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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Type 'q()' to quit R.
> x <- array(list(13
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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Learning'
+ ,'Connected'
+ ,'Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Learning','Connected','Separate','Software','Happiness','Depression'),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
Learning Connected Separate Software Happiness Depression
1 13 41 38 12 14 12.0
2 16 39 32 11 18 11.0
3 19 30 35 15 11 14.0
4 15 31 33 6 12 12.0
5 14 34 37 13 16 21.0
6 13 35 29 10 18 12.0
7 19 39 31 12 14 22.0
8 15 34 36 14 14 11.0
9 14 36 35 12 15 10.0
10 15 37 38 9 15 13.0
11 16 38 31 10 17 10.0
12 16 36 34 12 19 8.0
13 16 38 35 12 10 15.0
14 16 39 38 11 16 14.0
15 17 33 37 15 18 10.0
16 15 32 33 12 14 14.0
17 15 36 32 10 14 14.0
18 20 38 38 12 17 11.0
19 18 39 38 11 14 10.0
20 16 32 32 12 16 13.0
21 16 32 33 11 18 9.5
22 16 31 31 12 11 14.0
23 19 39 38 13 14 12.0
24 16 37 39 11 12 14.0
25 17 39 32 12 17 11.0
26 17 41 32 13 9 9.0
27 16 36 35 10 16 11.0
28 15 33 37 14 14 15.0
29 16 33 33 12 15 14.0
30 14 34 33 10 11 13.0
31 15 31 31 12 16 9.0
32 12 27 32 8 13 15.0
33 14 37 31 10 17 10.0
34 16 34 37 12 15 11.0
35 14 34 30 12 14 13.0
36 10 32 33 7 16 8.0
37 10 29 31 9 9 20.0
38 14 36 33 12 15 12.0
39 16 29 31 10 17 10.0
40 16 35 33 10 13 10.0
41 16 37 32 10 15 9.0
42 14 34 33 12 16 14.0
43 20 38 32 15 16 8.0
44 14 35 33 10 12 14.0
45 14 38 28 10 15 11.0
46 11 37 35 12 11 13.0
47 14 38 39 13 15 9.0
48 15 33 34 11 15 11.0
49 16 36 38 11 17 15.0
50 14 38 32 12 13 11.0
51 16 32 38 14 16 10.0
52 14 32 30 10 14 14.0
53 12 32 33 12 11 18.0
54 16 34 38 13 12 14.0
55 9 32 32 5 12 11.0
56 14 37 35 6 15 14.5
57 16 39 34 12 16 13.0
58 16 29 34 12 15 9.0
59 15 37 36 11 12 10.0
60 16 35 34 10 12 15.0
61 12 30 28 7 8 20.0
62 16 38 34 12 13 12.0
63 16 34 35 14 11 12.0
64 14 31 35 11 14 14.0
65 16 34 31 12 15 13.0
66 17 35 37 13 10 11.0
67 18 36 35 14 11 17.0
68 18 30 27 11 12 12.0
69 12 39 40 12 15 13.0
70 16 35 37 12 15 14.0
71 10 38 36 8 14 13.0
72 14 31 38 11 16 15.0
73 18 34 39 14 15 13.0
74 18 38 41 14 15 10.0
75 16 34 27 12 13 11.0
76 17 39 30 9 12 19.0
77 16 37 37 13 17 13.0
78 16 34 31 11 13 17.0
79 13 28 31 12 15 13.0
80 16 37 27 12 13 9.0
81 16 33 36 12 15 11.0
82 16 35 37 12 15 9.0
83 15 37 33 12 16 12.0
84 15 32 34 11 15 12.0
85 16 33 31 10 14 13.0
86 14 38 39 9 15 13.0
87 16 33 34 12 14 12.0
88 16 29 32 12 13 15.0
89 15 33 33 12 7 22.0
90 12 31 36 9 17 13.0
91 17 36 32 15 13 15.0
92 16 35 41 12 15 13.0
93 15 32 28 12 14 15.0
94 13 29 30 12 13 12.5
95 16 39 36 10 16 11.0
96 16 37 35 13 12 16.0
97 16 35 31 9 14 11.0
98 16 37 34 12 17 11.0
99 14 32 36 10 15 10.0
100 16 38 36 14 17 10.0
101 16 37 35 11 12 16.0
102 20 36 37 15 16 12.0
103 15 32 28 11 11 11.0
104 16 33 39 11 15 16.0
105 13 40 32 12 9 19.0
106 17 38 35 12 16 11.0
107 16 41 39 12 15 16.0
108 16 36 35 11 10 15.0
109 12 43 42 7 10 24.0
110 16 30 34 12 15 14.0
111 16 31 33 14 11 15.0
112 17 32 41 11 13 11.0
113 13 32 33 11 14 15.0
114 12 37 34 10 18 12.0
115 18 37 32 13 16 10.0
116 14 33 40 13 14 14.0
117 14 34 40 8 14 13.0
118 13 33 35 11 14 9.0
119 16 38 36 12 14 15.0
120 13 33 37 11 12 15.0
121 16 31 27 13 14 14.0
122 13 38 39 12 15 11.0
123 16 37 38 14 15 8.0
124 15 36 31 13 15 11.0
125 16 31 33 15 13 11.0
126 15 39 32 10 17 8.0
127 17 44 39 11 17 10.0
128 15 33 36 9 19 11.0
129 12 35 33 11 15 13.0
130 16 32 33 10 13 11.0
131 10 28 32 11 9 20.0
132 16 40 37 8 15 10.0
133 12 27 30 11 15 15.0
134 14 37 38 12 15 12.0
135 15 32 29 12 16 14.0
136 13 28 22 9 11 23.0
137 15 34 35 11 14 14.0
138 11 30 35 10 11 16.0
139 12 35 34 8 15 11.0
140 11 31 35 9 13 12.0
141 16 32 34 8 15 10.0
142 15 30 37 9 16 14.0
143 17 30 35 15 14 12.0
144 16 31 23 11 15 12.0
145 10 40 31 8 16 11.0
146 18 32 27 13 16 12.0
147 13 36 36 12 11 13.0
148 16 32 31 12 12 11.0
149 13 35 32 9 9 19.0
150 10 38 39 7 16 12.0
151 15 42 37 13 13 17.0
152 16 34 38 9 16 9.0
153 16 35 39 6 12 12.0
154 14 38 34 8 9 19.0
155 10 33 31 8 13 18.0
156 17 36 32 15 13 15.0
157 13 32 37 6 14 14.0
158 15 33 36 9 19 11.0
159 16 34 32 11 13 9.0
160 12 32 38 8 12 18.0
161 13 34 36 8 13 16.0
162 13 27 26 10 10 24.0
163 12 31 26 8 14 14.0
164 17 38 33 14 16 20.0
165 15 34 39 10 10 18.0
166 10 24 30 8 11 23.0
167 14 30 33 11 14 12.0
168 11 26 25 12 12 14.0
169 13 34 38 12 9 16.0
170 16 27 37 12 9 18.0
171 12 37 31 5 11 20.0
172 16 36 37 12 16 12.0
173 12 41 35 10 9 12.0
174 9 29 25 7 13 17.0
175 12 36 28 12 16 13.0
176 15 32 35 11 13 9.0
177 12 37 33 8 9 16.0
178 12 30 30 9 12 18.0
179 14 31 31 10 16 10.0
180 12 38 37 9 11 14.0
181 16 36 36 12 14 11.0
182 11 35 30 6 13 9.0
183 19 31 36 15 15 11.0
184 15 38 32 12 14 10.0
185 8 22 28 12 16 11.0
186 16 32 36 12 13 19.0
187 17 36 34 11 14 14.0
188 12 39 31 7 15 12.0
189 11 28 28 7 13 14.0
190 11 32 36 5 11 21.0
191 14 32 36 12 11 13.0
192 16 38 40 12 14 10.0
193 12 32 33 3 15 15.0
194 16 35 37 11 11 16.0
195 13 32 32 10 15 14.0
196 15 37 38 12 12 12.0
197 16 34 31 9 14 19.0
198 16 33 37 12 14 15.0
199 14 33 33 9 8 19.0
200 16 26 32 12 13 13.0
201 16 30 30 12 9 17.0
202 14 24 30 10 15 12.0
203 11 34 31 9 17 11.0
204 12 34 32 12 13 14.0
205 15 33 34 8 15 11.0
206 15 34 36 11 15 13.0
207 16 35 37 11 14 12.0
208 16 35 36 12 16 15.0
209 11 36 33 10 13 14.0
210 15 34 33 10 16 12.0
211 12 34 33 12 9 17.0
212 12 41 44 12 16 11.0
213 15 32 39 11 11 18.0
214 15 30 32 8 10 13.0
215 16 35 35 12 11 17.0
216 14 28 25 10 15 13.0
217 17 33 35 11 17 11.0
218 14 39 34 10 14 12.0
219 13 36 35 8 8 22.0
220 15 36 39 12 15 14.0
221 13 35 33 12 11 12.0
222 14 38 36 10 16 12.0
223 15 33 32 12 10 17.0
