R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(14
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+ ,3
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+ ,29
+ ,32
+ ,9
+ ,12
+ ,72
+ ,43
+ ,11)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Software'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Happiness','Connected','Separate','Software','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
Happiness Connected Separate Software Depression Sport1 Sport2 Month
1 14 41 38 12 12.0 53 32 9
2 18 39 32 11 11.0 83 51 9
3 11 30 35 15 14.0 66 42 9
4 12 31 33 6 12.0 67 41 9
5 16 34 37 13 21.0 76 46 9
6 18 35 29 10 12.0 78 47 9
7 14 39 31 12 22.0 53 37 9
8 14 34 36 14 11.0 80 49 9
9 15 36 35 12 10.0 74 45 9
10 15 37 38 9 13.0 76 47 9
11 17 38 31 10 10.0 79 49 9
12 19 36 34 12 8.0 54 33 9
13 10 38 35 12 15.0 67 42 9
14 16 39 38 11 14.0 54 33 9
15 18 33 37 15 10.0 87 53 9
16 14 32 33 12 14.0 58 36 9
17 14 36 32 10 14.0 75 45 9
18 17 38 38 12 11.0 88 54 9
19 14 39 38 11 10.0 64 41 9
20 16 32 32 12 13.0 57 36 9
21 18 32 33 11 9.5 66 41 9
22 11 31 31 12 14.0 68 44 9
23 14 39 38 13 12.0 54 33 9
24 12 37 39 11 14.0 56 37 9
25 17 39 32 12 11.0 86 52 9
26 9 41 32 13 9.0 80 47 9
27 16 36 35 10 11.0 76 43 9
28 14 33 37 14 15.0 69 44 9
29 15 33 33 12 14.0 78 45 9
30 11 34 33 10 13.0 67 44 9
31 16 31 31 12 9.0 80 49 9
32 13 27 32 8 15.0 54 33 9
33 17 37 31 10 10.0 71 43 9
34 15 34 37 12 11.0 84 54 9
35 14 34 30 12 13.0 74 42 9
36 16 32 33 7 8.0 71 44 9
37 9 29 31 9 20.0 63 37 9
38 15 36 33 12 12.0 71 43 9
39 17 29 31 10 10.0 76 46 9
40 13 35 33 10 10.0 69 42 9
41 15 37 32 10 9.0 74 45 9
42 16 34 33 12 14.0 75 44 9
43 16 38 32 15 8.0 54 33 9
44 12 35 33 10 14.0 52 31 9
45 15 38 28 10 11.0 69 42 9
46 11 37 35 12 13.0 68 40 9
47 15 38 39 13 9.0 65 43 9
48 15 33 34 11 11.0 75 46 9
49 17 36 38 11 15.0 74 42 9
50 13 38 32 12 11.0 75 45 9
51 16 32 38 14 10.0 72 44 9
52 14 32 30 10 14.0 67 40 9
53 11 32 33 12 18.0 63 37 9
54 12 34 38 13 14.0 62 46 9
55 12 32 32 5 11.0 63 36 9
56 15 37 35 6 14.5 76 47 9
57 16 39 34 12 13.0 74 45 9
58 15 29 34 12 9.0 67 42 9
59 12 37 36 11 10.0 73 43 9
60 12 35 34 10 15.0 70 43 9
61 8 30 28 7 20.0 53 32 9
62 13 38 34 12 12.0 77 45 9
63 11 34 35 14 12.0 80 48 9
64 14 31 35 11 14.0 52 31 9
65 15 34 31 12 13.0 54 33 9
66 10 35 37 13 11.0 80 49 10
67 11 36 35 14 17.0 66 42 10
68 12 30 27 11 12.0 73 41 10
69 15 39 40 12 13.0 63 38 10
70 15 35 37 12 14.0 69 42 10
71 14 38 36 8 13.0 67 44 10
72 16 31 38 11 15.0 54 33 10
73 15 34 39 14 13.0 81 48 10
74 15 38 41 14 10.0 69 40 10
75 13 34 27 12 11.0 84 50 10
76 12 39 30 9 19.0 80 49 10
77 17 37 37 13 13.0 70 43 10
78 13 34 31 11 17.0 69 44 10
79 15 28 31 12 13.0 77 47 10
80 13 37 27 12 9.0 54 33 10
81 15 33 36 12 11.0 79 46 10
82 15 35 37 12 9.0 71 45 10
83 16 37 33 12 12.0 73 43 10
84 15 32 34 11 12.0 72 44 10
85 14 33 31 10 13.0 77 47 10
86 15 38 39 9 13.0 75 45 10
87 14 33 34 12 12.0 69 42 10
88 13 29 32 12 15.0 54 33 10
89 7 33 33 12 22.0 70 43 10
90 17 31 36 9 13.0 73 46 10
91 13 36 32 15 15.0 54 33 10
92 15 35 41 12 13.0 77 46 10
93 14 32 28 12 15.0 82 48 10
94 13 29 30 12 12.5 80 47 10
95 16 39 36 10 11.0 80 47 10
96 12 37 35 13 16.0 69 43 10
97 14 35 31 9 11.0 78 46 10
98 17 37 34 12 11.0 81 48 10
99 15 32 36 10 10.0 76 46 10
100 17 38 36 14 10.0 76 45 10
101 12 37 35 11 16.0 73 45 10
102 16 36 37 15 12.0 85 52 10
103 11 32 28 11 11.0 66 42 10
104 15 33 39 11 16.0 79 47 10
105 9 40 32 12 19.0 68 41 10
106 16 38 35 12 11.0 76 47 10
107 15 41 39 12 16.0 71 43 10
108 10 36 35 11 15.0 54 33 10
109 10 43 42 7 24.0 46 30 10
110 15 30 34 12 14.0 85 52 10
111 11 31 33 14 15.0 74 44 10
112 13 32 41 11 11.0 88 55 10
113 14 32 33 11 15.0 38 11 10
114 18 37 34 10 12.0 76 47 10
115 16 37 32 13 10.0 86 53 10
116 14 33 40 13 14.0 54 33 10
117 14 34 40 8 13.0 67 44 10
118 14 33 35 11 9.0 69 42 10
119 14 38 36 12 15.0 90 55 10
120 12 33 37 11 15.0 54 33 10
121 14 31 27 13 14.0 76 46 10
122 15 38 39 12 11.0 89 54 10
123 15 37 38 14 8.0 76 47 10
124 15 36 31 13 11.0 73 45 10
125 13 31 33 15 11.0 79 47 10
126 17 39 32 10 8.0 90 55 10
127 17 44 39 11 10.0 74 44 10
128 19 33 36 9 11.0 81 53 10
129 15 35 33 11 13.0 72 44 10
130 13 32 33 10 11.0 71 42 10
131 9 28 32 11 20.0 66 40 10
132 15 40 37 8 10.0 77 46 10
133 15 27 30 11 15.0 65 40 10
134 15 37 38 12 12.0 74 46 10
135 16 32 29 12 14.0 85 53 10
136 11 28 22 9 23.0 54 33 10
137 14 34 35 11 14.0 63 42 10
138 11 30 35 10 16.0 54 35 10
139 15 35 34 8 11.0 64 40 10
140 13 31 35 9 12.0 69 41 10
141 15 32 34 8 10.0 54 33 10
142 16 30 37 9 14.0 84 51 10
143 14 30 35 15 12.0 86 53 10
144 15 31 23 11 12.0 77 46 10
145 16 40 31 8 11.0 89 55 10
146 16 32 27 13 12.0 76 47 10
147 11 36 36 12 13.0 60 38 10
148 12 32 31 12 11.0 75 46 10
149 9 35 32 9 19.0 73 46 10
150 16 38 39 7 12.0 85 53 10
151 13 42 37 13 17.0 79 47 10
152 16 34 38 9 9.0 71 41 10
153 12 35 39 6 12.0 72 44 10
154 9 38 34 8 19.0 69 43 9
155 13 33 31 8 18.0 78 51 10
156 13 36 32 15 15.0 54 33 10
157 14 32 37 6 14.0 69 43 10
158 19 33 36 9 11.0 81 53 10
159 13 34 32 11 9.0 84 51 10
160 12 32 38 8 18.0 84 50 10
161 13 34 36 8 16.0 69 46 10
162 10 27 26 10 24.0 66 43 11
163 14 31 26 8 14.0 81 47 11
164 16 38 33 14 20.0 82 50 11
165 10 34 39 10 18.0 72 43 11
166 11 24 30 8 23.0 54 33 11
167 14 30 33 11 12.0 78 48 11
168 12 26 25 12 14.0 74 44 11
169 9 34 38 12 16.0 82 50 11
170 9 27 37 12 18.0 73 41 11
171 11 37 31 5 20.0 55 34 11
172 16 36 37 12 12.0 72 44 11
173 9 41 35 10 12.0 78 47 11
174 13 29 25 7 17.0 59 35 11
175 16 36 28 12 13.0 72 44 11
176 13 32 35 11 9.0 78 44 11
177 9 37 33 8 16.0 68 43 11
178 12 30 30 9 18.0 69 41 11
179 16 31 31 10 10.0 67 41 11
180 11 38 37 9 14.0 74 42 11
181 14 36 36 12 11.0 54 33 11
182 13 35 30 6 9.0 67 41 11
183 15 31 36 15 11.0 70 44 11
184 14 38 32 12 10.0 80 48 11
185 16 22 28 12 11.0 89 55 11
186 13 32 36 12 19.0 76 44 11
