R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(37
+ ,159261
+ ,19
+ ,43
+ ,189672
+ ,20
+ ,0
+ ,7215
+ ,0
+ ,54
+ ,129098
+ ,27
+ ,86
+ ,230632
+ ,31
+ ,181
+ ,515038
+ ,36
+ ,42
+ ,180745
+ ,23
+ ,59
+ ,185559
+ ,30
+ ,46
+ ,154581
+ ,30
+ ,77
+ ,298001
+ ,26
+ ,49
+ ,121844
+ ,24
+ ,79
+ ,184039
+ ,30
+ ,37
+ ,100324
+ ,22
+ ,92
+ ,220269
+ ,28
+ ,31
+ ,168265
+ ,18
+ ,28
+ ,154647
+ ,22
+ ,103
+ ,142018
+ ,33
+ ,2
+ ,79030
+ ,15
+ ,48
+ ,167047
+ ,34
+ ,25
+ ,27997
+ ,18
+ ,16
+ ,73019
+ ,15
+ ,106
+ ,241082
+ ,30
+ ,35
+ ,195820
+ ,25
+ ,33
+ ,142001
+ ,34
+ ,45
+ ,145433
+ ,21
+ ,64
+ ,183744
+ ,21
+ ,73
+ ,202357
+ ,25
+ ,78
+ ,199532
+ ,31
+ ,63
+ ,354924
+ ,31
+ ,69
+ ,192399
+ ,20
+ ,36
+ ,182286
+ ,28
+ ,41
+ ,181590
+ ,22
+ ,59
+ ,133801
+ ,17
+ ,33
+ ,233686
+ ,25
+ ,76
+ ,219428
+ ,24
+ ,0
+ ,0
+ ,0
+ ,27
+ ,223044
+ ,28
+ ,44
+ ,100129
+ ,14
+ ,43
+ ,145864
+ ,35
+ ,104
+ ,249965
+ ,34
+ ,120
+ ,242379
+ ,22
+ ,44
+ ,145794
+ ,34
+ ,71
+ ,96404
+ ,23
+ ,78
+ ,195891
+ ,24
+ ,106
+ ,117156
+ ,26
+ ,61
+ ,157787
+ ,22
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+ ,81293
+ ,35
+ ,51
+ ,237435
+ ,24
+ ,46
+ ,233155
+ ,31
+ ,55
+ ,160344
+ ,26
+ ,14
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+ ,22
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+ ,161922
+ ,21
+ ,113
+ ,307432
+ ,27
+ ,55
+ ,235223
+ ,30
+ ,46
+ ,195583
+ ,33
+ ,39
+ ,146061
+ ,11
+ ,51
+ ,208834
+ ,26
+ ,31
+ ,93764
+ ,26
+ ,36
+ ,151985
+ ,23
+ ,47
+ ,193222
+ ,38
+ ,53
+ ,148922
+ ,31
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+ ,132856
+ ,20
+ ,52
+ ,129561
+ ,22
+ ,37
+ ,112718
+ ,26
+ ,11
+ ,160930
+ ,26
+ ,45
+ ,99184
+ ,33
+ ,59
+ ,192535
+ ,36
+ ,82
+ ,138708
+ ,25
+ ,49
+ ,114408
+ ,24
+ ,6
+ ,31970
+ ,21
+ ,81
+ ,225558
+ ,19
+ ,56
+ ,139220
+ ,12
+ ,105
+ ,113612
+ ,30
+ ,46
+ ,108641
+ ,21
+ ,46
+ ,162203
+ ,34
+ ,2
+ ,100098
+ ,32
+ ,51
+ ,174768
+ ,28
+ ,95
+ ,158459
+ ,28
+ ,18
+ ,80934
+ ,21
+ ,55
+ ,84971
+ ,31
+ ,48
+ ,80545
+ ,26
+ ,48
+ ,287191
+ ,29
+ ,39
+ ,62974
+ ,23
+ ,40
+ ,134091
+ ,25
+ ,36
+ ,75555
+ ,22
+ ,60
+ ,162154
+ ,26
+ ,114
+ ,226638
+ ,33
+ ,39
+ ,115367
+ ,24
+ ,45
+ ,108749
+ ,24
+ ,59
+ ,155537
+ ,21
+ ,59
+ ,153133
+ ,28
+ ,93
+ ,165618
+ ,27
+ ,35
+ ,151517
+ ,25
+ ,47
+ ,133686
+ ,15
+ ,36
+ ,61342
+ ,13
+ ,59
+ ,245196
+ ,36
+ ,79
+ ,195576
+ ,24
+ ,14
+ ,19349
+ ,1
+ ,42
+ ,225371
+ ,24
+ ,41
+ ,153213
+ ,31
+ ,8
+ ,59117
+ ,4
+ ,41
+ ,91762
+ ,21
+ ,24
+ ,136769
+ ,23
+ ,22
+ ,114798
+ ,23
+ ,18
+ ,85338
+ ,12
+ ,1
+ ,27676
+ ,16
+ ,53
+ ,153535
+ ,29
+ ,6
+ ,122417
+ ,26
+ ,0
+ ,0
+ ,0
+ ,49
+ ,91529
+ ,25
+ ,33
+ ,107205
+ ,21
+ ,50
+ ,144664
+ ,23
+ ,64
+ ,146445
+ ,21
+ ,53
+ ,76656
+ ,21
+ ,0
+ ,3616
+ ,0
+ ,0
+ ,0
+ ,0
+ ,48
+ ,183088
+ ,23
+ ,90
+ ,144677
+ ,33
+ ,46
+ ,159104
+ ,30
+ ,29
+ ,113273
+ ,23
+ ,1
+ ,43410
+ ,1
+ ,64
+ ,175774
+ ,29
+ ,29
+ ,95401
+ ,18
+ ,27
+ ,134837
+ ,33
+ ,4
+ ,60493
+ ,12
+ ,10
+ ,19764
+ ,2
+ ,47
+ ,164062
+ ,21
+ ,44
+ ,132696
+ ,28
+ ,51
+ ,155367
+ ,29
+ ,0
+ ,11796
+ ,2
+ ,0
+ ,10674
+ ,0
+ ,38
+ ,142261
+ ,18
+ ,0
+ ,6836
+ ,1
+ ,57
+ ,162563
+ ,21
+ ,0
+ ,5118
+ ,0
+ ,6
+ ,40248
+ ,4
+ ,0
+ ,0
+ ,0
+ ,22
+ ,122641
+ ,25
+ ,34
+ ,88837
+ ,26
+ ,0
+ ,7131
+ ,0
+ ,10
+ ,9056
+ ,4
+ ,16
+ ,76611
+ ,17
+ ,93
+ ,132697
+ ,21
+ ,22
+ ,100681
+ ,22)
+ ,dim=c(3
+ ,144)
+ ,dimnames=list(c('Compendium_Writing_total_number_of_included_blogs'
+ ,'Total_Time_spent_in_RFC_in_seconds'
+ ,'Total_Number_of_Reviewed_Compendiums
')
+ ,1:144))
> y <- array(NA,dim=c(3,144),dimnames=list(c('Compendium_Writing_total_number_of_included_blogs','Total_Time_spent_in_RFC_in_seconds','Total_Number_of_Reviewed_Compendiums
'),1:144))
