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(1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,9,0,1,0,0,1,1,1,1,0,1,0,0,1,1,1,0,0,0,1,0,1,0,1,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,0,1,0,0,1,1,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,1,0,1,0,1,1,1,1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,1,1,0,1,0,1,1,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0),dim=c(2,86),dimnames=list(c('Treatment','Outcome'),1:86))
> y <- array(NA,dim=c(2,86),dimnames=list(c('Treatment','Outcome'),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 = '1'
> 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, 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
Treatment Outcome
1 1 1
2 0 0
3 0 0
4 0 0
5 0 0
6 0 1
7 0 0
8 1 0
9 0 1
10 0 0
11 1 0
12 0 0
13 9 0
14 1 0
15 0 1
16 1 1
17 1 0
18 1 0
19 0 1
20 1 1
21 0 0
22 0 1
23 0 1
24 0 1
25 1 1
26 0 0
27 0 1
28 0 0
29 0 1
30 0 0
31 0 0
32 0 0
33 0 0
34 1 1
35 0 0
36 0 0
37 1 0
38 0 1
39 0 1
40 1 0
41 0 1
42 0 1
43 0 1
44 1 0
45 0 0
46 0 1
47 0 0
48 1 1
49 0 1
50 0 0
51 1 0
52 1 0
53 0 1
54 0 0
55 0 0
56 1 1
57 0 1
58 0 1
59 0 1
60 1 1
61 1 1
62 0 0
63 0 0
64 1 1
65 0 0
66 0 0
67 1 0
68 0 0
69 0 1
70 0 0
71 0 0
72 0 1
73 0 1
74 0 0
75 0 1
76 1 1
77 0 1
78 0 1
79 1 1
80 1 0
81 0 0
82 0 1
83 0 0
84 0 0
85 0 1
86 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Outcome
0.4565 -0.1565
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.4565 -0.4565 -0.3000 0.5435 8.5435
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4565 0.1541 2.962 0.00398 **
Outcome -0.1565 0.2260 -0.693 0.49053
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.045 on 84 degrees of freedom
Multiple R-squared: 0.005677, Adjusted R-squared: -0.006161
F-statistic: 0.4796 on 1 and 84 DF, p-value: 0.4905
> 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,] 0.000000000 0.000000e+00 1.000000e+00
[2,] 0.020584912 4.116982e-02 9.794151e-01
[3,] 0.005675708 1.135142e-02 9.943243e-01
[4,] 0.020677089 4.135418e-02 9.793229e-01
[5,] 0.011147663 2.229533e-02 9.888523e-01
[6,] 0.004444439 8.888878e-03 9.955556e-01
[7,] 0.006173661 1.234732e-02 9.938263e-01
[8,] 0.002778347 5.556693e-03 9.972217e-01
[9,] 1.000000000 1.895803e-22 9.479017e-23
[10,] 1.000000000 3.856150e-22 1.928075e-22
[11,] 1.000000000 1.732126e-21 8.660629e-22
[12,] 1.000000000 2.928391e-21 1.464196e-21
[13,] 1.000000000 4.811168e-21 2.405584e-21
[14,] 1.000000000 6.919264e-21 3.459632e-21
[15,] 1.000000000 2.736647e-20 1.368323e-20
[16,] 1.000000000 3.827234e-20 1.913617e-20
[17,] 1.000000000 1.118236e-19 5.591180e-20
[18,] 1.000000000 4.137393e-19 2.068697e-19
[19,] 1.000000000 1.492660e-18 7.463298e-19
[20,] 1.000000000 5.231419e-18 2.615710e-18
[21,] 1.000000000 6.589281e-18 3.294641e-18
[22,] 1.000000000 1.851497e-17 9.257485e-18
[23,] 1.000000000 6.210600e-17 3.105300e-17
[24,] 1.000000000 1.743569e-16 8.717845e-17
[25,] 1.000000000 5.584480e-16 2.792240e-16
[26,] 1.000000000 1.553594e-15 7.767970e-16
