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(41
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+ ,13
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+ ,15
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+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,27
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+ ,15
+ ,11
+ ,14
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+ ,7
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+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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
Separate Connected Learning Software Happiness Depression Sport1 Sport2
1 38 41 13 12 14 12.0 53 32
2 32 39 16 11 18 11.0 83 51
3 35 30 19 15 11 14.0 66 42
4 33 31 15 6 12 12.0 67 41
5 37 34 14 13 16 21.0 76 46
6 29 35 13 10 18 12.0 78 47
7 31 39 19 12 14 22.0 53 37
8 36 34 15 14 14 11.0 80 49
9 35 36 14 12 15 10.0 74 45
10 38 37 15 9 15 13.0 76 47
11 31 38 16 10 17 10.0 79 49
12 34 36 16 12 19 8.0 54 33
13 35 38 16 12 10 15.0 67 42
14 38 39 16 11 16 14.0 54 33
15 37 33 17 15 18 10.0 87 53
16 33 32 15 12 14 14.0 58 36
17 32 36 15 10 14 14.0 75 45
18 38 38 20 12 17 11.0 88 54
19 38 39 18 11 14 10.0 64 41
20 32 32 16 12 16 13.0 57 36
21 33 32 16 11 18 9.5 66 41
22 31 31 16 12 11 14.0 68 44
23 38 39 19 13 14 12.0 54 33
24 39 37 16 11 12 14.0 56 37
25 32 39 17 12 17 11.0 86 52
26 32 41 17 13 9 9.0 80 47
27 35 36 16 10 16 11.0 76 43
28 37 33 15 14 14 15.0 69 44
29 33 33 16 12 15 14.0 78 45
30 33 34 14 10 11 13.0 67 44
31 31 31 15 12 16 9.0 80 49
32 32 27 12 8 13 15.0 54 33
33 31 37 14 10 17 10.0 71 43
34 37 34 16 12 15 11.0 84 54
35 30 34 14 12 14 13.0 74 42
36 33 32 10 7 16 8.0 71 44
37 31 29 10 9 9 20.0 63 37
38 33 36 14 12 15 12.0 71 43
39 31 29 16 10 17 10.0 76 46
40 33 35 16 10 13 10.0 69 42
41 32 37 16 10 15 9.0 74 45
42 33 34 14 12 16 14.0 75 44
43 32 38 20 15 16 8.0 54 33
44 33 35 14 10 12 14.0 52 31
45 28 38 14 10 15 11.0 69 42
46 35 37 11 12 11 13.0 68 40
47 39 38 14 13 15 9.0 65 43
48 34 33 15 11 15 11.0 75 46
49 38 36 16 11 17 15.0 74 42
50 32 38 14 12 13 11.0 75 45
51 38 32 16 14 16 10.0 72 44
52 30 32 14 10 14 14.0 67 40
53 33 32 12 12 11 18.0 63 37
54 38 34 16 13 12 14.0 62 46
55 32 32 9 5 12 11.0 63 36
56 35 37 14 6 15 14.5 76 47
57 34 39 16 12 16 13.0 74 45
58 34 29 16 12 15 9.0 67 42
59 36 37 15 11 12 10.0 73 43
60 34 35 16 10 12 15.0 70 43
61 28 30 12 7 8 20.0 53 32
62 34 38 16 12 13 12.0 77 45
63 35 34 16 14 11 12.0 80 48
64 35 31 14 11 14 14.0 52 31
65 31 34 16 12 15 13.0 54 33
66 37 35 17 13 10 11.0 80 49
67 35 36 18 14 11 17.0 66 42
68 27 30 18 11 12 12.0 73 41
69 40 39 12 12 15 13.0 63 38
70 37 35 16 12 15 14.0 69 42
71 36 38 10 8 14 13.0 67 44
72 38 31 14 11 16 15.0 54 33
73 39 34 18 14 15 13.0 81 48
74 41 38 18 14 15 10.0 69 40
75 27 34 16 12 13 11.0 84 50
76 30 39 17 9 12 19.0 80 49
77 37 37 16 13 17 13.0 70 43
78 31 34 16 11 13 17.0 69 44
79 31 28 13 12 15 13.0 77 47
80 27 37 16 12 13 9.0 54 33
81 36 33 16 12 15 11.0 79 46
82 37 35 16 12 15 9.0 71 45
83 33 37 15 12 16 12.0 73 43
84 34 32 15 11 15 12.0 72 44
85 31 33 16 10 14 13.0 77 47
86 39 38 14 9 15 13.0 75 45
87 34 33 16 12 14 12.0 69 42
88 32 29 16 12 13 15.0 54 33
89 33 33 15 12 7 22.0 70 43
90 36 31 12 9 17 13.0 73 46
91 32 36 17 15 13 15.0 54 33
92 41 35 16 12 15 13.0 77 46
93 28 32 15 12 14 15.0 82 48
94 30 29 13 12 13 12.5 80 47
95 36 39 16 10 16 11.0 80 47
96 35 37 16 13 12 16.0 69 43
97 31 35 16 9 14 11.0 78 46
98 34 37 16 12 17 11.0 81 48
99 36 32 14 10 15 10.0 76 46
100 36 38 16 14 17 10.0 76 45
101 35 37 16 11 12 16.0 73 45
102 37 36 20 15 16 12.0 85 52
103 28 32 15 11 11 11.0 66 42
104 39 33 16 11 15 16.0 79 47
105 32 40 13 12 9 19.0 68 41
106 35 38 17 12 16 11.0 76 47
107 39 41 16 12 15 16.0 71 43
108 35 36 16 11 10 15.0 54 33
109 42 43 12 7 10 24.0 46 30
110 34 30 16 12 15 14.0 85 52
111 33 31 16 14 11 15.0 74 44
112 41 32 17 11 13 11.0 88 55
113 33 32 13 11 14 15.0 38 11
114 34 37 12 10 18 12.0 76 47
115 32 37 18 13 16 10.0 86 53
116 40 33 14 13 14 14.0 54 33
117 40 34 14 8 14 13.0 67 44
118 35 33 13 11 14 9.0 69 42
119 36 38 16 12 14 15.0 90 55
120 37 33 13 11 12 15.0 54 33
121 27 31 16 13 14 14.0 76 46
122 39 38 13 12 15 11.0 89 54
123 38 37 16 14 15 8.0 76 47
124 31 36 15 13 15 11.0 73 45
125 33 31 16 15 13 11.0 79 47
126 32 39 15 10 17 8.0 90 55
127 39 44 17 11 17 10.0 74 44
128 36 33 15 9 19 11.0 81 53
129 33 35 12 11 15 13.0 72 44
130 33 32 16 10 13 11.0 71 42
131 32 28 10 11 9 20.0 66 40
132 37 40 16 8 15 10.0 77 46
133 30 27 12 11 15 15.0 65 40
134 38 37 14 12 15 12.0 74 46
135 29 32 15 12 16 14.0 85 53
136 22 28 13 9 11 23.0 54 33
137 35 34 15 11 14 14.0 63 42
138 35 30 11 10 11 16.0 54 35
139 34 35 12 8 15 11.0 64 40
140 35 31 11 9 13 12.0 69 41
141 34 32 16 8 15 10.0 54 33
142 37 30 15 9 16 14.0 84 51
143 35 30 17 15 14 12.0 86 53
144 23 31 16 11 15 12.0 77 46
145 31 40 10 8 16 11.0 89 55
146 27 32 18 13 16 12.0 76 47
147 36 36 13 12 11 13.0 60 38
148 31 32 16 12 12 11.0 75 46
149 32 35 13 9 9 19.0 73 46
150 39 38 10 7 16 12.0 85 53
151 37 42 15 13 13 17.0 79 47
152 38 34 16 9 16 9.0 71 41
153 39 35 16 6 12 12.0 72 44
154 34 38 14 8 9 19.0 69 43
155 31 33 10 8 13 18.0 78 51
156 32 36 17 15 13 15.0 54 33
157 37 32 13 6 14 14.0 69 43
158 36 33 15 9 19 11.0 81 53
159 32 34 16 11 13 9.0 84 51
160 38 32 12 8 12 18.0 84 50
161 36 34 13 8 13 16.0 69 46
162 26 27 13 10 10 24.0 66 43
163 26 31 12 8 14 14.0 81 47
164 33 38 17 14 16 20.0 82 50
165 39 34 15 10 10 18.0 72 43
166 30 24 10 8 11 23.0 54 33
167 33 30 14 11 14 12.0 78 48
168 25 26 11 12 12 14.0 74 44
169 38 34 13 12 9 16.0 82 50
170 37 27 16 12 9 18.0 73 41
171 31 37 12 5 11 20.0 55 34
172 37 36 16 12 16 12.0 72 44
173 35 41 12 10 9 12.0 78 47
174 25 29 9 7 13 17.0 59 35
175 28 36 12 12 16 13.0 72 44
176 35 32 15 11 13 9.0 78 44
177 33 37 12 8 9 16.0 68 43
178 30 30 12 9 12 18.0 69 41
179 31 31 14 10 16 10.0 67 41
180 37 38 12 9 11 14.0 74 42
