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(1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,1,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,1,1,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0),dim=c(4,86),dimnames=list(c('Treatment','CA','Used','Outcome'),1:86))
> y <- array(NA,dim=c(4,86),dimnames=list(c('Treatment','CA','Used','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 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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
Outcome Treatment CA Used t
1 1 1 0 0 1
2 0 0 0 0 2
3 0 0 0 0 3
4 0 0 0 0 4
5 0 0 0 0 5
6 1 0 0 0 6
7 0 0 0 0 7
8 0 1 0 0 8
9 1 0 0 0 9
10 0 0 0 0 10
11 0 1 0 0 11
12 0 0 0 0 12
13 0 0 0 1 13
14 0 1 0 0 14
15 1 0 0 1 15
16 1 1 0 1 16
17 0 1 1 1 17
18 0 1 0 0 18
19 1 0 0 0 19
20 1 1 1 1 20
21 0 0 0 0 21
22 1 0 0 1 22
23 1 0 0 0 23
24 1 0 0 0 24
25 1 1 0 1 25
26 0 0 0 1 26
27 1 0 0 0 27
28 0 0 0 1 28
29 1 0 0 0 29
30 0 0 0 0 30
31 0 0 0 0 31
32 0 0 0 0 32
33 0 0 0 0 33
34 1 1 0 0 34
35 0 0 0 0 35
36 0 0 0 0 36
37 0 1 0 1 37
38 1 0 0 1 38
39 1 0 0 0 39
40 0 1 0 0 40
41 1 0 1 1 41
42 1 0 0 1 42
43 1 0 0 0 43
44 0 1 0 0 44
45 0 0 0 0 45
46 1 0 0 0 46
47 0 0 0 0 47
48 1 0 0 0 48
49 1 0 0 0 49
50 0 0 0 0 50
51 0 1 0 1 51
52 0 1 1 1 52
53 1 0 0 0 53
54 0 0 1 1 54
55 0 0 0 0 55
56 1 1 0 1 56
57 1 0 0 1 57
58 1 0 0 0 58
59 1 0 0 0 59
60 1 1 1 1 60
61 1 1 0 0 61
62 0 0 0 1 62
63 0 0 0 0 63
64 1 1 0 0 64
65 0 0 0 0 65
66 0 0 0 0 66
67 0 1 1 1 67
68 0 0 0 0 68
69 1 0 0 0 69
70 0 0 0 1 70
71 0 0 0 0 71
72 1 0 0 0 72
73 1 0 0 1 73
74 0 0 0 1 74
75 1 0 0 0 75
76 1 1 0 0 76
77 1 0 0 0 77
78 1 0 0 1 78
79 1 1 1 1 79
80 0 1 0 0 80
81 0 0 0 0 81
82 1 0 0 1 82
83 0 0 0 0 83
84 0 0 1 1 84
85 1 0 0 0 85
86 0 0 0 0 86
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Treatment CA Used t
0.339186 0.032335 -0.162233 0.137637 0.002056
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6290 -0.4534 -0.3484 0.5112 0.6485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.339186 0.120155 2.823 0.00599 **
Treatment 0.032335 0.131151 0.247 0.80588
CA -0.162233 0.213142 -0.761 0.44878
Used 0.137637 0.134716 1.022 0.30997
t 0.002056 0.002244 0.916 0.36228
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5074 on 81 degrees of freedom
Multiple R-squared: 0.02516, Adjusted R-squared: -0.02298
F-statistic: 0.5226 on 4 and 81 DF, p-value: 0.7194
> 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,] 0.7953116 0.4093768 0.2046884
[2,] 0.8649875 0.2700250 0.1350125
[3,] 0.8048199 0.3903601 0.1951801
[4,] 0.7475524 0.5048952 0.2524476
[5,] 0.6557471 0.6885057 0.3442529
[6,] 0.5623110 0.8753779 0.4376890
[7,] 0.4715687 0.9431374 0.5284313
[8,] 0.5596210 0.8807581 0.4403790
[9,] 0.5070946 0.9858108 0.4929054
[10,] 0.4257782 0.8515565 0.5742218
[11,] 0.3552805 0.7105610 0.6447195
[12,] 0.4935093 0.9870187 0.5064907
[13,] 0.5430272 0.9139456 0.4569728
[14,] 0.4993828 0.9987656 0.5006172
[15,] 0.4502386 0.9004772 0.5497614
