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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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Connected','Separate','Learning','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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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
Software Connected Separate Learning Happiness Depression
1 12 41 38 13 14 12.0
2 11 39 32 16 18 11.0
3 15 30 35 19 11 14.0
4 6 31 33 15 12 12.0
5 13 34 37 14 16 21.0
6 10 35 29 13 18 12.0
7 12 39 31 19 14 22.0
8 14 34 36 15 14 11.0
9 12 36 35 14 15 10.0
10 9 37 38 15 15 13.0
11 10 38 31 16 17 10.0
12 12 36 34 16 19 8.0
13 12 38 35 16 10 15.0
14 11 39 38 16 16 14.0
15 15 33 37 17 18 10.0
16 12 32 33 15 14 14.0
17 10 36 32 15 14 14.0
18 12 38 38 20 17 11.0
19 11 39 38 18 14 10.0
20 12 32 32 16 16 13.0
21 11 32 33 16 18 9.5
22 12 31 31 16 11 14.0
23 13 39 38 19 14 12.0
24 11 37 39 16 12 14.0
25 12 39 32 17 17 11.0
26 13 41 32 17 9 9.0
27 10 36 35 16 16 11.0
28 14 33 37 15 14 15.0
29 12 33 33 16 15 14.0
30 10 34 33 14 11 13.0
31 12 31 31 15 16 9.0
32 8 27 32 12 13 15.0
33 10 37 31 14 17 10.0
34 12 34 37 16 15 11.0
35 12 34 30 14 14 13.0
36 7 32 33 10 16 8.0
37 9 29 31 10 9 20.0
38 12 36 33 14 15 12.0
39 10 29 31 16 17 10.0
40 10 35 33 16 13 10.0
41 10 37 32 16 15 9.0
42 12 34 33 14 16 14.0
43 15 38 32 20 16 8.0
44 10 35 33 14 12 14.0
45 10 38 28 14 15 11.0
46 12 37 35 11 11 13.0
47 13 38 39 14 15 9.0
48 11 33 34 15 15 11.0
49 11 36 38 16 17 15.0
50 12 38 32 14 13 11.0
51 14 32 38 16 16 10.0
52 10 32 30 14 14 14.0
53 12 32 33 12 11 18.0
54 13 34 38 16 12 14.0
55 5 32 32 9 12 11.0
56 6 37 35 14 15 14.5
57 12 39 34 16 16 13.0
58 12 29 34 16 15 9.0
59 11 37 36 15 12 10.0
60 10 35 34 16 12 15.0
61 7 30 28 12 8 20.0
62 12 38 34 16 13 12.0
63 14 34 35 16 11 12.0
64 11 31 35 14 14 14.0
65 12 34 31 16 15 13.0
66 13 35 37 17 10 11.0
67 14 36 35 18 11 17.0
68 11 30 27 18 12 12.0
69 12 39 40 12 15 13.0
70 12 35 37 16 15 14.0
71 8 38 36 10 14 13.0
72 11 31 38 14 16 15.0
73 14 34 39 18 15 13.0
74 14 38 41 18 15 10.0
75 12 34 27 16 13 11.0
76 9 39 30 17 12 19.0
77 13 37 37 16 17 13.0
78 11 34 31 16 13 17.0
79 12 28 31 13 15 13.0
80 12 37 27 16 13 9.0
81 12 33 36 16 15 11.0
82 12 35 37 16 15 9.0
83 12 37 33 15 16 12.0
84 11 32 34 15 15 12.0
85 10 33 31 16 14 13.0
86 9 38 39 14 15 13.0
87 12 33 34 16 14 12.0
88 12 29 32 16 13 15.0
89 12 33 33 15 7 22.0
90 9 31 36 12 17 13.0
91 15 36 32 17 13 15.0
92 12 35 41 16 15 13.0
93 12 32 28 15 14 15.0
94 12 29 30 13 13 12.5
95 10 39 36 16 16 11.0
96 13 37 35 16 12 16.0
97 9 35 31 16 14 11.0
98 12 37 34 16 17 11.0
99 10 32 36 14 15 10.0
100 14 38 36 16 17 10.0
101 11 37 35 16 12 16.0
102 15 36 37 20 16 12.0
103 11 32 28 15 11 11.0
104 11 33 39 16 15 16.0
105 12 40 32 13 9 19.0
106 12 38 35 17 16 11.0
107 12 41 39 16 15 16.0
108 11 36 35 16 10 15.0
109 7 43 42 12 10 24.0
110 12 30 34 16 15 14.0
111 14 31 33 16 11 15.0
112 11 32 41 17 13 11.0
113 11 32 33 13 14 15.0
114 10 37 34 12 18 12.0
115 13 37 32 18 16 10.0
116 13 33 40 14 14 14.0
117 8 34 40 14 14 13.0
118 11 33 35 13 14 9.0
119 12 38 36 16 14 15.0
120 11 33 37 13 12 15.0
121 13 31 27 16 14 14.0
122 12 38 39 13 15 11.0
123 14 37 38 16 15 8.0
124 13 36 31 15 15 11.0
125 15 31 33 16 13 11.0
126 10 39 32 15 17 8.0
127 11 44 39 17 17 10.0
128 9 33 36 15 19 11.0
129 11 35 33 12 15 13.0
130 10 32 33 16 13 11.0
131 11 28 32 10 9 20.0
132 8 40 37 16 15 10.0
133 11 27 30 12 15 15.0
134 12 37 38 14 15 12.0
135 12 32 29 15 16 14.0
136 9 28 22 13 11 23.0
137 11 34 35 15 14 14.0
138 10 30 35 11 11 16.0
139 8 35 34 12 15 11.0
140 9 31 35 11 13 12.0
141 8 32 34 16 15 10.0
142 9 30 37 15 16 14.0
143 15 30 35 17 14 12.0
144 11 31 23 16 15 12.0
145 8 40 31 10 16 11.0
146 13 32 27 18 16 12.0
147 12 36 36 13 11 13.0
148 12 32 31 16 12 11.0
149 9 35 32 13 9 19.0
150 7 38 39 10 16 12.0
151 13 42 37 15 13 17.0
152 9 34 38 16 16 9.0
153 6 35 39 16 12 12.0
154 8 38 34 14 9 19.0
155 8 33 31 10 13 18.0
156 15 36 32 17 13 15.0
157 6 32 37 13 14 14.0
158 9 33 36 15 19 11.0
159 11 34 32 16 13 9.0
160 8 32 38 12 12 18.0
161 8 34 36 13 13 16.0
162 10 27 26 13 10 24.0
163 8 31 26 12 14 14.0
164 14 38 33 17 16 20.0
165 10 34 39 15 10 18.0
166 8 24 30 10 11 23.0
167 11 30 33 14 14 12.0
168 12 26 25 11 12 14.0
169 12 34 38 13 9 16.0
170 12 27 37 16 9 18.0
171 5 37 31 12 11 20.0
172 12 36 37 16 16 12.0
173 10 41 35 12 9 12.0
174 7 29 25 9 13 17.0
175 12 36 28 12 16 13.0
176 11 32 35 15 13 9.0
177 8 37 33 12 9 16.0
178 9 30 30 12 12 18.0
179 10 31 31 14 16 10.0
180 9 38 37 12 11 14.0
181 12 36 36 16 14 11.0
182 6 35 30 11 13 9.0
183 15 31 36 19 15 11.0
184 12 38 32 15 14 10.0
185 12 22 28 8 16 11.0
186 12 32 36 16 13 19.0
187 11 36 34 17 14 14.0
188 7 39 31 12 15 12.0
189 7 28 28 11 13 14.0
190 5 32 36 11 11 21.0
191 12 32 36 14 11 13.0
192 12 38 40 16 14 10.0
193 3 32 33 12 15 15.0
194 11 35 37 16 11 16.0
195 10 32 32 13 15 14.0
196 12 37 38 15 12 12.0
197 9 34 31 16 14 19.0
198 12 33 37 16 14 15.0
199 9 33 33 14 8 19.0
200 12 26 32 16 13 13.0
201 12 30 30 16 9 17.0
202 10 24 30 14 15 12.0
203 9 34 31 11 17 11.0
204 12 34 32 12 13 14.0
205 8 33 34 15 15 11.0
206 11 34 36 15 15 13.0
207 11 35 37 16 14 12.0
208 12 35 36 16 16 15.0
209 10 36 33 11 13 14.0
210 10 34 33 15 16 12.0
211 12 34 33 12 9 17.0
212 12 41 44 12 16 11.0
213 11 32 39 15 11 18.0
214 8 30 32 15 10 13.0
215 12 35 35 16 11 17.0
216 10 28 25 14 15 13.0
217 11 33 35 17 17 11.0
218 10 39 34 14 14 12.0
219 8 36 35 13 8 22.0
220 12 36 39 15 15 14.0
221 12 35 33 13 11 12.0
222 10 38 36 14 16 12.0
223 12 33 32 15 10 17.0
224 9 31 32 12 15 9.0
225 9 34 36 13 9 21.0
226 6 32 36 8 16 10.0
227 10 31 32 14 19 11.0
228 9 33 34 14 12 12.0
229 9 34 33 11 8 23.0
230 9 34 35 12 11 13.0
231 6 34 30 13 14 12.0
232 10 33 38 10 9 16.0
233 6 32 34 16 15 9.0
234 14 41 33 18 13 17.0
235 10 34 32 13 16 9.0
236 10 36 31 11 11 14.0
237 6 37 30 4 12 17.0
238 12 36 27 13 13 13.0
239 12 29 31 16 10 11.0
240 7 37 30 10 11 12.0
241 8 27 32 12 12 10.0
242 11 35 35 12 8 19.0
243 3 28 28 10 12 16.0
244 6 35 33 13 12 16.0
245 10 37 31 15 15 14.0
246 8 29 35 12 11 20.0
247 9 32 35 14 13 15.0
248 9 36 32 10 14 23.0