187 14 36 34 11 14.0 74 43 11
188 15 39 31 7 12.0 87 52 11
189 13 28 28 7 14.0 54 30 11
190 11 32 36 5 21.0 61 39 11
191 11 32 36 12 13.0 38 11 11
192 14 38 40 12 10.0 75 44 11
193 15 32 33 3 15.0 69 42 11
194 11 35 37 11 16.0 62 41 11
195 15 32 32 10 14.0 72 44 11
196 12 37 38 12 12.0 70 44 11
197 14 34 31 9 19.0 79 48 11
198 14 33 37 12 15.0 87 53 11
199 8 33 33 9 19.0 62 37 11
200 13 26 32 12 13.0 77 44 11
201 9 30 30 12 17.0 69 44 11
202 15 24 30 10 12.0 69 40 11
203 17 34 31 9 11.0 75 42 11
204 13 34 32 12 14.0 54 35 11
205 15 33 34 8 11.0 72 43 11
206 15 34 36 11 13.0 74 45 11
207 14 35 37 11 12.0 85 55 11
208 16 35 36 12 15.0 52 31 11
209 13 36 33 10 14.0 70 44 11
210 16 34 33 10 12.0 84 50 11
211 9 34 33 12 17.0 64 40 11
212 16 41 44 12 11.0 84 53 11
213 11 32 39 11 18.0 87 54 11
214 10 30 32 8 13.0 79 49 11
215 11 35 35 12 17.0 67 40 11
216 15 28 25 10 13.0 65 41 11
217 17 33 35 11 11.0 85 52 11
218 14 39 34 10 12.0 83 52 11
219 8 36 35 8 22.0 61 36 11
220 15 36 39 12 14.0 82 52 11
221 11 35 33 12 12.0 76 46 11
222 16 38 36 10 12.0 58 31 11
223 10 33 32 12 17.0 72 44 11
224 15 31 32 9 9.0 72 44 11
225 9 34 36 9 21.0 38 11 11
226 16 32 36 6 10.0 78 46 11
227 19 31 32 10 11.0 54 33 11
228 12 33 34 9 12.0 63 34 11
229 8 34 33 9 23.0 66 42 11
230 11 34 35 9 13.0 70 43 11
231 14 34 30 6 12.0 71 43 11
232 9 33 38 10 16.0 67 44 11
233 15 32 34 6 9.0 58 36 11
234 13 41 33 14 17.0 72 46 11
235 16 34 32 10 9.0 72 44 11
236 11 36 31 10 14.0 70 43 11
237 12 37 30 6 17.0 76 50 11
238 13 36 27 12 13.0 50 33 11
239 10 29 31 12 11.0 72 43 11
240 11 37 30 7 12.0 72 44 11
241 12 27 32 8 10.0 88 53 11
242 8 35 35 11 19.0 53 34 11
243 12 28 28 3 16.0 58 35 11
244 12 35 33 6 16.0 66 40 11
245 15 37 31 10 14.0 82 53 11
246 11 29 35 8 20.0 69 42 11
247 13 32 35 9 15.0 68 43 11
248 14 36 32 9 23.0 44 29 11
249 10 19 21 8 20.0 56 36 11
250 12 21 20 9 16.0 53 30 11
251 15 31 34 7 14.0 70 42 11
252 13 33 32 7 17.0 78 47 11
253 13 36 34 6 11.0 71 44 11
254 13 33 32 9 13.0 72 45 11
255 12 37 33 10 17.0 68 44 11
256 12 34 33 11 15.0 67 43 11
257 9 35 37 12 21.0 75 43 11
258 9 31 32 8 18.0 62 40 11
259 15 37 34 11 15.0 67 41 11
260 10 35 30 3 8.0 83 52 11
261 14 27 30 11 12.0 64 38 11
262 15 34 38 12 12.0 68 41 11
263 7 40 36 7 22.0 62 39 11
264 14 29 32 9 12.0 72 43 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Depression Sport1
19.147854 0.007275 0.015968 0.033689 -0.368687 0.019304
Sport2 Month
0.011774 -0.357276
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9592 -1.4159 0.2824 1.2301 5.3334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.147854 2.561844 7.474 1.23e-12 ***
Connected 0.007275 0.037550 0.194 0.8465
Separate 0.015968 0.038106 0.419 0.6755
Software 0.033689 0.056979 0.591 0.5549
Depression -0.368687 0.039641 -9.301 < 2e-16 ***
Sport1 0.019304 0.040788 0.473 0.6364
Sport2 0.011774 0.060608 0.194 0.8461
Month -0.357276 0.170901 -2.091 0.0376 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.015 on 256 degrees of freedom
Multiple R-squared: 0.3671, Adjusted R-squared: 0.3498
F-statistic: 21.21 on 7 and 256 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,] 0.0240453 0.048090604 0.975954698
[2,] 0.8030961 0.393807753 0.196903876
[3,] 0.9680211 0.063957824 0.031978912
[4,] 0.9536366 0.092726827 0.046363414
[5,] 0.9681201 0.063759729 0.031879865
[6,] 0.9467378 0.106524394 0.053262197
[7,] 0.9504936 0.099012837 0.049506418
[8,] 0.9303405 0.139319001 0.069659501
[9,] 0.8984154 0.203169112 0.101584556
[10,] 0.8947928 0.210414437 0.105207218
[11,] 0.9192834 0.161433251 0.080716625
[12,] 0.9314928 0.137014303 0.068507151
[13,] 0.9151499 0.169700104 0.084850052
[14,] 0.8852877 0.229424669 0.114712335
[15,] 0.8593934 0.281213283 0.140606642
[16,] 0.9989320 0.002135989 0.001067995
[17,] 0.9982723 0.003455464 0.001727732
[18,] 0.9972439 0.005512232 0.002756116
[19,] 0.9958031 0.008393756 0.004196878
[20,] 0.9969368 0.006126467 0.003063234
[21,] 0.9953808 0.009238419 0.004619209
[22,] 0.9933568 0.013286376 0.006643188
[23,] 0.9919678 0.016064438 0.008032219
[24,] 0.9884884 0.023023182 0.011511591
[25,] 0.9857776 0.028444818 0.014222409
[26,] 0.9804396 0.039120765 0.019560383
[27,] 0.9868961 0.026207729 0.013103864
[28,] 0.9818906 0.036218719 0.018109359
[29,] 0.9797861 0.040427707 0.020213854
[30,] 0.9797742 0.040451676 0.020225838
[31,] 0.9735541 0.052891829 0.026445915
[32,] 0.9701438 0.059712302 0.029856151
[33,] 0.9611111 0.077777759 0.038888880
[34,] 0.9546347 0.090730629 0.045365315
[35,] 0.9418828 0.116234437 0.058117218
[36,] 0.9590159 0.081968125 0.040984062
[37,] 0.9478134 0.104373151 0.052186575
[38,] 0.9338871 0.132225803 0.066112901
[39,] 0.9470793 0.105841338 0.052920669
[40,] 0.9476064 0.104787144 0.052393572
[41,] 0.9351119 0.129776178 0.064888089
[42,] 0.9196803 0.160639326 0.080319663
[43,] 0.9124738 0.175052420 0.087526210
[44,] 0.9013278 0.197344483 0.098672241
[45,] 0.9043164 0.191367208 0.095683604
[46,] 0.8909436 0.218112726 0.109056363
[47,] 0.8787908 0.242418332 0.121209166
[48,] 0.8556404 0.288719182 0.144359591
[49,] 0.8918027 0.216394522 0.108197261
[50,] 0.8831862 0.233627674 0.116813837
[51,] 0.8987944 0.202411237 0.101205618
[52,] 0.8981192 0.203761622 0.101880811
[53,] 0.9421859 0.115628119 0.057814059
[54,] 0.9323077 0.135384659 0.067692330
[55,] 0.9246703 0.150659305 0.075329652
[56,] 0.9341751 0.131649733 0.065824866
[57,] 0.9338368 0.132326314 0.066163157
[58,] 0.9274132 0.145173513 0.072586756
[59,] 0.9397310 0.120537948 0.060268974
[60,] 0.9446608 0.110678366 0.055339183
[61,] 0.9358600 0.128279983 0.064139991
[62,] 0.9573621 0.085275792 0.042637896
[63,] 0.9488621 0.102275885 0.051137942
[64,] 0.9375434 0.124913277 0.062456638
[65,] 0.9301711 0.139657857 0.069828928
[66,] 0.9160883 0.167823358 0.083911679
[67,] 0.9320502 0.135899564 0.067949782
[68,] 0.9201220 0.159755901 0.079877950
