R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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(41
+ ,12
+ ,12
+ ,13
+ ,39
+ ,11
+ ,11
+ ,16
+ ,30
+ ,15
+ ,14
+ ,19
+ ,31
+ ,6
+ ,12
+ ,15
+ ,34
+ ,13
+ ,21
+ ,14
+ ,35
+ ,10
+ ,12
+ ,13
+ ,39
+ ,12
+ ,22
+ ,19
+ ,34
+ ,14
+ ,11
+ ,15
+ ,36
+ ,12
+ ,10
+ ,14
+ ,37
+ ,6
+ ,13
+ ,15
+ ,38
+ ,10
+ ,10
+ ,16
+ ,36
+ ,12
+ ,8
+ ,16
+ ,38
+ ,12
+ ,15
+ ,16
+ ,39
+ ,11
+ ,14
+ ,16
+ ,33
+ ,15
+ ,10
+ ,17
+ ,32
+ ,12
+ ,14
+ ,15
+ ,36
+ ,10
+ ,14
+ ,15
+ ,38
+ ,12
+ ,11
+ ,20
+ ,39
+ ,11
+ ,10
+ ,18
+ ,32
+ ,12
+ ,13
+ ,16
+ ,32
+ ,11
+ ,7
+ ,16
+ ,31
+ ,12
+ ,14
+ ,16
+ ,39
+ ,13
+ ,12
+ ,19
+ ,37
+ ,11
+ ,14
+ ,16
+ ,39
+ ,9
+ ,11
+ ,17
+ ,41
+ ,13
+ ,9
+ ,17
+ ,36
+ ,10
+ ,11
+ ,16
+ ,33
+ ,14
+ ,15
+ ,15
+ ,33
+ ,12
+ ,14
+ ,16
+ ,34
+ ,10
+ ,13
+ ,14
+ ,31
+ ,12
+ ,9
+ ,15
+ ,27
+ ,8
+ ,15
+ ,12
+ ,37
+ ,10
+ ,10
+ ,14
+ ,34
+ ,12
+ ,11
+ ,16
+ ,34
+ ,12
+ ,13
+ ,14
+ ,32
+ ,7
+ ,8
+ ,7
+ ,29
+ ,6
+ ,20
+ ,10
+ ,36
+ ,12
+ ,12
+ ,14
+ ,29
+ ,10
+ ,10
+ ,16
+ ,35
+ ,10
+ ,10
+ ,16
+ ,37
+ ,10
+ ,9
+ ,16
+ ,34
+ ,12
+ ,14
+ ,14
+ ,38
+ ,15
+ ,8
+ ,20
+ ,35
+ ,10
+ ,14
+ ,14
+ ,38
+ ,10
+ ,11
+ ,14
+ ,37
+ ,12
+ ,13
+ ,11
+ ,38
+ ,13
+ ,9
+ ,14
+ ,33
+ ,11
+ ,11
+ ,15
+ ,36
+ ,11
+ ,15
+ ,16
+ ,38
+ ,12
+ ,11
+ ,14
+ ,32
+ ,14
+ ,10
+ ,16
+ ,32
+ ,10
+ ,14
+ ,14
+ ,32
+ ,12
+ ,18
+ ,12
+ ,34
+ ,13
+ ,14
+ ,16
+ ,32
+ ,5
+ ,11
+ ,9
+ ,37
+ ,6
+ ,12
+ ,14
+ ,39
+ ,12
+ ,13
+ ,16
+ ,29
+ ,12
+ ,9
+ ,16
+ ,37
+ ,11
+ ,10
+ ,15
+ ,35
+ ,10
+ ,15
+ ,16
+ ,30
+ ,7
+ ,20
+ ,12
+ ,38
+ ,12
+ ,12
+ ,16
+ ,34
+ ,14
+ ,12
+ ,16
+ ,31
+ ,11
+ ,14
+ ,14
+ ,34
+ ,12
+ ,13
+ ,16
+ ,35
+ ,13
+ ,11
+ ,17
+ ,36
+ ,14
+ ,17
+ ,18
+ ,30
+ ,11
+ ,12
+ ,18
+ ,39
+ ,12
+ ,13
+ ,12
+ ,35
+ ,12
+ ,14
+ ,16
+ ,38
+ ,8
+ ,13
+ ,10
+ ,31
+ ,11
+ ,15
+ ,14
+ ,34
+ ,14
+ ,13
+ ,18
+ ,38
+ ,14
+ ,10
+ ,18
+ ,34
+ ,12
+ ,11
+ ,16
+ ,39
+ ,9
+ ,19
+ ,17
+ ,37
+ ,13
+ ,13
+ ,16
+ ,34
+ ,11
+ ,17
+ ,16
+ ,28
+ ,12
+ ,13
+ ,13
+ ,37
+ ,12
+ ,9
+ ,16
+ ,33
+ ,12
+ ,11
+ ,16
+ ,37
+ ,12
+ ,10
+ ,20
+ ,35
+ ,12
+ ,9
+ ,16
+ ,37
+ ,12
+ ,12
+ ,15
+ ,32
+ ,11
+ ,12
+ ,15
+ ,33
+ ,10
+ ,13
+ ,16
+ ,38
+ ,9
+ ,13
+ ,14
+ ,33
+ ,12
+ ,12
+ ,16
+ ,29
+ ,12
+ ,15
+ ,16
+ ,33
+ ,12
+ ,22
+ ,15
+ ,31
+ ,9
+ ,13
+ ,12
+ ,36
+ ,15
+ ,15
+ ,17
+ ,35
+ ,12
+ ,13
+ ,16
+ ,32
+ ,12
+ ,15
+ ,15
+ ,29
+ ,12
+ ,10
+ ,13
+ ,39
+ ,10
+ ,11
+ ,16
+ ,37
+ ,13
+ ,16
+ ,16
+ ,35
+ ,9
+ ,11
+ ,16
+ ,37
+ ,12
+ ,11
+ ,16
+ ,32
+ ,10
+ ,10
+ ,14
+ ,38
+ ,14
+ ,10
+ ,16
+ ,37
+ ,11
+ ,16
+ ,16
+ ,36
+ ,15
+ ,12
+ ,20
+ ,32
+ ,11
+ ,11
+ ,15
+ ,33
+ ,11
+ ,16
+ ,16
+ ,40
+ ,12
+ ,19
+ ,13
+ ,38
+ ,12
+ ,11
+ ,17
+ ,41
+ ,12
+ ,16
+ ,16
+ ,36
+ ,11
+ ,15
+ ,16
+ ,43
+ ,7
+ ,24
+ ,12
+ ,30
+ ,12
+ ,14
+ ,16
+ ,31
+ ,14
+ ,15
+ ,16
+ ,32
+ ,11
+ ,11
+ ,17
+ ,32
+ ,11
+ ,15
+ ,13
+ ,37
+ ,10
+ ,12
+ ,12
+ ,37
+ ,13
+ ,10
+ ,18
+ ,33
+ ,13
+ ,14
+ ,14
+ ,34
+ ,8
+ ,13
+ ,14
+ ,33
+ ,11
+ ,9
+ ,13
+ ,38
+ ,12
+ ,15
+ ,16
+ ,33
+ ,11
+ ,15
+ ,13
+ ,31
+ ,13
+ ,14
+ ,16
+ ,38
+ ,12
+ ,11
+ ,13
+ ,37
+ ,14
+ ,8
+ ,16
+ ,33
+ ,13
+ ,11
+ ,15
+ ,31
+ ,15
+ ,11
+ ,16
+ ,39
+ ,10
+ ,8
+ ,15
+ ,44
+ ,11
+ ,10
+ ,17
+ ,33
+ ,9
+ ,11
+ ,15
+ ,35
+ ,11
+ ,13
+ ,12
+ ,32
+ ,10
+ ,11
+ ,16
+ ,28
+ ,11
+ ,20
+ ,10
+ ,40
+ ,8
+ ,10
+ ,16
+ ,27
+ ,11
+ ,15
+ ,12
+ ,37
+ ,12
+ ,12
+ ,14
+ ,32
+ ,12
+ ,14
+ ,15
+ ,28
+ ,9
+ ,23
+ ,13
+ ,34
+ ,11
+ ,14
+ ,15
+ ,30
+ ,10
+ ,16
+ ,11
+ ,35
+ ,8
+ ,11
+ ,12
+ ,31
+ ,9
+ ,12
+ ,8
+ ,32
+ ,8
+ ,10
+ ,16
+ ,30
+ ,9
+ ,14
+ ,15
+ ,30
+ ,15
+ ,12
+ ,17
+ ,31
+ ,11
+ ,12
+ ,16
+ ,40
+ ,8
+ ,11
+ ,10
+ ,32
+ ,13
+ ,12
+ ,18
+ ,36
+ ,12
+ ,13
+ ,13
+ ,32
+ ,12
+ ,11
+ ,16
+ ,35
+ ,9
+ ,19
+ ,13
+ ,38
+ ,7
+ ,12
+ ,10
+ ,42
+ ,13
+ ,17
+ ,15
+ ,34
+ ,9
+ ,9
+ ,16
+ ,35
+ ,6
+ ,12
+ ,16
+ ,35
+ ,8
+ ,19
+ ,14
+ ,33
+ ,8
+ ,18
+ ,10
+ ,36
+ ,15
+ ,15
+ ,17
+ ,32
+ ,6
+ ,14
+ ,13
+ ,33
+ ,9
+ ,11
+ ,15
