R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
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> x <- array(list(41
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+ ,dim=c(5
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:264))
> y <- array(NA,dim=c(5,264),dimnames=list(c('Connected','Separate','Software','Happiness','Depression'),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 = '1'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
Connected Separate Software Happiness Depression t
1 41 38 12 14 12.0 1
2 39 32 11 18 11.0 2
3 30 35 15 11 14.0 3
4 31 33 6 12 12.0 4
5 34 37 13 16 21.0 5
6 35 29 10 18 12.0 6
7 39 31 12 14 22.0 7
8 34 36 14 14 11.0 8
9 36 35 12 15 10.0 9
10 37 38 9 15 13.0 10
11 38 31 10 17 10.0 11
12 36 34 12 19 8.0 12
13 38 35 12 10 15.0 13
14 39 38 11 16 14.0 14
15 33 37 15 18 10.0 15
16 32 33 12 14 14.0 16
17 36 32 10 14 14.0 17
18 38 38 12 17 11.0 18
19 39 38 11 14 10.0 19
20 32 32 12 16 13.0 20
21 32 33 11 18 9.5 21
22 31 31 12 11 14.0 22
23 39 38 13 14 12.0 23
24 37 39 11 12 14.0 24
25 39 32 12 17 11.0 25
26 41 32 13 9 9.0 26
27 36 35 10 16 11.0 27
28 33 37 14 14 15.0 28
29 33 33 12 15 14.0 29
30 34 33 10 11 13.0 30
31 31 31 12 16 9.0 31
32 27 32 8 13 15.0 32
33 37 31 10 17 10.0 33
34 34 37 12 15 11.0 34
35 34 30 12 14 13.0 35
36 32 33 7 16 8.0 36
37 29 31 9 9 20.0 37
38 36 33 12 15 12.0 38
39 29 31 10 17 10.0 39
40 35 33 10 13 10.0 40
41 37 32 10 15 9.0 41
42 34 33 12 16 14.0 42
43 38 32 15 16 8.0 43
44 35 33 10 12 14.0 44
45 38 28 10 15 11.0 45
46 37 35 12 11 13.0 46
47 38 39 13 15 9.0 47
48 33 34 11 15 11.0 48
49 36 38 11 17 15.0 49
50 38 32 12 13 11.0 50
51 32 38 14 16 10.0 51
52 32 30 10 14 14.0 52
53 32 33 12 11 18.0 53
54 34 38 13 12 14.0 54
55 32 32 5 12 11.0 55
56 37 35 6 15 14.5 56
57 39 34 12 16 13.0 57
58 29 34 12 15 9.0 58
59 37 36 11 12 10.0 59
60 35 34 10 12 15.0 60
61 30 28 7 8 20.0 61
62 38 34 12 13 12.0 62
63 34 35 14 11 12.0 63
64 31 35 11 14 14.0 64
65 34 31 12 15 13.0 65
66 35 37 13 10 11.0 66
67 36 35 14 11 17.0 67
68 30 27 11 12 12.0 68
69 39 40 12 15 13.0 69
70 35 37 12 15 14.0 70
71 38 36 8 14 13.0 71
72 31 38 11 16 15.0 72
73 34 39 14 15 13.0 73
74 38 41 14 15 10.0 74
75 34 27 12 13 11.0 75
76 39 30 9 12 19.0 76
77 37 37 13 17 13.0 77
78 34 31 11 13 17.0 78
79 28 31 12 15 13.0 79
80 37 27 12 13 9.0 80
81 33 36 12 15 11.0 81
82 35 37 12 15 9.0 82
83 37 33 12 16 12.0 83
84 32 34 11 15 12.0 84
85 33 31 10 14 13.0 85
86 38 39 9 15 13.0 86
87 33 34 12 14 12.0 87
88 29 32 12 13 15.0 88
89 33 33 12 7 22.0 89
90 31 36 9 17 13.0 90
91 36 32 15 13 15.0 91
92 35 41 12 15 13.0 92
93 32 28 12 14 15.0 93
94 29 30 12 13 12.5 94
95 39 36 10 16 11.0 95
96 37 35 13 12 16.0 96
97 35 31 9 14 11.0 97
98 37 34 12 17 11.0 98
99 32 36 10 15 10.0 99
100 38 36 14 17 10.0 100
101 37 35 11 12 16.0 101
102 36 37 15 16 12.0 102
103 32 28 11 11 11.0 103
104 33 39 11 15 16.0 104
105 40 32 12 9 19.0 105
106 38 35 12 16 11.0 106
107 41 39 12 15 16.0 107
108 36 35 11 10 15.0 108
109 43 42 7 10 24.0 109
110 30 34 12 15 14.0 110
111 31 33 14 11 15.0 111
112 32 41 11 13 11.0 112
113 32 33 11 14 15.0 113
114 37 34 10 18 12.0 114
115 37 32 13 16 10.0 115
116 33 40 13 14 14.0 116
117 34 40 8 14 13.0 117
118 33 35 11 14 9.0 118
119 38 36 12 14 15.0 119
120 33 37 11 12 15.0 120
121 31 27 13 14 14.0 121
122 38 39 12 15 11.0 122
123 37 38 14 15 8.0 123
124 36 31 13 15 11.0 124
125 31 33 15 13 11.0 125
126 39 32 10 17 8.0 126
127 44 39 11 17 10.0 127
128 33 36 9 19 11.0 128
129 35 33 11 15 13.0 129
130 32 33 10 13 11.0 130
131 28 32 11 9 20.0 131
132 40 37 8 15 10.0 132
133 27 30 11 15 15.0 133
134 37 38 12 15 12.0 134
135 32 29 12 16 14.0 135
136 28 22 9 11 23.0 136
137 34 35 11 14 14.0 137
138 30 35 10 11 16.0 138
139 35 34 8 15 11.0 139
140 31 35 9 13 12.0 140
141 32 34 8 15 10.0 141
142 30 37 9 16 14.0 142
143 30 35 15 14 12.0 143
144 31 23 11 15 12.0 144
145 40 31 8 16 11.0 145
146 32 27 13 16 12.0 146
147 36 36 12 11 13.0 147
148 32 31 12 12 11.0 148
149 35 32 9 9 19.0 149
150 38 39 7 16 12.0 150
151 42 37 13 13 17.0 151
152 34 38 9 16 9.0 152
153 35 39 6 12 12.0 153
154 38 34 8 9 19.0 154
155 33 31 8 13 18.0 155
156 36 32 15 13 15.0 156
157 32 37 6 14 14.0 157
158 33 36 9 19 11.0 158
159 34 32 11 13 9.0 159
160 32 38 8 12 18.0 160
161 34 36 8 13 16.0 161
162 27 26 10 10 24.0 162
163 31 26 8 14 14.0 163
164 38 33 14 16 20.0 164
165 34 39 10 10 18.0 165
166 24 30 8 11 23.0 166
167 30 33 11 14 12.0 167
168 26 25 12 12 14.0 168
169 34 38 12 9 16.0 169
170 27 37 12 9 18.0 170
171 37 31 5 11 20.0 171
172 36 37 12 16 12.0 172
173 41 35 10 9 12.0 173
174 29 25 7 13 17.0 174
175 36 28 12 16 13.0 175
176 32 35 11 13 9.0 176
177 37 33 8 9 16.0 177
178 30 30 9 12 18.0 178
179 31 31 10 16 10.0 179
180 38 37 9 11 14.0 180
181 36 36 12 14 11.0 181
182 35 30 6 13 9.0 182
183 31 36 15 15 11.0 183
184 38 32 12 14 10.0 184
185 22 28 12 16 11.0 185
186 32 36 12 13 19.0 186
187 36 34 11 14 14.0 187
188 39 31 7 15 12.0 188
189 28 28 7 13 14.0 189
190 32 36 5 11 21.0 190
191 32 36 12 11 13.0 191
192 38 40 12 14 10.0 192
193 32 33 3 15 15.0 193
194 35 37 11 11 16.0 194
195 32 32 10 15 14.0 195
196 37 38 12 12 12.0 196
197 34 31 9 14 19.0 197
198 33 37 12 14 15.0 198
199 33 33 9 8 19.0 199
200 26 32 12 13 13.0 200
201 30 30 12 9 17.0 201
202 24 30 10 15 12.0 202
203 34 31 9 17 11.0 203
204 34 32 12 13 14.0 204
205 33 34 8 15 11.0 205
206 34 36 11 15 13.0 206
207 35 37 11 14 12.0 207
208 35 36 12 16 15.0 208
209 36 33 10 13 14.0 209
210 34 33 10 16 12.0 210
211 34 33 12 9 17.0 211
212 41 44 12 16 11.0 212
213 32 39 11 11 18.0 213
214 30 32 8 10 13.0 214
215 35 35 12 11 17.0 215
216 28 25 10 15 13.0 216
217 33 35 11 17 11.0 217
218 39 34 10 14 12.0 218
219 36 35 8 8 22.0 219
220 36 39 12 15 14.0 220
221 35 33 12 11 12.0 221
222 38 36 10 16 12.0 222
223 33 32 12 10 17.0 223
224 31 32 9 15 9.0 224
225 34 36 9 9 21.0 225
226 32 36 6 16 10.0 226
227 31 32 10 19 11.0 227