[69,] 0.9110472 0.177905592 0.088952796
[70,] 0.9033445 0.193311006 0.096655503
[71,] 0.8856522 0.228695690 0.114347845
[72,] 0.8664487 0.267102595 0.133551297
[73,] 0.8593291 0.281341767 0.140670884
[74,] 0.8399374 0.320125291 0.160062645
[75,] 0.8155052 0.368989509 0.184494754
[76,] 0.7916916 0.416616814 0.208308407
[77,] 0.7634592 0.473081642 0.236540821
[78,] 0.7337520 0.532496008 0.266248004
[79,] 0.8058517 0.388296556 0.194148278
[80,] 0.8330333 0.333933331 0.166966665
[81,] 0.8090887 0.381822589 0.190911295
[82,] 0.7847763 0.430447335 0.215223667
[83,] 0.7608598 0.478280443 0.239140221
[84,] 0.7400242 0.519951622 0.259975811
[85,] 0.7146743 0.570651434 0.285325717
[86,] 0.6894474 0.621105118 0.310552559
[87,] 0.6598851 0.680229745 0.340114873
[88,] 0.6581610 0.683678036 0.341839018
[89,] 0.6239204 0.752159113 0.376079556
[90,] 0.6105872 0.778825636 0.389412818
[91,] 0.5849385 0.830122946 0.415061473
[92,] 0.5586217 0.882756615 0.441378308
[93,] 0.6079611 0.784077839 0.392038919
[94,] 0.5959887 0.808022561 0.404011281
[95,] 0.6243232 0.751353505 0.375676752
[96,] 0.5993280 0.801344023 0.400672011
[97,] 0.5902160 0.819567924 0.409783962
[98,] 0.6236994 0.752601197 0.376300599
[99,] 0.5926856 0.814628770 0.407314385
[100,] 0.5666311 0.866737709 0.433368855
[101,] 0.5751117 0.849776504 0.424888252
[102,] 0.5957191 0.808561712 0.404280856
[103,] 0.5799361 0.840127818 0.420063909
[104,] 0.6547052 0.690589679 0.345294839
[105,] 0.6243750 0.751249977 0.375624989
[106,] 0.5938613 0.812277369 0.406138684
[107,] 0.5593265 0.881346912 0.440673456
[108,] 0.5387319 0.922536171 0.461268085
[109,] 0.5038435 0.992312995 0.496156497
[110,] 0.4735284 0.947056778 0.526471611
[111,] 0.4444850 0.888970063 0.555514969
[112,] 0.4126405 0.825281021 0.587359489
[113,] 0.3879951 0.775990197 0.612004902
[114,] 0.3563067 0.712613474 0.643693263
[115,] 0.3515571 0.703114160 0.648442920
[116,] 0.3231618 0.646323603 0.676838199
[117,] 0.3113329 0.622665855 0.688667073
[118,] 0.4176331 0.835266231 0.582366885
[119,] 0.3930768 0.786153695 0.606923153
[120,] 0.3792153 0.758430594 0.620784703
[121,] 0.3792577 0.758515433 0.620742284
[122,] 0.3499311 0.699862234 0.650068883
[123,] 0.3595352 0.719070311 0.640464844
[124,] 0.3292782 0.658556413 0.670721794
[125,] 0.3379793 0.675958544 0.662020728
[126,] 0.3282736 0.656547213 0.671726394
[127,] 0.2998376 0.599675112 0.700162444
[128,] 0.2817390 0.563478090 0.718260955
[129,] 0.2549525 0.509905006 0.745047497
[130,] 0.2357753 0.471550649 0.764224675
[131,] 0.2110011 0.422002179 0.788998911
[132,] 0.2171543 0.434308577 0.782845712
[133,] 0.1937152 0.387430388 0.806284806
[134,] 0.1752035 0.350406960 0.824796520
[135,] 0.1599027 0.319805380 0.840097310
[136,] 0.1571626 0.314325185 0.842837408
[137,] 0.1710537 0.342107320 0.828946340
[138,] 0.1864268 0.372853593 0.813573203
[139,] 0.2065849 0.413169869 0.793415066
[140,] 0.1967982 0.393596386 0.803201807
[141,] 0.1751185 0.350237061 0.824881469
[142,] 0.1571126 0.314225183 0.842887409
[143,] 0.1617773 0.323554699 0.838222650
[144,] 0.1925820 0.385164022 0.807417989
[145,] 0.1708551 0.341710201 0.829144900
[146,] 0.1525605 0.305121029 0.847439486
[147,] 0.1324977 0.264995361 0.867502319
[148,] 0.2022738 0.404547654 0.797726173
[149,] 0.2212550 0.442509964 0.778745018
[150,] 0.1974349 0.394869786 0.802565107
[151,] 0.1728676 0.345735106 0.827132447
[152,] 0.1524112 0.304822338 0.847588831
[153,] 0.1343220 0.268644050 0.865677975
[154,] 0.2406116 0.481223290 0.759388355
[155,] 0.2355735 0.471147039 0.764426481
[156,] 0.2266237 0.453247467 0.773376266
[157,] 0.2003294 0.400658766 0.799670617
[158,] 0.1836019 0.367203876 0.816398062
[159,] 0.2446406 0.489281126 0.755359437
[160,] 0.2637762 0.527552452 0.736223774
[161,] 0.2395592 0.479118413 0.760440793
[162,] 0.2382524 0.476504885 0.761747558
[163,] 0.4032824 0.806564825 0.596717587
[164,] 0.3866991 0.773398206 0.613300897
[165,] 0.4024671 0.804934245 0.597532878
[166,] 0.4069935 0.813987054 0.593006473
[167,] 0.4581147 0.916229303 0.541885348
[168,] 0.4243105 0.848620987 0.575689506
[169,] 0.4061360 0.812271979 0.593864011
[170,] 0.4065853 0.813170596 0.593414702
[171,] 0.3695512 0.739102323 0.630448838
[172,] 0.3585472 0.717094340 0.641452830
[173,] 0.3249273 0.649854679 0.675072661
[174,] 0.2967794 0.593558781 0.703220610
[175,] 0.2825854 0.565170867 0.717414566
[176,] 0.2784033 0.556806585 0.721596708
[177,] 0.2521141 0.504228285 0.747885858
[178,] 0.2297900 0.459579932 0.770210034
[179,] 0.2022675 0.404534995 0.797732503
[180,] 0.1835178 0.367035501 0.816482249
[181,] 0.1924884 0.384976767 0.807511616
[182,] 0.1733627 0.346725497 0.826637252
[183,] 0.2041045 0.408208922 0.795895539
[184,] 0.1861883 0.372376664 0.813811668
[185,] 0.1850121 0.370024254 0.814987873
[186,] 0.1908729 0.381745776 0.809127112
[187,] 0.2542096 0.508419198 0.745790401
[188,] 0.2380054 0.476010856 0.761994572
[189,] 0.2610797 0.522159345 0.738920328
[190,] 0.2289114 0.457822869 0.771088566
[191,] 0.2530601 0.506120183 0.746939909
[192,] 0.2330260 0.466052022 0.766973989
[193,] 0.2761546 0.552309294 0.723845353
[194,] 0.2472405 0.494481036 0.752759482
[195,] 0.2207434 0.441486771 0.779256615
[196,] 0.2039110 0.407821956 0.796089022
[197,] 0.1747819 0.349563846 0.825218077
[198,] 0.2017473 0.403494595 0.798252702
[199,] 0.1717552 0.343510456 0.828244772
[200,] 0.1929518 0.385903637 0.807048182
[201,] 0.2278391 0.455678131 0.772160934
[202,] 0.2075375 0.415075089 0.792462456
[203,] 0.1810781 0.362156218 0.818921891
[204,] 0.2103173 0.420634611 0.789682694
[205,] 0.1826318 0.365263619 0.817368191
[206,] 0.1742805 0.348561043 0.825719478
[207,] 0.2206614 0.441322829 0.779338585
[208,] 0.1929261 0.385852117 0.807073941
[209,] 0.1758794 0.351758819 0.824120590
[210,] 0.1859255 0.371850927 0.814074536
[211,] 0.1964700 0.392940048 0.803529976
