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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+ ,4
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+ ,11
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+ ,41
+ ,11
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+ ,11
+ ,7
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+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),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
Connected Separate Learning Software Happiness Depression Sport1 Sport2
1 41 38 13 12 14 12.0 53 32
2 39 32 16 11 18 11.0 83 51
3 30 35 19 15 11 14.0 66 42
4 31 33 15 6 12 12.0 67 41
5 34 37 14 13 16 21.0 76 46
6 35 29 13 10 18 12.0 78 47
7 39 31 19 12 14 22.0 53 37
8 34 36 15 14 14 11.0 80 49
9 36 35 14 12 15 10.0 74 45
10 37 38 15 9 15 13.0 76 47
11 38 31 16 10 17 10.0 79 49
12 36 34 16 12 19 8.0 54 33
13 38 35 16 12 10 15.0 67 42
14 39 38 16 11 16 14.0 54 33
15 33 37 17 15 18 10.0 87 53
16 32 33 15 12 14 14.0 58 36
17 36 32 15 10 14 14.0 75 45
18 38 38 20 12 17 11.0 88 54
19 39 38 18 11 14 10.0 64 41
20 32 32 16 12 16 13.0 57 36
21 32 33 16 11 18 9.5 66 41
22 31 31 16 12 11 14.0 68 44
23 39 38 19 13 14 12.0 54 33
24 37 39 16 11 12 14.0 56 37
25 39 32 17 12 17 11.0 86 52
26 41 32 17 13 9 9.0 80 47
27 36 35 16 10 16 11.0 76 43
28 33 37 15 14 14 15.0 69 44
29 33 33 16 12 15 14.0 78 45
30 34 33 14 10 11 13.0 67 44
31 31 31 15 12 16 9.0 80 49
32 27 32 12 8 13 15.0 54 33
33 37 31 14 10 17 10.0 71 43
34 34 37 16 12 15 11.0 84 54
35 34 30 14 12 14 13.0 74 42
36 32 33 10 7 16 8.0 71 44
37 29 31 10 9 9 20.0 63 37
38 36 33 14 12 15 12.0 71 43
39 29 31 16 10 17 10.0 76 46
40 35 33 16 10 13 10.0 69 42
41 37 32 16 10 15 9.0 74 45
42 34 33 14 12 16 14.0 75 44
43 38 32 20 15 16 8.0 54 33
44 35 33 14 10 12 14.0 52 31
45 38 28 14 10 15 11.0 69 42
46 37 35 11 12 11 13.0 68 40
47 38 39 14 13 15 9.0 65 43
48 33 34 15 11 15 11.0 75 46
49 36 38 16 11 17 15.0 74 42
50 38 32 14 12 13 11.0 75 45
51 32 38 16 14 16 10.0 72 44
52 32 30 14 10 14 14.0 67 40
53 32 33 12 12 11 18.0 63 37
54 34 38 16 13 12 14.0 62 46
55 32 32 9 5 12 11.0 63 36
56 37 35 14 6 15 14.5 76 47
57 39 34 16 12 16 13.0 74 45
58 29 34 16 12 15 9.0 67 42
59 37 36 15 11 12 10.0 73 43
60 35 34 16 10 12 15.0 70 43
61 30 28 12 7 8 20.0 53 32
62 38 34 16 12 13 12.0 77 45
63 34 35 16 14 11 12.0 80 48
64 31 35 14 11 14 14.0 52 31
65 34 31 16 12 15 13.0 54 33
66 35 37 17 13 10 11.0 80 49
67 36 35 18 14 11 17.0 66 42
68 30 27 18 11 12 12.0 73 41
69 39 40 12 12 15 13.0 63 38
70 35 37 16 12 15 14.0 69 42
71 38 36 10 8 14 13.0 67 44
72 31 38 14 11 16 15.0 54 33
73 34 39 18 14 15 13.0 81 48
74 38 41 18 14 15 10.0 69 40
75 34 27 16 12 13 11.0 84 50
76 39 30 17 9 12 19.0 80 49
77 37 37 16 13 17 13.0 70 43
78 34 31 16 11 13 17.0 69 44
79 28 31 13 12 15 13.0 77 47
80 37 27 16 12 13 9.0 54 33
81 33 36 16 12 15 11.0 79 46
82 35 37 16 12 15 9.0 71 45
83 37 33 15 12 16 12.0 73 43
84 32 34 15 11 15 12.0 72 44
85 33 31 16 10 14 13.0 77 47
86 38 39 14 9 15 13.0 75 45
87 33 34 16 12 14 12.0 69 42
88 29 32 16 12 13 15.0 54 33
89 33 33 15 12 7 22.0 70 43
90 31 36 12 9 17 13.0 73 46
91 36 32 17 15 13 15.0 54 33
92 35 41 16 12 15 13.0 77 46
93 32 28 15 12 14 15.0 82 48
94 29 30 13 12 13 12.5 80 47
95 39 36 16 10 16 11.0 80 47
96 37 35 16 13 12 16.0 69 43
97 35 31 16 9 14 11.0 78 46
98 37 34 16 12 17 11.0 81 48
99 32 36 14 10 15 10.0 76 46
100 38 36 16 14 17 10.0 76 45
101 37 35 16 11 12 16.0 73 45
102 36 37 20 15 16 12.0 85 52
103 32 28 15 11 11 11.0 66 42
104 33 39 16 11 15 16.0 79 47
105 40 32 13 12 9 19.0 68 41
106 38 35 17 12 16 11.0 76 47
107 41 39 16 12 15 16.0 71 43
108 36 35 16 11 10 15.0 54 33
109 43 42 12 7 10 24.0 46 30
110 30 34 16 12 15 14.0 85 52
111 31 33 16 14 11 15.0 74 44
112 32 41 17 11 13 11.0 88 55
113 32 33 13 11 14 15.0 38 11
114 37 34 12 10 18 12.0 76 47
115 37 32 18 13 16 10.0 86 53
116 33 40 14 13 14 14.0 54 33
117 34 40 14 8 14 13.0 67 44
118 33 35 13 11 14 9.0 69 42
119 38 36 16 12 14 15.0 90 55
120 33 37 13 11 12 15.0 54 33
121 31 27 16 13 14 14.0 76 46
122 38 39 13 12 15 11.0 89 54
123 37 38 16 14 15 8.0 76 47
124 36 31 15 13 15 11.0 73 45
125 31 33 16 15 13 11.0 79 47
126 39 32 15 10 17 8.0 90 55
127 44 39 17 11 17 10.0 74 44
128 33 36 15 9 19 11.0 81 53
129 35 33 12 11 15 13.0 72 44
130 32 33 16 10 13 11.0 71 42
131 28 32 10 11 9 20.0 66 40
132 40 37 16 8 15 10.0 77 46
133 27 30 12 11 15 15.0 65 40
134 37 38 14 12 15 12.0 74 46
135 32 29 15 12 16 14.0 85 53
136 28 22 13 9 11 23.0 54 33
137 34 35 15 11 14 14.0 63 42
138 30 35 11 10 11 16.0 54 35
139 35 34 12 8 15 11.0 64 40
140 31 35 11 9 13 12.0 69 41
141 32 34 16 8 15 10.0 54 33
142 30 37 15 9 16 14.0 84 51
143 30 35 17 15 14 12.0 86 53
144 31 23 16 11 15 12.0 77 46
145 40 31 10 8 16 11.0 89 55
146 32 27 18 13 16 12.0 76 47
147 36 36 13 12 11 13.0 60 38
148 32 31 16 12 12 11.0 75 46
149 35 32 13 9 9 19.0 73 46
150 38 39 10 7 16 12.0 85 53
151 42 37 15 13 13 17.0 79 47
152 34 38 16 9 16 9.0 71 41
153 35 39 16 6 12 12.0 72 44
154 38 34 14 8 9 19.0 69 43
155 33 31 10 8 13 18.0 78 51
156 36 32 17 15 13 15.0 54 33
157 32 37 13 6 14 14.0 69 43
158 33 36 15 9 19 11.0 81 53
159 34 32 16 11 13 9.0 84 51
160 32 38 12 8 12 18.0 84 50
161 34 36 13 8 13 16.0 69 46
162 27 26 13 10 10 24.0 66 43
163 31 26 12 8 14 14.0 81 47
164 38 33 17 14 16 20.0 82 50
165 34 39 15 10 10 18.0 72 43
166 24 30 10 8 11 23.0 54 33
167 30 33 14 11 14 12.0 78 48
168 26 25 11 12 12 14.0 74 44
169 34 38 13 12 9 16.0 82 50
170 27 37 16 12 9 18.0 73 41
171 37 31 12 5 11 20.0 55 34
172 36 37 16 12 16 12.0 72 44
173 41 35 12 10 9 12.0 78 47
174 29 25 9 7 13 17.0 59 35
175 36 28 12 12 16 13.0 72 44
176 32 35 15 11 13 9.0 78 44
177 37 33 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 38 37 12 9 11 14.0 74 42
181 36 36 16 12 14 11.0 54 33
182 35 30 11 6 13 9.0 67 41
183 31 36 19 15 15 11.0 70 44
184 38 32 15 12 14 10.0 80 48
185 22 28 8 12 16 11.0 89 55
186 32 36 16 12 13 19.0 76 44
187 36 34 17 11 14 14.0 74 43
188 39 31 12 7 15 12.0 87 52
189 28 28 11 7 13 14.0 54 30
190 32 36 11 5 11 21.0 61 39
