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)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
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+ ,dim=c(6
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+ ,dimnames=list(c('Connected'
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+ ,'Software'
+ ,'Happyness'
+ ,'Beloning')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Connected','Separated','Learning','Software','Happyness','Beloning'),1:264))
> 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 = '3'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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 Separated Software Happyness Beloning t
1 13 41 38 12 14 53 1
2 16 39 32 11 18 83 2
3 19 30 35 15 11 66 3
4 15 31 33 6 12 67 4
5 14 34 37 13 16 76 5
6 13 35 29 10 18 78 6
7 19 39 31 12 14 53 7
8 15 34 36 14 14 80 8
9 14 36 35 12 15 74 9
10 15 37 38 9 15 76 10
11 16 38 31 10 17 79 11
12 16 36 34 12 19 54 12
13 16 38 35 12 10 67 13
14 16 39 38 11 16 54 14
15 17 33 37 15 18 87 15
16 15 32 33 12 14 58 16
17 15 36 32 10 14 75 17
18 20 38 38 12 17 88 18
19 18 39 38 11 14 64 19
20 16 32 32 12 16 57 20
21 16 32 33 11 18 66 21
22 16 31 31 12 11 68 22
23 19 39 38 13 14 54 23
24 16 37 39 11 12 56 24
25 17 39 32 12 17 86 25
26 17 41 32 13 9 80 26
27 16 36 35 10 16 76 27
28 15 33 37 14 14 69 28
29 16 33 33 12 15 78 29
30 14 34 33 10 11 67 30
31 15 31 31 12 16 80 31
32 12 27 32 8 13 54 32
33 14 37 31 10 17 71 33
34 16 34 37 12 15 84 34
35 14 34 30 12 14 74 35
36 10 32 33 7 16 71 36
37 10 29 31 9 9 63 37
38 14 36 33 12 15 71 38
39 16 29 31 10 17 76 39
40 16 35 33 10 13 69 40
41 16 37 32 10 15 74 41
42 14 34 33 12 16 75 42
43 20 38 32 15 16 54 43
44 14 35 33 10 12 52 44
45 14 38 28 10 15 69 45
46 11 37 35 12 11 68 46
47 14 38 39 13 15 65 47
48 15 33 34 11 15 75 48
49 16 36 38 11 17 74 49
50 14 38 32 12 13 75 50
51 16 32 38 14 16 72 51
52 14 32 30 10 14 67 52
53 12 32 33 12 11 63 53
54 16 34 38 13 12 62 54
55 9 32 32 5 12 63 55
56 14 37 35 6 15 76 56
57 16 39 34 12 16 74 57
58 16 29 34 12 15 67 58
59 15 37 36 11 12 73 59
60 16 35 34 10 12 70 60
61 12 30 28 7 8 53 61
62 16 38 34 12 13 77 62
63 16 34 35 14 11 80 63
64 14 31 35 11 14 52 64
65 16 34 31 12 15 54 65
66 17 35 37 13 10 80 66
67 18 36 35 14 11 66 67
68 18 30 27 11 12 73 68
69 12 39 40 12 15 63 69
70 16 35 37 12 15 69 70
71 10 38 36 8 14 67 71
72 14 31 38 11 16 54 72
73 18 34 39 14 15 81 73
74 18 38 41 14 15 69 74
75 16 34 27 12 13 84 75
76 17 39 30 9 12 80 76
77 16 37 37 13 17 70 77
78 16 34 31 11 13 69 78
79 13 28 31 12 15 77 79
80 16 37 27 12 13 54 80
81 16 33 36 12 15 79 81
82 16 35 37 12 15 71 82
83 15 37 33 12 16 73 83
84 15 32 34 11 15 72 84
85 16 33 31 10 14 77 85
86 14 38 39 9 15 75 86
87 16 33 34 12 14 69 87
88 16 29 32 12 13 54 88
89 15 33 33 12 7 70 89
90 12 31 36 9 17 73 90
91 17 36 32 15 13 54 91
92 16 35 41 12 15 77 92
93 15 32 28 12 14 82 93
94 13 29 30 12 13 80 94
95 16 39 36 10 16 80 95
96 16 37 35 13 12 69 96
97 16 35 31 9 14 78 97
98 16 37 34 12 17 81 98
99 14 32 36 10 15 76 99
100 16 38 36 14 17 76 100
101 16 37 35 11 12 73 101
102 20 36 37 15 16 85 102
103 15 32 28 11 11 66 103
104 16 33 39 11 15 79 104
105 13 40 32 12 9 68 105
106 17 38 35 12 16 76 106
107 16 41 39 12 15 71 107
108 16 36 35 11 10 54 108
109 12 43 42 7 10 46 109
110 16 30 34 12 15 85 110
111 16 31 33 14 11 74 111
112 17 32 41 11 13 88 112
113 13 32 33 11 14 38 113
114 12 37 34 10 18 76 114
115 18 37 32 13 16 86 115
116 14 33 40 13 14 54 116
117 14 34 40 8 14 67 117
118 13 33 35 11 14 69 118
119 16 38 36 12 14 90 119
120 13 33 37 11 12 54 120
121 16 31 27 13 14 76 121
122 13 38 39 12 15 89 122
123 16 37 38 14 15 76 123
124 15 36 31 13 15 73 124
125 16 31 33 15 13 79 125
126 15 39 32 10 17 90 126
127 17 44 39 11 17 74 127
128 15 33 36 9 19 81 128
129 12 35 33 11 15 72 129
130 16 32 33 10 13 71 130
131 10 28 32 11 9 66 131
132 16 40 37 8 15 77 132
133 12 27 30 11 15 65 133
134 14 37 38 12 15 74 134
135 15 32 29 12 16 85 135
136 13 28 22 9 11 54 136
137 15 34 35 11 14 63 137
138 11 30 35 10 11 54 138
139 12 35 34 8 15 64 139
140 11 31 35 9 13 69 140
141 16 32 34 8 15 54 141
142 15 30 37 9 16 84 142
143 17 30 35 15 14 86 143
144 16 31 23 11 15 77 144
145 10 40 31 8 16 89 145
146 18 32 27 13 16 76 146
147 13 36 36 12 11 60 147
148 16 32 31 12 12 75 148
149 13 35 32 9 9 73 149
150 10 38 39 7 16 85 150
151 15 42 37 13 13 79 151
152 16 34 38 9 16 71 152
153 16 35 39 6 12 72 153
154 14 38 34 8 9 69 154
155 10 33 31 8 13 78 155
156 17 36 32 15 13 54 156
157 13 32 37 6 14 69 157
158 15 33 36 9 19 81 158
159 16 34 32 11 13 84 159
160 12 32 38 8 12 84 160
161 13 34 36 8 13 69 161
162 13 27 26 10 10 66 162
163 12 31 26 8 14 81 163
164 17 38 33 14 16 82 164
165 15 34 39 10 10 72 165
166 10 24 30 8 11 54 166
167 14 30 33 11 14 78 167
168 11 26 25 12 12 74 168
169 13 34 38 12 9 82 169
170 16 27 37 12 9 73 170
171 12 37 31 5 11 55 171
172 16 36 37 12 16 72 172
173 12 41 35 10 9 78 173
174 9 29 25 7 13 59 174
175 12 36 28 12 16 72 175
176 15 32 35 11 13 78 176
177 12 37 33 8 9 68 177
178 12 30 30 9 12 69 178
179 14 31 31 10 16 67 179
180 12 38 37 9 11 74 180
181 16 36 36 12 14 54 181
182 11 35 30 6 13 67 182
183 19 31 36 15 15 70 183
184 15 38 32 12 14 80 184
185 8 22 28 12 16 89 185
186 16 32 36 12 13 76 186
187 17 36 34 11 14 74 187
188 12 39 31 7 15 87 188
189 11 28 28 7 13 54 189
190 11 32 36 5 11 61 190
191 14 32 36 12 11 38 191
192 16 38 40 12 14 75 192
193 12 32 33 3 15 69 193
194 16 35 37 11 11 62 194
195 13 32 32 10 15 72 195
196 15 37 38 12 12 70 196
197 16 34 31 9 14 79 197
198 16 33 37 12 14 87 198
199 14 33 33 9 8 62 199
200 16 26 32 12 13 77 200
201 16 30 30 12 9 69 201
202 14 24 30 10 15 69 202
203 11 34 31 9 17 75 203
204 12 34 32 12 13 54 204
205 15 33 34 8 15 72 205
206 15 34 36 11 15 74 206
207 16 35 37 11 14 85 207
208 16 35 36 12 16 52 208
209 11 36 33 10 13 70 209
210 15 34 33 10 16 84 210
211 12 34 33 12 9 64 211
212 12 41 44 12 16 84 212
213 15 32 39 11 11 87 213
