -0.2698413
56 0.7301587 -0.3913043
57 -0.2698413 0.7301587
58 -0.2698413 -0.2698413
59 0.6086957 -0.2698413
60 -0.3913043 0.6086957
61 0.7301587 -0.3913043
62 -0.2698413 0.7301587
63 -0.3913043 -0.2698413
64 -0.2698413 -0.3913043
65 -0.2698413 -0.2698413
66 0.6086957 -0.2698413
67 -0.2698413 0.6086957
68 -0.2698413 -0.2698413
69 -0.2698413 -0.2698413
70 -0.2698413 -0.2698413
71 -0.2698413 -0.2698413
72 -0.2698413 -0.2698413
73 -0.2698413 -0.2698413
74 -0.2698413 -0.2698413
75 0.6086957 -0.2698413
76 -0.2698413 0.6086957
77 0.7301587 -0.2698413
78 -0.3913043 0.7301587
79 0.6086957 -0.3913043
80 -0.2698413 0.6086957
81 -0.2698413 -0.2698413
82 -0.2698413 -0.2698413
83 -0.2698413 -0.2698413
84 0.7301587 -0.2698413
85 -0.2698413 0.7301587
86 NA -0.2698413
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.2698413 -0.3913043
[2,] -0.2698413 -0.2698413
[3,] -0.2698413 -0.2698413
[4,] -0.2698413 -0.2698413
[5,] -0.2698413 -0.2698413
[6,] -0.2698413 -0.2698413
[7,] -0.3913043 -0.2698413
[8,] -0.2698413 -0.3913043
[9,] -0.2698413 -0.2698413
[10,] -0.3913043 -0.2698413
[11,] -0.2698413 -0.3913043
[12,] -0.2698413 -0.2698413
[13,] -0.3913043 -0.2698413
[14,] -0.2698413 -0.3913043
[15,] -0.3913043 -0.2698413
[16,] 0.6086957 -0.3913043
[17,] -0.3913043 0.6086957
[18,] -0.2698413 -0.3913043
[19,] 0.6086957 -0.2698413
[20,] 0.7301587 0.6086957
[21,] 0.7301587 0.7301587
[22,] 0.7301587 0.7301587
[23,] 0.7301587 0.7301587
[24,] -0.3913043 0.7301587
[25,] 0.7301587 -0.3913043
[26,] -0.2698413 0.7301587
[27,] -0.2698413 -0.2698413
[28,] -0.2698413 -0.2698413
[29,] 0.7301587 -0.2698413
[30,] -0.2698413 0.7301587
[31,] -0.2698413 -0.2698413
[32,] 0.7301587 -0.2698413
[33,] -0.3913043 0.7301587
[34,] -0.2698413 -0.3913043
[35,] -0.2698413 -0.2698413
[36,] 0.6086957 -0.2698413
[37,] -0.2698413 0.6086957
[38,] 0.7301587 -0.2698413
[39,] 0.6086957 0.7301587
[40,] 0.7301587 0.6086957
[41,] -0.2698413 0.7301587
[42,] 0.7301587 -0.2698413
[43,] -0.3913043 0.7301587
[44,] 0.7301587 -0.3913043
[45,] 0.7301587 0.7301587
[46,] -0.2698413 0.7301587
[47,] -0.2698413 -0.2698413
[48,] 0.7301587 -0.2698413
[49,] -0.2698413 0.7301587
[50,] -0.3913043 -0.2698413
[51,] 0.6086957 -0.3913043
[52,] -0.2698413 0.6086957
[53,] -0.2698413 -0.2698413
[54,] -0.2698413 -0.2698413
[55,] -0.3913043 -0.2698413
[56,] 0.7301587 -0.3913043
[57,] -0.2698413 0.7301587
[58,] -0.2698413 -0.2698413
[59,] 0.6086957 -0.2698413
[60,] -0.3913043 0.6086957
[61,] 0.7301587 -0.3913043
[62,] -0.2698413 0.7301587
[63,] -0.3913043 -0.2698413
[64,] -0.2698413 -0.3913043
[65,] -0.2698413 -0.2698413
[66,] 0.6086957 -0.2698413
[67,] -0.2698413 0.6086957
[68,] -0.2698413 -0.2698413
[69,] -0.2698413 -0.2698413
[70,] -0.2698413 -0.2698413
[71,] -0.2698413 -0.2698413
[72,] -0.2698413 -0.2698413
[73,] -0.2698413 -0.2698413
[74,] -0.2698413 -0.2698413
[75,] 0.6086957 -0.2698413
[76,] -0.2698413 0.6086957
[77,] 0.7301587 -0.2698413
[78,] -0.3913043 0.7301587
[79,] 0.6086957 -0.3913043
[80,] -0.2698413 0.6086957
[81,] -0.2698413 -0.2698413
[82,] -0.2698413 -0.2698413
[83,] -0.2698413 -0.2698413
[84,] 0.7301587 -0.2698413
[85,] -0.2698413 0.7301587
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.2698413 -0.3913043
2 -0.2698413 -0.2698413
3 -0.2698413 -0.2698413
4 -0.2698413 -0.2698413
5 -0.2698413 -0.2698413
6 -0.2698413 -0.2698413
7 -0.3913043 -0.2698413
8 -0.2698413 -0.3913043
9 -0.2698413 -0.2698413