181 36 36 16 12 14 11.0 54 33
182 30 35 11 6 13 9.0 67 41
183 36 31 19 15 15 11.0 70 44
184 32 38 15 12 14 10.0 80 48
185 28 22 8 12 16 11.0 89 55
186 36 32 16 12 13 19.0 76 44
187 34 36 17 11 14 14.0 74 43
188 31 39 12 7 15 12.0 87 52
189 28 28 11 7 13 14.0 54 30
190 36 32 11 5 11 21.0 61 39
191 36 32 14 12 11 13.0 38 11
192 40 38 16 12 14 10.0 75 44
193 33 32 12 3 15 15.0 69 42
194 37 35 16 11 11 16.0 62 41
195 32 32 13 10 15 14.0 72 44
196 38 37 15 12 12 12.0 70 44
197 31 34 16 9 14 19.0 79 48
198 37 33 16 12 14 15.0 87 53
199 33 33 14 9 8 19.0 62 37
200 32 26 16 12 13 13.0 77 44
201 30 30 16 12 9 17.0 69 44
202 30 24 14 10 15 12.0 69 40
203 31 34 11 9 17 11.0 75 42
204 32 34 12 12 13 14.0 54 35
205 34 33 15 8 15 11.0 72 43
206 36 34 15 11 15 13.0 74 45
207 37 35 16 11 14 12.0 85 55
208 36 35 16 12 16 15.0 52 31
209 33 36 11 10 13 14.0 70 44
210 33 34 15 10 16 12.0 84 50
211 33 34 12 12 9 17.0 64 40
212 44 41 12 12 16 11.0 84 53
213 39 32 15 11 11 18.0 87 54
214 32 30 15 8 10 13.0 79 49
215 35 35 16 12 11 17.0 67 40
216 25 28 14 10 15 13.0 65 41
217 35 33 17 11 17 11.0 85 52
218 34 39 14 10 14 12.0 83 52
219 35 36 13 8 8 22.0 61 36
220 39 36 15 12 15 14.0 82 52
221 33 35 13 12 11 12.0 76 46
222 36 38 14 10 16 12.0 58 31
223 32 33 15 12 10 17.0 72 44
224 32 31 12 9 15 9.0 72 44
225 36 34 13 9 9 21.0 38 11
226 36 32 8 6 16 10.0 78 46
227 32 31 14 10 19 11.0 54 33
228 34 33 14 9 12 12.0 63 34
229 33 34 11 9 8 23.0 66 42
230 35 34 12 9 11 13.0 70 43
231 30 34 13 6 14 12.0 71 43
232 38 33 10 10 9 16.0 67 44
233 34 32 16 6 15 9.0 58 36
234 33 41 18 14 13 17.0 72 46
235 32 34 13 10 16 9.0 72 44
236 31 36 11 10 11 14.0 70 43
237 30 37 4 6 12 17.0 76 50
238 27 36 13 12 13 13.0 50 33
239 31 29 16 12 10 11.0 72 43
240 30 37 10 7 11 12.0 72 44
241 32 27 12 8 12 10.0 88 53
242 35 35 12 11 8 19.0 53 34
243 28 28 10 3 12 16.0 58 35
244 33 35 13 6 12 16.0 66 40
245 31 37 15 10 15 14.0 82 53
246 35 29 12 8 11 20.0 69 42
247 35 32 14 9 13 15.0 68 43
248 32 36 10 9 14 23.0 44 29
249 21 19 12 8 10 20.0 56 36
250 20 21 12 9 12 16.0 53 30
251 34 31 11 7 15 14.0 70 42
252 32 33 10 7 13 17.0 78 47
253 34 36 12 6 13 11.0 71 44
254 32 33 16 9 13 13.0 72 45
255 33 37 12 10 12 17.0 68 44
256 33 34 14 11 12 15.0 67 43
257 37 35 16 12 9 21.0 75 43
258 32 31 14 8 9 18.0 62 40
259 34 37 13 11 15 15.0 67 41
260 30 35 4 3 10 8.0 83 52
261 30 27 15 11 14 12.0 64 38
262 38 34 11 12 15 12.0 68 41
263 36 40 11 7 7 22.0 62 39
264 32 29 14 9 14 12.0 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Learning Software Happiness Depression
15.457763 0.412499 0.128397 0.121252 0.026869 0.008557
Sport1 Sport2
-0.001634 0.016287
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.7624 -1.8056 0.1241 2.2684 7.6304
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.457763 3.237266 4.775 3.03e-06 ***
Connected 0.412499 0.055330 7.455 1.39e-12 ***
Learning 0.128397 0.108986 1.178 0.240
Software 0.121252 0.112099 1.082 0.280
Happiness 0.026869 0.101906 0.264 0.792
Depression 0.008557 0.074533 0.115 0.909
Sport1 -0.001634 0.066191 -0.025 0.980
Sport2 0.016287 0.098710 0.165 0.869
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.295 on 256 degrees of freedom
Multiple R-squared: 0.2297, Adjusted R-squared: 0.2087
F-statistic: 10.91 on 7 and 256 DF, p-value: 4.812e-12
> 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,] 0.104039739 0.20807948 0.89596026
[2,] 0.503879518 0.99224096 0.49612048
[3,] 0.498608344 0.99721669 0.50139166
[4,] 0.377816601 0.75563320 0.62218340
[5,] 0.336235588 0.67247118 0.66376441
[6,] 0.243832477 0.48766495 0.75616752
[7,] 0.357996748 0.71599350 0.64200325
[8,] 0.286719311 0.57343862 0.71328069
[9,] 0.297152294 0.59430459 0.70284771
[10,] 0.224358620 0.44871724 0.77564138
[11,] 0.172507361 0.34501472 0.82749264
[12,] 0.124218998 0.24843800 0.87578100
[13,] 0.102616492 0.20523298 0.89738351
[14,] 0.207639278 0.41527856 0.79236072
[15,] 0.245937028 0.49187406 0.75406297
[16,] 0.429827520 0.85965504 0.57017248
[17,] 0.362270185 0.72454037 0.63772981
[18,] 0.340289218 0.68057844 0.65971078
[19,] 0.289071439 0.57814288 0.71092856
[20,] 0.235102121 0.47020424 0.76489788
[21,] 0.206123413 0.41224683 0.79387659
[22,] 0.164497475 0.32899495 0.83550252
[23,] 0.156950267 0.31390053 0.84304973
[24,] 0.171712093 0.34342419 0.82828791
[25,] 0.182859685 0.36571937 0.81714032
[26,] 0.153391977 0.30678395 0.84660802
[27,] 0.121052345 0.24210469 0.87894766
[28,] 0.097925004 0.19585001 0.90207500
[29,] 0.077770980 0.15554196 0.92222902
[30,] 0.059859461 0.11971892 0.94014054
[31,] 0.050666414 0.10133283 0.94933359
[32,] 0.037756487 0.07551297 0.96224351
[33,] 0.049710622 0.09942124 0.95028938
[34,] 0.037448835 0.07489767 0.96255116
[35,] 0.080173270 0.16034654 0.91982673
[36,] 0.064680246 0.12936049 0.93531975
[37,] 0.063618271 0.12723654 0.93638173
[38,] 0.049776556 0.09955311 0.95022344
[39,] 0.075694785 0.15138957 0.92430521
[40,] 0.069501317 0.13900263 0.93049868
[41,] 0.078098179 0.15619636 0.92190182
[42,] 0.073287772 0.14657554 0.92671223
[43,] 0.057909968 0.11581994 0.94209003
[44,] 0.049314504 0.09862901 0.95068550
[45,] 0.042396617 0.08479323 0.95760338
[46,] 0.038027799 0.07605560 0.96197220
[47,] 0.030449383 0.06089877 0.96955062
[48,] 0.023870710 0.04774142 0.97612929
[49,] 0.021367927 0.04273585 0.97863207
[50,] 0.016120557 0.03224111 0.98387944
[51,] 0.017079050 0.03415810 0.98292095
[52,] 0.013047568 0.02609514 0.98695243
[53,] 0.009816842 0.01963368 0.99018316
[54,] 0.008525597 0.01705119 0.99147440
[55,] 0.008592165 0.01718433 0.99140783
[56,] 0.007650439 0.01530088 0.99234956
[57,] 0.005639328 0.01127866 0.99436067
[58,] 0.008266515 0.01653303 0.99173348
[59,] 0.011667054 0.02333411 0.98833295
[60,] 0.011240119 0.02248024 0.98875988
[61,] 0.008585359 0.01717072 0.99141464
[62,] 0.013511134 0.02702227 0.98648887
[63,] 0.019912652 0.03982530 0.98008735