+ ,34
+ ,11
+ ,9
+ ,16
+ ,32
+ ,8
+ ,18
+ ,12
+ ,34
+ ,8
+ ,16
+ ,13)
+ ,dim=c(4
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Software'
+ ,'Depression'
+ ,'Learning')
+ ,1:162))
> y <- array(NA,dim=c(4,162),dimnames=list(c('Connected','Software','Depression','Learning'),1:162))
> 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 = '4'
> par3 <- 'No 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
Learning Connected Software Depression
1 13 41 12 12
2 16 39 11 11
3 19 30 15 14
4 15 31 6 12
5 14 34 13 21
6 13 35 10 12
7 19 39 12 22
8 15 34 14 11
9 14 36 12 10
10 15 37 6 13
11 16 38 10 10
12 16 36 12 8
13 16 38 12 15
14 16 39 11 14
15 17 33 15 10
16 15 32 12 14
17 15 36 10 14
18 20 38 12 11
19 18 39 11 10
20 16 32 12 13
21 16 32 11 7
22 16 31 12 14
23 19 39 13 12
24 16 37 11 14
25 17 39 9 11
26 17 41 13 9
27 16 36 10 11
28 15 33 14 15
29 16 33 12 14
30 14 34 10 13
31 15 31 12 9
32 12 27 8 15
33 14 37 10 10
34 16 34 12 11
35 14 34 12 13
36 7 32 7 8
37 10 29 6 20
38 14 36 12 12
39 16 29 10 10
40 16 35 10 10
41 16 37 10 9
42 14 34 12 14
43 20 38 15 8
44 14 35 10 14
45 14 38 10 11
46 11 37 12 13
47 14 38 13 9
48 15 33 11 11
49 16 36 11 15
50 14 38 12 11
51 16 32 14 10
52 14 32 10 14
53 12 32 12 18
54 16 34 13 14
55 9 32 5 11
56 14 37 6 12
57 16 39 12 13
58 16 29 12 9
59 15 37 11 10
60 16 35 10 15
61 12 30 7 20
62 16 38 12 12
63 16 34 14 12
64 14 31 11 14
65 16 34 12 13
66 17 35 13 11
67 18 36 14 17
68 18 30 11 12
69 12 39 12 13
70 16 35 12 14
71 10 38 8 13
72 14 31 11 15
73 18 34 14 13
74 18 38 14 10
75 16 34 12 11
76 17 39 9 19
77 16 37 13 13
78 16 34 11 17
79 13 28 12 13
80 16 37 12 9
81 16 33 12 11
82 20 37 12 10
83 16 35 12 9
84 15 37 12 12
85 15 32 11 12
86 16 33 10 13
87 14 38 9 13
88 16 33 12 12
89 16 29 12 15
90 15 33 12 22
91 12 31 9 13
92 17 36 15 15
93 16 35 12 13
94 15 32 12 15
95 13 29 12 10
96 16 39 10 11
97 16 37 13 16
98 16 35 9 11
99 16 37 12 11
100 14 32 10 10
101 16 38 14 10
102 16 37 11 16
103 20 36 15 12
104 15 32 11 11
105 16 33 11 16
106 13 40 12 19
107 17 38 12 11
108 16 41 12 16
109 16 36 11 15
110 12 43 7 24
111 16 30 12 14
112 16 31 14 15
113 17 32 11 11
114 13 32 11 15
115 12 37 10 12
116 18 37 13 10
117 14 33 13 14
118 14 34 8 13
119 13 33 11 9
120 16 38 12 15
121 13 33 11 15
122 16 31 13 14
123 13 38 12 11
124 16 37 14 8
125 15 33 13 11
126 16 31 15 11
127 15 39 10 8
128 17 44 11 10
129 15 33 9 11
130 12 35 11 13
131 16 32 10 11
132 10 28 11 20
133 16 40 8 10
134 12 27 11 15
135 14 37 12 12
136 15 32 12 14
137 13 28 9 23
138 15 34 11 14
139 11 30 10 16
140 12 35 8 11
141 8 31 9 12
142 16 32 8 10
143 15 30 9 14
144 17 30 15 12
145 16 31 11 12
146 10 40 8 11
147 18 32 13 12
148 13 36 12 13
149 16 32 12 11
150 13 35 9 19
151 10 38 7 12
152 15 42 13 17
153 16 34 9 9
154 16 35 6 12
155 14 35 8 19
156 10 33 8 18
157 17 36 15 15
158 13 32 6 14
159 15 33 9 11
160 16 34 11 9
161 12 32 8 18
162 13 34 8 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Software Depression
6.4564 0.1099 0.5436 -0.1021
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9603 -1.0917 0.1221 1.1408 3.9826
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.45638 1.84538 3.499 0.000608 ***
Connected 0.10987 0.04324 2.541 0.012017 *
Software 0.54360 0.06827 7.963 3.1e-13 ***
Depression -0.10214 0.04643 -2.200 0.029258 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.836 on 158 degrees of freedom
Multiple R-squared: 0.35, Adjusted R-squared: 0.3376
F-statistic: 28.36 on 3 and 158 DF, p-value: 1.003e-14
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.96806075 0.06387850 0.03193925
[2,] 0.93935055 0.12129889 0.06064945
[3,] 0.89788737 0.20422526 0.10211263
[4,] 0.85558607 0.28882786 0.14441393
[5,] 0.83443459 0.33113081 0.16556541
[6,] 0.78978668 0.42042664 0.21021332
[7,] 0.71439409 0.57121182 0.28560591
[8,] 0.63941321 0.72117359 0.36058679
[9,] 0.57129804 0.85740391 0.42870196
