R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(14
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+ ,43
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+ ,40
+ ,15
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+ ,34
+ ,11
+ ,15
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+ ,10
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+ ,3
+ ,8
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+ ,52
+ ,14
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+ ,30
+ ,11
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+ ,15
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+ ,38
+ ,12
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+ ,7
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+ ,7
+ ,22
+ ,62
+ ,39
+ ,14
+ ,29
+ ,32
+ ,9
+ ,12
+ ,72
+ ,43)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Software'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Happiness','Connected','Separate','Software','Depression','Sport1','Sport2'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No 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
Happiness Connected Separate Software Depression Sport1 Sport2
1 14 41 38 12 12.0 53 32
2 18 39 32 11 11.0 83 51
3 11 30 35 15 14.0 66 42
4 12 31 33 6 12.0 67 41
5 16 34 37 13 21.0 76 46
6 18 35 29 10 12.0 78 47
7 14 39 31 12 22.0 53 37
8 14 34 36 14 11.0 80 49
9 15 36 35 12 10.0 74 45
10 15 37 38 9 13.0 76 47
11 17 38 31 10 10.0 79 49
12 19 36 34 12 8.0 54 33
13 10 38 35 12 15.0 67 42
14 16 39 38 11 14.0 54 33
15 18 33 37 15 10.0 87 53
16 14 32 33 12 14.0 58 36
17 14 36 32 10 14.0 75 45
18 17 38 38 12 11.0 88 54
19 14 39 38 11 10.0 64 41
20 16 32 32 12 13.0 57 36
21 18 32 33 11 9.5 66 41
22 11 31 31 12 14.0 68 44
23 14 39 38 13 12.0 54 33
24 12 37 39 11 14.0 56 37
25 17 39 32 12 11.0 86 52
26 9 41 32 13 9.0 80 47
27 16 36 35 10 11.0 76 43
28 14 33 37 14 15.0 69 44
29 15 33 33 12 14.0 78 45
30 11 34 33 10 13.0 67 44
31 16 31 31 12 9.0 80 49
32 13 27 32 8 15.0 54 33
33 17 37 31 10 10.0 71 43
34 15 34 37 12 11.0 84 54
35 14 34 30 12 13.0 74 42
36 16 32 33 7 8.0 71 44
37 9 29 31 9 20.0 63 37
38 15 36 33 12 12.0 71 43
39 17 29 31 10 10.0 76 46
40 13 35 33 10 10.0 69 42
41 15 37 32 10 9.0 74 45
42 16 34 33 12 14.0 75 44
43 16 38 32 15 8.0 54 33
44 12 35 33 10 14.0 52 31
45 15 38 28 10 11.0 69 42
46 11 37 35 12 13.0 68 40
47 15 38 39 13 9.0 65 43
48 15 33 34 11 11.0 75 46
49 17 36 38 11 15.0 74 42
50 13 38 32 12 11.0 75 45
51 16 32 38 14 10.0 72 44
52 14 32 30 10 14.0 67 40
53 11 32 33 12 18.0 63 37
54 12 34 38 13 14.0 62 46
55 12 32 32 5 11.0 63 36
56 15 37 35 6 14.5 76 47
57 16 39 34 12 13.0 74 45
58 15 29 34 12 9.0 67 42
59 12 37 36 11 10.0 73 43
60 12 35 34 10 15.0 70 43
61 8 30 28 7 20.0 53 32
62 13 38 34 12 12.0 77 45
63 11 34 35 14 12.0 80 48
64 14 31 35 11 14.0 52 31
65 15 34 31 12 13.0 54 33
66 10 35 37 13 11.0 80 49
67 11 36 35 14 17.0 66 42
68 12 30 27 11 12.0 73 41
69 15 39 40 12 13.0 63 38
70 15 35 37 12 14.0 69 42
71 14 38 36 8 13.0 67 44
72 16 31 38 11 15.0 54 33
73 15 34 39 14 13.0 81 48
74 15 38 41 14 10.0 69 40
75 13 34 27 12 11.0 84 50
76 12 39 30 9 19.0 80 49
77 17 37 37 13 13.0 70 43
78 13 34 31 11 17.0 69 44
79 15 28 31 12 13.0 77 47
80 13 37 27 12 9.0 54 33
81 15 33 36 12 11.0 79 46
82 15 35 37 12 9.0 71 45
83 16 37 33 12 12.0 73 43
84 15 32 34 11 12.0 72 44
85 14 33 31 10 13.0 77 47
86 15 38 39 9 13.0 75 45
87 14 33 34 12 12.0 69 42
88 13 29 32 12 15.0 54 33
89 7 33 33 12 22.0 70 43
90 17 31 36 9 13.0 73 46
91 13 36 32 15 15.0 54 33
92 15 35 41 12 13.0 77 46
93 14 32 28 12 15.0 82 48
94 13 29 30 12 12.5 80 47
95 16 39 36 10 11.0 80 47
96 12 37 35 13 16.0 69 43
97 14 35 31 9 11.0 78 46
98 17 37 34 12 11.0 81 48
99 15 32 36 10 10.0 76 46
100 17 38 36 14 10.0 76 45
101 12 37 35 11 16.0 73 45
102 16 36 37 15 12.0 85 52
103 11 32 28 11 11.0 66 42
104 15 33 39 11 16.0 79 47
105 9 40 32 12 19.0 68 41
106 16 38 35 12 11.0 76 47
107 15 41 39 12 16.0 71 43
108 10 36 35 11 15.0 54 33
109 10 43 42 7 24.0 46 30
110 15 30 34 12 14.0 85 52
111 11 31 33 14 15.0 74 44
112 13 32 41 11 11.0 88 55
113 14 32 33 11 15.0 38 11
114 18 37 34 10 12.0 76 47
115 16 37 32 13 10.0 86 53
116 14 33 40 13 14.0 54 33
117 14 34 40 8 13.0 67 44
118 14 33 35 11 9.0 69 42
119 14 38 36 12 15.0 90 55
120 12 33 37 11 15.0 54 33
121 14 31 27 13 14.0 76 46
122 15 38 39 12 11.0 89 54
123 15 37 38 14 8.0 76 47
124 15 36 31 13 11.0 73 45
125 13 31 33 15 11.0 79 47
126 17 39 32 10 8.0 90 55
127 17 44 39 11 10.0 74 44
128 19 33 36 9 11.0 81 53
129 15 35 33 11 13.0 72 44
130 13 32 33 10 11.0 71 42
131 9 28 32 11 20.0 66 40
132 15 40 37 8 10.0 77 46
133 15 27 30 11 15.0 65 40
134 15 37 38 12 12.0 74 46
135 16 32 29 12 14.0 85 53
136 11 28 22 9 23.0 54 33
137 14 34 35 11 14.0 63 42
138 11 30 35 10 16.0 54 35
139 15 35 34 8 11.0 64 40
140 13 31 35 9 12.0 69 41
141 15 32 34 8 10.0 54 33
142 16 30 37 9 14.0 84 51
143 14 30 35 15 12.0 86 53
144 15 31 23 11 12.0 77 46
145 16 40 31 8 11.0 89 55
146 16 32 27 13 12.0 76 47
147 11 36 36 12 13.0 60 38
148 12 32 31 12 11.0 75 46
149 9 35 32 9 19.0 73 46
150 16 38 39 7 12.0 85 53
151 13 42 37 13 17.0 79 47
152 16 34 38 9 9.0 71 41
153 12 35 39 6 12.0 72 44
154 9 38 34 8 19.0 69 43
155 13 33 31 8 18.0 78 51
156 13 36 32 15 15.0 54 33
157 14 32 37 6 14.0 69 43
158 19 33 36 9 11.0 81 53
159 13 34 32 11 9.0 84 51
160 12 32 38 8 18.0 84 50
161 13 34 36 8 16.0 69 46
162 10 27 26 10 24.0 66 43
163 14 31 26 8 14.0 81 47
164 16 38 33 14 20.0 82 50
165 10 34 39 10 18.0 72 43
166 11 24 30 8 23.0 54 33
167 14 30 33 11 12.0 78 48
168 12 26 25 12 14.0 74 44
169 9 34 38 12 16.0 82 50
170 9 27 37 12 18.0 73 41
171 11 37 31 5 20.0 55 34
172 16 36 37 12 12.0 72 44
173 9 41 35 10 12.0 78 47
174 13 29 25 7 17.0 59 35
175 16 36 28 12 13.0 72 44
176 13 32 35 11 9.0 78 44
177 9 37 33 8 16.0 68 43
178 12 30 30 9 18.0 69 41
179 16 31 31 10 10.0 67 41
180 11 38 37 9 14.0 74 42
181 14 36 36 12 11.0 54 33
182 13 35 30 6 9.0 67 41
183 15 31 36 15 11.0 70 44
184 14 38 32 12 10.0 80 48
185 16 22 28 12 11.0 89 55
186 13 32 36 12 19.0 76 44
187 14 36 34 11 14.0 74 43
188 15 39 31 7 12.0 87 52
189 13 28 28 7 14.0 54 30
190 11 32 36 5 21.0 61 39
191 11 32 36 12 13.0 38 11