224 12 31 32 9 15 9.0
225 13 34 36 9 9 21.0
226 8 32 36 6 16 10.0
227 14 31 32 10 19 11.0
228 14 33 34 9 12 12.0
229 11 34 33 9 8 23.0
230 12 34 35 9 11 13.0
231 13 34 30 6 14 12.0
232 10 33 38 10 9 16.0
233 16 32 34 6 15 9.0
234 18 41 33 14 13 17.0
235 13 34 32 10 16 9.0
236 11 36 31 10 11 14.0
237 4 37 30 6 12 17.0
238 13 36 27 12 13 13.0
239 16 29 31 12 10 11.0
240 10 37 30 7 11 12.0
241 12 27 32 8 12 10.0
242 12 35 35 11 8 19.0
243 10 28 28 3 12 16.0
244 13 35 33 6 12 16.0
245 15 37 31 10 15 14.0
246 12 29 35 8 11 20.0
247 14 32 35 9 13 15.0
248 10 36 32 9 14 23.0
249 12 19 21 8 10 20.0
250 12 21 20 9 12 16.0
251 11 31 34 7 15 14.0
252 10 33 32 7 13 17.0
253 12 36 34 6 13 11.0
254 16 33 32 9 13 13.0
255 12 37 33 10 12 17.0
256 14 34 33 11 12 15.0
257 16 35 37 12 9 21.0
258 14 31 32 8 9 18.0
259 13 37 34 11 15 15.0
260 4 35 30 3 10 8.0
261 15 27 30 11 14 12.0
262 11 34 38 12 15 12.0
263 11 40 36 7 7 22.0
264 14 29 32 9 14 12.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
4.09668 0.04645 0.04218 0.60869 0.10009 -0.04346
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.195 -1.315 0.302 1.225 4.301
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.09668 1.73594 2.360 0.0190 *
Connected 0.04645 0.03449 1.347 0.1792
Separate 0.04218 0.03551 1.188 0.2360
Software 0.60869 0.05159 11.800 <2e-16 ***
Happiness 0.10009 0.05736 1.745 0.0822 .
Depression -0.04346 0.04131 -1.052 0.2938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.878 on 258 degrees of freedom
Multiple R-squared: 0.4265, Adjusted R-squared: 0.4154
F-statistic: 38.37 on 5 and 258 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,] 0.577983842 0.844032317 0.42201616
[2,] 0.671036665 0.657926670 0.32896333
[3,] 0.602071847 0.795856307 0.39792815
[4,] 0.694739788 0.610520423 0.30526021
[5,] 0.617788814 0.764422373 0.38221119
[6,] 0.609251367 0.781497266 0.39074863
[7,] 0.578551689 0.842896622 0.42144831
[8,] 0.528066534 0.943866932 0.47193347
[9,] 0.447274634 0.894549268 0.55272537
[10,] 0.823052540 0.353894920 0.17694746
[11,] 0.833237526 0.333524948 0.16676247
[12,] 0.782545791 0.434908419 0.21745421
[13,] 0.731513753 0.536972493 0.26848625
[14,] 0.669887892 0.660224216 0.33011211
[15,] 0.691044735 0.617910529 0.30895526
[16,] 0.631634475 0.736731050 0.36836553
[17,] 0.577080458 0.845839084 0.42291954
[18,] 0.516957471 0.966085058 0.48304253
[19,] 0.458781499 0.917562998 0.54121850
[20,] 0.441681014 0.883362027 0.55831899
[21,] 0.381001486 0.762002972 0.61899851
[22,] 0.359405451 0.718810902 0.64059455
[23,] 0.307357468 0.614714936 0.69264253
[24,] 0.289588383 0.579176767 0.71041162
[25,] 0.265886526 0.531773052 0.73411347
[26,] 0.219928763 0.439857526 0.78007124
[27,] 0.208459913 0.416919827 0.79154009
[28,] 0.297168405 0.594336810 0.70283159
[29,] 0.384967440 0.769934880 0.61503256
[30,] 0.383692384 0.767384769 0.61630762
[31,] 0.412703948 0.825407896 0.58729605
[32,] 0.387846733 0.775693466 0.61215327
[33,] 0.351940623 0.703881246 0.64805938
[34,] 0.334293454 0.668586908 0.66570655
[35,] 0.344263653 0.688527306 0.65573635
[36,] 0.302722756 0.605445512 0.69727724
[37,] 0.272755521 0.545511041 0.72724448
[38,] 0.537948720 0.924102560 0.46205128
[39,] 0.601432320 0.797135360 0.39856768
[40,] 0.554564790 0.890870420 0.44543521
[41,] 0.512712352 0.974575296 0.48728765
[42,] 0.506158911 0.987682178 0.49384109
[43,] 0.464054441 0.928108882 0.53594556
[44,] 0.418162286 0.836324571 0.58183771
[45,] 0.451885419 0.903770837 0.54811458
[46,] 0.408555503 0.817111005 0.59144450
[47,] 0.418673651 0.837347302 0.58132635
[48,] 0.404910824 0.809821647 0.59508918
[49,] 0.363866659 0.727733319 0.63613334
[50,] 0.335785438 0.671570876 0.66421456
[51,] 0.296435551 0.592871103 0.70356445
[52,] 0.302662252 0.605324503 0.69733775
[53,] 0.275566692 0.551133383 0.72443331
[54,] 0.241762962 0.483525924 0.75823704
[55,] 0.209159675 0.418319350 0.79084033
[56,] 0.181061791 0.362123581 0.81893821
[57,] 0.157509932 0.315019864 0.84249007
[58,] 0.145226183 0.290452366 0.85477382
[59,] 0.142874265 0.285748530 0.85712573
[60,] 0.241224169 0.482448339 0.75877583
[61,] 0.362904941 0.725809883 0.63709506
[62,] 0.328192288 0.656384577 0.67180771
[63,] 0.409027780 0.818055560 0.59097222
[64,] 0.372825301 0.745650602 0.62717470
[65,] 0.358087165 0.716174330 0.64191284
[66,] 0.331726918 0.663453836 0.66827308
[67,] 0.301307714 0.602615427 0.69869229
[68,] 0.383671885 0.767343771 0.61632811
[69,] 0.348202115 0.696404231 0.65179788
[70,] 0.330901500 0.661803000 0.66909850
[71,] 0.337381316 0.674762631 0.66261868
[72,] 0.309946522 0.619893044 0.69005348
[73,] 0.280017049 0.560034098 0.71998295
[74,] 0.249437844 0.498875689 0.75056216
[75,] 0.227158044 0.454316088 0.77284196
[76,] 0.199484995 0.398969990 0.80051500
[77,] 0.200511640 0.401023280 0.79948836
[78,] 0.174393705 0.348787410 0.82560630
[79,] 0.153796878 0.307593756 0.84620312
[80,] 0.139903618 0.279807236 0.86009638
[81,] 0.121685975 0.243371950 0.87831402
[82,] 0.115861708 0.231723417 0.88413829
[83,] 0.100395140 0.200790280 0.89960486
[84,] 0.086529067 0.173058135 0.91347093
[85,] 0.073928619 0.147857238 0.92607138
[86,] 0.075545066 0.151090133 0.92445493
[87,] 0.068285310 0.136570620 0.93171469
[88,] 0.057239763 0.114479526 0.94276024
[89,] 0.063795100 0.127590200 0.93620490
[90,] 0.053216367 0.106432735 0.94678363
[91,] 0.043836821 0.087673642 0.95616318
[92,] 0.039045384 0.078090768 0.96095462
[93,] 0.035126463 0.070252925 0.96487354
[94,] 0.043536365 0.087072729 0.95646364
[95,] 0.037293032 0.074586065 0.96270697
[96,] 0.033961852 0.067923704 0.96603815
[97,] 0.040322179 0.080644358 0.95967782
[98,] 0.036011488 0.072022976 0.96398851
[99,] 0.029562417 0.059124835 0.97043758
