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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+ ,43
+ ,31
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+ ,14
+ ,8
+ ,9
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+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,52
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,38
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,41
+ ,40
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+ ,11
+ ,7
+ ,7
+ ,22
+ ,39
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,43)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Learning Software Depression Sport2
1 14 41 38 13 12 12.0 32
2 18 39 32 16 11 11.0 51
3 11 30 35 19 15 14.0 42
4 12 31 33 15 6 12.0 41
5 16 34 37 14 13 21.0 46
6 18 35 29 13 10 12.0 47
7 14 39 31 19 12 22.0 37
8 14 34 36 15 14 11.0 49
9 15 36 35 14 12 10.0 45
10 15 37 38 15 9 13.0 47
11 17 38 31 16 10 10.0 49
12 19 36 34 16 12 8.0 33
13 10 38 35 16 12 15.0 42
14 16 39 38 16 11 14.0 33
15 18 33 37 17 15 10.0 53
16 14 32 33 15 12 14.0 36
17 14 36 32 15 10 14.0 45
18 17 38 38 20 12 11.0 54
19 14 39 38 18 11 10.0 41
20 16 32 32 16 12 13.0 36
21 18 32 33 16 11 9.5 41
22 11 31 31 16 12 14.0 44
23 14 39 38 19 13 12.0 33
24 12 37 39 16 11 14.0 37
25 17 39 32 17 12 11.0 52
26 9 41 32 17 13 9.0 47
27 16 36 35 16 10 11.0 43
28 14 33 37 15 14 15.0 44
29 15 33 33 16 12 14.0 45
30 11 34 33 14 10 13.0 44
31 16 31 31 15 12 9.0 49
32 13 27 32 12 8 15.0 33
33 17 37 31 14 10 10.0 43
34 15 34 37 16 12 11.0 54
35 14 34 30 14 12 13.0 42
36 16 32 33 10 7 8.0 44
37 9 29 31 10 9 20.0 37
38 15 36 33 14 12 12.0 43
39 17 29 31 16 10 10.0 46
40 13 35 33 16 10 10.0 42
41 15 37 32 16 10 9.0 45
42 16 34 33 14 12 14.0 44
43 16 38 32 20 15 8.0 33
44 12 35 33 14 10 14.0 31
45 15 38 28 14 10 11.0 42
46 11 37 35 11 12 13.0 40
47 15 38 39 14 13 9.0 43
48 15 33 34 15 11 11.0 46
49 17 36 38 16 11 15.0 42
50 13 38 32 14 12 11.0 45
51 16 32 38 16 14 10.0 44
52 14 32 30 14 10 14.0 40
53 11 32 33 12 12 18.0 37
54 12 34 38 16 13 14.0 46
55 12 32 32 9 5 11.0 36
56 15 37 35 14 6 14.5 47
57 16 39 34 16 12 13.0 45
58 15 29 34 16 12 9.0 42
59 12 37 36 15 11 10.0 43
60 12 35 34 16 10 15.0 43
61 8 30 28 12 7 20.0 32
62 13 38 34 16 12 12.0 45
63 11 34 35 16 14 12.0 48
64 14 31 35 14 11 14.0 31
65 15 34 31 16 12 13.0 33
66 10 35 37 17 13 11.0 49
67 11 36 35 18 14 17.0 42
68 12 30 27 18 11 12.0 41
69 15 39 40 12 12 13.0 38
70 15 35 37 16 12 14.0 42
71 14 38 36 10 8 13.0 44
72 16 31 38 14 11 15.0 33
73 15 34 39 18 14 13.0 48
74 15 38 41 18 14 10.0 40
75 13 34 27 16 12 11.0 50
76 12 39 30 17 9 19.0 49
77 17 37 37 16 13 13.0 43
78 13 34 31 16 11 17.0 44
79 15 28 31 13 12 13.0 47
80 13 37 27 16 12 9.0 33
81 15 33 36 16 12 11.0 46
82 15 35 37 16 12 9.0 45
83 16 37 33 15 12 12.0 43
84 15 32 34 15 11 12.0 44
85 14 33 31 16 10 13.0 47
86 15 38 39 14 9 13.0 45
87 14 33 34 16 12 12.0 42
88 13 29 32 16 12 15.0 33
89 7 33 33 15 12 22.0 43
90 17 31 36 12 9 13.0 46
91 13 36 32 17 15 15.0 33
92 15 35 41 16 12 13.0 46
93 14 32 28 15 12 15.0 48
94 13 29 30 13 12 12.5 47
95 16 39 36 16 10 11.0 47
96 12 37 35 16 13 16.0 43
97 14 35 31 16 9 11.0 46
98 17 37 34 16 12 11.0 48
99 15 32 36 14 10 10.0 46
100 17 38 36 16 14 10.0 45
101 12 37 35 16 11 16.0 45
102 16 36 37 20 15 12.0 52
103 11 32 28 15 11 11.0 42
104 15 33 39 16 11 16.0 47
105 9 40 32 13 12 19.0 41
106 16 38 35 17 12 11.0 47
107 15 41 39 16 12 16.0 43
108 10 36 35 16 11 15.0 33
109 10 43 42 12 7 24.0 30
110 15 30 34 16 12 14.0 52
111 11 31 33 16 14 15.0 44
112 13 32 41 17 11 11.0 55
113 14 32 33 13 11 15.0 11
114 18 37 34 12 10 12.0 47
115 16 37 32 18 13 10.0 53
116 14 33 40 14 13 14.0 33
117 14 34 40 14 8 13.0 44
118 14 33 35 13 11 9.0 42
119 14 38 36 16 12 15.0 55
120 12 33 37 13 11 15.0 33
121 14 31 27 16 13 14.0 46
122 15 38 39 13 12 11.0 54
123 15 37 38 16 14 8.0 47
124 15 36 31 15 13 11.0 45
125 13 31 33 16 15 11.0 47
126 17 39 32 15 10 8.0 55
127 17 44 39 17 11 10.0 44
128 19 33 36 15 9 11.0 53
129 15 35 33 12 11 13.0 44
130 13 32 33 16 10 11.0 42
131 9 28 32 10 11 20.0 40
132 15 40 37 16 8 10.0 46
133 15 27 30 12 11 15.0 40
134 15 37 38 14 12 12.0 46
135 16 32 29 15 12 14.0 53
136 11 28 22 13 9 23.0 33
137 14 34 35 15 11 14.0 42
138 11 30 35 11 10 16.0 35
139 15 35 34 12 8 11.0 40
140 13 31 35 11 9 12.0 41
141 15 32 34 16 8 10.0 33
142 16 30 37 15 9 14.0 51
143 14 30 35 17 15 12.0 53
144 15 31 23 16 11 12.0 46
145 16 40 31 10 8 11.0 55
146 16 32 27 18 13 12.0 47
147 11 36 36 13 12 13.0 38
148 12 32 31 16 12 11.0 46
149 9 35 32 13 9 19.0 46
150 16 38 39 10 7 12.0 53
151 13 42 37 15 13 17.0 47
152 16 34 38 16 9 9.0 41
153 12 35 39 16 6 12.0 44
154 9 38 34 14 8 19.0 43
155 13 33 31 10 8 18.0 51
156 13 36 32 17 15 15.0 33
157 14 32 37 13 6 14.0 43
158 19 33 36 15 9 11.0 53
159 13 34 32 16 11 9.0 51
160 12 32 38 12 8 18.0 50
161 13 34 36 13 8 16.0 46
162 10 27 26 13 10 24.0 43
163 14 31 26 12 8 14.0 47
164 16 38 33 17 14 20.0 50
165 10 34 39 15 10 18.0 43
166 11 24 30 10 8 23.0 33
167 14 30 33 14 11 12.0 48
168 12 26 25 11 12 14.0 44
169 9 34 38 13 12 16.0 50
170 9 27 37 16 12 18.0 41
171 11 37 31 12 5 20.0 34
172 16 36 37 16 12 12.0 44
173 9 41 35 12 10 12.0 47
174 13 29 25 9 7 17.0 35
175 16 36 28 12 12 13.0 44
176 13 32 35 15 11 9.0 44
177 9 37 33 12 8 16.0 43
178 12 30 30 12 9 18.0 41
179 16 31 31 14 10 10.0 41
180 11 38 37 12 9 14.0 42
181 14 36 36 16 12 11.0 33
182 13 35 30 11 6 9.0 41
183 15 31 36 19 15 11.0 44
184 14 38 32 15 12 10.0 48
185 16 22 28 8 12 11.0 55
186 13 32 36 16 12 19.0 44
187 14 36 34 17 11 14.0 43
188 15 39 31 12 7 12.0 52
189 13 28 28 11 7 14.0 30
190 11 32 36 11 5 21.0 39
191 11 32 36 14 12 13.0 11
192 14 38 40 16 12 10.0 44
193 15 32 33 12 3 15.0 42
194 11 35 37 16 11 16.0 41
195 15 32 32 13 10 14.0 44
196 12 37 38 15 12 12.0 44
197 14 34 31 16 9 19.0 48
198 14 33 37 16 12 15.0 53
199 8 33 33 14 9 19.0 37
200 13 26 32 16 12 13.0 44
201 9 30 30 16 12 17.0 44
202 15 24 30 14 10 12.0 40
203 17 34 31 11 9 11.0 42
204 13 34 32 12 12 14.0 35
205 15 33 34 15 8 11.0 43
206 15 34 36 15 11 13.0 45