192 14 38 40 12 10.0 75 44
193 15 32 33 3 15.0 69 42
194 11 35 37 11 16.0 62 41
195 15 32 32 10 14.0 72 44
196 12 37 38 12 12.0 70 44
197 14 34 31 9 19.0 79 48
198 14 33 37 12 15.0 87 53
199 8 33 33 9 19.0 62 37
200 13 26 32 12 13.0 77 44
201 9 30 30 12 17.0 69 44
202 15 24 30 10 12.0 69 40
203 17 34 31 9 11.0 75 42
204 13 34 32 12 14.0 54 35
205 15 33 34 8 11.0 72 43
206 15 34 36 11 13.0 74 45
207 14 35 37 11 12.0 85 55
208 16 35 36 12 15.0 52 31
209 13 36 33 10 14.0 70 44
210 16 34 33 10 12.0 84 50
211 9 34 33 12 17.0 64 40
212 16 41 44 12 11.0 84 53
213 11 32 39 11 18.0 87 54
214 10 30 32 8 13.0 79 49
215 11 35 35 12 17.0 67 40
216 15 28 25 10 13.0 65 41
217 17 33 35 11 11.0 85 52
218 14 39 34 10 12.0 83 52
219 8 36 35 8 22.0 61 36
220 15 36 39 12 14.0 82 52
221 11 35 33 12 12.0 76 46
222 16 38 36 10 12.0 58 31
223 10 33 32 12 17.0 72 44
224 15 31 32 9 9.0 72 44
225 9 34 36 9 21.0 38 11
226 16 32 36 6 10.0 78 46
227 19 31 32 10 11.0 54 33
228 12 33 34 9 12.0 63 34
229 8 34 33 9 23.0 66 42
230 11 34 35 9 13.0 70 43
231 14 34 30 6 12.0 71 43
232 9 33 38 10 16.0 67 44
233 15 32 34 6 9.0 58 36
234 13 41 33 14 17.0 72 46
235 16 34 32 10 9.0 72 44
236 11 36 31 10 14.0 70 43
237 12 37 30 6 17.0 76 50
238 13 36 27 12 13.0 50 33
239 10 29 31 12 11.0 72 43
240 11 37 30 7 12.0 72 44
241 12 27 32 8 10.0 88 53
242 8 35 35 11 19.0 53 34
243 12 28 28 3 16.0 58 35
244 12 35 33 6 16.0 66 40
245 15 37 31 10 14.0 82 53
246 11 29 35 8 20.0 69 42
247 13 32 35 9 15.0 68 43
248 14 36 32 9 23.0 44 29
249 10 19 21 8 20.0 56 36
250 12 21 20 9 16.0 53 30
251 15 31 34 7 14.0 70 42
252 13 33 32 7 17.0 78 47
253 13 36 34 6 11.0 71 44
254 13 33 32 9 13.0 72 45
255 12 37 33 10 17.0 68 44
256 12 34 33 11 15.0 67 43
257 9 35 37 12 21.0 75 43
258 9 31 32 8 18.0 62 40
259 15 37 34 11 15.0 67 41
260 10 35 30 3 8.0 83 52
261 14 27 30 11 12.0 64 38
262 15 34 38 12 12.0 68 41
263 7 40 36 7 22.0 62 39
264 14 29 32 9 12.0 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Depression Sport1
15.369555 0.018321 0.015035 0.062414 -0.386905 0.008476
Sport2
0.023863
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7306 -1.4104 0.2848 1.2100 5.0231
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.369555 1.827523 8.410 2.86e-15 ***
Connected 0.018321 0.037420 0.490 0.625
Separate 0.015035 0.038353 0.392 0.695
Software 0.062414 0.055659 1.121 0.263
Depression -0.386905 0.038924 -9.940 < 2e-16 ***
Sport1 0.008476 0.040722 0.208 0.835
Sport2 0.023863 0.060726 0.393 0.695
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.028 on 257 degrees of freedom
Multiple R-squared: 0.3563, Adjusted R-squared: 0.3412
F-statistic: 23.7 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,] 0.023824045 0.047648090 0.976175955
[2,] 0.005114452 0.010228903 0.994885548
[3,] 0.695650225 0.608699550 0.304349775
[4,] 0.941760293 0.116479413 0.058239707
[5,] 0.922127127 0.155745747 0.077872873
[6,] 0.946858693 0.106282613 0.053141307
[7,] 0.917179970 0.165640059 0.082820030
[8,] 0.924428778 0.151142443 0.075571222
[9,] 0.898735842 0.202528317 0.101264158
[10,] 0.858618485 0.282763029 0.141381515
[11,] 0.856531230 0.286937540 0.143468770
[12,] 0.889736193 0.220527615 0.110263807
[13,] 0.905933510 0.188132979 0.094066490
[14,] 0.886293463 0.227413074 0.113706537
[15,] 0.850287217 0.299425567 0.149712783
[16,] 0.821255994 0.357488011 0.178744006
[17,] 0.998271316 0.003457368 0.001728684
[18,] 0.997305930 0.005388140 0.002694070
[19,] 0.995833284 0.008333431 0.004166716
[20,] 0.993864605 0.012270790 0.006135395
[21,] 0.995425423 0.009149154 0.004574577
[22,] 0.993271492 0.013457016 0.006728508
[23,] 0.990560780 0.018878440 0.009439220
[24,] 0.988875837 0.022248327 0.011124163
[25,] 0.984347286 0.031305428 0.015652714
[26,] 0.980945442 0.038109116 0.019054558
[27,] 0.974297558 0.051404884 0.025702442
[28,] 0.982347290 0.035305421 0.017652710
[29,] 0.976119104 0.047761792 0.023880896
[30,] 0.973941020 0.052117960 0.026058980
[31,] 0.973742854 0.052514292 0.026257146
[32,] 0.966058512 0.067882976 0.033941488
[33,] 0.962602503 0.074794993 0.037397497
[34,] 0.951986055 0.096027890 0.048013945
[35,] 0.944201468 0.111597063 0.055798532
[36,] 0.929635279 0.140729442 0.070364721
[37,] 0.949051777 0.101896446 0.050948223
[38,] 0.935732568 0.128534864 0.064267432
[39,] 0.919721278 0.160557444 0.080278722
[40,] 0.936975399 0.126049202 0.063024601
[41,] 0.937175909 0.125648183 0.062824091
[42,] 0.923274899 0.153450202 0.076725101
[43,] 0.906426856 0.187146288 0.093573144
[44,] 0.898184735 0.203630530 0.101815265
[45,] 0.884971021 0.230057958 0.115028979
[46,] 0.886369414 0.227261171 0.113630586
[47,] 0.873417713 0.253164575 0.126582287
[48,] 0.861783484 0.276433031 0.138216516
[49,] 0.836317614 0.327364773 0.163682386
[50,] 0.873433451 0.253133097 0.126566549
[51,] 0.862597305 0.274805391 0.137402695
[52,] 0.875477535 0.249044931 0.124522465
[53,] 0.872402218 0.255195565 0.127597782
[54,] 0.920293258 0.159413483 0.079706742
[55,] 0.908910155 0.182179690 0.091089845
[56,] 0.901705176 0.196589648 0.098294824
[57,] 0.962421875 0.075156249 0.037578125
[58,] 0.959270877 0.081458245 0.040729123
[59,] 0.958850058 0.082299885 0.041149942
[60,] 0.951323107 0.097353786 0.048676893
[61,] 0.944890857 0.110218286 0.055109143
[62,] 0.932856215 0.134287571 0.067143785
[63,] 0.947579922 0.104840155 0.052420078
[64,] 0.936827763 0.126344474 0.063172237
[65,] 0.924184785 0.151630431 0.075815215
[66,] 0.918371852 0.163256297 0.081628148
[67,] 0.903023011 0.193953978 0.096976989
[68,] 0.915890014 0.168219971 0.084109986
[69,] 0.901367684 0.197264631 0.098632316
[70,] 0.889549452 0.220901095 0.110450548
[71,] 0.880942723 0.238114554 0.119057277
[72,] 0.860689741 0.278620519 0.139310259
[73,] 0.839135281 0.321729438 0.160864719
[74,] 0.829773374 0.340453252 0.170226626
[75,] 0.807735011 0.384529978 0.192264989
[76,] 0.780471686 0.439056627 0.219528314
[77,] 0.755453823 0.489092354 0.244546177
[78,] 0.724880678 0.550238644 0.275119322
[79,] 0.693243762 0.613512476 0.306756238
[80,] 0.770198137 0.459603726 0.229801863
[81,] 0.801176694 0.397646613 0.198823306
[82,] 0.774424993 0.451150013 0.225575007