249 8 19 21 12 10 20.0
250 9 21 20 12 12 16.0
251 7 31 34 11 15 14.0
252 7 33 32 10 13 17.0
253 6 36 34 12 13 11.0
254 9 33 32 16 13 13.0
255 10 37 33 12 12 17.0
256 11 34 33 14 12 15.0
257 12 35 37 16 9 21.0
258 8 31 32 14 9 18.0
259 11 37 34 13 15 15.0
260 3 35 30 4 10 8.0
261 11 27 30 15 14 12.0
262 12 34 38 11 15 12.0
263 7 40 36 11 7 22.0
264 9 29 32 14 14 12.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Happiness Depression
1.66484 -0.01079 0.03779 0.57584 -0.00394 -0.01453
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1997 -1.1481 0.2106 1.1649 5.1305
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.66484 1.70342 0.977 0.329
Connected -0.01079 0.03366 -0.321 0.749
Separate 0.03779 0.03455 1.094 0.275
Learning 0.57583 0.04880 11.800 <2e-16 ***
Happiness -0.00394 0.05612 -0.070 0.944
Depression -0.01453 0.04025 -0.361 0.718
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.827 on 258 degrees of freedom
Multiple R-squared: 0.392, Adjusted R-squared: 0.3802
F-statistic: 33.27 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,] 0.906847190 0.186305619 0.09315281
[2,] 0.988760827 0.022478345 0.01123917
[3,] 0.979003686 0.041992629 0.02099631
[4,] 0.962617704 0.074764592 0.03738230
[5,] 0.944173002 0.111653997 0.05582700
[6,] 0.938085250 0.123829499 0.06191475
[7,] 0.923897930 0.152204140 0.07610207
[8,] 0.895991427 0.208017146 0.10400857
[9,] 0.853489815 0.293020370 0.14651019
[10,] 0.863158789 0.273682423 0.13684121
[11,] 0.832470323 0.335059354 0.16752968
[12,] 0.780518150 0.438963700 0.21948185
[13,] 0.734657243 0.530685514 0.26534276
[14,] 0.687975875 0.624048250 0.31202412
[15,] 0.626427626 0.747144749 0.37357237
[16,] 0.580832012 0.838335976 0.41916799
[17,] 0.532502486 0.934995027 0.46749751
[18,] 0.585129956 0.829740087 0.41487004
[19,] 0.568497492 0.863005016 0.43150251
[20,] 0.588816422 0.822367155 0.41118358
[21,] 0.527978373 0.944043254 0.47202163
[22,] 0.480710208 0.961420416 0.51928979
[23,] 0.434252826 0.868505653 0.56574717
[24,] 0.477203464 0.954406928 0.52279654
[25,] 0.419535210 0.839070419 0.58046479
[26,] 0.363613512 0.727227024 0.63638649
[27,] 0.357749498 0.715498996 0.64225050
[28,] 0.356478769 0.712957538 0.64352123
[29,] 0.307701787 0.615403574 0.69229821
[30,] 0.294189437 0.588378875 0.70581056
[31,] 0.274637537 0.549275075 0.72536246
[32,] 0.252989871 0.505979742 0.74701013
[33,] 0.226071096 0.452142192 0.77392890
[34,] 0.208302002 0.416604003 0.79169800
[35,] 0.225009662 0.450019324 0.77499034
[36,] 0.191132332 0.382264665 0.80886767
[37,] 0.158344332 0.316688664 0.84165567
[38,] 0.202277055 0.404554110 0.79772295
[39,] 0.203121644 0.406243288 0.79687836
[40,] 0.170113596 0.340227192 0.82988640
[41,] 0.154377332 0.308754664 0.84562267
[42,] 0.144755328 0.289510655 0.85524467
[43,] 0.152378949 0.304757899 0.84762105
[44,] 0.126649509 0.253299017 0.87335049
[45,] 0.134244386 0.268488771 0.86575561
[46,] 0.114680874 0.229361749 0.88531913
[47,] 0.184910643 0.369821286 0.81508936
[48,] 0.425359625 0.850719249 0.57464038
[49,] 0.384741252 0.769482504 0.61525875
[50,] 0.343973656 0.687947311 0.65602634
[51,] 0.305386647 0.610773293 0.69461335
[52,] 0.300959400 0.601918801 0.69904060
[53,] 0.308591625 0.617183249 0.69140838
[54,] 0.273775444 0.547550889 0.72622456
[55,] 0.293025142 0.586050284 0.70697486
[56,] 0.257959822 0.515919645 0.74204018
[57,] 0.230585916 0.461171832 0.76941408
[58,] 0.201258487 0.402516973 0.79874151
[59,] 0.184398685 0.368797370 0.81560132
[60,] 0.164708506 0.329417012 0.83529149
[61,] 0.163086732 0.326173464 0.83691327
[62,] 0.139289301 0.278578602 0.86071070
[63,] 0.123121524 0.246243049 0.87687848
[64,] 0.104768135 0.209536269 0.89523187
[65,] 0.089833496 0.179666992 0.91016650
[66,] 0.076060689 0.152121378 0.92393931
[67,] 0.070097006 0.140194013 0.92990299
[68,] 0.085343063 0.170686127 0.91465694
[69,] 0.075981696 0.151963392 0.92401830
[70,] 0.062718599 0.125437197 0.93728140
[71,] 0.068942638 0.137885277 0.93105736
[72,] 0.063577011 0.127154023 0.93642299
[73,] 0.052371197 0.104742394 0.94762880
[74,] 0.042980864 0.085961728 0.95701914
[75,] 0.037540528 0.075081056 0.96245947
[76,] 0.030335977 0.060671953 0.96966402
[77,] 0.027562766 0.055125532 0.97243723
[78,] 0.031024909 0.062049819 0.96897509
[79,] 0.024939515 0.049879030 0.97506048
[80,] 0.020059722 0.040119445 0.97994028
[81,] 0.017132141 0.034264281 0.98286786
[82,] 0.014354754 0.028709509 0.98564525
[83,] 0.023637765 0.047275529 0.97636224
[84,] 0.019446388 0.038892777 0.98055361
[85,] 0.018021109 0.036042218 0.98197889
[86,] 0.020085411 0.040170822 0.97991459
[87,] 0.019674352 0.039348704 0.98032565
[88,] 0.017743633 0.035487266 0.98225637
[89,] 0.021635641 0.043271282 0.97836436
[90,] 0.017504090 0.035008181 0.98249591
[91,] 0.014764201 0.029528401 0.98523580
[92,] 0.017654255 0.035308510 0.98234575
[93,] 0.014527297 0.029054594 0.98547270
[94,] 0.012370475 0.024740949 0.98762953
[95,] 0.009723828 0.019447656 0.99027617
[96,] 0.008506737 0.017013473 0.99149326
[97,] 0.009725003 0.019450007 0.99027500
[98,] 0.007576292 0.015152584 0.99242371
[99,] 0.005926373 0.011852746 0.99407363
[100,] 0.004843162 0.009686324 0.99515684
[101,] 0.007217528 0.014435055 0.99278247
[102,] 0.005637034 0.011274068 0.99436297
[103,] 0.006746922 0.013493843 0.99325308
[104,] 0.007015951 0.014031901 0.99298405
[105,] 0.005949145 0.011898290 0.99405085
[106,] 0.004773240 0.009546479 0.99522676
[107,] 0.003719163 0.007438326 0.99628084
[108,] 0.004219616 0.008439232 0.99578038
[109,] 0.006630863 0.013261726 0.99336914
[110,] 0.005500783 0.011001566 0.99449922
[111,] 0.004305096 0.008610192 0.99569490
[112,] 0.003533236 0.007066471 0.99646676
[113,] 0.003422710 0.006845420 0.99657729
[114,] 0.003573505 0.007147010 0.99642650
[115,] 0.004209790 0.008419580 0.99579021
[116,] 0.004747813 0.009495626 0.99525219
[117,] 0.009052665 0.018105329 0.99094734
[118,] 0.007509758 0.015019516 0.99249024
[119,] 0.006374981 0.012749963 0.99362502
[120,] 0.007058548 0.014117095 0.99294145
[121,] 0.006908244 0.013816488 0.99309176
[122,] 0.006782495 0.013564990 0.99321750
[123,] 0.008726828 0.017453656 0.99127317