214 15 30 32 8 10 79 214
215 16 35 35 12 11 67 215
216 14 28 25 10 15 65 216
217 17 33 35 11 17 85 217
218 14 39 34 10 14 83 218
219 13 36 35 8 8 61 219
220 15 36 39 12 15 82 220
221 13 35 33 12 11 76 221
222 14 38 36 10 16 58 222
223 15 33 32 12 10 72 223
224 12 31 32 9 15 72 224
225 13 34 36 9 9 38 225
226 8 32 36 6 16 78 226
227 14 31 32 10 19 54 227
228 14 33 34 9 12 63 228
229 11 34 33 9 8 66 229
230 12 34 35 9 11 70 230
231 13 34 30 6 14 71 231
232 10 33 38 10 9 67 232
233 16 32 34 6 15 58 233
234 18 41 33 14 13 72 234
235 13 34 32 10 16 72 235
236 11 36 31 10 11 70 236
237 4 37 30 6 12 76 237
238 13 36 27 12 13 50 238
239 16 29 31 12 10 72 239
240 10 37 30 7 11 72 240
241 12 27 32 8 12 88 241
242 12 35 35 11 8 53 242
243 10 28 28 3 12 58 243
244 13 35 33 6 12 66 244
245 15 37 31 10 15 82 245
246 12 29 35 8 11 69 246
247 14 32 35 9 13 68 247
248 10 36 32 9 14 44 248
249 12 19 21 8 10 56 249
250 12 21 20 9 12 53 250
251 11 31 34 7 15 70 251
252 10 33 32 7 13 78 252
253 12 36 34 6 13 71 253
254 16 33 32 9 13 72 254
255 12 37 33 10 12 68 255
256 14 34 33 11 12 67 256
257 16 35 37 12 9 75 257
258 14 31 32 8 9 62 258
259 13 37 34 11 15 67 259
260 4 35 30 3 10 83 260
261 15 27 30 11 14 64 261
262 11 34 38 12 15 68 262
263 11 40 36 7 7 62 263
264 14 29 32 9 14 72 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separated Software Happyness Beloning
4.576146 0.033033 0.042681 0.559971 0.092008 0.009306
t
-0.005012
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0622 -1.0532 0.2491 1.2464 4.8037
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.576146 1.546304 2.959 0.00337 **
Connected 0.033033 0.034375 0.961 0.33747
Separated 0.042681 0.035046 1.218 0.22439
Software 0.559971 0.053642 10.439 < 2e-16 ***
Happyness 0.092008 0.049842 1.846 0.06604 .
Beloning 0.009306 0.011603 0.802 0.42329
t -0.005012 0.001673 -2.995 0.00301 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.853 on 257 degrees of freedom
Multiple R-squared: 0.444, Adjusted R-squared: 0.431
F-statistic: 34.2 on 6 and 257 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,] 0.817357514 0.365284972 0.1826425
[2,] 0.698600690 0.602798620 0.3013993
[3,] 0.715751578 0.568496843 0.2842484
[4,] 0.681130342 0.637739316 0.3188697
[5,] 0.599385924 0.801228151 0.4006141
[6,] 0.546192801 0.907614399 0.4538072
[7,] 0.609691203 0.780617595 0.3903088
[8,] 0.533535566 0.932928868 0.4664644
[9,] 0.818309895 0.363380211 0.1816901
[10,] 0.784428324 0.431143352 0.2155717
[11,] 0.728178954 0.543642092 0.2718210
[12,] 0.659752340 0.680495320 0.3402477
[13,] 0.621058252 0.757883496 0.3789417
[14,] 0.581997164 0.836005671 0.4180028
[15,] 0.547833225 0.904333550 0.4521668
[16,] 0.484611542 0.969223084 0.5153885
[17,] 0.446345041 0.892690081 0.5536550
[18,] 0.387877295 0.775754590 0.6121227
[19,] 0.433137163 0.866274325 0.5668628
[20,] 0.375115954 0.750231907 0.6248840
[21,] 0.385922984 0.771845968 0.6140770
[22,] 0.340865565 0.681731129 0.6591344
[23,] 0.337562627 0.675125253 0.6624374
[24,] 0.320581324 0.641162648 0.6794187
[25,] 0.268916484 0.537832969 0.7310835
[26,] 0.259819594 0.519639187 0.7401804
[27,] 0.355197374 0.710394747 0.6448026
[28,] 0.442489272 0.884978544 0.5575107
[29,] 0.421021416 0.842042832 0.5789786
[30,] 0.472293200 0.944586400 0.5277068
[31,] 0.458895516 0.917791033 0.5411045
[32,] 0.426611634 0.853223267 0.5733884
[33,] 0.407099592 0.814199183 0.5929004
[34,] 0.443354078 0.886708156 0.5566459
[35,] 0.398608136 0.797216272 0.6013919
[36,] 0.362247478 0.724494956 0.6377525
[37,] 0.593538824 0.812922352 0.4064612
[38,] 0.604090994 0.791818013 0.3959090
[39,] 0.564941241 0.870117518 0.4350588
[40,] 0.533583934 0.932832131 0.4664161
[41,] 0.506548834 0.986902332 0.4934512
[42,] 0.463158484 0.926316969 0.5368415
[43,] 0.419660939 0.839321879 0.5803391
[44,] 0.443763976 0.887527952 0.5562360
[45,] 0.412338414 0.824676828 0.5876616
[46,] 0.404229625 0.808459250 0.5957704
[47,] 0.405116772 0.810233543 0.5948832
[48,] 0.363482521 0.726965041 0.6365175
[49,] 0.357038294 0.714076589 0.6429617
[50,] 0.320138437 0.640276874 0.6798616
[51,] 0.338714263 0.677428527 0.6612857
[52,] 0.308880785 0.617761569 0.6911192
[53,] 0.275728670 0.551457341 0.7242713
[54,] 0.242942328 0.485884656 0.7570577
[55,] 0.211692337 0.423384675 0.7883077
[56,] 0.188827455 0.377654910 0.8111725
[57,] 0.182121220 0.364242439 0.8178788
[58,] 0.180454518 0.360909036 0.8195455
[59,] 0.294883553 0.589767105 0.7051164
[60,] 0.412271358 0.824542717 0.5877286
[61,] 0.378460723 0.756921445 0.6215393
[62,] 0.453295920 0.906591840 0.5467041
[63,] 0.418820913 0.837641826 0.5811791
[64,] 0.409045442 0.818090885 0.5909546
[65,] 0.390557882 0.781115764 0.6094421
[66,] 0.355465114 0.710930229 0.6445349
[67,] 0.418846767 0.837693535 0.5811532
[68,] 0.381784244 0.763568489 0.6182158
[69,] 0.360001841 0.720003683 0.6399982
[70,] 0.370123429 0.740246857 0.6298766
[71,] 0.338305277 0.676610553 0.6616947
[72,] 0.307640791 0.615281582 0.6923592
[73,] 0.277099402 0.554198804 0.7229006
[74,] 0.251292416 0.502584833 0.7487076
[75,] 0.222478093 0.444956186 0.7775219
[76,] 0.219809805 0.439619610 0.7801902
[77,] 0.192252076 0.384504152 0.8077479
[78,] 0.170428678 0.340857357 0.8295713
[79,] 0.157363797 0.314727594 0.8426362
[80,] 0.135711058 0.271422116 0.8642889
[81,] 0.132102633 0.264205265 0.8678974
[82,] 0.113261764 0.226523528 0.8867382
[83,] 0.098166347 0.196332694 0.9018337
[84,] 0.084137839 0.168275678 0.9158622
[85,] 0.087637564 0.175275128 0.9123624
[86,] 0.078984365 0.157968731 0.9210156
[87,] 0.066032990 0.132065980 0.9339670
[88,] 0.071752018 0.143504036 0.9282480
[89,] 0.059803648 0.119607295 0.9401964
[90,] 0.049671121 0.099342243 0.9503289
[91,] 0.043904129 0.087808257 0.9560959
[92,] 0.038699382 0.077398763 0.9613006
[93,] 0.045793342 0.091586683 0.9542067
[94,] 0.038900311 0.077800621 0.9610997
[95,] 0.034305470 0.068610941 0.9656945
[96,] 0.041772800 0.083545601 0.9582272
[97,] 0.037002111 0.074004222 0.9629979