[83,] 0.749100230 0.501799540 0.250899770
[84,] 0.723715087 0.552569826 0.276284913
[85,] 0.699786600 0.600426799 0.300213400
[86,] 0.674043777 0.651912446 0.325956223
[87,] 0.647337187 0.705325627 0.352662813
[88,] 0.615801487 0.768397027 0.384198513
[89,] 0.615613789 0.768772423 0.384386211
[90,] 0.580781562 0.838436875 0.419218438
[91,] 0.568431224 0.863137552 0.431568776
[92,] 0.541897082 0.916205837 0.458102918
[93,] 0.516606784 0.966786433 0.483393216
[94,] 0.561669596 0.876660808 0.438330404
[95,] 0.554106407 0.891787187 0.445893593
[96,] 0.579574189 0.840851623 0.420425811
[97,] 0.554625016 0.890749968 0.445374984
[98,] 0.548774300 0.902451399 0.451225700
[99,] 0.580716709 0.838566583 0.419283291
[100,] 0.553453651 0.893092697 0.446546349
[101,] 0.529498983 0.941002035 0.470501017
[102,] 0.534621276 0.930757448 0.465378724
[103,] 0.556176737 0.887646525 0.443823263
[104,] 0.549679571 0.900640858 0.450320429
[105,] 0.627544775 0.744910449 0.372455225
[106,] 0.596627259 0.806745482 0.403372741
[107,] 0.567906137 0.864187725 0.432093863
[108,] 0.534795709 0.930408583 0.465204291
[109,] 0.513630138 0.972739725 0.486369862
[110,] 0.480449071 0.960898142 0.519550929
[111,] 0.450430790 0.900861579 0.549569210
[112,] 0.420738218 0.841476437 0.579261782
[113,] 0.390730317 0.781460634 0.609269683
[114,] 0.366493181 0.732986363 0.633506819
[115,] 0.334837938 0.669675875 0.665162062
[116,] 0.326428177 0.652856354 0.673571823
[117,] 0.299771507 0.599543014 0.700228493
[118,] 0.292934313 0.585868625 0.707065687
[119,] 0.401079379 0.802158758 0.598920621
[120,] 0.379130955 0.758261909 0.620869045
[121,] 0.363476870 0.726953740 0.636523130
[122,] 0.358428018 0.716856036 0.641571982
[123,] 0.332185907 0.664371813 0.667814093
[124,] 0.345005970 0.690011940 0.654994030
[125,] 0.317523153 0.635046306 0.682476847
[126,] 0.329829584 0.659659169 0.670170416
[127,] 0.322381156 0.644762313 0.677618844
[128,] 0.295279501 0.590559003 0.704720499
[129,] 0.275944722 0.551889443 0.724055278
[130,] 0.250854943 0.501709886 0.749145057
[131,] 0.231105783 0.462211565 0.768894217
[132,] 0.207737427 0.415474853 0.792262573
[133,] 0.217349546 0.434699092 0.782650454
[134,] 0.193711122 0.387422243 0.806288878
[135,] 0.175597548 0.351195097 0.824402452
[136,] 0.161237782 0.322475565 0.838762218
[137,] 0.158830101 0.317660201 0.841169899
[138,] 0.172795747 0.345591494 0.827204253
[139,] 0.185716384 0.371432769 0.814283616
[140,] 0.201652979 0.403305958 0.798347021
[141,] 0.196330700 0.392661399 0.803669300
[142,] 0.176679958 0.353359916 0.823320042
[143,] 0.160348653 0.320697306 0.839651347
[144,] 0.164419000 0.328838000 0.835581000
[145,] 0.174209778 0.348419557 0.825790222
[146,] 0.158375474 0.316750948 0.841624526
[147,] 0.137879770 0.275759540 0.862120230
[148,] 0.123447277 0.246894555 0.876552723
[149,] 0.207136356 0.414272711 0.792863644
[150,] 0.218994703 0.437989406 0.781005297
[151,] 0.198847602 0.397695205 0.801152398
[152,] 0.178211123 0.356422247 0.821788877
[153,] 0.157118898 0.314237796 0.842881102
[154,] 0.138611681 0.277223361 0.861388319
[155,] 0.247591137 0.495182274 0.752408863
[156,] 0.242379083 0.484758166 0.757620917
[157,] 0.233521099 0.467042199 0.766478901
[158,] 0.206834154 0.413668308 0.793165846
[159,] 0.189977070 0.379954140 0.810022930
[160,] 0.254493579 0.508987159 0.745506421
[161,] 0.276045869 0.552091738 0.723954131
[162,] 0.251394479 0.502788958 0.748605521
[163,] 0.248689214 0.497378429 0.751310786
[164,] 0.426894643 0.853789287 0.573105357
[165,] 0.407743160 0.815486321 0.592256840
[166,] 0.420162128 0.840324257 0.579837872
[167,] 0.428450786 0.856901573 0.571549214
[168,] 0.487589889 0.975179777 0.512410111
[169,] 0.453199101 0.906398201 0.546800899
[170,] 0.431702704 0.863405408 0.568297296
[171,] 0.436696675 0.873393349 0.563303325
[172,] 0.399144567 0.798289134 0.600855433
[173,] 0.392489577 0.784979155 0.607510423
[174,] 0.356798400 0.713596800 0.643201600
[175,] 0.330132688 0.660265376 0.669867312
[176,] 0.313751340 0.627502679 0.686248660
[177,] 0.308116664 0.616233327 0.691883336
[178,] 0.279762447 0.559524895 0.720237553
[179,] 0.255667715 0.511335431 0.744332285
[180,] 0.225852151 0.451704303 0.774147849
[181,] 0.205666374 0.411332747 0.794333626
[182,] 0.216553810 0.433107619 0.783446190
[183,] 0.197537532 0.395075065 0.802462468
[184,] 0.229187386 0.458374773 0.770812614
[185,] 0.212603238 0.425206476 0.787396762
[186,] 0.209724915 0.419449829 0.790275085
[187,] 0.219755667 0.439511334 0.780244333
[188,] 0.286379765 0.572759530 0.713620235
[189,] 0.269110682 0.538221365 0.730889318
[190,] 0.297335041 0.594670081 0.702664959
[191,] 0.263720947 0.527441893 0.736279053
[192,] 0.294626462 0.589252924 0.705373538
[193,] 0.271728198 0.543456396 0.728271802
[194,] 0.315091827 0.630183653 0.684908173
[195,] 0.283953472 0.567906944 0.716046528
[196,] 0.255024837 0.510049673 0.744975163
[197,] 0.235723402 0.471446803 0.764276598
[198,] 0.205020417 0.410040833 0.794979583
[199,] 0.230447357 0.460894714 0.769552643
[200,] 0.198504937 0.397009874 0.801495063
[201,] 0.221019256 0.442038513 0.778980744
[202,] 0.262100553 0.524201107 0.737899447
[203,] 0.239596265 0.479192529 0.760403735
[204,] 0.211682916 0.423365831 0.788317084
[205,] 0.246810605 0.493621210 0.753189395
[206,] 0.217580003 0.435160006 0.782419997
[207,] 0.207003502 0.414007004 0.792996498
[208,] 0.256920099 0.513840198 0.743079901
[209,] 0.227546653 0.455093305 0.772453347
[210,] 0.209952584 0.419905167 0.790047416
[211,] 0.220745527 0.441491054 0.779254473
[212,] 0.235856655 0.471713310 0.764143345
[213,] 0.237528294 0.475056588 0.762471706
[214,] 0.219658979 0.439317959 0.780341021
[215,] 0.185164003 0.370328007 0.814835997
[216,] 0.171920149 0.343840299 0.828079851
[217,] 0.188918726 0.377837452 0.811081274
[218,] 0.380512137 0.761024274 0.619487863
[219,] 0.360770402 0.721540805 0.639229598
[220,] 0.335963316 0.671926632 0.664036684
[221,] 0.326059071 0.652118142 0.673940929
[222,] 0.287986828 0.575973655 0.712013172
[223,] 0.351018326 0.702036652 0.648981674
[224,] 0.306298318 0.612596636 0.693701682
[225,] 0.261524154 0.523048309 0.738475846