[212,] 0.1996122 0.399224339 0.800387831
[213,] 0.1818562 0.363712469 0.818143766
[214,] 0.1508754 0.301750866 0.849124567
[215,] 0.1389031 0.277806104 0.861096948
[216,] 0.1535720 0.307143950 0.846428025
[217,] 0.3321929 0.664385868 0.667807066
[218,] 0.3117886 0.623577219 0.688211391
[219,] 0.2870737 0.574147327 0.712926336
[220,] 0.2753641 0.550728103 0.724635949
[221,] 0.2397014 0.479402796 0.760298602
[222,] 0.2942433 0.588486538 0.705756731
[223,] 0.2524646 0.504929155 0.747535422
[224,] 0.2121311 0.424262219 0.787868890
[225,] 0.2116716 0.423343189 0.788328406
[226,] 0.1872906 0.374581278 0.812709361
[227,] 0.1559509 0.311901796 0.844049102
[228,] 0.1213506 0.242701129 0.878649436
[229,] 0.2255149 0.451029885 0.774485058
[230,] 0.2226769 0.445353752 0.777323124
[231,] 0.1935978 0.387195635 0.806402182
[232,] 0.3961138 0.792227613 0.603886194
[233,] 0.3506322 0.701264426 0.649367787
[234,] 0.2857485 0.571497026 0.714251487
[235,] 0.3934397 0.786879376 0.606560312
[236,] 0.3151188 0.630237590 0.684881205
[237,] 0.2402980 0.480596071 0.759701965
[238,] 0.4058053 0.811610517 0.594194741
[239,] 0.3143736 0.628747142 0.685626429
[240,] 0.2259370 0.451873968 0.774063016
[241,] 0.3523955 0.704790978 0.647604511
[242,] 0.7903636 0.419272834 0.209636417
[243,] 0.6656956 0.668608856 0.334304428
> postscript(file="/var/wessaorg/rcomp/tmp/19b8r1384797171.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2j9io1384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ggag1384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zhia1384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mqxr1384797171.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
-0.21737648 2.75514166 -2.82183807 -2.23887644 4.52517046 3.37814216
7 8 9 10 11 12
3.53694647 -1.29195975 -0.42892715 0.66086522 1.54415360 3.37704601
13 14 15 16 17 18
-3.42959026 2.53715869 2.11474368 0.52169604 0.14179842 1.50107539
19 20 21 22 23 24
-1.22482670 2.18828148 2.68298830 -2.72633083 -0.26759384 -1.54996441
25 26 27 28 29 30
1.65176482 -6.95915132 0.99207842 0.44531456 1.02236465 -3.06209636
31 32 33 34 35 36
0.13971112 0.19002573 1.77650970 -0.37663775 -0.19315345 0.13287106
37 38 39 40 41 42
-2.21964886 0.42184404 1.70286792 -2.19049236 -0.68960675 2.08477668
43 44 45 46 47 48
0.29336280 -1.25805351 0.23620793 -3.15544432 -0.71243790 -0.01983552
49 50 51 52 53 54
3.43561562 -2.04619168 0.53527476 0.41614215 -1.11185276 -1.80134415
55 56 57 58 59 60
-2.42909234 1.36286751 1.67127516 -0.56026502 -3.37562803 -1.39410477
61 62 63 64 65 66
-2.85972647 -1.74804941 -3.89553070 0.70542271 1.28293465 -4.92423711
67 68 69 70 71 72
-1.36846387 -2.06279049 1.22751230 1.51028095 0.28555379 3.32132497
73 74 75 76 77 78
0.74725660 -0.09399441 -1.81258495 0.24268919 3.06227528 0.72956512
79 80 81 82 83 84
1.07502274 -1.79249065 0.19459824 -0.40708485 1.73323643 0.79486486
85 86 87 88 89 90
0.10602477 1.03775139 -0.16463825 0.39799374 -3.49288067 3.16341714
91 92 93 94 95 96
0.24599785 0.87619034 0.72290254 -1.15854123 1.18724623 -0.78042404
97 98 99 100 101 102
-0.61974027 2.13527526 -0.04152126 1.79184309 -0.81381123 1.23795076
103 104 105 106 107 108
-3.33863971 2.01204380 -2.57174351 1.22032810 2.12168354 -2.66714815
109 110 111 112 113 114
0.81284594 1.16794868 -2.21550839 -2.12398450 1.96179360 3.67963709
115 116 117 118 119 120
0.60944148 0.83877270 0.25078362 -1.25297722 0.31465200 -0.67725786
121 122 123 124 125 126
0.48314435 -0.17692040 -0.99373963 0.34652275 -1.85578989 0.85781932
127 128 129 130 131 132
1.85173847 4.17463772 1.15769350 -1.48131166 -2.03167978 -0.06761762
133 134 135 136 137 138
2.18340161 0.59876960 2.22146320 1.61551119 0.69900777 -1.24466826
139 140 141 142 143 144
0.70695235 -1.05321282 0.63555538 2.25219191 -0.71753987 0.85771683
145 146 147 148 149 150
1.05925559 1.72672101 -2.62887685 -2.64106937 -2.58969169 1.44920537
151 152 153 154 155 156
0.27980927 0.73238811 -2.13835363 -2.85450031 0.95043623 0.24599785
157 158 159 160 161 162
0.72246936 4.17463772 -2.60788304 -0.25811567 0.35855843 0.90179341
163 164 165 166 167 168
0.91653979 4.70919395 -1.68466422 1.90783530 0.01973660 -0.99541973
169 170 171 172 173 174
-3.74891260 -2.66493544 0.76121672 2.04144670 -5.04676455 1.65279601
175 176 177 178 179 180
2.55384474 -2.08571326 -3.20346019 0.60329959 1.63548095 -2.14972220
181 182 183 184 185 186
0.16572366 -1.61158171 0.66264506 -0.83217027 1.46063473 1.59010646
187 188 189 190 191 192
0.83357910 0.90011535 0.66150048 0.91174315 -1.49989675 -0.81629454
193 194 195 196 197 198
2.62514693 -1.21447460 1.95514027 -1.94318785 2.65145328 0.77379912
199 200 201 202 203 204
-2.91551566 -0.53379504 -2.90177844 1.41291568 2.84982188 0.32665795
205 206 207 208 209 210
0.88902175 1.42395859 -0.29806255 3.70990369 -0.05132039 1.88494977
211 212 213 214 215 216
-2.83516462 1.18698655 -1.12288570 -3.52561901 -0.93228891 1.89778376
217 218 219 220 221 222
2.41506008 -0.17163923 -1.79844899 1.45964649 -2.98817235 2.53356976
223 224 225 226 227 228
-2.01345347 0.15267091 -0.46388452 1.41190424 5.33335102 -1.49627286
229 230 231 232 233 234
-1.58413266 -2.39192857 0.40098809 -3.31404707 0.57898354 0.82144820
235 236 237 238 239 240
1.09715537 -2.00761026 0.04365422 0.12402600 -4.16873078 -2.68560612
241 242 243 244 245 246
-2.83068990 -2.82031889 0.39754292 -0.04759895 1.63571898 0.29002421
247 248 249 250 251 252
0.39860457 4.99504646 -0.09206878 0.52947052 2.09370545 1.00384461
253 254 255 256 257 258
-1.05789542 -0.39890680 0.08607347 -0.63208485 -1.67923523 -2.25531730
259 260 261 262 263 264
2.35366973 -5.31758739 0.47747057 1.15256989 -2.86445621 0.28505645
> postscript(file="/var/wessaorg/rcomp/tmp/6sna61384797171.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 -0.21737648 NA
1 2.75514166 -0.21737648
2 -2.82183807 2.75514166