[124,] 0.017159172 0.034318344 0.98284083
[125,] 0.016540378 0.033080757 0.98345962
[126,] 0.015430057 0.030860113 0.98456994
[127,] 0.013578220 0.027156440 0.98642178
[128,] 0.011155828 0.022311656 0.98884417
[129,] 0.008891475 0.017782949 0.99110853
[130,] 0.007722445 0.015444890 0.99227756
[131,] 0.007094980 0.014189961 0.99290502
[132,] 0.005730057 0.011460113 0.99426994
[133,] 0.012169429 0.024338858 0.98783057
[134,] 0.013574677 0.027149353 0.98642532
[135,] 0.018958080 0.037916159 0.98104192
[136,] 0.015228298 0.030456596 0.98477170
[137,] 0.012110473 0.024220946 0.98788953
[138,] 0.009851346 0.019702692 0.99014865
[139,] 0.010543524 0.021087047 0.98945648
[140,] 0.008551092 0.017102184 0.99144891
[141,] 0.007168666 0.014337332 0.99283133
[142,] 0.006413652 0.012827304 0.99358635
[143,] 0.007028684 0.014057369 0.99297132
[144,] 0.009196675 0.018393351 0.99080332
[145,] 0.057578651 0.115157302 0.94242135
[146,] 0.065083851 0.130167701 0.93491615
[147,] 0.054934106 0.109868212 0.94506589
[148,] 0.075857889 0.151715779 0.92414211
[149,] 0.135055749 0.270111498 0.86494425
[150,] 0.138018170 0.276036340 0.86198183
[151,] 0.120287200 0.240574400 0.87971280
[152,] 0.113957553 0.227915106 0.88604245
[153,] 0.114974444 0.229948888 0.88502556
[154,] 0.101196838 0.202393677 0.89880316
[155,] 0.090292796 0.180585591 0.90970720
[156,] 0.098508827 0.197017653 0.90149117
[157,] 0.088820422 0.177640844 0.91117958
[158,] 0.075834566 0.151669132 0.92416543
[159,] 0.065060996 0.130121991 0.93493900
[160,] 0.108387804 0.216775608 0.89161220
[161,] 0.111420065 0.222840129 0.88857994
[162,] 0.096764033 0.193528066 0.90323597
[163,] 0.166963906 0.333927812 0.83303609
[164,] 0.146003879 0.292007757 0.85399612
[165,] 0.129124347 0.258248693 0.87087565
[166,] 0.111657351 0.223314702 0.88834265
[167,] 0.150279643 0.300559287 0.84972036
[168,] 0.129967968 0.259935935 0.87003203
[169,] 0.117512204 0.235024408 0.88248780
[170,] 0.100890683 0.201781365 0.89910932
[171,] 0.085973197 0.171946395 0.91402680
[172,] 0.072433338 0.144866676 0.92756666
[173,] 0.061272949 0.122545898 0.93872705
[174,] 0.070340963 0.140681926 0.92965904
[175,] 0.072145913 0.144291826 0.92785409
[176,] 0.066596996 0.133193992 0.93340300
[177,] 0.237545198 0.475090395 0.76245480
[178,] 0.214361665 0.428723329 0.78563834
[179,] 0.193200842 0.386401684 0.80679916
[180,] 0.201996488 0.403992976 0.79800351
[181,] 0.187861730 0.375723460 0.81213827
[182,] 0.268369250 0.536738500 0.73163075
[183,] 0.266604616 0.533209233 0.73339538
[184,] 0.235758557 0.471517113 0.76424144
[185,] 0.610321293 0.779357415 0.38967871
[186,] 0.573069303 0.853861395 0.42693070
[187,] 0.536199859 0.927600281 0.46380014
[188,] 0.509096510 0.981806980 0.49090349
[189,] 0.535390033 0.929219934 0.46460997
[190,] 0.498589171 0.997178341 0.50141083
[191,] 0.474016169 0.948032338 0.52598383
[192,] 0.462408179 0.924816359 0.53759182
[193,] 0.441972197 0.883944394 0.55802780
[194,] 0.417616828 0.835233655 0.58238317
[195,] 0.380229833 0.760459666 0.61977017
[196,] 0.455584803 0.911169606 0.54441520
[197,] 0.494321384 0.988642767 0.50567862
[198,] 0.451913571 0.903827141 0.54808643
[199,] 0.410164077 0.820328155 0.58983592
[200,] 0.371511495 0.743022990 0.62848851
[201,] 0.354295232 0.708590463 0.64570477
[202,] 0.317702646 0.635405292 0.68229735
[203,] 0.392843790 0.785687579 0.60715621
[204,] 0.419898707 0.839797415 0.58010129
[205,] 0.378664798 0.757329596 0.62133520
[206,] 0.396829589 0.793659179 0.60317041
[207,] 0.359848233 0.719696465 0.64015177
[208,] 0.323641460 0.647282921 0.67635854
[209,] 0.286273972 0.572547944 0.71372603
[210,] 0.247475762 0.494951524 0.75252424
[211,] 0.249827899 0.499655798 0.75017210
[212,] 0.228997695 0.457995391 0.77100230
[213,] 0.273473855 0.546947710 0.72652614
[214,] 0.233748022 0.467496044 0.76625198
[215,] 0.223952116 0.447904232 0.77604788
[216,] 0.197145048 0.394290096 0.80285495
[217,] 0.168668886 0.337337771 0.83133111
[218,] 0.140736397 0.281472794 0.85926360
[219,] 0.116927190 0.233854380 0.88307281
[220,] 0.096201916 0.192403831 0.90379808
[221,] 0.075496748 0.150993495 0.92450325
[222,] 0.059111805 0.118223611 0.94088819
[223,] 0.089423289 0.178846579 0.91057671
[224,] 0.111022477 0.222044954 0.88897752
[225,] 0.319298614 0.638597228 0.68070139
[226,] 0.277390708 0.554781417 0.72260929
[227,] 0.231225052 0.462450103 0.76877495
[228,] 0.226201716 0.452403432 0.77379828
[229,] 0.226044876 0.452089752 0.77395512
[230,] 0.308943004 0.617886009 0.69105700
[231,] 0.316253170 0.632506341 0.68374683
[232,] 0.268287139 0.536574279 0.73171286
[233,] 0.218449532 0.436899064 0.78155047
[234,] 0.304895073 0.609790147 0.69510493
[235,] 0.600986857 0.798026286 0.39901314
[236,] 0.736960636 0.526078728 0.26303936
[237,] 0.676240034 0.647519932 0.32375997
[238,] 0.630650327 0.738699346 0.36934967
[239,] 0.584012251 0.831975497 0.41598775
[240,] 0.501040484 0.997919031 0.49895952
[241,] 0.409064443 0.818128886 0.59093556
[242,] 0.425957620 0.851915240 0.57404238
[243,] 0.570246045 0.859507910 0.42975396
[244,] 0.739615549 0.520768903 0.26038445
[245,] 0.797592392 0.404815217 0.20240761
[246,] 0.842474550 0.315050900 0.15752545
[247,] 0.798053996 0.403892007 0.20194600
> postscript(file="/var/wessaorg/rcomp/tmp/1csyd1383327634.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/27iar1383327634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3j6k01383327634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4sbu11383327634.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/55vas1383327634.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
2.0850498059 -0.4360492796 1.6419646784 -4.9934343023 2.6101005892
6 7 8 9 10
0.3762016722 -0.9817271504 2.9189103424 1.5435298197 -2.0913122102
11 12 13 14 15
-1.4275131136 0.4163570924 0.4663735843 -0.6271018236 2.7198904033
16 17 18 19 20
1.0542882885 -0.8647613537 -1.9808727616 -1.8447588493 0.5095980387
21 22 23 24 25
-0.5711583990 0.5314287444 -0.3915405048 -0.7022321287 -0.0158242403
26 27 28 29 30
0.9451832478 -1.5896740663 2.9284356864 0.4931823665 -0.3746434742
31 32 33 34 35
1.0543288372 -1.2237745006 -0.2866322309 0.3092226602 1.7505524681