191 32 36 14 12 11 13.0 38 11
192 38 40 16 12 14 10.0 75 44
193 32 33 12 3 15 15.0 69 42
194 35 37 16 11 11 16.0 62 41
195 32 32 13 10 15 14.0 72 44
196 37 38 15 12 12 12.0 70 44
197 34 31 16 9 14 19.0 79 48
198 33 37 16 12 14 15.0 87 53
199 33 33 14 9 8 19.0 62 37
200 26 32 16 12 13 13.0 77 44
201 30 30 16 12 9 17.0 69 44
202 24 30 14 10 15 12.0 69 40
203 34 31 11 9 17 11.0 75 42
204 34 32 12 12 13 14.0 54 35
205 33 34 15 8 15 11.0 72 43
206 34 36 15 11 15 13.0 74 45
207 35 37 16 11 14 12.0 85 55
208 35 36 16 12 16 15.0 52 31
209 36 33 11 10 13 14.0 70 44
210 34 33 15 10 16 12.0 84 50
211 34 33 12 12 9 17.0 64 40
212 41 44 12 12 16 11.0 84 53
213 32 39 15 11 11 18.0 87 54
214 30 32 15 8 10 13.0 79 49
215 35 35 16 12 11 17.0 67 40
216 28 25 14 10 15 13.0 65 41
217 33 35 17 11 17 11.0 85 52
218 39 34 14 10 14 12.0 83 52
219 36 35 13 8 8 22.0 61 36
220 36 39 15 12 15 14.0 82 52
221 35 33 13 12 11 12.0 76 46
222 38 36 14 10 16 12.0 58 31
223 33 32 15 12 10 17.0 72 44
224 31 32 12 9 15 9.0 72 44
225 34 36 13 9 9 21.0 38 11
226 32 36 8 6 16 10.0 78 46
227 31 32 14 10 19 11.0 54 33
228 33 34 14 9 12 12.0 63 34
229 34 33 11 9 8 23.0 66 42
230 34 35 12 9 11 13.0 70 43
231 34 30 13 6 14 12.0 71 43
232 33 38 10 10 9 16.0 67 44
233 32 34 16 6 15 9.0 58 36
234 41 33 18 14 13 17.0 72 46
235 34 32 13 10 16 9.0 72 44
236 36 31 11 10 11 14.0 70 43
237 37 30 4 6 12 17.0 76 50
238 36 27 13 12 13 13.0 50 33
239 29 31 16 12 10 11.0 72 43
240 37 30 10 7 11 12.0 72 44
241 27 32 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 35 33 13 6 12 16.0 66 40
245 37 31 15 10 15 14.0 82 53
246 29 35 12 8 11 20.0 69 42
247 32 35 14 9 13 15.0 68 43
248 36 32 10 9 14 23.0 44 29
249 19 21 12 8 10 20.0 56 36
250 21 20 12 9 12 16.0 53 30
251 31 34 11 7 15 14.0 70 42
252 33 32 10 7 13 17.0 78 47
253 36 34 12 6 13 11.0 71 44
254 33 32 16 9 13 13.0 72 45
255 37 33 12 10 12 17.0 68 44
256 34 33 14 11 12 15.0 67 43
257 35 37 16 12 9 21.0 75 43
258 31 32 14 8 9 18.0 62 40
259 37 34 13 11 15 15.0 67 41
260 35 30 4 3 10 8.0 83 52
261 27 30 15 11 14 12.0 64 38
262 34 38 11 12 15 12.0 68 41
263 40 36 11 7 7 22.0 62 39
264 29 32 14 9 14 12.0 72 43
Month
1 9
2 9
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264 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software Happiness Depression
23.55456 0.42900 0.11248 -0.05921 0.01112 -0.04134
Sport1 Sport2 Month
-0.05306 0.11966 -0.58511
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.9020 -2.2434 0.0338 2.3256 7.4029
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.55456 4.56667 5.158 5.02e-07 ***
Separate 0.42900 0.05768 7.438 1.56e-12 ***
Learning 0.11248 0.11239 1.001 0.3179
Software -0.05921 0.11489 -0.515 0.6068
Happiness 0.01112 0.10440 0.106 0.9153
Depression -0.04134 0.07632 -0.542 0.5886
Sport1 -0.05306 0.06791 -0.781 0.4353
Sport2 0.11966 0.10067 1.189 0.2357
Month -0.58511 0.28812 -2.031 0.0433 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.353 on 255 degrees of freedom
Multiple R-squared: 0.2436, Adjusted R-squared: 0.2198
F-statistic: 10.26 on 8 and 255 DF, p-value: 1.966e-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,] 0.55993628 0.88012745 0.4400637
[2,] 0.69621503 0.60756994 0.3037850
[3,] 0.67988303 0.64023394 0.3201170
[4,] 0.57816869 0.84366262 0.4218313
[5,] 0.60400012 0.79199977 0.3959999
[6,] 0.66030103 0.67939795 0.3396990
[7,] 0.65610835 0.68778331 0.3438917
[8,] 0.56790111 0.86419778 0.4320989
[9,] 0.59470367 0.81059266 0.4052963
[10,] 0.65114668 0.69770664 0.3488533
[11,] 0.62061315 0.75877370 0.3793868
[12,] 0.60621854 0.78756291 0.3937815
[13,] 0.53904660 0.92190681 0.4609534
[14,] 0.58798652 0.82402696 0.4120135
[15,] 0.74641696 0.50716607 0.2535830
[16,] 0.71945525 0.56108949 0.2805447
[17,] 0.67633608 0.64732785 0.3236639
[18,] 0.66639308 0.66721385 0.3336069
[19,] 0.60551233 0.78897534 0.3944877
[20,] 0.59626052 0.80747895 0.4037395
[21,] 0.73630630 0.52738739 0.2636937
[22,] 0.72362824 0.55274351 0.2763718
[23,] 0.68610134 0.62779732 0.3138987
[24,] 0.63309563 0.73380874 0.3669044
[25,] 0.58120806 0.83758388 0.4187919
[26,] 0.54166692 0.91666615 0.4583331
[27,] 0.50496992 0.99006017 0.4950301
[28,] 0.62225321 0.75549359 0.3777468
[29,] 0.56941584 0.86116832 0.4305842
[30,] 0.53313360 0.93373280 0.4668664
[31,] 0.47963242 0.95926484 0.5203676
[32,] 0.43779817 0.87559633 0.5622018
[33,] 0.38984039 0.77968077 0.6101596
[34,] 0.45776034 0.91552068 0.5422397
[35,] 0.46453043 0.92906086 0.5354696
[36,] 0.43399798 0.86799595 0.5660020
[37,] 0.40391689 0.80783379 0.5960831
[38,] 0.35779675 0.71559349 0.6422033
[39,] 0.36657861 0.73315722 0.6334214
[40,] 0.41423795 0.82847590 0.5857621
[41,] 0.37793255 0.75586509 0.6220675
[42,] 0.33788193 0.67576387 0.6621181
[43,] 0.30442806 0.60885613 0.6955719
[44,] 0.26673661 0.53347322 0.7332634
[45,] 0.25080419 0.50160838 0.7491958
[46,] 0.26212082 0.52424164 0.7378792
[47,] 0.37875937 0.75751875 0.6212406
[48,] 0.34031487 0.68062975 0.6596851
[49,] 0.29992433 0.59984867 0.7000757
[50,] 0.27030011 0.54060021 0.7296999
[51,] 0.25266099 0.50532198 0.7473390
[52,] 0.22646763 0.45293526 0.7735324
[53,] 0.23444168 0.46888335 0.7655583
[54,] 0.20237667 0.40475335 0.7976233
[55,] 0.17342286 0.34684572 0.8265771
[56,] 0.15185424 0.30370848 0.8481458
[57,] 0.15292720 0.30585440 0.8470728
[58,] 0.16878564 0.33757129 0.8312144
[59,] 0.14426836 0.28853673 0.8557316
[60,] 0.15377605 0.30755210 0.8462240
[61,] 0.17937644 0.35875287 0.8206236
[62,] 0.17098853 0.34197707 0.8290115
[63,] 0.14650596 0.29301193 0.8534940
[64,] 0.12962382 0.25924764 0.8703762
[65,] 0.17235889 0.34471778 0.8276411
[66,] 0.14976244 0.29952488 0.8502376
[67,] 0.12762038 0.25524075 0.8723796
[68,] 0.16143669 0.32287339 0.8385633
[69,] 0.18687960 0.37375920 0.8131204
[70,] 0.17770193 0.35540387 0.8222981
[71,] 0.15534252 0.31068505 0.8446575
[72,] 0.14835970 0.29671940 0.8516403
[73,] 0.14096498 0.28192996 0.8590350
[74,] 0.12259982 0.24519965 0.8774002
[75,] 0.10997179 0.21994358 0.8900282
[76,] 0.09719154 0.19438309 0.9028085
[77,] 0.11249070 0.22498141 0.8875093