207 14 35 37 16 11 12.0 55
208 16 35 36 16 12 15.0 31
209 13 36 33 11 10 14.0 44
210 16 34 33 15 10 12.0 50
211 9 34 33 12 12 17.0 40
212 16 41 44 12 12 11.0 53
213 11 32 39 15 11 18.0 54
214 10 30 32 15 8 13.0 49
215 11 35 35 16 12 17.0 40
216 15 28 25 14 10 13.0 41
217 17 33 35 17 11 11.0 52
218 14 39 34 14 10 12.0 52
219 8 36 35 13 8 22.0 36
220 15 36 39 15 12 14.0 52
221 11 35 33 13 12 12.0 46
222 16 38 36 14 10 12.0 31
223 10 33 32 15 12 17.0 44
224 15 31 32 12 9 9.0 44
225 9 34 36 13 9 21.0 11
226 16 32 36 8 6 10.0 46
227 19 31 32 14 10 11.0 33
228 12 33 34 14 9 12.0 34
229 8 34 33 11 9 23.0 42
230 11 34 35 12 9 13.0 43
231 14 34 30 13 6 12.0 43
232 9 33 38 10 10 16.0 44
233 15 32 34 16 6 9.0 36
234 13 41 33 18 14 17.0 46
235 16 34 32 13 10 9.0 44
236 11 36 31 11 10 14.0 43
237 12 37 30 4 6 17.0 50
238 13 36 27 13 12 13.0 33
239 10 29 31 16 12 11.0 43
240 11 37 30 10 7 12.0 44
241 12 27 32 12 8 10.0 53
242 8 35 35 12 11 19.0 34
243 12 28 28 10 3 16.0 35
244 12 35 33 13 6 16.0 40
245 15 37 31 15 10 14.0 53
246 11 29 35 12 8 20.0 42
247 13 32 35 14 9 15.0 43
248 14 36 32 10 9 23.0 29
249 10 19 21 12 8 20.0 36
250 12 21 20 12 9 16.0 30
251 15 31 34 11 7 14.0 42
252 13 33 32 10 7 17.0 47
253 13 36 34 12 6 11.0 44
254 13 33 32 16 9 13.0 45
255 12 37 33 12 10 17.0 44
256 12 34 33 14 11 15.0 43
257 9 35 37 16 12 21.0 43
258 9 31 32 14 8 18.0 40
259 15 37 34 13 11 15.0 41
260 10 35 30 4 3 8.0 52
261 14 27 30 15 11 12.0 38
262 15 34 38 11 12 12.0 41
263 7 40 36 11 7 22.0 39
264 14 29 32 14 9 12.0 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
14.857159 0.012387 0.010096 0.113690 -0.006907 -0.379372
Sport2
0.035112
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7669 -1.4125 0.2361 1.2899 5.1980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.857159 1.770018 8.394 3.19e-15 ***
Connected 0.012387 0.037269 0.332 0.7399
Separate 0.010096 0.038249 0.264 0.7920
Learning 0.113690 0.066487 1.710 0.0885 .
Software -0.006907 0.068750 -0.100 0.9201
Depression -0.379372 0.038273 -9.912 < 2e-16 ***
Sport2 0.035112 0.019004 1.848 0.0658 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.017 on 257 degrees of freedom
Multiple R-squared: 0.3634, Adjusted R-squared: 0.3485
F-statistic: 24.45 on 6 and 257 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.07873181 0.157463625 0.921268188
[2,] 0.02607973 0.052159469 0.973920266
[3,] 0.83513435 0.329731308 0.164865654
[4,] 0.96684920 0.066301599 0.033150799
[5,] 0.96823061 0.063538789 0.031769395
[6,] 0.97870791 0.042584176 0.021292088
[7,] 0.96446867 0.071062664 0.035531332
[8,] 0.95120620 0.097587597 0.048793798
[9,] 0.93551129 0.128977417 0.064488708
[10,] 0.91649472 0.167010551 0.083505276
[11,] 0.90860641 0.182787174 0.091393587
[12,] 0.92157427 0.156851456 0.078425728
[13,] 0.95579800 0.088404009 0.044202005
[14,] 0.93825432 0.123491363 0.061745682
[15,] 0.92587482 0.148250358 0.074125179
[16,] 0.90300294 0.193994130 0.096997065
[17,] 0.99833223 0.003335531 0.001667765
[18,] 0.99753334 0.004933321 0.002466661
[19,] 0.99621961 0.007560788 0.003780394
[20,] 0.99443717 0.011125666 0.005562833
[21,] 0.99727399 0.005452023 0.002726011
[22,] 0.99587647 0.008247051 0.004123525
[23,] 0.99398858 0.012022843 0.006011422
[24,] 0.99274991 0.014500183 0.007250091
[25,] 0.98965615 0.020687698 0.010343849
[26,] 0.98594350 0.028112993 0.014056496
[27,] 0.98059050 0.038818991 0.019409495
[28,] 0.98514097 0.029718050 0.014859025
[29,] 0.97983657 0.040326859 0.020163430
[30,] 0.97757269 0.044854615 0.022427308
[31,] 0.97780425 0.044391507 0.022195753
[32,] 0.97123627 0.057527456 0.028763728
[33,] 0.96984225 0.060315503 0.030157751
[34,] 0.96071981 0.078560386 0.039280193
[35,] 0.95335952 0.093280966 0.046640483
[36,] 0.94070591 0.118588182 0.059294091
[37,] 0.95010308 0.099793848 0.049896924
[38,] 0.93673647 0.126527051 0.063263525
[39,] 0.92089407 0.158211854 0.079105927
[40,] 0.94291094 0.114178127 0.057089063
[41,] 0.93879635 0.122407306 0.061203653
[42,] 0.92588499 0.148230027 0.074115014
[43,] 0.90949701 0.181005985 0.090502993
[44,] 0.89424006 0.211519871 0.105759936
[45,] 0.89607347 0.207853052 0.103926526
[46,] 0.88705672 0.225886560 0.112943280
[47,] 0.87052252 0.258954954 0.129477477
[48,] 0.85976323 0.280473546 0.140236773
[49,] 0.83437942 0.331241157 0.165620578
[50,] 0.86180977 0.276380468 0.138190234
[51,] 0.85626672 0.287466565 0.143733283
[52,] 0.87456735 0.250865292 0.125432646
[53,] 0.86732494 0.265350129 0.132675065
[54,] 0.91096332 0.178073366 0.089036683
[55,] 0.89958624 0.200827517 0.100413759
[56,] 0.88938634 0.221227315 0.110613657
[57,] 0.95865653 0.082686931 0.041343465
[58,] 0.95749352 0.085012966 0.042506483
[59,] 0.96046105 0.079077909 0.039538954
[60,] 0.95551599 0.088968019 0.044484010
[61,] 0.94934384 0.101312320 0.050656160
[62,] 0.93836920 0.123261591 0.061630795
[63,] 0.95286802 0.094263952 0.047131976
[64,] 0.94287884 0.114242311 0.057121155
[65,] 0.93099456 0.138010874 0.069005437
[66,] 0.92575275 0.148494498 0.074247249
[67,] 0.91169775 0.176604506 0.088302253
[68,] 0.92427976 0.151440471 0.075720235
[69,] 0.90997519 0.180049617 0.090024809
[70,] 0.90089725 0.198205500 0.099102750
[71,] 0.89488586 0.210228281 0.105114141
[72,] 0.87624215 0.247515708 0.123757854
[73,] 0.85695961 0.286080789 0.143040395
[74,] 0.85071605 0.298567898 0.149283949
[75,] 0.83014613 0.339707744 0.169853872
[76,] 0.80476604 0.390467927 0.195233963
[77,] 0.78157097 0.436858056 0.218429028
[78,] 0.75295542 0.494089152 0.247044576
[79,] 0.72228886 0.555422277 0.277711138
[80,] 0.79457396 0.410852088 0.205426044
[81,] 0.82672424 0.346551523 0.173275762
[82,] 0.80201523 0.395969534 0.197984767
[83,] 0.77842760 0.443144804 0.221572402
[84,] 0.75595194 0.488096125 0.244048062
[85,] 0.73025360 0.539492793 0.269746396
[86,] 0.70482065 0.590358702 0.295179351
[87,] 0.67965871 0.640682583 0.320341291
[88,] 0.65106401 0.697871975 0.348935987
[89,] 0.65204526 0.695909476 0.347954738
[90,] 0.61812490 0.763750204 0.381875102
[91,] 0.61038221 0.779235590 0.389617795