36 37 38 39 40
-1.1458168236 1.0441420752 1.6481678272 -1.5246189062 -1.5512251789
41 42 43 44 45
-1.4985011561 1.6595820674 1.1983605231 -0.3453874125 -0.1558187455
46 47 48 49 50
3.3096462908 2.3994131980 -0.0123549817 -0.6410038920 1.6851328376
51 52 53 54 55
2.2392643445 -0.2564998209 2.8280817563 1.3031915014 -2.5043680726
56 57 58 59 60
-4.3803101555 0.5095403110 0.3395981285 -0.0711273424 -1.5203232793
61 62 63 64 65
-1.9873018776 0.4724047431 2.3835748792 0.5437495083 0.5650295783
66 67 68 69 70
0.7244782196 1.3261174093 -1.5049760917 2.5821880223 0.3635925209
71 72 73 74 75
-0.1297019374 0.4527791076 1.1110213418 1.0350146883 0.6792654307
76 77 78 79 80
-2.8437243419 1.3785243161 -0.3847428236 2.2277980061 0.6825804761
81 82 83 84 85
0.3362253553 0.2909586406 1.0870619052 -0.0086177382 -1.4496997073
86 87 88 89 90
-1.5424796977 0.4223968336 0.4944637340 1.1537137579 -0.3450798209
91 92 93 94 95
2.9941552540 0.1978968342 1.2577762029 2.2612368720 -1.5950976965
96 97 98 99 100
1.4779903341 -2.4571741944 0.4628474469 -0.5374205380 2.3835257481
101 102 103 104 105
-0.5220096659 1.0459274240 0.1878498394 -0.7045174473 2.3830023064
106 107 108 109 110
-0.1439302089 0.3817988128 -0.5552054807 -2.3101425317 0.4230215413
111 112 113 114 115
2.4703710652 -1.4472400293 1.2204854798 0.7846548065 0.3682949579
116 117 118 119 120
2.3763674350 -2.6273698086 1.0685299006 0.4443403691 1.0722265955
121 122 123 124 125
1.6944169144 2.0043019577 2.2602187019 2.1333902988 3.4201434671
126 127 128 129 130
-0.9077341533 -1.2409489475 -2.0721804246 1.8035754859 -1.5690670004
131 132 133 134 135
2.9955603151 -3.6405669208 1.8596894610 1.4699962213 1.2133367054
136 137 138 139 140
-0.3025631967 0.0002828982 1.2776998914 -1.2632702939 0.2382618259
141 142 143 144 145
-3.6135064979 -2.1105801810 2.7764008008 -0.1795279739 0.0596642200
146 147 148 149 150
0.5323619858 2.1093941154 0.5025777019 -0.6709453562 -1.2497258903
151 152 153 154 155
2.0506552781 -2.7536833665 -5.7528647459 -2.2899964216 0.0740056656
156 157 158 159 160
2.9941552540 -3.9452102069 -2.0721804246 -0.5387492598 -1.3569396289
161 162 163 164 165
-1.8607251152 0.5460653831 -0.9644500275 2.0623952310 -1.1085379206
166 167 168 169 170
0.0794464747 0.5794908790 3.5873502390 2.0479314171 0.3117448464
171 172 173 174 175
-4.0133325736 0.3492682544 0.7545629310 -0.1810906669 3.0072659099
176 177 178 179 180
-0.0978698002 -1.1549036394 -0.0761808726 -0.3553091791 -0.3164570635
181 182 183 184 185
0.3646541998 -2.5731992339 1.5871406675 1.0987106070 5.1304997330
186 187 188 189 190
0.4337705251 -1.0920162243 -2.0922087047 -1.5005076259 -3.6658793909
191 192 193 194 195
1.4904007778 0.2205375781 -6.1997395595 -0.6231129394 0.2476906845
196 197 198 199 200
0.8823417544 -2.3517495184 0.3526004788 -1.3100916096 0.4330415843
201 202 203 204 205
0.5941322616 -0.3679298800 0.4230315704 2.8372254510 -3.0123549817
206 207 208 209 210
-0.0480963524 -0.6694007843 0.4198512777 1.3968474959 -0.9453066924
211 212 213 214 215
2.8272545391 2.4274843777 -0.1261772325 -2.9597843373 0.4669982920
216 217 218 219 220
-0.1212838355 -1.1939381184 -0.3611955560 -1.7338919316 0.8746328057
221 222 223 224 225
2.1974544899 -0.4396900377 1.1306913645 -0.2598975207 -0.8038502473
226 227 228 229 230
-1.0784695393 -0.3667553714 -1.4338122573 0.4863106500 -0.2985575143
231 232 233 234 235
-3.6881391003 1.7646475074 -5.6280332740 1.4635290334 0.2005756223
236 237 238 239 240
1.4645524451 1.5915007398 2.4574036707 0.4623295981 -0.9400841393
241 242 243 244 245
-1.3003481340 1.7875734153 -4.8995586192 -3.7404986528 -0.8122398405
246 247 248 249 250
-1.2508177444 -1.4348739362 1.1451556689 -0.8335616353 0.1755820593
251 252 253 254 255
-1.6870128881 -0.9783133381 -3.2603602675 -2.4914316881 0.8714423959
256 257 258 259 260
0.6583498310 0.4416414347 -2.3044654700 1.2405806678 -1.5686988160
261 262 263 264
0.0846637568 3.1651332466 -1.5807953671 -1.3935064259
> postscript(file="/var/wessaorg/rcomp/tmp/6zw0u1383327634.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 2.0850498059 NA
1 -0.4360492796 2.0850498059
2 1.6419646784 -0.4360492796
3 -4.9934343023 1.6419646784
4 2.6101005892 -4.9934343023
5 0.3762016722 2.6101005892
6 -0.9817271504 0.3762016722
7 2.9189103424 -0.9817271504
8 1.5435298197 2.9189103424
9 -2.0913122102 1.5435298197
10 -1.4275131136 -2.0913122102
11 0.4163570924 -1.4275131136
12 0.4663735843 0.4163570924
13 -0.6271018236 0.4663735843
14 2.7198904033 -0.6271018236
15 1.0542882885 2.7198904033
16 -0.8647613537 1.0542882885
17 -1.9808727616 -0.8647613537
18 -1.8447588493 -1.9808727616
19 0.5095980387 -1.8447588493
20 -0.5711583990 0.5095980387
21 0.5314287444 -0.5711583990
22 -0.3915405048 0.5314287444
23 -0.7022321287 -0.3915405048
24 -0.0158242403 -0.7022321287
25 0.9451832478 -0.0158242403
26 -1.5896740663 0.9451832478
27 2.9284356864 -1.5896740663
28 0.4931823665 2.9284356864
29 -0.3746434742 0.4931823665
30 1.0543288372 -0.3746434742
31 -1.2237745006 1.0543288372
32 -0.2866322309 -1.2237745006
33 0.3092226602 -0.2866322309
34 1.7505524681 0.3092226602
35 -1.1458168236 1.7505524681
36 1.0441420752 -1.1458168236
37 1.6481678272 1.0441420752
38 -1.5246189062 1.6481678272
39 -1.5512251789 -1.5246189062
40 -1.4985011561 -1.5512251789
41 1.6595820674 -1.4985011561
42 1.1983605231 1.6595820674
43 -0.3453874125 1.1983605231
44 -0.1558187455 -0.3453874125
45 3.3096462908 -0.1558187455
46 2.3994131980 3.3096462908
47 -0.0123549817 2.3994131980
48 -0.6410038920 -0.0123549817
49 1.6851328376 -0.6410038920
50 2.2392643445 1.6851328376
51 -0.2564998209 2.2392643445
52 2.8280817563 -0.2564998209
53 1.3031915014 2.8280817563
54 -2.5043680726 1.3031915014
55 -4.3803101555 -2.5043680726
56 0.5095403110 -4.3803101555
57 0.3395981285 0.5095403110
58 -0.0711273424 0.3395981285
59 -1.5203232793 -0.0711273424
60 -1.9873018776 -1.5203232793
61 0.4724047431 -1.9873018776
62 2.3835748792 0.4724047431
63 0.5437495083 2.3835748792
64 0.5650295783 0.5437495083
65 0.7244782196 0.5650295783
66 1.3261174093 0.7244782196
67 -1.5049760917 1.3261174093
68 2.5821880223 -1.5049760917
69 0.3635925209 2.5821880223
70 -0.1297019374 0.3635925209