[16,] 0.4684530 0.9369060 0.5315470
[17,] 0.4519010 0.9038020 0.5480990
[18,] 0.3966462 0.7932924 0.6033538
[19,] 0.4963431 0.9926862 0.5036569
[20,] 0.4748646 0.9497293 0.5251354
[21,] 0.5295378 0.9409243 0.4704622
[22,] 0.5067622 0.9864756 0.4932378
[23,] 0.5264054 0.9471892 0.4735946
[24,] 0.5236347 0.9527305 0.4763653
[25,] 0.5089972 0.9820056 0.4910028
[26,] 0.4886220 0.9772441 0.5113780
[27,] 0.4825722 0.9651444 0.5174278
[28,] 0.4658706 0.9317412 0.5341294
[29,] 0.4490108 0.8980215 0.5509892
[30,] 0.4801939 0.9603878 0.5198061
[31,] 0.4564116 0.9128232 0.5435884
[32,] 0.4608896 0.9217793 0.5391104
[33,] 0.4558854 0.9117707 0.5441146
[34,] 0.4489856 0.8979712 0.5510144
[35,] 0.4217541 0.8435081 0.5782459
[36,] 0.4241719 0.8483438 0.5758281
[37,] 0.4304039 0.8608078 0.5695961
[38,] 0.4222332 0.8444664 0.5777668
[39,] 0.4222418 0.8444835 0.5777582
[40,] 0.4154970 0.8309940 0.5845030
[41,] 0.4140065 0.8280129 0.5859935
[42,] 0.4182215 0.8364429 0.5817785
[43,] 0.4071213 0.8142426 0.5928787
[44,] 0.4770228 0.9540456 0.5229772
[45,] 0.4900192 0.9800384 0.5099808
[46,] 0.4917327 0.9834655 0.5082673
[47,] 0.4652519 0.9305038 0.5347481
[48,] 0.4590230 0.9180460 0.5409770
[49,] 0.4096071 0.8192143 0.5903929
[50,] 0.3751748 0.7503497 0.6248252
[51,] 0.3781708 0.7563416 0.6218292
[52,] 0.4048663 0.8097327 0.5951337
[53,] 0.4186301 0.8372601 0.5813699
[54,] 0.3937939 0.7875877 0.6062061
[55,] 0.3917528 0.7835056 0.6082472
[56,] 0.3561229 0.7122458 0.6438771
[57,] 0.3342327 0.6684654 0.6657673
[58,] 0.2994643 0.5989286 0.7005357
[59,] 0.2728734 0.5457468 0.7271266
[60,] 0.2627050 0.5254099 0.7372950
[61,] 0.2698385 0.5396769 0.7301615
[62,] 0.2403745 0.4807491 0.7596255
[63,] 0.3010958 0.6021916 0.6989042
[64,] 0.3736824 0.7473647 0.6263176
[65,] 0.3007693 0.6015385 0.6992307
[66,] 0.2258363 0.4516727 0.7741637
[67,] 0.5167404 0.9665191 0.4832596
[68,] 0.4079148 0.8158297 0.5920852
[69,] 0.3192139 0.6384278 0.6807861
[70,] 0.3352110 0.6704220 0.6647890
[71,] 0.2108070 0.4216140 0.7891930
> postscript(file="/var/fisher/rcomp/tmp/1rq7i1356028973.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/27bv61356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3v1kh1356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4bfr21356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5yn1s1356028973.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
0.6264221 -0.3432988 -0.3453551 -0.3474114 -0.3494676 0.6484761 -0.3535802
8 9 10 11 12 13 14
-0.3879718 0.6423073 -0.3597490 -0.3941406 -0.3638616 -0.5035550 -0.4003095
15 16 17 18 19 20 21
0.4923324 0.4579408 -0.3818824 -0.4085346 0.6217445 0.6119488 -0.3823680
22 23 24 25 26 27 28
0.4779385 0.6135194 0.6114631 0.4394343 -0.5302866 0.6052943 -0.5343991
29 30 31 32 33 34 35
0.6011818 -0.4008745 -0.4029308 -0.4049871 -0.4070433 0.5585650 -0.4111559
36 37 38 39 40 41 42
-0.4132122 -0.5852410 0.4450381 0.5806190 -0.4537726 0.6011023 0.4368130
43 44 45 46 47 48 49