[78,] 0.09494323 0.18988645 0.9050568
[79,] 0.09836087 0.19672174 0.9016391
[80,] 0.09254939 0.18509877 0.9074506
[81,] 0.08256182 0.16512364 0.9174382
[82,] 0.06933455 0.13866910 0.9306654
[83,] 0.06939318 0.13878636 0.9306068
[84,] 0.07057106 0.14114213 0.9294289
[85,] 0.06584173 0.13168346 0.9341583
[86,] 0.05633772 0.11267544 0.9436623
[87,] 0.05074540 0.10149081 0.9492546
[88,] 0.04974530 0.09949059 0.9502547
[89,] 0.04669151 0.09338302 0.9533085
[90,] 0.04204354 0.08408709 0.9579565
[91,] 0.03484677 0.06969355 0.9651532
[92,] 0.02839298 0.05678596 0.9716070
[93,] 0.02828834 0.05657668 0.9717117
[94,] 0.06559614 0.13119227 0.9344039
[95,] 0.06173343 0.12346686 0.9382666
[96,] 0.07556485 0.15112969 0.9244352
[97,] 0.06535353 0.13070706 0.9346465
[98,] 0.10367531 0.20735062 0.8963247
[99,] 0.12055945 0.24111890 0.8794406
[100,] 0.11571835 0.23143670 0.8842817
[101,] 0.15213615 0.30427230 0.8478639
[102,] 0.14068677 0.28137354 0.8593132
[103,] 0.13657597 0.27315194 0.8634240
[104,] 0.13016442 0.26032884 0.8698356
[105,] 0.12767424 0.25534848 0.8723258
[106,] 0.12452234 0.24904468 0.8754777
[107,] 0.10977539 0.21955078 0.8902246
[108,] 0.10358455 0.20716910 0.8964154
[109,] 0.09300709 0.18601418 0.9069929
[110,] 0.08114922 0.16229843 0.9188508
[111,] 0.07415336 0.14830672 0.9258466
[112,] 0.06344727 0.12689455 0.9365527
[113,] 0.06055195 0.12110391 0.9394480
[114,] 0.05722199 0.11444398 0.9427780
[115,] 0.07054883 0.14109766 0.9294512
[116,] 0.12680549 0.25361099 0.8731945
[117,] 0.12407023 0.24814045 0.8759298
[118,] 0.11043435 0.22086871 0.8895656
[119,] 0.10172611 0.20345222 0.8982739
[120,] 0.11045106 0.22090211 0.8895489
[121,] 0.11806407 0.23612815 0.8819359
[122,] 0.14211655 0.28423310 0.8578835
[123,] 0.12550691 0.25101381 0.8744931
[124,] 0.10844070 0.21688140 0.8915593
[125,] 0.09430599 0.18861198 0.9056940
[126,] 0.08099901 0.16199802 0.9190010
[127,] 0.08946784 0.17893567 0.9105322
[128,] 0.07648437 0.15296875 0.9235156
[129,] 0.07638977 0.15277954 0.9236102
[130,] 0.07402650 0.14805299 0.9259735
[131,] 0.10583260 0.21166519 0.8941674
[132,] 0.13164455 0.26328910 0.8683554
[133,] 0.11945759 0.23891518 0.8805424
[134,] 0.19533571 0.39067142 0.8046643
[135,] 0.17634231 0.35268462 0.8236577
[136,] 0.15917596 0.31835191 0.8408240
[137,] 0.14074683 0.28149367 0.8592532
[138,] 0.12730867 0.25461734 0.8726913
[139,] 0.11309372 0.22618744 0.8869063
[140,] 0.17837228 0.35674457 0.8216277
[141,] 0.16545820 0.33091640 0.8345418
[142,] 0.15093502 0.30187004 0.8490650
[143,] 0.16515929 0.33031858 0.8348407
[144,] 0.14517068 0.29034136 0.8548293
[145,] 0.15936405 0.31872811 0.8406359
[146,] 0.15310809 0.30621619 0.8468919
[147,] 0.14089453 0.28178905 0.8591055
[148,] 0.13583307 0.27166614 0.8641669
[149,] 0.12615807 0.25231614 0.8738419
[150,] 0.10822457 0.21644915 0.8917754
[151,] 0.10041901 0.20083802 0.8995810
[152,] 0.09209178 0.18418356 0.9079082
[153,] 0.12138356 0.24276712 0.8786164
[154,] 0.10885759 0.21771518 0.8911424
[155,] 0.18069083 0.36138165 0.8193092
[156,] 0.17600294 0.35200587 0.8239971
[157,] 0.16207883 0.32415766 0.8379212
[158,] 0.14533395 0.29066790 0.8546661
[159,] 0.23538626 0.47077251 0.7646137
[160,] 0.27809199 0.55618398 0.7219080
[161,] 0.24927240 0.49854480 0.7507276
[162,] 0.36227978 0.72455955 0.6377202
[163,] 0.32767970 0.65535940 0.6723203
[164,] 0.39980872 0.79961745 0.6001913
[165,] 0.37212659 0.74425318 0.6278734
[166,] 0.37835233 0.75670466 0.6216477
[167,] 0.34561620 0.69123240 0.6543838
[168,] 0.31521039 0.63042077 0.6847896
[169,] 0.31403471 0.62806943 0.6859653
[170,] 0.28361733 0.56723466 0.7163827
[171,] 0.27977959 0.55955918 0.7202204
[172,] 0.29495071 0.58990142 0.7050493
[173,] 0.36486413 0.72972827 0.6351359
[174,] 0.60221188 0.79557625 0.3977881
[175,] 0.58225519 0.83548963 0.4177448
[176,] 0.57877007 0.84245986 0.4212299
[177,] 0.72888179 0.54223641 0.2711182
[178,] 0.70545939 0.58908122 0.2945406
[179,] 0.70989602 0.58020795 0.2901040
[180,] 0.67440645 0.65118711 0.3255936
[181,] 0.64516516 0.70966967 0.3548348
[182,] 0.61059616 0.77880769 0.3894038
[183,] 0.57655026 0.84689949 0.4234497
[184,] 0.53674782 0.92650436 0.4632522
[185,] 0.49857614 0.99715229 0.5014239
[186,] 0.49131299 0.98262597 0.5086870
[187,] 0.46249016 0.92498031 0.5375098
[188,] 0.42099083 0.84198166 0.5790092
[189,] 0.49260456 0.98520912 0.5073954
[190,] 0.45808006 0.91616013 0.5419199
[191,] 0.60747474 0.78505051 0.3925253
[192,] 0.58770578 0.82458844 0.4122942
[193,] 0.55167120 0.89665760 0.4483288
[194,] 0.50862092 0.98275816 0.4913791
[195,] 0.46705870 0.93411739 0.5329413
[196,] 0.42665944 0.85331888 0.5733406
[197,] 0.38460989 0.76921979 0.6153901
[198,] 0.35939550 0.71879101 0.6406045
[199,] 0.32714161 0.65428321 0.6728584
[200,] 0.29081309 0.58162618 0.7091869
[201,] 0.26537754 0.53075509 0.7346225
[202,] 0.31330631 0.62661262 0.6866937
[203,] 0.29108745 0.58217491 0.7089125
[204,] 0.25279605 0.50559211 0.7472039
[205,] 0.21829979 0.43659959 0.7817002
[206,] 0.19000312 0.38000624 0.8099969
[207,] 0.22031317 0.44062633 0.7796868
[208,] 0.19656197 0.39312394 0.8034380
[209,] 0.17696660 0.35393320 0.8230334
[210,] 0.15140397 0.30280795 0.8485960
[211,] 0.16443476 0.32886953 0.8355652
[212,] 0.13438717 0.26877435 0.8656128
[213,] 0.11331942 0.22663883 0.8866806
[214,] 0.16998610 0.33997220 0.8300139
[215,] 0.15442270 0.30884541 0.8455773
[216,] 0.13385577 0.26771155 0.8661442
[217,] 0.14137912 0.28275824 0.8586209
[218,] 0.11374603 0.22749205 0.8862540
[219,] 0.08879973 0.17759947 0.9112003
[220,] 0.08843341 0.17686683 0.9115666
[221,] 0.20946572 0.41893144 0.7905343
[222,] 0.17152488 0.34304976 0.8284751
[223,] 0.28142757 0.56285514 0.7185724
[224,] 0.23617491 0.47234983 0.7638251
[225,] 0.24061804 0.48123608 0.7593820
[226,] 0.20650733 0.41301467 0.7934927
[227,] 0.28846774 0.57693548 0.7115323
[228,] 0.23707101 0.47414201 0.7629290
[229,] 0.39622008 0.79244016 0.6037799
[230,] 0.46480580 0.92961159 0.5351942
[231,] 0.38720388 0.77440777 0.6127961
[232,] 0.31428516 0.62857031 0.6857148