[27,] 1.000000000 4.329013e-15 2.164507e-15
[28,] 1.000000000 1.201640e-14 6.008198e-15
[29,] 1.000000000 3.308072e-14 1.654036e-14
[30,] 1.000000000 3.836425e-14 1.918213e-14
[31,] 1.000000000 1.041965e-13 5.209826e-14
[32,] 1.000000000 2.787058e-13 1.393529e-13
[33,] 1.000000000 3.305059e-13 1.652529e-13
[34,] 1.000000000 9.466865e-13 4.733433e-13
[35,] 1.000000000 2.642655e-12 1.321328e-12
[36,] 1.000000000 2.729745e-12 1.364872e-12
[37,] 1.000000000 7.409285e-12 3.704642e-12
[38,] 1.000000000 1.953064e-11 9.765318e-12
[39,] 1.000000000 4.990423e-11 2.495211e-11
[40,] 1.000000000 4.383716e-11 2.191858e-11
[41,] 1.000000000 1.181673e-10 5.908363e-11
[42,] 1.000000000 2.883657e-10 1.441828e-10
[43,] 1.000000000 7.552379e-10 3.776190e-10
[44,] 1.000000000 8.583387e-10 4.291693e-10
[45,] 0.999999999 2.083928e-09 1.041964e-09
[46,] 0.999999997 5.325839e-09 2.662920e-09
[47,] 0.999999998 4.218416e-09 2.109208e-09
[48,] 0.999999999 2.547335e-09 1.273668e-09
[49,] 0.999999997 5.965471e-09 2.982736e-09
[50,] 0.999999992 1.656855e-08 8.284274e-09
[51,] 0.999999977 4.512136e-08 2.256068e-08
[52,] 0.999999977 4.646845e-08 2.323423e-08
[53,] 0.999999946 1.085456e-07 5.427282e-08
[54,] 0.999999879 2.428242e-07 1.214121e-07
[55,] 0.999999741 5.170728e-07 2.585364e-07
[56,] 0.999999720 5.609723e-07 2.804861e-07
[57,] 0.999999756 4.889621e-07 2.444811e-07
[58,] 0.999999331 1.338651e-06 6.693255e-07
[59,] 0.999998216 3.567891e-06 1.783946e-06
[60,] 0.999998803 2.393118e-06 1.196559e-06
[61,] 0.999996765 6.470663e-06 3.235332e-06
[62,] 0.999991541 1.691734e-05 8.458671e-06
[63,] 0.999996284 7.432856e-06 3.716428e-06
[64,] 0.999989108 2.178381e-05 1.089190e-05
[65,] 0.999971009 5.798100e-05 2.899050e-05
[66,] 0.999920096 1.598083e-04 7.990415e-05
[67,] 0.999788980 4.220405e-04 2.110202e-04
[68,] 0.999490576 1.018848e-03 5.094238e-04
[69,] 0.998836309 2.327382e-03 1.163691e-03
[70,] 0.997219882 5.560235e-03 2.780118e-03
[71,] 0.994191098 1.161780e-02 5.808902e-03
[72,] 0.994803713 1.039257e-02 5.196287e-03
[73,] 0.987769744 2.446051e-02 1.223026e-02
[74,] 0.973769281 5.246144e-02 2.623072e-02
[75,] 0.983452894 3.309421e-02 1.654711e-02
[76,] 1.000000000 0.000000e+00 0.000000e+00
[77,] 1.000000000 0.000000e+00 0.000000e+00
> postscript(file="/var/wessaorg/rcomp/tmp/135zy1356129590.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xo721356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fjxb1356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4pmus1356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rngm1356129590.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
0.7000000 -0.4565217 -0.4565217 -0.4565217 -0.4565217 -0.3000000 -0.4565217
8 9 10 11 12 13 14
0.5434783 -0.3000000 -0.4565217 0.5434783 -0.4565217 8.5434783 0.5434783
15 16 17 18 19 20 21
-0.3000000 0.7000000 0.5434783 0.5434783 -0.3000000 0.7000000 -0.4565217
22 23 24 25 26 27 28
-0.3000000 -0.3000000 -0.3000000 0.7000000 -0.4565217 -0.3000000 -0.4565217
29 30 31 32 33 34 35