[92,] 0.58611065 0.827778700 0.413889350
[93,] 0.55709933 0.885801337 0.442900669
[94,] 0.61292517 0.774149656 0.387074828
[95,] 0.60397986 0.792040286 0.396020143
[96,] 0.61828059 0.763438813 0.381719406
[97,] 0.59067939 0.818641222 0.409320611
[98,] 0.58447147 0.831057061 0.415528530
[99,] 0.62259005 0.754819892 0.377409946
[100,] 0.59669155 0.806616908 0.403308454
[101,] 0.57116653 0.857666937 0.428833469
[102,] 0.57342969 0.853140617 0.426570309
[103,] 0.60415737 0.791685255 0.395842627
[104,] 0.61762550 0.764748992 0.382374496
[105,] 0.70068804 0.598623920 0.299311960
[106,] 0.67058185 0.658836306 0.329418153
[107,] 0.64812671 0.703746573 0.351873287
[108,] 0.61870612 0.762587766 0.381293883
[109,] 0.59605561 0.807888783 0.403944392
[110,] 0.56187271 0.876254572 0.438127286
[111,] 0.53195197 0.936096066 0.468048033
[112,] 0.50106563 0.997868732 0.498934366
[113,] 0.46876330 0.937526592 0.531236704
[114,] 0.44263242 0.885264834 0.557367583
[115,] 0.40904276 0.818085517 0.590957241
[116,] 0.39652702 0.793054039 0.603472981
[117,] 0.36668092 0.733361832 0.633319084
[118,] 0.35481127 0.709622544 0.645188728
[119,] 0.45243007 0.904860143 0.547569928
[120,] 0.43507682 0.870153641 0.564923180
[121,] 0.42199645 0.843992898 0.578003551
[122,] 0.40665231 0.813304615 0.593347693
[123,] 0.37723879 0.754477587 0.622761207
[124,] 0.39645412 0.792908245 0.603545878
[125,] 0.36960012 0.739200243 0.630399878
[126,] 0.37951964 0.759039283 0.620480359
[127,] 0.36741551 0.734831021 0.632584489
[128,] 0.33789975 0.675799509 0.662100246
[129,] 0.31546377 0.630927531 0.684536234
[130,] 0.28967276 0.579345517 0.710327241
[131,] 0.26519092 0.530381834 0.734809083
[132,] 0.23774631 0.475492616 0.762253692
[133,] 0.24497676 0.489953527 0.755023237
[134,] 0.21961714 0.439234275 0.780382863
[135,] 0.19830397 0.396607940 0.801696030
[136,] 0.18511814 0.370236279 0.814881860
[137,] 0.17617844 0.352356886 0.823821557
[138,] 0.18585678 0.371713553 0.814143223
[139,] 0.20324723 0.406494454 0.796752773
[140,] 0.22173314 0.443466271 0.778266864
[141,] 0.22251801 0.445036015 0.777481993
[142,] 0.20003506 0.400070115 0.799964943
[143,] 0.18017842 0.360356839 0.819821581
[144,] 0.19230451 0.384609021 0.807695490
[145,] 0.20852095 0.417041899 0.791479050
[146,] 0.19358706 0.387174114 0.806412943
[147,] 0.16993033 0.339860651 0.830069674
[148,] 0.15253501 0.305070022 0.847464989
[149,] 0.23707851 0.474157013 0.762921494
[150,] 0.25570936 0.511418712 0.744290644
[151,] 0.23314771 0.466295417 0.766852291
[152,] 0.21056736 0.421134714 0.789432643
[153,] 0.18642392 0.372847841 0.813576080
[154,] 0.16529972 0.330599444 0.834700278
[155,] 0.27074275 0.541485498 0.729257251
[156,] 0.26550889 0.531017785 0.734491107
[157,] 0.26330350 0.526607010 0.736696495
[158,] 0.23470841 0.469416828 0.765291586
[159,] 0.21302661 0.426053219 0.786973390
[160,] 0.27096003 0.541920069 0.729039966
[161,] 0.29550551 0.591011013 0.704494494
[162,] 0.26786161 0.535723213 0.732138394
[163,] 0.26338326 0.526766524 0.736616738
[164,] 0.43174355 0.863487109 0.568256446
[165,] 0.41796883 0.835937655 0.582031172
[166,] 0.44036062 0.880721244 0.559639378
[167,] 0.44992410 0.899848197 0.550075901
[168,] 0.50885962 0.982280770 0.491140385
[169,] 0.47529316 0.950586315 0.524706842
[170,] 0.45287589 0.905751776 0.547124112
[171,] 0.45235381 0.904707615 0.547646192
[172,] 0.41509929 0.830198585 0.584900707
[173,] 0.40839656 0.816793125 0.591603437
[174,] 0.37110044 0.742200885 0.628899558
[175,] 0.34410049 0.688200986 0.655899507
[176,] 0.36003582 0.720071640 0.639964180
[177,] 0.35138641 0.702772818 0.648613591
[178,] 0.31658444 0.633168872 0.683415564
[179,] 0.28737965 0.574759305 0.712620347
[180,] 0.25613155 0.512263092 0.743868454
[181,] 0.23477657 0.469553143 0.765223429
[182,] 0.24106156 0.482123114 0.758938443
[183,] 0.22249661 0.444993220 0.777503390
[184,] 0.24084779 0.481695571 0.759152214
[185,] 0.22944006 0.458880114 0.770559943
[186,] 0.22915840 0.458316799 0.770841600
[187,] 0.24068462 0.481369242 0.759315379
[188,] 0.28566303 0.571326055 0.714336972
[189,] 0.26515787 0.530315737 0.734842131
[190,] 0.30050744 0.601014890 0.699492555
[191,] 0.26802242 0.536044849 0.731977575
[192,] 0.30534289 0.610685773 0.694657113
[193,] 0.28216910 0.564338194 0.717830903
[194,] 0.32104187 0.642083734 0.678958133
[195,] 0.28346966 0.566939313 0.716530344
[196,] 0.25026014 0.500520281 0.749739860
[197,] 0.22920075 0.458401495 0.770799253
[198,] 0.19920842 0.398416849 0.800791576
[199,] 0.22828394 0.456567888 0.771716056
[200,] 0.19693604 0.393872083 0.803063958
[201,] 0.19936136 0.398722724 0.800638638
[202,] 0.22319668 0.446393353 0.776803323
[203,] 0.21310867 0.426217347 0.786891326
[204,] 0.18624224 0.372484490 0.813757755
[205,] 0.23579003 0.471580066 0.764209967
[206,] 0.21092296 0.421845916 0.789077042
[207,] 0.19922051 0.398441025 0.800779488
[208,] 0.22180215 0.443604305 0.778197847
[209,] 0.19062521 0.381250421 0.809374790
[210,] 0.18059852 0.361197032 0.819401484
[211,] 0.19069367 0.381387333 0.809306333
[212,] 0.20771606 0.415432112 0.792283944
[213,] 0.20149113 0.402982267 0.798508867
[214,] 0.19180656 0.383613118 0.808193441
[215,] 0.16093500 0.321870007 0.839064996
[216,] 0.17140551 0.342811024 0.828594488
[217,] 0.19871096 0.397421912 0.801289044
[218,] 0.40946359 0.818927189 0.590536406
[219,] 0.38663915 0.773278303 0.613360849
[220,] 0.35954575 0.719091504 0.640454248
[221,] 0.34442853 0.688857069 0.655571466
[222,] 0.30056946 0.601138918 0.699430541
[223,] 0.33530343 0.670606859 0.664696570
[224,] 0.29274280 0.585485609 0.707257195
[225,] 0.24814397 0.496287935 0.751856032
[226,] 0.24778843 0.495576860 0.752211570
[227,] 0.22553734 0.451074678 0.774462661
[228,] 0.19308330 0.386166607 0.806916697
[229,] 0.15325993 0.306519859 0.846740071
[230,] 0.28943414 0.578868286 0.710565857
[231,] 0.28465349 0.569306985 0.715346508
[232,] 0.25004216 0.500084327 0.749957837
[233,] 0.46558633 0.931172657 0.534413672
[234,] 0.42014888 0.840297763 0.579851118