[98,] 0.030202743 0.060405487 0.9697973
[99,] 0.029127711 0.058255421 0.9708723
[100,] 0.023964445 0.047928891 0.9760356
[101,] 0.019741485 0.039482970 0.9802585
[102,] 0.015783332 0.031566664 0.9842167
[103,] 0.017075470 0.034150941 0.9829245
[104,] 0.014839854 0.029679709 0.9851601
[105,] 0.020202855 0.040405709 0.9797971
[106,] 0.019566614 0.039133228 0.9804334
[107,] 0.019131127 0.038262254 0.9808689
[108,] 0.016296132 0.032592263 0.9837039
[109,] 0.015737964 0.031475927 0.9842620
[110,] 0.012773246 0.025546492 0.9872268
[111,] 0.011507065 0.023014130 0.9884929
[112,] 0.009323013 0.018646026 0.9906770
[113,] 0.013780177 0.027560354 0.9862198
[114,] 0.011343937 0.022687874 0.9886561
[115,] 0.009519553 0.019039107 0.9904804
[116,] 0.007728002 0.015456004 0.9922720
[117,] 0.006159744 0.012319488 0.9938403
[118,] 0.005631849 0.011263698 0.9943682
[119,] 0.004722048 0.009444095 0.9952780
[120,] 0.006483269 0.012966538 0.9935167
[121,] 0.007240808 0.014481616 0.9927592
[122,] 0.015508202 0.031016404 0.9844918
[123,] 0.018979283 0.037958565 0.9810207
[124,] 0.020473252 0.040946503 0.9795267
[125,] 0.019300482 0.038600965 0.9806995
[126,] 0.015504374 0.031008748 0.9844956
[127,] 0.012892609 0.025785217 0.9871074
[128,] 0.010469453 0.020938905 0.9895305
[129,] 0.013155302 0.026310604 0.9868447
[130,] 0.011193149 0.022386298 0.9888069
[131,] 0.013095366 0.026190731 0.9869046
[132,] 0.021789876 0.043579751 0.9782101
[133,] 0.019725614 0.039451227 0.9802744
[134,] 0.016047851 0.032095702 0.9839521
[135,] 0.016896350 0.033792700 0.9831037
[136,] 0.028498861 0.056997721 0.9715011
[137,] 0.035020820 0.070041639 0.9649792
[138,] 0.036084027 0.072168055 0.9639160
[139,] 0.032717788 0.065435575 0.9672822
[140,] 0.026779003 0.053558006 0.9732210
[141,] 0.037109071 0.074218141 0.9628909
[142,] 0.032147125 0.064294250 0.9678529
[143,] 0.035085921 0.070171841 0.9649141
[144,] 0.071221334 0.142442669 0.9287787
[145,] 0.068080630 0.136161260 0.9319194
[146,] 0.079410618 0.158821235 0.9205894
[147,] 0.067940975 0.135881951 0.9320590
[148,] 0.060827802 0.121655604 0.9391722
[149,] 0.053186595 0.106373190 0.9468134
[150,] 0.052268367 0.104536735 0.9477316
[151,] 0.045400985 0.090801970 0.9545990
[152,] 0.037402492 0.074804983 0.9625975
[153,] 0.030688302 0.061376604 0.9693117
[154,] 0.025656418 0.051312836 0.9743436
[155,] 0.021833669 0.043667338 0.9781663
[156,] 0.019019556 0.038039111 0.9809804
[157,] 0.020383386 0.040766771 0.9796166
[158,] 0.016381550 0.032763101 0.9836184
[159,] 0.023216241 0.046432482 0.9767838
[160,] 0.023516854 0.047033709 0.9764831
[161,] 0.021601344 0.043202688 0.9783987
[162,] 0.019864524 0.039729049 0.9801355
[163,] 0.016170632 0.032341263 0.9838294
[164,] 0.015200491 0.030400981 0.9847995
[165,] 0.017329900 0.034659799 0.9826701
[166,] 0.022017179 0.044034358 0.9779828
[167,] 0.017925474 0.035850948 0.9820745
[168,] 0.014249228 0.028498456 0.9857508
[169,] 0.011931768 0.023863535 0.9880682
[170,] 0.009402377 0.018804754 0.9905976
[171,] 0.008280460 0.016560921 0.9917195
[172,] 0.006816002 0.013632005 0.9931840
[173,] 0.005246438 0.010492876 0.9947536
[174,] 0.005577391 0.011154783 0.9944226
[175,] 0.004249911 0.008499822 0.9957501
[176,] 0.102128622 0.204257245 0.8978714
[177,] 0.089097346 0.178194693 0.9109027
[178,] 0.099928609 0.199857218 0.9000714
[179,] 0.084714303 0.169428607 0.9152857
[180,] 0.076238513 0.152477026 0.9237615
[181,] 0.063741322 0.127482645 0.9362587
[182,] 0.057182370 0.114364740 0.9428176
[183,] 0.047487688 0.094975375 0.9525123
[184,] 0.048677852 0.097355704 0.9513221
[185,] 0.045976756 0.091953512 0.9540232
[186,] 0.039811775 0.079623550 0.9601882
[187,] 0.031854569 0.063709138 0.9681454
[188,] 0.042738169 0.085476339 0.9572618
[189,] 0.035090966 0.070181932 0.9649090
[190,] 0.031619558 0.063239116 0.9683804
[191,] 0.027119601 0.054239201 0.9728804
[192,] 0.024948560 0.049897119 0.9750514
[193,] 0.021055256 0.042110513 0.9789447
[194,] 0.023311046 0.046622092 0.9766890
[195,] 0.030875378 0.061750757 0.9691246
[196,] 0.032853927 0.065707853 0.9671461
[197,] 0.025895792 0.051791583 0.9741042
[198,] 0.023243910 0.046487820 0.9767561
[199,] 0.018653834 0.037307668 0.9813462
[200,] 0.021717166 0.043434331 0.9782828
[201,] 0.018090129 0.036180258 0.9819099
[202,] 0.022102127 0.044204253 0.9778979
[203,] 0.038480175 0.076960350 0.9615198
[204,] 0.030286137 0.060572275 0.9697139
[205,] 0.038088820 0.076177640 0.9619112
[206,] 0.032513825 0.065027650 0.9674862
[207,] 0.025472997 0.050945993 0.9745270
[208,] 0.027965738 0.055931475 0.9720343
[209,] 0.023726364 0.047452729 0.9762736
[210,] 0.021455147 0.042910294 0.9785449
[211,] 0.016116035 0.032232071 0.9838840
[212,] 0.013171291 0.026342583 0.9868287
[213,] 0.009720330 0.019440659 0.9902797
[214,] 0.007438018 0.014876035 0.9925620
[215,] 0.005632008 0.011264017 0.9943680
[216,] 0.003978610 0.007957221 0.9960214
[217,] 0.008456105 0.016912211 0.9915439
[218,] 0.006843529 0.013687058 0.9931565
[219,] 0.005175273 0.010350546 0.9948247
[220,] 0.003841531 0.007683062 0.9961585
[221,] 0.002729020 0.005458039 0.9972710
[222,] 0.002985125 0.005970249 0.9970149
[223,] 0.009587966 0.019175932 0.9904120
[224,] 0.038447987 0.076895975 0.9615520
[225,] 0.058091123 0.116182245 0.9419089
[226,] 0.043661373 0.087322747 0.9563386
[227,] 0.036118115 0.072236230 0.9638819
[228,] 0.262257620 0.524515240 0.7377424
[229,] 0.219856983 0.439713967 0.7801430
[230,] 0.180695047 0.361390094 0.8193050
[231,] 0.153592669 0.307185339 0.8464073
[232,] 0.146463512 0.292927025 0.8535365
[233,] 0.199696221 0.399392441 0.8003038
[234,] 0.173717489 0.347434977 0.8262825
[235,] 0.191558970 0.383117940 0.8084410
[236,] 0.168751350 0.337502700 0.8312487
[237,] 0.154784897 0.309569793 0.8452151
[238,] 0.110381748 0.220763497 0.8896183
[239,] 0.105897860 0.211795721 0.8941021
[240,] 0.084879255 0.169758511 0.9151207
[241,] 0.195491507 0.390983013 0.8045085
[242,] 0.164708673 0.329417345 0.8352913
