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(210907
+ ,56
+ ,145
+ ,30
+ ,120982
+ ,56
+ ,101
+ ,28
+ ,176508
+ ,54
+ ,98
+ ,38
+ ,179321
+ ,89
+ ,132
+ ,30
+ ,123185
+ ,40
+ ,60
+ ,22
+ ,52746
+ ,25
+ ,38
+ ,26
+ ,385534
+ ,92
+ ,144
+ ,25
+ ,33170
+ ,18
+ ,5
+ ,18
+ ,149061
+ ,44
+ ,84
+ ,26
+ ,165446
+ ,33
+ ,79
+ ,25
+ ,237213
+ ,84
+ ,127
+ ,38
+ ,173326
+ ,88
+ ,78
+ ,44
+ ,133131
+ ,55
+ ,60
+ ,30
+ ,258873
+ ,60
+ ,131
+ ,40
+ ,180083
+ ,66
+ ,84
+ ,34
+ ,324799
+ ,154
+ ,133
+ ,47
+ ,230964
+ ,53
+ ,150
+ ,30
+ ,236785
+ ,119
+ ,91
+ ,31
+ ,135473
+ ,41
+ ,132
+ ,23
+ ,202925
+ ,61
+ ,136
+ ,36
+ ,215147
+ ,58
+ ,124
+ ,36
+ ,344297
+ ,75
+ ,118
+ ,30
+ ,153935
+ ,33
+ ,70
+ ,25
+ ,132943
+ ,40
+ ,107
+ ,39
+ ,174724
+ ,92
+ ,119
+ ,34
+ ,174415
+ ,100
+ ,89
+ ,31
+ ,225548
+ ,112
+ ,112
+ ,31
+ ,223632
+ ,73
+ ,108
+ ,33
+ ,124817
+ ,40
+ ,52
+ ,25
+ ,221698
+ ,45
+ ,112
+ ,33
+ ,210767
+ ,60
+ ,116
+ ,35
+ ,170266
+ ,62
+ ,123
+ ,42
+ ,260561
+ ,75
+ ,125
+ ,43
+ ,84853
+ ,31
+ ,27
+ ,30
+ ,294424
+ ,77
+ ,162
+ ,33
+ ,215641
+ ,46
+ ,64
+ ,32
+ ,325107
+ ,99
+ ,92
+ ,36
+ ,167542
+ ,66
+ ,83
+ ,28
+ ,106408
+ ,30
+ ,41
+ ,14
+ ,265769
+ ,146
+ ,120
+ ,32
+ ,269651
+ ,67
+ ,105
+ ,30
+ ,149112
+ ,56
+ ,79
+ ,35
+ ,152871
+ ,58
+ ,70
+ ,28
+ ,111665
+ ,34
+ ,55
+ ,28
+ ,116408
+ ,61
+ ,39
+ ,39
+ ,362301
+ ,119
+ ,67
+ ,34
+ ,78800
+ ,42
+ ,21
+ ,26
+ ,183167
+ ,66
+ ,127
+ ,39
+ ,277965
+ ,89
+ ,152
+ ,39
+ ,150629
+ ,44
+ ,113
+ ,33
+ ,168809
+ ,66
+ ,99
+ ,28
+ ,24188
+ ,24
+ ,7
+ ,4
+ ,329267
+ ,259
+ ,141
+ ,39
+ ,65029
+ ,17
+ ,21
+ ,18
+ ,101097
+ ,64
+ ,35
+ ,14
+ ,218946
+ ,41
+ ,109
+ ,29
+ ,244052
+ ,68
+ ,133
+ ,44
+ ,233328
+ ,132
+ ,230
+ ,28
+ ,256462
+ ,105
+ ,166
+ ,35
+ ,206161
+ ,71
+ ,68
+ ,28
+ ,311473
+ ,112
+ ,147
+ ,38
+ ,235800
+ ,94
+ ,179
+ ,23
+ ,177939
+ ,82
+ ,61
+ ,36
+ ,207176
+ ,70
+ ,101
+ ,32
+ ,196553
+ ,57
+ ,108
+ ,29
+ ,174184
+ ,53
+ ,90
+ ,25
+ ,143246
+ ,103
+ ,114
+ ,27
+ ,187559
+ ,121
+ ,103
+ ,36
+ ,187681
+ ,62
+ ,142
+ ,28
+ ,119016
+ ,52
+ ,79
+ ,23
+ ,182192
+ ,52
+ ,88
+ ,40
+ ,73566
+ ,32
+ ,25
+ ,23
+ ,194979
+ ,62
+ ,83
+ ,40
+ ,167488
+ ,45
+ ,113
+ ,28
+ ,143756
+ ,46
+ ,118
+ ,34
+ ,275541
+ ,63
+ ,110
+ ,33
+ ,243199
+ ,75
+ ,129
+ ,28
+ ,182999
+ ,88
+ ,51
+ ,34
+ ,135649
+ ,46
+ ,93
+ ,30
+ ,152299
+ ,53
+ ,76
+ ,33
+ ,120221
+ ,37
+ ,49
+ ,22
+ ,346485
+ ,90
+ ,118
+ ,38
+ ,145790
+ ,63
+ ,38
+ ,26
+ ,193339
+ ,78
+ ,141
+ ,35
+ ,80953
+ ,25
+ ,58
+ ,8
+ ,122774
+ ,45
+ ,27
+ ,24
+ ,130585
+ ,46
+ ,91
+ ,29
+ ,286468
+ ,144
+ ,63
+ ,29
+ ,241066
+ ,82
+ ,56
+ ,45
+ ,148446
+ ,91
+ ,144
+ ,37
+ ,204713
+ ,71
+ ,73
+ ,33
+ ,182079
+ ,63
+ ,168
+ ,33
+ ,140344
+ ,53
+ ,64
+ ,25
+ ,220516
+ ,62
+ ,97
+ ,32
+ ,243060
+ ,63
+ ,117
+ ,29
+ ,162765
+ ,32
+ ,100
+ ,28
+ ,182613
+ ,39
+ ,149
+ ,28
+ ,232138
+ ,62
+ ,187
+ ,31
+ ,265318
+ ,117
+ ,127
+ ,52
+ ,310839
+ ,92
+ ,245
+ ,24
+ ,225060
+ ,93
+ ,87
+ ,41
+ ,232317
+ ,54
+ ,177
+ ,33
+ ,144966
+ ,144
+ ,49
+ ,32
+ ,43287
+ ,14
+ ,49
+ ,19
+ ,155754
+ ,61
+ ,73
+ ,20
+ ,164709
+ ,109
+ ,177
+ ,31
+ ,201940
+ ,38
+ ,94
+ ,31
+ ,235454
+ ,73
+ ,117
+ ,32
+ ,99466
+ ,50
+ ,55
+ ,23
+ ,100750
+ ,72
+ ,58
+ ,30
+ ,224549
+ ,50
+ ,95
+ ,31
+ ,243511
+ ,71
+ ,129
+ ,42
+ ,22938
+ ,10
+ ,11
+ ,1
+ ,152474
+ ,65
+ ,101
+ ,32
+ ,61857
+ ,25
+ ,28
+ ,11
+ ,132487
+ ,41
+ ,89
+ ,36
+ ,317394
+ ,86
+ ,193
+ ,31
+ ,21054
+ ,16
+ ,4
+ ,0
+ ,209641
+ ,42
+ ,84
+ ,24
+ ,31414
+ ,19
+ ,39
+ ,8
+ ,244749
+ ,95
+ ,101
+ ,33
+ ,184510
+ ,49
+ ,82
+ ,40
+ ,128423
+ ,64
+ ,36
+ ,38
+ ,97839
+ ,38
+ ,75
+ ,24
+ ,38214
+ ,34
+ ,16
+ ,8
+ ,151101
+ ,32
+ ,55
+ ,35
+ ,272458
+ ,65
+ ,131
+ ,43
+ ,172494
+ ,52
+ ,131
+ ,43
+ ,328107
+ ,65
+ ,144
+ ,41
+ ,250579
+ ,83
+ ,139
+ ,38
+ ,351067
+ ,95
+ ,211
+ ,45
+ ,158015
+ ,29
+ ,78
+ ,31
+ ,85439
+ ,33
+ ,39
+ ,28
+ ,229242
+ ,247
+ ,90
+ ,31
+ ,351619
+ ,139
+ ,166
+ ,40
+ ,84207
+ ,29
+ ,12
+ ,30
+ ,324598
+ ,110
+ ,133
+ ,37
+ ,131069
+ ,67
+ ,69
+ ,30
+ ,204271
+ ,42
+ ,119
+ ,35
+ ,165543
+ ,65
+ ,119
+ ,32
+ ,141722
