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(14
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+ ,9)
+ ,dim=c(3
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Separate'
+ ,'Software')
+ ,1:264))
> y <- array(NA,dim=c(3,264),dimnames=list(c('Happiness','Separate','Software'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Separate Software
1 14 38 12
2 18 32 11
3 11 35 15
4 12 33 6
5 16 37 13
6 18 29 10
7 14 31 12
8 14 36 14
9 15 35 12
10 15 38 9
11 17 31 10
12 19 34 12
13 10 35 12
14 16 38 11
15 18 37 15
16 14 33 12
17 14 32 10
18 17 38 12
19 14 38 11
20 16 32 12
21 18 33 11
22 11 31 12
23 14 38 13
24 12 39 11
25 17 32 12
26 9 32 13
27 16 35 10
28 14 37 14
29 15 33 12
30 11 33 10
31 16 31 12
32 13 32 8
33 17 31 10
34 15 37 12
35 14 30 12
36 16 33 7
37 9 31 9
38 15 33 12
39 17 31 10
40 13 33 10
41 15 32 10
42 16 33 12
43 16 32 15
44 12 33 10
45 15 28 10
46 11 35 12
47 15 39 13
48 15 34 11
49 17 38 11
50 13 32 12
51 16 38 14
52 14 30 10
53 11 33 12
54 12 38 13
55 12 32 5
56 15 35 6
57 16 34 12
58 15 34 12
59 12 36 11
60 12 34 10
61 8 28 7
62 13 34 12
63 11 35 14
64 14 35 11
65 15 31 12
66 10 37 13
67 11 35 14
68 12 27 11
69 15 40 12
70 15 37 12
71 14 36 8
72 16 38 11
73 15 39 14
74 15 41 14
75 13 27 12
76 12 30 9
77 17 37 13
78 13 31 11
79 15 31 12
80 13 27 12
81 15 36 12
82 15 37 12
83 16 33 12
84 15 34 11
85 14 31 10
86 15 39 9
87 14 34 12
88 13 32 12
89 7 33 12
90 17 36 9
91 13 32 15
92 15 41 12
93 14 28 12
94 13 30 12
95 16 36 10
96 12 35 13
97 14 31 9
98 17 34 12
99 15 36 10
100 17 36 14
101 12 35 11
102 16 37 15
103 11 28 11
104 15 39 11
105 9 32 12
106 16 35 12
107 15 39 12
108 10 35 11
109 10 42 7
110 15 34 12
111 11 33 14
112 13 41 11
113 14 33 11
114 18 34 10
115 16 32 13
116 14 40 13
117 14 40 8
118 14 35 11
119 14 36 12
120 12 37 11
121 14 27 13
122 15 39 12
123 15 38 14
124 15 31 13
125 13 33 15
126 17 32 10
127 17 39 11
128 19 36 9
129 15 33 11
130 13 33 10
131 9 32 11
132 15 37 8
133 15 30 11
134 15 38 12
135 16 29 12
136 11 22 9
137 14 35 11
138 11 35 10
139 15 34 8
140 13 35 9
141 15 34 8
142 16 37 9
143 14 35 15
144 15 23 11
145 16 31 8
146 16 27 13
147 11 36 12
148 12 31 12
149 9 32 9
150 16 39 7
151 13 37 13
152 16 38 9
153 12 39 6
154 9 34 8
155 13 31 8
156 13 32 15
157 14 37 6
158 19 36 9
159 13 32 11
160 12 38 8
161 13 36 8
162 10 26 10
163 14 26 8
164 16 33 14
165 10 39 10
166 11 30 8
167 14 33 11
168 12 25 12
169 9 38 12
170 9 37 12
171 11 31 5
172 16 37 12
173 9 35 10
174 13 25 7
175 16 28 12
176 13 35 11
177 9 33 8
178 12 30 9
179 16 31 10
180 11 37 9
181 14 36 12
182 13 30 6
183 15 36 15
184 14 32 12
185 16 28 12
186 13 36 12
187 14 34 11
188 15 31 7
189 13 28 7
190 11 36 5
191 11 36 12
192 14 40 12
193 15 33 3
194 11 37 11
195 15 32 10
196 12 38 12
197 14 31 9
198 14 37 12
199 8 33 9
200 13 32 12
201 9 30 12
202 15 30 10
203 17 31 9
204 13 32 12
205 15 34 8
206 15 36 11
207 14 37 11
208 16 36 12
209 13 33 10
210 16 33 10
211 9 33 12
212 16 44 12
213 11 39 11
214 10 32 8
215 11 35 12
216 15 25 10
217 17 35 11
218 14 34 10
219 8 35 8
220 15 39 12
221 11 33 12
222 16 36 10
223 10 32 12
224 15 32 9
225 9 36 9
226 16 36 6
227 19 32 10
228 12 34 9
229 8 33 9
230 11 35 9
231 14 30 6
232 9 38 10
233 15 34 6
234 13 33 14
235 16 32 10
236 11 31 10
237 12 30 6
238 13 27 12
239 10 31 12
240 11 30 7
241 12 32 8
242 8 35 11
243 12 28 3
244 12 33 6
245 15 31 10
246 11 35 8
247 13 35 9
248 14 32 9
249 10 21 8
250 12 20 9
251 15 34 7
252 13 32 7
253 13 34 6
254 13 32 9
255 12 33 10
256 12 33 11
257 9 37 12
258 9 32 8
259 15 34 11
260 10 30 3