0.5723939 -0.4619977 -0.4317186 0.5662251 -0.4358312 0.5621125 0.5600563
50 51 52 53 54 55 56
-0.4420000 -0.6140288 -0.4538520 0.5518312 -0.4256292 -0.4522814 0.3756898
57 58 59 60 61 62 63
0.4059689 0.5415498 0.5394935 0.5296978 0.5030456 -0.6043125 -0.4687316
64 65 66 67 68 69 70
0.4968768 -0.4728441 -0.4749004 -0.4846962 -0.4790130 0.5189308 -0.6207627
71 72 73 74 75 76 77
-0.4851818 0.5127619 0.3730685 -0.6289878 0.5065931 0.4722015 0.5024806
78 79 80 81 82 83 84
0.3627871 0.4906285 -0.5360236 -0.5057445 0.3545620 -0.5098571 -0.4873175
85 86
0.4860304 -0.5160259
> postscript(file="/var/fisher/rcomp/tmp/65x9i1356028973.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 0.6264221 NA
1 -0.3432988 0.6264221
2 -0.3453551 -0.3432988
3 -0.3474114 -0.3453551
4 -0.3494676 -0.3474114
5 0.6484761 -0.3494676
6 -0.3535802 0.6484761
7 -0.3879718 -0.3535802
8 0.6423073 -0.3879718
9 -0.3597490 0.6423073
10 -0.3941406 -0.3597490
11 -0.3638616 -0.3941406
12 -0.5035550 -0.3638616
13 -0.4003095 -0.5035550
14 0.4923324 -0.4003095
15 0.4579408 0.4923324
16 -0.3818824 0.4579408
17 -0.4085346 -0.3818824
18 0.6217445 -0.4085346
19 0.6119488 0.6217445
20 -0.3823680 0.6119488
21 0.4779385 -0.3823680
22 0.6135194 0.4779385
23 0.6114631 0.6135194
24 0.4394343 0.6114631
25 -0.5302866 0.4394343
26 0.6052943 -0.5302866
27 -0.5343991 0.6052943
28 0.6011818 -0.5343991
29 -0.4008745 0.6011818
30 -0.4029308 -0.4008745
31 -0.4049871 -0.4029308
32 -0.4070433 -0.4049871
33 0.5585650 -0.4070433
34 -0.4111559 0.5585650
35 -0.4132122 -0.4111559
36 -0.5852410 -0.4132122
37 0.4450381 -0.5852410
38 0.5806190 0.4450381
39 -0.4537726 0.5806190
40 0.6011023 -0.4537726
41 0.4368130 0.6011023
42 0.5723939 0.4368130
43 -0.4619977 0.5723939
44 -0.4317186 -0.4619977
45 0.5662251 -0.4317186
46 -0.4358312 0.5662251
47 0.5621125 -0.4358312
48 0.5600563 0.5621125
49 -0.4420000 0.5600563
50 -0.6140288 -0.4420000
51 -0.4538520 -0.6140288
52 0.5518312 -0.4538520
53 -0.4256292 0.5518312
54 -0.4522814 -0.4256292
55 0.3756898 -0.4522814
56 0.4059689 0.3756898
57 0.5415498 0.4059689
58 0.5394935 0.5415498
59 0.5296978 0.5394935
60 0.5030456 0.5296978
61 -0.6043125 0.5030456
62 -0.4687316 -0.6043125
63 0.4968768 -0.4687316
64 -0.4728441 0.4968768
65 -0.4749004 -0.4728441
66 -0.4846962 -0.4749004
67 -0.4790130 -0.4846962
68 0.5189308 -0.4790130
69 -0.6207627 0.5189308
70 -0.4851818 -0.6207627
71 0.5127619 -0.4851818
72 0.3730685 0.5127619
73 -0.6289878 0.3730685
74 0.5065931 -0.6289878
75 0.4722015 0.5065931
76 0.5024806 0.4722015
77 0.3627871 0.5024806
78 0.4906285 0.3627871
79 -0.5360236 0.4906285
80 -0.5057445 -0.5360236
81 0.3545620 -0.5057445
82 -0.5098571 0.3545620
83 -0.4873175 -0.5098571
84 0.4860304 -0.4873175
85 -0.5160259 0.4860304
86 NA -0.5160259
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3432988 0.6264221
[2,] -0.3453551 -0.3432988
[3,] -0.3474114 -0.3453551
[4,] -0.3494676 -0.3474114
[5,] 0.6484761 -0.3494676
[6,] -0.3535802 0.6484761