[64,] 0.035358098 0.07071620 0.96464190
[65,] 0.073722214 0.14744443 0.92627779
[66,] 0.084786751 0.16957350 0.91521325
[67,] 0.072139309 0.14427862 0.92786069
[68,] 0.069671618 0.13934324 0.93032838
[69,] 0.060945938 0.12189188 0.93905406
[70,] 0.159211195 0.31842239 0.84078880
[71,] 0.153661512 0.30732302 0.84633849
[72,] 0.142640860 0.28528172 0.85735914
[73,] 0.128649900 0.25729980 0.87135010
[74,] 0.110098137 0.22019627 0.88990186
[75,] 0.098932781 0.19786556 0.90106722
[76,] 0.127278697 0.25455739 0.87272130
[77,] 0.108109283 0.21621857 0.89189072
[78,] 0.091612819 0.18322564 0.90838718
[79,] 0.076704036 0.15340807 0.92329596
[80,] 0.076580280 0.15316056 0.92341972
[81,] 0.079444551 0.15888910 0.92055545
[82,] 0.141516180 0.28303236 0.85848382
[83,] 0.178766371 0.35753274 0.82123363
[84,] 0.165061497 0.33012299 0.83493850
[85,] 0.148751356 0.29750271 0.85124864
[86,] 0.127968400 0.25593680 0.87203160
[87,] 0.120328425 0.24065685 0.87967158
[88,] 0.104971682 0.20994336 0.89502832
[89,] 0.104293162 0.20858632 0.89570684
[90,] 0.088207082 0.17641416 0.91179292
[91,] 0.075361410 0.15072282 0.92463859
[92,] 0.064769387 0.12953877 0.93523061
[93,] 0.083431128 0.16686226 0.91656887
[94,] 0.116074417 0.23214883 0.88392558
[95,] 0.119987306 0.23997461 0.88001269
[96,] 0.103759887 0.20751977 0.89624011
[97,] 0.097180002 0.19436000 0.90282000
[98,] 0.086454592 0.17290918 0.91354541
[99,] 0.126174138 0.25234828 0.87382586
[100,] 0.110213564 0.22042713 0.88978644
[101,] 0.094004756 0.18800951 0.90599524
[102,] 0.188667642 0.37733528 0.81133236
[103,] 0.180383085 0.36076617 0.81961691
[104,] 0.161705770 0.32341154 0.83829423
[105,] 0.169195204 0.33839041 0.83080480
[106,] 0.236930601 0.47386120 0.76306940
[107,] 0.328016648 0.65603330 0.67198335
[108,] 0.305448756 0.61089751 0.69455124
[109,] 0.275846670 0.55169334 0.72415333
[110,] 0.288613624 0.57722725 0.71138638
[111,] 0.379521028 0.75904206 0.62047897
[112,] 0.382255180 0.76451036 0.61774482
[113,] 0.366993560 0.73398712 0.63300644
[114,] 0.387249688 0.77449938 0.61275031
[115,] 0.353547751 0.70709550 0.64645225
[116,] 0.370258508 0.74051702 0.62974149
[117,] 0.346814256 0.69362851 0.65318574
[118,] 0.331891721 0.66378344 0.66810828
[119,] 0.306269939 0.61253988 0.69373006
[120,] 0.277752240 0.55550448 0.72224776
[121,] 0.254794062 0.50958812 0.74520594
[122,] 0.237527360 0.47505472 0.76247264
[123,] 0.221639696 0.44327939 0.77836030
[124,] 0.214371320 0.42874264 0.78562868
[125,] 0.242638671 0.48527734 0.75736133
[126,] 0.441485052 0.88297010 0.55851495
[127,] 0.413751893 0.82750379 0.58624811
[128,] 0.432023511 0.86404702 0.56797649
[129,] 0.399992236 0.79998447 0.60000776
[130,] 0.400627148 0.80125430 0.59937285
[131,] 0.375238140 0.75047628 0.62476186
[132,] 0.415303266 0.83060653 0.58469673
[133,] 0.392053519 0.78410704 0.60794648
[134,] 0.679678065 0.64064387 0.32032194
[135,] 0.719916806 0.56016639 0.28008319
[136,] 0.817384987 0.36523003 0.18261501
[137,] 0.803446670 0.39310666 0.19655333
[138,] 0.791899885 0.41620023 0.20810012
[139,] 0.773997608 0.45200478 0.22600239
[140,] 0.793471615 0.41305677 0.20652838
[141,] 0.771602884 0.45679423 0.22839712
[142,] 0.790864844 0.41827031 0.20913516
[143,] 0.828682389 0.34263522 0.17131761
[144,] 0.809350835 0.38129833 0.19064916
[145,] 0.791805908 0.41638818 0.20819409
[146,] 0.788667521 0.42266496 0.21133248
[147,] 0.822921764 0.35415647 0.17707824
[148,] 0.820221852 0.35955630 0.17977815
[149,] 0.812587118 0.37482576 0.18741288
[150,] 0.852195482 0.29560904 0.14780452
[151,] 0.854074457 0.29185109 0.14592554
[152,] 0.871456216 0.25708757 0.12854378
[153,] 0.919983029 0.16003394 0.08001697
[154,] 0.932633176 0.13473365 0.06736682
[155,] 0.947813030 0.10437394 0.05218697
[156,] 0.943734832 0.11253034 0.05626517
[157,] 0.932870460 0.13425908 0.06712954
[158,] 0.951889648 0.09622070 0.04811035
[159,] 0.954583331 0.09083334 0.04541667
[160,] 0.967985588 0.06402882 0.03201441
[161,] 0.965156376 0.06968725 0.03484362
[162,] 0.959879183 0.08024163 0.04012082
[163,] 0.954657380 0.09068524 0.04534262
[164,] 0.965803519 0.06839296 0.03419648
[165,] 0.984627143 0.03074571 0.01537286
[166,] 0.981375941 0.03724812 0.01862406
[167,] 0.977098352 0.04580330 0.02290165
[168,] 0.973053773 0.05389245 0.02694623
[169,] 0.967091018 0.06581796 0.03290898
[170,] 0.961199411 0.07760118 0.03880059
[171,] 0.955020109 0.08995978 0.04497989
[172,] 0.951580132 0.09683974 0.04841987
[173,] 0.948048533 0.10390293 0.05195147
[174,] 0.958589582 0.08282084 0.04141042
[175,] 0.948982023 0.10203595 0.05101798
[176,] 0.941612090 0.11677582 0.05838791
[177,] 0.934847135 0.13030573 0.06515287
[178,] 0.960805994 0.07838801 0.03919401
[179,] 0.953649688 0.09270062 0.04635031
[180,] 0.969393820 0.06121236 0.03060618
[181,] 0.966496267 0.06700747 0.03350373
[182,] 0.966377929 0.06724414 0.03362207
[183,] 0.960969370 0.07806126 0.03903063
[184,] 0.964773950 0.07045210 0.03522605
[185,] 0.956104760 0.08779048 0.04389524
[186,] 0.954580233 0.09083953 0.04541977
[187,] 0.962445868 0.07510826 0.03755413
[188,] 0.955978885 0.08804223 0.04402112
[189,] 0.944721084 0.11055783 0.05527892
[190,] 0.931619142 0.13676172 0.06838086
[191,] 0.921243274 0.15751345 0.07875673
[192,] 0.905059872 0.18988026 0.09494013
[193,] 0.914787031 0.17042594 0.08521297
[194,] 0.898332353 0.20333529 0.10166765
[195,] 0.877703136 0.24459373 0.12229686
[196,] 0.859078820 0.28184236 0.14092118
[197,] 0.846896111 0.30620778 0.15310389
[198,] 0.838057543 0.32388491 0.16194246
[199,] 0.810177265 0.37964547 0.18982273
[200,] 0.803043234 0.39391353 0.19695677
[201,] 0.768682975 0.46263405 0.23131702
[202,] 0.865767937 0.26846413 0.13423206
[203,] 0.895173296 0.20965341 0.10482670
[204,] 0.871413098 0.25717380 0.12858690
[205,] 0.844017405 0.31196519 0.15598259
[206,] 0.879599107 0.24080179 0.12040089
[207,] 0.852919382 0.29416124 0.14708062