261 14 30 11
262 15 38 12
263 7 36 7
264 14 32 9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Software
9.9683 0.0551 0.1602
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7086 -1.5609 0.3439 1.7357 5.6668
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.96827 1.45739 6.840 5.61e-11 ***
Separate 0.05510 0.04181 1.318 0.1888
Software 0.16019 0.06675 2.400 0.0171 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.466 on 261 degrees of freedom
Multiple R-squared: 0.0334, Adjusted R-squared: 0.026
F-statistic: 4.51 on 2 and 261 DF, p-value: 0.01187
> 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,] 0.86888379 0.2622324 0.1311162
[2,] 0.82258373 0.3548325 0.1774163
[3,] 0.72144209 0.5571158 0.2785579
[4,] 0.61671968 0.7665606 0.3832803
[5,] 0.54146598 0.9170680 0.4585340
[6,] 0.46817873 0.9363575 0.5318213
[7,] 0.62195177 0.7560965 0.3780482
[8,] 0.80070261 0.3985948 0.1992974
[9,] 0.77993319 0.4401336 0.2200668
[10,] 0.82202852 0.3559430 0.1779715
[11,] 0.78176985 0.4364603 0.2182302
[12,] 0.73585801 0.5282840 0.2641420
[13,] 0.72811245 0.5437751 0.2718875
[14,] 0.66823000 0.6635400 0.3317700
[15,] 0.60825446 0.7834911 0.3917455
[16,] 0.62833207 0.7433359 0.3716679
[17,] 0.74624804 0.5075039 0.2537520
[18,] 0.69677817 0.6064437 0.3032218
[19,] 0.69314514 0.6137097 0.3068549
[20,] 0.66967430 0.6606514 0.3303257
[21,] 0.86784670 0.2643066 0.1321533
[22,] 0.84396227 0.3120755 0.1560377
[23,] 0.80722884 0.3855423 0.1927712
[24,] 0.76691361 0.4661728 0.2330864
[25,] 0.81150474 0.3769905 0.1884953
[26,] 0.78442225 0.4311555 0.2155777
[27,] 0.75923358 0.4815328 0.2407664
[28,] 0.75696508 0.4860698 0.2430349
[29,] 0.71569695 0.5686061 0.2843030
[30,] 0.67529876 0.6494025 0.3247012
[31,] 0.64721754 0.7055649 0.3527825
[32,] 0.80596917 0.3880617 0.1940308
[33,] 0.77180998 0.4563800 0.2281900
[34,] 0.77680305 0.4463939 0.2231969
[35,] 0.75116880 0.4976624 0.2488312
[36,] 0.71499956 0.5700009 0.2850004
[37,] 0.68922980 0.6215404 0.3107702
[38,] 0.65679040 0.6864192 0.3432096
[39,] 0.65309055 0.6938189 0.3469095
[40,] 0.61427306 0.7714539 0.3857269
[41,] 0.65546566 0.6890687 0.3445343
[42,] 0.61449842 0.7710032 0.3855016
[43,] 0.57413241 0.8517352 0.4258676
[44,] 0.58495544 0.8300891 0.4150446
[45,] 0.55700510 0.8859898 0.4429949
[46,] 0.52644227 0.9471155 0.4735577
[47,] 0.48408021 0.9681604 0.5159198
[48,] 0.52649554 0.9470089 0.4735045
[49,] 0.52819300 0.9436140 0.4718070
[50,] 0.50621865 0.9875627 0.4937814
[51,] 0.47696065 0.9539213 0.5230394
[52,] 0.45521101 0.9104220 0.5447890
[53,] 0.41684642 0.8336928 0.5831536
[54,] 0.41124250 0.8224850 0.5887575
[55,] 0.40105484 0.8021097 0.5989452
[56,] 0.56619870 0.8676026 0.4338013
[57,] 0.53591104 0.9281779 0.4640890
[58,] 0.57985711 0.8402858 0.4201429
[59,] 0.53952882 0.9209424 0.4604712
[60,] 0.50559335 0.9888133 0.4944067
[61,] 0.59128487 0.8174303 0.4087151
[62,] 0.62243536 0.7551293 0.3775646
[63,] 0.60218997 0.7956201 0.3978100
[64,] 0.56618490 0.8676302 0.4338151
[65,] 0.53115213 0.9376957 0.4688479
[66,] 0.49249918 0.9849984 0.5075008
[67,] 0.47572449 0.9514490 0.5242755
[68,] 0.43806100 0.8761220 0.5619390
[69,] 0.40046308 0.8009262 0.5995369
[70,] 0.36495290 0.7299058 0.6350471
[71,] 0.34194621 0.6838924 0.6580538
[72,] 0.34957042 0.6991408 0.6504296
[73,] 0.31711697 0.6342339 0.6828830
[74,] 0.29177826 0.5835565 0.7082217
[75,] 0.26054902 0.5210980 0.7394510
[76,] 0.23403933 0.4680787 0.7659607
[77,] 0.20867175 0.4173435 0.7913283
[78,] 0.20160694 0.4032139 0.7983931
[79,] 0.18113065 0.3622613 0.8188694