[226,] 0.261533785 0.523067571 0.738466215
[227,] 0.236237725 0.472475451 0.763762275
[228,] 0.200884761 0.401769522 0.799115239
[229,] 0.160460575 0.320921150 0.839539425
[230,] 0.286698273 0.573396547 0.713301727
[231,] 0.286751187 0.573502373 0.713248813
[232,] 0.255426127 0.510852254 0.744573873
[233,] 0.483762776 0.967525552 0.516237224
[234,] 0.438044816 0.876089631 0.561955184
[235,] 0.369668759 0.739337518 0.630331241
[236,] 0.488498687 0.976997375 0.511501313
[237,] 0.407797715 0.815595431 0.592202285
[238,] 0.326429495 0.652858991 0.673570505
[239,] 0.512419547 0.975160907 0.487580453
[240,] 0.419932394 0.839864788 0.580067606
[241,] 0.323854195 0.647708389 0.676145805
[242,] 0.480630046 0.961260091 0.519369954
[243,] 0.890342968 0.219314065 0.109657032
[244,] 0.816192064 0.367615872 0.183807936
[245,] 0.691628848 0.616742304 0.308371152
> postscript(file="/var/fisher/rcomp/tmp/1rbu61384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/29pe81384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ioli1384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4qjtd1384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5uzn71384792467.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
-0.010933791 3.083768922 -2.526539595 -1.711488373 5.023067454 3.789303358
7 8 9 10 11 12
3.880690198 -0.998855157 -0.136236100 1.083621531 1.874260408 3.560849361
13 14 15 16 17 18
-3.107434751 2.829594646 2.400332767 0.865109551 0.572841316 1.835503276
19 20 21 22 23 24
-0.993682462 2.501714311 2.999332558 -2.362156359 -0.069044172 -1.261199271
25 26 27 28 29 30
1.972065607 -6.730634917 1.406271286 0.764596365 1.462514607 -2.700789640
31 32 33 34 35 36
0.482297615 0.713800045 2.103560960 -0.042276346 0.207881745 0.554665533
37 38 39 40 41 42
-1.607424921 0.740795301 2.136162733 -1.849052610 -0.371530612 2.493482937
43 44 45 46 47 48
0.367034321 -0.894858962 0.558062568 -2.823675036 -0.558330173 0.350742422
49 50 51 52 53 54
3.887188607 -1.749344615 0.807929437 0.863311060 -0.653509535 -1.581646307
55 56 57 58 59 60
-1.886051135 1.896325306 1.984551681 -0.248944096 -3.050976817 -0.961898602
61 62 63 64 65 66
-2.251745831 -1.409459455 -3.573052812 1.085941516 1.577121284 -4.969796474
67 68 69 70 71 72
-1.413334923 -1.965884288 1.154613508 1.513602458 0.305651423 3.363067138
73 74 75 76 77 78
0.745238900 -0.226222698 -1.796481194 0.407060423 2.995303171 0.797535430
79 80 81 82 83 84
1.158033328 -1.965324650 0.224357032 -0.509462830 1.705523264 0.829120381
85 86 87 88 89 90
0.191257023 1.106466726 -0.178462862 0.427501898 -3.326713234 3.272905156
91 92 93 94 95 96
0.112013357 0.903304475 0.837423543 -1.064132431 1.206921745 -0.805436279
97 98 99 100 101 102
-0.541394378 2.116466294 0.006027497 1.670308279 -0.762235414 1.159994375
103 104 105 106 107 108
-3.368999430 2.152331680 -2.535964790 1.149351257 2.106605045 -2.683433816
109 110 111 112 113 114
0.954274692 1.276076188 -2.181001659 -2.061123300 2.080503245 3.694440160
115 116 117 118 119 120
0.535525476 0.784622829 0.318796972 -1.291799244 0.372379920 -0.658540146
121 122 123 124 125 126
0.520037994 -0.188006804 -1.162975513 0.256868740 -1.905002428 0.830687995
127 128 129 130 131 132
1.743335295 4.227610552 1.176097521 -1.424135742 -1.825980968 -0.039221577
133 134 135 136 137 138
2.296357916 0.550287602 2.290744427 1.878652717 0.675259886 -1.171914059
139 140 141 142 143 144
0.737749262 -0.945750798 0.657599942 2.450553011 -0.732349313 0.922717996
145 146 147 148 149 150
1.121421317 1.704043942 -2.704859054 -2.648247242 -2.418808470 1.568733757
151 152 153 154 155 156
0.279589774 0.776515881 -1.988944413 -2.335936262 1.146686172 0.112013357
157 158 159 160 161 162
0.919187163 4.227610552 -2.606912807 0.032774594 0.474974799 0.820994811
163 164 165 166 167 168
0.780902577 4.414299013 -1.874985433 1.894074316 -0.265506801 -1.231198246
169 170 171 172 173 174
-4.010383052 -2.802248857 0.635056372 1.648319179 -5.410829077 1.528521663
175 176 177 178 179 180
2.170535083 -2.397483514 -3.454821247 0.469174567 1.295113651 -2.396495820
181 182 183 184 185 186
-0.308503696 -1.900384392 0.197761726 -1.250215334 1.246644626 1.411072234
187 188 189 190 191 192
0.536558809 0.677600308 0.502713498 0.868227630 -1.800825012 -1.232663495
193 194 195 196 197 198
2.577335301 -1.566981303 1.695414129 -2.368085086 2.515968482 0.522101635
199 200 201 202 203 204
-3.089207275 -0.748771844 -3.176560563 1.219116449 2.597803290 -0.098732934
205 206 207 208 209 210
0.634998944 1.108501233 -0.643615919 3.322114653 -0.375952825 1.625045248
211 212 213 214 215 216
-3.157119764 0.748098329 -1.290378952 -3.708662791 -1.230936391 1.617950499
217 218 219 220 221 222
2.107777116 -0.520843202 -1.918770848 1.116404975 -3.354849200 2.180410755
223 224 225 226 227 228
-2.287018983 -0.158377489 -0.554981976 1.238732710 4.968067000 -1.749467054
229 230 231 232 233 234
-1.713121747 -2.670008768 0.197025153 -3.596925329 0.290032971 0.378826130
235 236 237 238 239 240
0.724245734 -2.322021291 -0.132826391 -0.365480188 -4.496270296 -2.952689710
241 242 243 244 245 246
-2.986146259 -3.132878582 0.372965636 -0.204812591 1.319325815 0.224685132
247 248 249 250 251 252
0.157394808 4.761946219 -0.128265265 0.408693882 1.935584443 0.902610490
253 254 255 256 257 258
-1.310522512 -0.671260514 -0.216606618 -0.965530573 -1.852776238 -2.433609749
259 260 261 262 263 264
2.012197258 -5.498144242 0.191842555 0.775416290 -3.024737969 0.062842780
> postscript(file="/var/fisher/rcomp/tmp/6sk5u1384792467.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 -0.010933791 NA
1 3.083768922 -0.010933791
2 -2.526539595 3.083768922
3 -1.711488373 -2.526539595
4 5.023067454 -1.711488373
5 3.789303358 5.023067454
6 3.880690198 3.789303358
7 -0.998855157 3.880690198
8 -0.136236100 -0.998855157
9 1.083621531 -0.136236100
10 1.874260408 1.083621531
11 3.560849361 1.874260408
12 -3.107434751 3.560849361
13 2.829594646 -3.107434751
14 2.400332767 2.829594646
15 0.865109551 2.400332767
16 0.572841316 0.865109551
17 1.835503276 0.572841316
18 -0.993682462 1.835503276
19 2.501714311 -0.993682462
20 2.999332558 2.501714311
21 -2.362156359 2.999332558