[208,] 0.837929400 0.32414120 0.16207060
[209,] 0.805982989 0.38803402 0.19401701
[210,] 0.827229046 0.34554191 0.17277095
[211,] 0.796445119 0.40710976 0.20355488
[212,] 0.757128535 0.48574293 0.24287146
[213,] 0.722313240 0.55537352 0.27768676
[214,] 0.676418461 0.64716308 0.32358154
[215,] 0.635655407 0.72868919 0.36434459
[216,] 0.660279908 0.67944018 0.33972009
[217,] 0.633472464 0.73305507 0.36652754
[218,] 0.579112873 0.84177425 0.42088713
[219,] 0.521644715 0.95671057 0.47835528
[220,] 0.488930115 0.97786023 0.51106988
[221,] 0.468663849 0.93732770 0.53133615
[222,] 0.742819343 0.51436131 0.25718066
[223,] 0.749246562 0.50150688 0.25075344
[224,] 0.810541526 0.37891695 0.18945847
[225,] 0.763343393 0.47331321 0.23665661
[226,] 0.748565467 0.50286907 0.25143453
[227,] 0.720005074 0.55998985 0.27999493
[228,] 0.780234045 0.43953191 0.21976595
[229,] 0.725071480 0.54985704 0.27492852
[230,] 0.792821105 0.41435779 0.20717889
[231,] 0.757783785 0.48443243 0.24221621
[232,] 0.713055523 0.57388895 0.28694448
[233,] 0.640992832 0.71801434 0.35900717
[234,] 0.563139302 0.87372140 0.43686070
[235,] 0.764582503 0.47083499 0.23541750
[236,] 0.849534643 0.30093071 0.15046536
[237,] 0.841555538 0.31688892 0.15844446
[238,] 0.766134028 0.46773194 0.23386597
[239,] 0.694005635 0.61198873 0.30599437
[240,] 0.929208972 0.14158206 0.07079103
[241,] 0.903095601 0.19380880 0.09690440
[242,] 0.822561124 0.35487775 0.17743888
[243,] 0.713878593 0.57224281 0.28612141
> postscript(file="/var/wessaorg/rcomp/tmp/1iw4x1384525370.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2kk8u1384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35lwk1384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tvu21384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rkhm1384525370.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
1.59219268 -4.20609515 1.91740390 1.11792728 2.90885064 -5.00123746
7 8 9 10 11 12
-5.52020112 1.75618627 0.33912102 3.10700340 -4.61088190 -0.84527554
13 14 15 16 17 18
-0.61369077 2.06774455 2.63801161 -0.02620670 -2.55249822 1.55774111
19 20 21 22 23 24
1.78496212 -1.20141894 -0.17068189 -1.77545285 1.51090088 3.93833722
25 26 27 28 29 30
-4.44025966 -5.08281363 0.32524648 3.19791464 -0.70786958 -0.50672730
31 32 33 34 35 36
-1.80043822 1.96712077 -3.85694039 2.76852523 -3.78582324 1.11059286
37 38 39 40 41 42
0.29191679 -1.65032189 -0.85443696 -1.16824421 -3.07911165 -0.87905794
43 44 45 46 47 48
-4.46701071 -0.76743393 -7.21123978 0.46524843 3.41929505 0.54626097
49 50 51 52 53 54
3.15591594 -3.43906112 4.47596647 -2.70574566 0.39724784 3.79655123
55 56 57 58 59 60
0.68052017 0.58632032 -2.20770945 2.01579420 1.03102331 -0.19881373
61 62 63 64 65 66
-3.04291152 -1.70114444 0.71612541 2.70757065 -2.95558870 2.31562016
67 68 69 70 71 72
-0.33360935 -5.45121938 4.42878141 2.50128615 1.51876512 5.61597022
73 74 75 76 77 78
4.34493240 4.83129502 -7.11258875 -5.97156154 1.49520401 -2.96947141
79 80 81 82 83 84
-0.28583624 -8.10511804 2.30314897 2.49847905 -2.21481807 0.97787368
85 86 87 88 89 90
-2.46414857 3.85384215 0.37026624 0.14352730 -0.41347837 3.92483310
91 92 93 94 95 96
-3.23611482 6.45776917 -5.19098609 -1.63541565 0.02913976 -0.39775735
97 98 99 100 101 102
-3.13285825 -1.42988835 3.21859999 -0.03564337 -0.18129092 1.06496596
103 104 105 106 107 108
-4.88332530 5.36532805 -4.04293390 -0.93579969 1.99616237 0.45789607
109 110 111 112 113 114
5.52777597 1.42705633 -0.01670778 7.63036434 0.71777406 -0.70110564
115 116 117 118 119 120
-3.84577288 6.61076456 6.65516962 1.90238146 0.10469019 4.02684601
121 122 123 124 125 126
-5.99681109 3.51189474 2.41513292 -3.92071949 -0.19815861 -3.95761421
127 128 129 130 131 132
0.73774196 2.57708516 -0.88298681 0.06396274 1.41795343 0.90595495
133 134 135 136 137 138
-0.54640464 2.89322121 -4.31269923 -8.70974607 1.18049696 3.62812560
139 140 141 142 143 144
0.54995673 3.24416128 1.38008661 4.90699287 1.96423403 -9.76242796
145 146 147 148 149 150
-4.48605912 -6.71901490 1.64045212 -2.21028308 -1.68995017 4.47766956
151 152 153 154 155 156
-0.41608399 4.31301222 5.29884604 -0.89226720 -1.53069158 -3.23611482
157 158 159 160 161 162
4.86206599 2.57708516 -1.99050986 5.67797376 2.75545890 -4.54344701
163 164 165 166 167 168
-5.88507858 -3.29437278 5.37341706 1.44669181 0.90279428 -5.08803664
169 170 171 172 173 174
4.33402466 5.95108302 -2.79780973 2.05136170 -1.10601143 -5.39294937
175 176 177 178 179 180
-6.44360590 2.06708840 -1.19893687 -1.49621144 -1.32904347 2.25678216
181 182 183 184 185 186
1.26339738 -3.01967473 2.39706429 -3.62646008 -0.28929824 2.72859957
187 188 189 190 191 192
-0.89960484 -4.14519922 -2.13831028 4.31289333 3.56584573 4.30211946
193 194 195 196 197 198
1.33507969 2.71774620 -0.66119405 2.87146743 -2.81975655 3.19485403
199 200 201 202 203 204
0.16212496 1.25656757 -2.33325372 0.58775631 -2.09873394 -1.42939275
205 206 207 208 209 210
0.95397458 2.13130104 2.48093741 1.61723531 -1.00392416 -0.83085158
211 212 213 214 215 216
-0.41268130 7.38404344 5.89564979 0.22241761 0.61239581 -6.09361901
217 218 219 220 221 222
1.15434882 -1.74541744 1.16326002 4.07555986 -1.04264044 0.91451504
223 224 225 226 227 228
-1.46431787 0.04373914 3.21828209 4.45039090 -0.30915613 1.16504487
229 230 231 232 233 234
0.02569185 1.89250910 -2.94254823 5.44742708 1.58882308 -4.50518222
235 236 237 238 239 240
-1.47027434 -2.93389938 -3.11935336 -7.34819297 -0.87509146 -3.85014958
241 242 243 244 245 246
1.76659908 1.38557197 -1.59004960 -0.29484764 -4.11072225 4.03100635
247 248 249 250 251 252
2.38659086 -1.65634299 -5.74066290 -7.61360126 2.40116002 -0.33573442
253 254 255 256 257 258
0.37999103 -1.29162521 -1.54689109 -0.65567504 2.59611804 0.04120074
259 260 261 262 263 264
-0.81280558 -1.82098361 -0.84811670 4.58753857 0.87095439 0.62942598
> postscript(file="/var/wessaorg/rcomp/tmp/64fe01384525370.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 1.59219268 NA