> 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 = '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
> 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
Compendium_Writing_total_number_of_included_blogs
1 37
2 43
3 0
4 54
5 86
6 181
7 42
8 59
9 46
10 77
11 49
12 79
13 37
14 92
15 31
16 28
17 103
18 2
19 48
20 25
21 16
22 106
23 35
24 33
25 45
26 64
27 73
28 78
29 63
30 69
31 36
32 41
33 59
34 33
35 76
36 0
37 27
38 44
39 43
40 104
41 120
42 44
43 71
44 78
45 106
46 61
47 53
48 51
49 46
50 55
51 14
52 44
53 113
54 55
55 46
56 39
57 51
58 31
59 36
60 47
61 53
62 38
63 52
64 37
65 11
66 45
67 59
68 82
69 49
70 6
71 81
72 56
73 105
74 46
75 46
76 2
77 51
78 95
79 18
80 55
81 48
82 48
83 39
84 40
85 36
86 60
87 114
88 39
89 45
90 59
91 59
92 93
93 35
94 47
95 36
96 59
97 79
98 14
99 42
100 41
101 8
102 41
103 24
104 22
105 18
106 1
107 53
108 6
109 0
110 49
111 33
112 50
113 64
114 53
115 0
116 0
117 48
118 90
119 46
120 29
121 1
122 64
123 29
124 27
125 4
126 10
127 47
128 44
129 51
130 0
131 0
132 38
133 0
134 57
135 0
136 6
137 0
138 22
139 34
140 0
141 10
142 16
143 93
144 22
Total_Time_spent_in_RFC_in_seconds Total_Number_of_Reviewed_Compendiums\r
1 159261 19
2 189672 20
3 7215 0
4 129098 27
5 230632 31
6 515038 36
7 180745 23
8 185559 30
9 154581 30
10 298001 26
11 121844 24
12 184039 30
13 100324 22
14 220269 28
15 168265 18
16 154647 22
17 142018 33
18 79030 15
19 167047 34
20 27997 18
21 73019 15
22 241082 30
23 195820 25
24 142001 34
25 145433 21
26 183744 21
27 202357 25
28 199532 31
29 354924 31
30 192399 20
31 182286 28
32 181590 22
33 133801 17
34 233686 25
35 219428 24
36 0 0
37 223044 28
38 100129 14
39 145864 35
40 249965 34
41 242379 22
42 145794 34
43 96404 23
44 195891 24
45 117156 26
46 157787 22
47 81293 35
48 237435 24
49 233155 31
50 160344 26
51 48188 22
52 161922 21
53 307432 27
54 235223 30
55 195583 33
56 146061 11
57 208834 26
58 93764 26
59 151985 23
60 193222 38
61 148922 31
62 132856 20
63 129561 22
64 112718 26
65 160930 26
66 99184 33
67 192535 36
68 138708 25
69 114408 24
70 31970 21
71 225558 19
72 139220 12
73 113612 30
74 108641 21
75 162203 34
76 100098 32
77 174768 28
78 158459 28
79 80934 21
80 84971 31
81 80545 26
82 287191 29
83 62974 23
84 134091 25
85 75555 22
86 162154 26
87 226638 33
88 115367 24
89 108749 24
90 155537 21
91 153133 28
92 165618 27
93 151517 25
94 133686 15
95 61342 13
96 245196 36
97 195576 24
98 19349 1
99 225371 24
100 153213 31
101 59117 4
102 91762 21
103 136769 23
104 114798 23
105 85338 12
106 27676 16
107 153535 29
108 122417 26
109 0 0
110 91529 25
111 107205 21
112 144664 23
113 146445 21
114 76656 21
115 3616 0
116 0 0
117 183088 23
118 144677 33
119 159104 30
120 113273 23
121 43410 1
122 175774 29
123 95401 18
124 134837 33
125 60493 12
126 19764 2
127 164062 21
128 132696 28
129 155367 29
130 11796 2
131 10674 0
132 142261 18
133 6836 1
134 162563 21
135 5118 0
136 40248 4
137 0 0
138 122641 25
139 88837 26
140 7131 0
141 9056 4
142 76611 17
143 132697 21
144 100681 22
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
132 132
133 133
134 134
135 135
136 136
137 137
138 138
139 139
140 140
141 141
142 142
143 143
144 144
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept)
0.6797590
Total_Time_spent_in_RFC_in_seconds
0.0002337
`Total_Number_of_Reviewed_Compendiums\r`
0.6246783
t
-0.0160621
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-42.697 -13.062 -1.616 9.963 62.424
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6797590 6.6152701 0.103 0.9183
Total_Time_spent_in_RFC_in_seconds 0.0002337 0.0000312 7.490 7.04e-12
`Total_Number_of_Reviewed_Compendiums\r` 0.6246783 0.2428087 2.573 0.0111
t -0.0160621 0.0454831 -0.353 0.7245
(Intercept)
Total_Time_spent_in_RFC_in_seconds ***