[80,] 0.15799006 0.3159801 0.8420099
[81,] 0.13970472 0.2794094 0.8602953
[82,] 0.11996627 0.2399325 0.8800337
[83,] 0.10439358 0.2087872 0.8956064
[84,] 0.28154495 0.5630899 0.7184550
[85,] 0.30303890 0.6060778 0.6969611
[86,] 0.27575566 0.5515113 0.7242443
[87,] 0.24799240 0.4959848 0.7520076
[88,] 0.22127836 0.4425567 0.7787216
[89,] 0.19599049 0.3919810 0.8040095
[90,] 0.18904912 0.3780982 0.8109509
[91,] 0.18281328 0.3656266 0.8171867
[92,] 0.16090547 0.3218109 0.8390945
[93,] 0.17596757 0.3519351 0.8240324
[94,] 0.15807767 0.3161553 0.8419223
[95,] 0.16592157 0.3318431 0.8340784
[96,] 0.15907411 0.3181482 0.8409259
[97,] 0.14765473 0.2953095 0.8523453
[98,] 0.14501068 0.2900214 0.8549893
[99,] 0.12836429 0.2567286 0.8716357
[100,] 0.18870672 0.3774134 0.8112933
[101,] 0.18229341 0.3645868 0.8177066
[102,] 0.16272796 0.3254559 0.8372720
[103,] 0.20143198 0.4028640 0.7985680
[104,] 0.25551359 0.5110272 0.7444864
[105,] 0.23429947 0.4685989 0.7657005
[106,] 0.24749198 0.4949840 0.7525080
[107,] 0.22879524 0.4575905 0.7712048
[108,] 0.20348893 0.4069779 0.7965111
[109,] 0.26515670 0.5303134 0.7348433
[110,] 0.26096921 0.5219384 0.7390308
[111,] 0.23477427 0.4695485 0.7652257
[112,] 0.21047549 0.4209510 0.7895245
[113,] 0.18668457 0.3733691 0.8133154
[114,] 0.16452980 0.3290596 0.8354702
[115,] 0.15652937 0.3130587 0.8434706
[116,] 0.13718042 0.2743608 0.8628196
[117,] 0.12211806 0.2442361 0.8778819
[118,] 0.10757806 0.2151561 0.8924219
[119,] 0.09668718 0.1933744 0.9033128
[120,] 0.08482633 0.1696527 0.9151737
[121,] 0.10186022 0.2037204 0.8981398
[122,] 0.11276605 0.2255321 0.8872340
[123,] 0.19633092 0.3926618 0.8036691
[124,] 0.18169342 0.3633868 0.8183066
[125,] 0.16122187 0.3224437 0.8387781
[126,] 0.22045185 0.4409037 0.7795481
[127,] 0.20672118 0.4134424 0.7932788
[128,] 0.19343407 0.3868681 0.8065659
[129,] 0.17706854 0.3541371 0.8229315
[130,] 0.18112344 0.3622469 0.8188766
[131,] 0.17099919 0.3419984 0.8290008
[132,] 0.15138746 0.3027749 0.8486125
[133,] 0.15470903 0.3094181 0.8452910
[134,] 0.14535075 0.2907015 0.8546492
[135,] 0.12806965 0.2561393 0.8719303
[136,] 0.12009356 0.2401871 0.8799064
[137,] 0.12316843 0.2463369 0.8768316
[138,] 0.10683401 0.2136680 0.8931660
[139,] 0.10161445 0.2032289 0.8983856
[140,] 0.10844404 0.2168881 0.8915560
[141,] 0.11247256 0.2249451 0.8875274
[142,] 0.11725191 0.2345038 0.8827481
[143,] 0.10679428 0.2135886 0.8932057
[144,] 0.14549667 0.2909933 0.8545033
[145,] 0.15240454 0.3048091 0.8475955
[146,] 0.13558216 0.2711643 0.8644178
[147,] 0.14076675 0.2815335 0.8592332
[148,] 0.13001243 0.2600249 0.8699876
[149,] 0.17120288 0.3424058 0.8287971
[150,] 0.15020950 0.3004190 0.8497905
[151,] 0.13243853 0.2648771 0.8675615
[152,] 0.11941222 0.2388244 0.8805878
[153,] 0.22651264 0.4530253 0.7734874
[154,] 0.20175155 0.4035031 0.7982485
[155,] 0.18629944 0.3725989 0.8137006
[156,] 0.16579889 0.3315978 0.8342011
[157,] 0.18081866 0.3616373 0.8191813
[158,] 0.16259956 0.3251991 0.8374004
[159,] 0.16171559 0.3234312 0.8382844
[160,] 0.18381059 0.3676212 0.8161894
[161,] 0.17514718 0.3502944 0.8248528
[162,] 0.15542074 0.3108415 0.8445793
[163,] 0.14033860 0.2806772 0.8596614
[164,] 0.19569907 0.3913981 0.8043009
[165,] 0.26194464 0.5238893 0.7380554
[166,] 0.24473884 0.4894777 0.7552612
[167,] 0.24813786 0.4962757 0.7518621
[168,] 0.30791939 0.6158388 0.6920806
[169,] 0.27655722 0.5531144 0.7234428
[170,] 0.28267795 0.5653559 0.7173220
[171,] 0.25365077 0.5073015 0.7463492
[172,] 0.30138554 0.6027711 0.6986145