[243,] 0.151793827 0.303587654 0.8482062
[244,] 0.103160469 0.206320937 0.8968395
[245,] 0.441637480 0.883274959 0.5583625
> postscript(file="/var/wessaorg/rcomp/tmp/1rwkx1351933113.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2l9ew1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zfx31351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/45z6j1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rbo91351933113.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 = 264
Frequency = 1
1 2 3 4 5 6
-3.048326795 0.191608234 1.928241555 2.924007764 -2.712383208 -1.921670935
7 8 9 10 11 12
3.346576308 -2.067844390 -2.002450322 0.502787317 1.001628926 -0.126654841
13 14 15 16 17 18
0.476709698 0.449540691 -1.035552749 -0.508976960 0.368330701 3.534251683
19 20 21 22 23 24
2.565559464 0.379040279 0.633575691 0.822456560 2.558720008 0.872463815
25 26 27 28 29 30
0.810997530 0.981870009 1.097083050 -1.874897002 0.245022370 -0.192663326
31 32 33 34 35 36
-0.704145407 -0.851829129 -0.780636060 0.010490413 -1.500667194 -2.913876561
37 38 39 40 41 42
-3.125844814 -1.743832434 1.467170582 1.621793324 1.372875894 -1.786949933
43 44 45 46 47 48
2.644114680 -0.107956959 -0.422858556 -4.426184912 -2.525016657 -0.114548607
49 50 51 52 53 54
0.445929523 -1.560283416 -0.981208765 -0.164321143 -3.094047948 -0.011180722
55 56 57 58 59 60
-2.213554445 1.541281354 0.089684885 0.582174164 0.017721233 1.762048744
61 62 63 64 65 66
0.394451696 0.395883824 -0.473495964 -0.704939931 0.701106290 1.075123205
67 68 69 70 71 72
1.610762866 3.678186609 -3.911891172 0.297462151 -3.403440995 -0.995516556
73 74 75 76 77 78
1.028559306 0.927743839 0.826795826 3.347742819 -0.486814780 1.370661754
79 80 81 82 83 84
-2.244560052 1.031922604 0.368282066 0.338991343 -0.661958362 0.126822249
85 86 87 88 89 90
1.832295000 -0.182732208 0.668778060 1.122875664 0.356233211 -1.968816112
91 92 93 94 95 96
0.226766349 0.162550914 -0.133005414 -2.003637317 1.258875957 0.163115243
97 98 99 100 101 102
2.377034708 0.204083404 -0.360615558 -0.977702349 1.270893452 2.503992954
103 104 105 106 107 108
0.901996030 0.915479179 -1.917534938 1.306998910 0.180723520 1.699830519
109 110 111 112 113 114
-0.510827186 0.642248862 0.007360036 2.003509716 -1.276759771 -2.641267548
115 116 117 118 119 120
1.860153313 -1.862355914 0.788505314 -1.658569305 0.383207952 -1.410308043
121 122 123 124 125 126
0.578898564 -2.812502358 -0.730746137 -0.806046791 -0.712991252 0.399898582
127 128 129 130 131 132
1.529896486 0.897101501 -2.704069581 2.153333965 -3.812252091 2.608461545
133 134 135 136 137 138
-2.226576429 -1.537064263 -0.177127994 0.687209076 0.459453365 -2.483657341
139 140 141 142 143 144
-0.942275866 -2.270295789 3.259902478 1.271790824 0.167743254 1.883519904
145 146 147 148 149 150
-3.273975572 2.487141821 -1.855206868 1.263750124 0.101530103 -2.927105457
151 152 153 154 155 156
-0.996832510 2.268067181 4.236004560 1.539320845 -2.614241737 0.552526607
157 158 159 160 161 162
1.284413076 1.047452389 1.594344459 -0.818742478 0.253140744 0.100192372
163 164 165 166 167 168
-0.414601989 0.507263706 1.273309959 -1.811784633 -0.312285327 -3.172426234
169 170 171 172 173 174
-1.784952093 1.577722134 1.411770769 0.655697329 -1.710929325 -2.394024397
175 176 177 178 179 180
-2.945138796 0.673399946 -0.260390537 -0.741405352 0.278500250 -1.248933017
181 182 183 184 185 186
1.095000137 -0.280007608 2.349378330 -0.027252073 -6.590754067 1.139476069
187 188 189 190 191 192
2.584291670 -0.354849345 -0.367330929 0.402919861 -0.297837261 0.717921593
193 194 195 196 197 198
2.223463075 1.912054791 -0.671546486 0.086907387 2.901931801 0.929532957
199 200 201 202 203 204
1.569869478 1.569256418 1.969974738 0.741078454 -2.306799853 -2.460932800
205 206 207 208 209 210
2.380117531 0.568209987 1.487154320 1.097944041 -2.573568633 1.091206863
211 212 213 214 215 216
-2.193555507 -3.719433859 0.788374677 3.004585080 1.496163433 0.929737223
217 218 219 220 221 222
2.412675172 0.116775911 1.054918920 -0.190150962 -1.472154668 0.133117216
223 224 225 226 227 228
0.775846170 -0.933203221 0.670423503 -3.594865239 0.321329095 1.295189905
229 230 231 232 233 234
-1.350035004 -0.743631741 1.869368179 -3.176656410 4.803698263 2.128063274
235 236 237 238 239 240
-0.629152779 -2.168874777 -7.062172584 -1.105974013 2.030846471 -1.477878145
241 242 243 244 245 246
-0.028766316 -1.402247449 1.197970503 2.003988266 1.363498261 0.070997239
247 248 249 250 251 252
1.242228247 -2.625523416 1.226877064 0.492433257 -0.744696158 -1.610817148
253 254 255 256 257 258
0.834843552 3.335097678 -1.265444436 0.288000953 1.730864178 2.442269602
259 260 261 262 263 264
-1.114768228 -5.082047362 1.516234327 -3.740634513 -0.256706107 1.425338774
> postscript(file="/var/wessaorg/rcomp/tmp/63f761351933113.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.048326795 NA
1 0.191608234 -3.048326795
2 1.928241555 0.191608234
3 2.924007764 1.928241555
4 -2.712383208 2.924007764
5 -1.921670935 -2.712383208
6 3.346576308 -1.921670935
7 -2.067844390 3.346576308
8 -2.002450322 -2.067844390
9 0.502787317 -2.002450322
10 1.001628926 0.502787317
11 -0.126654841 1.001628926
12 0.476709698 -0.126654841
13 0.449540691 0.476709698
14 -1.035552749 0.449540691
15 -0.508976960 -1.035552749
16 0.368330701 -0.508976960
17 3.534251683 0.368330701
18 2.565559464 3.534251683
19 0.379040279 2.565559464
20 0.633575691 0.379040279
21 0.822456560 0.633575691
22 2.558720008 0.822456560
23 0.872463815 2.558720008
24 0.810997530 0.872463815
25 0.981870009 0.810997530
26 1.097083050 0.981870009
27 -1.874897002 1.097083050
28 0.245022370 -1.874897002
29 -0.192663326 0.245022370
30 -0.704145407 -0.192663326
31 -0.851829129 -0.704145407
32 -0.780636060 -0.851829129
33 0.010490413 -0.780636060
34 -1.500667194 0.010490413
35 -2.913876561 -1.500667194
36 -3.125844814 -2.913876561
37 -1.743832434 -3.125844814
38 1.467170582 -1.743832434
39 1.621793324 1.467170582
40 1.372875894 1.621793324