`Total_Number_of_Reviewed_Compendiums\r` *
t
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.23 on 140 degrees of freedom
Multiple R-squared: 0.5652, Adjusted R-squared: 0.5559
F-statistic: 60.66 on 3 and 140 DF, p-value: < 2.2e-16
> 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,] 0.48845833 0.976916659 0.511541670
[2,] 0.33155005 0.663100107 0.668449946
[3,] 0.21305774 0.426115472 0.786942264
[4,] 0.18498111 0.369962220 0.815018890
[5,] 0.16702800 0.334056006 0.832971997
[6,] 0.15439243 0.308784854 0.845607573
[7,] 0.09632381 0.192647629 0.903676186
[8,] 0.08012955 0.160259090 0.919870455
[9,] 0.11111327 0.222226532 0.888886734
[10,] 0.11678699 0.233573987 0.883213007
[11,] 0.38157280 0.763145594 0.618427203
[12,] 0.36067508 0.721350168 0.639324916
[13,] 0.38268998 0.765379966 0.617310017
[14,] 0.36705490 0.734109800 0.632945100
[15,] 0.30086354 0.601727077 0.699136462
[16,] 0.32575201 0.651504020 0.674247990
[17,] 0.40557792 0.811155835 0.594422083
[18,] 0.46506734 0.930134674 0.534932663
[19,] 0.40341625 0.806832497 0.596583751
[20,] 0.36478171 0.729563426 0.635218287
[21,] 0.31799888 0.635997757 0.682001121
[22,] 0.26896533 0.537930669 0.731034665
[23,] 0.49241868 0.984837365 0.507581317
[24,] 0.48788852 0.975777048 0.512111476
[25,] 0.49991755 0.999835095 0.500082452
[26,] 0.45183853 0.903677054 0.548161473
[27,] 0.49636924 0.992738482 0.503630759
[28,] 0.59034667 0.819306660 0.409653330
[29,] 0.56862648 0.862747036 0.431373518
[30,] 0.55350164 0.892996711 0.446498355
[31,] 0.69610384 0.607792325 0.303896163
[32,] 0.69525634 0.609487326 0.304743663
[33,] 0.65881471 0.682370586 0.341185293
[34,] 0.69394319 0.612113614 0.306056807
[35,] 0.88045365 0.239092709 0.119546354
[36,] 0.86061224 0.278775524 0.139387762
[37,] 0.90409164 0.191816727 0.095908363
[38,] 0.89336660 0.213266796 0.106633398
[39,] 0.98390728 0.032185439 0.016092720
[40,] 0.97897890 0.042042210 0.021021105
[41,] 0.97305863 0.053882744 0.026941372
[42,] 0.97490882 0.050182365 0.025091183
[43,] 0.98257312 0.034853760 0.017426880
[44,] 0.97647498 0.047050040 0.023525020
[45,] 0.97174588 0.056508242 0.028254121
[46,] 0.96405361 0.071892780 0.035946390
[47,] 0.96678584 0.066428328 0.033214164
[48,] 0.96621586 0.067568285 0.033784142
[49,] 0.96683090 0.066338195 0.033169098
[50,] 0.95664533 0.086709349 0.043354674
[51,] 0.95033366 0.099332683 0.049666342
[52,] 0.93841452 0.123170960 0.061585480
[53,] 0.92995589 0.140088213 0.070044107
[54,] 0.93235111 0.135297789 0.067648895
[55,] 0.91515376 0.169692482 0.084846241
[56,] 0.89672417 0.206551654 0.103275827
[57,] 0.87721093 0.245578135 0.122789068
[58,] 0.85353524 0.292929519 0.146464759
[59,] 0.93069943 0.138601149 0.069300574
[60,] 0.91416226 0.171675474 0.085837737
[61,] 0.89907071 0.201858585 0.100929293
[62,] 0.93041364 0.139172724 0.069586362
[63,] 0.91490224 0.170195529 0.085097765
[64,] 0.90940233 0.181195333 0.090597667
[65,] 0.90139487 0.197210252 0.098605126
[66,] 0.89116543 0.217669136 0.108834568
[67,] 0.98269335 0.034613293 0.017306647
[68,] 0.97736820 0.045263608 0.022631804
[69,] 0.97372676 0.052546487 0.026273244
[70,] 0.99224659 0.015506813 0.007753407
[71,] 0.98985715 0.020285693 0.010142847
[72,] 0.99638628 0.007227450 0.003613725
[73,] 0.99607296 0.007854089 0.003927045
[74,] 0.99503978 0.009920443 0.004960222
[75,] 0.99351504 0.012969915 0.006484957
[76,] 0.99766087 0.004678266 0.002339133
[77,] 0.99685348 0.006293035 0.003146518
[78,] 0.99571619 0.008567610 0.004283805
[79,] 0.99391056 0.012178871 0.006089435
[80,] 0.99146024 0.017079516 0.008539758
[81,] 0.99748465 0.005030693 0.002515347
[82,] 0.99630005 0.007399897 0.003699949
[83,] 0.99474171 0.010516584 0.005258292
[84,] 0.99313549 0.013729022 0.006864511