+ ,94
+ ,65
+ ,27
+ ,299775
+ ,95
+ ,101
+ ,31
+ ,195838
+ ,67
+ ,196
+ ,31
+ ,173260
+ ,63
+ ,15
+ ,21
+ ,254488
+ ,83
+ ,136
+ ,39
+ ,104389
+ ,45
+ ,89
+ ,41
+ ,199476
+ ,70
+ ,123
+ ,32
+ ,224330
+ ,83
+ ,163
+ ,39
+ ,14688
+ ,10
+ ,5
+ ,0
+ ,181633
+ ,70
+ ,96
+ ,30
+ ,271856
+ ,103
+ ,151
+ ,37
+ ,7199
+ ,5
+ ,6
+ ,0
+ ,46660
+ ,20
+ ,13
+ ,5
+ ,17547
+ ,5
+ ,3
+ ,1
+ ,95227
+ ,34
+ ,23
+ ,32
+ ,152601
+ ,48
+ ,57
+ ,24)
+ ,dim=c(4
+ ,156)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'totblogs'
+ ,'compendiums_reviewed')
+ ,1:156))
> y <- array(NA,dim=c(4,156),dimnames=list(c('time_in_rfc','logins','totblogs','compendiums_reviewed'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
time_in_rfc logins totblogs compendiums_reviewed
1 210907 56 145 30
2 120982 56 101 28
3 176508 54 98 38
4 179321 89 132 30
5 123185 40 60 22
6 52746 25 38 26
7 385534 92 144 25
8 33170 18 5 18
9 149061 44 84 26
10 165446 33 79 25
11 237213 84 127 38
12 173326 88 78 44
13 133131 55 60 30
14 258873 60 131 40
15 180083 66 84 34
16 324799 154 133 47
17 230964 53 150 30
18 236785 119 91 31
19 135473 41 132 23
20 202925 61 136 36
21 215147 58 124 36
22 344297 75 118 30
23 153935 33 70 25
24 132943 40 107 39
25 174724 92 119 34
26 174415 100 89 31
27 225548 112 112 31
28 223632 73 108 33
29 124817 40 52 25
30 221698 45 112 33
31 210767 60 116 35
32 170266 62 123 42
33 260561 75 125 43
34 84853 31 27 30
35 294424 77 162 33
36 215641 46 64 32
37 325107 99 92 36
38 167542 66 83 28
39 106408 30 41 14
40 265769 146 120 32
41 269651 67 105 30
42 149112 56 79 35
43 152871 58 70 28
44 111665 34 55 28
45 116408 61 39 39
46 362301 119 67 34
47 78800 42 21 26
48 183167 66 127 39
49 277965 89 152 39
50 150629 44 113 33
51 168809 66 99 28
52 24188 24 7 4
53 329267 259 141 39
54 65029 17 21 18
55 101097 64 35 14
56 218946 41 109 29
57 244052 68 133 44
58 233328 132 230 28
59 256462 105 166 35
60 206161 71 68 28
61 311473 112 147 38
62 235800 94 179 23
63 177939 82 61 36
64 207176 70 101 32
65 196553 57 108 29
66 174184 53 90 25
67 143246 103 114 27
68 187559 121 103 36
69 187681 62 142 28
70 119016 52 79 23
71 182192 52 88 40
72 73566 32 25 23
73 194979 62 83 40
74 167488 45 113 28
75 143756 46 118 34
76 275541 63 110 33
77 243199 75 129 28
78 182999 88 51 34
79 135649 46 93 30
80 152299 53 76 33
81 120221 37 49 22
82 346485 90 118 38
83 145790 63 38 26
84 193339 78 141 35
85 80953 25 58 8
86 122774 45 27 24
87 130585 46 91 29
88 286468 144 63 29
89 241066 82 56 45
90 148446 91 144 37
91 204713 71 73 33
92 182079 63 168 33
93 140344 53 64 25
94 220516 62 97 32
95 243060 63 117 29
96 162765 32 100 28
97 182613 39 149 28
98 232138 62 187 31
99 265318 117 127 52
100 310839 92 245 24
101 225060 93 87 41
102 232317 54 177 33
103 144966 144 49 32
104 43287 14 49 19
105 155754 61 73 20
106 164709 109 177 31
107 201940 38 94 31
108 235454 73 117 32
109 99466 50 55 23
110 100750 72 58 30
111 224549 50 95 31
112 243511 71 129 42
113 22938 10 11 1
114 152474 65 101 32
115 61857 25 28 11
116 132487 41 89 36
117 317394 86 193 31
118 21054 16 4 0
119 209641 42 84 24
120 31414 19 39 8
121 244749 95 101 33
122 184510 49 82 40
123 128423 64 36 38
124 97839 38 75 24
125 38214 34 16 8
126 151101 32 55 35
127 272458 65 131 43
128 172494 52 131 43
129 328107 65 144 41
130 250579 83 139 38
131 351067 95 211 45
132 158015 29 78 31
133 85439 33 39 28
134 229242 247 90 31
135 351619 139 166 40
136 84207 29 12 30
137 324598 110 133 37
138 131069 67 69 30
139 204271 42 119 35
140 165543 65 119 32
141 141722 94 65 27
142 299775 95 101 31
143 195838 67 196 31
144 173260 63 15 21
145 254488 83 136 39
146 104389 45 89 41
147 199476 70 123 32
148 224330 83 163 39
149 14688 10 5 0
150 181633 70 96 30
151 271856 103 151 37
152 7199 5 6 0
153 46660 20 13 5
154 17547 5 3 1
155 95227 34 23 32
156 152601 48 57 24
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins totblogs
2350.6 727.3 709.3
compendiums_reviewed
2168.4
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-109688 -28630 -706 18968 159919