41 -1.786949933 1.372875894
42 2.644114680 -1.786949933
43 -0.107956959 2.644114680
44 -0.422858556 -0.107956959
45 -4.426184912 -0.422858556
46 -2.525016657 -4.426184912
47 -0.114548607 -2.525016657
48 0.445929523 -0.114548607
49 -1.560283416 0.445929523
50 -0.981208765 -1.560283416
51 -0.164321143 -0.981208765
52 -3.094047948 -0.164321143
53 -0.011180722 -3.094047948
54 -2.213554445 -0.011180722
55 1.541281354 -2.213554445
56 0.089684885 1.541281354
57 0.582174164 0.089684885
58 0.017721233 0.582174164
59 1.762048744 0.017721233
60 0.394451696 1.762048744
61 0.395883824 0.394451696
62 -0.473495964 0.395883824
63 -0.704939931 -0.473495964
64 0.701106290 -0.704939931
65 1.075123205 0.701106290
66 1.610762866 1.075123205
67 3.678186609 1.610762866
68 -3.911891172 3.678186609
69 0.297462151 -3.911891172
70 -3.403440995 0.297462151
71 -0.995516556 -3.403440995
72 1.028559306 -0.995516556
73 0.927743839 1.028559306
74 0.826795826 0.927743839
75 3.347742819 0.826795826
76 -0.486814780 3.347742819
77 1.370661754 -0.486814780
78 -2.244560052 1.370661754
79 1.031922604 -2.244560052
80 0.368282066 1.031922604
81 0.338991343 0.368282066
82 -0.661958362 0.338991343
83 0.126822249 -0.661958362
84 1.832295000 0.126822249
85 -0.182732208 1.832295000
86 0.668778060 -0.182732208
87 1.122875664 0.668778060
88 0.356233211 1.122875664
89 -1.968816112 0.356233211
90 0.226766349 -1.968816112
91 0.162550914 0.226766349
92 -0.133005414 0.162550914
93 -2.003637317 -0.133005414
94 1.258875957 -2.003637317
95 0.163115243 1.258875957
96 2.377034708 0.163115243
97 0.204083404 2.377034708
98 -0.360615558 0.204083404
99 -0.977702349 -0.360615558
100 1.270893452 -0.977702349
101 2.503992954 1.270893452
102 0.901996030 2.503992954
103 0.915479179 0.901996030
104 -1.917534938 0.915479179
105 1.306998910 -1.917534938
106 0.180723520 1.306998910
107 1.699830519 0.180723520
108 -0.510827186 1.699830519
109 0.642248862 -0.510827186
110 0.007360036 0.642248862
111 2.003509716 0.007360036
112 -1.276759771 2.003509716
113 -2.641267548 -1.276759771
114 1.860153313 -2.641267548
115 -1.862355914 1.860153313
116 0.788505314 -1.862355914
117 -1.658569305 0.788505314
118 0.383207952 -1.658569305
119 -1.410308043 0.383207952
120 0.578898564 -1.410308043
121 -2.812502358 0.578898564
122 -0.730746137 -2.812502358
123 -0.806046791 -0.730746137
124 -0.712991252 -0.806046791
125 0.399898582 -0.712991252
126 1.529896486 0.399898582
127 0.897101501 1.529896486
128 -2.704069581 0.897101501
129 2.153333965 -2.704069581
130 -3.812252091 2.153333965
131 2.608461545 -3.812252091
132 -2.226576429 2.608461545
133 -1.537064263 -2.226576429
134 -0.177127994 -1.537064263
135 0.687209076 -0.177127994
136 0.459453365 0.687209076
137 -2.483657341 0.459453365
138 -0.942275866 -2.483657341
139 -2.270295789 -0.942275866
140 3.259902478 -2.270295789
141 1.271790824 3.259902478
142 0.167743254 1.271790824
143 1.883519904 0.167743254
144 -3.273975572 1.883519904
145 2.487141821 -3.273975572
146 -1.855206868 2.487141821
147 1.263750124 -1.855206868
148 0.101530103 1.263750124
149 -2.927105457 0.101530103
150 -0.996832510 -2.927105457
151 2.268067181 -0.996832510
152 4.236004560 2.268067181
153 1.539320845 4.236004560
154 -2.614241737 1.539320845
155 0.552526607 -2.614241737
156 1.284413076 0.552526607
157 1.047452389 1.284413076
158 1.594344459 1.047452389
159 -0.818742478 1.594344459
160 0.253140744 -0.818742478
161 0.100192372 0.253140744
162 -0.414601989 0.100192372
163 0.507263706 -0.414601989
164 1.273309959 0.507263706
165 -1.811784633 1.273309959
166 -0.312285327 -1.811784633
167 -3.172426234 -0.312285327
168 -1.784952093 -3.172426234
169 1.577722134 -1.784952093
170 1.411770769 1.577722134
171 0.655697329 1.411770769
172 -1.710929325 0.655697329
173 -2.394024397 -1.710929325
174 -2.945138796 -2.394024397
175 0.673399946 -2.945138796
176 -0.260390537 0.673399946
177 -0.741405352 -0.260390537
178 0.278500250 -0.741405352
179 -1.248933017 0.278500250
180 1.095000137 -1.248933017
181 -0.280007608 1.095000137
182 2.349378330 -0.280007608
183 -0.027252073 2.349378330
184 -6.590754067 -0.027252073
185 1.139476069 -6.590754067
186 2.584291670 1.139476069
187 -0.354849345 2.584291670
188 -0.367330929 -0.354849345
189 0.402919861 -0.367330929
190 -0.297837261 0.402919861
191 0.717921593 -0.297837261
192 2.223463075 0.717921593
193 1.912054791 2.223463075
194 -0.671546486 1.912054791
195 0.086907387 -0.671546486
196 2.901931801 0.086907387
197 0.929532957 2.901931801
198 1.569869478 0.929532957
199 1.569256418 1.569869478
200 1.969974738 1.569256418
201 0.741078454 1.969974738
202 -2.306799853 0.741078454
203 -2.460932800 -2.306799853
204 2.380117531 -2.460932800
205 0.568209987 2.380117531
206 1.487154320 0.568209987
207 1.097944041 1.487154320
208 -2.573568633 1.097944041
209 1.091206863 -2.573568633
210 -2.193555507 1.091206863
211 -3.719433859 -2.193555507
212 0.788374677 -3.719433859
213 3.004585080 0.788374677
214 1.496163433 3.004585080
215 0.929737223 1.496163433
216 2.412675172 0.929737223
217 0.116775911 2.412675172
218 1.054918920 0.116775911
219 -0.190150962 1.054918920
220 -1.472154668 -0.190150962
221 0.133117216 -1.472154668
222 0.775846170 0.133117216
223 -0.933203221 0.775846170
224 0.670423503 -0.933203221
225 -3.594865239 0.670423503
226 0.321329095 -3.594865239
227 1.295189905 0.321329095
228 -1.350035004 1.295189905
229 -0.743631741 -1.350035004
230 1.869368179 -0.743631741
231 -3.176656410 1.869368179
232 4.803698263 -3.176656410
233 2.128063274 4.803698263
234 -0.629152779 2.128063274
235 -2.168874777 -0.629152779
236 -7.062172584 -2.168874777
237 -1.105974013 -7.062172584
238 2.030846471 -1.105974013
239 -1.477878145 2.030846471
240 -0.028766316 -1.477878145
241 -1.402247449 -0.028766316
242 1.197970503 -1.402247449
243 2.003988266 1.197970503
244 1.363498261 2.003988266
245 0.070997239 1.363498261
246 1.242228247 0.070997239
247 -2.625523416 1.242228247
248 1.226877064 -2.625523416
249 0.492433257 1.226877064