[85,] 0.99072762 0.018544762 0.009272381
[86,] 0.99773300 0.004533995 0.002266998
[87,] 0.99718275 0.005634500 0.002817250
[88,] 0.99623160 0.007536794 0.003768397
[89,] 0.99610003 0.007799940 0.003899970
[90,] 0.99572726 0.008545487 0.004272743
[91,] 0.99674493 0.006510134 0.003255067
[92,] 0.99628066 0.007438672 0.003719336
[93,] 0.99667808 0.006643842 0.003321921
[94,] 0.99545513 0.009089732 0.004544866
[95,] 0.99341534 0.013169321 0.006584661
[96,] 0.99195377 0.016092452 0.008046226
[97,] 0.99186149 0.016277020 0.008138510
[98,] 0.99077558 0.018448850 0.009224425
[99,] 0.98719012 0.025619761 0.012809880
[100,] 0.98355842 0.032883161 0.016441581
[101,] 0.97635166 0.047296672 0.023648336
[102,] 0.99385595 0.012288095 0.006144048
[103,] 0.99055762 0.018884768 0.009442384
[104,] 0.98802250 0.023954990 0.011977495
[105,] 0.98268944 0.034621111 0.017310556
[106,] 0.97454808 0.050903841 0.025451921
[107,] 0.97238367 0.055232662 0.027616331
[108,] 0.98168886 0.036622275 0.018311138
[109,] 0.97348401 0.053031975 0.026515988
[110,] 0.96363214 0.072735722 0.036367861
[111,] 0.95227848 0.095443041 0.047721520
[112,] 0.99797308 0.004053843 0.002026921
[113,] 0.99646500 0.007070003 0.003535002
[114,] 0.99399813 0.012003746 0.006001873
[115,] 0.99049054 0.019018918 0.009509459
[116,] 0.98758313 0.024833735 0.012416868
[117,] 0.98164148 0.036717030 0.018358515
[118,] 0.97184366 0.056312683 0.028156342
[119,] 0.95856857 0.082862870 0.041431435
[120,] 0.94720344 0.105593114 0.052796557
[121,] 0.92199714 0.156005715 0.078002858
[122,] 0.89512564 0.209748730 0.104874365
[123,] 0.86388651 0.272226979 0.136113490
[124,] 0.81804643 0.363907148 0.181953574
[125,] 0.75671707 0.486565856 0.243282928
[126,] 0.68339604 0.633207920 0.316603960
[127,] 0.60116722 0.797665557 0.398832779
[128,] 0.49349582 0.986991648 0.506504176
[129,] 0.37792500 0.755850003 0.622074999
[130,] 0.27584820 0.551696399 0.724151800
[131,] 0.16540338 0.330806751 0.834596624
> postscript(file="/var/wessaorg/rcomp/tmp/1c2ri1324655713.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/21e9m1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a0it1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4h19t1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/57b1o1324655713.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 = 144
Frequency = 1
1 2 3 4 5 6
-12.74876067 -14.46383193 -2.31757679 6.35050208 12.14134376 37.57390204
7 8 9 10 11 12
-15.17149092 -3.65311413 -9.39810004 -9.39777020 5.03208962 16.76632869
13 14 15 16 17 18
-0.65762700 22.58156800 -20.00327086 -22.30366184 48.79208941 -26.22857766
19 20 21 22 23 24
-12.64925171 6.85492454 -10.77573825 30.59711813 -26.68658541 -21.71618127
25 26 27 28 29 30
-2.38129292 7.68223878 9.85009429 11.77823404 -39.51777111 11.34866161
31 32 33 34 35 36
-24.26949228 -15.34271900 16.96408788 -37.35844447 9.61411131 -0.10152182
37 38 39 40 41 42
-42.69746583 11.78692038 -13.00263482 24.31174054 49.59664146 -11.31341249
43 44 45 46 47 48
34.11558630 17.25880658 62.42433980 10.44444598 12.21484052 -19.38496407
49 50 51 52 53 54
-27.74149757 1.41246069 -10.86410332 -6.80077141 24.46438374 -18.51975969
55 56 57 58 59 60
-20.11464062 -1.78333514 -13.80626770 -6.90060002 -13.61561062 -21.60600174
61 62 63 64 65 66
-0.86514931 -5.22331672 8.31336588 -5.23340577 -42.48354387 1.58860145
67 68 69 70 71 72
-8.08367176 34.38216704 7.70134157 -14.14441531 16.88327886 16.44758864
73 74 75 76 77 78
60.20353020 8.00332204 -12.61782368 -40.83968274 -6.77382426 41.05333180
79 80 81 82 83 84
-13.44178393 16.38412776 13.55785073 -36.58924528 10.57000840 -6.28193616
85 86 87 88 89 90
5.28687763 6.56773634 41.14240267 -2.21757720 5.34498194 10.30163954
91 92 93 94 95 96