[10,] 0.51149516 0.97700968 0.48850484
[11,] 0.42970753 0.85941506 0.57029247
[12,] 0.77035162 0.45929675 0.22964838
[13,] 0.79327547 0.41344906 0.20672453
[14,] 0.73772754 0.52454493 0.26227246
[15,] 0.67993202 0.64013596 0.32006798
[16,] 0.61618002 0.76763996 0.38381998
[17,] 0.66083159 0.67833683 0.33916841
[18,] 0.59908135 0.80183729 0.40091865
[19,] 0.58325876 0.83348248 0.41674124
[20,] 0.51754763 0.96490475 0.48245237
[21,] 0.45960291 0.91920582 0.54039709
[22,] 0.43388646 0.86777293 0.56611354
[23,] 0.37475075 0.74950149 0.62524925
[24,] 0.35036177 0.70072354 0.64963823
[25,] 0.30029424 0.60058849 0.69970576
[26,] 0.28717007 0.57434015 0.71282993
[27,] 0.27750305 0.55500611 0.72249695
[28,] 0.23073875 0.46147749 0.76926125
[29,] 0.22748054 0.45496108 0.77251946
[30,] 0.76300340 0.47399320 0.23699660
[31,] 0.75024942 0.49950117 0.24975058
[32,] 0.75242261 0.49515478 0.24757739
[33,] 0.77815156 0.44369689 0.22184844
[34,] 0.75435515 0.49128970 0.24564485
[35,] 0.71948613 0.56102773 0.28051387
[36,] 0.70395319 0.59209361 0.29604681
[37,] 0.70940700 0.58118600 0.29059300
[38,] 0.66930642 0.66138716 0.33069358
[39,] 0.64450382 0.71099236 0.35549618
[40,] 0.87079458 0.25841085 0.12920542
[41,] 0.89875300 0.20249401 0.10124700
[42,] 0.87495863 0.25008274 0.12504137
[43,] 0.85474014 0.29051972 0.14525986
[44,] 0.86112574 0.27774851 0.13887426
[45,] 0.83367227 0.33265546 0.16632773
[46,] 0.80135668 0.39728665 0.19864332
[47,] 0.83879798 0.32240404 0.16120202
[48,] 0.80774125 0.38451750 0.19225875
[49,] 0.83396292 0.33207416 0.16603708
[50,] 0.81818607 0.36362787 0.18181393
[51,] 0.78555327 0.42889346 0.21444673
[52,] 0.76252927 0.47494146 0.23747073
[53,] 0.72695295 0.54609410 0.27304705
[54,] 0.71985276 0.56029447 0.28014724
[55,] 0.68059871 0.63880259 0.31940129
[56,] 0.63757715 0.72484570 0.36242285
[57,] 0.59626739 0.80746522 0.40373261
[58,] 0.55213006 0.89573989 0.44786994
[59,] 0.51058933 0.97882134 0.48941067
[60,] 0.47245685 0.94491370 0.52754315
[61,] 0.46038605 0.92077211 0.53961395
[62,] 0.58443409 0.83113183 0.41556591
[63,] 0.73725091 0.52549819 0.26274909
[64,] 0.70197883 0.59604234 0.29802117
[65,] 0.80651340 0.38697320 0.19348660
[66,] 0.77484870 0.45030259 0.22515130
[67,] 0.76274313 0.47451374 0.23725687
[68,] 0.73275683 0.53448634 0.26724317
[69,] 0.69562438 0.60875125 0.30437562
[70,] 0.77205548 0.45588905 0.22794452
[71,] 0.73721300 0.52557400 0.26278700
[72,] 0.72618908 0.54762185 0.27381092
[73,] 0.72159354 0.55681292 0.27840646
[74,] 0.68220327 0.63559345 0.31779673
[75,] 0.64390021 0.71219959 0.35609979
[76,] 0.78647443 0.42705114 0.21352557
[77,] 0.75182264 0.49635471 0.24817736
[78,] 0.72168163 0.55663674 0.27831837
[79,] 0.68282537 0.63434925 0.31717463
[80,] 0.68112026 0.63775948 0.31887974
[81,] 0.64000756 0.71998489 0.35999244
[82,] 0.60171061 0.79657878 0.39828939
[83,] 0.58232161 0.83535678 0.41767839
[84,] 0.55218257 0.89563486 0.44781743
[85,] 0.53433381 0.93133239 0.46566619
[86,] 0.49246778 0.98493556 0.50753222
[87,] 0.45165811 0.90331621 0.54834189
[88,] 0.40770549 0.81541098 0.59229451
[89,] 0.42350897 0.84701794 0.57649103
[90,] 0.39100993 0.78201986 0.60899007
[91,] 0.35226130 0.70452260 0.64773870
[92,] 0.35498752 0.70997504 0.64501248
[93,] 0.31317598 0.62635195 0.68682402
[94,] 0.27520903 0.55041807 0.72479097
[95,] 0.25009130 0.50018260 0.74990870
[96,] 0.23420199 0.46840399 0.76579801
[97,] 0.29511858 0.59023715 0.70488142
[98,] 0.25582839 0.51165679 0.74417161
[99,] 0.25414587 0.50829174 0.74585413
[100,] 0.26940165 0.53880330 0.73059835
[101,] 0.24772557 0.49545114 0.75227443
[102,] 0.22226135 0.44452271 0.77773865
[103,] 0.21142133 0.42284266 0.78857867
[104,] 0.19491935 0.38983871 0.80508065
[105,] 0.17783710 0.35567419 0.82216290
[106,] 0.15202814 0.30405628 0.84797186
[107,] 0.16349800 0.32699599 0.83650200
[108,] 0.14601554 0.29203107 0.85398446