[233,] 0.30119019 0.60238039 0.6988098
[234,] 0.32308984 0.64617967 0.6769102
[235,] 0.53420784 0.93158432 0.4657922
[236,] 0.52663240 0.94673520 0.4733676
[237,] 0.45765106 0.91530213 0.5423489
[238,] 0.67207880 0.65584240 0.3279212
[239,] 0.60077133 0.79845734 0.3992287
[240,] 0.56117976 0.87764047 0.4388202
[241,] 0.66347881 0.67304238 0.3365212
> postscript(file="/var/wessaorg/rcomp/tmp/1gcro1384526060.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d6ue1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3m1sg1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ji421384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5s7ke1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
4.981001822 4.390800158 -5.620051568 -3.766063581 -1.748357336 2.210717473
7 8 9 10 11 12
5.124191770 -1.910496452 0.620402430 0.034099795 3.757458676 1.072043230
13 14 15 16 17 18
2.645275789 2.578195760 -3.698278618 -2.229618323 1.906040423 0.343515537
19 20 21 22 23 24
1.783435204 -2.029726495 -2.805612927 -2.877434817 2.298739476 -0.178868696
25 26 27 28 29 30
4.388167558 6.733593421 0.652701691 -3.159511768 -1.368958673 -0.723295374
31 32 33 34 35 36
-2.988806295 -6.500811632 3.275905566 -2.967581598 1.259531044 -2.501027198
37 38 39 40 41 42
-3.537598272 1.641222292 -5.042734241 0.250956415 2.522703450 -0.194641151
43 44 45 46 47 48
3.691110364 1.066630811 5.640024468 2.406264582 0.684038660 -2.147546543
49 50 51 52 53 54
-0.407330730 4.024041935 -4.770722667 -0.949649786 -1.547844470 -3.390057941
55 56 57 58 59 60
-1.376664031 1.317967311 3.937339525 -6.229325827 1.239334103 -0.026850594
61 62 63 64 65 66
-1.515139279 3.088540640 -1.399619456 -3.754401823 0.610200444 -0.994082819
67 68 69 70 71 72
1.142335695 -1.329970612 2.663433742 -0.618421214 2.872950723 -4.570404088
73 74 75 76 77 78
-2.705548623 0.633011698 2.408440290 6.080557938 1.310612363 0.803300961
79 80 81 82 83 84
-4.922137291 5.768209125 -2.261467521 -1.077969864 3.208858298 -2.440958839
85 86 87 88 89 90
-0.366867419 1.488949996 -1.402968803 -4.128786246 -0.436907924 -4.247109365
91 92 93 94 95 96
2.936357147 -2.429929923 0.379347067 -3.332383435 3.542398399 2.295151329
97 98 99 100 101 102
1.663978795 2.441097222 -3.355446454 2.753854893 2.149655414 -0.332155666
103 104 105 106 107 108
0.057147951 -3.520665242 7.204011606 2.765091078 4.592705352 1.558352478
109 110 111 112 113 114
6.074943302 -4.679069048 -2.672211890 -6.155350041 0.492941189 2.657169327
115 116 117 118 119 120
2.770122941 -3.329095412 -3.292969830 -1.677752694 2.421695823 -1.984453702
121 122 123 124 125 126
-0.365298627 1.362263204 0.596085707 2.856523823 -2.894268121 4.809063884
127 128 129 130 131 132
7.190289808 -3.102605099 1.366813004 -2.016476156 -4.462885045 3.925242889
133 134 135 136 137 138
-5.156283082 0.881512184 -0.552361138 -0.326027748 -1.014397192 -4.147574613
139 140 141 142 143 144
0.731680955 -3.316421708 -2.452534357 -5.975734524 -5.181084783 1.191568280
145 146 147 148 149 150
6.764105371 0.185174684 1.152250876 -1.295349292 1.693378564 1.341297711
151 152 153 154 155 156
6.631802489 -2.217055582 -1.961130863 3.225326207 -0.018210758 2.936357147
157 158 159 160 161 162
-3.744776436 -3.102605099 0.001885863 -3.797032207 -1.462561919 -2.905168467
163 164 165 166 167 168
0.948331665 4.658019786 -1.636801189 -6.894711203 -3.463539171 -3.263563604
169 170 171 172 173 174
-1.243015602 -7.469353556 5.083099168 0.792754398 7.019456502 0.059299535
175 176 177 178 179 180
5.145022440 -2.068256719 3.872431551 -1.439645970 -1.515678220 3.548755032
181 182 183 184 185 186
1.563851395 3.005945960 -4.074397023 4.935643289 -8.901995296 -2.243292575
187 188 189 190 191 192
2.238770324 6.370373196 -2.243659281 -2.188048767 -0.311547360 1.604495052
193 194 195 196 197 198
-1.358919905 -0.217144097 -0.749425160 1.414577230 1.393509172 -2.342054784
199 200 201 202 203 204
0.241422720 -6.722238016 -2.078910971 -7.767099523 2.097594703 1.625569874
205 206 207 208 209 210
-0.955143801 -0.686059843 -0.870720564 0.840165061 2.962640392 0.421581989
211 212 213 214 215 216
1.297341483 2.758076893 -4.109091715 -3.305432054 1.126374842 -1.912661806
217 218 219 220 221 222
-1.840890992 4.834903493 2.627299308 -0.285678676 1.874703159 4.141067435
223 224 225 226 227 228
0.323629169 -1.902834479 1.976232506 -2.237293855 -1.669185706 -0.109348927
229 230 231 232 233 234
1.358127032 0.033513831 1.866799003 -2.101935401 -2.173921685 7.402927128
235 236 237 238 239 240
1.032777194 3.962543948 5.535675314 5.643849243 -3.488198638 5.230189938
241 242 243 244 245 246
-6.115343729 1.608226167 -2.660293445 1.861055429 3.908261679 -4.669738769
247 248 249 250 251 252
-2.237127911 4.221179152 -8.624405295 -5.764976500 -2.426890575 0.516007673
253 254 255 256 257 258
2.113385371 -0.284830507 3.879165196 0.697346914 0.521449773 -1.800227173
259 260 261 262 263 264
3.586797465 3.140367740 -4.835232451 -0.915995514 6.069233137 -3.873000711
> postscript(file="/var/wessaorg/rcomp/tmp/6c5b41384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 4.981001822 NA
1 4.390800158 4.981001822
2 -5.620051568 4.390800158
3 -3.766063581 -5.620051568
4 -1.748357336 -3.766063581
5 2.210717473 -1.748357336
6 5.124191770 2.210717473
7 -1.910496452 5.124191770
8 0.620402430 -1.910496452
9 0.034099795 0.620402430
10 3.757458676 0.034099795
11 1.072043230 3.757458676
12 2.645275789 1.072043230
13 2.578195760 2.645275789
14 -3.698278618 2.578195760
15 -2.229618323 -3.698278618
16 1.906040423 -2.229618323
17 0.343515537 1.906040423
18 1.783435204 0.343515537
19 -2.029726495 1.783435204
20 -2.805612927 -2.029726495
21 -2.877434817 -2.805612927
22 2.298739476 -2.877434817
23 -0.178868696 2.298739476
24 4.388167558 -0.178868696
25 6.733593421 4.388167558
26 0.652701691 6.733593421
27 -3.159511768 0.652701691
28 -1.368958673 -3.159511768
29 -0.723295374 -1.368958673
30 -2.988806295 -0.723295374
31 -6.500811632 -2.988806295
32 3.275905566 -6.500811632
33 -2.967581598 3.275905566
34 1.259531044 -2.967581598