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2350.62 11884.02 0.198 0.843
logins 727.32 116.94 6.220 4.60e-09 ***
totblogs 709.32 92.61 7.659 2.04e-12 ***
compendiums_reviewed 2168.38 475.41 4.561 1.04e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 45090 on 152 degrees of freedom
Multiple R-squared: 0.6971, Adjusted R-squared: 0.6911
F-statistic: 116.6 on 3 and 152 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,] 0.9798362 0.0403275007 0.0201637504
[2,] 0.9565095 0.0869810748 0.0434905374
[3,] 0.9196634 0.1606731913 0.0803365956
[4,] 0.8963698 0.2072604960 0.1036302480
[5,] 0.8506884 0.2986232928 0.1493116464
[6,] 0.7968091 0.4063818266 0.2031909133
[7,] 0.7219393 0.5561214378 0.2780607189
[8,] 0.7479989 0.5040021659 0.2520010830
[9,] 0.6764149 0.6471701263 0.3235850632
[10,] 0.5948172 0.8103656122 0.4051828061
[11,] 0.5181126 0.9637748871 0.4818874436
[12,] 0.4404343 0.8808686049 0.5595656976
[13,] 0.5365736 0.9268527511 0.4634263755
[14,] 0.4681282 0.9362563011 0.5318718495
[15,] 0.4008236 0.8016471798 0.5991764101
[16,] 0.8061432 0.3877136381 0.1938568190
[17,] 0.7807735 0.4384530400 0.2192265200
[18,] 0.7524189 0.4951621375 0.2475810687
[19,] 0.8119984 0.3760031844 0.1880015922
[20,] 0.8071728 0.3856544230 0.1928272115
[21,] 0.7786817 0.4426365442 0.2213182721
[22,] 0.7386660 0.5226680752 0.2613340376
[23,] 0.6922879 0.6154242705 0.3077121353
[24,] 0.6816947 0.6366106669 0.3183053335
[25,] 0.6283724 0.7432551183 0.3716275592
[26,] 0.6143311 0.7713377965 0.3856688983
[27,] 0.5969159 0.8061682982 0.4030841491
[28,] 0.5524364 0.8951271082 0.4475635541
[29,] 0.5200579 0.9598841731 0.4799420866
[30,] 0.6458567 0.7082865943 0.3541432972
[31,] 0.8316520 0.3366959561 0.1683479781
[32,] 0.7969249 0.4061502488 0.2030751244
[33,] 0.7605136 0.4789727765 0.2394863883
[34,] 0.7422914 0.5154172682 0.2577086341
[35,] 0.7999160 0.4001680121 0.2000840060
[36,] 0.7676894 0.4646211823 0.2323105912
[37,] 0.7258075 0.5483849192 0.2741924596
[38,] 0.6826698 0.6346604732 0.3173302366
[39,] 0.6530125 0.6939750961 0.3469875480
[40,] 0.9375171 0.1249657941 0.0624828970
[41,] 0.9244946 0.1510107424 0.0755053712
[42,] 0.9198021 0.1603957850 0.0801978925
[43,] 0.9017732 0.1964535366 0.0982267683
[44,] 0.8905092 0.2189816266 0.1094908133
[45,] 0.8728379 0.2543242464 0.1271621232
[46,] 0.8569180 0.2861639097 0.1430819549
[47,] 0.9262709 0.1474581878 0.0737290939
[48,] 0.9078649 0.1842702368 0.0921351184
[49,] 0.8881657 0.2236685082 0.1118342541
[50,] 0.8847608 0.2304784058 0.1152392029
[51,] 0.8598833 0.2802334399 0.1401167199
[52,] 0.9454231 0.1091538233 0.0545769116
[53,] 0.9332973 0.1334053860 0.0667026930
[54,] 0.9306384 0.1387232247 0.0693616124
[55,] 0.9271663 0.1456674398 0.0728337199
[56,] 0.9119546 0.1760907240 0.0880453620
[57,] 0.8918492 0.2163015307 0.1081507654
[58,] 0.8701477 0.2597046391 0.1298523196
[59,] 0.8456818 0.3086363844 0.1543181922
[60,] 0.8190568 0.3618864848 0.1809432424
[61,] 0.8655798 0.2688404649 0.1344202325
[62,] 0.8746684 0.2506631292 0.1253315646
[63,] 0.8549461 0.2901078986 0.1450539493
[64,] 0.8373452 0.3253095131 0.1626547566
[65,] 0.8075696 0.3848608378 0.1924304189
[66,] 0.7806871 0.4386258492 0.2193129246
[67,] 0.7446791 0.5106418963 0.2553209481
[68,] 0.7071366 0.5857267010 0.2928633505
[69,] 0.7134884 0.5730231352 0.2865115676
[70,] 0.7836442 0.4327116185 0.2163558093
[71,] 0.7682989 0.4634022836 0.2317011418
[72,] 0.7320844 0.5358312990 0.2679156495
[73,] 0.7111590 0.5776819328 0.2888409664
[74,] 0.6739652 0.6520696311 0.3260348155
[75,] 0.6321002 0.7357995139 0.3678997570
[76,] 0.8274601 0.3450797018 0.1725398509
[77,] 0.7994133 0.4011733820 0.2005866910
[78,] 0.7929070 0.4141860998 0.2070930499
[79,] 0.7578055 0.4843889261 0.2421944631
[80,] 0.7248899 0.5502201965 0.2751100982
[81,] 0.7050091 0.5899817108 0.2949908554
[82,] 0.7767726 0.4464548966 0.2232274483
[83,] 0.7766544 0.4466911778 0.2233455889
[84,] 0.8918280 0.2163439621 0.1081719811
[85,] 0.8794482 0.2411036096 0.1205518048
[86,] 0.8956304 0.2087392334 0.1043696167