228 33 34 9 12 12.0 228
229 34 33 9 8 23.0 229
230 34 35 9 11 13.0 230
231 34 30 6 14 12.0 231
232 33 38 10 9 16.0 232
233 32 34 6 15 9.0 233
234 41 33 14 13 17.0 234
235 34 32 10 16 9.0 235
236 36 31 10 11 14.0 236
237 37 30 6 12 17.0 237
238 36 27 12 13 13.0 238
239 29 31 12 10 11.0 239
240 37 30 7 11 12.0 240
241 27 32 8 12 10.0 241
242 35 35 11 8 19.0 242
243 28 28 3 12 16.0 243
244 35 33 6 12 16.0 244
245 37 31 10 15 14.0 245
246 29 35 8 11 20.0 246
247 32 35 9 13 15.0 247
248 36 32 9 14 23.0 248
249 19 21 8 10 20.0 249
250 21 20 9 12 16.0 250
251 31 34 7 15 14.0 251
252 33 32 7 13 17.0 252
253 36 34 6 13 11.0 253
254 33 32 9 13 13.0 254
255 37 33 10 12 17.0 255
256 34 33 11 12 15.0 256
257 35 37 12 9 21.0 257
258 31 32 8 9 18.0 258
259 37 34 11 15 15.0 259
260 35 30 3 10 8.0 260
261 27 30 11 14 12.0 261
262 34 38 12 15 12.0 262
263 40 36 7 7 22.0 263
264 29 32 9 14 12.0 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Software Happiness Depression t
20.434668 0.442650 -0.008831 0.030825 -0.058087 -0.006321
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.4077 -2.4103 0.1604 2.2912 8.1474
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.434668 3.049091 6.702 1.29e-10 ***
Separate 0.442650 0.057273 7.729 2.42e-13 ***
Software -0.008831 0.097104 -0.091 0.9276
Happiness 0.030825 0.103867 0.297 0.7669
Depression -0.058087 0.073974 -0.785 0.4330
t -0.006321 0.003000 -2.107 0.0361 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.361 on 258 degrees of freedom
Multiple R-squared: 0.2311, Adjusted R-squared: 0.2162
F-statistic: 15.51 on 5 and 258 DF, p-value: 2.426e-13
> 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,] 0.91244374 0.17511252 0.08755626
[2,] 0.84381931 0.31236137 0.15618069
[3,] 0.77854162 0.44291676 0.22145838
[4,] 0.71438707 0.57122587 0.28561293
[5,] 0.77029277 0.45941446 0.22970723
[6,] 0.68902356 0.62195289 0.31097644
[7,] 0.71596436 0.56807128 0.28403564
[8,] 0.69695066 0.60609867 0.30304934
[9,] 0.62094539 0.75810922 0.37905461
[10,] 0.54645095 0.90709811 0.45354905
[11,] 0.51328836 0.97342328 0.48671164
[12,] 0.50432381 0.99135238 0.49567619
[13,] 0.50381410 0.99237181 0.49618590
[14,] 0.43420129 0.86840259 0.56579871
[15,] 0.43896777 0.87793554 0.56103223
[16,] 0.36905441 0.73810882 0.63094559
[17,] 0.42053138 0.84106276 0.57946862
[18,] 0.65071322 0.69857355 0.34928678
[19,] 0.59466004 0.81067993 0.40533996
[20,] 0.58640650 0.82718699 0.41359350
[21,] 0.54827855 0.90344290 0.45172145
[22,] 0.49572988 0.99145976 0.50427012
[23,] 0.49548598 0.99097196 0.50451402
[24,] 0.65225753 0.69548493 0.34774247
[25,] 0.65187750 0.69624500 0.34812250
[26,] 0.60686402 0.78627196 0.39313598
[27,] 0.55916069 0.88167862 0.44083931
[28,] 0.52796092 0.94407816 0.47203908
[29,] 0.49700462 0.99400923 0.50299538
[30,] 0.47082421 0.94164842 0.52917579
[31,] 0.49428115 0.98856230 0.50571885
[32,] 0.45473967 0.90947934 0.54526033
[33,] 0.46023940 0.92047880 0.53976060
[34,] 0.41312566 0.82625132 0.58687434
[35,] 0.40999948 0.81999896 0.59000052
[36,] 0.37909363 0.75818726 0.62090637
[37,] 0.45686601 0.91373202 0.54313399
[38,] 0.43337785 0.86675571 0.56662215
[39,] 0.39105445 0.78210890 0.60894555
[40,] 0.35655784 0.71311568 0.64344216
[41,] 0.31789691 0.63579381 0.68210309
[42,] 0.32550189 0.65100379 0.67449811
[43,] 0.36155684 0.72311367 0.63844316
[44,] 0.32072834 0.64145667 0.67927166
[45,] 0.28395261 0.56790522 0.71604739
[46,] 0.25067714 0.50135429 0.74932286
[47,] 0.21775863 0.43551726 0.78224137
[48,] 0.22998252 0.45996504 0.77001748
[49,] 0.26310330 0.52620660 0.73689670
[50,] 0.34898539 0.69797078 0.65101461
[51,] 0.32388366 0.64776732 0.67611634
[52,] 0.29109120 0.58218241 0.70890880
[53,] 0.25715775 0.51431550 0.74284225
[54,] 0.26310977 0.52621954 0.73689023
[55,] 0.23112184 0.46224367 0.76887816
[56,] 0.23107358 0.46214715 0.76892642
[57,] 0.20083419 0.40166838 0.79916581
[58,] 0.17253212 0.34506424 0.82746788
[59,] 0.15599969 0.31199938 0.84400031
[60,] 0.14160072 0.28320144 0.85839928
[61,] 0.13576460 0.27152919 0.86423540
[62,] 0.11441544 0.22883088 0.88558456
[63,] 0.11471431 0.22942863 0.88528569
[64,] 0.13286964 0.26573928 0.86713036
[65,] 0.11957805 0.23915611 0.88042195
[66,] 0.10225487 0.20450975 0.89774513
[67,] 0.09106249 0.18212497 0.90893751
[68,] 0.16338204 0.32676408 0.83661796
[69,] 0.14372113 0.28744225 0.85627887
[70,] 0.12371573 0.24743146 0.87628427
[71,] 0.16385234 0.32770468 0.83614766
[72,] 0.18854280 0.37708560 0.81145720
[73,] 0.17382702 0.34765404 0.82617298
[74,] 0.15050419 0.30100838 0.84949581
[75,] 0.14435723 0.28871445 0.85564277
[76,] 0.13390825 0.26781651 0.86609175
[77,] 0.11409257 0.22818513 0.88590743
[78,] 0.10571148 0.21142296 0.89428852
[79,] 0.09174959 0.18349918 0.90825041
[80,] 0.10352685 0.20705369 0.89647315
[81,] 0.08765618 0.17531236 0.91234382
[82,] 0.09196626 0.18393253 0.90803374
[83,] 0.08623294 0.17246588 0.91376706
[84,] 0.07572626 0.15145251 0.92427374
[85,] 0.06320253 0.12640507 0.93679747
[86,] 0.06693748 0.13387496 0.93306252
[87,] 0.07552207 0.15104413 0.92447793
[88,] 0.07264649 0.14529297 0.92735351
[89,] 0.06418997 0.12837995 0.93581003
[90,] 0.05950204 0.11900407 0.94049796
[91,] 0.05791857 0.11583715 0.94208143
[92,] 0.05414575 0.10829150 0.94585425
[93,] 0.05194283 0.10388567 0.94805717
[94,] 0.04284650 0.08569301 0.95715350
[95,] 0.03543731 0.07087463 0.96456269
[96,] 0.03325794 0.06651589 0.96674206
[97,] 0.06817473 0.13634946 0.93182527
[98,] 0.06719361 0.13438722 0.93280639
[99,] 0.08475278 0.16950557 0.91524722
[100,] 0.07462197 0.14924395 0.92537803
[101,] 0.11667217 0.23334435 0.88332783
[102,] 0.13088948 0.26177896 0.86911052
[103,] 0.12556193 0.25112385 0.87443807
[104,] 0.15020205 0.30040409 0.84979795
[105,] 0.13556958 0.27113917 0.86443042
[106,] 0.12781587 0.25563173 0.87218413
[107,] 0.12802927 0.25605854 0.87197073
[108,] 0.13021133 0.26042265 0.86978867