250 -0.744696158 0.492433257
251 -1.610817148 -0.744696158
252 0.834843552 -1.610817148
253 3.335097678 0.834843552
254 -1.265444436 3.335097678
255 0.288000953 -1.265444436
256 1.730864178 0.288000953
257 2.442269602 1.730864178
258 -1.114768228 2.442269602
259 -5.082047362 -1.114768228
260 1.516234327 -5.082047362
261 -3.740634513 1.516234327
262 -0.256706107 -3.740634513
263 1.425338774 -0.256706107
264 NA 1.425338774
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.191608234 -3.048326795
[2,] 1.928241555 0.191608234
[3,] 2.924007764 1.928241555
[4,] -2.712383208 2.924007764
[5,] -1.921670935 -2.712383208
[6,] 3.346576308 -1.921670935
[7,] -2.067844390 3.346576308
[8,] -2.002450322 -2.067844390
[9,] 0.502787317 -2.002450322
[10,] 1.001628926 0.502787317
[11,] -0.126654841 1.001628926
[12,] 0.476709698 -0.126654841
[13,] 0.449540691 0.476709698
[14,] -1.035552749 0.449540691
[15,] -0.508976960 -1.035552749
[16,] 0.368330701 -0.508976960
[17,] 3.534251683 0.368330701
[18,] 2.565559464 3.534251683
[19,] 0.379040279 2.565559464
[20,] 0.633575691 0.379040279
[21,] 0.822456560 0.633575691
[22,] 2.558720008 0.822456560
[23,] 0.872463815 2.558720008
[24,] 0.810997530 0.872463815
[25,] 0.981870009 0.810997530
[26,] 1.097083050 0.981870009
[27,] -1.874897002 1.097083050
[28,] 0.245022370 -1.874897002
[29,] -0.192663326 0.245022370
[30,] -0.704145407 -0.192663326
[31,] -0.851829129 -0.704145407
[32,] -0.780636060 -0.851829129
[33,] 0.010490413 -0.780636060
[34,] -1.500667194 0.010490413
[35,] -2.913876561 -1.500667194
[36,] -3.125844814 -2.913876561
[37,] -1.743832434 -3.125844814
[38,] 1.467170582 -1.743832434
[39,] 1.621793324 1.467170582
[40,] 1.372875894 1.621793324
[41,] -1.786949933 1.372875894
[42,] 2.644114680 -1.786949933
[43,] -0.107956959 2.644114680
[44,] -0.422858556 -0.107956959
[45,] -4.426184912 -0.422858556
[46,] -2.525016657 -4.426184912
[47,] -0.114548607 -2.525016657
[48,] 0.445929523 -0.114548607
[49,] -1.560283416 0.445929523
[50,] -0.981208765 -1.560283416
[51,] -0.164321143 -0.981208765
[52,] -3.094047948 -0.164321143
[53,] -0.011180722 -3.094047948
[54,] -2.213554445 -0.011180722
[55,] 1.541281354 -2.213554445
[56,] 0.089684885 1.541281354
[57,] 0.582174164 0.089684885
[58,] 0.017721233 0.582174164
[59,] 1.762048744 0.017721233
[60,] 0.394451696 1.762048744
[61,] 0.395883824 0.394451696
[62,] -0.473495964 0.395883824
[63,] -0.704939931 -0.473495964
[64,] 0.701106290 -0.704939931
[65,] 1.075123205 0.701106290
[66,] 1.610762866 1.075123205
[67,] 3.678186609 1.610762866
[68,] -3.911891172 3.678186609
[69,] 0.297462151 -3.911891172
[70,] -3.403440995 0.297462151
[71,] -0.995516556 -3.403440995
[72,] 1.028559306 -0.995516556
[73,] 0.927743839 1.028559306
[74,] 0.826795826 0.927743839
[75,] 3.347742819 0.826795826
[76,] -0.486814780 3.347742819
[77,] 1.370661754 -0.486814780
[78,] -2.244560052 1.370661754
[79,] 1.031922604 -2.244560052
[80,] 0.368282066 1.031922604
[81,] 0.338991343 0.368282066
[82,] -0.661958362 0.338991343
[83,] 0.126822249 -0.661958362
[84,] 1.832295000 0.126822249
[85,] -0.182732208 1.832295000
[86,] 0.668778060 -0.182732208
[87,] 1.122875664 0.668778060
[88,] 0.356233211 1.122875664
[89,] -1.968816112 0.356233211
[90,] 0.226766349 -1.968816112
[91,] 0.162550914 0.226766349
[92,] -0.133005414 0.162550914
[93,] -2.003637317 -0.133005414
[94,] 1.258875957 -2.003637317
[95,] 0.163115243 1.258875957
[96,] 2.377034708 0.163115243
[97,] 0.204083404 2.377034708
[98,] -0.360615558 0.204083404
[99,] -0.977702349 -0.360615558
[100,] 1.270893452 -0.977702349
[101,] 2.503992954 1.270893452
[102,] 0.901996030 2.503992954
[103,] 0.915479179 0.901996030
[104,] -1.917534938 0.915479179
[105,] 1.306998910 -1.917534938
[106,] 0.180723520 1.306998910
[107,] 1.699830519 0.180723520
[108,] -0.510827186 1.699830519
[109,] 0.642248862 -0.510827186
[110,] 0.007360036 0.642248862
[111,] 2.003509716 0.007360036
[112,] -1.276759771 2.003509716
[113,] -2.641267548 -1.276759771
[114,] 1.860153313 -2.641267548
[115,] -1.862355914 1.860153313
[116,] 0.788505314 -1.862355914
[117,] -1.658569305 0.788505314
[118,] 0.383207952 -1.658569305
[119,] -1.410308043 0.383207952
[120,] 0.578898564 -1.410308043
[121,] -2.812502358 0.578898564
[122,] -0.730746137 -2.812502358
[123,] -0.806046791 -0.730746137
[124,] -0.712991252 -0.806046791
[125,] 0.399898582 -0.712991252
[126,] 1.529896486 0.399898582
[127,] 0.897101501 1.529896486
[128,] -2.704069581 0.897101501
[129,] 2.153333965 -2.704069581
[130,] -3.812252091 2.153333965
[131,] 2.608461545 -3.812252091
[132,] -2.226576429 2.608461545
[133,] -1.537064263 -2.226576429
[134,] -0.177127994 -1.537064263
[135,] 0.687209076 -0.177127994
[136,] 0.459453365 0.687209076
[137,] -2.483657341 0.459453365
[138,] -0.942275866 -2.483657341
[139,] -2.270295789 -0.942275866
[140,] 3.259902478 -2.270295789
[141,] 1.271790824 3.259902478
[142,] 0.167743254 1.271790824
[143,] 1.883519904 0.167743254
[144,] -3.273975572 1.883519904
[145,] 2.487141821 -3.273975572
[146,] -1.855206868 2.487141821
[147,] 1.263750124 -1.855206868
[148,] 0.101530103 1.263750124
[149,] -2.927105457 0.101530103
[150,] -0.996832510 -2.927105457
[151,] 2.268067181 -0.996832510
[152,] 4.236004560 2.268067181
[153,] 1.539320845 4.236004560
[154,] -2.614241737 1.539320845
[155,] 0.552526607 -2.614241737
[156,] 1.284413076 0.552526607
[157,] 1.047452389 1.284413076
[158,] 1.594344459 1.047452389
[159,] -0.818742478 1.594344459
[160,] 0.253140744 -0.818742478
[161,] 0.100192372 0.253140744
[162,] -0.414601989 0.100192372
[163,] 0.507263706 -0.414601989
[164,] 1.273309959 0.507263706
[165,] -1.811784633 1.273309959
[166,] -0.312285327 -1.811784633
[167,] -3.172426234 -0.312285327
[168,] -1.784952093 -3.172426234
[169,] 1.577722134 -1.784952093
[170,] 1.411770769 1.577722134
[171,] 0.655697329 1.411770769
[172,] -1.710929325 0.655697329
[173,] -2.394024397 -1.710929325
[174,] -2.945138796 -2.394024397
[175,] 0.673399946 -2.945138796