35 -2.501027198 1.259531044
36 -3.537598272 -2.501027198
37 1.641222292 -3.537598272
38 -5.042734241 1.641222292
39 0.250956415 -5.042734241
40 2.522703450 0.250956415
41 -0.194641151 2.522703450
42 3.691110364 -0.194641151
43 1.066630811 3.691110364
44 5.640024468 1.066630811
45 2.406264582 5.640024468
46 0.684038660 2.406264582
47 -2.147546543 0.684038660
48 -0.407330730 -2.147546543
49 4.024041935 -0.407330730
50 -4.770722667 4.024041935
51 -0.949649786 -4.770722667
52 -1.547844470 -0.949649786
53 -3.390057941 -1.547844470
54 -1.376664031 -3.390057941
55 1.317967311 -1.376664031
56 3.937339525 1.317967311
57 -6.229325827 3.937339525
58 1.239334103 -6.229325827
59 -0.026850594 1.239334103
60 -1.515139279 -0.026850594
61 3.088540640 -1.515139279
62 -1.399619456 3.088540640
63 -3.754401823 -1.399619456
64 0.610200444 -3.754401823
65 -0.994082819 0.610200444
66 1.142335695 -0.994082819
67 -1.329970612 1.142335695
68 2.663433742 -1.329970612
69 -0.618421214 2.663433742
70 2.872950723 -0.618421214
71 -4.570404088 2.872950723
72 -2.705548623 -4.570404088
73 0.633011698 -2.705548623
74 2.408440290 0.633011698
75 6.080557938 2.408440290
76 1.310612363 6.080557938
77 0.803300961 1.310612363
78 -4.922137291 0.803300961
79 5.768209125 -4.922137291
80 -2.261467521 5.768209125
81 -1.077969864 -2.261467521
82 3.208858298 -1.077969864
83 -2.440958839 3.208858298
84 -0.366867419 -2.440958839
85 1.488949996 -0.366867419
86 -1.402968803 1.488949996
87 -4.128786246 -1.402968803
88 -0.436907924 -4.128786246
89 -4.247109365 -0.436907924
90 2.936357147 -4.247109365
91 -2.429929923 2.936357147
92 0.379347067 -2.429929923
93 -3.332383435 0.379347067
94 3.542398399 -3.332383435
95 2.295151329 3.542398399
96 1.663978795 2.295151329
97 2.441097222 1.663978795
98 -3.355446454 2.441097222
99 2.753854893 -3.355446454
100 2.149655414 2.753854893
101 -0.332155666 2.149655414
102 0.057147951 -0.332155666
103 -3.520665242 0.057147951
104 7.204011606 -3.520665242
105 2.765091078 7.204011606
106 4.592705352 2.765091078
107 1.558352478 4.592705352
108 6.074943302 1.558352478
109 -4.679069048 6.074943302
110 -2.672211890 -4.679069048
111 -6.155350041 -2.672211890
112 0.492941189 -6.155350041
113 2.657169327 0.492941189
114 2.770122941 2.657169327
115 -3.329095412 2.770122941
116 -3.292969830 -3.329095412
117 -1.677752694 -3.292969830
118 2.421695823 -1.677752694
119 -1.984453702 2.421695823
120 -0.365298627 -1.984453702
121 1.362263204 -0.365298627
122 0.596085707 1.362263204
123 2.856523823 0.596085707
124 -2.894268121 2.856523823
125 4.809063884 -2.894268121
126 7.190289808 4.809063884
127 -3.102605099 7.190289808
128 1.366813004 -3.102605099
129 -2.016476156 1.366813004
130 -4.462885045 -2.016476156
131 3.925242889 -4.462885045
132 -5.156283082 3.925242889
133 0.881512184 -5.156283082
134 -0.552361138 0.881512184
135 -0.326027748 -0.552361138
136 -1.014397192 -0.326027748
137 -4.147574613 -1.014397192
138 0.731680955 -4.147574613
139 -3.316421708 0.731680955
140 -2.452534357 -3.316421708
141 -5.975734524 -2.452534357
142 -5.181084783 -5.975734524
143 1.191568280 -5.181084783
144 6.764105371 1.191568280
145 0.185174684 6.764105371
146 1.152250876 0.185174684
147 -1.295349292 1.152250876
148 1.693378564 -1.295349292
149 1.341297711 1.693378564
150 6.631802489 1.341297711
151 -2.217055582 6.631802489
152 -1.961130863 -2.217055582
153 3.225326207 -1.961130863
154 -0.018210758 3.225326207
155 2.936357147 -0.018210758
156 -3.744776436 2.936357147
157 -3.102605099 -3.744776436
158 0.001885863 -3.102605099
159 -3.797032207 0.001885863
160 -1.462561919 -3.797032207
161 -2.905168467 -1.462561919
162 0.948331665 -2.905168467
163 4.658019786 0.948331665
164 -1.636801189 4.658019786
165 -6.894711203 -1.636801189
166 -3.463539171 -6.894711203
167 -3.263563604 -3.463539171
168 -1.243015602 -3.263563604
169 -7.469353556 -1.243015602
170 5.083099168 -7.469353556
171 0.792754398 5.083099168
172 7.019456502 0.792754398
173 0.059299535 7.019456502
174 5.145022440 0.059299535
175 -2.068256719 5.145022440
176 3.872431551 -2.068256719
177 -1.439645970 3.872431551
178 -1.515678220 -1.439645970
179 3.548755032 -1.515678220
180 1.563851395 3.548755032
181 3.005945960 1.563851395
182 -4.074397023 3.005945960
183 4.935643289 -4.074397023
184 -8.901995296 4.935643289
185 -2.243292575 -8.901995296
186 2.238770324 -2.243292575
187 6.370373196 2.238770324
188 -2.243659281 6.370373196
189 -2.188048767 -2.243659281
190 -0.311547360 -2.188048767
191 1.604495052 -0.311547360
192 -1.358919905 1.604495052
193 -0.217144097 -1.358919905
194 -0.749425160 -0.217144097
195 1.414577230 -0.749425160
196 1.393509172 1.414577230
197 -2.342054784 1.393509172
198 0.241422720 -2.342054784
199 -6.722238016 0.241422720
200 -2.078910971 -6.722238016
201 -7.767099523 -2.078910971
202 2.097594703 -7.767099523
203 1.625569874 2.097594703
204 -0.955143801 1.625569874
205 -0.686059843 -0.955143801
206 -0.870720564 -0.686059843
207 0.840165061 -0.870720564
208 2.962640392 0.840165061
209 0.421581989 2.962640392
210 1.297341483 0.421581989
211 2.758076893 1.297341483
212 -4.109091715 2.758076893
213 -3.305432054 -4.109091715
214 1.126374842 -3.305432054
215 -1.912661806 1.126374842
216 -1.840890992 -1.912661806
217 4.834903493 -1.840890992
218 2.627299308 4.834903493
219 -0.285678676 2.627299308
220 1.874703159 -0.285678676
221 4.141067435 1.874703159
222 0.323629169 4.141067435
223 -1.902834479 0.323629169
224 1.976232506 -1.902834479
225 -2.237293855 1.976232506
226 -1.669185706 -2.237293855
227 -0.109348927 -1.669185706
228 1.358127032 -0.109348927
229 0.033513831 1.358127032
230 1.866799003 0.033513831
231 -2.101935401 1.866799003
232 -2.173921685 -2.101935401
233 7.402927128 -2.173921685
234 1.032777194 7.402927128
235 3.962543948 1.032777194
236 5.535675314 3.962543948
237 5.643849243 5.535675314
238 -3.488198638 5.643849243
239 5.230189938 -3.488198638
240 -6.115343729 5.230189938
241 1.608226167 -6.115343729
242 -2.660293445 1.608226167
243 1.861055429 -2.660293445
244 3.908261679 1.861055429
245 -4.669738769 3.908261679