1 -4.20609515 1.59219268
2 1.91740390 -4.20609515
3 1.11792728 1.91740390
4 2.90885064 1.11792728
5 -5.00123746 2.90885064
6 -5.52020112 -5.00123746
7 1.75618627 -5.52020112
8 0.33912102 1.75618627
9 3.10700340 0.33912102
10 -4.61088190 3.10700340
11 -0.84527554 -4.61088190
12 -0.61369077 -0.84527554
13 2.06774455 -0.61369077
14 2.63801161 2.06774455
15 -0.02620670 2.63801161
16 -2.55249822 -0.02620670
17 1.55774111 -2.55249822
18 1.78496212 1.55774111
19 -1.20141894 1.78496212
20 -0.17068189 -1.20141894
21 -1.77545285 -0.17068189
22 1.51090088 -1.77545285
23 3.93833722 1.51090088
24 -4.44025966 3.93833722
25 -5.08281363 -4.44025966
26 0.32524648 -5.08281363
27 3.19791464 0.32524648
28 -0.70786958 3.19791464
29 -0.50672730 -0.70786958
30 -1.80043822 -0.50672730
31 1.96712077 -1.80043822
32 -3.85694039 1.96712077
33 2.76852523 -3.85694039
34 -3.78582324 2.76852523
35 1.11059286 -3.78582324
36 0.29191679 1.11059286
37 -1.65032189 0.29191679
38 -0.85443696 -1.65032189
39 -1.16824421 -0.85443696
40 -3.07911165 -1.16824421
41 -0.87905794 -3.07911165
42 -4.46701071 -0.87905794
43 -0.76743393 -4.46701071
44 -7.21123978 -0.76743393
45 0.46524843 -7.21123978
46 3.41929505 0.46524843
47 0.54626097 3.41929505
48 3.15591594 0.54626097
49 -3.43906112 3.15591594
50 4.47596647 -3.43906112
51 -2.70574566 4.47596647
52 0.39724784 -2.70574566
53 3.79655123 0.39724784
54 0.68052017 3.79655123
55 0.58632032 0.68052017
56 -2.20770945 0.58632032
57 2.01579420 -2.20770945
58 1.03102331 2.01579420
59 -0.19881373 1.03102331
60 -3.04291152 -0.19881373
61 -1.70114444 -3.04291152
62 0.71612541 -1.70114444
63 2.70757065 0.71612541
64 -2.95558870 2.70757065
65 2.31562016 -2.95558870
66 -0.33360935 2.31562016
67 -5.45121938 -0.33360935
68 4.42878141 -5.45121938
69 2.50128615 4.42878141
70 1.51876512 2.50128615
71 5.61597022 1.51876512
72 4.34493240 5.61597022
73 4.83129502 4.34493240
74 -7.11258875 4.83129502
75 -5.97156154 -7.11258875
76 1.49520401 -5.97156154
77 -2.96947141 1.49520401
78 -0.28583624 -2.96947141
79 -8.10511804 -0.28583624
80 2.30314897 -8.10511804
81 2.49847905 2.30314897
82 -2.21481807 2.49847905
83 0.97787368 -2.21481807
84 -2.46414857 0.97787368
85 3.85384215 -2.46414857
86 0.37026624 3.85384215
87 0.14352730 0.37026624
88 -0.41347837 0.14352730
89 3.92483310 -0.41347837
90 -3.23611482 3.92483310
91 6.45776917 -3.23611482
92 -5.19098609 6.45776917
93 -1.63541565 -5.19098609
94 0.02913976 -1.63541565
95 -0.39775735 0.02913976
96 -3.13285825 -0.39775735
97 -1.42988835 -3.13285825
98 3.21859999 -1.42988835
99 -0.03564337 3.21859999
100 -0.18129092 -0.03564337
101 1.06496596 -0.18129092
102 -4.88332530 1.06496596
103 5.36532805 -4.88332530
104 -4.04293390 5.36532805
105 -0.93579969 -4.04293390
106 1.99616237 -0.93579969
107 0.45789607 1.99616237
108 5.52777597 0.45789607
109 1.42705633 5.52777597
110 -0.01670778 1.42705633
111 7.63036434 -0.01670778
112 0.71777406 7.63036434
113 -0.70110564 0.71777406
114 -3.84577288 -0.70110564
115 6.61076456 -3.84577288
116 6.65516962 6.61076456
117 1.90238146 6.65516962
118 0.10469019 1.90238146
119 4.02684601 0.10469019
120 -5.99681109 4.02684601
121 3.51189474 -5.99681109
122 2.41513292 3.51189474
123 -3.92071949 2.41513292
124 -0.19815861 -3.92071949
125 -3.95761421 -0.19815861
126 0.73774196 -3.95761421
127 2.57708516 0.73774196
128 -0.88298681 2.57708516
129 0.06396274 -0.88298681
130 1.41795343 0.06396274
131 0.90595495 1.41795343
132 -0.54640464 0.90595495
133 2.89322121 -0.54640464
134 -4.31269923 2.89322121
135 -8.70974607 -4.31269923
136 1.18049696 -8.70974607
137 3.62812560 1.18049696
138 0.54995673 3.62812560
139 3.24416128 0.54995673
140 1.38008661 3.24416128
141 4.90699287 1.38008661
142 1.96423403 4.90699287
143 -9.76242796 1.96423403
144 -4.48605912 -9.76242796
145 -6.71901490 -4.48605912
146 1.64045212 -6.71901490
147 -2.21028308 1.64045212
148 -1.68995017 -2.21028308
149 4.47766956 -1.68995017
150 -0.41608399 4.47766956
151 4.31301222 -0.41608399
152 5.29884604 4.31301222
153 -0.89226720 5.29884604
154 -1.53069158 -0.89226720
155 -3.23611482 -1.53069158
156 4.86206599 -3.23611482
157 2.57708516 4.86206599
158 -1.99050986 2.57708516
159 5.67797376 -1.99050986
160 2.75545890 5.67797376
161 -4.54344701 2.75545890
162 -5.88507858 -4.54344701
163 -3.29437278 -5.88507858
164 5.37341706 -3.29437278
165 1.44669181 5.37341706
166 0.90279428 1.44669181
167 -5.08803664 0.90279428
168 4.33402466 -5.08803664
169 5.95108302 4.33402466
170 -2.79780973 5.95108302
171 2.05136170 -2.79780973
172 -1.10601143 2.05136170
173 -5.39294937 -1.10601143
174 -6.44360590 -5.39294937
175 2.06708840 -6.44360590
176 -1.19893687 2.06708840
177 -1.49621144 -1.19893687
178 -1.32904347 -1.49621144
179 2.25678216 -1.32904347
180 1.26339738 2.25678216
181 -3.01967473 1.26339738
182 2.39706429 -3.01967473
183 -3.62646008 2.39706429
184 -0.28929824 -3.62646008
185 2.72859957 -0.28929824
186 -0.89960484 2.72859957
187 -4.14519922 -0.89960484
188 -2.13831028 -4.14519922
189 4.31289333 -2.13831028
190 3.56584573 4.31289333
191 4.30211946 3.56584573
192 1.33507969 4.30211946
193 2.71774620 1.33507969
194 -0.66119405 2.71774620
195 2.87146743 -0.66119405
196 -2.81975655 2.87146743
197 3.19485403 -2.81975655
198 0.16212496 3.19485403
199 1.25656757 0.16212496
200 -2.33325372 1.25656757
201 0.58775631 -2.33325372
202 -2.09873394 0.58775631
203 -1.42939275 -2.09873394
204 0.95397458 -1.42939275
205 2.13130104 0.95397458
206 2.48093741 2.13130104
207 1.61723531 2.48093741
208 -1.00392416 1.61723531
209 -0.83085158 -1.00392416
210 -0.41268130 -0.83085158
211 7.38404344 -0.41268130
212 5.89564979 7.38404344
213 0.22241761 5.89564979
214 0.61239581 0.22241761
215 -6.09361901 0.61239581
216 1.15434882 -6.09361901
217 -1.74541744 1.15434882
218 1.16326002 -1.74541744
219 4.07555986 1.16326002
220 -1.04264044 4.07555986