22 -0.069044172 -2.362156359
23 -1.261199271 -0.069044172
24 1.972065607 -1.261199271
25 -6.730634917 1.972065607
26 1.406271286 -6.730634917
27 0.764596365 1.406271286
28 1.462514607 0.764596365
29 -2.700789640 1.462514607
30 0.482297615 -2.700789640
31 0.713800045 0.482297615
32 2.103560960 0.713800045
33 -0.042276346 2.103560960
34 0.207881745 -0.042276346
35 0.554665533 0.207881745
36 -1.607424921 0.554665533
37 0.740795301 -1.607424921
38 2.136162733 0.740795301
39 -1.849052610 2.136162733
40 -0.371530612 -1.849052610
41 2.493482937 -0.371530612
42 0.367034321 2.493482937
43 -0.894858962 0.367034321
44 0.558062568 -0.894858962
45 -2.823675036 0.558062568
46 -0.558330173 -2.823675036
47 0.350742422 -0.558330173
48 3.887188607 0.350742422
49 -1.749344615 3.887188607
50 0.807929437 -1.749344615
51 0.863311060 0.807929437
52 -0.653509535 0.863311060
53 -1.581646307 -0.653509535
54 -1.886051135 -1.581646307
55 1.896325306 -1.886051135
56 1.984551681 1.896325306
57 -0.248944096 1.984551681
58 -3.050976817 -0.248944096
59 -0.961898602 -3.050976817
60 -2.251745831 -0.961898602
61 -1.409459455 -2.251745831
62 -3.573052812 -1.409459455
63 1.085941516 -3.573052812
64 1.577121284 1.085941516
65 -4.969796474 1.577121284
66 -1.413334923 -4.969796474
67 -1.965884288 -1.413334923
68 1.154613508 -1.965884288
69 1.513602458 1.154613508
70 0.305651423 1.513602458
71 3.363067138 0.305651423
72 0.745238900 3.363067138
73 -0.226222698 0.745238900
74 -1.796481194 -0.226222698
75 0.407060423 -1.796481194
76 2.995303171 0.407060423
77 0.797535430 2.995303171
78 1.158033328 0.797535430
79 -1.965324650 1.158033328
80 0.224357032 -1.965324650
81 -0.509462830 0.224357032
82 1.705523264 -0.509462830
83 0.829120381 1.705523264
84 0.191257023 0.829120381
85 1.106466726 0.191257023
86 -0.178462862 1.106466726
87 0.427501898 -0.178462862
88 -3.326713234 0.427501898
89 3.272905156 -3.326713234
90 0.112013357 3.272905156
91 0.903304475 0.112013357
92 0.837423543 0.903304475
93 -1.064132431 0.837423543
94 1.206921745 -1.064132431
95 -0.805436279 1.206921745
96 -0.541394378 -0.805436279
97 2.116466294 -0.541394378
98 0.006027497 2.116466294
99 1.670308279 0.006027497
100 -0.762235414 1.670308279
101 1.159994375 -0.762235414
102 -3.368999430 1.159994375
103 2.152331680 -3.368999430
104 -2.535964790 2.152331680
105 1.149351257 -2.535964790
106 2.106605045 1.149351257
107 -2.683433816 2.106605045
108 0.954274692 -2.683433816
109 1.276076188 0.954274692
110 -2.181001659 1.276076188
111 -2.061123300 -2.181001659
112 2.080503245 -2.061123300
113 3.694440160 2.080503245
114 0.535525476 3.694440160
115 0.784622829 0.535525476
116 0.318796972 0.784622829
117 -1.291799244 0.318796972
118 0.372379920 -1.291799244
119 -0.658540146 0.372379920
120 0.520037994 -0.658540146
121 -0.188006804 0.520037994
122 -1.162975513 -0.188006804
123 0.256868740 -1.162975513
124 -1.905002428 0.256868740
125 0.830687995 -1.905002428
126 1.743335295 0.830687995
127 4.227610552 1.743335295
128 1.176097521 4.227610552
129 -1.424135742 1.176097521
130 -1.825980968 -1.424135742
131 -0.039221577 -1.825980968
132 2.296357916 -0.039221577
133 0.550287602 2.296357916
134 2.290744427 0.550287602
135 1.878652717 2.290744427
136 0.675259886 1.878652717
137 -1.171914059 0.675259886
138 0.737749262 -1.171914059
139 -0.945750798 0.737749262
140 0.657599942 -0.945750798
141 2.450553011 0.657599942
142 -0.732349313 2.450553011
143 0.922717996 -0.732349313
144 1.121421317 0.922717996
145 1.704043942 1.121421317
146 -2.704859054 1.704043942
147 -2.648247242 -2.704859054
148 -2.418808470 -2.648247242
149 1.568733757 -2.418808470
150 0.279589774 1.568733757
151 0.776515881 0.279589774
152 -1.988944413 0.776515881
153 -2.335936262 -1.988944413
154 1.146686172 -2.335936262
155 0.112013357 1.146686172
156 0.919187163 0.112013357
157 4.227610552 0.919187163
158 -2.606912807 4.227610552
159 0.032774594 -2.606912807
160 0.474974799 0.032774594
161 0.820994811 0.474974799
162 0.780902577 0.820994811
163 4.414299013 0.780902577
164 -1.874985433 4.414299013
165 1.894074316 -1.874985433
166 -0.265506801 1.894074316
167 -1.231198246 -0.265506801
168 -4.010383052 -1.231198246
169 -2.802248857 -4.010383052
170 0.635056372 -2.802248857
171 1.648319179 0.635056372
172 -5.410829077 1.648319179
173 1.528521663 -5.410829077
174 2.170535083 1.528521663
175 -2.397483514 2.170535083
176 -3.454821247 -2.397483514
177 0.469174567 -3.454821247
178 1.295113651 0.469174567
179 -2.396495820 1.295113651
180 -0.308503696 -2.396495820
181 -1.900384392 -0.308503696
182 0.197761726 -1.900384392
183 -1.250215334 0.197761726
184 1.246644626 -1.250215334
185 1.411072234 1.246644626
186 0.536558809 1.411072234
187 0.677600308 0.536558809
188 0.502713498 0.677600308
189 0.868227630 0.502713498
190 -1.800825012 0.868227630
191 -1.232663495 -1.800825012
192 2.577335301 -1.232663495
193 -1.566981303 2.577335301
194 1.695414129 -1.566981303
195 -2.368085086 1.695414129
196 2.515968482 -2.368085086
197 0.522101635 2.515968482
198 -3.089207275 0.522101635
199 -0.748771844 -3.089207275
200 -3.176560563 -0.748771844
201 1.219116449 -3.176560563
202 2.597803290 1.219116449
203 -0.098732934 2.597803290
204 0.634998944 -0.098732934
205 1.108501233 0.634998944
206 -0.643615919 1.108501233
207 3.322114653 -0.643615919
208 -0.375952825 3.322114653
209 1.625045248 -0.375952825
210 -3.157119764 1.625045248
211 0.748098329 -3.157119764
212 -1.290378952 0.748098329
213 -3.708662791 -1.290378952
214 -1.230936391 -3.708662791
215 1.617950499 -1.230936391
216 2.107777116 1.617950499
217 -0.520843202 2.107777116
218 -1.918770848 -0.520843202
219 1.116404975 -1.918770848
220 -3.354849200 1.116404975
221 2.180410755 -3.354849200
222 -2.287018983 2.180410755
223 -0.158377489 -2.287018983
224 -0.554981976 -0.158377489
225 1.238732710 -0.554981976
226 4.968067000 1.238732710
227 -1.749467054 4.968067000
228 -1.713121747 -1.749467054
229 -2.670008768 -1.713121747
230 0.197025153 -2.670008768
231 -3.596925329 0.197025153
232 0.290032971 -3.596925329
233 0.378826130 0.290032971
234 0.724245734 0.378826130
235 -2.322021291 0.724245734
236 -0.132826391 -2.322021291
237 -0.365480188 -0.132826391