[7,] -0.3879718 -0.3535802
[8,] 0.6423073 -0.3879718
[9,] -0.3597490 0.6423073
[10,] -0.3941406 -0.3597490
[11,] -0.3638616 -0.3941406
[12,] -0.5035550 -0.3638616
[13,] -0.4003095 -0.5035550
[14,] 0.4923324 -0.4003095
[15,] 0.4579408 0.4923324
[16,] -0.3818824 0.4579408
[17,] -0.4085346 -0.3818824
[18,] 0.6217445 -0.4085346
[19,] 0.6119488 0.6217445
[20,] -0.3823680 0.6119488
[21,] 0.4779385 -0.3823680
[22,] 0.6135194 0.4779385
[23,] 0.6114631 0.6135194
[24,] 0.4394343 0.6114631
[25,] -0.5302866 0.4394343
[26,] 0.6052943 -0.5302866
[27,] -0.5343991 0.6052943
[28,] 0.6011818 -0.5343991
[29,] -0.4008745 0.6011818
[30,] -0.4029308 -0.4008745
[31,] -0.4049871 -0.4029308
[32,] -0.4070433 -0.4049871
[33,] 0.5585650 -0.4070433
[34,] -0.4111559 0.5585650
[35,] -0.4132122 -0.4111559
[36,] -0.5852410 -0.4132122
[37,] 0.4450381 -0.5852410
[38,] 0.5806190 0.4450381
[39,] -0.4537726 0.5806190
[40,] 0.6011023 -0.4537726
[41,] 0.4368130 0.6011023
[42,] 0.5723939 0.4368130
[43,] -0.4619977 0.5723939
[44,] -0.4317186 -0.4619977
[45,] 0.5662251 -0.4317186
[46,] -0.4358312 0.5662251
[47,] 0.5621125 -0.4358312
[48,] 0.5600563 0.5621125
[49,] -0.4420000 0.5600563
[50,] -0.6140288 -0.4420000
[51,] -0.4538520 -0.6140288
[52,] 0.5518312 -0.4538520
[53,] -0.4256292 0.5518312
[54,] -0.4522814 -0.4256292
[55,] 0.3756898 -0.4522814
[56,] 0.4059689 0.3756898
[57,] 0.5415498 0.4059689
[58,] 0.5394935 0.5415498
[59,] 0.5296978 0.5394935
[60,] 0.5030456 0.5296978
[61,] -0.6043125 0.5030456
[62,] -0.4687316 -0.6043125
[63,] 0.4968768 -0.4687316
[64,] -0.4728441 0.4968768
[65,] -0.4749004 -0.4728441
[66,] -0.4846962 -0.4749004
[67,] -0.4790130 -0.4846962
[68,] 0.5189308 -0.4790130
[69,] -0.6207627 0.5189308
[70,] -0.4851818 -0.6207627
[71,] 0.5127619 -0.4851818
[72,] 0.3730685 0.5127619
[73,] -0.6289878 0.3730685
[74,] 0.5065931 -0.6289878
[75,] 0.4722015 0.5065931
[76,] 0.5024806 0.4722015
[77,] 0.3627871 0.5024806
[78,] 0.4906285 0.3627871
[79,] -0.5360236 0.4906285
[80,] -0.5057445 -0.5360236
[81,] 0.3545620 -0.5057445
[82,] -0.5098571 0.3545620
[83,] -0.4873175 -0.5098571
[84,] 0.4860304 -0.4873175
[85,] -0.5160259 0.4860304
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3432988 0.6264221
2 -0.3453551 -0.3432988
3 -0.3474114 -0.3453551
4 -0.3494676 -0.3474114
5 0.6484761 -0.3494676
6 -0.3535802 0.6484761
7 -0.3879718 -0.3535802
8 0.6423073 -0.3879718
9 -0.3597490 0.6423073
10 -0.3941406 -0.3597490
11 -0.3638616 -0.3941406
12 -0.5035550 -0.3638616
13 -0.4003095 -0.5035550
14 0.4923324 -0.4003095
15 0.4579408 0.4923324
16 -0.3818824 0.4579408
17 -0.4085346 -0.3818824
18 0.6217445 -0.4085346
19 0.6119488 0.6217445
20 -0.3823680 0.6119488
21 0.4779385 -0.3823680
22 0.6135194 0.4779385
23 0.6114631 0.6135194
24 0.4394343 0.6114631
25 -0.5302866 0.4394343
26 0.6052943 -0.5302866
27 -0.5343991 0.6052943
28 0.6011818 -0.5343991
29 -0.4008745 0.6011818
30 -0.4029308 -0.4008745
31 -0.4049871 -0.4029308
32 -0.4070433 -0.4049871