[109,] 0.12197705 0.24395409 0.87802295
[110,] 0.10863947 0.21727893 0.89136053
[111,] 0.10609840 0.21219679 0.89390160
[112,] 0.09679080 0.19358160 0.90320920
[113,] 0.08407049 0.16814098 0.91592951
[114,] 0.07495183 0.14990365 0.92504817
[115,] 0.06425966 0.12851932 0.93574034
[116,] 0.06165844 0.12331687 0.93834156
[117,] 0.05864263 0.11728525 0.94135737
[118,] 0.07648483 0.15296965 0.92351517
[119,] 0.14296103 0.28592207 0.85703897
[120,] 0.13406934 0.26813868 0.86593066
[121,] 0.11859602 0.23719205 0.88140398
[122,] 0.10646268 0.21292536 0.89353732
[123,] 0.12268717 0.24537435 0.87731283
[124,] 0.13731594 0.27463187 0.86268406
[125,] 0.17046874 0.34093747 0.82953126
[126,] 0.15145231 0.30290461 0.84854769
[127,] 0.13257230 0.26514459 0.86742770
[128,] 0.11571508 0.23143017 0.88428492
[129,] 0.09941371 0.19882742 0.90058629
[130,] 0.10619360 0.21238721 0.89380640
[131,] 0.09185169 0.18370338 0.90814831
[132,] 0.09165472 0.18330944 0.90834528
[133,] 0.08312792 0.16625584 0.91687208
[134,] 0.10406874 0.20813748 0.89593126
[135,] 0.11585904 0.23171809 0.88414096
[136,] 0.10532581 0.21065163 0.89467419
[137,] 0.17674746 0.35349492 0.82325254
[138,] 0.15923449 0.31846897 0.84076551
[139,] 0.14239962 0.28479925 0.85760038
[140,] 0.12372547 0.24745094 0.87627453
[141,] 0.11693024 0.23386048 0.88306976
[142,] 0.10452912 0.20905823 0.89547088
[143,] 0.17318124 0.34636248 0.82681876
[144,] 0.15760892 0.31521784 0.84239108
[145,] 0.13918945 0.27837890 0.86081055
[146,] 0.15903457 0.31806913 0.84096543
[147,] 0.14071927 0.28143854 0.85928073
[148,] 0.14332711 0.28665422 0.85667289
[149,] 0.14057135 0.28114269 0.85942865
[150,] 0.12796600 0.25593200 0.87203400
[151,] 0.11265895 0.22531791 0.88734105
[152,] 0.11266917 0.22533834 0.88733083
[153,] 0.09675349 0.19350697 0.90324651
[154,] 0.08890313 0.17780626 0.91109687
[155,] 0.07823794 0.15647589 0.92176206
[156,] 0.10147735 0.20295470 0.89852265
[157,] 0.09016883 0.18033766 0.90983117
[158,] 0.15220726 0.30441451 0.84779274
[159,] 0.14980681 0.29961363 0.85019319
[160,] 0.14665004 0.29330007 0.85334996
[161,] 0.13053369 0.26106738 0.86946631
[162,] 0.24445678 0.48891357 0.75554322
[163,] 0.27393067 0.54786133 0.72606933
[164,] 0.24501062 0.49002124 0.75498938
[165,] 0.33916423 0.67832845 0.66083577
[166,] 0.30734151 0.61468301 0.69265849
[167,] 0.37611759 0.75223519 0.62388241
[168,] 0.35645846 0.71291692 0.64354154
[169,] 0.37232357 0.74464714 0.62767643
[170,] 0.34107208 0.68214416 0.65892792
[171,] 0.31152238 0.62304476 0.68847762
[172,] 0.30261827 0.60523654 0.69738173
[173,] 0.27587816 0.55175632 0.72412184
[174,] 0.28279893 0.56559787 0.71720107
[175,] 0.28342648 0.56685296 0.71657352
[176,] 0.35648732 0.71297464 0.64351268
[177,] 0.53776026 0.92447948 0.46223974
[178,] 0.52070967 0.95858065 0.47929033
[179,] 0.50432049 0.99135903 0.49567951
[180,] 0.66068372 0.67863257 0.33931628
[181,] 0.63596496 0.72807009 0.36403504
[182,] 0.61536600 0.76926799 0.38463400
[183,] 0.59429755 0.81140490 0.40570245
[184,] 0.56070523 0.87858953 0.43929477
[185,] 0.52291735 0.95416531 0.47708265
[186,] 0.48284978 0.96569956 0.51715022
[187,] 0.44250938 0.88501877 0.55749062
[188,] 0.41019762 0.82039524 0.58980238
[189,] 0.38879349 0.77758698 0.61120651
[190,] 0.36527801 0.73055602 0.63472199
[191,] 0.32739495 0.65478991 0.67260505
[192,] 0.42515363 0.85030726 0.57484637
[193,] 0.38960521 0.77921042 0.61039479
[194,] 0.56144783 0.87710434 0.43855217
[195,] 0.52957360 0.94085280 0.47042640
[196,] 0.49165361 0.98330721 0.50834639
[197,] 0.45047164 0.90094329 0.54952836
[198,] 0.41354770 0.82709540 0.58645230
[199,] 0.37457815 0.74915630 0.62542185
[200,] 0.33640504 0.67281008 0.66359496
[201,] 0.31958014 0.63916029 0.68041986
[202,] 0.28164688 0.56329377 0.71835312
[203,] 0.24718076 0.49436153 0.75281924
[204,] 0.22082490 0.44164981 0.77917510
[205,] 0.26655798 0.53311596 0.73344202
[206,] 0.25553611 0.51107221 0.74446389
[207,] 0.22274879 0.44549758 0.77725121
[208,] 0.19239882 0.38479763 0.80760118
[209,] 0.17390186 0.34780371 0.82609814
[210,] 0.19866695 0.39733390 0.80133305
[211,] 0.17252330 0.34504661 0.82747670
[212,] 0.15154254 0.30308508 0.84845746
[213,] 0.12848550 0.25697099 0.87151450
[214,] 0.11667517 0.23335034 0.88332483
[215,] 0.09442922 0.18885844 0.90557078
[216,] 0.07943820 0.15887640 0.92056180
[217,] 0.06777759 0.13555518 0.93222241
[218,] 0.06807388 0.13614775 0.93192612
[219,] 0.06419153 0.12838307 0.93580847
[220,] 0.05336947 0.10673893 0.94663053
[221,] 0.04184167 0.08368335 0.95815833
[222,] 0.03374890 0.06749780 0.96625110
[223,] 0.02605176 0.05210352 0.97394824
[224,] 0.03957367 0.07914734 0.96042633
[225,] 0.04326800 0.08653601 0.95673200
[226,] 0.07073862 0.14147724 0.92926138
[227,] 0.05325238 0.10650475 0.94674762
[228,] 0.04847535 0.09695069 0.95152465
[229,] 0.05920008 0.11840017 0.94079992
[230,] 0.18456778 0.36913556 0.81543222
[231,] 0.14995642 0.29991284 0.85004358
[232,] 0.29192502 0.58385003 0.70807498
[233,] 0.32084676 0.64169353 0.67915324
[234,] 0.26568401 0.53136802 0.73431599
[235,] 0.23543500 0.47086999 0.76456500
[236,] 0.18650966 0.37301933 0.81349034
[237,] 0.43189940 0.86379881 0.56810060
[238,] 0.60794855 0.78410291 0.39205145
[239,] 0.59456709 0.81086583 0.40543291
[240,] 0.60751648 0.78496704 0.39248352
[241,] 0.63294334 0.73411331 0.36705666
[242,] 0.54688432 0.90623135 0.45311568
[243,] 0.63977658 0.72044684 0.36022342
[244,] 0.69352115 0.61295770 0.30647885
[245,] 0.75858515 0.48282969 0.24141485
[246,] 0.76149820 0.47700361 0.23850180
[247,] 0.60397346 0.79205309 0.39602654
> postscript(file="/var/wessaorg/rcomp/tmp/1d3181384710363.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/20gbv1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37eay1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wmyn1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5scnw1384710363.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