238 -4.496270296 -0.365480188
239 -2.952689710 -4.496270296
240 -2.986146259 -2.952689710
241 -3.132878582 -2.986146259
242 0.372965636 -3.132878582
243 -0.204812591 0.372965636
244 1.319325815 -0.204812591
245 0.224685132 1.319325815
246 0.157394808 0.224685132
247 4.761946219 0.157394808
248 -0.128265265 4.761946219
249 0.408693882 -0.128265265
250 1.935584443 0.408693882
251 0.902610490 1.935584443
252 -1.310522512 0.902610490
253 -0.671260514 -1.310522512
254 -0.216606618 -0.671260514
255 -0.965530573 -0.216606618
256 -1.852776238 -0.965530573
257 -2.433609749 -1.852776238
258 2.012197258 -2.433609749
259 -5.498144242 2.012197258
260 0.191842555 -5.498144242
261 0.775416290 0.191842555
262 -3.024737969 0.775416290
263 0.062842780 -3.024737969
264 NA 0.062842780
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.083768922 -0.010933791
[2,] -2.526539595 3.083768922
[3,] -1.711488373 -2.526539595
[4,] 5.023067454 -1.711488373
[5,] 3.789303358 5.023067454
[6,] 3.880690198 3.789303358
[7,] -0.998855157 3.880690198
[8,] -0.136236100 -0.998855157
[9,] 1.083621531 -0.136236100
[10,] 1.874260408 1.083621531
[11,] 3.560849361 1.874260408
[12,] -3.107434751 3.560849361
[13,] 2.829594646 -3.107434751
[14,] 2.400332767 2.829594646
[15,] 0.865109551 2.400332767
[16,] 0.572841316 0.865109551
[17,] 1.835503276 0.572841316
[18,] -0.993682462 1.835503276
[19,] 2.501714311 -0.993682462
[20,] 2.999332558 2.501714311
[21,] -2.362156359 2.999332558
[22,] -0.069044172 -2.362156359
[23,] -1.261199271 -0.069044172
[24,] 1.972065607 -1.261199271
[25,] -6.730634917 1.972065607
[26,] 1.406271286 -6.730634917
[27,] 0.764596365 1.406271286
[28,] 1.462514607 0.764596365
[29,] -2.700789640 1.462514607
[30,] 0.482297615 -2.700789640
[31,] 0.713800045 0.482297615
[32,] 2.103560960 0.713800045
[33,] -0.042276346 2.103560960
[34,] 0.207881745 -0.042276346
[35,] 0.554665533 0.207881745
[36,] -1.607424921 0.554665533
[37,] 0.740795301 -1.607424921
[38,] 2.136162733 0.740795301
[39,] -1.849052610 2.136162733
[40,] -0.371530612 -1.849052610
[41,] 2.493482937 -0.371530612
[42,] 0.367034321 2.493482937
[43,] -0.894858962 0.367034321
[44,] 0.558062568 -0.894858962
[45,] -2.823675036 0.558062568
[46,] -0.558330173 -2.823675036
[47,] 0.350742422 -0.558330173
[48,] 3.887188607 0.350742422
[49,] -1.749344615 3.887188607
[50,] 0.807929437 -1.749344615
[51,] 0.863311060 0.807929437
[52,] -0.653509535 0.863311060
[53,] -1.581646307 -0.653509535
[54,] -1.886051135 -1.581646307
[55,] 1.896325306 -1.886051135
[56,] 1.984551681 1.896325306
[57,] -0.248944096 1.984551681
[58,] -3.050976817 -0.248944096
[59,] -0.961898602 -3.050976817
[60,] -2.251745831 -0.961898602
[61,] -1.409459455 -2.251745831
[62,] -3.573052812 -1.409459455
[63,] 1.085941516 -3.573052812
[64,] 1.577121284 1.085941516
[65,] -4.969796474 1.577121284
[66,] -1.413334923 -4.969796474
[67,] -1.965884288 -1.413334923
[68,] 1.154613508 -1.965884288
[69,] 1.513602458 1.154613508
[70,] 0.305651423 1.513602458
[71,] 3.363067138 0.305651423
[72,] 0.745238900 3.363067138
[73,] -0.226222698 0.745238900
[74,] -1.796481194 -0.226222698
[75,] 0.407060423 -1.796481194
[76,] 2.995303171 0.407060423
[77,] 0.797535430 2.995303171
[78,] 1.158033328 0.797535430
[79,] -1.965324650 1.158033328
[80,] 0.224357032 -1.965324650
[81,] -0.509462830 0.224357032
[82,] 1.705523264 -0.509462830
[83,] 0.829120381 1.705523264
[84,] 0.191257023 0.829120381
[85,] 1.106466726 0.191257023
[86,] -0.178462862 1.106466726
[87,] 0.427501898 -0.178462862
[88,] -3.326713234 0.427501898
[89,] 3.272905156 -3.326713234
[90,] 0.112013357 3.272905156
[91,] 0.903304475 0.112013357
[92,] 0.837423543 0.903304475
[93,] -1.064132431 0.837423543
[94,] 1.206921745 -1.064132431
[95,] -0.805436279 1.206921745
[96,] -0.541394378 -0.805436279
[97,] 2.116466294 -0.541394378
[98,] 0.006027497 2.116466294
[99,] 1.670308279 0.006027497
[100,] -0.762235414 1.670308279
[101,] 1.159994375 -0.762235414
[102,] -3.368999430 1.159994375
[103,] 2.152331680 -3.368999430
[104,] -2.535964790 2.152331680
[105,] 1.149351257 -2.535964790
[106,] 2.106605045 1.149351257
[107,] -2.683433816 2.106605045
[108,] 0.954274692 -2.683433816
[109,] 1.276076188 0.954274692
[110,] -2.181001659 1.276076188
[111,] -2.061123300 -2.181001659
[112,] 2.080503245 -2.061123300
[113,] 3.694440160 2.080503245
[114,] 0.535525476 3.694440160
[115,] 0.784622829 0.535525476
[116,] 0.318796972 0.784622829
[117,] -1.291799244 0.318796972
[118,] 0.372379920 -1.291799244
[119,] -0.658540146 0.372379920
[120,] 0.520037994 -0.658540146
[121,] -0.188006804 0.520037994
[122,] -1.162975513 -0.188006804
[123,] 0.256868740 -1.162975513
[124,] -1.905002428 0.256868740
[125,] 0.830687995 -1.905002428
[126,] 1.743335295 0.830687995
[127,] 4.227610552 1.743335295
[128,] 1.176097521 4.227610552
[129,] -1.424135742 1.176097521
[130,] -1.825980968 -1.424135742
[131,] -0.039221577 -1.825980968
[132,] 2.296357916 -0.039221577
[133,] 0.550287602 2.296357916
[134,] 2.290744427 0.550287602
[135,] 1.878652717 2.290744427
[136,] 0.675259886 1.878652717
[137,] -1.171914059 0.675259886
[138,] 0.737749262 -1.171914059
[139,] -0.945750798 0.737749262
[140,] 0.657599942 -0.945750798
[141,] 2.450553011 0.657599942
[142,] -0.732349313 2.450553011
[143,] 0.922717996 -0.732349313
[144,] 1.121421317 0.922717996
[145,] 1.704043942 1.121421317
[146,] -2.704859054 1.704043942
[147,] -2.648247242 -2.704859054
[148,] -2.418808470 -2.648247242
[149,] 1.568733757 -2.418808470
[150,] 0.279589774 1.568733757
[151,] 0.776515881 0.279589774
[152,] -1.988944413 0.776515881
[153,] -2.335936262 -1.988944413
[154,] 1.146686172 -2.335936262
[155,] 0.112013357 1.146686172
[156,] 0.919187163 0.112013357
[157,] 4.227610552 0.919187163
[158,] -2.606912807 4.227610552
[159,] 0.032774594 -2.606912807
[160,] 0.474974799 0.032774594
[161,] 0.820994811 0.474974799
[162,] 0.780902577 0.820994811
[163,] 4.414299013 0.780902577
[164,] -1.874985433 4.414299013
[165,] 1.894074316 -1.874985433
[166,] -0.265506801 1.894074316
[167,] -1.231198246 -0.265506801
[168,] -4.010383052 -1.231198246
[169,] -2.802248857 -4.010383052
[170,] 0.635056372 -2.802248857