246 -2.237127911 -4.669738769
247 4.221179152 -2.237127911
248 -8.624405295 4.221179152
249 -5.764976500 -8.624405295
250 -2.426890575 -5.764976500
251 0.516007673 -2.426890575
252 2.113385371 0.516007673
253 -0.284830507 2.113385371
254 3.879165196 -0.284830507
255 0.697346914 3.879165196
256 0.521449773 0.697346914
257 -1.800227173 0.521449773
258 3.586797465 -1.800227173
259 3.140367740 3.586797465
260 -4.835232451 3.140367740
261 -0.915995514 -4.835232451
262 6.069233137 -0.915995514
263 -3.873000711 6.069233137
264 NA -3.873000711
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.390800158 4.981001822
[2,] -5.620051568 4.390800158
[3,] -3.766063581 -5.620051568
[4,] -1.748357336 -3.766063581
[5,] 2.210717473 -1.748357336
[6,] 5.124191770 2.210717473
[7,] -1.910496452 5.124191770
[8,] 0.620402430 -1.910496452
[9,] 0.034099795 0.620402430
[10,] 3.757458676 0.034099795
[11,] 1.072043230 3.757458676
[12,] 2.645275789 1.072043230
[13,] 2.578195760 2.645275789
[14,] -3.698278618 2.578195760
[15,] -2.229618323 -3.698278618
[16,] 1.906040423 -2.229618323
[17,] 0.343515537 1.906040423
[18,] 1.783435204 0.343515537
[19,] -2.029726495 1.783435204
[20,] -2.805612927 -2.029726495
[21,] -2.877434817 -2.805612927
[22,] 2.298739476 -2.877434817
[23,] -0.178868696 2.298739476
[24,] 4.388167558 -0.178868696
[25,] 6.733593421 4.388167558
[26,] 0.652701691 6.733593421
[27,] -3.159511768 0.652701691
[28,] -1.368958673 -3.159511768
[29,] -0.723295374 -1.368958673
[30,] -2.988806295 -0.723295374
[31,] -6.500811632 -2.988806295
[32,] 3.275905566 -6.500811632
[33,] -2.967581598 3.275905566
[34,] 1.259531044 -2.967581598
[35,] -2.501027198 1.259531044
[36,] -3.537598272 -2.501027198
[37,] 1.641222292 -3.537598272
[38,] -5.042734241 1.641222292
[39,] 0.250956415 -5.042734241
[40,] 2.522703450 0.250956415
[41,] -0.194641151 2.522703450
[42,] 3.691110364 -0.194641151
[43,] 1.066630811 3.691110364
[44,] 5.640024468 1.066630811
[45,] 2.406264582 5.640024468
[46,] 0.684038660 2.406264582
[47,] -2.147546543 0.684038660
[48,] -0.407330730 -2.147546543
[49,] 4.024041935 -0.407330730
[50,] -4.770722667 4.024041935
[51,] -0.949649786 -4.770722667
[52,] -1.547844470 -0.949649786
[53,] -3.390057941 -1.547844470
[54,] -1.376664031 -3.390057941
[55,] 1.317967311 -1.376664031
[56,] 3.937339525 1.317967311
[57,] -6.229325827 3.937339525
[58,] 1.239334103 -6.229325827
[59,] -0.026850594 1.239334103
[60,] -1.515139279 -0.026850594
[61,] 3.088540640 -1.515139279
[62,] -1.399619456 3.088540640
[63,] -3.754401823 -1.399619456
[64,] 0.610200444 -3.754401823
[65,] -0.994082819 0.610200444
[66,] 1.142335695 -0.994082819
[67,] -1.329970612 1.142335695
[68,] 2.663433742 -1.329970612
[69,] -0.618421214 2.663433742
[70,] 2.872950723 -0.618421214
[71,] -4.570404088 2.872950723
[72,] -2.705548623 -4.570404088
[73,] 0.633011698 -2.705548623
[74,] 2.408440290 0.633011698
[75,] 6.080557938 2.408440290
[76,] 1.310612363 6.080557938
[77,] 0.803300961 1.310612363
[78,] -4.922137291 0.803300961
[79,] 5.768209125 -4.922137291
[80,] -2.261467521 5.768209125
[81,] -1.077969864 -2.261467521
[82,] 3.208858298 -1.077969864
[83,] -2.440958839 3.208858298
[84,] -0.366867419 -2.440958839
[85,] 1.488949996 -0.366867419
[86,] -1.402968803 1.488949996
[87,] -4.128786246 -1.402968803
[88,] -0.436907924 -4.128786246
[89,] -4.247109365 -0.436907924
[90,] 2.936357147 -4.247109365
[91,] -2.429929923 2.936357147
[92,] 0.379347067 -2.429929923
[93,] -3.332383435 0.379347067
[94,] 3.542398399 -3.332383435
[95,] 2.295151329 3.542398399
[96,] 1.663978795 2.295151329
[97,] 2.441097222 1.663978795
[98,] -3.355446454 2.441097222
[99,] 2.753854893 -3.355446454
[100,] 2.149655414 2.753854893
[101,] -0.332155666 2.149655414
[102,] 0.057147951 -0.332155666
[103,] -3.520665242 0.057147951
[104,] 7.204011606 -3.520665242
[105,] 2.765091078 7.204011606
[106,] 4.592705352 2.765091078
[107,] 1.558352478 4.592705352
[108,] 6.074943302 1.558352478
[109,] -4.679069048 6.074943302
[110,] -2.672211890 -4.679069048
[111,] -6.155350041 -2.672211890
[112,] 0.492941189 -6.155350041
[113,] 2.657169327 0.492941189
[114,] 2.770122941 2.657169327
[115,] -3.329095412 2.770122941
[116,] -3.292969830 -3.329095412
[117,] -1.677752694 -3.292969830
[118,] 2.421695823 -1.677752694
[119,] -1.984453702 2.421695823
[120,] -0.365298627 -1.984453702
[121,] 1.362263204 -0.365298627
[122,] 0.596085707 1.362263204
[123,] 2.856523823 0.596085707
[124,] -2.894268121 2.856523823
[125,] 4.809063884 -2.894268121
[126,] 7.190289808 4.809063884
[127,] -3.102605099 7.190289808
[128,] 1.366813004 -3.102605099
[129,] -2.016476156 1.366813004
[130,] -4.462885045 -2.016476156
[131,] 3.925242889 -4.462885045
[132,] -5.156283082 3.925242889
[133,] 0.881512184 -5.156283082
[134,] -0.552361138 0.881512184
[135,] -0.326027748 -0.552361138
[136,] -1.014397192 -0.326027748
[137,] -4.147574613 -1.014397192
[138,] 0.731680955 -4.147574613
[139,] -3.316421708 0.731680955
[140,] -2.452534357 -3.316421708
[141,] -5.975734524 -2.452534357
[142,] -5.181084783 -5.975734524
[143,] 1.191568280 -5.181084783
[144,] 6.764105371 1.191568280
[145,] 0.185174684 6.764105371
[146,] 1.152250876 0.185174684
[147,] -1.295349292 1.152250876
[148,] 1.693378564 -1.295349292
[149,] 1.341297711 1.693378564
[150,] 6.631802489 1.341297711
[151,] -2.217055582 6.631802489
[152,] -1.961130863 -2.217055582
[153,] 3.225326207 -1.961130863
[154,] -0.018210758 3.225326207
[155,] 2.936357147 -0.018210758
[156,] -3.744776436 2.936357147
[157,] -3.102605099 -3.744776436
[158,] 0.001885863 -3.102605099
[159,] -3.797032207 0.001885863
[160,] -1.462561919 -3.797032207
[161,] -2.905168467 -1.462561919
[162,] 0.948331665 -2.905168467
[163,] 4.658019786 0.948331665
[164,] -1.636801189 4.658019786
[165,] -6.894711203 -1.636801189
[166,] -3.463539171 -6.894711203
[167,] -3.263563604 -3.463539171
[168,] -1.243015602 -3.263563604
[169,] -7.469353556 -1.243015602
[170,] 5.083099168 -7.469353556
[171,] 0.792754398 5.083099168
[172,] 7.019456502 0.792754398
[173,] 0.059299535 7.019456502