[100,] 0.027989359 0.055978718 0.97201064
[101,] 0.023041404 0.046082809 0.97695860
[102,] 0.019491275 0.038982549 0.98050873
[103,] 0.015621484 0.031242968 0.98437852
[104,] 0.018028789 0.036057578 0.98197121
[105,] 0.016986358 0.033972717 0.98301364
[106,] 0.022356643 0.044713286 0.97764336
[107,] 0.022201725 0.044403450 0.97779827
[108,] 0.022678153 0.045356307 0.97732185
[109,] 0.019205549 0.038411099 0.98079445
[110,] 0.019478117 0.038956235 0.98052188
[111,] 0.015995139 0.031990278 0.98400486
[112,] 0.014808933 0.029617865 0.98519107
[113,] 0.012230824 0.024461647 0.98776918
[114,] 0.016617223 0.033234446 0.98338278
[115,] 0.014082053 0.028164106 0.98591795
[116,] 0.012383966 0.024767933 0.98761603
[117,] 0.010219181 0.020438362 0.98978082
[118,] 0.008281767 0.016563534 0.99171823
[119,] 0.007458880 0.014917760 0.99254112
[120,] 0.006248793 0.012497585 0.99375121
[121,] 0.008799521 0.017599043 0.99120048
[122,] 0.009368298 0.018736597 0.99063170
[123,] 0.018123119 0.036246238 0.98187688
[124,] 0.021812494 0.043624988 0.97818751
[125,] 0.023434806 0.046869612 0.97656519
[126,] 0.022393272 0.044786543 0.97760673
[127,] 0.018118029 0.036236059 0.98188197
[128,] 0.015472626 0.030945253 0.98452737
[129,] 0.012446364 0.024892727 0.98755364
[130,] 0.015253844 0.030507688 0.98474616
[131,] 0.013409212 0.026818423 0.98659079
[132,] 0.015268727 0.030537454 0.98473127
[133,] 0.022011238 0.044022476 0.97798876
[134,] 0.020607874 0.041215749 0.97939213
[135,] 0.016724264 0.033448527 0.98327574
[136,] 0.017955820 0.035911640 0.98204418
[137,] 0.029822987 0.059645975 0.97017701
[138,] 0.036611003 0.073222007 0.96338900
[139,] 0.038433316 0.076866631 0.96156668
[140,] 0.034771336 0.069542672 0.96522866
[141,] 0.028851793 0.057703586 0.97114821
[142,] 0.038666544 0.077333088 0.96133346
[143,] 0.033545885 0.067091769 0.96645412
[144,] 0.034686168 0.069372336 0.96531383
[145,] 0.070136618 0.140273237 0.92986338
[146,] 0.069196513 0.138393026 0.93080349
[147,] 0.077252439 0.154504879 0.92274756
[148,] 0.066536109 0.133072219 0.93346389
[149,] 0.059549259 0.119098519 0.94045074
[150,] 0.052084238 0.104168477 0.94791576
[151,] 0.051025358 0.102050716 0.94897464
[152,] 0.043902357 0.087804714 0.95609764
[153,] 0.036172039 0.072344079 0.96382796
[154,] 0.029810876 0.059621753 0.97018912
[155,] 0.025100094 0.050200188 0.97489991
[156,] 0.021320143 0.042640286 0.97867986
[157,] 0.018726747 0.037453494 0.98127325
[158,] 0.019255721 0.038511442 0.98074428
[159,] 0.015521636 0.031043273 0.98447836
[160,] 0.022727065 0.045454130 0.97727294
[161,] 0.022560240 0.045120480 0.97743976
[162,] 0.020808277 0.041616554 0.97919172
[163,] 0.020104439 0.040208878 0.97989556
[164,] 0.016351716 0.032703432 0.98364828
[165,] 0.016270728 0.032541455 0.98372927
[166,] 0.018178425 0.036356850 0.98182158
[167,] 0.023903343 0.047806687 0.97609666
[168,] 0.019474574 0.038949148 0.98052543
[169,] 0.015828907 0.031657815 0.98417109
[170,] 0.012930183 0.025860365 0.98706982
[171,] 0.010170747 0.020341494 0.98982925
[172,] 0.008857207 0.017714413 0.99114279
[173,] 0.007229256 0.014458512 0.99277074
[174,] 0.005875176 0.011750352 0.99412482
[175,] 0.005918880 0.011837761 0.99408112
[176,] 0.004771843 0.009543686 0.99522816
[177,] 0.088100956 0.176201912 0.91189904
[178,] 0.076469332 0.152938664 0.92353067
[179,] 0.091999357 0.183998713 0.90800064
[180,] 0.082800550 0.165601101 0.91719945
[181,] 0.070777458 0.141554916 0.92922254
[182,] 0.058717994 0.117435988 0.94128201
[183,] 0.050754430 0.101508861 0.94924557
[184,] 0.043498131 0.086996262 0.95650187
[185,] 0.049601987 0.099203975 0.95039801
[186,] 0.048192717 0.096385434 0.95180728
[187,] 0.040685855 0.081371709 0.95931415
[188,] 0.033250327 0.066500655 0.96674967
[189,] 0.049232105 0.098464210 0.95076789
[190,] 0.040905350 0.081810700 0.95909465
[191,] 0.038419428 0.076838857 0.96158057
[192,] 0.032440125 0.064880249 0.96755988
[193,] 0.030313406 0.060626811 0.96968659
[194,] 0.024973154 0.049946308 0.97502685
[195,] 0.027280739 0.054561478 0.97271926
[196,] 0.034857472 0.069714945 0.96514253
[197,] 0.039013592 0.078027184 0.96098641
[198,] 0.031140945 0.062281890 0.96885905
[199,] 0.028558676 0.057117352 0.97144132
[200,] 0.023214562 0.046429124 0.97678544
[201,] 0.026418265 0.052836530 0.97358173
[202,] 0.022620920 0.045241840 0.97737908
[203,] 0.025913074 0.051826148 0.97408693
[204,] 0.044179626 0.088359253 0.95582037
[205,] 0.035099440 0.070198880 0.96490056
[206,] 0.047031172 0.094062343 0.95296883
[207,] 0.041305693 0.082611386 0.95869431
[208,] 0.032503866 0.065007731 0.96749613
[209,] 0.034196494 0.068392989 0.96580351
[210,] 0.028490559 0.056981117 0.97150944
[211,] 0.026531214 0.053062429 0.97346879
[212,] 0.020178582 0.040357164 0.97982142
[213,] 0.018018588 0.036037176 0.98198141
[214,] 0.013849797 0.027699594 0.98615020
[215,] 0.010393045 0.020786091 0.98960695
[216,] 0.008484020 0.016968039 0.99151598
[217,] 0.006407699 0.012815398 0.99359230
[218,] 0.013938818 0.027877637 0.98606118
[219,] 0.010577665 0.021155330 0.98942233
[220,] 0.008291408 0.016582815 0.99170859
[221,] 0.006137457 0.012274914 0.99386254
[222,] 0.004435910 0.008871820 0.99556409
[223,] 0.005015569 0.010031137 0.99498443
[224,] 0.011757955 0.023515910 0.98824205
[225,] 0.054154999 0.108309998 0.94584500
[226,] 0.079292179 0.158584358 0.92070782
[227,] 0.061226329 0.122452658 0.93877367
[228,] 0.054124632 0.108249263 0.94587537
[229,] 0.297408897 0.594817793 0.70259110
[230,] 0.253273264 0.506546528 0.74672674
[231,] 0.226278913 0.452557826 0.77372109
[232,] 0.185245694 0.370491388 0.81475431
[233,] 0.144343645 0.288687291 0.85565635
[234,] 0.142805229 0.285610458 0.85719477