-0.3000000 -0.4565217 -0.4565217 -0.4565217 -0.4565217 0.7000000 -0.4565217
36 37 38 39 40 41 42
-0.4565217 0.5434783 -0.3000000 -0.3000000 0.5434783 -0.3000000 -0.3000000
43 44 45 46 47 48 49
-0.3000000 0.5434783 -0.4565217 -0.3000000 -0.4565217 0.7000000 -0.3000000
50 51 52 53 54 55 56
-0.4565217 0.5434783 0.5434783 -0.3000000 -0.4565217 -0.4565217 0.7000000
57 58 59 60 61 62 63
-0.3000000 -0.3000000 -0.3000000 0.7000000 0.7000000 -0.4565217 -0.4565217
64 65 66 67 68 69 70
0.7000000 -0.4565217 -0.4565217 0.5434783 -0.4565217 -0.3000000 -0.4565217
71 72 73 74 75 76 77
-0.4565217 -0.3000000 -0.3000000 -0.4565217 -0.3000000 0.7000000 -0.3000000
78 79 80 81 82 83 84
-0.3000000 0.7000000 0.5434783 -0.4565217 -0.3000000 -0.4565217 -0.4565217
85 86
-0.3000000 -0.4565217
> postscript(file="/var/wessaorg/rcomp/tmp/6a4e51356129590.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 0.7000000 NA
1 -0.4565217 0.7000000
2 -0.4565217 -0.4565217
3 -0.4565217 -0.4565217
4 -0.4565217 -0.4565217
5 -0.3000000 -0.4565217
6 -0.4565217 -0.3000000
7 0.5434783 -0.4565217
8 -0.3000000 0.5434783
9 -0.4565217 -0.3000000
10 0.5434783 -0.4565217
11 -0.4565217 0.5434783
12 8.5434783 -0.4565217
13 0.5434783 8.5434783
14 -0.3000000 0.5434783
15 0.7000000 -0.3000000
16 0.5434783 0.7000000
17 0.5434783 0.5434783
18 -0.3000000 0.5434783
19 0.7000000 -0.3000000
20 -0.4565217 0.7000000
21 -0.3000000 -0.4565217
22 -0.3000000 -0.3000000
23 -0.3000000 -0.3000000
24 0.7000000 -0.3000000
25 -0.4565217 0.7000000
26 -0.3000000 -0.4565217
27 -0.4565217 -0.3000000
28 -0.3000000 -0.4565217
29 -0.4565217 -0.3000000
30 -0.4565217 -0.4565217
31 -0.4565217 -0.4565217
32 -0.4565217 -0.4565217
33 0.7000000 -0.4565217
34 -0.4565217 0.7000000
35 -0.4565217 -0.4565217
36 0.5434783 -0.4565217
37 -0.3000000 0.5434783
38 -0.3000000 -0.3000000
39 0.5434783 -0.3000000
40 -0.3000000 0.5434783
41 -0.3000000 -0.3000000
42 -0.3000000 -0.3000000
43 0.5434783 -0.3000000
44 -0.4565217 0.5434783
45 -0.3000000 -0.4565217
46 -0.4565217 -0.3000000
47 0.7000000 -0.4565217
48 -0.3000000 0.7000000
49 -0.4565217 -0.3000000
50 0.5434783 -0.4565217
51 0.5434783 0.5434783
52 -0.3000000 0.5434783
53 -0.4565217 -0.3000000
54 -0.4565217 -0.4565217
55 0.7000000 -0.4565217
56 -0.3000000 0.7000000
57 -0.3000000 -0.3000000
58 -0.3000000 -0.3000000
59 0.7000000 -0.3000000
60 0.7000000 0.7000000
61 -0.4565217 0.7000000
62 -0.4565217 -0.4565217
63 0.7000000 -0.4565217
64 -0.4565217 0.7000000
65 -0.4565217 -0.4565217
66 0.5434783 -0.4565217
67 -0.4565217 0.5434783
68 -0.3000000 -0.4565217
69 -0.4565217 -0.3000000
70 -0.4565217 -0.4565217
71 -0.3000000 -0.4565217
72 -0.3000000 -0.3000000
73 -0.4565217 -0.3000000
74 -0.3000000 -0.4565217
75 0.7000000 -0.3000000
76 -0.3000000 0.7000000
77 -0.3000000 -0.3000000
78 0.7000000 -0.3000000
79 0.5434783 0.7000000
80 -0.4565217 0.5434783
81 -0.3000000 -0.4565217
82 -0.4565217 -0.3000000
83 -0.4565217 -0.4565217
84 -0.3000000 -0.4565217
85 -0.4565217 -0.3000000
86 NA -0.4565217
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.4565217 0.7000000