6.50672157 38.22996198 -15.20949180 7.22010846 14.39090319 -19.92371399
97 98 99 100 101 102
19.18370956 9.74817049 -24.74667416 -13.24144834 -7.37068067 7.39735384
103 104 105 106 107 108
-21.35319505 -18.20294047 -8.43119238 -14.43936310 0.04509810 -37.79313777
109 110 111 112 113 114
1.07101472 13.08158549 -4.06681354 2.94645736 17.79569121 23.12007594
115 116 117 118 119 120
0.32239920 1.18344973 -7.95216824 36.79300977 -8.68820067 -10.58958353
121 122 123 124 125 126
-8.50498469 6.08921148 -3.24166938 -23.81120206 -16.30415954 5.47625490
127 128 129 130 131 132
-3.09618667 -3.12325241 -2.02963725 -2.59753096 -1.06992287 -5.04737440
133 134 135 136 137 138
-0.76561140 7.36653528 0.29265411 -4.39918983 1.52075477 -20.73893959
139 140 141 142 143 144
-1.44822290 -0.09743385 6.97008045 -10.92095535 50.49019391 -13.63690999
> postscript(file="/var/wessaorg/rcomp/tmp/6x3y61324655713.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 -12.74876067 NA
1 -14.46383193 -12.74876067
2 -2.31757679 -14.46383193
3 6.35050208 -2.31757679
4 12.14134376 6.35050208
5 37.57390204 12.14134376
6 -15.17149092 37.57390204
7 -3.65311413 -15.17149092
8 -9.39810004 -3.65311413
9 -9.39777020 -9.39810004
10 5.03208962 -9.39777020
11 16.76632869 5.03208962
12 -0.65762700 16.76632869
13 22.58156800 -0.65762700
14 -20.00327086 22.58156800
15 -22.30366184 -20.00327086
16 48.79208941 -22.30366184
17 -26.22857766 48.79208941
18 -12.64925171 -26.22857766
19 6.85492454 -12.64925171
20 -10.77573825 6.85492454
21 30.59711813 -10.77573825
22 -26.68658541 30.59711813
23 -21.71618127 -26.68658541
24 -2.38129292 -21.71618127
25 7.68223878 -2.38129292
26 9.85009429 7.68223878
27 11.77823404 9.85009429
28 -39.51777111 11.77823404
29 11.34866161 -39.51777111
30 -24.26949228 11.34866161
31 -15.34271900 -24.26949228
32 16.96408788 -15.34271900
33 -37.35844447 16.96408788
34 9.61411131 -37.35844447
35 -0.10152182 9.61411131
36 -42.69746583 -0.10152182
37 11.78692038 -42.69746583
38 -13.00263482 11.78692038
39 24.31174054 -13.00263482
40 49.59664146 24.31174054
41 -11.31341249 49.59664146
42 34.11558630 -11.31341249
43 17.25880658 34.11558630
44 62.42433980 17.25880658
45 10.44444598 62.42433980
46 12.21484052 10.44444598
47 -19.38496407 12.21484052
48 -27.74149757 -19.38496407
49 1.41246069 -27.74149757
50 -10.86410332 1.41246069
51 -6.80077141 -10.86410332
52 24.46438374 -6.80077141
53 -18.51975969 24.46438374
54 -20.11464062 -18.51975969
55 -1.78333514 -20.11464062
56 -13.80626770 -1.78333514
57 -6.90060002 -13.80626770
58 -13.61561062 -6.90060002
59 -21.60600174 -13.61561062
60 -0.86514931 -21.60600174
61 -5.22331672 -0.86514931
62 8.31336588 -5.22331672
63 -5.23340577 8.31336588
64 -42.48354387 -5.23340577
65 1.58860145 -42.48354387
66 -8.08367176 1.58860145
67 34.38216704 -8.08367176
68 7.70134157 34.38216704
69 -14.14441531 7.70134157
70 16.88327886 -14.14441531
71 16.44758864 16.88327886
72 60.20353020 16.44758864
73 8.00332204 60.20353020
74 -12.61782368 8.00332204
75 -40.83968274 -12.61782368
76 -6.77382426 -40.83968274
77 41.05333180 -6.77382426
78 -13.44178393 41.05333180
79 16.38412776 -13.44178393
80 13.55785073 16.38412776
81 -36.58924528 13.55785073
82 10.57000840 -36.58924528
83 -6.28193616 10.57000840
84 5.28687763 -6.28193616
85 6.56773634 5.28687763
86 41.14240267 6.56773634
87 -2.21757720 41.14240267
88 5.34498194 -2.21757720
89 10.30163954 5.34498194
90 6.50672157 10.30163954
91 38.22996198 6.50672157
92 -15.20949180 38.22996198
93 7.22010846 -15.20949180
94 14.39090319 7.22010846
95 -19.92371399 14.39090319
96 19.18370956 -19.92371399
97 9.74817049 19.18370956
98 -24.74667416 9.74817049
99 -13.24144834 -24.74667416
100 -7.37068067 -13.24144834
101 7.39735384 -7.37068067