4.122410159 4.594415651 -5.301856200 -3.636709238 -1.939690306 1.996907829
7 8 9 10 11 12
5.839759451 -1.988465962 0.353932402 0.180072214 4.057864831 0.576073228
13 14 15 16 17 18
2.823776815 2.250280968 -3.559422590 -2.453345162 1.977965032 1.079311033
19 20 21 22 23 24
2.111189152 -2.105146368 -2.815260071 -2.437642431 2.270309737 -0.005856784
25 26 27 28 29 30
4.779461366 6.925037895 0.477316944 -3.072341821 -1.401992893 -0.348120607
31 32 33 34 35 36
-2.825309753 -6.855964725 3.196933754 -2.315248099 0.936623373 -2.781243836
37 38 39 40 41 42
-3.959142977 1.538724922 -4.765138357 0.479181870 2.808416719 -0.350640624
43 44 45 46 47 48
3.776300790 0.767639985 5.720476749 1.885381361 0.774285253 -1.907629782
49 50 51 52 53 54
-0.501210830 4.060793522 -4.721686459 -0.915488577 -1.894634017 -2.355905846
55 56 57 58 59 60
-1.938589663 1.859441805 4.243441899 -5.951759998 1.310991761 0.484217841
61 62 63 64 65 66
-1.466317346 3.309435931 -1.047581989 -4.044053087 0.652787804 -0.950011263
67 68 69 70 71 72
1.268138313 -1.532090695 1.694221369 -0.913419743 2.472966956 -5.324995770
73 74 75 76 77 78
-2.820181876 0.126578037 2.432077331 6.579477010 1.019923461 1.020131920
79 80 81 82 83 84
-5.258713790 5.347509887 -2.575496119 -1.127999010 2.792359261 -2.621975444
85 86 87 88 89 90
-0.207622388 1.217841944 -1.563356024 -4.466648484 -0.311419364 -4.490571877
91 92 93 94 95 96
2.578807423 -2.603038582 0.304734020 -3.688637741 3.464516156 2.353713954
97 98 99 100 101 102
1.743228716 2.355616963 -3.537460772 2.442533516 2.367659219 0.168355451
103 104 105 106 107 108
0.219242945 -3.476452057 6.996461189 2.994362114 4.551342542 1.415471057
109 110 111 112 113 114
5.810701515 -4.332616590 -2.684597481 -5.539967315 -1.790921282 2.466358876
115 116 117 118 119 120
3.329948157 -3.910934355 -3.006853321 -1.993137137 2.927886696 -2.455623187
121 122 123 124 125 126
-0.124875334 1.355727219 0.648099004 2.918402009 -2.881266100 5.225991816
127 128 129 130 131 132
7.258766485 -2.428185580 1.163220914 -1.893812798 -4.789928274 4.210831116
133 134 135 136 137 138
-5.367369245 0.932320192 0.007842396 -0.236869745 -0.582597114 -4.376457970
139 140 141 142 143 144
0.641117892 -3.666643674 -2.404326487 -5.515601870 -4.625520625 1.626455509
145 146 147 148 149 150
6.976171523 0.855333872 1.081183957 -0.846242626 2.248107073 1.515832945
151 152 153 154 155 156
6.843348072 -2.185473955 -1.350734476 4.385582616 0.538468198 2.989692879
157 158 159 160 161 162
-3.385624468 -2.238546139 0.624812153 -3.497651724 -0.753028873 -2.745373831
163 164 165 166 167 168
0.539116800 4.786743130 -1.829384386 -7.597262465 -3.623831314 -3.889654177
169 170 171 172 173 174
-1.429136825 -7.863991305 4.790940931 0.584355517 6.674089655 -0.752443357
175 176 177 178 179 180
4.645258164 -2.595676062 3.799362016 -1.833835899 -1.849329477 2.878732200
181 182 183 184 185 186
1.087460173 2.611349496 -3.904230056 4.818937862 -9.407702706 -2.385412362
187 188 189 190 191 192
2.176118810 6.328069232 -3.159835227 -2.244118022 -2.640678196 1.328306873
193 194 195 196 197 198
-1.386686163 0.101065932 -0.927665773 1.416716170 1.840056256 -2.015379630
199 200 201 202 203 204
0.152347442 -6.874835255 -1.627566240 -8.114290211 1.320813608 1.208537013
205 206 207 208 209 210
-0.941675337 -0.677988418 -0.141579478 0.428834060 2.779832089 0.577504920
211 212 213 214 215 216
1.107696342 2.680569779 -3.547955448 -2.729185000 1.186031580 -1.754453855
217 218 219 220 221 222
-1.343627430 5.247074894 2.558903731 0.149477086 1.818824801 3.325410132
223 224 225 226 227 228
0.595377485 -2.043613344 0.074100255 -2.800801245 -2.022944017 -0.636892969
229 230 231 232 233 234
1.574334918 0.022011303 2.064530120 -2.048555308 -1.898513852 8.147448600
235 236 237 238 239 240
1.003926950 3.897457607 5.454542696 5.578625645 -3.209353344 5.222727074
241 242 243 244 245 246
-5.794420173 1.556524907 -2.706808011 2.112754326 4.831050151 -4.479069256
247 248 249 250 251 252
-1.816001974 3.952141152 -8.232177854 -6.068373375 -2.485464489 0.642067873
253 254 255 256 257 258
2.405736104 0.440023776 4.275698414 1.174676365 0.860224100 -1.129787287
259 260 261 262 263 264
3.658515655 3.112307538 -4.701677167 -1.258551526 6.416385558 -3.585674908
> postscript(file="/var/wessaorg/rcomp/tmp/60uyb1384710363.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 4.122410159 NA
1 4.594415651 4.122410159
2 -5.301856200 4.594415651
3 -3.636709238 -5.301856200
4 -1.939690306 -3.636709238
5 1.996907829 -1.939690306
6 5.839759451 1.996907829
7 -1.988465962 5.839759451
8 0.353932402 -1.988465962
9 0.180072214 0.353932402
10 4.057864831 0.180072214
11 0.576073228 4.057864831
12 2.823776815 0.576073228
13 2.250280968 2.823776815
14 -3.559422590 2.250280968
15 -2.453345162 -3.559422590
16 1.977965032 -2.453345162
17 1.079311033 1.977965032
18 2.111189152 1.079311033
19 -2.105146368 2.111189152
20 -2.815260071 -2.105146368
21 -2.437642431 -2.815260071
22 2.270309737 -2.437642431
23 -0.005856784 2.270309737
24 4.779461366 -0.005856784
25 6.925037895 4.779461366
26 0.477316944 6.925037895
27 -3.072341821 0.477316944
28 -1.401992893 -3.072341821
29 -0.348120607 -1.401992893
30 -2.825309753 -0.348120607
31 -6.855964725 -2.825309753
32 3.196933754 -6.855964725
33 -2.315248099 3.196933754
34 0.936623373 -2.315248099
35 -2.781243836 0.936623373
36 -3.959142977 -2.781243836
37 1.538724922 -3.959142977
38 -4.765138357 1.538724922
39 0.479181870 -4.765138357
40 2.808416719 0.479181870
41 -0.350640624 2.808416719
42 3.776300790 -0.350640624
43 0.767639985 3.776300790
44 5.720476749 0.767639985
45 1.885381361 5.720476749
46 0.774285253 1.885381361
47 -1.907629782 0.774285253
48 -0.501210830 -1.907629782
49 4.060793522 -0.501210830
50 -4.721686459 4.060793522
51 -0.915488577 -4.721686459
52 -1.894634017 -0.915488577
53 -2.355905846 -1.894634017