33 0.5585650 -0.4070433
34 -0.4111559 0.5585650
35 -0.4132122 -0.4111559
36 -0.5852410 -0.4132122
37 0.4450381 -0.5852410
38 0.5806190 0.4450381
39 -0.4537726 0.5806190
40 0.6011023 -0.4537726
41 0.4368130 0.6011023
42 0.5723939 0.4368130
43 -0.4619977 0.5723939
44 -0.4317186 -0.4619977
45 0.5662251 -0.4317186
46 -0.4358312 0.5662251
47 0.5621125 -0.4358312
48 0.5600563 0.5621125
49 -0.4420000 0.5600563
50 -0.6140288 -0.4420000
51 -0.4538520 -0.6140288
52 0.5518312 -0.4538520
53 -0.4256292 0.5518312
54 -0.4522814 -0.4256292
55 0.3756898 -0.4522814
56 0.4059689 0.3756898
57 0.5415498 0.4059689
58 0.5394935 0.5415498
59 0.5296978 0.5394935
60 0.5030456 0.5296978
61 -0.6043125 0.5030456
62 -0.4687316 -0.6043125
63 0.4968768 -0.4687316
64 -0.4728441 0.4968768
65 -0.4749004 -0.4728441
66 -0.4846962 -0.4749004
67 -0.4790130 -0.4846962
68 0.5189308 -0.4790130
69 -0.6207627 0.5189308
70 -0.4851818 -0.6207627
71 0.5127619 -0.4851818
72 0.3730685 0.5127619
73 -0.6289878 0.3730685
74 0.5065931 -0.6289878
75 0.4722015 0.5065931
76 0.5024806 0.4722015
77 0.3627871 0.5024806
78 0.4906285 0.3627871
79 -0.5360236 0.4906285
80 -0.5057445 -0.5360236
81 0.3545620 -0.5057445
82 -0.5098571 0.3545620
83 -0.4873175 -0.5098571
84 0.4860304 -0.4873175
85 -0.5160259 0.4860304
> 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/fisher/rcomp/tmp/7eltk1356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8opj51356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9919y1356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10zxz21356028973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/119cq21356028973.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/fisher/rcomp/tmp/12n0h81356028973.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/fisher/rcomp/tmp/1346tu1356028973.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/fisher/rcomp/tmp/14erfj1356028973.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/fisher/rcomp/tmp/15ums01356028973.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/fisher/rcomp/tmp/16n2uh1356028973.tab")
+ }
>
> try(system("convert tmp/1rq7i1356028973.ps tmp/1rq7i1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/27bv61356028973.ps tmp/27bv61356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v1kh1356028973.ps tmp/3v1kh1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bfr21356028973.ps tmp/4bfr21356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yn1s1356028973.ps tmp/5yn1s1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/65x9i1356028973.ps tmp/65x9i1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/7eltk1356028973.ps tmp/7eltk1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/8opj51356028973.ps tmp/8opj51356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/9919y1356028973.ps tmp/9919y1356028973.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zxz21356028973.ps tmp/10zxz21356028973.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.828 1.781 8.632