3 -2.23887644 -2.82183807
4 4.52517046 -2.23887644
5 3.37814216 4.52517046
6 3.53694647 3.37814216
7 -1.29195975 3.53694647
8 -0.42892715 -1.29195975
9 0.66086522 -0.42892715
10 1.54415360 0.66086522
11 3.37704601 1.54415360
12 -3.42959026 3.37704601
13 2.53715869 -3.42959026
14 2.11474368 2.53715869
15 0.52169604 2.11474368
16 0.14179842 0.52169604
17 1.50107539 0.14179842
18 -1.22482670 1.50107539
19 2.18828148 -1.22482670
20 2.68298830 2.18828148
21 -2.72633083 2.68298830
22 -0.26759384 -2.72633083
23 -1.54996441 -0.26759384
24 1.65176482 -1.54996441
25 -6.95915132 1.65176482
26 0.99207842 -6.95915132
27 0.44531456 0.99207842
28 1.02236465 0.44531456
29 -3.06209636 1.02236465
30 0.13971112 -3.06209636
31 0.19002573 0.13971112
32 1.77650970 0.19002573
33 -0.37663775 1.77650970
34 -0.19315345 -0.37663775
35 0.13287106 -0.19315345
36 -2.21964886 0.13287106
37 0.42184404 -2.21964886
38 1.70286792 0.42184404
39 -2.19049236 1.70286792
40 -0.68960675 -2.19049236
41 2.08477668 -0.68960675
42 0.29336280 2.08477668
43 -1.25805351 0.29336280
44 0.23620793 -1.25805351
45 -3.15544432 0.23620793
46 -0.71243790 -3.15544432
47 -0.01983552 -0.71243790
48 3.43561562 -0.01983552
49 -2.04619168 3.43561562
50 0.53527476 -2.04619168
51 0.41614215 0.53527476
52 -1.11185276 0.41614215
53 -1.80134415 -1.11185276
54 -2.42909234 -1.80134415
55 1.36286751 -2.42909234
56 1.67127516 1.36286751
57 -0.56026502 1.67127516
58 -3.37562803 -0.56026502
59 -1.39410477 -3.37562803
60 -2.85972647 -1.39410477
61 -1.74804941 -2.85972647
62 -3.89553070 -1.74804941
63 0.70542271 -3.89553070
64 1.28293465 0.70542271
65 -4.92423711 1.28293465
66 -1.36846387 -4.92423711
67 -2.06279049 -1.36846387
68 1.22751230 -2.06279049
69 1.51028095 1.22751230
70 0.28555379 1.51028095
71 3.32132497 0.28555379
72 0.74725660 3.32132497
73 -0.09399441 0.74725660
74 -1.81258495 -0.09399441
75 0.24268919 -1.81258495
76 3.06227528 0.24268919
77 0.72956512 3.06227528
78 1.07502274 0.72956512
79 -1.79249065 1.07502274
80 0.19459824 -1.79249065
81 -0.40708485 0.19459824
82 1.73323643 -0.40708485
83 0.79486486 1.73323643
84 0.10602477 0.79486486
85 1.03775139 0.10602477
86 -0.16463825 1.03775139
87 0.39799374 -0.16463825
88 -3.49288067 0.39799374
89 3.16341714 -3.49288067
90 0.24599785 3.16341714
91 0.87619034 0.24599785
92 0.72290254 0.87619034
93 -1.15854123 0.72290254
94 1.18724623 -1.15854123
95 -0.78042404 1.18724623
96 -0.61974027 -0.78042404
97 2.13527526 -0.61974027
98 -0.04152126 2.13527526
99 1.79184309 -0.04152126
100 -0.81381123 1.79184309
101 1.23795076 -0.81381123
102 -3.33863971 1.23795076
103 2.01204380 -3.33863971
104 -2.57174351 2.01204380
105 1.22032810 -2.57174351
106 2.12168354 1.22032810
107 -2.66714815 2.12168354
108 0.81284594 -2.66714815
109 1.16794868 0.81284594
110 -2.21550839 1.16794868
111 -2.12398450 -2.21550839
112 1.96179360 -2.12398450
113 3.67963709 1.96179360
114 0.60944148 3.67963709
115 0.83877270 0.60944148
116 0.25078362 0.83877270
117 -1.25297722 0.25078362
118 0.31465200 -1.25297722
119 -0.67725786 0.31465200
120 0.48314435 -0.67725786
121 -0.17692040 0.48314435
122 -0.99373963 -0.17692040
123 0.34652275 -0.99373963
124 -1.85578989 0.34652275
125 0.85781932 -1.85578989
126 1.85173847 0.85781932
127 4.17463772 1.85173847
128 1.15769350 4.17463772
129 -1.48131166 1.15769350
130 -2.03167978 -1.48131166
131 -0.06761762 -2.03167978
132 2.18340161 -0.06761762
133 0.59876960 2.18340161
134 2.22146320 0.59876960
135 1.61551119 2.22146320
136 0.69900777 1.61551119
137 -1.24466826 0.69900777
138 0.70695235 -1.24466826
139 -1.05321282 0.70695235
140 0.63555538 -1.05321282
141 2.25219191 0.63555538
142 -0.71753987 2.25219191
143 0.85771683 -0.71753987
144 1.05925559 0.85771683
145 1.72672101 1.05925559
146 -2.62887685 1.72672101
147 -2.64106937 -2.62887685
148 -2.58969169 -2.64106937
149 1.44920537 -2.58969169
150 0.27980927 1.44920537
151 0.73238811 0.27980927
152 -2.13835363 0.73238811
153 -2.85450031 -2.13835363
154 0.95043623 -2.85450031
155 0.24599785 0.95043623
156 0.72246936 0.24599785
157 4.17463772 0.72246936
158 -2.60788304 4.17463772
159 -0.25811567 -2.60788304
160 0.35855843 -0.25811567
161 0.90179341 0.35855843
162 0.91653979 0.90179341
163 4.70919395 0.91653979
164 -1.68466422 4.70919395
165 1.90783530 -1.68466422
166 0.01973660 1.90783530
167 -0.99541973 0.01973660
168 -3.74891260 -0.99541973
169 -2.66493544 -3.74891260
170 0.76121672 -2.66493544
171 2.04144670 0.76121672
172 -5.04676455 2.04144670
173 1.65279601 -5.04676455
174 2.55384474 1.65279601
175 -2.08571326 2.55384474
176 -3.20346019 -2.08571326
177 0.60329959 -3.20346019
178 1.63548095 0.60329959
179 -2.14972220 1.63548095
180 0.16572366 -2.14972220
181 -1.61158171 0.16572366
182 0.66264506 -1.61158171
183 -0.83217027 0.66264506
184 1.46063473 -0.83217027
185 1.59010646 1.46063473
186 0.83357910 1.59010646
187 0.90011535 0.83357910
188 0.66150048 0.90011535
189 0.91174315 0.66150048
190 -1.49989675 0.91174315
191 -0.81629454 -1.49989675
192 2.62514693 -0.81629454
193 -1.21447460 2.62514693
194 1.95514027 -1.21447460
195 -1.94318785 1.95514027
196 2.65145328 -1.94318785
197 0.77379912 2.65145328
198 -2.91551566 0.77379912
199 -0.53379504 -2.91551566
200 -2.90177844 -0.53379504
201 1.41291568 -2.90177844
202 2.84982188 1.41291568
203 0.32665795 2.84982188
204 0.88902175 0.32665795
205 1.42395859 0.88902175
206 -0.29806255 1.42395859
207 3.70990369 -0.29806255
208 -0.05132039 3.70990369
209 1.88494977 -0.05132039
210 -2.83516462 1.88494977
211 1.18698655 -2.83516462
212 -1.12288570 1.18698655
213 -3.52561901 -1.12288570
214 -0.93228891 -3.52561901
215 1.89778376 -0.93228891
216 2.41506008 1.89778376
217 -0.17163923 2.41506008
218 -1.79844899 -0.17163923
219 1.45964649 -1.79844899
220 -2.98817235 1.45964649
221 2.53356976 -2.98817235
222 -2.01345347 2.53356976
223 0.15267091 -2.01345347