[87,] 0.8723156 0.2553688706 0.1276844353
[88,] 0.8637548 0.2724903393 0.1362451696
[89,] 0.8705274 0.2589451660 0.1294725830
[90,] 0.8435242 0.3129515010 0.1564757505
[91,] 0.8184547 0.3630906656 0.1815453328
[92,] 0.7924897 0.4150205649 0.2075102824
[93,] 0.7628840 0.4742320042 0.2371160021
[94,] 0.7272494 0.5455011737 0.2727505869
[95,] 0.6883319 0.6233362106 0.3116681053
[96,] 0.6489335 0.7021330837 0.3510665419
[97,] 0.6747676 0.6504648471 0.3252324235
[98,] 0.6781738 0.6436523528 0.3218261764
[99,] 0.6371513 0.7256973096 0.3628486548
[100,] 0.8572783 0.2854434066 0.1427217033
[101,] 0.8472727 0.3054546803 0.1527273401
[102,] 0.8258080 0.3483839831 0.1741919916
[103,] 0.8028752 0.3942496277 0.1971248138
[104,] 0.8218418 0.3563163925 0.1781581962
[105,] 0.8314704 0.3370592781 0.1685296390
[106,] 0.7964793 0.4070414406 0.2035207203
[107,] 0.7563395 0.4873209305 0.2436604653
[108,] 0.7458713 0.5082573017 0.2541286508
[109,] 0.7003452 0.5993096178 0.2996548089
[110,] 0.6924899 0.6150202475 0.3075101237
[111,] 0.6785299 0.6429401176 0.3214700588
[112,] 0.6275020 0.7449960506 0.3724980253
[113,] 0.6805546 0.6388908181 0.3194454091
[114,] 0.6514148 0.6971703057 0.3485851529
[115,] 0.6256455 0.7487089466 0.3743544733
[116,] 0.5696097 0.8607805407 0.4303902703
[117,] 0.5243573 0.9512853153 0.4756426577
[118,] 0.5072676 0.9854647357 0.4927323678
[119,] 0.4556108 0.9112215851 0.5443892075
[120,] 0.3996181 0.7992361308 0.6003819346
[121,] 0.3774718 0.7549435087 0.6225282457
[122,] 0.4047305 0.8094610628 0.5952694686
[123,] 0.5625336 0.8749328525 0.4374664263
[124,] 0.4996726 0.9993451442 0.5003274279
[125,] 0.4737122 0.9474244237 0.5262877882
[126,] 0.4139524 0.8279048314 0.5860475843
[127,] 0.3692533 0.7385066493 0.6307466753
[128,] 0.8985730 0.2028540506 0.1014270253
[129,] 0.8636582 0.2726836250 0.1363418125
[130,] 0.8164906 0.3670187280 0.1835093640
[131,] 0.8333398 0.3333204193 0.1666602096
[132,] 0.8296828 0.3406343711 0.1703171856
[133,] 0.8938250 0.2123499541 0.1061749770
[134,] 0.8555872 0.2888256049 0.1444128025
[135,] 0.9996279 0.0007441278 0.0003720639
[136,] 0.9998086 0.0003828753 0.0001914377
[137,] 0.9995169 0.0009661642 0.0004830821
[138,] 0.9986029 0.0027941998 0.0013970999
[139,] 0.9991171 0.0017658646 0.0008829323
[140,] 0.9991320 0.0017359967 0.0008679984
[141,] 0.9971058 0.0057883070 0.0028941535
[142,] 0.9884345 0.0231310455 0.0115655228
[143,] 0.9614199 0.0771602403 0.0385801202
> postscript(file="/var/fisher/rcomp/tmp/1yqdd1353352791.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/2l8651353352791.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3u5vl1353352791.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/444mw1353352791.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5qsvd1353352791.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5
-75.84705 -54454.08441 -17029.29388 -46442.13657 1478.29738
6 7 8 9 10
-51119.47990 159918.99451 -24849.72864 -1252.12318 28848.32337
11 12 13 14 15
1285.99762 -43763.94926 -16832.47906 33227.54854 -3578.11138
16 17 18 19 20
14188.55385 18616.50956 16116.05344 -40200.31286 -18320.84025
21 22 23 24 25
4594.92676 138646.74259 23721.18770 -58964.11429 -52673.46557
26 27 28 29 30
-31016.30668 -4925.41824 20024.41826 2279.70460 35617.99228
31 32 33 34 35
6603.22051 -55496.29655 21756.57975 -24247.39332 49603.96840
36 37 38 39 40
65049.33237 107433.15642 -2399.51542 22798.54090 2723.91321
41 42 43 44 45
79040.40768 -25897.74004 -2030.85032 -15141.49323 -42539.10798
46 47 48 49 50
152150.55280 -25371.44092 -41836.69460 18500.08169 -35433.01001
51 52 53 54 55
-12481.60756 -9256.94632 -46039.13177 -3612.50482 -2985.29240
56 57 58 59 60
46576.72939 2495.86582 -88886.15328 -15896.91093 43222.67864
61 62 63 64 65
40994.78537 -11759.01726 -5381.60612 12883.97359 13255.99219
66 67 68 69 70
15237.50319 -73426.69259 -53918.29569 -21201.02883 -27063.92076
71 72 73 74 75
-7134.23859 -19664.41544 1926.19304 -8459.42789 -49475.61286
76 77 78 79 80
77787.94140 34083.00097 6744.43991 -31176.13791 -14064.07812
81 82 83 84 85