71 0.4527791076 -0.1297019374
72 1.1110213418 0.4527791076
73 1.0350146883 1.1110213418
74 0.6792654307 1.0350146883
75 -2.8437243419 0.6792654307
76 1.3785243161 -2.8437243419
77 -0.3847428236 1.3785243161
78 2.2277980061 -0.3847428236
79 0.6825804761 2.2277980061
80 0.3362253553 0.6825804761
81 0.2909586406 0.3362253553
82 1.0870619052 0.2909586406
83 -0.0086177382 1.0870619052
84 -1.4496997073 -0.0086177382
85 -1.5424796977 -1.4496997073
86 0.4223968336 -1.5424796977
87 0.4944637340 0.4223968336
88 1.1537137579 0.4944637340
89 -0.3450798209 1.1537137579
90 2.9941552540 -0.3450798209
91 0.1978968342 2.9941552540
92 1.2577762029 0.1978968342
93 2.2612368720 1.2577762029
94 -1.5950976965 2.2612368720
95 1.4779903341 -1.5950976965
96 -2.4571741944 1.4779903341
97 0.4628474469 -2.4571741944
98 -0.5374205380 0.4628474469
99 2.3835257481 -0.5374205380
100 -0.5220096659 2.3835257481
101 1.0459274240 -0.5220096659
102 0.1878498394 1.0459274240
103 -0.7045174473 0.1878498394
104 2.3830023064 -0.7045174473
105 -0.1439302089 2.3830023064
106 0.3817988128 -0.1439302089
107 -0.5552054807 0.3817988128
108 -2.3101425317 -0.5552054807
109 0.4230215413 -2.3101425317
110 2.4703710652 0.4230215413
111 -1.4472400293 2.4703710652
112 1.2204854798 -1.4472400293
113 0.7846548065 1.2204854798
114 0.3682949579 0.7846548065
115 2.3763674350 0.3682949579
116 -2.6273698086 2.3763674350
117 1.0685299006 -2.6273698086
118 0.4443403691 1.0685299006
119 1.0722265955 0.4443403691
120 1.6944169144 1.0722265955
121 2.0043019577 1.6944169144
122 2.2602187019 2.0043019577
123 2.1333902988 2.2602187019
124 3.4201434671 2.1333902988
125 -0.9077341533 3.4201434671
126 -1.2409489475 -0.9077341533
127 -2.0721804246 -1.2409489475
128 1.8035754859 -2.0721804246
129 -1.5690670004 1.8035754859
130 2.9955603151 -1.5690670004
131 -3.6405669208 2.9955603151
132 1.8596894610 -3.6405669208
133 1.4699962213 1.8596894610
134 1.2133367054 1.4699962213
135 -0.3025631967 1.2133367054
136 0.0002828982 -0.3025631967
137 1.2776998914 0.0002828982
138 -1.2632702939 1.2776998914
139 0.2382618259 -1.2632702939
140 -3.6135064979 0.2382618259
141 -2.1105801810 -3.6135064979
142 2.7764008008 -2.1105801810
143 -0.1795279739 2.7764008008
144 0.0596642200 -0.1795279739
145 0.5323619858 0.0596642200
146 2.1093941154 0.5323619858
147 0.5025777019 2.1093941154
148 -0.6709453562 0.5025777019
149 -1.2497258903 -0.6709453562
150 2.0506552781 -1.2497258903
151 -2.7536833665 2.0506552781
152 -5.7528647459 -2.7536833665
153 -2.2899964216 -5.7528647459
154 0.0740056656 -2.2899964216
155 2.9941552540 0.0740056656
156 -3.9452102069 2.9941552540
157 -2.0721804246 -3.9452102069
158 -0.5387492598 -2.0721804246
159 -1.3569396289 -0.5387492598
160 -1.8607251152 -1.3569396289
161 0.5460653831 -1.8607251152
162 -0.9644500275 0.5460653831
163 2.0623952310 -0.9644500275
164 -1.1085379206 2.0623952310
165 0.0794464747 -1.1085379206
166 0.5794908790 0.0794464747
167 3.5873502390 0.5794908790
168 2.0479314171 3.5873502390
169 0.3117448464 2.0479314171
170 -4.0133325736 0.3117448464
171 0.3492682544 -4.0133325736
172 0.7545629310 0.3492682544
173 -0.1810906669 0.7545629310
174 3.0072659099 -0.1810906669
175 -0.0978698002 3.0072659099
176 -1.1549036394 -0.0978698002
177 -0.0761808726 -1.1549036394
178 -0.3553091791 -0.0761808726
179 -0.3164570635 -0.3553091791
180 0.3646541998 -0.3164570635
181 -2.5731992339 0.3646541998
182 1.5871406675 -2.5731992339
183 1.0987106070 1.5871406675
184 5.1304997330 1.0987106070
185 0.4337705251 5.1304997330
186 -1.0920162243 0.4337705251
187 -2.0922087047 -1.0920162243
188 -1.5005076259 -2.0922087047
189 -3.6658793909 -1.5005076259
190 1.4904007778 -3.6658793909
191 0.2205375781 1.4904007778
192 -6.1997395595 0.2205375781
193 -0.6231129394 -6.1997395595
194 0.2476906845 -0.6231129394
195 0.8823417544 0.2476906845
196 -2.3517495184 0.8823417544
197 0.3526004788 -2.3517495184
198 -1.3100916096 0.3526004788
199 0.4330415843 -1.3100916096
200 0.5941322616 0.4330415843
201 -0.3679298800 0.5941322616
202 0.4230315704 -0.3679298800
203 2.8372254510 0.4230315704
204 -3.0123549817 2.8372254510
205 -0.0480963524 -3.0123549817
206 -0.6694007843 -0.0480963524
207 0.4198512777 -0.6694007843
208 1.3968474959 0.4198512777
209 -0.9453066924 1.3968474959
210 2.8272545391 -0.9453066924
211 2.4274843777 2.8272545391
212 -0.1261772325 2.4274843777
213 -2.9597843373 -0.1261772325
214 0.4669982920 -2.9597843373
215 -0.1212838355 0.4669982920
216 -1.1939381184 -0.1212838355
217 -0.3611955560 -1.1939381184
218 -1.7338919316 -0.3611955560
219 0.8746328057 -1.7338919316
220 2.1974544899 0.8746328057
221 -0.4396900377 2.1974544899
222 1.1306913645 -0.4396900377
223 -0.2598975207 1.1306913645
224 -0.8038502473 -0.2598975207
225 -1.0784695393 -0.8038502473
226 -0.3667553714 -1.0784695393
227 -1.4338122573 -0.3667553714
228 0.4863106500 -1.4338122573
229 -0.2985575143 0.4863106500
230 -3.6881391003 -0.2985575143
231 1.7646475074 -3.6881391003
232 -5.6280332740 1.7646475074
233 1.4635290334 -5.6280332740
234 0.2005756223 1.4635290334
235 1.4645524451 0.2005756223
236 1.5915007398 1.4645524451
237 2.4574036707 1.5915007398
238 0.4623295981 2.4574036707
239 -0.9400841393 0.4623295981
240 -1.3003481340 -0.9400841393
241 1.7875734153 -1.3003481340
242 -4.8995586192 1.7875734153
243 -3.7404986528 -4.8995586192
244 -0.8122398405 -3.7404986528
245 -1.2508177444 -0.8122398405
246 -1.4348739362 -1.2508177444
247 1.1451556689 -1.4348739362
248 -0.8335616353 1.1451556689
249 0.1755820593 -0.8335616353
250 -1.6870128881 0.1755820593
251 -0.9783133381 -1.6870128881
252 -3.2603602675 -0.9783133381
253 -2.4914316881 -3.2603602675
254 0.8714423959 -2.4914316881
255 0.6583498310 0.8714423959
256 0.4416414347 0.6583498310
257 -2.3044654700 0.4416414347
258 1.2405806678 -2.3044654700
259 -1.5686988160 1.2405806678
260 0.0846637568 -1.5686988160
261 3.1651332466 0.0846637568
262 -1.5807953671 3.1651332466
263 -1.3935064259 -1.5807953671
264 NA -1.3935064259
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.4360492796 2.0850498059
[2,] 1.6419646784 -0.4360492796
[3,] -4.9934343023 1.6419646784
[4,] 2.6101005892 -4.9934343023