[174,] 5.145022440 0.059299535
[175,] -2.068256719 5.145022440
[176,] 3.872431551 -2.068256719
[177,] -1.439645970 3.872431551
[178,] -1.515678220 -1.439645970
[179,] 3.548755032 -1.515678220
[180,] 1.563851395 3.548755032
[181,] 3.005945960 1.563851395
[182,] -4.074397023 3.005945960
[183,] 4.935643289 -4.074397023
[184,] -8.901995296 4.935643289
[185,] -2.243292575 -8.901995296
[186,] 2.238770324 -2.243292575
[187,] 6.370373196 2.238770324
[188,] -2.243659281 6.370373196
[189,] -2.188048767 -2.243659281
[190,] -0.311547360 -2.188048767
[191,] 1.604495052 -0.311547360
[192,] -1.358919905 1.604495052
[193,] -0.217144097 -1.358919905
[194,] -0.749425160 -0.217144097
[195,] 1.414577230 -0.749425160
[196,] 1.393509172 1.414577230
[197,] -2.342054784 1.393509172
[198,] 0.241422720 -2.342054784
[199,] -6.722238016 0.241422720
[200,] -2.078910971 -6.722238016
[201,] -7.767099523 -2.078910971
[202,] 2.097594703 -7.767099523
[203,] 1.625569874 2.097594703
[204,] -0.955143801 1.625569874
[205,] -0.686059843 -0.955143801
[206,] -0.870720564 -0.686059843
[207,] 0.840165061 -0.870720564
[208,] 2.962640392 0.840165061
[209,] 0.421581989 2.962640392
[210,] 1.297341483 0.421581989
[211,] 2.758076893 1.297341483
[212,] -4.109091715 2.758076893
[213,] -3.305432054 -4.109091715
[214,] 1.126374842 -3.305432054
[215,] -1.912661806 1.126374842
[216,] -1.840890992 -1.912661806
[217,] 4.834903493 -1.840890992
[218,] 2.627299308 4.834903493
[219,] -0.285678676 2.627299308
[220,] 1.874703159 -0.285678676
[221,] 4.141067435 1.874703159
[222,] 0.323629169 4.141067435
[223,] -1.902834479 0.323629169
[224,] 1.976232506 -1.902834479
[225,] -2.237293855 1.976232506
[226,] -1.669185706 -2.237293855
[227,] -0.109348927 -1.669185706
[228,] 1.358127032 -0.109348927
[229,] 0.033513831 1.358127032
[230,] 1.866799003 0.033513831
[231,] -2.101935401 1.866799003
[232,] -2.173921685 -2.101935401
[233,] 7.402927128 -2.173921685
[234,] 1.032777194 7.402927128
[235,] 3.962543948 1.032777194
[236,] 5.535675314 3.962543948
[237,] 5.643849243 5.535675314
[238,] -3.488198638 5.643849243
[239,] 5.230189938 -3.488198638
[240,] -6.115343729 5.230189938
[241,] 1.608226167 -6.115343729
[242,] -2.660293445 1.608226167
[243,] 1.861055429 -2.660293445
[244,] 3.908261679 1.861055429
[245,] -4.669738769 3.908261679
[246,] -2.237127911 -4.669738769
[247,] 4.221179152 -2.237127911
[248,] -8.624405295 4.221179152
[249,] -5.764976500 -8.624405295
[250,] -2.426890575 -5.764976500
[251,] 0.516007673 -2.426890575
[252,] 2.113385371 0.516007673
[253,] -0.284830507 2.113385371
[254,] 3.879165196 -0.284830507
[255,] 0.697346914 3.879165196
[256,] 0.521449773 0.697346914
[257,] -1.800227173 0.521449773
[258,] 3.586797465 -1.800227173
[259,] 3.140367740 3.586797465
[260,] -4.835232451 3.140367740
[261,] -0.915995514 -4.835232451
[262,] 6.069233137 -0.915995514
[263,] -3.873000711 6.069233137
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.390800158 4.981001822
2 -5.620051568 4.390800158
3 -3.766063581 -5.620051568
4 -1.748357336 -3.766063581
5 2.210717473 -1.748357336
6 5.124191770 2.210717473
7 -1.910496452 5.124191770
8 0.620402430 -1.910496452
9 0.034099795 0.620402430
10 3.757458676 0.034099795
11 1.072043230 3.757458676
12 2.645275789 1.072043230
13 2.578195760 2.645275789
14 -3.698278618 2.578195760
15 -2.229618323 -3.698278618
16 1.906040423 -2.229618323
17 0.343515537 1.906040423
18 1.783435204 0.343515537
19 -2.029726495 1.783435204
20 -2.805612927 -2.029726495
21 -2.877434817 -2.805612927
22 2.298739476 -2.877434817
23 -0.178868696 2.298739476
24 4.388167558 -0.178868696
25 6.733593421 4.388167558
26 0.652701691 6.733593421
27 -3.159511768 0.652701691
28 -1.368958673 -3.159511768
29 -0.723295374 -1.368958673
30 -2.988806295 -0.723295374
31 -6.500811632 -2.988806295
32 3.275905566 -6.500811632
33 -2.967581598 3.275905566
34 1.259531044 -2.967581598
35 -2.501027198 1.259531044
36 -3.537598272 -2.501027198
37 1.641222292 -3.537598272
38 -5.042734241 1.641222292
39 0.250956415 -5.042734241
40 2.522703450 0.250956415
41 -0.194641151 2.522703450
42 3.691110364 -0.194641151
43 1.066630811 3.691110364
44 5.640024468 1.066630811
45 2.406264582 5.640024468
46 0.684038660 2.406264582
47 -2.147546543 0.684038660
48 -0.407330730 -2.147546543
49 4.024041935 -0.407330730
50 -4.770722667 4.024041935
51 -0.949649786 -4.770722667
52 -1.547844470 -0.949649786
53 -3.390057941 -1.547844470
54 -1.376664031 -3.390057941
55 1.317967311 -1.376664031
56 3.937339525 1.317967311
57 -6.229325827 3.937339525
58 1.239334103 -6.229325827
59 -0.026850594 1.239334103
60 -1.515139279 -0.026850594
61 3.088540640 -1.515139279
62 -1.399619456 3.088540640
63 -3.754401823 -1.399619456
64 0.610200444 -3.754401823
65 -0.994082819 0.610200444
66 1.142335695 -0.994082819
67 -1.329970612 1.142335695
68 2.663433742 -1.329970612
69 -0.618421214 2.663433742
70 2.872950723 -0.618421214
71 -4.570404088 2.872950723
72 -2.705548623 -4.570404088
73 0.633011698 -2.705548623
74 2.408440290 0.633011698
75 6.080557938 2.408440290
76 1.310612363 6.080557938
77 0.803300961 1.310612363
78 -4.922137291 0.803300961
79 5.768209125 -4.922137291
80 -2.261467521 5.768209125
81 -1.077969864 -2.261467521
82 3.208858298 -1.077969864
83 -2.440958839 3.208858298
84 -0.366867419 -2.440958839
85 1.488949996 -0.366867419
86 -1.402968803 1.488949996
87 -4.128786246 -1.402968803
88 -0.436907924 -4.128786246
89 -4.247109365 -0.436907924
90 2.936357147 -4.247109365
91 -2.429929923 2.936357147
92 0.379347067 -2.429929923
93 -3.332383435 0.379347067
94 3.542398399 -3.332383435
95 2.295151329 3.542398399
96 1.663978795 2.295151329
97 2.441097222 1.663978795
98 -3.355446454 2.441097222
99 2.753854893 -3.355446454
100 2.149655414 2.753854893
101 -0.332155666 2.149655414
102 0.057147951 -0.332155666
103 -3.520665242 0.057147951
104 7.204011606 -3.520665242
105 2.765091078 7.204011606
106 4.592705352 2.765091078
107 1.558352478 4.592705352
108 6.074943302 1.558352478
109 -4.679069048 6.074943302
110 -2.672211890 -4.679069048