[173,] 0.27448017 0.5489603 0.7255198
[174,] 0.28753853 0.5750771 0.7124615
[175,] 0.27996516 0.5599303 0.7200348
[176,] 0.25211208 0.5042242 0.7478879
[177,] 0.22374747 0.4474949 0.7762525
[178,] 0.20560163 0.4112033 0.7943984
[179,] 0.18268701 0.3653740 0.8173130
[180,] 0.19377948 0.3875590 0.8062205
[181,] 0.17020376 0.3404075 0.8297962
[182,] 0.15064927 0.3012985 0.8493507
[183,] 0.14746414 0.2949283 0.8525359
[184,] 0.12709067 0.2541813 0.8729093
[185,] 0.11736955 0.2347391 0.8826304
[186,] 0.11498655 0.2299731 0.8850135
[187,] 0.09890631 0.1978126 0.9010937
[188,] 0.10034302 0.2006860 0.8996570
[189,] 0.09756655 0.1951331 0.9024335
[190,] 0.09248028 0.1849606 0.9075197
[191,] 0.08215789 0.1643158 0.9178421
[192,] 0.07153230 0.1430646 0.9284677
[193,] 0.06010435 0.1202087 0.9398957
[194,] 0.10047564 0.2009513 0.8995244
[195,] 0.08426728 0.1685346 0.9157327
[196,] 0.11301812 0.2260362 0.8869819
[197,] 0.10733292 0.2146658 0.8926671
[198,] 0.14661875 0.2932375 0.8533812
[199,] 0.12461514 0.2492303 0.8753849
[200,] 0.12123681 0.2424736 0.8787632
[201,] 0.11269596 0.2253919 0.8873040
[202,] 0.09664143 0.1932829 0.9033586
[203,] 0.10300315 0.2060063 0.8969969
[204,] 0.08585686 0.1717137 0.9141431
[205,] 0.09806541 0.1961308 0.9019346
[206,] 0.12856915 0.2571383 0.8714309
[207,] 0.13998760 0.2799752 0.8600124
[208,] 0.12971364 0.2594273 0.8702864
[209,] 0.13034468 0.2606894 0.8696553
[210,] 0.12191634 0.2438327 0.8780837
[211,] 0.11836781 0.2367356 0.8816322
[212,] 0.16372464 0.3274493 0.8362754
[213,] 0.14575390 0.2915078 0.8542461
[214,] 0.21323739 0.4264748 0.7867626
[215,] 0.20855371 0.4171074 0.7914463
[216,] 0.19086103 0.3817221 0.8091390
[217,] 0.22418131 0.4483626 0.7758187
[218,] 0.22862223 0.4572445 0.7713778
[219,] 0.22975781 0.4595156 0.7702422
[220,] 0.26439268 0.5287854 0.7356073
[221,] 0.32572429 0.6514486 0.6742757
[222,] 0.64461707 0.7107659 0.3553829
[223,] 0.59633190 0.8073362 0.4036681
[224,] 0.70388874 0.5922225 0.2961113
[225,] 0.66975617 0.6604877 0.3302438
[226,] 0.65211974 0.6957605 0.3478803
[227,] 0.70660015 0.5867997 0.2933999
[228,] 0.73929018 0.5214196 0.2607098
[229,] 0.69012633 0.6197473 0.3098737
[230,] 0.76282560 0.4743488 0.2371744
[231,] 0.72894292 0.5421142 0.2710571
[232,] 0.67674650 0.6465070 0.3232535
[233,] 0.62166402 0.7566720 0.3783360
[234,] 0.63410494 0.7317901 0.3658951
[235,] 0.58132218 0.8373556 0.4186778
[236,] 0.51665122 0.9666976 0.4833488
[237,] 0.70739411 0.5852118 0.2926059
[238,] 0.66309286 0.6738143 0.3369071
[239,] 0.59924486 0.8015103 0.4007551
[240,] 0.59534311 0.8093138 0.4046569
[241,] 0.53668368 0.9266326 0.4633163
[242,] 0.46386655 0.9277331 0.5361335
[243,] 0.42383690 0.8476738 0.5761631
[244,] 0.39822196 0.7964439 0.6017780
[245,] 0.36319366 0.7263873 0.6368063
[246,] 0.48471765 0.9694353 0.5152823
[247,] 0.42695085 0.8539017 0.5730492
[248,] 0.51489231 0.9702154 0.4851077
[249,] 0.41871366 0.8374273 0.5812863
[250,] 0.31383112 0.6276622 0.6861689
[251,] 0.23571386 0.4714277 0.7642861
[252,] 0.37537112 0.7507422 0.6246289
[253,] 0.43479118 0.8695824 0.5652088
> postscript(file="/var/wessaorg/rcomp/tmp/194fk1384685343.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29gxm1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3088g1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4uur91384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59qkw1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
0.01588851 4.50664547 -3.29938170 -0.74752312 1.91079844 4.83211661
7 8 9 10 11 12
0.40155540 -0.19429163 1.18117432 1.49644452 3.72192607 5.23626959