[235,] 0.35421625 0.708432504 0.645783748
[236,] 0.50743927 0.985121453 0.492560726
[237,] 0.42253962 0.845079245 0.577460377
[238,] 0.34337453 0.686749069 0.656625466
[239,] 0.49593650 0.991872991 0.504063504
[240,] 0.39558873 0.791177462 0.604411269
[241,] 0.29755763 0.595115254 0.702442373
[242,] 0.43991123 0.879822465 0.560088768
[243,] 0.94025058 0.119498841 0.059749420
[244,] 0.87362499 0.252750012 0.126375006
[245,] 0.81099886 0.378002279 0.189001139
> postscript(file="/var/wessaorg/rcomp/tmp/1kryy1384525145.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fla41384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xuf21384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4q2pn1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5o1pe1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
0.285155758 2.976032135 -2.802093584 -2.125325798 5.197962910 3.909848378
7 8 9 10 11 12
3.316619314 -0.797780271 0.048491597 0.939299327 1.682458459 3.493801576
13 14 15 16 17 18
-3.201467557 2.685584113 2.464219311 0.838032685 0.468761764 1.374657314
19 20 21 22 23 24
-1.340177552 2.355065837 2.834702842 -2.523972455 -0.400416461 -1.440183974
25 26 27 28 29 30
1.834137468 -6.766915313 1.256891919 0.897557452 1.395951116 -2.747130139
31 32 33 34 35 36
0.517299413 0.708211606 2.132895505 -0.110938455 0.367193991 0.814822742
37 38 39 40 41 42
-1.315764932 0.897649973 1.899275422 -2.054790643 -0.554175829 2.646056354
43 44 45 46 47 48
0.055179299 -0.923694182 0.565278801 -2.309150250 -0.318905927 0.319410831
49 50 51 52 53 54
3.786112530 -1.566623614 0.889298167 0.827748675 -0.338519764 -1.695117721
55 56 57 58 59 60
-1.656196852 1.631612630 1.932162082 -0.356122377 -3.024365100 -1.203137337
61 62 63 64 65 66
-2.363502281 -1.434823093 -3.486891356 1.112569639 1.445722325 -5.054550438
67 68 69 70 71 72
-1.631515714 -2.358900639 1.572131322 1.436130068 0.613982392 3.391432002
73 74 75 76 77 78
0.624718151 -0.302243952 -1.869536987 -0.026081678 3.003779778 0.570075864
79 80 81 82 83 84
1.369552209 -2.068544697 0.192436749 -0.566065140 1.771572793 0.781393018
85 86 87 88 89 90
-0.047267559 1.100730383 -0.267553760 0.256305519 -3.385158754 3.409994206
91 92 93 94 95 96
0.076628510 0.875929562 0.846543121 -0.822425232 1.069189295 -0.837912961
97 98 99 100 101 102
-0.802581146 2.092856936 0.039017710 1.800056138 -0.921950569 0.879843532
103 104 105 106 107 108
-3.467182822 2.016991834 -2.302284102 0.991795847 2.065250175 -2.867596560
109 110 111 112 113 114
0.921842346 1.177235141 -2.150976977 -2.282255747 2.315668075 3.948287300
115 116 117 118 119 120
0.337644534 0.980909501 0.168386899 -1.081601899 0.331986153 -0.509556032
121 122 123 124 125 126
0.453093582 0.160393720 -1.036715331 0.361462641 -1.766892897 0.804252561
127 128 129 130 131 132
1.596147881 4.039624215 1.474770706 -1.638257838 -1.404993917 -0.311368015
133 134 135 136 137 138
2.503343047 0.729450733 2.281517538 1.724973364 0.575491058 -0.922582069
139 140 141 142 143 144
0.825655812 -0.670034103 0.274464549 2.275028875 -0.719684787 0.720917036
145 146 147 148 149 150
1.494714365 1.419470365 -2.464016200 -2.744698807 -2.436629174 1.881412195
151 152 153 154 155 156
0.432577449 0.555951039 -2.454471541 -2.509243594 1.377473688 0.076628510
157 158 159 160 161 162
0.737806780 4.039624215 -2.720777493 0.126923095 0.390352252 0.732141543
163 164 165 166 167 168
0.848302558 4.334815218 -1.989421446 2.027920549 -0.210493781 -0.833013507
169 170 171 172 173 174
-3.742656336 -2.912174744 0.435465237 1.594775799 -5.111355837 1.776790925
175 176 177 178 179 180
2.519768541 -2.366818736 -3.397496879 0.555372562 1.277440147 -2.166991177
181 182 183 184 185 186
-0.388273191 -1.827942059 -0.032915443 -1.165020765 2.002974589 1.310023512
187 188 189 190 191 192
0.298320605 0.757520398 0.568952510 0.764426599 -1.580145680 -1.219028703
193 194 195 196 197 198
2.285641147 -1.776921374 1.780801539 -2.314016343 2.174559224 0.454048380
199 200 201 202 203 204
-3.219636752 -0.851505508 -3.363373793 1.168099770 2.918703523 0.199536796
205 206 207 208 209 210
0.404024011 1.080688673 -0.785972084 3.211825243 -0.051461051 1.549137914
211 212 213 214 215 216
-2.848000457 1.221557333 -1.343968011 -3.990549679 -1.335339473 1.513290161
217 218 219 220 221 222
1.871265185 -0.479425055 -1.996977575 1.166126433 -3.081607642 2.250114409
223 224 225 226 227 228
-2.307035257 0.003111103 -0.476974179 1.693530528 4.927609511 -1.780002040
229 230 231 232 233 234
-1.549022503 -2.511736213 0.024957352 -3.192343427 -0.224056740 0.186294369
235 236 237 238 239 240
0.859167313 -1.996158438 0.662088311 -0.197598640 -4.602203289 -2.699336944
241 242 243 244 245 246
-2.890880778 -2.918071828 0.238200133 -0.394892953 1.185577575 0.234007294
247 248 249 250 251 252
0.044401219 5.006440245 -0.290116937 0.395293223 2.049879560 1.121545287
253 254 255 256 257 258
-1.340991900 -0.994047075 -0.039421985 -0.946367170 -1.943376954 -2.676381513
259 260 261 262 263 264
2.190290071 -4.818431429 0.094379177 1.283240269 -2.941482167 -0.026267783
> postscript(file="/var/wessaorg/rcomp/tmp/6ds1c1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 0.285155758 NA
1 2.976032135 0.285155758
2 -2.802093584 2.976032135
3 -2.125325798 -2.802093584
4 5.197962910 -2.125325798
5 3.909848378 5.197962910
6 3.316619314 3.909848378
7 -0.797780271 3.316619314
8 0.048491597 -0.797780271
9 0.939299327 0.048491597
10 1.682458459 0.939299327
11 3.493801576 1.682458459
12 -3.201467557 3.493801576
13 2.685584113 -3.201467557
14 2.464219311 2.685584113
15 0.838032685 2.464219311
16 0.468761764 0.838032685
17 1.374657314 0.468761764
18 -1.340177552 1.374657314
19 2.355065837 -1.340177552
20 2.834702842 2.355065837
21 -2.523972455 2.834702842
22 -0.400416461 -2.523972455
23 -1.440183974 -0.400416461
24 1.834137468 -1.440183974
25 -6.766915313 1.834137468
26 1.256891919 -6.766915313
27 0.897557452 1.256891919
28 1.395951116 0.897557452
29 -2.747130139 1.395951116
30 0.517299413 -2.747130139
31 0.708211606 0.517299413
32 2.132895505 0.708211606