102 -21.35319505 7.39735384
103 -18.20294047 -21.35319505
104 -8.43119238 -18.20294047
105 -14.43936310 -8.43119238
106 0.04509810 -14.43936310
107 -37.79313777 0.04509810
108 1.07101472 -37.79313777
109 13.08158549 1.07101472
110 -4.06681354 13.08158549
111 2.94645736 -4.06681354
112 17.79569121 2.94645736
113 23.12007594 17.79569121
114 0.32239920 23.12007594
115 1.18344973 0.32239920
116 -7.95216824 1.18344973
117 36.79300977 -7.95216824
118 -8.68820067 36.79300977
119 -10.58958353 -8.68820067
120 -8.50498469 -10.58958353
121 6.08921148 -8.50498469
122 -3.24166938 6.08921148
123 -23.81120206 -3.24166938
124 -16.30415954 -23.81120206
125 5.47625490 -16.30415954
126 -3.09618667 5.47625490
127 -3.12325241 -3.09618667
128 -2.02963725 -3.12325241
129 -2.59753096 -2.02963725
130 -1.06992287 -2.59753096
131 -5.04737440 -1.06992287
132 -0.76561140 -5.04737440
133 7.36653528 -0.76561140
134 0.29265411 7.36653528
135 -4.39918983 0.29265411
136 1.52075477 -4.39918983
137 -20.73893959 1.52075477
138 -1.44822290 -20.73893959
139 -0.09743385 -1.44822290
140 6.97008045 -0.09743385
141 -10.92095535 6.97008045
142 50.49019391 -10.92095535
143 -13.63690999 50.49019391
144 NA -13.63690999
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -14.46383193 -12.74876067
[2,] -2.31757679 -14.46383193
[3,] 6.35050208 -2.31757679
[4,] 12.14134376 6.35050208
[5,] 37.57390204 12.14134376
[6,] -15.17149092 37.57390204
[7,] -3.65311413 -15.17149092
[8,] -9.39810004 -3.65311413
[9,] -9.39777020 -9.39810004
[10,] 5.03208962 -9.39777020
[11,] 16.76632869 5.03208962
[12,] -0.65762700 16.76632869
[13,] 22.58156800 -0.65762700
[14,] -20.00327086 22.58156800
[15,] -22.30366184 -20.00327086
[16,] 48.79208941 -22.30366184
[17,] -26.22857766 48.79208941
[18,] -12.64925171 -26.22857766
[19,] 6.85492454 -12.64925171
[20,] -10.77573825 6.85492454
[21,] 30.59711813 -10.77573825
[22,] -26.68658541 30.59711813
[23,] -21.71618127 -26.68658541
[24,] -2.38129292 -21.71618127
[25,] 7.68223878 -2.38129292
[26,] 9.85009429 7.68223878
[27,] 11.77823404 9.85009429
[28,] -39.51777111 11.77823404
[29,] 11.34866161 -39.51777111
[30,] -24.26949228 11.34866161
[31,] -15.34271900 -24.26949228
[32,] 16.96408788 -15.34271900
[33,] -37.35844447 16.96408788
[34,] 9.61411131 -37.35844447
[35,] -0.10152182 9.61411131
[36,] -42.69746583 -0.10152182
[37,] 11.78692038 -42.69746583
[38,] -13.00263482 11.78692038
[39,] 24.31174054 -13.00263482
[40,] 49.59664146 24.31174054
[41,] -11.31341249 49.59664146
[42,] 34.11558630 -11.31341249
[43,] 17.25880658 34.11558630
[44,] 62.42433980 17.25880658
[45,] 10.44444598 62.42433980
[46,] 12.21484052 10.44444598
[47,] -19.38496407 12.21484052
[48,] -27.74149757 -19.38496407
[49,] 1.41246069 -27.74149757
[50,] -10.86410332 1.41246069
[51,] -6.80077141 -10.86410332
[52,] 24.46438374 -6.80077141
[53,] -18.51975969 24.46438374
[54,] -20.11464062 -18.51975969
[55,] -1.78333514 -20.11464062
[56,] -13.80626770 -1.78333514
[57,] -6.90060002 -13.80626770
[58,] -13.61561062 -6.90060002
[59,] -21.60600174 -13.61561062
[60,] -0.86514931 -21.60600174
[61,] -5.22331672 -0.86514931
[62,] 8.31336588 -5.22331672
[63,] -5.23340577 8.31336588
[64,] -42.48354387 -5.23340577
[65,] 1.58860145 -42.48354387
[66,] -8.08367176 1.58860145
[67,] 34.38216704 -8.08367176
[68,] 7.70134157 34.38216704
[69,] -14.14441531 7.70134157
[70,] 16.88327886 -14.14441531
[71,] 16.44758864 16.88327886
[72,] 60.20353020 16.44758864
[73,] 8.00332204 60.20353020
[74,] -12.61782368 8.00332204
[75,] -40.83968274 -12.61782368
[76,] -6.77382426 -40.83968274
[77,] 41.05333180 -6.77382426
[78,] -13.44178393 41.05333180
[79,] 16.38412776 -13.44178393
[80,] 13.55785073 16.38412776
[81,] -36.58924528 13.55785073
[82,] 10.57000840 -36.58924528