54 -1.938589663 -2.355905846
55 1.859441805 -1.938589663
56 4.243441899 1.859441805
57 -5.951759998 4.243441899
58 1.310991761 -5.951759998
59 0.484217841 1.310991761
60 -1.466317346 0.484217841
61 3.309435931 -1.466317346
62 -1.047581989 3.309435931
63 -4.044053087 -1.047581989
64 0.652787804 -4.044053087
65 -0.950011263 0.652787804
66 1.268138313 -0.950011263
67 -1.532090695 1.268138313
68 1.694221369 -1.532090695
69 -0.913419743 1.694221369
70 2.472966956 -0.913419743
71 -5.324995770 2.472966956
72 -2.820181876 -5.324995770
73 0.126578037 -2.820181876
74 2.432077331 0.126578037
75 6.579477010 2.432077331
76 1.019923461 6.579477010
77 1.020131920 1.019923461
78 -5.258713790 1.020131920
79 5.347509887 -5.258713790
80 -2.575496119 5.347509887
81 -1.127999010 -2.575496119
82 2.792359261 -1.127999010
83 -2.621975444 2.792359261
84 -0.207622388 -2.621975444
85 1.217841944 -0.207622388
86 -1.563356024 1.217841944
87 -4.466648484 -1.563356024
88 -0.311419364 -4.466648484
89 -4.490571877 -0.311419364
90 2.578807423 -4.490571877
91 -2.603038582 2.578807423
92 0.304734020 -2.603038582
93 -3.688637741 0.304734020
94 3.464516156 -3.688637741
95 2.353713954 3.464516156
96 1.743228716 2.353713954
97 2.355616963 1.743228716
98 -3.537460772 2.355616963
99 2.442533516 -3.537460772
100 2.367659219 2.442533516
101 0.168355451 2.367659219
102 0.219242945 0.168355451
103 -3.476452057 0.219242945
104 6.996461189 -3.476452057
105 2.994362114 6.996461189
106 4.551342542 2.994362114
107 1.415471057 4.551342542
108 5.810701515 1.415471057
109 -4.332616590 5.810701515
110 -2.684597481 -4.332616590
111 -5.539967315 -2.684597481
112 -1.790921282 -5.539967315
113 2.466358876 -1.790921282
114 3.329948157 2.466358876
115 -3.910934355 3.329948157
116 -3.006853321 -3.910934355
117 -1.993137137 -3.006853321
118 2.927886696 -1.993137137
119 -2.455623187 2.927886696
120 -0.124875334 -2.455623187
121 1.355727219 -0.124875334
122 0.648099004 1.355727219
123 2.918402009 0.648099004
124 -2.881266100 2.918402009
125 5.225991816 -2.881266100
126 7.258766485 5.225991816
127 -2.428185580 7.258766485
128 1.163220914 -2.428185580
129 -1.893812798 1.163220914
130 -4.789928274 -1.893812798
131 4.210831116 -4.789928274
132 -5.367369245 4.210831116
133 0.932320192 -5.367369245
134 0.007842396 0.932320192
135 -0.236869745 0.007842396
136 -0.582597114 -0.236869745
137 -4.376457970 -0.582597114
138 0.641117892 -4.376457970
139 -3.666643674 0.641117892
140 -2.404326487 -3.666643674
141 -5.515601870 -2.404326487
142 -4.625520625 -5.515601870
143 1.626455509 -4.625520625
144 6.976171523 1.626455509
145 0.855333872 6.976171523
146 1.081183957 0.855333872
147 -0.846242626 1.081183957
148 2.248107073 -0.846242626
149 1.515832945 2.248107073
150 6.843348072 1.515832945
151 -2.185473955 6.843348072
152 -1.350734476 -2.185473955
153 4.385582616 -1.350734476
154 0.538468198 4.385582616
155 2.989692879 0.538468198
156 -3.385624468 2.989692879
157 -2.238546139 -3.385624468
158 0.624812153 -2.238546139
159 -3.497651724 0.624812153
160 -0.753028873 -3.497651724
161 -2.745373831 -0.753028873
162 0.539116800 -2.745373831
163 4.786743130 0.539116800
164 -1.829384386 4.786743130
165 -7.597262465 -1.829384386
166 -3.623831314 -7.597262465
167 -3.889654177 -3.623831314
168 -1.429136825 -3.889654177
169 -7.863991305 -1.429136825
170 4.790940931 -7.863991305
171 0.584355517 4.790940931
172 6.674089655 0.584355517
173 -0.752443357 6.674089655
174 4.645258164 -0.752443357
175 -2.595676062 4.645258164
176 3.799362016 -2.595676062
177 -1.833835899 3.799362016
178 -1.849329477 -1.833835899
179 2.878732200 -1.849329477
180 1.087460173 2.878732200
181 2.611349496 1.087460173
182 -3.904230056 2.611349496
183 4.818937862 -3.904230056
184 -9.407702706 4.818937862
185 -2.385412362 -9.407702706
186 2.176118810 -2.385412362
187 6.328069232 2.176118810
188 -3.159835227 6.328069232
189 -2.244118022 -3.159835227
190 -2.640678196 -2.244118022
191 1.328306873 -2.640678196
192 -1.386686163 1.328306873
193 0.101065932 -1.386686163
194 -0.927665773 0.101065932
195 1.416716170 -0.927665773
196 1.840056256 1.416716170
197 -2.015379630 1.840056256
198 0.152347442 -2.015379630
199 -6.874835255 0.152347442
200 -1.627566240 -6.874835255
201 -8.114290211 -1.627566240
202 1.320813608 -8.114290211
203 1.208537013 1.320813608
204 -0.941675337 1.208537013
205 -0.677988418 -0.941675337
206 -0.141579478 -0.677988418
207 0.428834060 -0.141579478
208 2.779832089 0.428834060
209 0.577504920 2.779832089
210 1.107696342 0.577504920
211 2.680569779 1.107696342
212 -3.547955448 2.680569779
213 -2.729185000 -3.547955448
214 1.186031580 -2.729185000
215 -1.754453855 1.186031580
216 -1.343627430 -1.754453855
217 5.247074894 -1.343627430
218 2.558903731 5.247074894
219 0.149477086 2.558903731
220 1.818824801 0.149477086
221 3.325410132 1.818824801
222 0.595377485 3.325410132
223 -2.043613344 0.595377485
224 0.074100255 -2.043613344
225 -2.800801245 0.074100255
226 -2.022944017 -2.800801245
227 -0.636892969 -2.022944017
228 1.574334918 -0.636892969
229 0.022011303 1.574334918
230 2.064530120 0.022011303
231 -2.048555308 2.064530120
232 -1.898513852 -2.048555308
233 8.147448600 -1.898513852
234 1.003926950 8.147448600
235 3.897457607 1.003926950
236 5.454542696 3.897457607
237 5.578625645 5.454542696
238 -3.209353344 5.578625645
239 5.222727074 -3.209353344
240 -5.794420173 5.222727074
241 1.556524907 -5.794420173
242 -2.706808011 1.556524907
243 2.112754326 -2.706808011
244 4.831050151 2.112754326
245 -4.479069256 4.831050151
246 -1.816001974 -4.479069256
247 3.952141152 -1.816001974
248 -8.232177854 3.952141152
249 -6.068373375 -8.232177854
250 -2.485464489 -6.068373375
251 0.642067873 -2.485464489
252 2.405736104 0.642067873
253 0.440023776 2.405736104
254 4.275698414 0.440023776
255 1.174676365 4.275698414
256 0.860224100 1.174676365
257 -1.129787287 0.860224100
258 3.658515655 -1.129787287