33 -0.110938455 2.132895505
34 0.367193991 -0.110938455
35 0.814822742 0.367193991
36 -1.315764932 0.814822742
37 0.897649973 -1.315764932
38 1.899275422 0.897649973
39 -2.054790643 1.899275422
40 -0.554175829 -2.054790643
41 2.646056354 -0.554175829
42 0.055179299 2.646056354
43 -0.923694182 0.055179299
44 0.565278801 -0.923694182
45 -2.309150250 0.565278801
46 -0.318905927 -2.309150250
47 0.319410831 -0.318905927
48 3.786112530 0.319410831
49 -1.566623614 3.786112530
50 0.889298167 -1.566623614
51 0.827748675 0.889298167
52 -0.338519764 0.827748675
53 -1.695117721 -0.338519764
54 -1.656196852 -1.695117721
55 1.631612630 -1.656196852
56 1.932162082 1.631612630
57 -0.356122377 1.932162082
58 -3.024365100 -0.356122377
59 -1.203137337 -3.024365100
60 -2.363502281 -1.203137337
61 -1.434823093 -2.363502281
62 -3.486891356 -1.434823093
63 1.112569639 -3.486891356
64 1.445722325 1.112569639
65 -5.054550438 1.445722325
66 -1.631515714 -5.054550438
67 -2.358900639 -1.631515714
68 1.572131322 -2.358900639
69 1.436130068 1.572131322
70 0.613982392 1.436130068
71 3.391432002 0.613982392
72 0.624718151 3.391432002
73 -0.302243952 0.624718151
74 -1.869536987 -0.302243952
75 -0.026081678 -1.869536987
76 3.003779778 -0.026081678
77 0.570075864 3.003779778
78 1.369552209 0.570075864
79 -2.068544697 1.369552209
80 0.192436749 -2.068544697
81 -0.566065140 0.192436749
82 1.771572793 -0.566065140
83 0.781393018 1.771572793
84 -0.047267559 0.781393018
85 1.100730383 -0.047267559
86 -0.267553760 1.100730383
87 0.256305519 -0.267553760
88 -3.385158754 0.256305519
89 3.409994206 -3.385158754
90 0.076628510 3.409994206
91 0.875929562 0.076628510
92 0.846543121 0.875929562
93 -0.822425232 0.846543121
94 1.069189295 -0.822425232
95 -0.837912961 1.069189295
96 -0.802581146 -0.837912961
97 2.092856936 -0.802581146
98 0.039017710 2.092856936
99 1.800056138 0.039017710
100 -0.921950569 1.800056138
101 0.879843532 -0.921950569
102 -3.467182822 0.879843532
103 2.016991834 -3.467182822
104 -2.302284102 2.016991834
105 0.991795847 -2.302284102
106 2.065250175 0.991795847
107 -2.867596560 2.065250175
108 0.921842346 -2.867596560
109 1.177235141 0.921842346
110 -2.150976977 1.177235141
111 -2.282255747 -2.150976977
112 2.315668075 -2.282255747
113 3.948287300 2.315668075
114 0.337644534 3.948287300
115 0.980909501 0.337644534
116 0.168386899 0.980909501
117 -1.081601899 0.168386899
118 0.331986153 -1.081601899
119 -0.509556032 0.331986153
120 0.453093582 -0.509556032
121 0.160393720 0.453093582
122 -1.036715331 0.160393720
123 0.361462641 -1.036715331
124 -1.766892897 0.361462641
125 0.804252561 -1.766892897
126 1.596147881 0.804252561
127 4.039624215 1.596147881
128 1.474770706 4.039624215
129 -1.638257838 1.474770706
130 -1.404993917 -1.638257838
131 -0.311368015 -1.404993917
132 2.503343047 -0.311368015
133 0.729450733 2.503343047
134 2.281517538 0.729450733
135 1.724973364 2.281517538
136 0.575491058 1.724973364
137 -0.922582069 0.575491058
138 0.825655812 -0.922582069
139 -0.670034103 0.825655812
140 0.274464549 -0.670034103
141 2.275028875 0.274464549
142 -0.719684787 2.275028875
143 0.720917036 -0.719684787
144 1.494714365 0.720917036
145 1.419470365 1.494714365
146 -2.464016200 1.419470365
147 -2.744698807 -2.464016200
148 -2.436629174 -2.744698807
149 1.881412195 -2.436629174
150 0.432577449 1.881412195
151 0.555951039 0.432577449
152 -2.454471541 0.555951039
153 -2.509243594 -2.454471541
154 1.377473688 -2.509243594
155 0.076628510 1.377473688
156 0.737806780 0.076628510
157 4.039624215 0.737806780
158 -2.720777493 4.039624215
159 0.126923095 -2.720777493
160 0.390352252 0.126923095
161 0.732141543 0.390352252
162 0.848302558 0.732141543
163 4.334815218 0.848302558
164 -1.989421446 4.334815218
165 2.027920549 -1.989421446
166 -0.210493781 2.027920549
167 -0.833013507 -0.210493781
168 -3.742656336 -0.833013507
169 -2.912174744 -3.742656336
170 0.435465237 -2.912174744
171 1.594775799 0.435465237
172 -5.111355837 1.594775799
173 1.776790925 -5.111355837
174 2.519768541 1.776790925
175 -2.366818736 2.519768541
176 -3.397496879 -2.366818736
177 0.555372562 -3.397496879
178 1.277440147 0.555372562
179 -2.166991177 1.277440147
180 -0.388273191 -2.166991177
181 -1.827942059 -0.388273191
182 -0.032915443 -1.827942059
183 -1.165020765 -0.032915443
184 2.002974589 -1.165020765
185 1.310023512 2.002974589
186 0.298320605 1.310023512
187 0.757520398 0.298320605
188 0.568952510 0.757520398
189 0.764426599 0.568952510
190 -1.580145680 0.764426599
191 -1.219028703 -1.580145680
192 2.285641147 -1.219028703
193 -1.776921374 2.285641147
194 1.780801539 -1.776921374
195 -2.314016343 1.780801539
196 2.174559224 -2.314016343
197 0.454048380 2.174559224
198 -3.219636752 0.454048380
199 -0.851505508 -3.219636752
200 -3.363373793 -0.851505508
201 1.168099770 -3.363373793
202 2.918703523 1.168099770
203 0.199536796 2.918703523
204 0.404024011 0.199536796
205 1.080688673 0.404024011
206 -0.785972084 1.080688673
207 3.211825243 -0.785972084
208 -0.051461051 3.211825243
209 1.549137914 -0.051461051
210 -2.848000457 1.549137914
211 1.221557333 -2.848000457
212 -1.343968011 1.221557333
213 -3.990549679 -1.343968011
214 -1.335339473 -3.990549679
215 1.513290161 -1.335339473
216 1.871265185 1.513290161
217 -0.479425055 1.871265185
218 -1.996977575 -0.479425055
219 1.166126433 -1.996977575
220 -3.081607642 1.166126433
221 2.250114409 -3.081607642
222 -2.307035257 2.250114409
223 0.003111103 -2.307035257
224 -0.476974179 0.003111103
225 1.693530528 -0.476974179
226 4.927609511 1.693530528
227 -1.780002040 4.927609511
228 -1.549022503 -1.780002040
229 -2.511736213 -1.549022503
230 0.024957352 -2.511736213
231 -3.192343427 0.024957352
232 -0.224056740 -3.192343427
233 0.186294369 -0.224056740
234 0.859167313 0.186294369
235 -1.996158438 0.859167313
236 0.662088311 -1.996158438
237 -0.197598640 0.662088311
238 -4.602203289 -0.197598640
239 -2.699336944 -4.602203289
240 -2.890880778 -2.699336944
241 -2.918071828 -2.890880778
242 0.238200133 -2.918071828
243 -0.394892953 0.238200133