8498.74612 112577.96615 14286.51338 -41649.42336 1931.98804
86 87 88 89 90
16501.46086 -32653.12178 71813.82476 41776.56862 -102462.24494
91 92 93 94 95
27386.18926 -56814.51762 -160.22208 34879.77435 49015.23206
96 97 98 99 100
5493.81704 -14505.98942 -15168.48934 -24967.74392 15751.22996
101 102 103 104 105
4454.74543 -6414.53825 -66262.85846 -45201.84779 13889.28395
106 107 108 109 110
-109688.15718 38055.69194 27630.93354 -28135.65061 -60159.21397
111 112 113 114 115
51227.58209 6946.95019 3343.34303 -38181.44658 -2389.60305
116 117 118 119 120
-40874.56275 48376.01791 4229.05466 65119.26799 -29766.06924
121 122 123 124 125
30105.69481 1621.61887 -28409.72148 -37389.60381 -17561.48878
126 127 128 129 130
10570.48138 36670.82986 -53838.06258 87435.45172 6867.49447
131 132 133 134 135
32378.13090 12025.62779 -29291.08512 -83814.07202 43689.44812
136 137 138 139 140
-12798.98750 67673.25255 -34006.13499 11070.95313 -37880.17524
141 142 143 144 145
-33648.25420 89468.45404 -61488.93351 68912.73142 10736.06963
146 147 148 149 150
-82723.72470 -10421.02811 -38573.52336 1517.63220 -4775.67588
151 152 153 154 155
7254.73565 -3044.10623 9700.02838 7263.46893 -17554.82742
156
22866.96519
> postscript(file="/var/fisher/rcomp/tmp/6fp5j1353352791.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -75.84705 NA
1 -54454.08441 -75.84705
2 -17029.29388 -54454.08441
3 -46442.13657 -17029.29388
4 1478.29738 -46442.13657
5 -51119.47990 1478.29738
6 159918.99451 -51119.47990
7 -24849.72864 159918.99451
8 -1252.12318 -24849.72864
9 28848.32337 -1252.12318
10 1285.99762 28848.32337
11 -43763.94926 1285.99762
12 -16832.47906 -43763.94926
13 33227.54854 -16832.47906
14 -3578.11138 33227.54854
15 14188.55385 -3578.11138
16 18616.50956 14188.55385
17 16116.05344 18616.50956
18 -40200.31286 16116.05344
19 -18320.84025 -40200.31286
20 4594.92676 -18320.84025
21 138646.74259 4594.92676
22 23721.18770 138646.74259
23 -58964.11429 23721.18770
24 -52673.46557 -58964.11429
25 -31016.30668 -52673.46557
26 -4925.41824 -31016.30668
27 20024.41826 -4925.41824
28 2279.70460 20024.41826
29 35617.99228 2279.70460
30 6603.22051 35617.99228
31 -55496.29655 6603.22051
32 21756.57975 -55496.29655
33 -24247.39332 21756.57975
34 49603.96840 -24247.39332
35 65049.33237 49603.96840
36 107433.15642 65049.33237
37 -2399.51542 107433.15642
38 22798.54090 -2399.51542
39 2723.91321 22798.54090
40 79040.40768 2723.91321
41 -25897.74004 79040.40768
42 -2030.85032 -25897.74004
43 -15141.49323 -2030.85032
44 -42539.10798 -15141.49323
45 152150.55280 -42539.10798
46 -25371.44092 152150.55280
47 -41836.69460 -25371.44092
48 18500.08169 -41836.69460
49 -35433.01001 18500.08169
50 -12481.60756 -35433.01001
51 -9256.94632 -12481.60756
52 -46039.13177 -9256.94632
53 -3612.50482 -46039.13177
54 -2985.29240 -3612.50482
55 46576.72939 -2985.29240
56 2495.86582 46576.72939
57 -88886.15328 2495.86582
58 -15896.91093 -88886.15328
59 43222.67864 -15896.91093
60 40994.78537 43222.67864
61 -11759.01726 40994.78537
62 -5381.60612 -11759.01726
63 12883.97359 -5381.60612
64 13255.99219 12883.97359
65 15237.50319 13255.99219
66 -73426.69259 15237.50319
67 -53918.29569 -73426.69259
68 -21201.02883 -53918.29569
69 -27063.92076 -21201.02883
70 -7134.23859 -27063.92076
71 -19664.41544 -7134.23859
72 1926.19304 -19664.41544
73 -8459.42789 1926.19304
74 -49475.61286 -8459.42789
75 77787.94140 -49475.61286
76 34083.00097 77787.94140
77 6744.43991 34083.00097
78 -31176.13791 6744.43991
79 -14064.07812 -31176.13791
80 8498.74612 -14064.07812
81 112577.96615 8498.74612
82 14286.51338 112577.96615
83 -41649.42336 14286.51338
84 1931.98804 -41649.42336
85 16501.46086 1931.98804
86 -32653.12178 16501.46086
87 71813.82476 -32653.12178
88 41776.56862 71813.82476
89 -102462.24494 41776.56862
90 27386.18926 -102462.24494
91 -56814.51762 27386.18926
92 -160.22208 -56814.51762
93 34879.77435 -160.22208
94 49015.23206 34879.77435
95 5493.81704 49015.23206
96 -14505.98942 5493.81704
97 -15168.48934 -14505.98942
98 -24967.74392 -15168.48934
99 15751.22996 -24967.74392