111 -6.155350041 -2.672211890
112 0.492941189 -6.155350041
113 2.657169327 0.492941189
114 2.770122941 2.657169327
115 -3.329095412 2.770122941
116 -3.292969830 -3.329095412
117 -1.677752694 -3.292969830
118 2.421695823 -1.677752694
119 -1.984453702 2.421695823
120 -0.365298627 -1.984453702
121 1.362263204 -0.365298627
122 0.596085707 1.362263204
123 2.856523823 0.596085707
124 -2.894268121 2.856523823
125 4.809063884 -2.894268121
126 7.190289808 4.809063884
127 -3.102605099 7.190289808
128 1.366813004 -3.102605099
129 -2.016476156 1.366813004
130 -4.462885045 -2.016476156
131 3.925242889 -4.462885045
132 -5.156283082 3.925242889
133 0.881512184 -5.156283082
134 -0.552361138 0.881512184
135 -0.326027748 -0.552361138
136 -1.014397192 -0.326027748
137 -4.147574613 -1.014397192
138 0.731680955 -4.147574613
139 -3.316421708 0.731680955
140 -2.452534357 -3.316421708
141 -5.975734524 -2.452534357
142 -5.181084783 -5.975734524
143 1.191568280 -5.181084783
144 6.764105371 1.191568280
145 0.185174684 6.764105371
146 1.152250876 0.185174684
147 -1.295349292 1.152250876
148 1.693378564 -1.295349292
149 1.341297711 1.693378564
150 6.631802489 1.341297711
151 -2.217055582 6.631802489
152 -1.961130863 -2.217055582
153 3.225326207 -1.961130863
154 -0.018210758 3.225326207
155 2.936357147 -0.018210758
156 -3.744776436 2.936357147
157 -3.102605099 -3.744776436
158 0.001885863 -3.102605099
159 -3.797032207 0.001885863
160 -1.462561919 -3.797032207
161 -2.905168467 -1.462561919
162 0.948331665 -2.905168467
163 4.658019786 0.948331665
164 -1.636801189 4.658019786
165 -6.894711203 -1.636801189
166 -3.463539171 -6.894711203
167 -3.263563604 -3.463539171
168 -1.243015602 -3.263563604
169 -7.469353556 -1.243015602
170 5.083099168 -7.469353556
171 0.792754398 5.083099168
172 7.019456502 0.792754398
173 0.059299535 7.019456502
174 5.145022440 0.059299535
175 -2.068256719 5.145022440
176 3.872431551 -2.068256719
177 -1.439645970 3.872431551
178 -1.515678220 -1.439645970
179 3.548755032 -1.515678220
180 1.563851395 3.548755032
181 3.005945960 1.563851395
182 -4.074397023 3.005945960
183 4.935643289 -4.074397023
184 -8.901995296 4.935643289
185 -2.243292575 -8.901995296
186 2.238770324 -2.243292575
187 6.370373196 2.238770324
188 -2.243659281 6.370373196
189 -2.188048767 -2.243659281
190 -0.311547360 -2.188048767
191 1.604495052 -0.311547360
192 -1.358919905 1.604495052
193 -0.217144097 -1.358919905
194 -0.749425160 -0.217144097
195 1.414577230 -0.749425160
196 1.393509172 1.414577230
197 -2.342054784 1.393509172
198 0.241422720 -2.342054784
199 -6.722238016 0.241422720
200 -2.078910971 -6.722238016
201 -7.767099523 -2.078910971
202 2.097594703 -7.767099523
203 1.625569874 2.097594703
204 -0.955143801 1.625569874
205 -0.686059843 -0.955143801
206 -0.870720564 -0.686059843
207 0.840165061 -0.870720564
208 2.962640392 0.840165061
209 0.421581989 2.962640392
210 1.297341483 0.421581989
211 2.758076893 1.297341483
212 -4.109091715 2.758076893
213 -3.305432054 -4.109091715
214 1.126374842 -3.305432054
215 -1.912661806 1.126374842
216 -1.840890992 -1.912661806
217 4.834903493 -1.840890992
218 2.627299308 4.834903493
219 -0.285678676 2.627299308
220 1.874703159 -0.285678676
221 4.141067435 1.874703159
222 0.323629169 4.141067435
223 -1.902834479 0.323629169
224 1.976232506 -1.902834479
225 -2.237293855 1.976232506
226 -1.669185706 -2.237293855
227 -0.109348927 -1.669185706
228 1.358127032 -0.109348927
229 0.033513831 1.358127032
230 1.866799003 0.033513831
231 -2.101935401 1.866799003
232 -2.173921685 -2.101935401
233 7.402927128 -2.173921685
234 1.032777194 7.402927128
235 3.962543948 1.032777194
236 5.535675314 3.962543948
237 5.643849243 5.535675314
238 -3.488198638 5.643849243
239 5.230189938 -3.488198638
240 -6.115343729 5.230189938
241 1.608226167 -6.115343729
242 -2.660293445 1.608226167
243 1.861055429 -2.660293445
244 3.908261679 1.861055429
245 -4.669738769 3.908261679
246 -2.237127911 -4.669738769
247 4.221179152 -2.237127911
248 -8.624405295 4.221179152
249 -5.764976500 -8.624405295
250 -2.426890575 -5.764976500
251 0.516007673 -2.426890575
252 2.113385371 0.516007673
253 -0.284830507 2.113385371
254 3.879165196 -0.284830507
255 0.697346914 3.879165196
256 0.521449773 0.697346914
257 -1.800227173 0.521449773
258 3.586797465 -1.800227173
259 3.140367740 3.586797465
260 -4.835232451 3.140367740
261 -0.915995514 -4.835232451
262 6.069233137 -0.915995514
263 -3.873000711 6.069233137
> 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/7uod41384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8nypa1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/93zu91384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10scur1384526060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111xpi1384526060.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/12vsxz1384526060.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/13gps11384526061.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/14u7sp1384526061.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/15923o1384526061.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/16p5r51384526061.tab")
+ }
>
> try(system("convert tmp/1gcro1384526060.ps tmp/1gcro1384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d6ue1384526060.ps tmp/2d6ue1384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m1sg1384526060.ps tmp/3m1sg1384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ji421384526060.ps tmp/4ji421384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s7ke1384526060.ps tmp/5s7ke1384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c5b41384526060.ps tmp/6c5b41384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uod41384526060.ps tmp/7uod41384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nypa1384526060.ps tmp/8nypa1384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/93zu91384526060.ps tmp/93zu91384526060.png",intern=TRUE))
character(0)
> try(system("convert tmp/10scur1384526060.ps tmp/10scur1384526060.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.898 3.125 20.108