259 3.112307538 3.658515655
260 -4.701677167 3.112307538
261 -1.258551526 -4.701677167
262 6.416385558 -1.258551526
263 -3.585674908 6.416385558
264 NA -3.585674908
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.594415651 4.122410159
[2,] -5.301856200 4.594415651
[3,] -3.636709238 -5.301856200
[4,] -1.939690306 -3.636709238
[5,] 1.996907829 -1.939690306
[6,] 5.839759451 1.996907829
[7,] -1.988465962 5.839759451
[8,] 0.353932402 -1.988465962
[9,] 0.180072214 0.353932402
[10,] 4.057864831 0.180072214
[11,] 0.576073228 4.057864831
[12,] 2.823776815 0.576073228
[13,] 2.250280968 2.823776815
[14,] -3.559422590 2.250280968
[15,] -2.453345162 -3.559422590
[16,] 1.977965032 -2.453345162
[17,] 1.079311033 1.977965032
[18,] 2.111189152 1.079311033
[19,] -2.105146368 2.111189152
[20,] -2.815260071 -2.105146368
[21,] -2.437642431 -2.815260071
[22,] 2.270309737 -2.437642431
[23,] -0.005856784 2.270309737
[24,] 4.779461366 -0.005856784
[25,] 6.925037895 4.779461366
[26,] 0.477316944 6.925037895
[27,] -3.072341821 0.477316944
[28,] -1.401992893 -3.072341821
[29,] -0.348120607 -1.401992893
[30,] -2.825309753 -0.348120607
[31,] -6.855964725 -2.825309753
[32,] 3.196933754 -6.855964725
[33,] -2.315248099 3.196933754
[34,] 0.936623373 -2.315248099
[35,] -2.781243836 0.936623373
[36,] -3.959142977 -2.781243836
[37,] 1.538724922 -3.959142977
[38,] -4.765138357 1.538724922
[39,] 0.479181870 -4.765138357
[40,] 2.808416719 0.479181870
[41,] -0.350640624 2.808416719
[42,] 3.776300790 -0.350640624
[43,] 0.767639985 3.776300790
[44,] 5.720476749 0.767639985
[45,] 1.885381361 5.720476749
[46,] 0.774285253 1.885381361
[47,] -1.907629782 0.774285253
[48,] -0.501210830 -1.907629782
[49,] 4.060793522 -0.501210830
[50,] -4.721686459 4.060793522
[51,] -0.915488577 -4.721686459
[52,] -1.894634017 -0.915488577
[53,] -2.355905846 -1.894634017
[54,] -1.938589663 -2.355905846
[55,] 1.859441805 -1.938589663
[56,] 4.243441899 1.859441805
[57,] -5.951759998 4.243441899
[58,] 1.310991761 -5.951759998
[59,] 0.484217841 1.310991761
[60,] -1.466317346 0.484217841
[61,] 3.309435931 -1.466317346
[62,] -1.047581989 3.309435931
[63,] -4.044053087 -1.047581989
[64,] 0.652787804 -4.044053087
[65,] -0.950011263 0.652787804
[66,] 1.268138313 -0.950011263
[67,] -1.532090695 1.268138313
[68,] 1.694221369 -1.532090695
[69,] -0.913419743 1.694221369
[70,] 2.472966956 -0.913419743
[71,] -5.324995770 2.472966956
[72,] -2.820181876 -5.324995770
[73,] 0.126578037 -2.820181876
[74,] 2.432077331 0.126578037
[75,] 6.579477010 2.432077331
[76,] 1.019923461 6.579477010
[77,] 1.020131920 1.019923461
[78,] -5.258713790 1.020131920
[79,] 5.347509887 -5.258713790
[80,] -2.575496119 5.347509887
[81,] -1.127999010 -2.575496119
[82,] 2.792359261 -1.127999010
[83,] -2.621975444 2.792359261
[84,] -0.207622388 -2.621975444
[85,] 1.217841944 -0.207622388
[86,] -1.563356024 1.217841944
[87,] -4.466648484 -1.563356024
[88,] -0.311419364 -4.466648484
[89,] -4.490571877 -0.311419364
[90,] 2.578807423 -4.490571877
[91,] -2.603038582 2.578807423
[92,] 0.304734020 -2.603038582
[93,] -3.688637741 0.304734020
[94,] 3.464516156 -3.688637741
[95,] 2.353713954 3.464516156
[96,] 1.743228716 2.353713954
[97,] 2.355616963 1.743228716
[98,] -3.537460772 2.355616963
[99,] 2.442533516 -3.537460772
[100,] 2.367659219 2.442533516
[101,] 0.168355451 2.367659219
[102,] 0.219242945 0.168355451
[103,] -3.476452057 0.219242945
[104,] 6.996461189 -3.476452057
[105,] 2.994362114 6.996461189
[106,] 4.551342542 2.994362114
[107,] 1.415471057 4.551342542
[108,] 5.810701515 1.415471057
[109,] -4.332616590 5.810701515
[110,] -2.684597481 -4.332616590
[111,] -5.539967315 -2.684597481
[112,] -1.790921282 -5.539967315
[113,] 2.466358876 -1.790921282
[114,] 3.329948157 2.466358876
[115,] -3.910934355 3.329948157
[116,] -3.006853321 -3.910934355
[117,] -1.993137137 -3.006853321
[118,] 2.927886696 -1.993137137
[119,] -2.455623187 2.927886696
[120,] -0.124875334 -2.455623187
[121,] 1.355727219 -0.124875334
[122,] 0.648099004 1.355727219
[123,] 2.918402009 0.648099004
[124,] -2.881266100 2.918402009
[125,] 5.225991816 -2.881266100
[126,] 7.258766485 5.225991816
[127,] -2.428185580 7.258766485
[128,] 1.163220914 -2.428185580
[129,] -1.893812798 1.163220914
[130,] -4.789928274 -1.893812798
[131,] 4.210831116 -4.789928274
[132,] -5.367369245 4.210831116
[133,] 0.932320192 -5.367369245
[134,] 0.007842396 0.932320192
[135,] -0.236869745 0.007842396
[136,] -0.582597114 -0.236869745
[137,] -4.376457970 -0.582597114
[138,] 0.641117892 -4.376457970
[139,] -3.666643674 0.641117892
[140,] -2.404326487 -3.666643674
[141,] -5.515601870 -2.404326487
[142,] -4.625520625 -5.515601870
[143,] 1.626455509 -4.625520625
[144,] 6.976171523 1.626455509
[145,] 0.855333872 6.976171523
[146,] 1.081183957 0.855333872
[147,] -0.846242626 1.081183957
[148,] 2.248107073 -0.846242626
[149,] 1.515832945 2.248107073
[150,] 6.843348072 1.515832945
[151,] -2.185473955 6.843348072
[152,] -1.350734476 -2.185473955
[153,] 4.385582616 -1.350734476
[154,] 0.538468198 4.385582616
[155,] 2.989692879 0.538468198
[156,] -3.385624468 2.989692879
[157,] -2.238546139 -3.385624468
[158,] 0.624812153 -2.238546139
[159,] -3.497651724 0.624812153
[160,] -0.753028873 -3.497651724
[161,] -2.745373831 -0.753028873
[162,] 0.539116800 -2.745373831
[163,] 4.786743130 0.539116800
[164,] -1.829384386 4.786743130
[165,] -7.597262465 -1.829384386
[166,] -3.623831314 -7.597262465
[167,] -3.889654177 -3.623831314
[168,] -1.429136825 -3.889654177
[169,] -7.863991305 -1.429136825
[170,] 4.790940931 -7.863991305
[171,] 0.584355517 4.790940931
[172,] 6.674089655 0.584355517
[173,] -0.752443357 6.674089655
[174,] 4.645258164 -0.752443357
[175,] -2.595676062 4.645258164
[176,] 3.799362016 -2.595676062
[177,] -1.833835899 3.799362016
[178,] -1.849329477 -1.833835899
[179,] 2.878732200 -1.849329477
[180,] 1.087460173 2.878732200
[181,] 2.611349496 1.087460173