[109,] 0.16937429 0.33874859 0.83062571
[110,] 0.16919703 0.33839407 0.83080297
[111,] 0.15392025 0.30784050 0.84607975
[112,] 0.13042201 0.26084402 0.86957799
[113,] 0.14038978 0.28077956 0.85961022
[114,] 0.12692451 0.25384902 0.87307549
[115,] 0.11145907 0.22291813 0.88854093
[116,] 0.09397463 0.18794926 0.90602537
[117,] 0.11453940 0.22907881 0.88546060
[118,] 0.09784253 0.19568506 0.90215747
[119,] 0.08068755 0.16137509 0.91931245
[120,] 0.06485128 0.12970256 0.93514872
[121,] 0.05016419 0.10032839 0.94983581
[122,] 0.04336896 0.08673793 0.95663104
[123,] 0.03451197 0.06902395 0.96548803
[124,] 0.04480159 0.08960318 0.95519841
[125,] 0.03961221 0.07922443 0.96038779
[126,] 0.06352431 0.12704863 0.93647569
[127,] 0.07324481 0.14648961 0.92675519
[128,] 0.09224807 0.18449614 0.90775193
[129,] 0.07761687 0.15523374 0.92238313
[130,] 0.05766606 0.11533212 0.94233394
[131,] 0.04328691 0.08657382 0.95671309
[132,] 0.03124589 0.06249178 0.96875411
[133,] 0.04888739 0.09777478 0.95111261
[134,] 0.04040613 0.08081227 0.95959387
[135,] 0.55815269 0.88369462 0.44184731
[136,] 0.54210745 0.91578509 0.45789255
[137,] 0.47449976 0.94899953 0.52550024
[138,] 0.44176847 0.88353694 0.55823153
[139,] 0.36889378 0.73778756 0.63110622
[140,] 0.47565829 0.95131658 0.52434171
[141,] 0.44732349 0.89464697 0.55267651
[142,] 0.51481935 0.97036129 0.48518065
[143,] 0.42595360 0.85190720 0.57404640
[144,] 0.33394547 0.66789094 0.66605453
[145,] 0.80409638 0.39180723 0.19590362
[146,] 0.92196438 0.15607123 0.07803562
[147,] 0.85766364 0.28467271 0.14233636
[148,] 0.76022965 0.47954070 0.23977035
[149,] 0.71935044 0.56129913 0.28064956
> postscript(file="/var/wessaorg/rcomp/tmp/1knfb1351952601.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2rj5r1351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37d371351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44mq81351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52oyv1351952601.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 = 162
Frequency = 1
1 2 3 4 5 6
-3.25855064 0.40264788 2.52350117 3.10174373 -1.11379631 -1.51213285
7 8 9 10 11 12
3.98259408 -1.67880055 -1.91348226 2.54466498 0.95397653 -0.11776325
13 14 15 16 17 18
0.37748048 0.70906937 -0.21467044 -0.06544077 0.58227827 3.96891849
19 20 21 22 23 24
2.30050739 0.83241874 0.76317503 1.04442911 2.41758984 0.92880913
25 26 27 28 29 30
2.48984642 -0.10857140 1.27585678 -1.16036869 0.82468936 -0.30012248
31 32 33 34 35 36
-0.46627337 -0.23955381 -0.93615359 0.40839799 -1.38732101 -5.96028740
37 38 39 40 41 42
-0.86139254 -1.70920126 1.94280541 1.28358616 0.96170591 -1.28518052
43 44 45 46 47 48
2.03169919 -0.30785185 -0.94388297 -4.71693064 -2.77896177 0.06186714
49 50 51 52 53 54
1.14081950 -2.03108151 -0.56120129 0.02175777 -2.65687878 0.17122021
55 56 57 58 59 60
-2.56666737 1.44252448 0.06332961 0.75346638 -0.47975286 1.79428864
61 62 63 64 65 66
0.48513831 0.07105899 -0.57666005 -0.41197162 0.61267899 0.75492885
67 68 69 70 71 72
1.71430268 3.49361726 -3.93667039 0.60494961 -3.65240344 -0.30983112
73 74 75 76 77 78
1.52548045 0.77957945 0.40839799 3.30697040 -0.26052991 1.56484024
79 80 81 82 83 84
-1.72810176 -0.12549263 0.51826787 3.97664787 0.09424712 -0.81907114
85 86 87 88 89 90
0.27387751 1.80974740 -0.19600271 0.62040836 1.36630936 0.64181333
91 92 93 94 95 96
-1.42691358 -0.03357758 0.50280911 0.03669973 -2.14439313 0.94624715
97 98 99 100 101 102
0.04589158 1.92932593 0.07878837 -0.38680421 -1.22042055 1.13309012
103 104 105 106 107 108
2.66000093 0.17173701 1.57256962 -2.43369729 0.96891849 0.15001135
109 110 111 112 113 114
1.14081950 -0.53460808 1.15429898 0.05937107 2.17173701 -1.41970100
115 116 117 118 119 120
-2.73187260 1.43304860 -1.71890991 0.78707606 -2.14241386 0.37748048
121 122 123 124 125 126
-1.52957088 0.50082984 -3.03108151 -1.31483166 -1.02533140 -0.89279019
127 128 129 130 131 132