221 0.91451504 -1.04264044
222 -1.46431787 0.91451504
223 0.04373914 -1.46431787
224 3.21828209 0.04373914
225 4.45039090 3.21828209
226 -0.30915613 4.45039090
227 1.16504487 -0.30915613
228 0.02569185 1.16504487
229 1.89250910 0.02569185
230 -2.94254823 1.89250910
231 5.44742708 -2.94254823
232 1.58882308 5.44742708
233 -4.50518222 1.58882308
234 -1.47027434 -4.50518222
235 -2.93389938 -1.47027434
236 -3.11935336 -2.93389938
237 -7.34819297 -3.11935336
238 -0.87509146 -7.34819297
239 -3.85014958 -0.87509146
240 1.76659908 -3.85014958
241 1.38557197 1.76659908
242 -1.59004960 1.38557197
243 -0.29484764 -1.59004960
244 -4.11072225 -0.29484764
245 4.03100635 -4.11072225
246 2.38659086 4.03100635
247 -1.65634299 2.38659086
248 -5.74066290 -1.65634299
249 -7.61360126 -5.74066290
250 2.40116002 -7.61360126
251 -0.33573442 2.40116002
252 0.37999103 -0.33573442
253 -1.29162521 0.37999103
254 -1.54689109 -1.29162521
255 -0.65567504 -1.54689109
256 2.59611804 -0.65567504
257 0.04120074 2.59611804
258 -0.81280558 0.04120074
259 -1.82098361 -0.81280558
260 -0.84811670 -1.82098361
261 4.58753857 -0.84811670
262 0.87095439 4.58753857
263 0.62942598 0.87095439
264 NA 0.62942598
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.20609515 1.59219268
[2,] 1.91740390 -4.20609515
[3,] 1.11792728 1.91740390
[4,] 2.90885064 1.11792728
[5,] -5.00123746 2.90885064
[6,] -5.52020112 -5.00123746
[7,] 1.75618627 -5.52020112
[8,] 0.33912102 1.75618627
[9,] 3.10700340 0.33912102
[10,] -4.61088190 3.10700340
[11,] -0.84527554 -4.61088190
[12,] -0.61369077 -0.84527554
[13,] 2.06774455 -0.61369077
[14,] 2.63801161 2.06774455
[15,] -0.02620670 2.63801161
[16,] -2.55249822 -0.02620670
[17,] 1.55774111 -2.55249822
[18,] 1.78496212 1.55774111
[19,] -1.20141894 1.78496212
[20,] -0.17068189 -1.20141894
[21,] -1.77545285 -0.17068189
[22,] 1.51090088 -1.77545285
[23,] 3.93833722 1.51090088
[24,] -4.44025966 3.93833722
[25,] -5.08281363 -4.44025966
[26,] 0.32524648 -5.08281363
[27,] 3.19791464 0.32524648
[28,] -0.70786958 3.19791464
[29,] -0.50672730 -0.70786958
[30,] -1.80043822 -0.50672730
[31,] 1.96712077 -1.80043822
[32,] -3.85694039 1.96712077
[33,] 2.76852523 -3.85694039
[34,] -3.78582324 2.76852523
[35,] 1.11059286 -3.78582324
[36,] 0.29191679 1.11059286
[37,] -1.65032189 0.29191679
[38,] -0.85443696 -1.65032189
[39,] -1.16824421 -0.85443696
[40,] -3.07911165 -1.16824421
[41,] -0.87905794 -3.07911165
[42,] -4.46701071 -0.87905794
[43,] -0.76743393 -4.46701071
[44,] -7.21123978 -0.76743393
[45,] 0.46524843 -7.21123978
[46,] 3.41929505 0.46524843
[47,] 0.54626097 3.41929505
[48,] 3.15591594 0.54626097
[49,] -3.43906112 3.15591594
[50,] 4.47596647 -3.43906112
[51,] -2.70574566 4.47596647
[52,] 0.39724784 -2.70574566
[53,] 3.79655123 0.39724784
[54,] 0.68052017 3.79655123
[55,] 0.58632032 0.68052017
[56,] -2.20770945 0.58632032
[57,] 2.01579420 -2.20770945
[58,] 1.03102331 2.01579420
[59,] -0.19881373 1.03102331
[60,] -3.04291152 -0.19881373
[61,] -1.70114444 -3.04291152
[62,] 0.71612541 -1.70114444
[63,] 2.70757065 0.71612541
[64,] -2.95558870 2.70757065
[65,] 2.31562016 -2.95558870
[66,] -0.33360935 2.31562016
[67,] -5.45121938 -0.33360935
[68,] 4.42878141 -5.45121938
[69,] 2.50128615 4.42878141
[70,] 1.51876512 2.50128615
[71,] 5.61597022 1.51876512
[72,] 4.34493240 5.61597022
[73,] 4.83129502 4.34493240
[74,] -7.11258875 4.83129502
[75,] -5.97156154 -7.11258875
[76,] 1.49520401 -5.97156154
[77,] -2.96947141 1.49520401
[78,] -0.28583624 -2.96947141
[79,] -8.10511804 -0.28583624
[80,] 2.30314897 -8.10511804
[81,] 2.49847905 2.30314897
[82,] -2.21481807 2.49847905
[83,] 0.97787368 -2.21481807
[84,] -2.46414857 0.97787368
[85,] 3.85384215 -2.46414857
[86,] 0.37026624 3.85384215
[87,] 0.14352730 0.37026624
[88,] -0.41347837 0.14352730
[89,] 3.92483310 -0.41347837
[90,] -3.23611482 3.92483310
[91,] 6.45776917 -3.23611482
[92,] -5.19098609 6.45776917
[93,] -1.63541565 -5.19098609
[94,] 0.02913976 -1.63541565
[95,] -0.39775735 0.02913976
[96,] -3.13285825 -0.39775735
[97,] -1.42988835 -3.13285825
[98,] 3.21859999 -1.42988835
[99,] -0.03564337 3.21859999
[100,] -0.18129092 -0.03564337
[101,] 1.06496596 -0.18129092
[102,] -4.88332530 1.06496596
[103,] 5.36532805 -4.88332530
[104,] -4.04293390 5.36532805
[105,] -0.93579969 -4.04293390
[106,] 1.99616237 -0.93579969
[107,] 0.45789607 1.99616237
[108,] 5.52777597 0.45789607
[109,] 1.42705633 5.52777597
[110,] -0.01670778 1.42705633
[111,] 7.63036434 -0.01670778
[112,] 0.71777406 7.63036434
[113,] -0.70110564 0.71777406
[114,] -3.84577288 -0.70110564
[115,] 6.61076456 -3.84577288
[116,] 6.65516962 6.61076456
[117,] 1.90238146 6.65516962
[118,] 0.10469019 1.90238146
[119,] 4.02684601 0.10469019
[120,] -5.99681109 4.02684601
[121,] 3.51189474 -5.99681109
[122,] 2.41513292 3.51189474
[123,] -3.92071949 2.41513292
[124,] -0.19815861 -3.92071949
[125,] -3.95761421 -0.19815861
[126,] 0.73774196 -3.95761421
[127,] 2.57708516 0.73774196
[128,] -0.88298681 2.57708516
[129,] 0.06396274 -0.88298681
[130,] 1.41795343 0.06396274
[131,] 0.90595495 1.41795343
[132,] -0.54640464 0.90595495
[133,] 2.89322121 -0.54640464
[134,] -4.31269923 2.89322121
[135,] -8.70974607 -4.31269923
[136,] 1.18049696 -8.70974607
[137,] 3.62812560 1.18049696
[138,] 0.54995673 3.62812560
[139,] 3.24416128 0.54995673
[140,] 1.38008661 3.24416128
[141,] 4.90699287 1.38008661
[142,] 1.96423403 4.90699287
[143,] -9.76242796 1.96423403
[144,] -4.48605912 -9.76242796
[145,] -6.71901490 -4.48605912
[146,] 1.64045212 -6.71901490
[147,] -2.21028308 1.64045212
[148,] -1.68995017 -2.21028308
[149,] 4.47766956 -1.68995017
[150,] -0.41608399 4.47766956
[151,] 4.31301222 -0.41608399
[152,] 5.29884604 4.31301222
[153,] -0.89226720 5.29884604
[154,] -1.53069158 -0.89226720
[155,] -3.23611482 -1.53069158
[156,] 4.86206599 -3.23611482
[157,] 2.57708516 4.86206599
[158,] -1.99050986 2.57708516
[159,] 5.67797376 -1.99050986