[176,] -0.260390537 0.673399946
[177,] -0.741405352 -0.260390537
[178,] 0.278500250 -0.741405352
[179,] -1.248933017 0.278500250
[180,] 1.095000137 -1.248933017
[181,] -0.280007608 1.095000137
[182,] 2.349378330 -0.280007608
[183,] -0.027252073 2.349378330
[184,] -6.590754067 -0.027252073
[185,] 1.139476069 -6.590754067
[186,] 2.584291670 1.139476069
[187,] -0.354849345 2.584291670
[188,] -0.367330929 -0.354849345
[189,] 0.402919861 -0.367330929
[190,] -0.297837261 0.402919861
[191,] 0.717921593 -0.297837261
[192,] 2.223463075 0.717921593
[193,] 1.912054791 2.223463075
[194,] -0.671546486 1.912054791
[195,] 0.086907387 -0.671546486
[196,] 2.901931801 0.086907387
[197,] 0.929532957 2.901931801
[198,] 1.569869478 0.929532957
[199,] 1.569256418 1.569869478
[200,] 1.969974738 1.569256418
[201,] 0.741078454 1.969974738
[202,] -2.306799853 0.741078454
[203,] -2.460932800 -2.306799853
[204,] 2.380117531 -2.460932800
[205,] 0.568209987 2.380117531
[206,] 1.487154320 0.568209987
[207,] 1.097944041 1.487154320
[208,] -2.573568633 1.097944041
[209,] 1.091206863 -2.573568633
[210,] -2.193555507 1.091206863
[211,] -3.719433859 -2.193555507
[212,] 0.788374677 -3.719433859
[213,] 3.004585080 0.788374677
[214,] 1.496163433 3.004585080
[215,] 0.929737223 1.496163433
[216,] 2.412675172 0.929737223
[217,] 0.116775911 2.412675172
[218,] 1.054918920 0.116775911
[219,] -0.190150962 1.054918920
[220,] -1.472154668 -0.190150962
[221,] 0.133117216 -1.472154668
[222,] 0.775846170 0.133117216
[223,] -0.933203221 0.775846170
[224,] 0.670423503 -0.933203221
[225,] -3.594865239 0.670423503
[226,] 0.321329095 -3.594865239
[227,] 1.295189905 0.321329095
[228,] -1.350035004 1.295189905
[229,] -0.743631741 -1.350035004
[230,] 1.869368179 -0.743631741
[231,] -3.176656410 1.869368179
[232,] 4.803698263 -3.176656410
[233,] 2.128063274 4.803698263
[234,] -0.629152779 2.128063274
[235,] -2.168874777 -0.629152779
[236,] -7.062172584 -2.168874777
[237,] -1.105974013 -7.062172584
[238,] 2.030846471 -1.105974013
[239,] -1.477878145 2.030846471
[240,] -0.028766316 -1.477878145
[241,] -1.402247449 -0.028766316
[242,] 1.197970503 -1.402247449
[243,] 2.003988266 1.197970503
[244,] 1.363498261 2.003988266
[245,] 0.070997239 1.363498261
[246,] 1.242228247 0.070997239
[247,] -2.625523416 1.242228247
[248,] 1.226877064 -2.625523416
[249,] 0.492433257 1.226877064
[250,] -0.744696158 0.492433257
[251,] -1.610817148 -0.744696158
[252,] 0.834843552 -1.610817148
[253,] 3.335097678 0.834843552
[254,] -1.265444436 3.335097678
[255,] 0.288000953 -1.265444436
[256,] 1.730864178 0.288000953
[257,] 2.442269602 1.730864178
[258,] -1.114768228 2.442269602
[259,] -5.082047362 -1.114768228
[260,] 1.516234327 -5.082047362
[261,] -3.740634513 1.516234327
[262,] -0.256706107 -3.740634513
[263,] 1.425338774 -0.256706107
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.191608234 -3.048326795
2 1.928241555 0.191608234
3 2.924007764 1.928241555
4 -2.712383208 2.924007764
5 -1.921670935 -2.712383208
6 3.346576308 -1.921670935
7 -2.067844390 3.346576308
8 -2.002450322 -2.067844390
9 0.502787317 -2.002450322
10 1.001628926 0.502787317
11 -0.126654841 1.001628926
12 0.476709698 -0.126654841
13 0.449540691 0.476709698
14 -1.035552749 0.449540691
15 -0.508976960 -1.035552749
16 0.368330701 -0.508976960
17 3.534251683 0.368330701
18 2.565559464 3.534251683
19 0.379040279 2.565559464
20 0.633575691 0.379040279
21 0.822456560 0.633575691
22 2.558720008 0.822456560
23 0.872463815 2.558720008
24 0.810997530 0.872463815
25 0.981870009 0.810997530
26 1.097083050 0.981870009
27 -1.874897002 1.097083050
28 0.245022370 -1.874897002
29 -0.192663326 0.245022370
30 -0.704145407 -0.192663326
31 -0.851829129 -0.704145407
32 -0.780636060 -0.851829129
33 0.010490413 -0.780636060
34 -1.500667194 0.010490413
35 -2.913876561 -1.500667194
36 -3.125844814 -2.913876561
37 -1.743832434 -3.125844814
38 1.467170582 -1.743832434
39 1.621793324 1.467170582
40 1.372875894 1.621793324
41 -1.786949933 1.372875894
42 2.644114680 -1.786949933
43 -0.107956959 2.644114680
44 -0.422858556 -0.107956959
45 -4.426184912 -0.422858556
46 -2.525016657 -4.426184912
47 -0.114548607 -2.525016657
48 0.445929523 -0.114548607
49 -1.560283416 0.445929523
50 -0.981208765 -1.560283416
51 -0.164321143 -0.981208765
52 -3.094047948 -0.164321143
53 -0.011180722 -3.094047948
54 -2.213554445 -0.011180722
55 1.541281354 -2.213554445
56 0.089684885 1.541281354
57 0.582174164 0.089684885
58 0.017721233 0.582174164
59 1.762048744 0.017721233
60 0.394451696 1.762048744
61 0.395883824 0.394451696
62 -0.473495964 0.395883824
63 -0.704939931 -0.473495964
64 0.701106290 -0.704939931
65 1.075123205 0.701106290
66 1.610762866 1.075123205
67 3.678186609 1.610762866
68 -3.911891172 3.678186609
69 0.297462151 -3.911891172
70 -3.403440995 0.297462151
71 -0.995516556 -3.403440995
72 1.028559306 -0.995516556
73 0.927743839 1.028559306
74 0.826795826 0.927743839
75 3.347742819 0.826795826
76 -0.486814780 3.347742819
77 1.370661754 -0.486814780
78 -2.244560052 1.370661754
79 1.031922604 -2.244560052
80 0.368282066 1.031922604
81 0.338991343 0.368282066
82 -0.661958362 0.338991343
83 0.126822249 -0.661958362
84 1.832295000 0.126822249
85 -0.182732208 1.832295000
86 0.668778060 -0.182732208
87 1.122875664 0.668778060
88 0.356233211 1.122875664
89 -1.968816112 0.356233211
90 0.226766349 -1.968816112
91 0.162550914 0.226766349
92 -0.133005414 0.162550914
93 -2.003637317 -0.133005414
94 1.258875957 -2.003637317
95 0.163115243 1.258875957
96 2.377034708 0.163115243
97 0.204083404 2.377034708
98 -0.360615558 0.204083404
99 -0.977702349 -0.360615558
100 1.270893452 -0.977702349
101 2.503992954 1.270893452
102 0.901996030 2.503992954
103 0.915479179 0.901996030
104 -1.917534938 0.915479179
105 1.306998910 -1.917534938
106 0.180723520 1.306998910
107 1.699830519 0.180723520
108 -0.510827186 1.699830519
109 0.642248862 -0.510827186
110 0.007360036 0.642248862
111 2.003509716 0.007360036
112 -1.276759771 2.003509716