[171,] 1.648319179 0.635056372
[172,] -5.410829077 1.648319179
[173,] 1.528521663 -5.410829077
[174,] 2.170535083 1.528521663
[175,] -2.397483514 2.170535083
[176,] -3.454821247 -2.397483514
[177,] 0.469174567 -3.454821247
[178,] 1.295113651 0.469174567
[179,] -2.396495820 1.295113651
[180,] -0.308503696 -2.396495820
[181,] -1.900384392 -0.308503696
[182,] 0.197761726 -1.900384392
[183,] -1.250215334 0.197761726
[184,] 1.246644626 -1.250215334
[185,] 1.411072234 1.246644626
[186,] 0.536558809 1.411072234
[187,] 0.677600308 0.536558809
[188,] 0.502713498 0.677600308
[189,] 0.868227630 0.502713498
[190,] -1.800825012 0.868227630
[191,] -1.232663495 -1.800825012
[192,] 2.577335301 -1.232663495
[193,] -1.566981303 2.577335301
[194,] 1.695414129 -1.566981303
[195,] -2.368085086 1.695414129
[196,] 2.515968482 -2.368085086
[197,] 0.522101635 2.515968482
[198,] -3.089207275 0.522101635
[199,] -0.748771844 -3.089207275
[200,] -3.176560563 -0.748771844
[201,] 1.219116449 -3.176560563
[202,] 2.597803290 1.219116449
[203,] -0.098732934 2.597803290
[204,] 0.634998944 -0.098732934
[205,] 1.108501233 0.634998944
[206,] -0.643615919 1.108501233
[207,] 3.322114653 -0.643615919
[208,] -0.375952825 3.322114653
[209,] 1.625045248 -0.375952825
[210,] -3.157119764 1.625045248
[211,] 0.748098329 -3.157119764
[212,] -1.290378952 0.748098329
[213,] -3.708662791 -1.290378952
[214,] -1.230936391 -3.708662791
[215,] 1.617950499 -1.230936391
[216,] 2.107777116 1.617950499
[217,] -0.520843202 2.107777116
[218,] -1.918770848 -0.520843202
[219,] 1.116404975 -1.918770848
[220,] -3.354849200 1.116404975
[221,] 2.180410755 -3.354849200
[222,] -2.287018983 2.180410755
[223,] -0.158377489 -2.287018983
[224,] -0.554981976 -0.158377489
[225,] 1.238732710 -0.554981976
[226,] 4.968067000 1.238732710
[227,] -1.749467054 4.968067000
[228,] -1.713121747 -1.749467054
[229,] -2.670008768 -1.713121747
[230,] 0.197025153 -2.670008768
[231,] -3.596925329 0.197025153
[232,] 0.290032971 -3.596925329
[233,] 0.378826130 0.290032971
[234,] 0.724245734 0.378826130
[235,] -2.322021291 0.724245734
[236,] -0.132826391 -2.322021291
[237,] -0.365480188 -0.132826391
[238,] -4.496270296 -0.365480188
[239,] -2.952689710 -4.496270296
[240,] -2.986146259 -2.952689710
[241,] -3.132878582 -2.986146259
[242,] 0.372965636 -3.132878582
[243,] -0.204812591 0.372965636
[244,] 1.319325815 -0.204812591
[245,] 0.224685132 1.319325815
[246,] 0.157394808 0.224685132
[247,] 4.761946219 0.157394808
[248,] -0.128265265 4.761946219
[249,] 0.408693882 -0.128265265
[250,] 1.935584443 0.408693882
[251,] 0.902610490 1.935584443
[252,] -1.310522512 0.902610490
[253,] -0.671260514 -1.310522512
[254,] -0.216606618 -0.671260514
[255,] -0.965530573 -0.216606618
[256,] -1.852776238 -0.965530573
[257,] -2.433609749 -1.852776238
[258,] 2.012197258 -2.433609749
[259,] -5.498144242 2.012197258
[260,] 0.191842555 -5.498144242
[261,] 0.775416290 0.191842555
[262,] -3.024737969 0.775416290
[263,] 0.062842780 -3.024737969
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.083768922 -0.010933791
2 -2.526539595 3.083768922
3 -1.711488373 -2.526539595
4 5.023067454 -1.711488373
5 3.789303358 5.023067454
6 3.880690198 3.789303358
7 -0.998855157 3.880690198
8 -0.136236100 -0.998855157
9 1.083621531 -0.136236100
10 1.874260408 1.083621531
11 3.560849361 1.874260408
12 -3.107434751 3.560849361
13 2.829594646 -3.107434751
14 2.400332767 2.829594646
15 0.865109551 2.400332767
16 0.572841316 0.865109551
17 1.835503276 0.572841316
18 -0.993682462 1.835503276
19 2.501714311 -0.993682462
20 2.999332558 2.501714311
21 -2.362156359 2.999332558
22 -0.069044172 -2.362156359
23 -1.261199271 -0.069044172
24 1.972065607 -1.261199271
25 -6.730634917 1.972065607
26 1.406271286 -6.730634917
27 0.764596365 1.406271286
28 1.462514607 0.764596365
29 -2.700789640 1.462514607
30 0.482297615 -2.700789640
31 0.713800045 0.482297615
32 2.103560960 0.713800045
33 -0.042276346 2.103560960
34 0.207881745 -0.042276346
35 0.554665533 0.207881745
36 -1.607424921 0.554665533
37 0.740795301 -1.607424921
38 2.136162733 0.740795301
39 -1.849052610 2.136162733
40 -0.371530612 -1.849052610
41 2.493482937 -0.371530612
42 0.367034321 2.493482937
43 -0.894858962 0.367034321
44 0.558062568 -0.894858962
45 -2.823675036 0.558062568
46 -0.558330173 -2.823675036
47 0.350742422 -0.558330173
48 3.887188607 0.350742422
49 -1.749344615 3.887188607
50 0.807929437 -1.749344615
51 0.863311060 0.807929437
52 -0.653509535 0.863311060
53 -1.581646307 -0.653509535
54 -1.886051135 -1.581646307
55 1.896325306 -1.886051135
56 1.984551681 1.896325306
57 -0.248944096 1.984551681
58 -3.050976817 -0.248944096
59 -0.961898602 -3.050976817
60 -2.251745831 -0.961898602
61 -1.409459455 -2.251745831
62 -3.573052812 -1.409459455
63 1.085941516 -3.573052812
64 1.577121284 1.085941516
65 -4.969796474 1.577121284
66 -1.413334923 -4.969796474
67 -1.965884288 -1.413334923
68 1.154613508 -1.965884288
69 1.513602458 1.154613508
70 0.305651423 1.513602458
71 3.363067138 0.305651423
72 0.745238900 3.363067138
73 -0.226222698 0.745238900
74 -1.796481194 -0.226222698
75 0.407060423 -1.796481194
76 2.995303171 0.407060423
77 0.797535430 2.995303171
78 1.158033328 0.797535430
79 -1.965324650 1.158033328
80 0.224357032 -1.965324650
81 -0.509462830 0.224357032
82 1.705523264 -0.509462830
83 0.829120381 1.705523264
84 0.191257023 0.829120381
85 1.106466726 0.191257023
86 -0.178462862 1.106466726
87 0.427501898 -0.178462862
88 -3.326713234 0.427501898
89 3.272905156 -3.326713234
90 0.112013357 3.272905156
91 0.903304475 0.112013357
92 0.837423543 0.903304475
93 -1.064132431 0.837423543
94 1.206921745 -1.064132431
95 -0.805436279 1.206921745
96 -0.541394378 -0.805436279
97 2.116466294 -0.541394378
98 0.006027497 2.116466294
99 1.670308279 0.006027497
100 -0.762235414 1.670308279
101 1.159994375 -0.762235414
102 -3.368999430 1.159994375
103 2.152331680 -3.368999430
104 -2.535964790 2.152331680
105 1.149351257 -2.535964790
106 2.106605045 1.149351257
107 -2.683433816 2.106605045
108 0.954274692 -2.683433816
109 1.276076188 0.954274692
110 -2.181001659 1.276076188
111 -2.061123300 -2.181001659
112 2.080503245 -2.061123300