[235,] 0.126023703 0.252047407 0.87397630
[236,] 0.173908225 0.347816450 0.82609178
[237,] 0.225821101 0.451642201 0.77417890
[238,] 0.189036633 0.378073265 0.81096337
[239,] 0.147705563 0.295411126 0.85229444
[240,] 0.133100952 0.266201904 0.86689905
[241,] 0.106964182 0.213928364 0.89303582
[242,] 0.144333511 0.288667021 0.85566649
[243,] 0.097261888 0.194523776 0.90273811
[244,] 0.227323912 0.454647824 0.77267609
[245,] 0.385157620 0.770315241 0.61484238
[246,] 0.933490746 0.133018508 0.06650925
[247,] 0.905250853 0.189498295 0.09474915
> postscript(file="/var/wessaorg/rcomp/tmp/1fz3m1383506161.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25j171383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3356z1383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4n77c1383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ujv21383506161.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
-2.78780877 0.72302422 2.41077593 3.73985091 -1.83825614 -1.31250953
7 8 9 10 11 12
4.03489113 -1.63914489 -1.61603774 1.16740571 1.47696889 -0.06113055
13 14 15 16 17 18
1.00879459 0.80050535 -0.68737460 -0.07197698 1.00177996 4.00781013
19 20 21 22 23 24
2.82686049 0.72656547 0.94079794 1.35909517 2.69640160 1.25158195
25 26 27 28 29 30
1.21442837 1.22665193 1.54469959 -1.46105058 0.78148549 0.30931093
31 32 33 34 35 36
-0.35863300 -0.21927219 -0.47658319 0.43595873 -1.08179576 -2.48946501
37 38 39 40 41 42
-2.26103724 -1.44477032 1.89500018 1.93231572 1.63796232 -1.36505205
43 44 45 46 47 48
2.40454145 0.20622942 -0.15286278 -4.13175915 -2.52978585 0.21762520
49 50 51 52 53 54
0.88321552 -1.33876526 -0.87423968 0.27192703 -2.59788405 0.21573248
55 56 57 58 59 60
-1.69918987 2.18517921 0.31707463 0.70781937 0.20429095 2.20750774
61 62 63 64 65 66
1.13650846 0.62033538 -0.25324233 -0.50119898 0.77593693 1.28127341
67 68 69 70 71 72
1.87114193 3.99593767 -3.83590191 0.51987887 -3.08591175 -0.78445527
73 74 75 76 77 78
1.22114446 0.82062932 1.05791489 3.97293638 -0.42533768 1.75862571
79 80 81 82 83 84
-1.94537554 0.83165909 0.52458435 0.30259877 -0.59130785 0.30752914
85 86 87 88 89 90
2.13984540 0.07878010 0.75248537 1.25309011 0.92985057 -1.66973061
91 92 93 94 95 96
0.10189827 0.30771208 0.18236751 -1.77119455 1.36317813 0.28983384
97 98 99 100 101 102
2.56872298 0.22296882 -0.25505283 -1.16866144 1.50720477 2.46037290
103 104 105 106 107 108
0.91749773 1.22401683 -1.68365448 1.23443282 0.24374800 1.71037589
109 110 111 112 113 114
-0.08415736 0.87865156 0.10082488 2.16900849 -1.41983549 -2.61629384
115 116 117 118 119 120
1.75527234 -2.02235422 0.93116916 -1.81137493 0.56625844 -1.43481496
121 122 123 124 125 126
0.61885164 -2.83418834 -1.09330172 -1.01255641 -0.88186390 0.30143123
127 128 129 130 131 132
1.25217435 0.95028228 -2.74618091 2.11511550 -3.47413794 2.54855704
133 134 135 136 137 138
-2.16115242 -1.70210670 -0.10344543 1.09519876 0.35945726 -2.45888471
139 140 141 142 143 144
-1.04921425 -2.27065048 3.04667349 1.47808526 0.02359504 1.81793169
145 146 147 148 149 150
-3.25501039 2.28531245 -2.12748892 1.08218957 0.37464152 -2.84739461
151 152 153 154 155 156
-1.08339475 2.03283577 4.30099307 1.75962783 -2.32541396 0.10189827
157 158 159 160 161 162
1.41142503 0.95028228 1.36879985 -0.47412028 0.33033761 0.50779607
163 164 165 166 167 168
-0.29554335 0.49252147 1.37361449 -1.44774564 -0.45730771 -3.25568868
169 170 171 172 173 174
-1.78840118 1.66582401 1.60194202 0.28642930 -1.94345670 -2.32132667
175 176 177 178 179 180
-3.29051544 0.33516261 -0.28211461 -0.65248835 -0.09780605 -1.39305004
181 182 183 184 185 186
0.48533020 -0.54986536 1.79142379 -0.48231089 -6.72715659 1.11885966
187 188 189 190 191 192
2.30873911 -0.45633137 -0.53177989 0.66674918 -0.94169724 0.18026756
193 194 195 196 197 198
2.34955861 1.61583483 -0.91251797 -0.40183786 2.96281907 0.75632035
199 200 201 202 203 204
1.52544928 1.30552183 1.77826808 0.45650874 -2.68509794 -3.02260551
205 206 207 208 209 210
2.04368159 0.17373393 1.14174191 0.50542297 -2.94030812 0.76540684
211 212 213 214 215 216
-2.53405669 -4.28451018 0.75773525 2.85474085 1.13496078 0.52506155
217 218 219 220 221 222
1.97526828 -0.30883123 1.04080366 -0.61092443 -1.99796394 -0.54691792
223 224 225 226 227 228
0.45447931 -1.47466469 0.33929071 -3.92040058 -0.39679657 0.77872099
229 230 231 232 233 234
-1.34717456 -1.16635899 1.52686100 -3.52458233 4.22058840 1.52307848
235 236 237 238 239 240
-1.32278353 -2.65577350 -7.19502343 -1.94806891 1.42171256 -1.92089938
241 242 243 244 245 246
-0.33646268 -1.86917288 1.08996362 1.72788331 0.89742012 -0.02124178
247 248 249 250 251 252
0.81326966 -2.99843039 1.13381475 0.10040783 -1.12436904 -1.80236221
253 254 255 256 257 258
0.32188798 2.80644278 -1.75629836 -0.31255211 1.42460870 2.12566265
259 260 261 262 263 264
-1.79434240 -5.46699615 0.80856913 -4.56276294 -0.47839064 0.84868883
> postscript(file="/var/wessaorg/rcomp/tmp/60ve51383506161.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 -2.78780877 NA
1 0.72302422 -2.78780877
2 2.41077593 0.72302422
3 3.73985091 2.41077593
4 -1.83825614 3.73985091
5 -1.31250953 -1.83825614
6 4.03489113 -1.31250953
7 -1.63914489 4.03489113
8 -1.61603774 -1.63914489
9 1.16740571 -1.61603774
10 1.47696889 1.16740571
11 -0.06113055 1.47696889
12 1.00879459 -0.06113055
13 0.80050535 1.00879459
14 -0.68737460 0.80050535
15 -0.07197698 -0.68737460
16 1.00177996 -0.07197698
17 4.00781013 1.00177996
18 2.82686049 4.00781013
19 0.72656547 2.82686049
20 0.94079794 0.72656547
21 1.35909517 0.94079794
22 2.69640160 1.35909517
23 1.25158195 2.69640160
24 1.21442837 1.25158195
25 1.22665193 1.21442837
26 1.54469959 1.22665193
27 -1.46105058 1.54469959