100 4454.74543 15751.22996
101 -6414.53825 4454.74543
102 -66262.85846 -6414.53825
103 -45201.84779 -66262.85846
104 13889.28395 -45201.84779
105 -109688.15718 13889.28395
106 38055.69194 -109688.15718
107 27630.93354 38055.69194
108 -28135.65061 27630.93354
109 -60159.21397 -28135.65061
110 51227.58209 -60159.21397
111 6946.95019 51227.58209
112 3343.34303 6946.95019
113 -38181.44658 3343.34303
114 -2389.60305 -38181.44658
115 -40874.56275 -2389.60305
116 48376.01791 -40874.56275
117 4229.05466 48376.01791
118 65119.26799 4229.05466
119 -29766.06924 65119.26799
120 30105.69481 -29766.06924
121 1621.61887 30105.69481
122 -28409.72148 1621.61887
123 -37389.60381 -28409.72148
124 -17561.48878 -37389.60381
125 10570.48138 -17561.48878
126 36670.82986 10570.48138
127 -53838.06258 36670.82986
128 87435.45172 -53838.06258
129 6867.49447 87435.45172
130 32378.13090 6867.49447
131 12025.62779 32378.13090
132 -29291.08512 12025.62779
133 -83814.07202 -29291.08512
134 43689.44812 -83814.07202
135 -12798.98750 43689.44812
136 67673.25255 -12798.98750
137 -34006.13499 67673.25255
138 11070.95313 -34006.13499
139 -37880.17524 11070.95313
140 -33648.25420 -37880.17524
141 89468.45404 -33648.25420
142 -61488.93351 89468.45404
143 68912.73142 -61488.93351
144 10736.06963 68912.73142
145 -82723.72470 10736.06963
146 -10421.02811 -82723.72470
147 -38573.52336 -10421.02811
148 1517.63220 -38573.52336
149 -4775.67588 1517.63220
150 7254.73565 -4775.67588
151 -3044.10623 7254.73565
152 9700.02838 -3044.10623
153 7263.46893 9700.02838
154 -17554.82742 7263.46893
155 22866.96519 -17554.82742
156 NA 22866.96519
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -54454.0844 -75.84705
[2,] -17029.2939 -54454.08441
[3,] -46442.1366 -17029.29388
[4,] 1478.2974 -46442.13657
[5,] -51119.4799 1478.29738
[6,] 159918.9945 -51119.47990
[7,] -24849.7286 159918.99451
[8,] -1252.1232 -24849.72864
[9,] 28848.3234 -1252.12318
[10,] 1285.9976 28848.32337
[11,] -43763.9493 1285.99762
[12,] -16832.4791 -43763.94926
[13,] 33227.5485 -16832.47906
[14,] -3578.1114 33227.54854
[15,] 14188.5538 -3578.11138
[16,] 18616.5096 14188.55385
[17,] 16116.0534 18616.50956
[18,] -40200.3129 16116.05344
[19,] -18320.8403 -40200.31286
[20,] 4594.9268 -18320.84025
[21,] 138646.7426 4594.92676
[22,] 23721.1877 138646.74259
[23,] -58964.1143 23721.18770
[24,] -52673.4656 -58964.11429
[25,] -31016.3067 -52673.46557
[26,] -4925.4182 -31016.30668
[27,] 20024.4183 -4925.41824
[28,] 2279.7046 20024.41826
[29,] 35617.9923 2279.70460
[30,] 6603.2205 35617.99228
[31,] -55496.2966 6603.22051
[32,] 21756.5797 -55496.29655
[33,] -24247.3933 21756.57975
[34,] 49603.9684 -24247.39332
[35,] 65049.3324 49603.96840
[36,] 107433.1564 65049.33237
[37,] -2399.5154 107433.15642
[38,] 22798.5409 -2399.51542
[39,] 2723.9132 22798.54090
[40,] 79040.4077 2723.91321
[41,] -25897.7400 79040.40768
[42,] -2030.8503 -25897.74004
[43,] -15141.4932 -2030.85032
[44,] -42539.1080 -15141.49323
[45,] 152150.5528 -42539.10798
[46,] -25371.4409 152150.55280
[47,] -41836.6946 -25371.44092
[48,] 18500.0817 -41836.69460
[49,] -35433.0100 18500.08169
[50,] -12481.6076 -35433.01001
[51,] -9256.9463 -12481.60756
[52,] -46039.1318 -9256.94632
[53,] -3612.5048 -46039.13177
[54,] -2985.2924 -3612.50482
[55,] 46576.7294 -2985.29240
[56,] 2495.8658 46576.72939
[57,] -88886.1533 2495.86582
[58,] -15896.9109 -88886.15328
[59,] 43222.6786 -15896.91093
[60,] 40994.7854 43222.67864
[61,] -11759.0173 40994.78537
[62,] -5381.6061 -11759.01726
[63,] 12883.9736 -5381.60612
[64,] 13255.9922 12883.97359
[65,] 15237.5032 13255.99219
[66,] -73426.6926 15237.50319
[67,] -53918.2957 -73426.69259
[68,] -21201.0288 -53918.29569
[69,] -27063.9208 -21201.02883
[70,] -7134.2386 -27063.92076
[71,] -19664.4154 -7134.23859
[72,] 1926.1930 -19664.41544
[73,] -8459.4279 1926.19304
[74,] -49475.6129 -8459.42789
[75,] 77787.9414 -49475.61286
[76,] 34083.0010 77787.94140
[77,] 6744.4399 34083.00097
[78,] -31176.1379 6744.43991
[79,] -14064.0781 -31176.13791
[80,] 8498.7461 -14064.07812