13 14 15 16 17 18
-3.81882568 2.17607384 3.59042776 0.29136486 0.66683080 3.01588851
19 20 21 22 23 24
0.17607384 2.34646013 4.45155020 -2.59844460 -0.14429683 -1.87902143
25 26 27 28 29 30
3.34646013 -4.81372521 2.50154499 -0.24938690 1.29136486 -2.38826447
31 32 33 34 35 36
2.40155540 -0.01279852 3.72192607 1.07098378 0.45665067 3.09229155
37 38 39 40 41 42
-4.11788859 1.29136486 3.72192607 -0.38826447 1.66683080 2.29136486
43 44 45 46 47 48
1.86590411 -1.38826447 1.88721188 -2.81882568 0.80060790 1.39645493
49 50 51 52 53 54
3.17607384 -0.65353987 1.69551783 0.77702134 -2.70863514 -2.14429683
55 56 57 58 59 60
-0.53224251 2.14228634 2.23626959 1.23626959 -1.71373562 -1.44335974
61 62 63 64 65 66
-4.63223210 -0.76373041 -3.13919636 0.34135965 1.40155540 -4.08920156
67 68 69 70 71 72
-3.13919636 -1.21787818 0.90569797 1.07098378 0.76682040 2.17607384
73 74 75 76 77 78
0.64042256 0.53023202 -0.37806352 -1.06279332 2.91079844 -0.43825926
79 80 81 82 83 84
1.40155540 -0.37806352 1.12607905 1.07098378 2.29136486 1.39645493
85 86 87 88 89 90
0.72192607 1.44134925 0.23626959 -0.65353987 -6.70863514 3.60663506
91 92 93 94 95 96
-1.13409589 0.85060270 0.56684121 -0.54334933 2.44644972 -1.97901102
97 98 99 100 101 102
0.88211141 3.23626959 1.44644972 2.80570837 -1.65864035 1.59042776
103 104 105 106 107 108
-2.27297345 1.12097857 -4.65353987 2.18117432 0.96079324 -3.65864035
109 110 111 112 113 114
-3.40356589 1.23626959 -3.02900582 -0.98921197 0.45155020 4.55664026
115 116 117 118 119 120
2.18627479 -0.25448737 0.54643932 0.34135965 0.12607905 -1.76883089
121 122 123 124 125 126
0.46175114 0.96079324 0.69551783 1.24137006 -1.18919116 3.66683080
127 128 129 130 131 132
3.12097857 5.60663506 1.45155020 -0.38826447 -4.49335453 1.71172513
133 134 135 136 137 138
1.61683601 1.01588851 2.51174594 -1.62203116 0.34135965 -2.49845501
139 140 141 142 143 144
1.87701094 -0.33826967 1.87701094 2.55153979 -0.29938170 2.00250290
145 146 147 148 149 150
3.04229675 2.46175114 -2.87392095 -1.59844460 -4.17298386 2.76171993
151 152 153 154 155 156
-1.08920156 2.49644452 -1.07809474 -4.12298906 0.04229675 -1.13409589
157 158 159 160 161 162
1.03209580 5.60663506 -0.49335453 -1.34337014 -0.23317960 -3.00259757
163 164 165 166 167 168
1.31777310 1.97099418 -3.71883609 -1.90260798 0.45155020 -1.26787298
169 170 171 172 173 174
-4.98411149 -4.92901622 -1.47714724 2.07098378 -4.49845501 0.53305371
175 176 177 178 179 180
2.56684121 -0.65864035 -4.06789379 -1.06279332 2.72192607 -2.44846021
181 182 183 184 185 186
0.12607905 0.41776270 0.64552303 0.34646013 2.56684121 -0.87392095
187 188 189 190 191 192
0.39645493 2.20248209 0.36776790 -1.75262359 -2.87392095 -0.09430203
193 194 195 196 197 198
2.73303290 -2.76883089 1.66683080 -1.98411149 0.88211141 0.07098378
199 200 201 202 203 204
-5.22807913 -0.65353987 -4.54334933 1.77702134 3.88211141 -0.65353987
205 206 207 208 209 210
1.87701094 1.28626438 0.23116911 2.12607905 -0.38826447 2.61173553
211 212 213 214 215 216
-4.70863514 1.68531688 -2.87902143 -3.01279852 -2.81882568 2.05249770
217 218 219 220 221 222
3.34135965 0.55664026 -5.17808433 0.96079324 -2.70863514 2.44644972
223 224 225 226 227 228
-3.65353987 1.82701614 -4.39336494 3.08719107 5.66683080 -1.28317440
229 230 231 232 233 234
-5.22807913 -2.33826967 1.41776270 -4.66374082 2.19738161 -1.02900582
235 236 237 238 239 240
2.66683080 -2.27807393 -0.58223730 -0.37806352 -3.59844460 -1.74242264
241 242 243 244 245 246
-1.01279852 -5.65864035 0.00850925 -0.74752312 1.72192607 -2.17808433