28 0.78148549 -1.46105058
29 0.30931093 0.78148549
30 -0.35863300 0.30931093
31 -0.21927219 -0.35863300
32 -0.47658319 -0.21927219
33 0.43595873 -0.47658319
34 -1.08179576 0.43595873
35 -2.48946501 -1.08179576
36 -2.26103724 -2.48946501
37 -1.44477032 -2.26103724
38 1.89500018 -1.44477032
39 1.93231572 1.89500018
40 1.63796232 1.93231572
41 -1.36505205 1.63796232
42 2.40454145 -1.36505205
43 0.20622942 2.40454145
44 -0.15286278 0.20622942
45 -4.13175915 -0.15286278
46 -2.52978585 -4.13175915
47 0.21762520 -2.52978585
48 0.88321552 0.21762520
49 -1.33876526 0.88321552
50 -0.87423968 -1.33876526
51 0.27192703 -0.87423968
52 -2.59788405 0.27192703
53 0.21573248 -2.59788405
54 -1.69918987 0.21573248
55 2.18517921 -1.69918987
56 0.31707463 2.18517921
57 0.70781937 0.31707463
58 0.20429095 0.70781937
59 2.20750774 0.20429095
60 1.13650846 2.20750774
61 0.62033538 1.13650846
62 -0.25324233 0.62033538
63 -0.50119898 -0.25324233
64 0.77593693 -0.50119898
65 1.28127341 0.77593693
66 1.87114193 1.28127341
67 3.99593767 1.87114193
68 -3.83590191 3.99593767
69 0.51987887 -3.83590191
70 -3.08591175 0.51987887
71 -0.78445527 -3.08591175
72 1.22114446 -0.78445527
73 0.82062932 1.22114446
74 1.05791489 0.82062932
75 3.97293638 1.05791489
76 -0.42533768 3.97293638
77 1.75862571 -0.42533768
78 -1.94537554 1.75862571
79 0.83165909 -1.94537554
80 0.52458435 0.83165909
81 0.30259877 0.52458435
82 -0.59130785 0.30259877
83 0.30752914 -0.59130785
84 2.13984540 0.30752914
85 0.07878010 2.13984540
86 0.75248537 0.07878010
87 1.25309011 0.75248537
88 0.92985057 1.25309011
89 -1.66973061 0.92985057
90 0.10189827 -1.66973061
91 0.30771208 0.10189827
92 0.18236751 0.30771208
93 -1.77119455 0.18236751
94 1.36317813 -1.77119455
95 0.28983384 1.36317813
96 2.56872298 0.28983384
97 0.22296882 2.56872298
98 -0.25505283 0.22296882
99 -1.16866144 -0.25505283
100 1.50720477 -1.16866144
101 2.46037290 1.50720477
102 0.91749773 2.46037290
103 1.22401683 0.91749773
104 -1.68365448 1.22401683
105 1.23443282 -1.68365448
106 0.24374800 1.23443282
107 1.71037589 0.24374800
108 -0.08415736 1.71037589
109 0.87865156 -0.08415736
110 0.10082488 0.87865156
111 2.16900849 0.10082488
112 -1.41983549 2.16900849
113 -2.61629384 -1.41983549
114 1.75527234 -2.61629384
115 -2.02235422 1.75527234
116 0.93116916 -2.02235422
117 -1.81137493 0.93116916
118 0.56625844 -1.81137493
119 -1.43481496 0.56625844
120 0.61885164 -1.43481496
121 -2.83418834 0.61885164
122 -1.09330172 -2.83418834
123 -1.01255641 -1.09330172
124 -0.88186390 -1.01255641
125 0.30143123 -0.88186390
126 1.25217435 0.30143123
127 0.95028228 1.25217435
128 -2.74618091 0.95028228
129 2.11511550 -2.74618091
130 -3.47413794 2.11511550
131 2.54855704 -3.47413794
132 -2.16115242 2.54855704
133 -1.70210670 -2.16115242
134 -0.10344543 -1.70210670
135 1.09519876 -0.10344543
136 0.35945726 1.09519876
137 -2.45888471 0.35945726
138 -1.04921425 -2.45888471
139 -2.27065048 -1.04921425
140 3.04667349 -2.27065048
141 1.47808526 3.04667349
142 0.02359504 1.47808526
143 1.81793169 0.02359504
144 -3.25501039 1.81793169
145 2.28531245 -3.25501039
146 -2.12748892 2.28531245
147 1.08218957 -2.12748892
148 0.37464152 1.08218957
149 -2.84739461 0.37464152
150 -1.08339475 -2.84739461
151 2.03283577 -1.08339475
152 4.30099307 2.03283577
153 1.75962783 4.30099307
154 -2.32541396 1.75962783
155 0.10189827 -2.32541396
156 1.41142503 0.10189827
157 0.95028228 1.41142503
158 1.36879985 0.95028228
159 -0.47412028 1.36879985
160 0.33033761 -0.47412028
161 0.50779607 0.33033761
162 -0.29554335 0.50779607
163 0.49252147 -0.29554335
164 1.37361449 0.49252147
165 -1.44774564 1.37361449
166 -0.45730771 -1.44774564
167 -3.25568868 -0.45730771
168 -1.78840118 -3.25568868
169 1.66582401 -1.78840118
170 1.60194202 1.66582401
171 0.28642930 1.60194202
172 -1.94345670 0.28642930
173 -2.32132667 -1.94345670
174 -3.29051544 -2.32132667
175 0.33516261 -3.29051544
176 -0.28211461 0.33516261
177 -0.65248835 -0.28211461
178 -0.09780605 -0.65248835
179 -1.39305004 -0.09780605
180 0.48533020 -1.39305004
181 -0.54986536 0.48533020
182 1.79142379 -0.54986536
183 -0.48231089 1.79142379
184 -6.72715659 -0.48231089
185 1.11885966 -6.72715659
186 2.30873911 1.11885966
187 -0.45633137 2.30873911
188 -0.53177989 -0.45633137
189 0.66674918 -0.53177989
190 -0.94169724 0.66674918
191 0.18026756 -0.94169724
192 2.34955861 0.18026756
193 1.61583483 2.34955861
194 -0.91251797 1.61583483
195 -0.40183786 -0.91251797
196 2.96281907 -0.40183786
197 0.75632035 2.96281907
198 1.52544928 0.75632035
199 1.30552183 1.52544928
200 1.77826808 1.30552183
201 0.45650874 1.77826808
202 -2.68509794 0.45650874
203 -3.02260551 -2.68509794
204 2.04368159 -3.02260551
205 0.17373393 2.04368159
206 1.14174191 0.17373393
207 0.50542297 1.14174191
208 -2.94030812 0.50542297
209 0.76540684 -2.94030812
210 -2.53405669 0.76540684
211 -4.28451018 -2.53405669
212 0.75773525 -4.28451018
213 2.85474085 0.75773525
214 1.13496078 2.85474085
215 0.52506155 1.13496078
216 1.97526828 0.52506155
217 -0.30883123 1.97526828
218 1.04080366 -0.30883123
219 -0.61092443 1.04080366
220 -1.99796394 -0.61092443
221 -0.54691792 -1.99796394
222 0.45447931 -0.54691792
223 -1.47466469 0.45447931
224 0.33929071 -1.47466469
225 -3.92040058 0.33929071
226 -0.39679657 -3.92040058
227 0.77872099 -0.39679657
228 -1.34717456 0.77872099
229 -1.16635899 -1.34717456
230 1.52686100 -1.16635899
231 -3.52458233 1.52686100
232 4.22058840 -3.52458233
233 1.52307848 4.22058840
234 -1.32278353 1.52307848
235 -2.65577350 -1.32278353
236 -7.19502343 -2.65577350
237 -1.94806891 -7.19502343
238 1.42171256 -1.94806891
239 -1.92089938 1.42171256
240 -0.33646268 -1.92089938
241 -1.86917288 -0.33646268