-0.36017434 0.75115801 1.14906568 -2.95359162 1.71533628 -3.46951901
133 134 135 136 137 138
1.82143532 -1.87035162 -1.81907114 -0.06544077 0.92410101 0.25841875
139 140 141 142 143 144
-2.55422148 -1.52707480 -5.52905408 2.70039433 1.78509679 0.31922018
145 146 147 148 149 150
1.38374739 -4.07642418 2.18667897 -2.60706077 0.62813774 -0.25355010
151 152 153 154 155 156
-3.21094466 -1.40131730 1.83491481 3.66226423 1.29004917 -2.59235158
157 158 159 160 161 162
-0.03357758 1.19615485 1.14906568 0.74771627 -0.48248170 0.09349755
> postscript(file="/var/wessaorg/rcomp/tmp/6vnsw1351952601.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.25855064 NA
1 0.40264788 -3.25855064
2 2.52350117 0.40264788
3 3.10174373 2.52350117
4 -1.11379631 3.10174373
5 -1.51213285 -1.11379631
6 3.98259408 -1.51213285
7 -1.67880055 3.98259408
8 -1.91348226 -1.67880055
9 2.54466498 -1.91348226
10 0.95397653 2.54466498
11 -0.11776325 0.95397653
12 0.37748048 -0.11776325
13 0.70906937 0.37748048
14 -0.21467044 0.70906937
15 -0.06544077 -0.21467044
16 0.58227827 -0.06544077
17 3.96891849 0.58227827
18 2.30050739 3.96891849
19 0.83241874 2.30050739
20 0.76317503 0.83241874
21 1.04442911 0.76317503
22 2.41758984 1.04442911
23 0.92880913 2.41758984
24 2.48984642 0.92880913
25 -0.10857140 2.48984642
26 1.27585678 -0.10857140
27 -1.16036869 1.27585678
28 0.82468936 -1.16036869
29 -0.30012248 0.82468936
30 -0.46627337 -0.30012248
31 -0.23955381 -0.46627337
32 -0.93615359 -0.23955381
33 0.40839799 -0.93615359
34 -1.38732101 0.40839799
35 -5.96028740 -1.38732101
36 -0.86139254 -5.96028740
37 -1.70920126 -0.86139254
38 1.94280541 -1.70920126
39 1.28358616 1.94280541
40 0.96170591 1.28358616
41 -1.28518052 0.96170591
42 2.03169919 -1.28518052
43 -0.30785185 2.03169919
44 -0.94388297 -0.30785185
45 -4.71693064 -0.94388297
46 -2.77896177 -4.71693064
47 0.06186714 -2.77896177
48 1.14081950 0.06186714
49 -2.03108151 1.14081950
50 -0.56120129 -2.03108151
51 0.02175777 -0.56120129
52 -2.65687878 0.02175777
53 0.17122021 -2.65687878
54 -2.56666737 0.17122021
55 1.44252448 -2.56666737
56 0.06332961 1.44252448
57 0.75346638 0.06332961
58 -0.47975286 0.75346638
59 1.79428864 -0.47975286
60 0.48513831 1.79428864
61 0.07105899 0.48513831
62 -0.57666005 0.07105899
63 -0.41197162 -0.57666005
64 0.61267899 -0.41197162
65 0.75492885 0.61267899
66 1.71430268 0.75492885
67 3.49361726 1.71430268
68 -3.93667039 3.49361726
69 0.60494961 -3.93667039
70 -3.65240344 0.60494961
71 -0.30983112 -3.65240344
72 1.52548045 -0.30983112
73 0.77957945 1.52548045
74 0.40839799 0.77957945
75 3.30697040 0.40839799
76 -0.26052991 3.30697040
77 1.56484024 -0.26052991
78 -1.72810176 1.56484024
79 -0.12549263 -1.72810176
80 0.51826787 -0.12549263
81 3.97664787 0.51826787
82 0.09424712 3.97664787
83 -0.81907114 0.09424712
84 0.27387751 -0.81907114
85 1.80974740 0.27387751
86 -0.19600271 1.80974740
87 0.62040836 -0.19600271
88 1.36630936 0.62040836
89 0.64181333 1.36630936
90 -1.42691358 0.64181333
91 -0.03357758 -1.42691358
92 0.50280911 -0.03357758
93 0.03669973 0.50280911
94 -2.14439313 0.03669973
95 0.94624715 -2.14439313
96 0.04589158 0.94624715
97 1.92932593 0.04589158
98 0.07878837 1.92932593
99 -0.38680421 0.07878837
100 -1.22042055 -0.38680421
101 1.13309012 -1.22042055
102 2.66000093 1.13309012
103 0.17173701 2.66000093
104 1.57256962 0.17173701
105 -2.43369729 1.57256962
106 0.96891849 -2.43369729
107 0.15001135 0.96891849
108 1.14081950 0.15001135
109 -0.53460808 1.14081950
110 1.15429898 -0.53460808
111 0.05937107 1.15429898
112 2.17173701 0.05937107
113 -1.41970100 2.17173701
114 -2.73187260 -1.41970100
115 1.43304860 -2.73187260
116 -1.71890991 1.43304860
117 0.78707606 -1.71890991
118 -2.14241386 0.78707606
119 0.37748048 -2.14241386
120 -1.52957088 0.37748048
121 0.50082984 -1.52957088
122 -3.03108151 0.50082984
123 -1.31483166 -3.03108151
124 -1.02533140 -1.31483166