244 1.185577575 -0.394892953
245 0.234007294 1.185577575
246 0.044401219 0.234007294
247 5.006440245 0.044401219
248 -0.290116937 5.006440245
249 0.395293223 -0.290116937
250 2.049879560 0.395293223
251 1.121545287 2.049879560
252 -1.340991900 1.121545287
253 -0.994047075 -1.340991900
254 -0.039421985 -0.994047075
255 -0.946367170 -0.039421985
256 -1.943376954 -0.946367170
257 -2.676381513 -1.943376954
258 2.190290071 -2.676381513
259 -4.818431429 2.190290071
260 0.094379177 -4.818431429
261 1.283240269 0.094379177
262 -2.941482167 1.283240269
263 -0.026267783 -2.941482167
264 NA -0.026267783
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.976032135 0.285155758
[2,] -2.802093584 2.976032135
[3,] -2.125325798 -2.802093584
[4,] 5.197962910 -2.125325798
[5,] 3.909848378 5.197962910
[6,] 3.316619314 3.909848378
[7,] -0.797780271 3.316619314
[8,] 0.048491597 -0.797780271
[9,] 0.939299327 0.048491597
[10,] 1.682458459 0.939299327
[11,] 3.493801576 1.682458459
[12,] -3.201467557 3.493801576
[13,] 2.685584113 -3.201467557
[14,] 2.464219311 2.685584113
[15,] 0.838032685 2.464219311
[16,] 0.468761764 0.838032685
[17,] 1.374657314 0.468761764
[18,] -1.340177552 1.374657314
[19,] 2.355065837 -1.340177552
[20,] 2.834702842 2.355065837
[21,] -2.523972455 2.834702842
[22,] -0.400416461 -2.523972455
[23,] -1.440183974 -0.400416461
[24,] 1.834137468 -1.440183974
[25,] -6.766915313 1.834137468
[26,] 1.256891919 -6.766915313
[27,] 0.897557452 1.256891919
[28,] 1.395951116 0.897557452
[29,] -2.747130139 1.395951116
[30,] 0.517299413 -2.747130139
[31,] 0.708211606 0.517299413
[32,] 2.132895505 0.708211606
[33,] -0.110938455 2.132895505
[34,] 0.367193991 -0.110938455
[35,] 0.814822742 0.367193991
[36,] -1.315764932 0.814822742
[37,] 0.897649973 -1.315764932
[38,] 1.899275422 0.897649973
[39,] -2.054790643 1.899275422
[40,] -0.554175829 -2.054790643
[41,] 2.646056354 -0.554175829
[42,] 0.055179299 2.646056354
[43,] -0.923694182 0.055179299
[44,] 0.565278801 -0.923694182
[45,] -2.309150250 0.565278801
[46,] -0.318905927 -2.309150250
[47,] 0.319410831 -0.318905927
[48,] 3.786112530 0.319410831
[49,] -1.566623614 3.786112530
[50,] 0.889298167 -1.566623614
[51,] 0.827748675 0.889298167
[52,] -0.338519764 0.827748675
[53,] -1.695117721 -0.338519764
[54,] -1.656196852 -1.695117721
[55,] 1.631612630 -1.656196852
[56,] 1.932162082 1.631612630
[57,] -0.356122377 1.932162082
[58,] -3.024365100 -0.356122377
[59,] -1.203137337 -3.024365100
[60,] -2.363502281 -1.203137337
[61,] -1.434823093 -2.363502281
[62,] -3.486891356 -1.434823093
[63,] 1.112569639 -3.486891356
[64,] 1.445722325 1.112569639
[65,] -5.054550438 1.445722325
[66,] -1.631515714 -5.054550438
[67,] -2.358900639 -1.631515714
[68,] 1.572131322 -2.358900639
[69,] 1.436130068 1.572131322
[70,] 0.613982392 1.436130068
[71,] 3.391432002 0.613982392
[72,] 0.624718151 3.391432002
[73,] -0.302243952 0.624718151
[74,] -1.869536987 -0.302243952
[75,] -0.026081678 -1.869536987
[76,] 3.003779778 -0.026081678
[77,] 0.570075864 3.003779778
[78,] 1.369552209 0.570075864
[79,] -2.068544697 1.369552209
[80,] 0.192436749 -2.068544697
[81,] -0.566065140 0.192436749
[82,] 1.771572793 -0.566065140
[83,] 0.781393018 1.771572793
[84,] -0.047267559 0.781393018
[85,] 1.100730383 -0.047267559
[86,] -0.267553760 1.100730383
[87,] 0.256305519 -0.267553760
[88,] -3.385158754 0.256305519
[89,] 3.409994206 -3.385158754
[90,] 0.076628510 3.409994206
[91,] 0.875929562 0.076628510
[92,] 0.846543121 0.875929562
[93,] -0.822425232 0.846543121
[94,] 1.069189295 -0.822425232
[95,] -0.837912961 1.069189295
[96,] -0.802581146 -0.837912961
[97,] 2.092856936 -0.802581146
[98,] 0.039017710 2.092856936
[99,] 1.800056138 0.039017710
[100,] -0.921950569 1.800056138
[101,] 0.879843532 -0.921950569
[102,] -3.467182822 0.879843532
[103,] 2.016991834 -3.467182822
[104,] -2.302284102 2.016991834
[105,] 0.991795847 -2.302284102
[106,] 2.065250175 0.991795847
[107,] -2.867596560 2.065250175
[108,] 0.921842346 -2.867596560
[109,] 1.177235141 0.921842346
[110,] -2.150976977 1.177235141
[111,] -2.282255747 -2.150976977
[112,] 2.315668075 -2.282255747
[113,] 3.948287300 2.315668075
[114,] 0.337644534 3.948287300
[115,] 0.980909501 0.337644534
[116,] 0.168386899 0.980909501
[117,] -1.081601899 0.168386899
[118,] 0.331986153 -1.081601899
[119,] -0.509556032 0.331986153
[120,] 0.453093582 -0.509556032
[121,] 0.160393720 0.453093582
[122,] -1.036715331 0.160393720
[123,] 0.361462641 -1.036715331
[124,] -1.766892897 0.361462641
[125,] 0.804252561 -1.766892897
[126,] 1.596147881 0.804252561
[127,] 4.039624215 1.596147881
[128,] 1.474770706 4.039624215
[129,] -1.638257838 1.474770706
[130,] -1.404993917 -1.638257838
[131,] -0.311368015 -1.404993917
[132,] 2.503343047 -0.311368015
[133,] 0.729450733 2.503343047
[134,] 2.281517538 0.729450733
[135,] 1.724973364 2.281517538
[136,] 0.575491058 1.724973364
[137,] -0.922582069 0.575491058
[138,] 0.825655812 -0.922582069
[139,] -0.670034103 0.825655812
[140,] 0.274464549 -0.670034103
[141,] 2.275028875 0.274464549
[142,] -0.719684787 2.275028875
[143,] 0.720917036 -0.719684787
[144,] 1.494714365 0.720917036
[145,] 1.419470365 1.494714365
[146,] -2.464016200 1.419470365
[147,] -2.744698807 -2.464016200
[148,] -2.436629174 -2.744698807
[149,] 1.881412195 -2.436629174
[150,] 0.432577449 1.881412195
[151,] 0.555951039 0.432577449
[152,] -2.454471541 0.555951039
[153,] -2.509243594 -2.454471541
[154,] 1.377473688 -2.509243594
[155,] 0.076628510 1.377473688
[156,] 0.737806780 0.076628510
[157,] 4.039624215 0.737806780
[158,] -2.720777493 4.039624215
[159,] 0.126923095 -2.720777493
[160,] 0.390352252 0.126923095
[161,] 0.732141543 0.390352252
[162,] 0.848302558 0.732141543
[163,] 4.334815218 0.848302558
[164,] -1.989421446 4.334815218
[165,] 2.027920549 -1.989421446
[166,] -0.210493781 2.027920549
[167,] -0.833013507 -0.210493781
[168,] -3.742656336 -0.833013507
[169,] -2.912174744 -3.742656336
[170,] 0.435465237 -2.912174744
[171,] 1.594775799 0.435465237
[172,] -5.111355837 1.594775799