242 1.08996362 -1.86917288
243 1.72788331 1.08996362
244 0.89742012 1.72788331
245 -0.02124178 0.89742012
246 0.81326966 -0.02124178
247 -2.99843039 0.81326966
248 1.13381475 -2.99843039
249 0.10040783 1.13381475
250 -1.12436904 0.10040783
251 -1.80236221 -1.12436904
252 0.32188798 -1.80236221
253 2.80644278 0.32188798
254 -1.75629836 2.80644278
255 -0.31255211 -1.75629836
256 1.42460870 -0.31255211
257 2.12566265 1.42460870
258 -1.79434240 2.12566265
259 -5.46699615 -1.79434240
260 0.80856913 -5.46699615
261 -4.56276294 0.80856913
262 -0.47839064 -4.56276294
263 0.84868883 -0.47839064
264 NA 0.84868883
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.72302422 -2.78780877
[2,] 2.41077593 0.72302422
[3,] 3.73985091 2.41077593
[4,] -1.83825614 3.73985091
[5,] -1.31250953 -1.83825614
[6,] 4.03489113 -1.31250953
[7,] -1.63914489 4.03489113
[8,] -1.61603774 -1.63914489
[9,] 1.16740571 -1.61603774
[10,] 1.47696889 1.16740571
[11,] -0.06113055 1.47696889
[12,] 1.00879459 -0.06113055
[13,] 0.80050535 1.00879459
[14,] -0.68737460 0.80050535
[15,] -0.07197698 -0.68737460
[16,] 1.00177996 -0.07197698
[17,] 4.00781013 1.00177996
[18,] 2.82686049 4.00781013
[19,] 0.72656547 2.82686049
[20,] 0.94079794 0.72656547
[21,] 1.35909517 0.94079794
[22,] 2.69640160 1.35909517
[23,] 1.25158195 2.69640160
[24,] 1.21442837 1.25158195
[25,] 1.22665193 1.21442837
[26,] 1.54469959 1.22665193
[27,] -1.46105058 1.54469959
[28,] 0.78148549 -1.46105058
[29,] 0.30931093 0.78148549
[30,] -0.35863300 0.30931093
[31,] -0.21927219 -0.35863300
[32,] -0.47658319 -0.21927219
[33,] 0.43595873 -0.47658319
[34,] -1.08179576 0.43595873
[35,] -2.48946501 -1.08179576
[36,] -2.26103724 -2.48946501
[37,] -1.44477032 -2.26103724
[38,] 1.89500018 -1.44477032
[39,] 1.93231572 1.89500018
[40,] 1.63796232 1.93231572
[41,] -1.36505205 1.63796232
[42,] 2.40454145 -1.36505205
[43,] 0.20622942 2.40454145
[44,] -0.15286278 0.20622942
[45,] -4.13175915 -0.15286278
[46,] -2.52978585 -4.13175915
[47,] 0.21762520 -2.52978585
[48,] 0.88321552 0.21762520
[49,] -1.33876526 0.88321552
[50,] -0.87423968 -1.33876526
[51,] 0.27192703 -0.87423968
[52,] -2.59788405 0.27192703
[53,] 0.21573248 -2.59788405
[54,] -1.69918987 0.21573248
[55,] 2.18517921 -1.69918987
[56,] 0.31707463 2.18517921
[57,] 0.70781937 0.31707463
[58,] 0.20429095 0.70781937
[59,] 2.20750774 0.20429095
[60,] 1.13650846 2.20750774
[61,] 0.62033538 1.13650846
[62,] -0.25324233 0.62033538
[63,] -0.50119898 -0.25324233
[64,] 0.77593693 -0.50119898
[65,] 1.28127341 0.77593693
[66,] 1.87114193 1.28127341
[67,] 3.99593767 1.87114193
[68,] -3.83590191 3.99593767
[69,] 0.51987887 -3.83590191
[70,] -3.08591175 0.51987887
[71,] -0.78445527 -3.08591175
[72,] 1.22114446 -0.78445527
[73,] 0.82062932 1.22114446
[74,] 1.05791489 0.82062932
[75,] 3.97293638 1.05791489
[76,] -0.42533768 3.97293638
[77,] 1.75862571 -0.42533768
[78,] -1.94537554 1.75862571
[79,] 0.83165909 -1.94537554
[80,] 0.52458435 0.83165909
[81,] 0.30259877 0.52458435
[82,] -0.59130785 0.30259877
[83,] 0.30752914 -0.59130785
[84,] 2.13984540 0.30752914
[85,] 0.07878010 2.13984540
[86,] 0.75248537 0.07878010
[87,] 1.25309011 0.75248537
[88,] 0.92985057 1.25309011
[89,] -1.66973061 0.92985057
[90,] 0.10189827 -1.66973061
[91,] 0.30771208 0.10189827
[92,] 0.18236751 0.30771208
[93,] -1.77119455 0.18236751
[94,] 1.36317813 -1.77119455
[95,] 0.28983384 1.36317813
[96,] 2.56872298 0.28983384
[97,] 0.22296882 2.56872298
[98,] -0.25505283 0.22296882
[99,] -1.16866144 -0.25505283
[100,] 1.50720477 -1.16866144
[101,] 2.46037290 1.50720477
[102,] 0.91749773 2.46037290
[103,] 1.22401683 0.91749773
[104,] -1.68365448 1.22401683
[105,] 1.23443282 -1.68365448
[106,] 0.24374800 1.23443282
[107,] 1.71037589 0.24374800
[108,] -0.08415736 1.71037589
[109,] 0.87865156 -0.08415736
[110,] 0.10082488 0.87865156
[111,] 2.16900849 0.10082488
[112,] -1.41983549 2.16900849
[113,] -2.61629384 -1.41983549
[114,] 1.75527234 -2.61629384
[115,] -2.02235422 1.75527234
[116,] 0.93116916 -2.02235422
[117,] -1.81137493 0.93116916
[118,] 0.56625844 -1.81137493
[119,] -1.43481496 0.56625844
[120,] 0.61885164 -1.43481496
[121,] -2.83418834 0.61885164
[122,] -1.09330172 -2.83418834
[123,] -1.01255641 -1.09330172
[124,] -0.88186390 -1.01255641
[125,] 0.30143123 -0.88186390
[126,] 1.25217435 0.30143123
[127,] 0.95028228 1.25217435
[128,] -2.74618091 0.95028228
[129,] 2.11511550 -2.74618091
[130,] -3.47413794 2.11511550
[131,] 2.54855704 -3.47413794
[132,] -2.16115242 2.54855704
[133,] -1.70210670 -2.16115242
[134,] -0.10344543 -1.70210670
[135,] 1.09519876 -0.10344543
[136,] 0.35945726 1.09519876
[137,] -2.45888471 0.35945726
[138,] -1.04921425 -2.45888471
[139,] -2.27065048 -1.04921425
[140,] 3.04667349 -2.27065048
[141,] 1.47808526 3.04667349
[142,] 0.02359504 1.47808526
[143,] 1.81793169 0.02359504
[144,] -3.25501039 1.81793169
[145,] 2.28531245 -3.25501039
[146,] -2.12748892 2.28531245
[147,] 1.08218957 -2.12748892
[148,] 0.37464152 1.08218957
[149,] -2.84739461 0.37464152
[150,] -1.08339475 -2.84739461
[151,] 2.03283577 -1.08339475
[152,] 4.30099307 2.03283577
[153,] 1.75962783 4.30099307
[154,] -2.32541396 1.75962783
[155,] 0.10189827 -2.32541396
[156,] 1.41142503 0.10189827
[157,] 0.95028228 1.41142503
[158,] 1.36879985 0.95028228
[159,] -0.47412028 1.36879985
[160,] 0.33033761 -0.47412028
[161,] 0.50779607 0.33033761
[162,] -0.29554335 0.50779607
[163,] 0.49252147 -0.29554335
[164,] 1.37361449 0.49252147
[165,] -1.44774564 1.37361449
[166,] -0.45730771 -1.44774564
[167,] -3.25568868 -0.45730771
[168,] -1.78840118 -3.25568868
[169,] 1.66582401 -1.78840118
[170,] 1.60194202 1.66582401
[171,] 0.28642930 1.60194202
[172,] -1.94345670 0.28642930