[2,] -0.4565217 -0.4565217
[3,] -0.4565217 -0.4565217
[4,] -0.4565217 -0.4565217
[5,] -0.3000000 -0.4565217
[6,] -0.4565217 -0.3000000
[7,] 0.5434783 -0.4565217
[8,] -0.3000000 0.5434783
[9,] -0.4565217 -0.3000000
[10,] 0.5434783 -0.4565217
[11,] -0.4565217 0.5434783
[12,] 8.5434783 -0.4565217
[13,] 0.5434783 8.5434783
[14,] -0.3000000 0.5434783
[15,] 0.7000000 -0.3000000
[16,] 0.5434783 0.7000000
[17,] 0.5434783 0.5434783
[18,] -0.3000000 0.5434783
[19,] 0.7000000 -0.3000000
[20,] -0.4565217 0.7000000
[21,] -0.3000000 -0.4565217
[22,] -0.3000000 -0.3000000
[23,] -0.3000000 -0.3000000
[24,] 0.7000000 -0.3000000
[25,] -0.4565217 0.7000000
[26,] -0.3000000 -0.4565217
[27,] -0.4565217 -0.3000000
[28,] -0.3000000 -0.4565217
[29,] -0.4565217 -0.3000000
[30,] -0.4565217 -0.4565217
[31,] -0.4565217 -0.4565217
[32,] -0.4565217 -0.4565217
[33,] 0.7000000 -0.4565217
[34,] -0.4565217 0.7000000
[35,] -0.4565217 -0.4565217
[36,] 0.5434783 -0.4565217
[37,] -0.3000000 0.5434783
[38,] -0.3000000 -0.3000000
[39,] 0.5434783 -0.3000000
[40,] -0.3000000 0.5434783
[41,] -0.3000000 -0.3000000
[42,] -0.3000000 -0.3000000
[43,] 0.5434783 -0.3000000
[44,] -0.4565217 0.5434783
[45,] -0.3000000 -0.4565217
[46,] -0.4565217 -0.3000000
[47,] 0.7000000 -0.4565217
[48,] -0.3000000 0.7000000
[49,] -0.4565217 -0.3000000
[50,] 0.5434783 -0.4565217
[51,] 0.5434783 0.5434783
[52,] -0.3000000 0.5434783
[53,] -0.4565217 -0.3000000
[54,] -0.4565217 -0.4565217
[55,] 0.7000000 -0.4565217
[56,] -0.3000000 0.7000000
[57,] -0.3000000 -0.3000000
[58,] -0.3000000 -0.3000000
[59,] 0.7000000 -0.3000000
[60,] 0.7000000 0.7000000
[61,] -0.4565217 0.7000000
[62,] -0.4565217 -0.4565217
[63,] 0.7000000 -0.4565217
[64,] -0.4565217 0.7000000
[65,] -0.4565217 -0.4565217
[66,] 0.5434783 -0.4565217
[67,] -0.4565217 0.5434783
[68,] -0.3000000 -0.4565217
[69,] -0.4565217 -0.3000000
[70,] -0.4565217 -0.4565217
[71,] -0.3000000 -0.4565217
[72,] -0.3000000 -0.3000000
[73,] -0.4565217 -0.3000000
[74,] -0.3000000 -0.4565217
[75,] 0.7000000 -0.3000000
[76,] -0.3000000 0.7000000
[77,] -0.3000000 -0.3000000
[78,] 0.7000000 -0.3000000
[79,] 0.5434783 0.7000000
[80,] -0.4565217 0.5434783
[81,] -0.3000000 -0.4565217
[82,] -0.4565217 -0.3000000
[83,] -0.4565217 -0.4565217
[84,] -0.3000000 -0.4565217
[85,] -0.4565217 -0.3000000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.4565217 0.7000000
2 -0.4565217 -0.4565217
3 -0.4565217 -0.4565217
4 -0.4565217 -0.4565217
5 -0.3000000 -0.4565217
6 -0.4565217 -0.3000000
7 0.5434783 -0.4565217
8 -0.3000000 0.5434783
9 -0.4565217 -0.3000000
10 0.5434783 -0.4565217
11 -0.4565217 0.5434783
12 8.5434783 -0.4565217
13 0.5434783 8.5434783
14 -0.3000000 0.5434783
15 0.7000000 -0.3000000
16 0.5434783 0.7000000
17 0.5434783 0.5434783
18 -0.3000000 0.5434783
19 0.7000000 -0.3000000
20 -0.4565217 0.7000000
21 -0.3000000 -0.4565217
22 -0.3000000 -0.3000000
23 -0.3000000 -0.3000000
24 0.7000000 -0.3000000
25 -0.4565217 0.7000000
26 -0.3000000 -0.4565217
27 -0.4565217 -0.3000000