[182,] -3.904230056 2.611349496
[183,] 4.818937862 -3.904230056
[184,] -9.407702706 4.818937862
[185,] -2.385412362 -9.407702706
[186,] 2.176118810 -2.385412362
[187,] 6.328069232 2.176118810
[188,] -3.159835227 6.328069232
[189,] -2.244118022 -3.159835227
[190,] -2.640678196 -2.244118022
[191,] 1.328306873 -2.640678196
[192,] -1.386686163 1.328306873
[193,] 0.101065932 -1.386686163
[194,] -0.927665773 0.101065932
[195,] 1.416716170 -0.927665773
[196,] 1.840056256 1.416716170
[197,] -2.015379630 1.840056256
[198,] 0.152347442 -2.015379630
[199,] -6.874835255 0.152347442
[200,] -1.627566240 -6.874835255
[201,] -8.114290211 -1.627566240
[202,] 1.320813608 -8.114290211
[203,] 1.208537013 1.320813608
[204,] -0.941675337 1.208537013
[205,] -0.677988418 -0.941675337
[206,] -0.141579478 -0.677988418
[207,] 0.428834060 -0.141579478
[208,] 2.779832089 0.428834060
[209,] 0.577504920 2.779832089
[210,] 1.107696342 0.577504920
[211,] 2.680569779 1.107696342
[212,] -3.547955448 2.680569779
[213,] -2.729185000 -3.547955448
[214,] 1.186031580 -2.729185000
[215,] -1.754453855 1.186031580
[216,] -1.343627430 -1.754453855
[217,] 5.247074894 -1.343627430
[218,] 2.558903731 5.247074894
[219,] 0.149477086 2.558903731
[220,] 1.818824801 0.149477086
[221,] 3.325410132 1.818824801
[222,] 0.595377485 3.325410132
[223,] -2.043613344 0.595377485
[224,] 0.074100255 -2.043613344
[225,] -2.800801245 0.074100255
[226,] -2.022944017 -2.800801245
[227,] -0.636892969 -2.022944017
[228,] 1.574334918 -0.636892969
[229,] 0.022011303 1.574334918
[230,] 2.064530120 0.022011303
[231,] -2.048555308 2.064530120
[232,] -1.898513852 -2.048555308
[233,] 8.147448600 -1.898513852
[234,] 1.003926950 8.147448600
[235,] 3.897457607 1.003926950
[236,] 5.454542696 3.897457607
[237,] 5.578625645 5.454542696
[238,] -3.209353344 5.578625645
[239,] 5.222727074 -3.209353344
[240,] -5.794420173 5.222727074
[241,] 1.556524907 -5.794420173
[242,] -2.706808011 1.556524907
[243,] 2.112754326 -2.706808011
[244,] 4.831050151 2.112754326
[245,] -4.479069256 4.831050151
[246,] -1.816001974 -4.479069256
[247,] 3.952141152 -1.816001974
[248,] -8.232177854 3.952141152
[249,] -6.068373375 -8.232177854
[250,] -2.485464489 -6.068373375
[251,] 0.642067873 -2.485464489
[252,] 2.405736104 0.642067873
[253,] 0.440023776 2.405736104
[254,] 4.275698414 0.440023776
[255,] 1.174676365 4.275698414
[256,] 0.860224100 1.174676365
[257,] -1.129787287 0.860224100
[258,] 3.658515655 -1.129787287
[259,] 3.112307538 3.658515655
[260,] -4.701677167 3.112307538
[261,] -1.258551526 -4.701677167
[262,] 6.416385558 -1.258551526
[263,] -3.585674908 6.416385558
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.594415651 4.122410159
2 -5.301856200 4.594415651
3 -3.636709238 -5.301856200
4 -1.939690306 -3.636709238
5 1.996907829 -1.939690306
6 5.839759451 1.996907829
7 -1.988465962 5.839759451
8 0.353932402 -1.988465962
9 0.180072214 0.353932402
10 4.057864831 0.180072214
11 0.576073228 4.057864831
12 2.823776815 0.576073228
13 2.250280968 2.823776815
14 -3.559422590 2.250280968
15 -2.453345162 -3.559422590
16 1.977965032 -2.453345162
17 1.079311033 1.977965032
18 2.111189152 1.079311033
19 -2.105146368 2.111189152
20 -2.815260071 -2.105146368
21 -2.437642431 -2.815260071
22 2.270309737 -2.437642431
23 -0.005856784 2.270309737
24 4.779461366 -0.005856784
25 6.925037895 4.779461366
26 0.477316944 6.925037895
27 -3.072341821 0.477316944
28 -1.401992893 -3.072341821
29 -0.348120607 -1.401992893
30 -2.825309753 -0.348120607
31 -6.855964725 -2.825309753
32 3.196933754 -6.855964725
33 -2.315248099 3.196933754
34 0.936623373 -2.315248099
35 -2.781243836 0.936623373
36 -3.959142977 -2.781243836
37 1.538724922 -3.959142977
38 -4.765138357 1.538724922
39 0.479181870 -4.765138357
40 2.808416719 0.479181870
41 -0.350640624 2.808416719
42 3.776300790 -0.350640624
43 0.767639985 3.776300790
44 5.720476749 0.767639985
45 1.885381361 5.720476749
46 0.774285253 1.885381361
47 -1.907629782 0.774285253
48 -0.501210830 -1.907629782
49 4.060793522 -0.501210830
50 -4.721686459 4.060793522
51 -0.915488577 -4.721686459
52 -1.894634017 -0.915488577
53 -2.355905846 -1.894634017
54 -1.938589663 -2.355905846
55 1.859441805 -1.938589663
56 4.243441899 1.859441805
57 -5.951759998 4.243441899
58 1.310991761 -5.951759998
59 0.484217841 1.310991761
60 -1.466317346 0.484217841
61 3.309435931 -1.466317346
62 -1.047581989 3.309435931
63 -4.044053087 -1.047581989
64 0.652787804 -4.044053087
65 -0.950011263 0.652787804
66 1.268138313 -0.950011263
67 -1.532090695 1.268138313
68 1.694221369 -1.532090695
69 -0.913419743 1.694221369
70 2.472966956 -0.913419743
71 -5.324995770 2.472966956
72 -2.820181876 -5.324995770
73 0.126578037 -2.820181876
74 2.432077331 0.126578037
75 6.579477010 2.432077331
76 1.019923461 6.579477010
77 1.020131920 1.019923461
78 -5.258713790 1.020131920
79 5.347509887 -5.258713790
80 -2.575496119 5.347509887
81 -1.127999010 -2.575496119
82 2.792359261 -1.127999010
83 -2.621975444 2.792359261
84 -0.207622388 -2.621975444
85 1.217841944 -0.207622388
86 -1.563356024 1.217841944
87 -4.466648484 -1.563356024
88 -0.311419364 -4.466648484
89 -4.490571877 -0.311419364
90 2.578807423 -4.490571877
91 -2.603038582 2.578807423
92 0.304734020 -2.603038582
93 -3.688637741 0.304734020
94 3.464516156 -3.688637741
95 2.353713954 3.464516156
96 1.743228716 2.353713954
97 2.355616963 1.743228716
98 -3.537460772 2.355616963
99 2.442533516 -3.537460772
100 2.367659219 2.442533516
101 0.168355451 2.367659219
102 0.219242945 0.168355451
103 -3.476452057 0.219242945
104 6.996461189 -3.476452057
105 2.994362114 6.996461189
106 4.551342542 2.994362114
107 1.415471057 4.551342542
108 5.810701515 1.415471057
109 -4.332616590 5.810701515
110 -2.684597481 -4.332616590
111 -5.539967315 -2.684597481
112 -1.790921282 -5.539967315
113 2.466358876 -1.790921282
114 3.329948157 2.466358876