[173,] -2.32132667 -1.94345670
[174,] -3.29051544 -2.32132667
[175,] 0.33516261 -3.29051544
[176,] -0.28211461 0.33516261
[177,] -0.65248835 -0.28211461
[178,] -0.09780605 -0.65248835
[179,] -1.39305004 -0.09780605
[180,] 0.48533020 -1.39305004
[181,] -0.54986536 0.48533020
[182,] 1.79142379 -0.54986536
[183,] -0.48231089 1.79142379
[184,] -6.72715659 -0.48231089
[185,] 1.11885966 -6.72715659
[186,] 2.30873911 1.11885966
[187,] -0.45633137 2.30873911
[188,] -0.53177989 -0.45633137
[189,] 0.66674918 -0.53177989
[190,] -0.94169724 0.66674918
[191,] 0.18026756 -0.94169724
[192,] 2.34955861 0.18026756
[193,] 1.61583483 2.34955861
[194,] -0.91251797 1.61583483
[195,] -0.40183786 -0.91251797
[196,] 2.96281907 -0.40183786
[197,] 0.75632035 2.96281907
[198,] 1.52544928 0.75632035
[199,] 1.30552183 1.52544928
[200,] 1.77826808 1.30552183
[201,] 0.45650874 1.77826808
[202,] -2.68509794 0.45650874
[203,] -3.02260551 -2.68509794
[204,] 2.04368159 -3.02260551
[205,] 0.17373393 2.04368159
[206,] 1.14174191 0.17373393
[207,] 0.50542297 1.14174191
[208,] -2.94030812 0.50542297
[209,] 0.76540684 -2.94030812
[210,] -2.53405669 0.76540684
[211,] -4.28451018 -2.53405669
[212,] 0.75773525 -4.28451018
[213,] 2.85474085 0.75773525
[214,] 1.13496078 2.85474085
[215,] 0.52506155 1.13496078
[216,] 1.97526828 0.52506155
[217,] -0.30883123 1.97526828
[218,] 1.04080366 -0.30883123
[219,] -0.61092443 1.04080366
[220,] -1.99796394 -0.61092443
[221,] -0.54691792 -1.99796394
[222,] 0.45447931 -0.54691792
[223,] -1.47466469 0.45447931
[224,] 0.33929071 -1.47466469
[225,] -3.92040058 0.33929071
[226,] -0.39679657 -3.92040058
[227,] 0.77872099 -0.39679657
[228,] -1.34717456 0.77872099
[229,] -1.16635899 -1.34717456
[230,] 1.52686100 -1.16635899
[231,] -3.52458233 1.52686100
[232,] 4.22058840 -3.52458233
[233,] 1.52307848 4.22058840
[234,] -1.32278353 1.52307848
[235,] -2.65577350 -1.32278353
[236,] -7.19502343 -2.65577350
[237,] -1.94806891 -7.19502343
[238,] 1.42171256 -1.94806891
[239,] -1.92089938 1.42171256
[240,] -0.33646268 -1.92089938
[241,] -1.86917288 -0.33646268
[242,] 1.08996362 -1.86917288
[243,] 1.72788331 1.08996362
[244,] 0.89742012 1.72788331
[245,] -0.02124178 0.89742012
[246,] 0.81326966 -0.02124178
[247,] -2.99843039 0.81326966
[248,] 1.13381475 -2.99843039
[249,] 0.10040783 1.13381475
[250,] -1.12436904 0.10040783
[251,] -1.80236221 -1.12436904
[252,] 0.32188798 -1.80236221
[253,] 2.80644278 0.32188798
[254,] -1.75629836 2.80644278
[255,] -0.31255211 -1.75629836
[256,] 1.42460870 -0.31255211
[257,] 2.12566265 1.42460870
[258,] -1.79434240 2.12566265
[259,] -5.46699615 -1.79434240
[260,] 0.80856913 -5.46699615
[261,] -4.56276294 0.80856913
[262,] -0.47839064 -4.56276294
[263,] 0.84868883 -0.47839064
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.72302422 -2.78780877
2 2.41077593 0.72302422
3 3.73985091 2.41077593
4 -1.83825614 3.73985091
5 -1.31250953 -1.83825614
6 4.03489113 -1.31250953
7 -1.63914489 4.03489113
8 -1.61603774 -1.63914489
9 1.16740571 -1.61603774
10 1.47696889 1.16740571
11 -0.06113055 1.47696889
12 1.00879459 -0.06113055
13 0.80050535 1.00879459
14 -0.68737460 0.80050535
15 -0.07197698 -0.68737460
16 1.00177996 -0.07197698
17 4.00781013 1.00177996
18 2.82686049 4.00781013
19 0.72656547 2.82686049
20 0.94079794 0.72656547
21 1.35909517 0.94079794
22 2.69640160 1.35909517
23 1.25158195 2.69640160
24 1.21442837 1.25158195
25 1.22665193 1.21442837
26 1.54469959 1.22665193
27 -1.46105058 1.54469959
28 0.78148549 -1.46105058
29 0.30931093 0.78148549
30 -0.35863300 0.30931093
31 -0.21927219 -0.35863300
32 -0.47658319 -0.21927219
33 0.43595873 -0.47658319
34 -1.08179576 0.43595873
35 -2.48946501 -1.08179576
36 -2.26103724 -2.48946501
37 -1.44477032 -2.26103724
38 1.89500018 -1.44477032
39 1.93231572 1.89500018
40 1.63796232 1.93231572
41 -1.36505205 1.63796232
42 2.40454145 -1.36505205
43 0.20622942 2.40454145
44 -0.15286278 0.20622942
45 -4.13175915 -0.15286278
46 -2.52978585 -4.13175915
47 0.21762520 -2.52978585
48 0.88321552 0.21762520
49 -1.33876526 0.88321552
50 -0.87423968 -1.33876526
51 0.27192703 -0.87423968
52 -2.59788405 0.27192703
53 0.21573248 -2.59788405
54 -1.69918987 0.21573248
55 2.18517921 -1.69918987
56 0.31707463 2.18517921
57 0.70781937 0.31707463
58 0.20429095 0.70781937
59 2.20750774 0.20429095
60 1.13650846 2.20750774
61 0.62033538 1.13650846
62 -0.25324233 0.62033538
63 -0.50119898 -0.25324233
64 0.77593693 -0.50119898
65 1.28127341 0.77593693
66 1.87114193 1.28127341
67 3.99593767 1.87114193
68 -3.83590191 3.99593767
69 0.51987887 -3.83590191
70 -3.08591175 0.51987887
71 -0.78445527 -3.08591175
72 1.22114446 -0.78445527
73 0.82062932 1.22114446
74 1.05791489 0.82062932
75 3.97293638 1.05791489
76 -0.42533768 3.97293638
77 1.75862571 -0.42533768
78 -1.94537554 1.75862571
79 0.83165909 -1.94537554
80 0.52458435 0.83165909
81 0.30259877 0.52458435
82 -0.59130785 0.30259877
83 0.30752914 -0.59130785
84 2.13984540 0.30752914
85 0.07878010 2.13984540
86 0.75248537 0.07878010
87 1.25309011 0.75248537
88 0.92985057 1.25309011
89 -1.66973061 0.92985057
90 0.10189827 -1.66973061
91 0.30771208 0.10189827
92 0.18236751 0.30771208
93 -1.77119455 0.18236751
94 1.36317813 -1.77119455
95 0.28983384 1.36317813
96 2.56872298 0.28983384
97 0.22296882 2.56872298
98 -0.25505283 0.22296882
99 -1.16866144 -0.25505283
100 1.50720477 -1.16866144
101 2.46037290 1.50720477
102 0.91749773 2.46037290
103 1.22401683 0.91749773
104 -1.68365448 1.22401683
105 1.23443282 -1.68365448
106 0.24374800 1.23443282
107 1.71037589 0.24374800
108 -0.08415736 1.71037589
109 0.87865156 -0.08415736
110 0.10082488 0.87865156
111 2.16900849 0.10082488
112 -1.41983549 2.16900849