125 -0.89279019 -1.02533140
126 -0.36017434 -0.89279019
127 0.75115801 -0.36017434
128 1.14906568 0.75115801
129 -2.95359162 1.14906568
130 1.71533628 -2.95359162
131 -3.46951901 1.71533628
132 1.82143532 -3.46951901
133 -1.87035162 1.82143532
134 -1.81907114 -1.87035162
135 -0.06544077 -1.81907114
136 0.92410101 -0.06544077
137 0.25841875 0.92410101
138 -2.55422148 0.25841875
139 -1.52707480 -2.55422148
140 -5.52905408 -1.52707480
141 2.70039433 -5.52905408
142 1.78509679 2.70039433
143 0.31922018 1.78509679
144 1.38374739 0.31922018
145 -4.07642418 1.38374739
146 2.18667897 -4.07642418
147 -2.60706077 2.18667897
148 0.62813774 -2.60706077
149 -0.25355010 0.62813774
150 -3.21094466 -0.25355010
151 -1.40131730 -3.21094466
152 1.83491481 -1.40131730
153 3.66226423 1.83491481
154 1.29004917 3.66226423
155 -2.59235158 1.29004917
156 -0.03357758 -2.59235158
157 1.19615485 -0.03357758
158 1.14906568 1.19615485
159 0.74771627 1.14906568
160 -0.48248170 0.74771627
161 0.09349755 -0.48248170
162 NA 0.09349755
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.40264788 -3.25855064
[2,] 2.52350117 0.40264788
[3,] 3.10174373 2.52350117
[4,] -1.11379631 3.10174373
[5,] -1.51213285 -1.11379631
[6,] 3.98259408 -1.51213285
[7,] -1.67880055 3.98259408
[8,] -1.91348226 -1.67880055
[9,] 2.54466498 -1.91348226
[10,] 0.95397653 2.54466498
[11,] -0.11776325 0.95397653
[12,] 0.37748048 -0.11776325
[13,] 0.70906937 0.37748048
[14,] -0.21467044 0.70906937
[15,] -0.06544077 -0.21467044
[16,] 0.58227827 -0.06544077
[17,] 3.96891849 0.58227827
[18,] 2.30050739 3.96891849
[19,] 0.83241874 2.30050739
[20,] 0.76317503 0.83241874
[21,] 1.04442911 0.76317503
[22,] 2.41758984 1.04442911
[23,] 0.92880913 2.41758984
[24,] 2.48984642 0.92880913
[25,] -0.10857140 2.48984642
[26,] 1.27585678 -0.10857140
[27,] -1.16036869 1.27585678
[28,] 0.82468936 -1.16036869
[29,] -0.30012248 0.82468936
[30,] -0.46627337 -0.30012248
[31,] -0.23955381 -0.46627337
[32,] -0.93615359 -0.23955381
[33,] 0.40839799 -0.93615359
[34,] -1.38732101 0.40839799
[35,] -5.96028740 -1.38732101
[36,] -0.86139254 -5.96028740
[37,] -1.70920126 -0.86139254
[38,] 1.94280541 -1.70920126
[39,] 1.28358616 1.94280541
[40,] 0.96170591 1.28358616
[41,] -1.28518052 0.96170591
[42,] 2.03169919 -1.28518052
[43,] -0.30785185 2.03169919
[44,] -0.94388297 -0.30785185
[45,] -4.71693064 -0.94388297
[46,] -2.77896177 -4.71693064
[47,] 0.06186714 -2.77896177
[48,] 1.14081950 0.06186714
[49,] -2.03108151 1.14081950
[50,] -0.56120129 -2.03108151
[51,] 0.02175777 -0.56120129
[52,] -2.65687878 0.02175777
[53,] 0.17122021 -2.65687878
[54,] -2.56666737 0.17122021
[55,] 1.44252448 -2.56666737
[56,] 0.06332961 1.44252448
[57,] 0.75346638 0.06332961
[58,] -0.47975286 0.75346638
[59,] 1.79428864 -0.47975286
[60,] 0.48513831 1.79428864
[61,] 0.07105899 0.48513831
[62,] -0.57666005 0.07105899
[63,] -0.41197162 -0.57666005
[64,] 0.61267899 -0.41197162
[65,] 0.75492885 0.61267899
[66,] 1.71430268 0.75492885
[67,] 3.49361726 1.71430268
[68,] -3.93667039 3.49361726
[69,] 0.60494961 -3.93667039
[70,] -3.65240344 0.60494961
[71,] -0.30983112 -3.65240344
[72,] 1.52548045 -0.30983112
[73,] 0.77957945 1.52548045
[74,] 0.40839799 0.77957945
[75,] 3.30697040 0.40839799
[76,] -0.26052991 3.30697040
[77,] 1.56484024 -0.26052991
[78,] -1.72810176 1.56484024
[79,] -0.12549263 -1.72810176
[80,] 0.51826787 -0.12549263
[81,] 3.97664787 0.51826787
[82,] 0.09424712 3.97664787
[83,] -0.81907114 0.09424712
[84,] 0.27387751 -0.81907114
[85,] 1.80974740 0.27387751
[86,] -0.19600271 1.80974740
[87,] 0.62040836 -0.19600271
[88,] 1.36630936 0.62040836
[89,] 0.64181333 1.36630936
[90,] -1.42691358 0.64181333
[91,] -0.03357758 -1.42691358
[92,] 0.50280911 -0.03357758
[93,] 0.03669973 0.50280911
[94,] -2.14439313 0.03669973
[95,] 0.94624715 -2.14439313
[96,] 0.04589158 0.94624715
[97,] 1.92932593 0.04589158