[81,] 112577.9661 8498.74612
[82,] 14286.5134 112577.96615
[83,] -41649.4234 14286.51338
[84,] 1931.9880 -41649.42336
[85,] 16501.4609 1931.98804
[86,] -32653.1218 16501.46086
[87,] 71813.8248 -32653.12178
[88,] 41776.5686 71813.82476
[89,] -102462.2449 41776.56862
[90,] 27386.1893 -102462.24494
[91,] -56814.5176 27386.18926
[92,] -160.2221 -56814.51762
[93,] 34879.7744 -160.22208
[94,] 49015.2321 34879.77435
[95,] 5493.8170 49015.23206
[96,] -14505.9894 5493.81704
[97,] -15168.4893 -14505.98942
[98,] -24967.7439 -15168.48934
[99,] 15751.2300 -24967.74392
[100,] 4454.7454 15751.22996
[101,] -6414.5383 4454.74543
[102,] -66262.8585 -6414.53825
[103,] -45201.8478 -66262.85846
[104,] 13889.2839 -45201.84779
[105,] -109688.1572 13889.28395
[106,] 38055.6919 -109688.15718
[107,] 27630.9335 38055.69194
[108,] -28135.6506 27630.93354
[109,] -60159.2140 -28135.65061
[110,] 51227.5821 -60159.21397
[111,] 6946.9502 51227.58209
[112,] 3343.3430 6946.95019
[113,] -38181.4466 3343.34303
[114,] -2389.6030 -38181.44658
[115,] -40874.5627 -2389.60305
[116,] 48376.0179 -40874.56275
[117,] 4229.0547 48376.01791
[118,] 65119.2680 4229.05466
[119,] -29766.0692 65119.26799
[120,] 30105.6948 -29766.06924
[121,] 1621.6189 30105.69481
[122,] -28409.7215 1621.61887
[123,] -37389.6038 -28409.72148
[124,] -17561.4888 -37389.60381
[125,] 10570.4814 -17561.48878
[126,] 36670.8299 10570.48138
[127,] -53838.0626 36670.82986
[128,] 87435.4517 -53838.06258
[129,] 6867.4945 87435.45172
[130,] 32378.1309 6867.49447
[131,] 12025.6278 32378.13090
[132,] -29291.0851 12025.62779
[133,] -83814.0720 -29291.08512
[134,] 43689.4481 -83814.07202
[135,] -12798.9875 43689.44812
[136,] 67673.2526 -12798.98750
[137,] -34006.1350 67673.25255
[138,] 11070.9531 -34006.13499
[139,] -37880.1752 11070.95313
[140,] -33648.2542 -37880.17524
[141,] 89468.4540 -33648.25420
[142,] -61488.9335 89468.45404
[143,] 68912.7314 -61488.93351
[144,] 10736.0696 68912.73142
[145,] -82723.7247 10736.06963
[146,] -10421.0281 -82723.72470
[147,] -38573.5234 -10421.02811
[148,] 1517.6322 -38573.52336
[149,] -4775.6759 1517.63220
[150,] 7254.7357 -4775.67588
[151,] -3044.1062 7254.73565
[152,] 9700.0284 -3044.10623
[153,] 7263.4689 9700.02838
[154,] -17554.8274 7263.46893
[155,] 22866.9652 -17554.82742
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -54454.0844 -75.84705
2 -17029.2939 -54454.08441
3 -46442.1366 -17029.29388
4 1478.2974 -46442.13657
5 -51119.4799 1478.29738
6 159918.9945 -51119.47990
7 -24849.7286 159918.99451
8 -1252.1232 -24849.72864
9 28848.3234 -1252.12318
10 1285.9976 28848.32337
11 -43763.9493 1285.99762
12 -16832.4791 -43763.94926
13 33227.5485 -16832.47906
14 -3578.1114 33227.54854
15 14188.5538 -3578.11138
16 18616.5096 14188.55385
17 16116.0534 18616.50956
18 -40200.3129 16116.05344
19 -18320.8403 -40200.31286
20 4594.9268 -18320.84025
21 138646.7426 4594.92676
22 23721.1877 138646.74259
23 -58964.1143 23721.18770
24 -52673.4656 -58964.11429
25 -31016.3067 -52673.46557
26 -4925.4182 -31016.30668
27 20024.4183 -4925.41824
28 2279.7046 20024.41826
29 35617.9923 2279.70460
30 6603.2205 35617.99228
31 -55496.2966 6603.22051
32 21756.5797 -55496.29655
33 -24247.3933 21756.57975
34 49603.9684 -24247.39332
35 65049.3324 49603.96840
36 107433.1564 65049.33237
37 -2399.5154 107433.15642
38 22798.5409 -2399.51542
39 2723.9132 22798.54090
40 79040.4077 2723.91321
41 -25897.7400 79040.40768
42 -2030.8503 -25897.74004
43 -15141.4932 -2030.85032
44 -42539.1080 -15141.49323
45 152150.5528 -42539.10798
46 -25371.4409 152150.55280
47 -41836.6946 -25371.44092
48 18500.0817 -41836.69460
49 -35433.0100 18500.08169
50 -12481.6076 -35433.01001
51 -9256.9463 -12481.60756
52 -46039.1318 -9256.94632
53 -3612.5048 -46039.13177
54 -2985.2924 -3612.50482
55 46576.7294 -2985.29240
56 2495.8658 46576.72939
57 -88886.1533 2495.86582
58 -15896.9109 -88886.15328
59 43222.6786 -15896.91093
60 40994.7854 43222.67864
61 -11759.0173 40994.78537
62 -5381.6061 -11759.01726
63 12883.9736 -5381.60612
64 13255.9922 12883.97359
65 15237.5032 13255.99219