113 -2.641267548 -1.276759771
114 1.860153313 -2.641267548
115 -1.862355914 1.860153313
116 0.788505314 -1.862355914
117 -1.658569305 0.788505314
118 0.383207952 -1.658569305
119 -1.410308043 0.383207952
120 0.578898564 -1.410308043
121 -2.812502358 0.578898564
122 -0.730746137 -2.812502358
123 -0.806046791 -0.730746137
124 -0.712991252 -0.806046791
125 0.399898582 -0.712991252
126 1.529896486 0.399898582
127 0.897101501 1.529896486
128 -2.704069581 0.897101501
129 2.153333965 -2.704069581
130 -3.812252091 2.153333965
131 2.608461545 -3.812252091
132 -2.226576429 2.608461545
133 -1.537064263 -2.226576429
134 -0.177127994 -1.537064263
135 0.687209076 -0.177127994
136 0.459453365 0.687209076
137 -2.483657341 0.459453365
138 -0.942275866 -2.483657341
139 -2.270295789 -0.942275866
140 3.259902478 -2.270295789
141 1.271790824 3.259902478
142 0.167743254 1.271790824
143 1.883519904 0.167743254
144 -3.273975572 1.883519904
145 2.487141821 -3.273975572
146 -1.855206868 2.487141821
147 1.263750124 -1.855206868
148 0.101530103 1.263750124
149 -2.927105457 0.101530103
150 -0.996832510 -2.927105457
151 2.268067181 -0.996832510
152 4.236004560 2.268067181
153 1.539320845 4.236004560
154 -2.614241737 1.539320845
155 0.552526607 -2.614241737
156 1.284413076 0.552526607
157 1.047452389 1.284413076
158 1.594344459 1.047452389
159 -0.818742478 1.594344459
160 0.253140744 -0.818742478
161 0.100192372 0.253140744
162 -0.414601989 0.100192372
163 0.507263706 -0.414601989
164 1.273309959 0.507263706
165 -1.811784633 1.273309959
166 -0.312285327 -1.811784633
167 -3.172426234 -0.312285327
168 -1.784952093 -3.172426234
169 1.577722134 -1.784952093
170 1.411770769 1.577722134
171 0.655697329 1.411770769
172 -1.710929325 0.655697329
173 -2.394024397 -1.710929325
174 -2.945138796 -2.394024397
175 0.673399946 -2.945138796
176 -0.260390537 0.673399946
177 -0.741405352 -0.260390537
178 0.278500250 -0.741405352
179 -1.248933017 0.278500250
180 1.095000137 -1.248933017
181 -0.280007608 1.095000137
182 2.349378330 -0.280007608
183 -0.027252073 2.349378330
184 -6.590754067 -0.027252073
185 1.139476069 -6.590754067
186 2.584291670 1.139476069
187 -0.354849345 2.584291670
188 -0.367330929 -0.354849345
189 0.402919861 -0.367330929
190 -0.297837261 0.402919861
191 0.717921593 -0.297837261
192 2.223463075 0.717921593
193 1.912054791 2.223463075
194 -0.671546486 1.912054791
195 0.086907387 -0.671546486
196 2.901931801 0.086907387
197 0.929532957 2.901931801
198 1.569869478 0.929532957
199 1.569256418 1.569869478
200 1.969974738 1.569256418
201 0.741078454 1.969974738
202 -2.306799853 0.741078454
203 -2.460932800 -2.306799853
204 2.380117531 -2.460932800
205 0.568209987 2.380117531
206 1.487154320 0.568209987
207 1.097944041 1.487154320
208 -2.573568633 1.097944041
209 1.091206863 -2.573568633
210 -2.193555507 1.091206863
211 -3.719433859 -2.193555507
212 0.788374677 -3.719433859
213 3.004585080 0.788374677
214 1.496163433 3.004585080
215 0.929737223 1.496163433
216 2.412675172 0.929737223
217 0.116775911 2.412675172
218 1.054918920 0.116775911
219 -0.190150962 1.054918920
220 -1.472154668 -0.190150962
221 0.133117216 -1.472154668
222 0.775846170 0.133117216
223 -0.933203221 0.775846170
224 0.670423503 -0.933203221
225 -3.594865239 0.670423503
226 0.321329095 -3.594865239
227 1.295189905 0.321329095
228 -1.350035004 1.295189905
229 -0.743631741 -1.350035004
230 1.869368179 -0.743631741
231 -3.176656410 1.869368179
232 4.803698263 -3.176656410
233 2.128063274 4.803698263
234 -0.629152779 2.128063274
235 -2.168874777 -0.629152779
236 -7.062172584 -2.168874777
237 -1.105974013 -7.062172584
238 2.030846471 -1.105974013
239 -1.477878145 2.030846471
240 -0.028766316 -1.477878145
241 -1.402247449 -0.028766316
242 1.197970503 -1.402247449
243 2.003988266 1.197970503
244 1.363498261 2.003988266
245 0.070997239 1.363498261
246 1.242228247 0.070997239
247 -2.625523416 1.242228247
248 1.226877064 -2.625523416
249 0.492433257 1.226877064
250 -0.744696158 0.492433257
251 -1.610817148 -0.744696158
252 0.834843552 -1.610817148
253 3.335097678 0.834843552
254 -1.265444436 3.335097678
255 0.288000953 -1.265444436
256 1.730864178 0.288000953
257 2.442269602 1.730864178
258 -1.114768228 2.442269602
259 -5.082047362 -1.114768228
260 1.516234327 -5.082047362
261 -3.740634513 1.516234327
262 -0.256706107 -3.740634513
263 1.425338774 -0.256706107
> 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/7jy0c1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/82dfp1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9rgxl1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/100xwv1351933113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qs9g1351933113.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/12s3c11351933114.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/13hdzj1351933114.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/14eqtc1351933114.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/15tbxb1351933114.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/16ojj11351933114.tab")
+ }
>
> try(system("convert tmp/1rwkx1351933113.ps tmp/1rwkx1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l9ew1351933113.ps tmp/2l9ew1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zfx31351933113.ps tmp/3zfx31351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/45z6j1351933113.ps tmp/45z6j1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rbo91351933113.ps tmp/5rbo91351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/63f761351933113.ps tmp/63f761351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jy0c1351933113.ps tmp/7jy0c1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/82dfp1351933113.ps tmp/82dfp1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rgxl1351933113.ps tmp/9rgxl1351933113.png",intern=TRUE))
character(0)
> try(system("convert tmp/100xwv1351933113.ps tmp/100xwv1351933113.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.302 0.822 11.117