[98,] 0.07878837 1.92932593
[99,] -0.38680421 0.07878837
[100,] -1.22042055 -0.38680421
[101,] 1.13309012 -1.22042055
[102,] 2.66000093 1.13309012
[103,] 0.17173701 2.66000093
[104,] 1.57256962 0.17173701
[105,] -2.43369729 1.57256962
[106,] 0.96891849 -2.43369729
[107,] 0.15001135 0.96891849
[108,] 1.14081950 0.15001135
[109,] -0.53460808 1.14081950
[110,] 1.15429898 -0.53460808
[111,] 0.05937107 1.15429898
[112,] 2.17173701 0.05937107
[113,] -1.41970100 2.17173701
[114,] -2.73187260 -1.41970100
[115,] 1.43304860 -2.73187260
[116,] -1.71890991 1.43304860
[117,] 0.78707606 -1.71890991
[118,] -2.14241386 0.78707606
[119,] 0.37748048 -2.14241386
[120,] -1.52957088 0.37748048
[121,] 0.50082984 -1.52957088
[122,] -3.03108151 0.50082984
[123,] -1.31483166 -3.03108151
[124,] -1.02533140 -1.31483166
[125,] -0.89279019 -1.02533140
[126,] -0.36017434 -0.89279019
[127,] 0.75115801 -0.36017434
[128,] 1.14906568 0.75115801
[129,] -2.95359162 1.14906568
[130,] 1.71533628 -2.95359162
[131,] -3.46951901 1.71533628
[132,] 1.82143532 -3.46951901
[133,] -1.87035162 1.82143532
[134,] -1.81907114 -1.87035162
[135,] -0.06544077 -1.81907114
[136,] 0.92410101 -0.06544077
[137,] 0.25841875 0.92410101
[138,] -2.55422148 0.25841875
[139,] -1.52707480 -2.55422148
[140,] -5.52905408 -1.52707480
[141,] 2.70039433 -5.52905408
[142,] 1.78509679 2.70039433
[143,] 0.31922018 1.78509679
[144,] 1.38374739 0.31922018
[145,] -4.07642418 1.38374739
[146,] 2.18667897 -4.07642418
[147,] -2.60706077 2.18667897
[148,] 0.62813774 -2.60706077
[149,] -0.25355010 0.62813774
[150,] -3.21094466 -0.25355010
[151,] -1.40131730 -3.21094466
[152,] 1.83491481 -1.40131730
[153,] 3.66226423 1.83491481
[154,] 1.29004917 3.66226423
[155,] -2.59235158 1.29004917
[156,] -0.03357758 -2.59235158
[157,] 1.19615485 -0.03357758
[158,] 1.14906568 1.19615485
[159,] 0.74771627 1.14906568
[160,] -0.48248170 0.74771627
[161,] 0.09349755 -0.48248170
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.40264788 -3.25855064
2 2.52350117 0.40264788
3 3.10174373 2.52350117
4 -1.11379631 3.10174373
5 -1.51213285 -1.11379631
6 3.98259408 -1.51213285
7 -1.67880055 3.98259408
8 -1.91348226 -1.67880055
9 2.54466498 -1.91348226
10 0.95397653 2.54466498
11 -0.11776325 0.95397653
12 0.37748048 -0.11776325
13 0.70906937 0.37748048
14 -0.21467044 0.70906937
15 -0.06544077 -0.21467044
16 0.58227827 -0.06544077
17 3.96891849 0.58227827
18 2.30050739 3.96891849
19 0.83241874 2.30050739
20 0.76317503 0.83241874
21 1.04442911 0.76317503
22 2.41758984 1.04442911
23 0.92880913 2.41758984
24 2.48984642 0.92880913
25 -0.10857140 2.48984642
26 1.27585678 -0.10857140
27 -1.16036869 1.27585678
28 0.82468936 -1.16036869
29 -0.30012248 0.82468936
30 -0.46627337 -0.30012248
31 -0.23955381 -0.46627337
32 -0.93615359 -0.23955381
33 0.40839799 -0.93615359
34 -1.38732101 0.40839799
35 -5.96028740 -1.38732101
36 -0.86139254 -5.96028740
37 -1.70920126 -0.86139254
38 1.94280541 -1.70920126
39 1.28358616 1.94280541
40 0.96170591 1.28358616
41 -1.28518052 0.96170591
42 2.03169919 -1.28518052
43 -0.30785185 2.03169919
44 -0.94388297 -0.30785185
45 -4.71693064 -0.94388297
46 -2.77896177 -4.71693064
47 0.06186714 -2.77896177
48 1.14081950 0.06186714
49 -2.03108151 1.14081950
50 -0.56120129 -2.03108151
51 0.02175777 -0.56120129
52 -2.65687878 0.02175777
53 0.17122021 -2.65687878
54 -2.56666737 0.17122021
55 1.44252448 -2.56666737
56 0.06332961 1.44252448
57 0.75346638 0.06332961
58 -0.47975286 0.75346638
59 1.79428864 -0.47975286
60 0.48513831 1.79428864
61 0.07105899 0.48513831
62 -0.57666005 0.07105899
63 -0.41197162 -0.57666005
64 0.61267899 -0.41197162
65 0.75492885 0.61267899
66 1.71430268 0.75492885
67 3.49361726 1.71430268
68 -3.93667039 3.49361726
69 0.60494961 -3.93667039
70 -3.65240344 0.60494961
71 -0.30983112 -3.65240344
72 1.52548045 -0.30983112
73 0.77957945 1.52548045
74 0.40839799 0.77957945
75 3.30697040 0.40839799