224 -0.46388452 0.15267091
225 1.41190424 -0.46388452
226 5.33335102 1.41190424
227 -1.49627286 5.33335102
228 -1.58413266 -1.49627286
229 -2.39192857 -1.58413266
230 0.40098809 -2.39192857
231 -3.31404707 0.40098809
232 0.57898354 -3.31404707
233 0.82144820 0.57898354
234 1.09715537 0.82144820
235 -2.00761026 1.09715537
236 0.04365422 -2.00761026
237 0.12402600 0.04365422
238 -4.16873078 0.12402600
239 -2.68560612 -4.16873078
240 -2.83068990 -2.68560612
241 -2.82031889 -2.83068990
242 0.39754292 -2.82031889
243 -0.04759895 0.39754292
244 1.63571898 -0.04759895
245 0.29002421 1.63571898
246 0.39860457 0.29002421
247 4.99504646 0.39860457
248 -0.09206878 4.99504646
249 0.52947052 -0.09206878
250 2.09370545 0.52947052
251 1.00384461 2.09370545
252 -1.05789542 1.00384461
253 -0.39890680 -1.05789542
254 0.08607347 -0.39890680
255 -0.63208485 0.08607347
256 -1.67923523 -0.63208485
257 -2.25531730 -1.67923523
258 2.35366973 -2.25531730
259 -5.31758739 2.35366973
260 0.47747057 -5.31758739
261 1.15256989 0.47747057
262 -2.86445621 1.15256989
263 0.28505645 -2.86445621
264 NA 0.28505645
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.75514166 -0.21737648
[2,] -2.82183807 2.75514166
[3,] -2.23887644 -2.82183807
[4,] 4.52517046 -2.23887644
[5,] 3.37814216 4.52517046
[6,] 3.53694647 3.37814216
[7,] -1.29195975 3.53694647
[8,] -0.42892715 -1.29195975
[9,] 0.66086522 -0.42892715
[10,] 1.54415360 0.66086522
[11,] 3.37704601 1.54415360
[12,] -3.42959026 3.37704601
[13,] 2.53715869 -3.42959026
[14,] 2.11474368 2.53715869
[15,] 0.52169604 2.11474368
[16,] 0.14179842 0.52169604
[17,] 1.50107539 0.14179842
[18,] -1.22482670 1.50107539
[19,] 2.18828148 -1.22482670
[20,] 2.68298830 2.18828148
[21,] -2.72633083 2.68298830
[22,] -0.26759384 -2.72633083
[23,] -1.54996441 -0.26759384
[24,] 1.65176482 -1.54996441
[25,] -6.95915132 1.65176482
[26,] 0.99207842 -6.95915132
[27,] 0.44531456 0.99207842
[28,] 1.02236465 0.44531456
[29,] -3.06209636 1.02236465
[30,] 0.13971112 -3.06209636
[31,] 0.19002573 0.13971112
[32,] 1.77650970 0.19002573
[33,] -0.37663775 1.77650970
[34,] -0.19315345 -0.37663775
[35,] 0.13287106 -0.19315345
[36,] -2.21964886 0.13287106
[37,] 0.42184404 -2.21964886
[38,] 1.70286792 0.42184404
[39,] -2.19049236 1.70286792
[40,] -0.68960675 -2.19049236
[41,] 2.08477668 -0.68960675
[42,] 0.29336280 2.08477668
[43,] -1.25805351 0.29336280
[44,] 0.23620793 -1.25805351
[45,] -3.15544432 0.23620793
[46,] -0.71243790 -3.15544432
[47,] -0.01983552 -0.71243790
[48,] 3.43561562 -0.01983552
[49,] -2.04619168 3.43561562
[50,] 0.53527476 -2.04619168
[51,] 0.41614215 0.53527476
[52,] -1.11185276 0.41614215
[53,] -1.80134415 -1.11185276
[54,] -2.42909234 -1.80134415
[55,] 1.36286751 -2.42909234
[56,] 1.67127516 1.36286751
[57,] -0.56026502 1.67127516
[58,] -3.37562803 -0.56026502
[59,] -1.39410477 -3.37562803
[60,] -2.85972647 -1.39410477
[61,] -1.74804941 -2.85972647
[62,] -3.89553070 -1.74804941
[63,] 0.70542271 -3.89553070
[64,] 1.28293465 0.70542271
[65,] -4.92423711 1.28293465
[66,] -1.36846387 -4.92423711
[67,] -2.06279049 -1.36846387
[68,] 1.22751230 -2.06279049
[69,] 1.51028095 1.22751230
[70,] 0.28555379 1.51028095
[71,] 3.32132497 0.28555379
[72,] 0.74725660 3.32132497
[73,] -0.09399441 0.74725660
[74,] -1.81258495 -0.09399441
[75,] 0.24268919 -1.81258495
[76,] 3.06227528 0.24268919
[77,] 0.72956512 3.06227528
[78,] 1.07502274 0.72956512
[79,] -1.79249065 1.07502274
[80,] 0.19459824 -1.79249065
[81,] -0.40708485 0.19459824
[82,] 1.73323643 -0.40708485
[83,] 0.79486486 1.73323643
[84,] 0.10602477 0.79486486
[85,] 1.03775139 0.10602477
[86,] -0.16463825 1.03775139
[87,] 0.39799374 -0.16463825
[88,] -3.49288067 0.39799374
[89,] 3.16341714 -3.49288067
[90,] 0.24599785 3.16341714
[91,] 0.87619034 0.24599785
[92,] 0.72290254 0.87619034
[93,] -1.15854123 0.72290254
[94,] 1.18724623 -1.15854123
[95,] -0.78042404 1.18724623
[96,] -0.61974027 -0.78042404
[97,] 2.13527526 -0.61974027
[98,] -0.04152126 2.13527526
[99,] 1.79184309 -0.04152126
[100,] -0.81381123 1.79184309
[101,] 1.23795076 -0.81381123
[102,] -3.33863971 1.23795076
[103,] 2.01204380 -3.33863971
[104,] -2.57174351 2.01204380
[105,] 1.22032810 -2.57174351
[106,] 2.12168354 1.22032810
[107,] -2.66714815 2.12168354
[108,] 0.81284594 -2.66714815
[109,] 1.16794868 0.81284594
[110,] -2.21550839 1.16794868
[111,] -2.12398450 -2.21550839
[112,] 1.96179360 -2.12398450
[113,] 3.67963709 1.96179360
[114,] 0.60944148 3.67963709
[115,] 0.83877270 0.60944148
[116,] 0.25078362 0.83877270
[117,] -1.25297722 0.25078362
[118,] 0.31465200 -1.25297722
[119,] -0.67725786 0.31465200
[120,] 0.48314435 -0.67725786
[121,] -0.17692040 0.48314435
[122,] -0.99373963 -0.17692040
[123,] 0.34652275 -0.99373963
[124,] -1.85578989 0.34652275
[125,] 0.85781932 -1.85578989
[126,] 1.85173847 0.85781932
[127,] 4.17463772 1.85173847
[128,] 1.15769350 4.17463772
[129,] -1.48131166 1.15769350
[130,] -2.03167978 -1.48131166
[131,] -0.06761762 -2.03167978
[132,] 2.18340161 -0.06761762
[133,] 0.59876960 2.18340161
[134,] 2.22146320 0.59876960
[135,] 1.61551119 2.22146320
[136,] 0.69900777 1.61551119
[137,] -1.24466826 0.69900777
[138,] 0.70695235 -1.24466826
[139,] -1.05321282 0.70695235
[140,] 0.63555538 -1.05321282
[141,] 2.25219191 0.63555538
[142,] -0.71753987 2.25219191
[143,] 0.85771683 -0.71753987
[144,] 1.05925559 0.85771683
[145,] 1.72672101 1.05925559
[146,] -2.62887685 1.72672101
[147,] -2.64106937 -2.62887685
[148,] -2.58969169 -2.64106937
[149,] 1.44920537 -2.58969169
[150,] 0.27980927 1.44920537
[151,] 0.73238811 0.27980927
[152,] -2.13835363 0.73238811
[153,] -2.85450031 -2.13835363
[154,] 0.95043623 -2.85450031
[155,] 0.24599785 0.95043623
[156,] 0.72246936 0.24599785
[157,] 4.17463772 0.72246936
[158,] -2.60788304 4.17463772
[159,] -0.25811567 -2.60788304
[160,] 0.35855843 -0.25811567
[161,] 0.90179341 0.35855843