[83,] -6.28193616 10.57000840
[84,] 5.28687763 -6.28193616
[85,] 6.56773634 5.28687763
[86,] 41.14240267 6.56773634
[87,] -2.21757720 41.14240267
[88,] 5.34498194 -2.21757720
[89,] 10.30163954 5.34498194
[90,] 6.50672157 10.30163954
[91,] 38.22996198 6.50672157
[92,] -15.20949180 38.22996198
[93,] 7.22010846 -15.20949180
[94,] 14.39090319 7.22010846
[95,] -19.92371399 14.39090319
[96,] 19.18370956 -19.92371399
[97,] 9.74817049 19.18370956
[98,] -24.74667416 9.74817049
[99,] -13.24144834 -24.74667416
[100,] -7.37068067 -13.24144834
[101,] 7.39735384 -7.37068067
[102,] -21.35319505 7.39735384
[103,] -18.20294047 -21.35319505
[104,] -8.43119238 -18.20294047
[105,] -14.43936310 -8.43119238
[106,] 0.04509810 -14.43936310
[107,] -37.79313777 0.04509810
[108,] 1.07101472 -37.79313777
[109,] 13.08158549 1.07101472
[110,] -4.06681354 13.08158549
[111,] 2.94645736 -4.06681354
[112,] 17.79569121 2.94645736
[113,] 23.12007594 17.79569121
[114,] 0.32239920 23.12007594
[115,] 1.18344973 0.32239920
[116,] -7.95216824 1.18344973
[117,] 36.79300977 -7.95216824
[118,] -8.68820067 36.79300977
[119,] -10.58958353 -8.68820067
[120,] -8.50498469 -10.58958353
[121,] 6.08921148 -8.50498469
[122,] -3.24166938 6.08921148
[123,] -23.81120206 -3.24166938
[124,] -16.30415954 -23.81120206
[125,] 5.47625490 -16.30415954
[126,] -3.09618667 5.47625490
[127,] -3.12325241 -3.09618667
[128,] -2.02963725 -3.12325241
[129,] -2.59753096 -2.02963725
[130,] -1.06992287 -2.59753096
[131,] -5.04737440 -1.06992287
[132,] -0.76561140 -5.04737440
[133,] 7.36653528 -0.76561140
[134,] 0.29265411 7.36653528
[135,] -4.39918983 0.29265411
[136,] 1.52075477 -4.39918983
[137,] -20.73893959 1.52075477
[138,] -1.44822290 -20.73893959
[139,] -0.09743385 -1.44822290
[140,] 6.97008045 -0.09743385
[141,] -10.92095535 6.97008045
[142,] 50.49019391 -10.92095535
[143,] -13.63690999 50.49019391
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -14.46383193 -12.74876067
2 -2.31757679 -14.46383193
3 6.35050208 -2.31757679
4 12.14134376 6.35050208
5 37.57390204 12.14134376
6 -15.17149092 37.57390204
7 -3.65311413 -15.17149092
8 -9.39810004 -3.65311413
9 -9.39777020 -9.39810004
10 5.03208962 -9.39777020
11 16.76632869 5.03208962
12 -0.65762700 16.76632869
13 22.58156800 -0.65762700
14 -20.00327086 22.58156800
15 -22.30366184 -20.00327086
16 48.79208941 -22.30366184
17 -26.22857766 48.79208941
18 -12.64925171 -26.22857766
19 6.85492454 -12.64925171
20 -10.77573825 6.85492454
21 30.59711813 -10.77573825
22 -26.68658541 30.59711813
23 -21.71618127 -26.68658541
24 -2.38129292 -21.71618127
25 7.68223878 -2.38129292
26 9.85009429 7.68223878
27 11.77823404 9.85009429
28 -39.51777111 11.77823404
29 11.34866161 -39.51777111
30 -24.26949228 11.34866161
31 -15.34271900 -24.26949228
32 16.96408788 -15.34271900
33 -37.35844447 16.96408788
34 9.61411131 -37.35844447
35 -0.10152182 9.61411131
36 -42.69746583 -0.10152182
37 11.78692038 -42.69746583
38 -13.00263482 11.78692038
39 24.31174054 -13.00263482
40 49.59664146 24.31174054
41 -11.31341249 49.59664146
42 34.11558630 -11.31341249
43 17.25880658 34.11558630
44 62.42433980 17.25880658
45 10.44444598 62.42433980
46 12.21484052 10.44444598
47 -19.38496407 12.21484052
48 -27.74149757 -19.38496407
49 1.41246069 -27.74149757
50 -10.86410332 1.41246069
51 -6.80077141 -10.86410332
52 24.46438374 -6.80077141
53 -18.51975969 24.46438374
54 -20.11464062 -18.51975969
55 -1.78333514 -20.11464062
56 -13.80626770 -1.78333514
57 -6.90060002 -13.80626770
58 -13.61561062 -6.90060002
59 -21.60600174 -13.61561062
60 -0.86514931 -21.60600174
61 -5.22331672 -0.86514931
62 8.31336588 -5.22331672
63 -5.23340577 8.31336588
64 -42.48354387 -5.23340577
65 1.58860145 -42.48354387