113 -2.61629384 -1.41983549
114 1.75527234 -2.61629384
115 -2.02235422 1.75527234
116 0.93116916 -2.02235422
117 -1.81137493 0.93116916
118 0.56625844 -1.81137493
119 -1.43481496 0.56625844
120 0.61885164 -1.43481496
121 -2.83418834 0.61885164
122 -1.09330172 -2.83418834
123 -1.01255641 -1.09330172
124 -0.88186390 -1.01255641
125 0.30143123 -0.88186390
126 1.25217435 0.30143123
127 0.95028228 1.25217435
128 -2.74618091 0.95028228
129 2.11511550 -2.74618091
130 -3.47413794 2.11511550
131 2.54855704 -3.47413794
132 -2.16115242 2.54855704
133 -1.70210670 -2.16115242
134 -0.10344543 -1.70210670
135 1.09519876 -0.10344543
136 0.35945726 1.09519876
137 -2.45888471 0.35945726
138 -1.04921425 -2.45888471
139 -2.27065048 -1.04921425
140 3.04667349 -2.27065048
141 1.47808526 3.04667349
142 0.02359504 1.47808526
143 1.81793169 0.02359504
144 -3.25501039 1.81793169
145 2.28531245 -3.25501039
146 -2.12748892 2.28531245
147 1.08218957 -2.12748892
148 0.37464152 1.08218957
149 -2.84739461 0.37464152
150 -1.08339475 -2.84739461
151 2.03283577 -1.08339475
152 4.30099307 2.03283577
153 1.75962783 4.30099307
154 -2.32541396 1.75962783
155 0.10189827 -2.32541396
156 1.41142503 0.10189827
157 0.95028228 1.41142503
158 1.36879985 0.95028228
159 -0.47412028 1.36879985
160 0.33033761 -0.47412028
161 0.50779607 0.33033761
162 -0.29554335 0.50779607
163 0.49252147 -0.29554335
164 1.37361449 0.49252147
165 -1.44774564 1.37361449
166 -0.45730771 -1.44774564
167 -3.25568868 -0.45730771
168 -1.78840118 -3.25568868
169 1.66582401 -1.78840118
170 1.60194202 1.66582401
171 0.28642930 1.60194202
172 -1.94345670 0.28642930
173 -2.32132667 -1.94345670
174 -3.29051544 -2.32132667
175 0.33516261 -3.29051544
176 -0.28211461 0.33516261
177 -0.65248835 -0.28211461
178 -0.09780605 -0.65248835
179 -1.39305004 -0.09780605
180 0.48533020 -1.39305004
181 -0.54986536 0.48533020
182 1.79142379 -0.54986536
183 -0.48231089 1.79142379
184 -6.72715659 -0.48231089
185 1.11885966 -6.72715659
186 2.30873911 1.11885966
187 -0.45633137 2.30873911
188 -0.53177989 -0.45633137
189 0.66674918 -0.53177989
190 -0.94169724 0.66674918
191 0.18026756 -0.94169724
192 2.34955861 0.18026756
193 1.61583483 2.34955861
194 -0.91251797 1.61583483
195 -0.40183786 -0.91251797
196 2.96281907 -0.40183786
197 0.75632035 2.96281907
198 1.52544928 0.75632035
199 1.30552183 1.52544928
200 1.77826808 1.30552183
201 0.45650874 1.77826808
202 -2.68509794 0.45650874
203 -3.02260551 -2.68509794
204 2.04368159 -3.02260551
205 0.17373393 2.04368159
206 1.14174191 0.17373393
207 0.50542297 1.14174191
208 -2.94030812 0.50542297
209 0.76540684 -2.94030812
210 -2.53405669 0.76540684
211 -4.28451018 -2.53405669
212 0.75773525 -4.28451018
213 2.85474085 0.75773525
214 1.13496078 2.85474085
215 0.52506155 1.13496078
216 1.97526828 0.52506155
217 -0.30883123 1.97526828
218 1.04080366 -0.30883123
219 -0.61092443 1.04080366
220 -1.99796394 -0.61092443
221 -0.54691792 -1.99796394
222 0.45447931 -0.54691792
223 -1.47466469 0.45447931
224 0.33929071 -1.47466469
225 -3.92040058 0.33929071
226 -0.39679657 -3.92040058
227 0.77872099 -0.39679657
228 -1.34717456 0.77872099
229 -1.16635899 -1.34717456
230 1.52686100 -1.16635899
231 -3.52458233 1.52686100
232 4.22058840 -3.52458233
233 1.52307848 4.22058840
234 -1.32278353 1.52307848
235 -2.65577350 -1.32278353
236 -7.19502343 -2.65577350
237 -1.94806891 -7.19502343
238 1.42171256 -1.94806891
239 -1.92089938 1.42171256
240 -0.33646268 -1.92089938
241 -1.86917288 -0.33646268
242 1.08996362 -1.86917288
243 1.72788331 1.08996362
244 0.89742012 1.72788331
245 -0.02124178 0.89742012
246 0.81326966 -0.02124178
247 -2.99843039 0.81326966
248 1.13381475 -2.99843039
249 0.10040783 1.13381475
250 -1.12436904 0.10040783
251 -1.80236221 -1.12436904
252 0.32188798 -1.80236221
253 2.80644278 0.32188798
254 -1.75629836 2.80644278
255 -0.31255211 -1.75629836
256 1.42460870 -0.31255211
257 2.12566265 1.42460870
258 -1.79434240 2.12566265
259 -5.46699615 -1.79434240
260 0.80856913 -5.46699615
261 -4.56276294 0.80856913
262 -0.47839064 -4.56276294
263 0.84868883 -0.47839064
> 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/7dkf11383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/83fvd1383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/98u4r1383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10jpa81383506161.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qr3s1383506161.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/124pcg1383506161.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/13i8551383506161.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/14fgp91383506161.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/15bfn81383506161.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/16zxqv1383506161.tab")
+ }
>
> try(system("convert tmp/1fz3m1383506161.ps tmp/1fz3m1383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/25j171383506161.ps tmp/25j171383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/3356z1383506161.ps tmp/3356z1383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n77c1383506161.ps tmp/4n77c1383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ujv21383506161.ps tmp/5ujv21383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/60ve51383506161.ps tmp/60ve51383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dkf11383506161.ps tmp/7dkf11383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/83fvd1383506161.ps tmp/83fvd1383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/98u4r1383506161.ps tmp/98u4r1383506161.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jpa81383506161.ps tmp/10jpa81383506161.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.284 2.762 19.030