[160,] 2.75545890 5.67797376
[161,] -4.54344701 2.75545890
[162,] -5.88507858 -4.54344701
[163,] -3.29437278 -5.88507858
[164,] 5.37341706 -3.29437278
[165,] 1.44669181 5.37341706
[166,] 0.90279428 1.44669181
[167,] -5.08803664 0.90279428
[168,] 4.33402466 -5.08803664
[169,] 5.95108302 4.33402466
[170,] -2.79780973 5.95108302
[171,] 2.05136170 -2.79780973
[172,] -1.10601143 2.05136170
[173,] -5.39294937 -1.10601143
[174,] -6.44360590 -5.39294937
[175,] 2.06708840 -6.44360590
[176,] -1.19893687 2.06708840
[177,] -1.49621144 -1.19893687
[178,] -1.32904347 -1.49621144
[179,] 2.25678216 -1.32904347
[180,] 1.26339738 2.25678216
[181,] -3.01967473 1.26339738
[182,] 2.39706429 -3.01967473
[183,] -3.62646008 2.39706429
[184,] -0.28929824 -3.62646008
[185,] 2.72859957 -0.28929824
[186,] -0.89960484 2.72859957
[187,] -4.14519922 -0.89960484
[188,] -2.13831028 -4.14519922
[189,] 4.31289333 -2.13831028
[190,] 3.56584573 4.31289333
[191,] 4.30211946 3.56584573
[192,] 1.33507969 4.30211946
[193,] 2.71774620 1.33507969
[194,] -0.66119405 2.71774620
[195,] 2.87146743 -0.66119405
[196,] -2.81975655 2.87146743
[197,] 3.19485403 -2.81975655
[198,] 0.16212496 3.19485403
[199,] 1.25656757 0.16212496
[200,] -2.33325372 1.25656757
[201,] 0.58775631 -2.33325372
[202,] -2.09873394 0.58775631
[203,] -1.42939275 -2.09873394
[204,] 0.95397458 -1.42939275
[205,] 2.13130104 0.95397458
[206,] 2.48093741 2.13130104
[207,] 1.61723531 2.48093741
[208,] -1.00392416 1.61723531
[209,] -0.83085158 -1.00392416
[210,] -0.41268130 -0.83085158
[211,] 7.38404344 -0.41268130
[212,] 5.89564979 7.38404344
[213,] 0.22241761 5.89564979
[214,] 0.61239581 0.22241761
[215,] -6.09361901 0.61239581
[216,] 1.15434882 -6.09361901
[217,] -1.74541744 1.15434882
[218,] 1.16326002 -1.74541744
[219,] 4.07555986 1.16326002
[220,] -1.04264044 4.07555986
[221,] 0.91451504 -1.04264044
[222,] -1.46431787 0.91451504
[223,] 0.04373914 -1.46431787
[224,] 3.21828209 0.04373914
[225,] 4.45039090 3.21828209
[226,] -0.30915613 4.45039090
[227,] 1.16504487 -0.30915613
[228,] 0.02569185 1.16504487
[229,] 1.89250910 0.02569185
[230,] -2.94254823 1.89250910
[231,] 5.44742708 -2.94254823
[232,] 1.58882308 5.44742708
[233,] -4.50518222 1.58882308
[234,] -1.47027434 -4.50518222
[235,] -2.93389938 -1.47027434
[236,] -3.11935336 -2.93389938
[237,] -7.34819297 -3.11935336
[238,] -0.87509146 -7.34819297
[239,] -3.85014958 -0.87509146
[240,] 1.76659908 -3.85014958
[241,] 1.38557197 1.76659908
[242,] -1.59004960 1.38557197
[243,] -0.29484764 -1.59004960
[244,] -4.11072225 -0.29484764
[245,] 4.03100635 -4.11072225
[246,] 2.38659086 4.03100635
[247,] -1.65634299 2.38659086
[248,] -5.74066290 -1.65634299
[249,] -7.61360126 -5.74066290
[250,] 2.40116002 -7.61360126
[251,] -0.33573442 2.40116002
[252,] 0.37999103 -0.33573442
[253,] -1.29162521 0.37999103
[254,] -1.54689109 -1.29162521
[255,] -0.65567504 -1.54689109
[256,] 2.59611804 -0.65567504
[257,] 0.04120074 2.59611804
[258,] -0.81280558 0.04120074
[259,] -1.82098361 -0.81280558
[260,] -0.84811670 -1.82098361
[261,] 4.58753857 -0.84811670
[262,] 0.87095439 4.58753857
[263,] 0.62942598 0.87095439
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.20609515 1.59219268
2 1.91740390 -4.20609515
3 1.11792728 1.91740390
4 2.90885064 1.11792728
5 -5.00123746 2.90885064
6 -5.52020112 -5.00123746
7 1.75618627 -5.52020112
8 0.33912102 1.75618627
9 3.10700340 0.33912102
10 -4.61088190 3.10700340
11 -0.84527554 -4.61088190
12 -0.61369077 -0.84527554
13 2.06774455 -0.61369077
14 2.63801161 2.06774455
15 -0.02620670 2.63801161
16 -2.55249822 -0.02620670
17 1.55774111 -2.55249822
18 1.78496212 1.55774111
19 -1.20141894 1.78496212
20 -0.17068189 -1.20141894
21 -1.77545285 -0.17068189
22 1.51090088 -1.77545285
23 3.93833722 1.51090088
24 -4.44025966 3.93833722
25 -5.08281363 -4.44025966
26 0.32524648 -5.08281363
27 3.19791464 0.32524648
28 -0.70786958 3.19791464
29 -0.50672730 -0.70786958
30 -1.80043822 -0.50672730
31 1.96712077 -1.80043822
32 -3.85694039 1.96712077
33 2.76852523 -3.85694039
34 -3.78582324 2.76852523
35 1.11059286 -3.78582324
36 0.29191679 1.11059286
37 -1.65032189 0.29191679
38 -0.85443696 -1.65032189
39 -1.16824421 -0.85443696
40 -3.07911165 -1.16824421
41 -0.87905794 -3.07911165
42 -4.46701071 -0.87905794
43 -0.76743393 -4.46701071
44 -7.21123978 -0.76743393
45 0.46524843 -7.21123978
46 3.41929505 0.46524843
47 0.54626097 3.41929505
48 3.15591594 0.54626097
49 -3.43906112 3.15591594
50 4.47596647 -3.43906112
51 -2.70574566 4.47596647
52 0.39724784 -2.70574566
53 3.79655123 0.39724784
54 0.68052017 3.79655123
55 0.58632032 0.68052017
56 -2.20770945 0.58632032
57 2.01579420 -2.20770945
58 1.03102331 2.01579420
59 -0.19881373 1.03102331
60 -3.04291152 -0.19881373
61 -1.70114444 -3.04291152
62 0.71612541 -1.70114444
63 2.70757065 0.71612541
64 -2.95558870 2.70757065
65 2.31562016 -2.95558870
66 -0.33360935 2.31562016
67 -5.45121938 -0.33360935
68 4.42878141 -5.45121938
69 2.50128615 4.42878141
70 1.51876512 2.50128615
71 5.61597022 1.51876512
72 4.34493240 5.61597022
73 4.83129502 4.34493240
74 -7.11258875 4.83129502
75 -5.97156154 -7.11258875
76 1.49520401 -5.97156154
77 -2.96947141 1.49520401
78 -0.28583624 -2.96947141
79 -8.10511804 -0.28583624
80 2.30314897 -8.10511804
81 2.49847905 2.30314897
82 -2.21481807 2.49847905
83 0.97787368 -2.21481807
84 -2.46414857 0.97787368
85 3.85384215 -2.46414857
86 0.37026624 3.85384215
87 0.14352730 0.37026624
88 -0.41347837 0.14352730
89 3.92483310 -0.41347837
90 -3.23611482 3.92483310
91 6.45776917 -3.23611482
92 -5.19098609 6.45776917
93 -1.63541565 -5.19098609
94 0.02913976 -1.63541565
95 -0.39775735 0.02913976
96 -3.13285825 -0.39775735
97 -1.42988835 -3.13285825
98 3.21859999 -1.42988835
99 -0.03564337 3.21859999
100 -0.18129092 -0.03564337
101 1.06496596 -0.18129092
102 -4.88332530 1.06496596
103 5.36532805 -4.88332530
104 -4.04293390 5.36532805