[173,] 1.776790925 -5.111355837
[174,] 2.519768541 1.776790925
[175,] -2.366818736 2.519768541
[176,] -3.397496879 -2.366818736
[177,] 0.555372562 -3.397496879
[178,] 1.277440147 0.555372562
[179,] -2.166991177 1.277440147
[180,] -0.388273191 -2.166991177
[181,] -1.827942059 -0.388273191
[182,] -0.032915443 -1.827942059
[183,] -1.165020765 -0.032915443
[184,] 2.002974589 -1.165020765
[185,] 1.310023512 2.002974589
[186,] 0.298320605 1.310023512
[187,] 0.757520398 0.298320605
[188,] 0.568952510 0.757520398
[189,] 0.764426599 0.568952510
[190,] -1.580145680 0.764426599
[191,] -1.219028703 -1.580145680
[192,] 2.285641147 -1.219028703
[193,] -1.776921374 2.285641147
[194,] 1.780801539 -1.776921374
[195,] -2.314016343 1.780801539
[196,] 2.174559224 -2.314016343
[197,] 0.454048380 2.174559224
[198,] -3.219636752 0.454048380
[199,] -0.851505508 -3.219636752
[200,] -3.363373793 -0.851505508
[201,] 1.168099770 -3.363373793
[202,] 2.918703523 1.168099770
[203,] 0.199536796 2.918703523
[204,] 0.404024011 0.199536796
[205,] 1.080688673 0.404024011
[206,] -0.785972084 1.080688673
[207,] 3.211825243 -0.785972084
[208,] -0.051461051 3.211825243
[209,] 1.549137914 -0.051461051
[210,] -2.848000457 1.549137914
[211,] 1.221557333 -2.848000457
[212,] -1.343968011 1.221557333
[213,] -3.990549679 -1.343968011
[214,] -1.335339473 -3.990549679
[215,] 1.513290161 -1.335339473
[216,] 1.871265185 1.513290161
[217,] -0.479425055 1.871265185
[218,] -1.996977575 -0.479425055
[219,] 1.166126433 -1.996977575
[220,] -3.081607642 1.166126433
[221,] 2.250114409 -3.081607642
[222,] -2.307035257 2.250114409
[223,] 0.003111103 -2.307035257
[224,] -0.476974179 0.003111103
[225,] 1.693530528 -0.476974179
[226,] 4.927609511 1.693530528
[227,] -1.780002040 4.927609511
[228,] -1.549022503 -1.780002040
[229,] -2.511736213 -1.549022503
[230,] 0.024957352 -2.511736213
[231,] -3.192343427 0.024957352
[232,] -0.224056740 -3.192343427
[233,] 0.186294369 -0.224056740
[234,] 0.859167313 0.186294369
[235,] -1.996158438 0.859167313
[236,] 0.662088311 -1.996158438
[237,] -0.197598640 0.662088311
[238,] -4.602203289 -0.197598640
[239,] -2.699336944 -4.602203289
[240,] -2.890880778 -2.699336944
[241,] -2.918071828 -2.890880778
[242,] 0.238200133 -2.918071828
[243,] -0.394892953 0.238200133
[244,] 1.185577575 -0.394892953
[245,] 0.234007294 1.185577575
[246,] 0.044401219 0.234007294
[247,] 5.006440245 0.044401219
[248,] -0.290116937 5.006440245
[249,] 0.395293223 -0.290116937
[250,] 2.049879560 0.395293223
[251,] 1.121545287 2.049879560
[252,] -1.340991900 1.121545287
[253,] -0.994047075 -1.340991900
[254,] -0.039421985 -0.994047075
[255,] -0.946367170 -0.039421985
[256,] -1.943376954 -0.946367170
[257,] -2.676381513 -1.943376954
[258,] 2.190290071 -2.676381513
[259,] -4.818431429 2.190290071
[260,] 0.094379177 -4.818431429
[261,] 1.283240269 0.094379177
[262,] -2.941482167 1.283240269
[263,] -0.026267783 -2.941482167
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.976032135 0.285155758
2 -2.802093584 2.976032135
3 -2.125325798 -2.802093584
4 5.197962910 -2.125325798
5 3.909848378 5.197962910
6 3.316619314 3.909848378
7 -0.797780271 3.316619314
8 0.048491597 -0.797780271
9 0.939299327 0.048491597
10 1.682458459 0.939299327
11 3.493801576 1.682458459
12 -3.201467557 3.493801576
13 2.685584113 -3.201467557
14 2.464219311 2.685584113
15 0.838032685 2.464219311
16 0.468761764 0.838032685
17 1.374657314 0.468761764
18 -1.340177552 1.374657314
19 2.355065837 -1.340177552
20 2.834702842 2.355065837
21 -2.523972455 2.834702842
22 -0.400416461 -2.523972455
23 -1.440183974 -0.400416461
24 1.834137468 -1.440183974
25 -6.766915313 1.834137468
26 1.256891919 -6.766915313
27 0.897557452 1.256891919
28 1.395951116 0.897557452
29 -2.747130139 1.395951116
30 0.517299413 -2.747130139
31 0.708211606 0.517299413
32 2.132895505 0.708211606
33 -0.110938455 2.132895505
34 0.367193991 -0.110938455
35 0.814822742 0.367193991
36 -1.315764932 0.814822742
37 0.897649973 -1.315764932
38 1.899275422 0.897649973
39 -2.054790643 1.899275422
40 -0.554175829 -2.054790643
41 2.646056354 -0.554175829
42 0.055179299 2.646056354
43 -0.923694182 0.055179299
44 0.565278801 -0.923694182
45 -2.309150250 0.565278801
46 -0.318905927 -2.309150250
47 0.319410831 -0.318905927
48 3.786112530 0.319410831
49 -1.566623614 3.786112530
50 0.889298167 -1.566623614
51 0.827748675 0.889298167
52 -0.338519764 0.827748675
53 -1.695117721 -0.338519764
54 -1.656196852 -1.695117721
55 1.631612630 -1.656196852
56 1.932162082 1.631612630
57 -0.356122377 1.932162082
58 -3.024365100 -0.356122377
59 -1.203137337 -3.024365100
60 -2.363502281 -1.203137337
61 -1.434823093 -2.363502281
62 -3.486891356 -1.434823093
63 1.112569639 -3.486891356
64 1.445722325 1.112569639
65 -5.054550438 1.445722325
66 -1.631515714 -5.054550438
67 -2.358900639 -1.631515714
68 1.572131322 -2.358900639
69 1.436130068 1.572131322
70 0.613982392 1.436130068
71 3.391432002 0.613982392
72 0.624718151 3.391432002
73 -0.302243952 0.624718151
74 -1.869536987 -0.302243952
75 -0.026081678 -1.869536987
76 3.003779778 -0.026081678
77 0.570075864 3.003779778
78 1.369552209 0.570075864
79 -2.068544697 1.369552209
80 0.192436749 -2.068544697
81 -0.566065140 0.192436749
82 1.771572793 -0.566065140
83 0.781393018 1.771572793
84 -0.047267559 0.781393018
85 1.100730383 -0.047267559
86 -0.267553760 1.100730383
87 0.256305519 -0.267553760
88 -3.385158754 0.256305519
89 3.409994206 -3.385158754
90 0.076628510 3.409994206
91 0.875929562 0.076628510
92 0.846543121 0.875929562
93 -0.822425232 0.846543121
94 1.069189295 -0.822425232
95 -0.837912961 1.069189295
96 -0.802581146 -0.837912961
97 2.092856936 -0.802581146
98 0.039017710 2.092856936
99 1.800056138 0.039017710
100 -0.921950569 1.800056138
101 0.879843532 -0.921950569
102 -3.467182822 0.879843532
103 2.016991834 -3.467182822
104 -2.302284102 2.016991834
105 0.991795847 -2.302284102
106 2.065250175 0.991795847
107 -2.867596560 2.065250175
108 0.921842346 -2.867596560
109 1.177235141 0.921842346
110 -2.150976977 1.177235141