247 248 249 250 251 252
-0.33826967 0.82701614 -2.40675055 -0.51184061 2.03719628 0.14738682
253 254 255 256 257 258
0.19738161 -0.17298386 -1.38826447 -1.54844980 -4.92901622 -4.01279852
259 260 261 262 263 264
1.39645493 -2.10168129 0.61683601 1.01588851 -6.07299426 0.82701614
> postscript(file="/var/wessaorg/rcomp/tmp/6z0xq1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 0.01588851 NA
1 4.50664547 0.01588851
2 -3.29938170 4.50664547
3 -0.74752312 -3.29938170
4 1.91079844 -0.74752312
5 4.83211661 1.91079844
6 0.40155540 4.83211661
7 -0.19429163 0.40155540
8 1.18117432 -0.19429163
9 1.49644452 1.18117432
10 3.72192607 1.49644452
11 5.23626959 3.72192607
12 -3.81882568 5.23626959
13 2.17607384 -3.81882568
14 3.59042776 2.17607384
15 0.29136486 3.59042776
16 0.66683080 0.29136486
17 3.01588851 0.66683080
18 0.17607384 3.01588851
19 2.34646013 0.17607384
20 4.45155020 2.34646013
21 -2.59844460 4.45155020
22 -0.14429683 -2.59844460
23 -1.87902143 -0.14429683
24 3.34646013 -1.87902143
25 -4.81372521 3.34646013
26 2.50154499 -4.81372521
27 -0.24938690 2.50154499
28 1.29136486 -0.24938690
29 -2.38826447 1.29136486
30 2.40155540 -2.38826447
31 -0.01279852 2.40155540
32 3.72192607 -0.01279852
33 1.07098378 3.72192607
34 0.45665067 1.07098378
35 3.09229155 0.45665067
36 -4.11788859 3.09229155
37 1.29136486 -4.11788859
38 3.72192607 1.29136486
39 -0.38826447 3.72192607
40 1.66683080 -0.38826447
41 2.29136486 1.66683080
42 1.86590411 2.29136486
43 -1.38826447 1.86590411
44 1.88721188 -1.38826447
45 -2.81882568 1.88721188
46 0.80060790 -2.81882568
47 1.39645493 0.80060790
48 3.17607384 1.39645493
49 -0.65353987 3.17607384
50 1.69551783 -0.65353987
51 0.77702134 1.69551783
52 -2.70863514 0.77702134
53 -2.14429683 -2.70863514
54 -0.53224251 -2.14429683
55 2.14228634 -0.53224251
56 2.23626959 2.14228634
57 1.23626959 2.23626959
58 -1.71373562 1.23626959
59 -1.44335974 -1.71373562
60 -4.63223210 -1.44335974
61 -0.76373041 -4.63223210
62 -3.13919636 -0.76373041
63 0.34135965 -3.13919636
64 1.40155540 0.34135965
65 -4.08920156 1.40155540
66 -3.13919636 -4.08920156
67 -1.21787818 -3.13919636
68 0.90569797 -1.21787818
69 1.07098378 0.90569797
70 0.76682040 1.07098378
71 2.17607384 0.76682040
72 0.64042256 2.17607384
73 0.53023202 0.64042256
74 -0.37806352 0.53023202
75 -1.06279332 -0.37806352
76 2.91079844 -1.06279332
77 -0.43825926 2.91079844
78 1.40155540 -0.43825926
79 -0.37806352 1.40155540
80 1.12607905 -0.37806352
81 1.07098378 1.12607905
82 2.29136486 1.07098378
83 1.39645493 2.29136486
84 0.72192607 1.39645493
85 1.44134925 0.72192607
86 0.23626959 1.44134925
87 -0.65353987 0.23626959
88 -6.70863514 -0.65353987
89 3.60663506 -6.70863514
90 -1.13409589 3.60663506
91 0.85060270 -1.13409589
92 0.56684121 0.85060270
93 -0.54334933 0.56684121
94 2.44644972 -0.54334933
95 -1.97901102 2.44644972
96 0.88211141 -1.97901102
97 3.23626959 0.88211141
98 1.44644972 3.23626959
99 2.80570837 1.44644972
100 -1.65864035 2.80570837
101 1.59042776 -1.65864035
102 -2.27297345 1.59042776
103 1.12097857 -2.27297345
104 -4.65353987 1.12097857
105 2.18117432 -4.65353987
106 0.96079324 2.18117432
107 -3.65864035 0.96079324
108 -3.40356589 -3.65864035
109 1.23626959 -3.40356589
110 -3.02900582 1.23626959
111 -0.98921197 -3.02900582
112 0.45155020 -0.98921197
113 4.55664026 0.45155020
114 2.18627479 4.55664026
115 -0.25448737 2.18627479
116 0.54643932 -0.25448737
117 0.34135965 0.54643932
118 0.12607905 0.34135965
119 -1.76883089 0.12607905
120 0.46175114 -1.76883089
121 0.96079324 0.46175114