105 -0.93579969 -4.04293390
106 1.99616237 -0.93579969
107 0.45789607 1.99616237
108 5.52777597 0.45789607
109 1.42705633 5.52777597
110 -0.01670778 1.42705633
111 7.63036434 -0.01670778
112 0.71777406 7.63036434
113 -0.70110564 0.71777406
114 -3.84577288 -0.70110564
115 6.61076456 -3.84577288
116 6.65516962 6.61076456
117 1.90238146 6.65516962
118 0.10469019 1.90238146
119 4.02684601 0.10469019
120 -5.99681109 4.02684601
121 3.51189474 -5.99681109
122 2.41513292 3.51189474
123 -3.92071949 2.41513292
124 -0.19815861 -3.92071949
125 -3.95761421 -0.19815861
126 0.73774196 -3.95761421
127 2.57708516 0.73774196
128 -0.88298681 2.57708516
129 0.06396274 -0.88298681
130 1.41795343 0.06396274
131 0.90595495 1.41795343
132 -0.54640464 0.90595495
133 2.89322121 -0.54640464
134 -4.31269923 2.89322121
135 -8.70974607 -4.31269923
136 1.18049696 -8.70974607
137 3.62812560 1.18049696
138 0.54995673 3.62812560
139 3.24416128 0.54995673
140 1.38008661 3.24416128
141 4.90699287 1.38008661
142 1.96423403 4.90699287
143 -9.76242796 1.96423403
144 -4.48605912 -9.76242796
145 -6.71901490 -4.48605912
146 1.64045212 -6.71901490
147 -2.21028308 1.64045212
148 -1.68995017 -2.21028308
149 4.47766956 -1.68995017
150 -0.41608399 4.47766956
151 4.31301222 -0.41608399
152 5.29884604 4.31301222
153 -0.89226720 5.29884604
154 -1.53069158 -0.89226720
155 -3.23611482 -1.53069158
156 4.86206599 -3.23611482
157 2.57708516 4.86206599
158 -1.99050986 2.57708516
159 5.67797376 -1.99050986
160 2.75545890 5.67797376
161 -4.54344701 2.75545890
162 -5.88507858 -4.54344701
163 -3.29437278 -5.88507858
164 5.37341706 -3.29437278
165 1.44669181 5.37341706
166 0.90279428 1.44669181
167 -5.08803664 0.90279428
168 4.33402466 -5.08803664
169 5.95108302 4.33402466
170 -2.79780973 5.95108302
171 2.05136170 -2.79780973
172 -1.10601143 2.05136170
173 -5.39294937 -1.10601143
174 -6.44360590 -5.39294937
175 2.06708840 -6.44360590
176 -1.19893687 2.06708840
177 -1.49621144 -1.19893687
178 -1.32904347 -1.49621144
179 2.25678216 -1.32904347
180 1.26339738 2.25678216
181 -3.01967473 1.26339738
182 2.39706429 -3.01967473
183 -3.62646008 2.39706429
184 -0.28929824 -3.62646008
185 2.72859957 -0.28929824
186 -0.89960484 2.72859957
187 -4.14519922 -0.89960484
188 -2.13831028 -4.14519922
189 4.31289333 -2.13831028
190 3.56584573 4.31289333
191 4.30211946 3.56584573
192 1.33507969 4.30211946
193 2.71774620 1.33507969
194 -0.66119405 2.71774620
195 2.87146743 -0.66119405
196 -2.81975655 2.87146743
197 3.19485403 -2.81975655
198 0.16212496 3.19485403
199 1.25656757 0.16212496
200 -2.33325372 1.25656757
201 0.58775631 -2.33325372
202 -2.09873394 0.58775631
203 -1.42939275 -2.09873394
204 0.95397458 -1.42939275
205 2.13130104 0.95397458
206 2.48093741 2.13130104
207 1.61723531 2.48093741
208 -1.00392416 1.61723531
209 -0.83085158 -1.00392416
210 -0.41268130 -0.83085158
211 7.38404344 -0.41268130
212 5.89564979 7.38404344
213 0.22241761 5.89564979
214 0.61239581 0.22241761
215 -6.09361901 0.61239581
216 1.15434882 -6.09361901
217 -1.74541744 1.15434882
218 1.16326002 -1.74541744
219 4.07555986 1.16326002
220 -1.04264044 4.07555986
221 0.91451504 -1.04264044
222 -1.46431787 0.91451504
223 0.04373914 -1.46431787
224 3.21828209 0.04373914
225 4.45039090 3.21828209
226 -0.30915613 4.45039090
227 1.16504487 -0.30915613
228 0.02569185 1.16504487
229 1.89250910 0.02569185
230 -2.94254823 1.89250910
231 5.44742708 -2.94254823
232 1.58882308 5.44742708
233 -4.50518222 1.58882308
234 -1.47027434 -4.50518222
235 -2.93389938 -1.47027434
236 -3.11935336 -2.93389938
237 -7.34819297 -3.11935336
238 -0.87509146 -7.34819297
239 -3.85014958 -0.87509146
240 1.76659908 -3.85014958
241 1.38557197 1.76659908
242 -1.59004960 1.38557197
243 -0.29484764 -1.59004960
244 -4.11072225 -0.29484764
245 4.03100635 -4.11072225
246 2.38659086 4.03100635
247 -1.65634299 2.38659086
248 -5.74066290 -1.65634299
249 -7.61360126 -5.74066290
250 2.40116002 -7.61360126
251 -0.33573442 2.40116002
252 0.37999103 -0.33573442
253 -1.29162521 0.37999103
254 -1.54689109 -1.29162521
255 -0.65567504 -1.54689109
256 2.59611804 -0.65567504
257 0.04120074 2.59611804
258 -0.81280558 0.04120074
259 -1.82098361 -0.81280558
260 -0.84811670 -1.82098361
261 4.58753857 -0.84811670
262 0.87095439 4.58753857
263 0.62942598 0.87095439
> 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/7w2rb1384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8bnbm1384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9glid1384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10mn871384525370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hswt1384525370.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/12ew651384525370.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/133idz1384525370.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/143pcm1384525370.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/15doqp1384525370.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/16647i1384525370.tab")
+ }
>
> try(system("convert tmp/1iw4x1384525370.ps tmp/1iw4x1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kk8u1384525370.ps tmp/2kk8u1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/35lwk1384525370.ps tmp/35lwk1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tvu21384525370.ps tmp/4tvu21384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rkhm1384525370.ps tmp/5rkhm1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/64fe01384525370.ps tmp/64fe01384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w2rb1384525370.ps tmp/7w2rb1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bnbm1384525370.ps tmp/8bnbm1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/9glid1384525370.ps tmp/9glid1384525370.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mn871384525370.ps tmp/10mn871384525370.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.834 3.012 20.213