66 -73426.6926 15237.50319
67 -53918.2957 -73426.69259
68 -21201.0288 -53918.29569
69 -27063.9208 -21201.02883
70 -7134.2386 -27063.92076
71 -19664.4154 -7134.23859
72 1926.1930 -19664.41544
73 -8459.4279 1926.19304
74 -49475.6129 -8459.42789
75 77787.9414 -49475.61286
76 34083.0010 77787.94140
77 6744.4399 34083.00097
78 -31176.1379 6744.43991
79 -14064.0781 -31176.13791
80 8498.7461 -14064.07812
81 112577.9661 8498.74612
82 14286.5134 112577.96615
83 -41649.4234 14286.51338
84 1931.9880 -41649.42336
85 16501.4609 1931.98804
86 -32653.1218 16501.46086
87 71813.8248 -32653.12178
88 41776.5686 71813.82476
89 -102462.2449 41776.56862
90 27386.1893 -102462.24494
91 -56814.5176 27386.18926
92 -160.2221 -56814.51762
93 34879.7744 -160.22208
94 49015.2321 34879.77435
95 5493.8170 49015.23206
96 -14505.9894 5493.81704
97 -15168.4893 -14505.98942
98 -24967.7439 -15168.48934
99 15751.2300 -24967.74392
100 4454.7454 15751.22996
101 -6414.5383 4454.74543
102 -66262.8585 -6414.53825
103 -45201.8478 -66262.85846
104 13889.2839 -45201.84779
105 -109688.1572 13889.28395
106 38055.6919 -109688.15718
107 27630.9335 38055.69194
108 -28135.6506 27630.93354
109 -60159.2140 -28135.65061
110 51227.5821 -60159.21397
111 6946.9502 51227.58209
112 3343.3430 6946.95019
113 -38181.4466 3343.34303
114 -2389.6030 -38181.44658
115 -40874.5627 -2389.60305
116 48376.0179 -40874.56275
117 4229.0547 48376.01791
118 65119.2680 4229.05466
119 -29766.0692 65119.26799
120 30105.6948 -29766.06924
121 1621.6189 30105.69481
122 -28409.7215 1621.61887
123 -37389.6038 -28409.72148
124 -17561.4888 -37389.60381
125 10570.4814 -17561.48878
126 36670.8299 10570.48138
127 -53838.0626 36670.82986
128 87435.4517 -53838.06258
129 6867.4945 87435.45172
130 32378.1309 6867.49447
131 12025.6278 32378.13090
132 -29291.0851 12025.62779
133 -83814.0720 -29291.08512
134 43689.4481 -83814.07202
135 -12798.9875 43689.44812
136 67673.2526 -12798.98750
137 -34006.1350 67673.25255
138 11070.9531 -34006.13499
139 -37880.1752 11070.95313
140 -33648.2542 -37880.17524
141 89468.4540 -33648.25420
142 -61488.9335 89468.45404
143 68912.7314 -61488.93351
144 10736.0696 68912.73142
145 -82723.7247 10736.06963
146 -10421.0281 -82723.72470
147 -38573.5234 -10421.02811
148 1517.6322 -38573.52336
149 -4775.6759 1517.63220
150 7254.7357 -4775.67588
151 -3044.1062 7254.73565
152 9700.0284 -3044.10623
153 7263.4689 9700.02838
154 -17554.8274 7263.46893
155 22866.9652 -17554.82742
> 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/7lhrs1353352791.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/8hdsm1353352791.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/9wx0h1353352791.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/10o5oa1353352791.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1127fw1353352791.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12v00d1353352791.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13n1yx1353352791.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/145qvs1353352791.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/15ky5t1353352791.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/16bazz1353352791.tab")
+ }
>
> try(system("convert tmp/1yqdd1353352791.ps tmp/1yqdd1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l8651353352791.ps tmp/2l8651353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u5vl1353352791.ps tmp/3u5vl1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/444mw1353352791.ps tmp/444mw1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qsvd1353352791.ps tmp/5qsvd1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fp5j1353352791.ps tmp/6fp5j1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lhrs1353352791.ps tmp/7lhrs1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hdsm1353352791.ps tmp/8hdsm1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wx0h1353352791.ps tmp/9wx0h1353352791.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o5oa1353352791.ps tmp/10o5oa1353352791.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.532 1.286 8.820