28 -0.3000000 -0.4565217
29 -0.4565217 -0.3000000
30 -0.4565217 -0.4565217
31 -0.4565217 -0.4565217
32 -0.4565217 -0.4565217
33 0.7000000 -0.4565217
34 -0.4565217 0.7000000
35 -0.4565217 -0.4565217
36 0.5434783 -0.4565217
37 -0.3000000 0.5434783
38 -0.3000000 -0.3000000
39 0.5434783 -0.3000000
40 -0.3000000 0.5434783
41 -0.3000000 -0.3000000
42 -0.3000000 -0.3000000
43 0.5434783 -0.3000000
44 -0.4565217 0.5434783
45 -0.3000000 -0.4565217
46 -0.4565217 -0.3000000
47 0.7000000 -0.4565217
48 -0.3000000 0.7000000
49 -0.4565217 -0.3000000
50 0.5434783 -0.4565217
51 0.5434783 0.5434783
52 -0.3000000 0.5434783
53 -0.4565217 -0.3000000
54 -0.4565217 -0.4565217
55 0.7000000 -0.4565217
56 -0.3000000 0.7000000
57 -0.3000000 -0.3000000
58 -0.3000000 -0.3000000
59 0.7000000 -0.3000000
60 0.7000000 0.7000000
61 -0.4565217 0.7000000
62 -0.4565217 -0.4565217
63 0.7000000 -0.4565217
64 -0.4565217 0.7000000
65 -0.4565217 -0.4565217
66 0.5434783 -0.4565217
67 -0.4565217 0.5434783
68 -0.3000000 -0.4565217
69 -0.4565217 -0.3000000
70 -0.4565217 -0.4565217
71 -0.3000000 -0.4565217
72 -0.3000000 -0.3000000
73 -0.4565217 -0.3000000
74 -0.3000000 -0.4565217
75 0.7000000 -0.3000000
76 -0.3000000 0.7000000
77 -0.3000000 -0.3000000
78 0.7000000 -0.3000000
79 0.5434783 0.7000000
80 -0.4565217 0.5434783
81 -0.3000000 -0.4565217
82 -0.4565217 -0.3000000
83 -0.4565217 -0.4565217
84 -0.3000000 -0.4565217
85 -0.4565217 -0.3000000
> 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/7bseh1356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87wwq1356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9naav1356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10jzi81356129590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c22q1356129590.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/12d9ip1356129590.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/1391ze1356129590.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/1493ru1356129590.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/153q801356129590.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/16bsn81356129590.tab")
+ }
>
> try(system("convert tmp/135zy1356129590.ps tmp/135zy1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xo721356129590.ps tmp/2xo721356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fjxb1356129590.ps tmp/3fjxb1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pmus1356129590.ps tmp/4pmus1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rngm1356129590.ps tmp/5rngm1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a4e51356129590.ps tmp/6a4e51356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bseh1356129590.ps tmp/7bseh1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/87wwq1356129590.ps tmp/87wwq1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/9naav1356129590.ps tmp/9naav1356129590.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jzi81356129590.ps tmp/10jzi81356129590.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.049 0.851 6.901