122 0.69551783 0.96079324
123 1.24137006 0.69551783
124 -1.18919116 1.24137006
125 3.66683080 -1.18919116
126 3.12097857 3.66683080
127 5.60663506 3.12097857
128 1.45155020 5.60663506
129 -0.38826447 1.45155020
130 -4.49335453 -0.38826447
131 1.71172513 -4.49335453
132 1.61683601 1.71172513
133 1.01588851 1.61683601
134 2.51174594 1.01588851
135 -1.62203116 2.51174594
136 0.34135965 -1.62203116
137 -2.49845501 0.34135965
138 1.87701094 -2.49845501
139 -0.33826967 1.87701094
140 1.87701094 -0.33826967
141 2.55153979 1.87701094
142 -0.29938170 2.55153979
143 2.00250290 -0.29938170
144 3.04229675 2.00250290
145 2.46175114 3.04229675
146 -2.87392095 2.46175114
147 -1.59844460 -2.87392095
148 -4.17298386 -1.59844460
149 2.76171993 -4.17298386
150 -1.08920156 2.76171993
151 2.49644452 -1.08920156
152 -1.07809474 2.49644452
153 -4.12298906 -1.07809474
154 0.04229675 -4.12298906
155 -1.13409589 0.04229675
156 1.03209580 -1.13409589
157 5.60663506 1.03209580
158 -0.49335453 5.60663506
159 -1.34337014 -0.49335453
160 -0.23317960 -1.34337014
161 -3.00259757 -0.23317960
162 1.31777310 -3.00259757
163 1.97099418 1.31777310
164 -3.71883609 1.97099418
165 -1.90260798 -3.71883609
166 0.45155020 -1.90260798
167 -1.26787298 0.45155020
168 -4.98411149 -1.26787298
169 -4.92901622 -4.98411149
170 -1.47714724 -4.92901622
171 2.07098378 -1.47714724
172 -4.49845501 2.07098378
173 0.53305371 -4.49845501
174 2.56684121 0.53305371
175 -0.65864035 2.56684121
176 -4.06789379 -0.65864035
177 -1.06279332 -4.06789379
178 2.72192607 -1.06279332
179 -2.44846021 2.72192607
180 0.12607905 -2.44846021
181 0.41776270 0.12607905
182 0.64552303 0.41776270
183 0.34646013 0.64552303
184 2.56684121 0.34646013
185 -0.87392095 2.56684121
186 0.39645493 -0.87392095
187 2.20248209 0.39645493
188 0.36776790 2.20248209
189 -1.75262359 0.36776790
190 -2.87392095 -1.75262359
191 -0.09430203 -2.87392095
192 2.73303290 -0.09430203
193 -2.76883089 2.73303290
194 1.66683080 -2.76883089
195 -1.98411149 1.66683080
196 0.88211141 -1.98411149
197 0.07098378 0.88211141
198 -5.22807913 0.07098378
199 -0.65353987 -5.22807913
200 -4.54334933 -0.65353987
201 1.77702134 -4.54334933
202 3.88211141 1.77702134
203 -0.65353987 3.88211141
204 1.87701094 -0.65353987
205 1.28626438 1.87701094
206 0.23116911 1.28626438
207 2.12607905 0.23116911
208 -0.38826447 2.12607905
209 2.61173553 -0.38826447
210 -4.70863514 2.61173553
211 1.68531688 -4.70863514
212 -2.87902143 1.68531688
213 -3.01279852 -2.87902143
214 -2.81882568 -3.01279852
215 2.05249770 -2.81882568
216 3.34135965 2.05249770
217 0.55664026 3.34135965
218 -5.17808433 0.55664026
219 0.96079324 -5.17808433
220 -2.70863514 0.96079324
221 2.44644972 -2.70863514
222 -3.65353987 2.44644972
223 1.82701614 -3.65353987
224 -4.39336494 1.82701614
225 3.08719107 -4.39336494
226 5.66683080 3.08719107
227 -1.28317440 5.66683080
228 -5.22807913 -1.28317440
229 -2.33826967 -5.22807913
230 1.41776270 -2.33826967
231 -4.66374082 1.41776270
232 2.19738161 -4.66374082
233 -1.02900582 2.19738161
234 2.66683080 -1.02900582
235 -2.27807393 2.66683080
236 -0.58223730 -2.27807393
237 -0.37806352 -0.58223730
238 -3.59844460 -0.37806352
239 -1.74242264 -3.59844460
240 -1.01279852 -1.74242264
241 -5.65864035 -1.01279852
242 0.00850925 -5.65864035
243 -0.74752312 0.00850925
244 1.72192607 -0.74752312
245 -2.17808433 1.72192607
246 -0.33826967 -2.17808433
247 0.82701614 -0.33826967
248 -2.40675055 0.82701614
249 -0.51184061 -2.40675055
250 2.03719628 -0.51184061
251 0.14738682 2.03719628
252 0.19738161 0.14738682