10 -0.3913043 -0.2698413
11 -0.2698413 -0.3913043
12 -0.2698413 -0.2698413
13 -0.3913043 -0.2698413
14 -0.2698413 -0.3913043
15 -0.3913043 -0.2698413
16 0.6086957 -0.3913043
17 -0.3913043 0.6086957
18 -0.2698413 -0.3913043
19 0.6086957 -0.2698413
20 0.7301587 0.6086957
21 0.7301587 0.7301587
22 0.7301587 0.7301587
23 0.7301587 0.7301587
24 -0.3913043 0.7301587
25 0.7301587 -0.3913043
26 -0.2698413 0.7301587
27 -0.2698413 -0.2698413
28 -0.2698413 -0.2698413
29 0.7301587 -0.2698413
30 -0.2698413 0.7301587
31 -0.2698413 -0.2698413
32 0.7301587 -0.2698413
33 -0.3913043 0.7301587
34 -0.2698413 -0.3913043
35 -0.2698413 -0.2698413
36 0.6086957 -0.2698413
37 -0.2698413 0.6086957
38 0.7301587 -0.2698413
39 0.6086957 0.7301587
40 0.7301587 0.6086957
41 -0.2698413 0.7301587
42 0.7301587 -0.2698413
43 -0.3913043 0.7301587
44 0.7301587 -0.3913043
45 0.7301587 0.7301587
46 -0.2698413 0.7301587
47 -0.2698413 -0.2698413
48 0.7301587 -0.2698413
49 -0.2698413 0.7301587
50 -0.3913043 -0.2698413
51 0.6086957 -0.3913043
52 -0.2698413 0.6086957
53 -0.2698413 -0.2698413
54 -0.2698413 -0.2698413
55 -0.3913043 -0.2698413
56 0.7301587 -0.3913043
57 -0.2698413 0.7301587
58 -0.2698413 -0.2698413
59 0.6086957 -0.2698413
60 -0.3913043 0.6086957
61 0.7301587 -0.3913043
62 -0.2698413 0.7301587
63 -0.3913043 -0.2698413
64 -0.2698413 -0.3913043
65 -0.2698413 -0.2698413
66 0.6086957 -0.2698413
67 -0.2698413 0.6086957
68 -0.2698413 -0.2698413
69 -0.2698413 -0.2698413
70 -0.2698413 -0.2698413
71 -0.2698413 -0.2698413
72 -0.2698413 -0.2698413
73 -0.2698413 -0.2698413
74 -0.2698413 -0.2698413
75 0.6086957 -0.2698413
76 -0.2698413 0.6086957
77 0.7301587 -0.2698413
78 -0.3913043 0.7301587
79 0.6086957 -0.3913043
80 -0.2698413 0.6086957
81 -0.2698413 -0.2698413
82 -0.2698413 -0.2698413
83 -0.2698413 -0.2698413
84 0.7301587 -0.2698413
85 -0.2698413 0.7301587
> 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/7xv4y1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8a7jg1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91ztf1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10vklr1354718140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uw9r1354718140.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/12a0yg1354718140.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/13qjkq1354718140.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/146xhe1354718140.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/15limo1354718140.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/16ljmf1354718140.tab")
+ }
>
> try(system("convert tmp/1ongt1354718140.ps tmp/1ongt1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lu5c1354718140.ps tmp/2lu5c1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nx861354718140.ps tmp/3nx861354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tyow1354718140.ps tmp/4tyow1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/56e2j1354718140.ps tmp/56e2j1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p3j51354718140.ps tmp/6p3j51354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xv4y1354718140.ps tmp/7xv4y1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a7jg1354718140.ps tmp/8a7jg1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/91ztf1354718140.ps tmp/91ztf1354718140.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vklr1354718140.ps tmp/10vklr1354718140.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.288 1.163 7.839