[5,] 0.3762016722 2.6101005892
[6,] -0.9817271504 0.3762016722
[7,] 2.9189103424 -0.9817271504
[8,] 1.5435298197 2.9189103424
[9,] -2.0913122102 1.5435298197
[10,] -1.4275131136 -2.0913122102
[11,] 0.4163570924 -1.4275131136
[12,] 0.4663735843 0.4163570924
[13,] -0.6271018236 0.4663735843
[14,] 2.7198904033 -0.6271018236
[15,] 1.0542882885 2.7198904033
[16,] -0.8647613537 1.0542882885
[17,] -1.9808727616 -0.8647613537
[18,] -1.8447588493 -1.9808727616
[19,] 0.5095980387 -1.8447588493
[20,] -0.5711583990 0.5095980387
[21,] 0.5314287444 -0.5711583990
[22,] -0.3915405048 0.5314287444
[23,] -0.7022321287 -0.3915405048
[24,] -0.0158242403 -0.7022321287
[25,] 0.9451832478 -0.0158242403
[26,] -1.5896740663 0.9451832478
[27,] 2.9284356864 -1.5896740663
[28,] 0.4931823665 2.9284356864
[29,] -0.3746434742 0.4931823665
[30,] 1.0543288372 -0.3746434742
[31,] -1.2237745006 1.0543288372
[32,] -0.2866322309 -1.2237745006
[33,] 0.3092226602 -0.2866322309
[34,] 1.7505524681 0.3092226602
[35,] -1.1458168236 1.7505524681
[36,] 1.0441420752 -1.1458168236
[37,] 1.6481678272 1.0441420752
[38,] -1.5246189062 1.6481678272
[39,] -1.5512251789 -1.5246189062
[40,] -1.4985011561 -1.5512251789
[41,] 1.6595820674 -1.4985011561
[42,] 1.1983605231 1.6595820674
[43,] -0.3453874125 1.1983605231
[44,] -0.1558187455 -0.3453874125
[45,] 3.3096462908 -0.1558187455
[46,] 2.3994131980 3.3096462908
[47,] -0.0123549817 2.3994131980
[48,] -0.6410038920 -0.0123549817
[49,] 1.6851328376 -0.6410038920
[50,] 2.2392643445 1.6851328376
[51,] -0.2564998209 2.2392643445
[52,] 2.8280817563 -0.2564998209
[53,] 1.3031915014 2.8280817563
[54,] -2.5043680726 1.3031915014
[55,] -4.3803101555 -2.5043680726
[56,] 0.5095403110 -4.3803101555
[57,] 0.3395981285 0.5095403110
[58,] -0.0711273424 0.3395981285
[59,] -1.5203232793 -0.0711273424
[60,] -1.9873018776 -1.5203232793
[61,] 0.4724047431 -1.9873018776
[62,] 2.3835748792 0.4724047431
[63,] 0.5437495083 2.3835748792
[64,] 0.5650295783 0.5437495083
[65,] 0.7244782196 0.5650295783
[66,] 1.3261174093 0.7244782196
[67,] -1.5049760917 1.3261174093
[68,] 2.5821880223 -1.5049760917
[69,] 0.3635925209 2.5821880223
[70,] -0.1297019374 0.3635925209
[71,] 0.4527791076 -0.1297019374
[72,] 1.1110213418 0.4527791076
[73,] 1.0350146883 1.1110213418
[74,] 0.6792654307 1.0350146883
[75,] -2.8437243419 0.6792654307
[76,] 1.3785243161 -2.8437243419
[77,] -0.3847428236 1.3785243161
[78,] 2.2277980061 -0.3847428236
[79,] 0.6825804761 2.2277980061
[80,] 0.3362253553 0.6825804761
[81,] 0.2909586406 0.3362253553
[82,] 1.0870619052 0.2909586406
[83,] -0.0086177382 1.0870619052
[84,] -1.4496997073 -0.0086177382
[85,] -1.5424796977 -1.4496997073
[86,] 0.4223968336 -1.5424796977
[87,] 0.4944637340 0.4223968336
[88,] 1.1537137579 0.4944637340
[89,] -0.3450798209 1.1537137579
[90,] 2.9941552540 -0.3450798209
[91,] 0.1978968342 2.9941552540
[92,] 1.2577762029 0.1978968342
[93,] 2.2612368720 1.2577762029
[94,] -1.5950976965 2.2612368720
[95,] 1.4779903341 -1.5950976965
[96,] -2.4571741944 1.4779903341
[97,] 0.4628474469 -2.4571741944
[98,] -0.5374205380 0.4628474469
[99,] 2.3835257481 -0.5374205380
[100,] -0.5220096659 2.3835257481
[101,] 1.0459274240 -0.5220096659
[102,] 0.1878498394 1.0459274240
[103,] -0.7045174473 0.1878498394
[104,] 2.3830023064 -0.7045174473
[105,] -0.1439302089 2.3830023064
[106,] 0.3817988128 -0.1439302089
[107,] -0.5552054807 0.3817988128
[108,] -2.3101425317 -0.5552054807
[109,] 0.4230215413 -2.3101425317
[110,] 2.4703710652 0.4230215413
[111,] -1.4472400293 2.4703710652
[112,] 1.2204854798 -1.4472400293
[113,] 0.7846548065 1.2204854798
[114,] 0.3682949579 0.7846548065
[115,] 2.3763674350 0.3682949579
[116,] -2.6273698086 2.3763674350
[117,] 1.0685299006 -2.6273698086
[118,] 0.4443403691 1.0685299006
[119,] 1.0722265955 0.4443403691
[120,] 1.6944169144 1.0722265955
[121,] 2.0043019577 1.6944169144
[122,] 2.2602187019 2.0043019577
[123,] 2.1333902988 2.2602187019
[124,] 3.4201434671 2.1333902988
[125,] -0.9077341533 3.4201434671
[126,] -1.2409489475 -0.9077341533
[127,] -2.0721804246 -1.2409489475
[128,] 1.8035754859 -2.0721804246
[129,] -1.5690670004 1.8035754859
[130,] 2.9955603151 -1.5690670004
[131,] -3.6405669208 2.9955603151
[132,] 1.8596894610 -3.6405669208
[133,] 1.4699962213 1.8596894610
[134,] 1.2133367054 1.4699962213
[135,] -0.3025631967 1.2133367054
[136,] 0.0002828982 -0.3025631967
[137,] 1.2776998914 0.0002828982
[138,] -1.2632702939 1.2776998914
[139,] 0.2382618259 -1.2632702939
[140,] -3.6135064979 0.2382618259
[141,] -2.1105801810 -3.6135064979
[142,] 2.7764008008 -2.1105801810
[143,] -0.1795279739 2.7764008008
[144,] 0.0596642200 -0.1795279739
[145,] 0.5323619858 0.0596642200
[146,] 2.1093941154 0.5323619858
[147,] 0.5025777019 2.1093941154
[148,] -0.6709453562 0.5025777019
[149,] -1.2497258903 -0.6709453562
[150,] 2.0506552781 -1.2497258903
[151,] -2.7536833665 2.0506552781
[152,] -5.7528647459 -2.7536833665
[153,] -2.2899964216 -5.7528647459
[154,] 0.0740056656 -2.2899964216
[155,] 2.9941552540 0.0740056656
[156,] -3.9452102069 2.9941552540
[157,] -2.0721804246 -3.9452102069
[158,] -0.5387492598 -2.0721804246
[159,] -1.3569396289 -0.5387492598
[160,] -1.8607251152 -1.3569396289
[161,] 0.5460653831 -1.8607251152
[162,] -0.9644500275 0.5460653831
[163,] 2.0623952310 -0.9644500275
[164,] -1.1085379206 2.0623952310
[165,] 0.0794464747 -1.1085379206
[166,] 0.5794908790 0.0794464747
[167,] 3.5873502390 0.5794908790
[168,] 2.0479314171 3.5873502390
[169,] 0.3117448464 2.0479314171
[170,] -4.0133325736 0.3117448464
[171,] 0.3492682544 -4.0133325736
[172,] 0.7545629310 0.3492682544
[173,] -0.1810906669 0.7545629310
[174,] 3.0072659099 -0.1810906669
[175,] -0.0978698002 3.0072659099
[176,] -1.1549036394 -0.0978698002
[177,] -0.0761808726 -1.1549036394
[178,] -0.3553091791 -0.0761808726
[179,] -0.3164570635 -0.3553091791
[180,] 0.3646541998 -0.3164570635
[181,] -2.5731992339 0.3646541998
[182,] 1.5871406675 -2.5731992339
[183,] 1.0987106070 1.5871406675
[184,] 5.1304997330 1.0987106070
[185,] 0.4337705251 5.1304997330
[186,] -1.0920162243 0.4337705251
[187,] -2.0922087047 -1.0920162243
[188,] -1.5005076259 -2.0922087047