111 -2.282255747 -2.150976977
112 2.315668075 -2.282255747
113 3.948287300 2.315668075
114 0.337644534 3.948287300
115 0.980909501 0.337644534
116 0.168386899 0.980909501
117 -1.081601899 0.168386899
118 0.331986153 -1.081601899
119 -0.509556032 0.331986153
120 0.453093582 -0.509556032
121 0.160393720 0.453093582
122 -1.036715331 0.160393720
123 0.361462641 -1.036715331
124 -1.766892897 0.361462641
125 0.804252561 -1.766892897
126 1.596147881 0.804252561
127 4.039624215 1.596147881
128 1.474770706 4.039624215
129 -1.638257838 1.474770706
130 -1.404993917 -1.638257838
131 -0.311368015 -1.404993917
132 2.503343047 -0.311368015
133 0.729450733 2.503343047
134 2.281517538 0.729450733
135 1.724973364 2.281517538
136 0.575491058 1.724973364
137 -0.922582069 0.575491058
138 0.825655812 -0.922582069
139 -0.670034103 0.825655812
140 0.274464549 -0.670034103
141 2.275028875 0.274464549
142 -0.719684787 2.275028875
143 0.720917036 -0.719684787
144 1.494714365 0.720917036
145 1.419470365 1.494714365
146 -2.464016200 1.419470365
147 -2.744698807 -2.464016200
148 -2.436629174 -2.744698807
149 1.881412195 -2.436629174
150 0.432577449 1.881412195
151 0.555951039 0.432577449
152 -2.454471541 0.555951039
153 -2.509243594 -2.454471541
154 1.377473688 -2.509243594
155 0.076628510 1.377473688
156 0.737806780 0.076628510
157 4.039624215 0.737806780
158 -2.720777493 4.039624215
159 0.126923095 -2.720777493
160 0.390352252 0.126923095
161 0.732141543 0.390352252
162 0.848302558 0.732141543
163 4.334815218 0.848302558
164 -1.989421446 4.334815218
165 2.027920549 -1.989421446
166 -0.210493781 2.027920549
167 -0.833013507 -0.210493781
168 -3.742656336 -0.833013507
169 -2.912174744 -3.742656336
170 0.435465237 -2.912174744
171 1.594775799 0.435465237
172 -5.111355837 1.594775799
173 1.776790925 -5.111355837
174 2.519768541 1.776790925
175 -2.366818736 2.519768541
176 -3.397496879 -2.366818736
177 0.555372562 -3.397496879
178 1.277440147 0.555372562
179 -2.166991177 1.277440147
180 -0.388273191 -2.166991177
181 -1.827942059 -0.388273191
182 -0.032915443 -1.827942059
183 -1.165020765 -0.032915443
184 2.002974589 -1.165020765
185 1.310023512 2.002974589
186 0.298320605 1.310023512
187 0.757520398 0.298320605
188 0.568952510 0.757520398
189 0.764426599 0.568952510
190 -1.580145680 0.764426599
191 -1.219028703 -1.580145680
192 2.285641147 -1.219028703
193 -1.776921374 2.285641147
194 1.780801539 -1.776921374
195 -2.314016343 1.780801539
196 2.174559224 -2.314016343
197 0.454048380 2.174559224
198 -3.219636752 0.454048380
199 -0.851505508 -3.219636752
200 -3.363373793 -0.851505508
201 1.168099770 -3.363373793
202 2.918703523 1.168099770
203 0.199536796 2.918703523
204 0.404024011 0.199536796
205 1.080688673 0.404024011
206 -0.785972084 1.080688673
207 3.211825243 -0.785972084
208 -0.051461051 3.211825243
209 1.549137914 -0.051461051
210 -2.848000457 1.549137914
211 1.221557333 -2.848000457
212 -1.343968011 1.221557333
213 -3.990549679 -1.343968011
214 -1.335339473 -3.990549679
215 1.513290161 -1.335339473
216 1.871265185 1.513290161
217 -0.479425055 1.871265185
218 -1.996977575 -0.479425055
219 1.166126433 -1.996977575
220 -3.081607642 1.166126433
221 2.250114409 -3.081607642
222 -2.307035257 2.250114409
223 0.003111103 -2.307035257
224 -0.476974179 0.003111103
225 1.693530528 -0.476974179
226 4.927609511 1.693530528
227 -1.780002040 4.927609511
228 -1.549022503 -1.780002040
229 -2.511736213 -1.549022503
230 0.024957352 -2.511736213
231 -3.192343427 0.024957352
232 -0.224056740 -3.192343427
233 0.186294369 -0.224056740
234 0.859167313 0.186294369
235 -1.996158438 0.859167313
236 0.662088311 -1.996158438
237 -0.197598640 0.662088311
238 -4.602203289 -0.197598640
239 -2.699336944 -4.602203289
240 -2.890880778 -2.699336944
241 -2.918071828 -2.890880778
242 0.238200133 -2.918071828
243 -0.394892953 0.238200133
244 1.185577575 -0.394892953
245 0.234007294 1.185577575
246 0.044401219 0.234007294
247 5.006440245 0.044401219
248 -0.290116937 5.006440245
249 0.395293223 -0.290116937
250 2.049879560 0.395293223
251 1.121545287 2.049879560
252 -1.340991900 1.121545287
253 -0.994047075 -1.340991900
254 -0.039421985 -0.994047075
255 -0.946367170 -0.039421985
256 -1.943376954 -0.946367170
257 -2.676381513 -1.943376954
258 2.190290071 -2.676381513
259 -4.818431429 2.190290071
260 0.094379177 -4.818431429
261 1.283240269 0.094379177
262 -2.941482167 1.283240269
263 -0.026267783 -2.941482167
> 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/77y561384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8v47u1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/92x0n1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10zv7x1384525145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11b8k41384525145.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/1260r41384525145.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/13urxf1384525145.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/1406w91384525146.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/15pazp1384525146.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/16ix3f1384525146.tab")
+ }
>
> try(system("convert tmp/1kryy1384525145.ps tmp/1kryy1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fla41384525145.ps tmp/2fla41384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xuf21384525145.ps tmp/3xuf21384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q2pn1384525145.ps tmp/4q2pn1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o1pe1384525145.ps tmp/5o1pe1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ds1c1384525145.ps tmp/6ds1c1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/77y561384525145.ps tmp/77y561384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v47u1384525145.ps tmp/8v47u1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/92x0n1384525145.ps tmp/92x0n1384525145.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zv7x1384525145.ps tmp/10zv7x1384525145.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.553 2.674 17.384