76 -0.26052991 3.30697040
77 1.56484024 -0.26052991
78 -1.72810176 1.56484024
79 -0.12549263 -1.72810176
80 0.51826787 -0.12549263
81 3.97664787 0.51826787
82 0.09424712 3.97664787
83 -0.81907114 0.09424712
84 0.27387751 -0.81907114
85 1.80974740 0.27387751
86 -0.19600271 1.80974740
87 0.62040836 -0.19600271
88 1.36630936 0.62040836
89 0.64181333 1.36630936
90 -1.42691358 0.64181333
91 -0.03357758 -1.42691358
92 0.50280911 -0.03357758
93 0.03669973 0.50280911
94 -2.14439313 0.03669973
95 0.94624715 -2.14439313
96 0.04589158 0.94624715
97 1.92932593 0.04589158
98 0.07878837 1.92932593
99 -0.38680421 0.07878837
100 -1.22042055 -0.38680421
101 1.13309012 -1.22042055
102 2.66000093 1.13309012
103 0.17173701 2.66000093
104 1.57256962 0.17173701
105 -2.43369729 1.57256962
106 0.96891849 -2.43369729
107 0.15001135 0.96891849
108 1.14081950 0.15001135
109 -0.53460808 1.14081950
110 1.15429898 -0.53460808
111 0.05937107 1.15429898
112 2.17173701 0.05937107
113 -1.41970100 2.17173701
114 -2.73187260 -1.41970100
115 1.43304860 -2.73187260
116 -1.71890991 1.43304860
117 0.78707606 -1.71890991
118 -2.14241386 0.78707606
119 0.37748048 -2.14241386
120 -1.52957088 0.37748048
121 0.50082984 -1.52957088
122 -3.03108151 0.50082984
123 -1.31483166 -3.03108151
124 -1.02533140 -1.31483166
125 -0.89279019 -1.02533140
126 -0.36017434 -0.89279019
127 0.75115801 -0.36017434
128 1.14906568 0.75115801
129 -2.95359162 1.14906568
130 1.71533628 -2.95359162
131 -3.46951901 1.71533628
132 1.82143532 -3.46951901
133 -1.87035162 1.82143532
134 -1.81907114 -1.87035162
135 -0.06544077 -1.81907114
136 0.92410101 -0.06544077
137 0.25841875 0.92410101
138 -2.55422148 0.25841875
139 -1.52707480 -2.55422148
140 -5.52905408 -1.52707480
141 2.70039433 -5.52905408
142 1.78509679 2.70039433
143 0.31922018 1.78509679
144 1.38374739 0.31922018
145 -4.07642418 1.38374739
146 2.18667897 -4.07642418
147 -2.60706077 2.18667897
148 0.62813774 -2.60706077
149 -0.25355010 0.62813774
150 -3.21094466 -0.25355010
151 -1.40131730 -3.21094466
152 1.83491481 -1.40131730
153 3.66226423 1.83491481
154 1.29004917 3.66226423
155 -2.59235158 1.29004917
156 -0.03357758 -2.59235158
157 1.19615485 -0.03357758
158 1.14906568 1.19615485
159 0.74771627 1.14906568
160 -0.48248170 0.74771627
161 0.09349755 -0.48248170
> 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/7k5xy1351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8pp2q1351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zchb1351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10b7je1351952601.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ou981351952601.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/12n6ch1351952601.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/13pb821351952601.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/14942i1351952601.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/151qvh1351952601.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/16199a1351952601.tab")
+ }
>
> try(system("convert tmp/1knfb1351952601.ps tmp/1knfb1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rj5r1351952601.ps tmp/2rj5r1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/37d371351952601.ps tmp/37d371351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/44mq81351952601.ps tmp/44mq81351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/52oyv1351952601.ps tmp/52oyv1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vnsw1351952601.ps tmp/6vnsw1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k5xy1351952601.ps tmp/7k5xy1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pp2q1351952601.ps tmp/8pp2q1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zchb1351952601.ps tmp/9zchb1351952601.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b7je1351952601.ps tmp/10b7je1351952601.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.653 1.151 8.844