115 -3.910934355 3.329948157
116 -3.006853321 -3.910934355
117 -1.993137137 -3.006853321
118 2.927886696 -1.993137137
119 -2.455623187 2.927886696
120 -0.124875334 -2.455623187
121 1.355727219 -0.124875334
122 0.648099004 1.355727219
123 2.918402009 0.648099004
124 -2.881266100 2.918402009
125 5.225991816 -2.881266100
126 7.258766485 5.225991816
127 -2.428185580 7.258766485
128 1.163220914 -2.428185580
129 -1.893812798 1.163220914
130 -4.789928274 -1.893812798
131 4.210831116 -4.789928274
132 -5.367369245 4.210831116
133 0.932320192 -5.367369245
134 0.007842396 0.932320192
135 -0.236869745 0.007842396
136 -0.582597114 -0.236869745
137 -4.376457970 -0.582597114
138 0.641117892 -4.376457970
139 -3.666643674 0.641117892
140 -2.404326487 -3.666643674
141 -5.515601870 -2.404326487
142 -4.625520625 -5.515601870
143 1.626455509 -4.625520625
144 6.976171523 1.626455509
145 0.855333872 6.976171523
146 1.081183957 0.855333872
147 -0.846242626 1.081183957
148 2.248107073 -0.846242626
149 1.515832945 2.248107073
150 6.843348072 1.515832945
151 -2.185473955 6.843348072
152 -1.350734476 -2.185473955
153 4.385582616 -1.350734476
154 0.538468198 4.385582616
155 2.989692879 0.538468198
156 -3.385624468 2.989692879
157 -2.238546139 -3.385624468
158 0.624812153 -2.238546139
159 -3.497651724 0.624812153
160 -0.753028873 -3.497651724
161 -2.745373831 -0.753028873
162 0.539116800 -2.745373831
163 4.786743130 0.539116800
164 -1.829384386 4.786743130
165 -7.597262465 -1.829384386
166 -3.623831314 -7.597262465
167 -3.889654177 -3.623831314
168 -1.429136825 -3.889654177
169 -7.863991305 -1.429136825
170 4.790940931 -7.863991305
171 0.584355517 4.790940931
172 6.674089655 0.584355517
173 -0.752443357 6.674089655
174 4.645258164 -0.752443357
175 -2.595676062 4.645258164
176 3.799362016 -2.595676062
177 -1.833835899 3.799362016
178 -1.849329477 -1.833835899
179 2.878732200 -1.849329477
180 1.087460173 2.878732200
181 2.611349496 1.087460173
182 -3.904230056 2.611349496
183 4.818937862 -3.904230056
184 -9.407702706 4.818937862
185 -2.385412362 -9.407702706
186 2.176118810 -2.385412362
187 6.328069232 2.176118810
188 -3.159835227 6.328069232
189 -2.244118022 -3.159835227
190 -2.640678196 -2.244118022
191 1.328306873 -2.640678196
192 -1.386686163 1.328306873
193 0.101065932 -1.386686163
194 -0.927665773 0.101065932
195 1.416716170 -0.927665773
196 1.840056256 1.416716170
197 -2.015379630 1.840056256
198 0.152347442 -2.015379630
199 -6.874835255 0.152347442
200 -1.627566240 -6.874835255
201 -8.114290211 -1.627566240
202 1.320813608 -8.114290211
203 1.208537013 1.320813608
204 -0.941675337 1.208537013
205 -0.677988418 -0.941675337
206 -0.141579478 -0.677988418
207 0.428834060 -0.141579478
208 2.779832089 0.428834060
209 0.577504920 2.779832089
210 1.107696342 0.577504920
211 2.680569779 1.107696342
212 -3.547955448 2.680569779
213 -2.729185000 -3.547955448
214 1.186031580 -2.729185000
215 -1.754453855 1.186031580
216 -1.343627430 -1.754453855
217 5.247074894 -1.343627430
218 2.558903731 5.247074894
219 0.149477086 2.558903731
220 1.818824801 0.149477086
221 3.325410132 1.818824801
222 0.595377485 3.325410132
223 -2.043613344 0.595377485
224 0.074100255 -2.043613344
225 -2.800801245 0.074100255
226 -2.022944017 -2.800801245
227 -0.636892969 -2.022944017
228 1.574334918 -0.636892969
229 0.022011303 1.574334918
230 2.064530120 0.022011303
231 -2.048555308 2.064530120
232 -1.898513852 -2.048555308
233 8.147448600 -1.898513852
234 1.003926950 8.147448600
235 3.897457607 1.003926950
236 5.454542696 3.897457607
237 5.578625645 5.454542696
238 -3.209353344 5.578625645
239 5.222727074 -3.209353344
240 -5.794420173 5.222727074
241 1.556524907 -5.794420173
242 -2.706808011 1.556524907
243 2.112754326 -2.706808011
244 4.831050151 2.112754326
245 -4.479069256 4.831050151
246 -1.816001974 -4.479069256
247 3.952141152 -1.816001974
248 -8.232177854 3.952141152
249 -6.068373375 -8.232177854
250 -2.485464489 -6.068373375
251 0.642067873 -2.485464489
252 2.405736104 0.642067873
253 0.440023776 2.405736104
254 4.275698414 0.440023776
255 1.174676365 4.275698414
256 0.860224100 1.174676365
257 -1.129787287 0.860224100
258 3.658515655 -1.129787287
259 3.112307538 3.658515655
260 -4.701677167 3.112307538
261 -1.258551526 -4.701677167
262 6.416385558 -1.258551526
263 -3.585674908 6.416385558
> 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/7sv2s1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87en71384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9368o1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10oulp1384710363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11s3gy1384710363.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1282c71384710363.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13z5x01384710364.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/146p0d1384710364.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15xp661384710364.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16ii8e1384710364.tab")
+ }
>
> try(system("convert tmp/1d3181384710363.ps tmp/1d3181384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/20gbv1384710363.ps tmp/20gbv1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/37eay1384710363.ps tmp/37eay1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wmyn1384710363.ps tmp/4wmyn1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/5scnw1384710363.ps tmp/5scnw1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/60uyb1384710363.ps tmp/60uyb1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sv2s1384710363.ps tmp/7sv2s1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/87en71384710363.ps tmp/87en71384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/9368o1384710363.ps tmp/9368o1384710363.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oulp1384710363.ps tmp/10oulp1384710363.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.256 2.119 14.369