[162,] 0.91653979 0.90179341
[163,] 4.70919395 0.91653979
[164,] -1.68466422 4.70919395
[165,] 1.90783530 -1.68466422
[166,] 0.01973660 1.90783530
[167,] -0.99541973 0.01973660
[168,] -3.74891260 -0.99541973
[169,] -2.66493544 -3.74891260
[170,] 0.76121672 -2.66493544
[171,] 2.04144670 0.76121672
[172,] -5.04676455 2.04144670
[173,] 1.65279601 -5.04676455
[174,] 2.55384474 1.65279601
[175,] -2.08571326 2.55384474
[176,] -3.20346019 -2.08571326
[177,] 0.60329959 -3.20346019
[178,] 1.63548095 0.60329959
[179,] -2.14972220 1.63548095
[180,] 0.16572366 -2.14972220
[181,] -1.61158171 0.16572366
[182,] 0.66264506 -1.61158171
[183,] -0.83217027 0.66264506
[184,] 1.46063473 -0.83217027
[185,] 1.59010646 1.46063473
[186,] 0.83357910 1.59010646
[187,] 0.90011535 0.83357910
[188,] 0.66150048 0.90011535
[189,] 0.91174315 0.66150048
[190,] -1.49989675 0.91174315
[191,] -0.81629454 -1.49989675
[192,] 2.62514693 -0.81629454
[193,] -1.21447460 2.62514693
[194,] 1.95514027 -1.21447460
[195,] -1.94318785 1.95514027
[196,] 2.65145328 -1.94318785
[197,] 0.77379912 2.65145328
[198,] -2.91551566 0.77379912
[199,] -0.53379504 -2.91551566
[200,] -2.90177844 -0.53379504
[201,] 1.41291568 -2.90177844
[202,] 2.84982188 1.41291568
[203,] 0.32665795 2.84982188
[204,] 0.88902175 0.32665795
[205,] 1.42395859 0.88902175
[206,] -0.29806255 1.42395859
[207,] 3.70990369 -0.29806255
[208,] -0.05132039 3.70990369
[209,] 1.88494977 -0.05132039
[210,] -2.83516462 1.88494977
[211,] 1.18698655 -2.83516462
[212,] -1.12288570 1.18698655
[213,] -3.52561901 -1.12288570
[214,] -0.93228891 -3.52561901
[215,] 1.89778376 -0.93228891
[216,] 2.41506008 1.89778376
[217,] -0.17163923 2.41506008
[218,] -1.79844899 -0.17163923
[219,] 1.45964649 -1.79844899
[220,] -2.98817235 1.45964649
[221,] 2.53356976 -2.98817235
[222,] -2.01345347 2.53356976
[223,] 0.15267091 -2.01345347
[224,] -0.46388452 0.15267091
[225,] 1.41190424 -0.46388452
[226,] 5.33335102 1.41190424
[227,] -1.49627286 5.33335102
[228,] -1.58413266 -1.49627286
[229,] -2.39192857 -1.58413266
[230,] 0.40098809 -2.39192857
[231,] -3.31404707 0.40098809
[232,] 0.57898354 -3.31404707
[233,] 0.82144820 0.57898354
[234,] 1.09715537 0.82144820
[235,] -2.00761026 1.09715537
[236,] 0.04365422 -2.00761026
[237,] 0.12402600 0.04365422
[238,] -4.16873078 0.12402600
[239,] -2.68560612 -4.16873078
[240,] -2.83068990 -2.68560612
[241,] -2.82031889 -2.83068990
[242,] 0.39754292 -2.82031889
[243,] -0.04759895 0.39754292
[244,] 1.63571898 -0.04759895
[245,] 0.29002421 1.63571898
[246,] 0.39860457 0.29002421
[247,] 4.99504646 0.39860457
[248,] -0.09206878 4.99504646
[249,] 0.52947052 -0.09206878
[250,] 2.09370545 0.52947052
[251,] 1.00384461 2.09370545
[252,] -1.05789542 1.00384461
[253,] -0.39890680 -1.05789542
[254,] 0.08607347 -0.39890680
[255,] -0.63208485 0.08607347
[256,] -1.67923523 -0.63208485
[257,] -2.25531730 -1.67923523
[258,] 2.35366973 -2.25531730
[259,] -5.31758739 2.35366973
[260,] 0.47747057 -5.31758739
[261,] 1.15256989 0.47747057
[262,] -2.86445621 1.15256989
[263,] 0.28505645 -2.86445621
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.75514166 -0.21737648
2 -2.82183807 2.75514166
3 -2.23887644 -2.82183807
4 4.52517046 -2.23887644
5 3.37814216 4.52517046
6 3.53694647 3.37814216
7 -1.29195975 3.53694647
8 -0.42892715 -1.29195975
9 0.66086522 -0.42892715
10 1.54415360 0.66086522
11 3.37704601 1.54415360
12 -3.42959026 3.37704601
13 2.53715869 -3.42959026
14 2.11474368 2.53715869
15 0.52169604 2.11474368
16 0.14179842 0.52169604
17 1.50107539 0.14179842
18 -1.22482670 1.50107539
19 2.18828148 -1.22482670
20 2.68298830 2.18828148
21 -2.72633083 2.68298830
22 -0.26759384 -2.72633083
23 -1.54996441 -0.26759384
24 1.65176482 -1.54996441
25 -6.95915132 1.65176482
26 0.99207842 -6.95915132
27 0.44531456 0.99207842
28 1.02236465 0.44531456
29 -3.06209636 1.02236465
30 0.13971112 -3.06209636
31 0.19002573 0.13971112
32 1.77650970 0.19002573
33 -0.37663775 1.77650970
34 -0.19315345 -0.37663775
35 0.13287106 -0.19315345
36 -2.21964886 0.13287106
37 0.42184404 -2.21964886
38 1.70286792 0.42184404
39 -2.19049236 1.70286792
40 -0.68960675 -2.19049236
41 2.08477668 -0.68960675
42 0.29336280 2.08477668
43 -1.25805351 0.29336280
44 0.23620793 -1.25805351
45 -3.15544432 0.23620793
46 -0.71243790 -3.15544432
47 -0.01983552 -0.71243790
48 3.43561562 -0.01983552
49 -2.04619168 3.43561562
50 0.53527476 -2.04619168
51 0.41614215 0.53527476
52 -1.11185276 0.41614215
53 -1.80134415 -1.11185276
54 -2.42909234 -1.80134415
55 1.36286751 -2.42909234
56 1.67127516 1.36286751
57 -0.56026502 1.67127516
58 -3.37562803 -0.56026502
59 -1.39410477 -3.37562803
60 -2.85972647 -1.39410477
61 -1.74804941 -2.85972647
62 -3.89553070 -1.74804941
63 0.70542271 -3.89553070
64 1.28293465 0.70542271
65 -4.92423711 1.28293465
66 -1.36846387 -4.92423711
67 -2.06279049 -1.36846387
68 1.22751230 -2.06279049
69 1.51028095 1.22751230
70 0.28555379 1.51028095
71 3.32132497 0.28555379
72 0.74725660 3.32132497
73 -0.09399441 0.74725660
74 -1.81258495 -0.09399441
75 0.24268919 -1.81258495
76 3.06227528 0.24268919
77 0.72956512 3.06227528
78 1.07502274 0.72956512
79 -1.79249065 1.07502274
80 0.19459824 -1.79249065
81 -0.40708485 0.19459824
82 1.73323643 -0.40708485
83 0.79486486 1.73323643
84 0.10602477 0.79486486
85 1.03775139 0.10602477
86 -0.16463825 1.03775139
87 0.39799374 -0.16463825
88 -3.49288067 0.39799374
89 3.16341714 -3.49288067
90 0.24599785 3.16341714
91 0.87619034 0.24599785
92 0.72290254 0.87619034
93 -1.15854123 0.72290254
94 1.18724623 -1.15854123
95 -0.78042404 1.18724623
96 -0.61974027 -0.78042404
97 2.13527526 -0.61974027
98 -0.04152126 2.13527526
99 1.79184309 -0.04152126
100 -0.81381123 1.79184309
101 1.23795076 -0.81381123
102 -3.33863971 1.23795076
103 2.01204380 -3.33863971
104 -2.57174351 2.01204380
105 1.22032810 -2.57174351