113 3.694440160 2.080503245
114 0.535525476 3.694440160
115 0.784622829 0.535525476
116 0.318796972 0.784622829
117 -1.291799244 0.318796972
118 0.372379920 -1.291799244
119 -0.658540146 0.372379920
120 0.520037994 -0.658540146
121 -0.188006804 0.520037994
122 -1.162975513 -0.188006804
123 0.256868740 -1.162975513
124 -1.905002428 0.256868740
125 0.830687995 -1.905002428
126 1.743335295 0.830687995
127 4.227610552 1.743335295
128 1.176097521 4.227610552
129 -1.424135742 1.176097521
130 -1.825980968 -1.424135742
131 -0.039221577 -1.825980968
132 2.296357916 -0.039221577
133 0.550287602 2.296357916
134 2.290744427 0.550287602
135 1.878652717 2.290744427
136 0.675259886 1.878652717
137 -1.171914059 0.675259886
138 0.737749262 -1.171914059
139 -0.945750798 0.737749262
140 0.657599942 -0.945750798
141 2.450553011 0.657599942
142 -0.732349313 2.450553011
143 0.922717996 -0.732349313
144 1.121421317 0.922717996
145 1.704043942 1.121421317
146 -2.704859054 1.704043942
147 -2.648247242 -2.704859054
148 -2.418808470 -2.648247242
149 1.568733757 -2.418808470
150 0.279589774 1.568733757
151 0.776515881 0.279589774
152 -1.988944413 0.776515881
153 -2.335936262 -1.988944413
154 1.146686172 -2.335936262
155 0.112013357 1.146686172
156 0.919187163 0.112013357
157 4.227610552 0.919187163
158 -2.606912807 4.227610552
159 0.032774594 -2.606912807
160 0.474974799 0.032774594
161 0.820994811 0.474974799
162 0.780902577 0.820994811
163 4.414299013 0.780902577
164 -1.874985433 4.414299013
165 1.894074316 -1.874985433
166 -0.265506801 1.894074316
167 -1.231198246 -0.265506801
168 -4.010383052 -1.231198246
169 -2.802248857 -4.010383052
170 0.635056372 -2.802248857
171 1.648319179 0.635056372
172 -5.410829077 1.648319179
173 1.528521663 -5.410829077
174 2.170535083 1.528521663
175 -2.397483514 2.170535083
176 -3.454821247 -2.397483514
177 0.469174567 -3.454821247
178 1.295113651 0.469174567
179 -2.396495820 1.295113651
180 -0.308503696 -2.396495820
181 -1.900384392 -0.308503696
182 0.197761726 -1.900384392
183 -1.250215334 0.197761726
184 1.246644626 -1.250215334
185 1.411072234 1.246644626
186 0.536558809 1.411072234
187 0.677600308 0.536558809
188 0.502713498 0.677600308
189 0.868227630 0.502713498
190 -1.800825012 0.868227630
191 -1.232663495 -1.800825012
192 2.577335301 -1.232663495
193 -1.566981303 2.577335301
194 1.695414129 -1.566981303
195 -2.368085086 1.695414129
196 2.515968482 -2.368085086
197 0.522101635 2.515968482
198 -3.089207275 0.522101635
199 -0.748771844 -3.089207275
200 -3.176560563 -0.748771844
201 1.219116449 -3.176560563
202 2.597803290 1.219116449
203 -0.098732934 2.597803290
204 0.634998944 -0.098732934
205 1.108501233 0.634998944
206 -0.643615919 1.108501233
207 3.322114653 -0.643615919
208 -0.375952825 3.322114653
209 1.625045248 -0.375952825
210 -3.157119764 1.625045248
211 0.748098329 -3.157119764
212 -1.290378952 0.748098329
213 -3.708662791 -1.290378952
214 -1.230936391 -3.708662791
215 1.617950499 -1.230936391
216 2.107777116 1.617950499
217 -0.520843202 2.107777116
218 -1.918770848 -0.520843202
219 1.116404975 -1.918770848
220 -3.354849200 1.116404975
221 2.180410755 -3.354849200
222 -2.287018983 2.180410755
223 -0.158377489 -2.287018983
224 -0.554981976 -0.158377489
225 1.238732710 -0.554981976
226 4.968067000 1.238732710
227 -1.749467054 4.968067000
228 -1.713121747 -1.749467054
229 -2.670008768 -1.713121747
230 0.197025153 -2.670008768
231 -3.596925329 0.197025153
232 0.290032971 -3.596925329
233 0.378826130 0.290032971
234 0.724245734 0.378826130
235 -2.322021291 0.724245734
236 -0.132826391 -2.322021291
237 -0.365480188 -0.132826391
238 -4.496270296 -0.365480188
239 -2.952689710 -4.496270296
240 -2.986146259 -2.952689710
241 -3.132878582 -2.986146259
242 0.372965636 -3.132878582
243 -0.204812591 0.372965636
244 1.319325815 -0.204812591
245 0.224685132 1.319325815
246 0.157394808 0.224685132
247 4.761946219 0.157394808
248 -0.128265265 4.761946219
249 0.408693882 -0.128265265
250 1.935584443 0.408693882
251 0.902610490 1.935584443
252 -1.310522512 0.902610490
253 -0.671260514 -1.310522512
254 -0.216606618 -0.671260514
255 -0.965530573 -0.216606618
256 -1.852776238 -0.965530573
257 -2.433609749 -1.852776238
258 2.012197258 -2.433609749
259 -5.498144242 2.012197258
260 0.191842555 -5.498144242
261 0.775416290 0.191842555
262 -3.024737969 0.775416290
263 0.062842780 -3.024737969
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7m0lh1384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/84pkg1384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9q1n01384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10aige1384792467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, 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/fisher/rcomp/tmp/11ivkp1384792467.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/fisher/rcomp/tmp/121maj1384792467.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/fisher/rcomp/tmp/13r4b91384792467.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/fisher/rcomp/tmp/14an9r1384792467.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/fisher/rcomp/tmp/155i2h1384792467.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/fisher/rcomp/tmp/16kgrt1384792467.tab")
+ }
>
> try(system("convert tmp/1rbu61384792467.ps tmp/1rbu61384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/29pe81384792467.ps tmp/29pe81384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ioli1384792467.ps tmp/3ioli1384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qjtd1384792467.ps tmp/4qjtd1384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uzn71384792467.ps tmp/5uzn71384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sk5u1384792467.ps tmp/6sk5u1384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m0lh1384792467.ps tmp/7m0lh1384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/84pkg1384792467.ps tmp/84pkg1384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q1n01384792467.ps tmp/9q1n01384792467.png",intern=TRUE))
character(0)
> try(system("convert tmp/10aige1384792467.ps tmp/10aige1384792467.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.468 1.819 13.287