66 -8.08367176 1.58860145
67 34.38216704 -8.08367176
68 7.70134157 34.38216704
69 -14.14441531 7.70134157
70 16.88327886 -14.14441531
71 16.44758864 16.88327886
72 60.20353020 16.44758864
73 8.00332204 60.20353020
74 -12.61782368 8.00332204
75 -40.83968274 -12.61782368
76 -6.77382426 -40.83968274
77 41.05333180 -6.77382426
78 -13.44178393 41.05333180
79 16.38412776 -13.44178393
80 13.55785073 16.38412776
81 -36.58924528 13.55785073
82 10.57000840 -36.58924528
83 -6.28193616 10.57000840
84 5.28687763 -6.28193616
85 6.56773634 5.28687763
86 41.14240267 6.56773634
87 -2.21757720 41.14240267
88 5.34498194 -2.21757720
89 10.30163954 5.34498194
90 6.50672157 10.30163954
91 38.22996198 6.50672157
92 -15.20949180 38.22996198
93 7.22010846 -15.20949180
94 14.39090319 7.22010846
95 -19.92371399 14.39090319
96 19.18370956 -19.92371399
97 9.74817049 19.18370956
98 -24.74667416 9.74817049
99 -13.24144834 -24.74667416
100 -7.37068067 -13.24144834
101 7.39735384 -7.37068067
102 -21.35319505 7.39735384
103 -18.20294047 -21.35319505
104 -8.43119238 -18.20294047
105 -14.43936310 -8.43119238
106 0.04509810 -14.43936310
107 -37.79313777 0.04509810
108 1.07101472 -37.79313777
109 13.08158549 1.07101472
110 -4.06681354 13.08158549
111 2.94645736 -4.06681354
112 17.79569121 2.94645736
113 23.12007594 17.79569121
114 0.32239920 23.12007594
115 1.18344973 0.32239920
116 -7.95216824 1.18344973
117 36.79300977 -7.95216824
118 -8.68820067 36.79300977
119 -10.58958353 -8.68820067
120 -8.50498469 -10.58958353
121 6.08921148 -8.50498469
122 -3.24166938 6.08921148
123 -23.81120206 -3.24166938
124 -16.30415954 -23.81120206
125 5.47625490 -16.30415954
126 -3.09618667 5.47625490
127 -3.12325241 -3.09618667
128 -2.02963725 -3.12325241
129 -2.59753096 -2.02963725
130 -1.06992287 -2.59753096
131 -5.04737440 -1.06992287
132 -0.76561140 -5.04737440
133 7.36653528 -0.76561140
134 0.29265411 7.36653528
135 -4.39918983 0.29265411
136 1.52075477 -4.39918983
137 -20.73893959 1.52075477
138 -1.44822290 -20.73893959
139 -0.09743385 -1.44822290
140 6.97008045 -0.09743385
141 -10.92095535 6.97008045
142 50.49019391 -10.92095535
143 -13.63690999 50.49019391
> 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/7pnly1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ytlb1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9vdux1324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10r4j21324655713.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1141vl1324655713.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/1235eb1324655713.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/13ggzd1324655713.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/14kyk91324655713.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/15m58k1324655713.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/16ztwv1324655713.tab")
+ }
>
> try(system("convert tmp/1c2ri1324655713.ps tmp/1c2ri1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/21e9m1324655713.ps tmp/21e9m1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a0it1324655713.ps tmp/3a0it1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h19t1324655713.ps tmp/4h19t1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/57b1o1324655713.ps tmp/57b1o1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/6x3y61324655713.ps tmp/6x3y61324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/7pnly1324655713.ps tmp/7pnly1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ytlb1324655713.ps tmp/8ytlb1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vdux1324655713.ps tmp/9vdux1324655713.png",intern=TRUE))
character(0)
> try(system("convert tmp/10r4j21324655713.ps tmp/10r4j21324655713.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.342 0.587 5.019