[189,] -3.6658793909 -1.5005076259
[190,] 1.4904007778 -3.6658793909
[191,] 0.2205375781 1.4904007778
[192,] -6.1997395595 0.2205375781
[193,] -0.6231129394 -6.1997395595
[194,] 0.2476906845 -0.6231129394
[195,] 0.8823417544 0.2476906845
[196,] -2.3517495184 0.8823417544
[197,] 0.3526004788 -2.3517495184
[198,] -1.3100916096 0.3526004788
[199,] 0.4330415843 -1.3100916096
[200,] 0.5941322616 0.4330415843
[201,] -0.3679298800 0.5941322616
[202,] 0.4230315704 -0.3679298800
[203,] 2.8372254510 0.4230315704
[204,] -3.0123549817 2.8372254510
[205,] -0.0480963524 -3.0123549817
[206,] -0.6694007843 -0.0480963524
[207,] 0.4198512777 -0.6694007843
[208,] 1.3968474959 0.4198512777
[209,] -0.9453066924 1.3968474959
[210,] 2.8272545391 -0.9453066924
[211,] 2.4274843777 2.8272545391
[212,] -0.1261772325 2.4274843777
[213,] -2.9597843373 -0.1261772325
[214,] 0.4669982920 -2.9597843373
[215,] -0.1212838355 0.4669982920
[216,] -1.1939381184 -0.1212838355
[217,] -0.3611955560 -1.1939381184
[218,] -1.7338919316 -0.3611955560
[219,] 0.8746328057 -1.7338919316
[220,] 2.1974544899 0.8746328057
[221,] -0.4396900377 2.1974544899
[222,] 1.1306913645 -0.4396900377
[223,] -0.2598975207 1.1306913645
[224,] -0.8038502473 -0.2598975207
[225,] -1.0784695393 -0.8038502473
[226,] -0.3667553714 -1.0784695393
[227,] -1.4338122573 -0.3667553714
[228,] 0.4863106500 -1.4338122573
[229,] -0.2985575143 0.4863106500
[230,] -3.6881391003 -0.2985575143
[231,] 1.7646475074 -3.6881391003
[232,] -5.6280332740 1.7646475074
[233,] 1.4635290334 -5.6280332740
[234,] 0.2005756223 1.4635290334
[235,] 1.4645524451 0.2005756223
[236,] 1.5915007398 1.4645524451
[237,] 2.4574036707 1.5915007398
[238,] 0.4623295981 2.4574036707
[239,] -0.9400841393 0.4623295981
[240,] -1.3003481340 -0.9400841393
[241,] 1.7875734153 -1.3003481340
[242,] -4.8995586192 1.7875734153
[243,] -3.7404986528 -4.8995586192
[244,] -0.8122398405 -3.7404986528
[245,] -1.2508177444 -0.8122398405
[246,] -1.4348739362 -1.2508177444
[247,] 1.1451556689 -1.4348739362
[248,] -0.8335616353 1.1451556689
[249,] 0.1755820593 -0.8335616353
[250,] -1.6870128881 0.1755820593
[251,] -0.9783133381 -1.6870128881
[252,] -3.2603602675 -0.9783133381
[253,] -2.4914316881 -3.2603602675
[254,] 0.8714423959 -2.4914316881
[255,] 0.6583498310 0.8714423959
[256,] 0.4416414347 0.6583498310
[257,] -2.3044654700 0.4416414347
[258,] 1.2405806678 -2.3044654700
[259,] -1.5686988160 1.2405806678
[260,] 0.0846637568 -1.5686988160
[261,] 3.1651332466 0.0846637568
[262,] -1.5807953671 3.1651332466
[263,] -1.3935064259 -1.5807953671
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.4360492796 2.0850498059
2 1.6419646784 -0.4360492796
3 -4.9934343023 1.6419646784
4 2.6101005892 -4.9934343023
5 0.3762016722 2.6101005892
6 -0.9817271504 0.3762016722
7 2.9189103424 -0.9817271504
8 1.5435298197 2.9189103424
9 -2.0913122102 1.5435298197
10 -1.4275131136 -2.0913122102
11 0.4163570924 -1.4275131136
12 0.4663735843 0.4163570924
13 -0.6271018236 0.4663735843
14 2.7198904033 -0.6271018236
15 1.0542882885 2.7198904033
16 -0.8647613537 1.0542882885
17 -1.9808727616 -0.8647613537
18 -1.8447588493 -1.9808727616
19 0.5095980387 -1.8447588493
20 -0.5711583990 0.5095980387
21 0.5314287444 -0.5711583990
22 -0.3915405048 0.5314287444
23 -0.7022321287 -0.3915405048
24 -0.0158242403 -0.7022321287
25 0.9451832478 -0.0158242403
26 -1.5896740663 0.9451832478
27 2.9284356864 -1.5896740663
28 0.4931823665 2.9284356864
29 -0.3746434742 0.4931823665
30 1.0543288372 -0.3746434742
31 -1.2237745006 1.0543288372
32 -0.2866322309 -1.2237745006
33 0.3092226602 -0.2866322309
34 1.7505524681 0.3092226602
35 -1.1458168236 1.7505524681
36 1.0441420752 -1.1458168236
37 1.6481678272 1.0441420752
38 -1.5246189062 1.6481678272
39 -1.5512251789 -1.5246189062
40 -1.4985011561 -1.5512251789
41 1.6595820674 -1.4985011561
42 1.1983605231 1.6595820674
43 -0.3453874125 1.1983605231
44 -0.1558187455 -0.3453874125
45 3.3096462908 -0.1558187455
46 2.3994131980 3.3096462908
47 -0.0123549817 2.3994131980
48 -0.6410038920 -0.0123549817
49 1.6851328376 -0.6410038920
50 2.2392643445 1.6851328376
51 -0.2564998209 2.2392643445
52 2.8280817563 -0.2564998209
53 1.3031915014 2.8280817563
54 -2.5043680726 1.3031915014
55 -4.3803101555 -2.5043680726
56 0.5095403110 -4.3803101555
57 0.3395981285 0.5095403110
58 -0.0711273424 0.3395981285
59 -1.5203232793 -0.0711273424
60 -1.9873018776 -1.5203232793
61 0.4724047431 -1.9873018776
62 2.3835748792 0.4724047431
63 0.5437495083 2.3835748792
64 0.5650295783 0.5437495083
65 0.7244782196 0.5650295783
66 1.3261174093 0.7244782196
67 -1.5049760917 1.3261174093
68 2.5821880223 -1.5049760917
69 0.3635925209 2.5821880223
70 -0.1297019374 0.3635925209
71 0.4527791076 -0.1297019374
72 1.1110213418 0.4527791076
73 1.0350146883 1.1110213418
74 0.6792654307 1.0350146883
75 -2.8437243419 0.6792654307
76 1.3785243161 -2.8437243419
77 -0.3847428236 1.3785243161
78 2.2277980061 -0.3847428236
79 0.6825804761 2.2277980061
80 0.3362253553 0.6825804761
81 0.2909586406 0.3362253553
82 1.0870619052 0.2909586406
83 -0.0086177382 1.0870619052
84 -1.4496997073 -0.0086177382
85 -1.5424796977 -1.4496997073
86 0.4223968336 -1.5424796977
87 0.4944637340 0.4223968336
88 1.1537137579 0.4944637340
89 -0.3450798209 1.1537137579
90 2.9941552540 -0.3450798209
91 0.1978968342 2.9941552540
92 1.2577762029 0.1978968342
93 2.2612368720 1.2577762029
94 -1.5950976965 2.2612368720
95 1.4779903341 -1.5950976965
96 -2.4571741944 1.4779903341
97 0.4628474469 -2.4571741944
98 -0.5374205380 0.4628474469
99 2.3835257481 -0.5374205380
100 -0.5220096659 2.3835257481
101 1.0459274240 -0.5220096659
102 0.1878498394 1.0459274240
103 -0.7045174473 0.1878498394
104 2.3830023064 -0.7045174473
105 -0.1439302089 2.3830023064
106 0.3817988128 -0.1439302089
107 -0.5552054807 0.3817988128
108 -2.3101425317 -0.5552054807
109 0.4230215413 -2.3101425317
110 2.4703710652 0.4230215413
111 -1.4472400293 2.4703710652
112 1.2204854798 -1.4472400293
113 0.7846548065 1.2204854798
114 0.3682949579 0.7846548065
115 2.3763674350 0.3682949579
116 -2.6273698086 2.3763674350
117 1.0685299006 -2.6273698086
118 0.4443403691 1.0685299006