106 2.12168354 1.22032810
107 -2.66714815 2.12168354
108 0.81284594 -2.66714815
109 1.16794868 0.81284594
110 -2.21550839 1.16794868
111 -2.12398450 -2.21550839
112 1.96179360 -2.12398450
113 3.67963709 1.96179360
114 0.60944148 3.67963709
115 0.83877270 0.60944148
116 0.25078362 0.83877270
117 -1.25297722 0.25078362
118 0.31465200 -1.25297722
119 -0.67725786 0.31465200
120 0.48314435 -0.67725786
121 -0.17692040 0.48314435
122 -0.99373963 -0.17692040
123 0.34652275 -0.99373963
124 -1.85578989 0.34652275
125 0.85781932 -1.85578989
126 1.85173847 0.85781932
127 4.17463772 1.85173847
128 1.15769350 4.17463772
129 -1.48131166 1.15769350
130 -2.03167978 -1.48131166
131 -0.06761762 -2.03167978
132 2.18340161 -0.06761762
133 0.59876960 2.18340161
134 2.22146320 0.59876960
135 1.61551119 2.22146320
136 0.69900777 1.61551119
137 -1.24466826 0.69900777
138 0.70695235 -1.24466826
139 -1.05321282 0.70695235
140 0.63555538 -1.05321282
141 2.25219191 0.63555538
142 -0.71753987 2.25219191
143 0.85771683 -0.71753987
144 1.05925559 0.85771683
145 1.72672101 1.05925559
146 -2.62887685 1.72672101
147 -2.64106937 -2.62887685
148 -2.58969169 -2.64106937
149 1.44920537 -2.58969169
150 0.27980927 1.44920537
151 0.73238811 0.27980927
152 -2.13835363 0.73238811
153 -2.85450031 -2.13835363
154 0.95043623 -2.85450031
155 0.24599785 0.95043623
156 0.72246936 0.24599785
157 4.17463772 0.72246936
158 -2.60788304 4.17463772
159 -0.25811567 -2.60788304
160 0.35855843 -0.25811567
161 0.90179341 0.35855843
162 0.91653979 0.90179341
163 4.70919395 0.91653979
164 -1.68466422 4.70919395
165 1.90783530 -1.68466422
166 0.01973660 1.90783530
167 -0.99541973 0.01973660
168 -3.74891260 -0.99541973
169 -2.66493544 -3.74891260
170 0.76121672 -2.66493544
171 2.04144670 0.76121672
172 -5.04676455 2.04144670
173 1.65279601 -5.04676455
174 2.55384474 1.65279601
175 -2.08571326 2.55384474
176 -3.20346019 -2.08571326
177 0.60329959 -3.20346019
178 1.63548095 0.60329959
179 -2.14972220 1.63548095
180 0.16572366 -2.14972220
181 -1.61158171 0.16572366
182 0.66264506 -1.61158171
183 -0.83217027 0.66264506
184 1.46063473 -0.83217027
185 1.59010646 1.46063473
186 0.83357910 1.59010646
187 0.90011535 0.83357910
188 0.66150048 0.90011535
189 0.91174315 0.66150048
190 -1.49989675 0.91174315
191 -0.81629454 -1.49989675
192 2.62514693 -0.81629454
193 -1.21447460 2.62514693
194 1.95514027 -1.21447460
195 -1.94318785 1.95514027
196 2.65145328 -1.94318785
197 0.77379912 2.65145328
198 -2.91551566 0.77379912
199 -0.53379504 -2.91551566
200 -2.90177844 -0.53379504
201 1.41291568 -2.90177844
202 2.84982188 1.41291568
203 0.32665795 2.84982188
204 0.88902175 0.32665795
205 1.42395859 0.88902175
206 -0.29806255 1.42395859
207 3.70990369 -0.29806255
208 -0.05132039 3.70990369
209 1.88494977 -0.05132039
210 -2.83516462 1.88494977
211 1.18698655 -2.83516462
212 -1.12288570 1.18698655
213 -3.52561901 -1.12288570
214 -0.93228891 -3.52561901
215 1.89778376 -0.93228891
216 2.41506008 1.89778376
217 -0.17163923 2.41506008
218 -1.79844899 -0.17163923
219 1.45964649 -1.79844899
220 -2.98817235 1.45964649
221 2.53356976 -2.98817235
222 -2.01345347 2.53356976
223 0.15267091 -2.01345347
224 -0.46388452 0.15267091
225 1.41190424 -0.46388452
226 5.33335102 1.41190424
227 -1.49627286 5.33335102
228 -1.58413266 -1.49627286
229 -2.39192857 -1.58413266
230 0.40098809 -2.39192857
231 -3.31404707 0.40098809
232 0.57898354 -3.31404707
233 0.82144820 0.57898354
234 1.09715537 0.82144820
235 -2.00761026 1.09715537
236 0.04365422 -2.00761026
237 0.12402600 0.04365422
238 -4.16873078 0.12402600
239 -2.68560612 -4.16873078
240 -2.83068990 -2.68560612
241 -2.82031889 -2.83068990
242 0.39754292 -2.82031889
243 -0.04759895 0.39754292
244 1.63571898 -0.04759895
245 0.29002421 1.63571898
246 0.39860457 0.29002421
247 4.99504646 0.39860457
248 -0.09206878 4.99504646
249 0.52947052 -0.09206878
250 2.09370545 0.52947052
251 1.00384461 2.09370545
252 -1.05789542 1.00384461
253 -0.39890680 -1.05789542
254 0.08607347 -0.39890680
255 -0.63208485 0.08607347
256 -1.67923523 -0.63208485
257 -2.25531730 -1.67923523
258 2.35366973 -2.25531730
259 -5.31758739 2.35366973
260 0.47747057 -5.31758739
261 1.15256989 0.47747057
262 -2.86445621 1.15256989
263 0.28505645 -2.86445621
> 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/7ocuf1384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8g6bn1384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/98i761384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10opl51384797171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o1ms1384797171.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/12e1kv1384797171.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/131m2y1384797171.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/14xcdc1384797171.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/15l6b11384797172.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/16geea1384797172.tab")
+ }
>
> try(system("convert tmp/19b8r1384797171.ps tmp/19b8r1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j9io1384797171.ps tmp/2j9io1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ggag1384797171.ps tmp/3ggag1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zhia1384797171.ps tmp/4zhia1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mqxr1384797171.ps tmp/5mqxr1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sna61384797171.ps tmp/6sna61384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ocuf1384797171.ps tmp/7ocuf1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g6bn1384797171.ps tmp/8g6bn1384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/98i761384797171.ps tmp/98i761384797171.png",intern=TRUE))
character(0)
> try(system("convert tmp/10opl51384797171.ps tmp/10opl51384797171.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.090 2.729 18.834