119 1.0722265955 0.4443403691
120 1.6944169144 1.0722265955
121 2.0043019577 1.6944169144
122 2.2602187019 2.0043019577
123 2.1333902988 2.2602187019
124 3.4201434671 2.1333902988
125 -0.9077341533 3.4201434671
126 -1.2409489475 -0.9077341533
127 -2.0721804246 -1.2409489475
128 1.8035754859 -2.0721804246
129 -1.5690670004 1.8035754859
130 2.9955603151 -1.5690670004
131 -3.6405669208 2.9955603151
132 1.8596894610 -3.6405669208
133 1.4699962213 1.8596894610
134 1.2133367054 1.4699962213
135 -0.3025631967 1.2133367054
136 0.0002828982 -0.3025631967
137 1.2776998914 0.0002828982
138 -1.2632702939 1.2776998914
139 0.2382618259 -1.2632702939
140 -3.6135064979 0.2382618259
141 -2.1105801810 -3.6135064979
142 2.7764008008 -2.1105801810
143 -0.1795279739 2.7764008008
144 0.0596642200 -0.1795279739
145 0.5323619858 0.0596642200
146 2.1093941154 0.5323619858
147 0.5025777019 2.1093941154
148 -0.6709453562 0.5025777019
149 -1.2497258903 -0.6709453562
150 2.0506552781 -1.2497258903
151 -2.7536833665 2.0506552781
152 -5.7528647459 -2.7536833665
153 -2.2899964216 -5.7528647459
154 0.0740056656 -2.2899964216
155 2.9941552540 0.0740056656
156 -3.9452102069 2.9941552540
157 -2.0721804246 -3.9452102069
158 -0.5387492598 -2.0721804246
159 -1.3569396289 -0.5387492598
160 -1.8607251152 -1.3569396289
161 0.5460653831 -1.8607251152
162 -0.9644500275 0.5460653831
163 2.0623952310 -0.9644500275
164 -1.1085379206 2.0623952310
165 0.0794464747 -1.1085379206
166 0.5794908790 0.0794464747
167 3.5873502390 0.5794908790
168 2.0479314171 3.5873502390
169 0.3117448464 2.0479314171
170 -4.0133325736 0.3117448464
171 0.3492682544 -4.0133325736
172 0.7545629310 0.3492682544
173 -0.1810906669 0.7545629310
174 3.0072659099 -0.1810906669
175 -0.0978698002 3.0072659099
176 -1.1549036394 -0.0978698002
177 -0.0761808726 -1.1549036394
178 -0.3553091791 -0.0761808726
179 -0.3164570635 -0.3553091791
180 0.3646541998 -0.3164570635
181 -2.5731992339 0.3646541998
182 1.5871406675 -2.5731992339
183 1.0987106070 1.5871406675
184 5.1304997330 1.0987106070
185 0.4337705251 5.1304997330
186 -1.0920162243 0.4337705251
187 -2.0922087047 -1.0920162243
188 -1.5005076259 -2.0922087047
189 -3.6658793909 -1.5005076259
190 1.4904007778 -3.6658793909
191 0.2205375781 1.4904007778
192 -6.1997395595 0.2205375781
193 -0.6231129394 -6.1997395595
194 0.2476906845 -0.6231129394
195 0.8823417544 0.2476906845
196 -2.3517495184 0.8823417544
197 0.3526004788 -2.3517495184
198 -1.3100916096 0.3526004788
199 0.4330415843 -1.3100916096
200 0.5941322616 0.4330415843
201 -0.3679298800 0.5941322616
202 0.4230315704 -0.3679298800
203 2.8372254510 0.4230315704
204 -3.0123549817 2.8372254510
205 -0.0480963524 -3.0123549817
206 -0.6694007843 -0.0480963524
207 0.4198512777 -0.6694007843
208 1.3968474959 0.4198512777
209 -0.9453066924 1.3968474959
210 2.8272545391 -0.9453066924
211 2.4274843777 2.8272545391
212 -0.1261772325 2.4274843777
213 -2.9597843373 -0.1261772325
214 0.4669982920 -2.9597843373
215 -0.1212838355 0.4669982920
216 -1.1939381184 -0.1212838355
217 -0.3611955560 -1.1939381184
218 -1.7338919316 -0.3611955560
219 0.8746328057 -1.7338919316
220 2.1974544899 0.8746328057
221 -0.4396900377 2.1974544899
222 1.1306913645 -0.4396900377
223 -0.2598975207 1.1306913645
224 -0.8038502473 -0.2598975207
225 -1.0784695393 -0.8038502473
226 -0.3667553714 -1.0784695393
227 -1.4338122573 -0.3667553714
228 0.4863106500 -1.4338122573
229 -0.2985575143 0.4863106500
230 -3.6881391003 -0.2985575143
231 1.7646475074 -3.6881391003
232 -5.6280332740 1.7646475074
233 1.4635290334 -5.6280332740
234 0.2005756223 1.4635290334
235 1.4645524451 0.2005756223
236 1.5915007398 1.4645524451
237 2.4574036707 1.5915007398
238 0.4623295981 2.4574036707
239 -0.9400841393 0.4623295981
240 -1.3003481340 -0.9400841393
241 1.7875734153 -1.3003481340
242 -4.8995586192 1.7875734153
243 -3.7404986528 -4.8995586192
244 -0.8122398405 -3.7404986528
245 -1.2508177444 -0.8122398405
246 -1.4348739362 -1.2508177444
247 1.1451556689 -1.4348739362
248 -0.8335616353 1.1451556689
249 0.1755820593 -0.8335616353
250 -1.6870128881 0.1755820593
251 -0.9783133381 -1.6870128881
252 -3.2603602675 -0.9783133381
253 -2.4914316881 -3.2603602675
254 0.8714423959 -2.4914316881
255 0.6583498310 0.8714423959
256 0.4416414347 0.6583498310
257 -2.3044654700 0.4416414347
258 1.2405806678 -2.3044654700
259 -1.5686988160 1.2405806678
260 0.0846637568 -1.5686988160
261 3.1651332466 0.0846637568
262 -1.5807953671 3.1651332466
263 -1.3935064259 -1.5807953671
> 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/7du2y1383327635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8srtl1383327635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9pfqv1383327635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10cwmw1383327635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11msst1383327635.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/12h9gw1383327635.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/13olc31383327635.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/14f0fc1383327635.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/15kjpc1383327635.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/16wnhj1383327635.tab")
+ }
>
> try(system("convert tmp/1csyd1383327634.ps tmp/1csyd1383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/27iar1383327634.ps tmp/27iar1383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j6k01383327634.ps tmp/3j6k01383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sbu11383327634.ps tmp/4sbu11383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/55vas1383327634.ps tmp/55vas1383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zw0u1383327634.ps tmp/6zw0u1383327634.png",intern=TRUE))
character(0)
> try(system("convert tmp/7du2y1383327635.ps tmp/7du2y1383327635.png",intern=TRUE))
character(0)
> try(system("convert tmp/8srtl1383327635.ps tmp/8srtl1383327635.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pfqv1383327635.ps tmp/9pfqv1383327635.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cwmw1383327635.ps tmp/10cwmw1383327635.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.318 2.622 17.917