253 -0.17298386 0.19738161
254 -1.38826447 -0.17298386
255 -1.54844980 -1.38826447
256 -4.92901622 -1.54844980
257 -4.01279852 -4.92901622
258 1.39645493 -4.01279852
259 -2.10168129 1.39645493
260 0.61683601 -2.10168129
261 1.01588851 0.61683601
262 -6.07299426 1.01588851
263 0.82701614 -6.07299426
264 NA 0.82701614
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.50664547 0.01588851
[2,] -3.29938170 4.50664547
[3,] -0.74752312 -3.29938170
[4,] 1.91079844 -0.74752312
[5,] 4.83211661 1.91079844
[6,] 0.40155540 4.83211661
[7,] -0.19429163 0.40155540
[8,] 1.18117432 -0.19429163
[9,] 1.49644452 1.18117432
[10,] 3.72192607 1.49644452
[11,] 5.23626959 3.72192607
[12,] -3.81882568 5.23626959
[13,] 2.17607384 -3.81882568
[14,] 3.59042776 2.17607384
[15,] 0.29136486 3.59042776
[16,] 0.66683080 0.29136486
[17,] 3.01588851 0.66683080
[18,] 0.17607384 3.01588851
[19,] 2.34646013 0.17607384
[20,] 4.45155020 2.34646013
[21,] -2.59844460 4.45155020
[22,] -0.14429683 -2.59844460
[23,] -1.87902143 -0.14429683
[24,] 3.34646013 -1.87902143
[25,] -4.81372521 3.34646013
[26,] 2.50154499 -4.81372521
[27,] -0.24938690 2.50154499
[28,] 1.29136486 -0.24938690
[29,] -2.38826447 1.29136486
[30,] 2.40155540 -2.38826447
[31,] -0.01279852 2.40155540
[32,] 3.72192607 -0.01279852
[33,] 1.07098378 3.72192607
[34,] 0.45665067 1.07098378
[35,] 3.09229155 0.45665067
[36,] -4.11788859 3.09229155
[37,] 1.29136486 -4.11788859
[38,] 3.72192607 1.29136486
[39,] -0.38826447 3.72192607
[40,] 1.66683080 -0.38826447
[41,] 2.29136486 1.66683080
[42,] 1.86590411 2.29136486
[43,] -1.38826447 1.86590411
[44,] 1.88721188 -1.38826447
[45,] -2.81882568 1.88721188
[46,] 0.80060790 -2.81882568
[47,] 1.39645493 0.80060790
[48,] 3.17607384 1.39645493
[49,] -0.65353987 3.17607384
[50,] 1.69551783 -0.65353987
[51,] 0.77702134 1.69551783
[52,] -2.70863514 0.77702134
[53,] -2.14429683 -2.70863514
[54,] -0.53224251 -2.14429683
[55,] 2.14228634 -0.53224251
[56,] 2.23626959 2.14228634
[57,] 1.23626959 2.23626959
[58,] -1.71373562 1.23626959
[59,] -1.44335974 -1.71373562
[60,] -4.63223210 -1.44335974
[61,] -0.76373041 -4.63223210
[62,] -3.13919636 -0.76373041
[63,] 0.34135965 -3.13919636
[64,] 1.40155540 0.34135965
[65,] -4.08920156 1.40155540
[66,] -3.13919636 -4.08920156
[67,] -1.21787818 -3.13919636
[68,] 0.90569797 -1.21787818
[69,] 1.07098378 0.90569797
[70,] 0.76682040 1.07098378
[71,] 2.17607384 0.76682040
[72,] 0.64042256 2.17607384
[73,] 0.53023202 0.64042256
[74,] -0.37806352 0.53023202
[75,] -1.06279332 -0.37806352
[76,] 2.91079844 -1.06279332
[77,] -0.43825926 2.91079844
[78,] 1.40155540 -0.43825926
[79,] -0.37806352 1.40155540
[80,] 1.12607905 -0.37806352
[81,] 1.07098378 1.12607905
[82,] 2.29136486 1.07098378
[83,] 1.39645493 2.29136486
[84,] 0.72192607 1.39645493
[85,] 1.44134925 0.72192607
[86,] 0.23626959 1.44134925
[87,] -0.65353987 0.23626959
[88,] -6.70863514 -0.65353987
[89,] 3.60663506 -6.70863514
[90,] -1.13409589 3.60663506
[91,] 0.85060270 -1.13409589
[92,] 0.56684121 0.85060270
[93,] -0.54334933 0.56684121
[94,] 2.44644972 -0.54334933
[95,] -1.97901102 2.44644972
[96,] 0.88211141 -1.97901102
[97,] 3.23626959 0.88211141
[98,] 1.44644972 3.23626959
[99,] 2.80570837 1.44644972
[100,] -1.65864035 2.80570837
[101,] 1.59042776 -1.65864035
[102,] -2.27297345 1.59042776
[103,] 1.12097857 -2.27297345
[104,] -4.65353987 1.12097857
[105,] 2.18117432 -4.65353987
[106,] 0.96079324 2.18117432
[107,] -3.65864035 0.96079324
[108,] -3.40356589 -3.65864035
[109,] 1.23626959 -3.40356589
[110,] -3.02900582 1.23626959
[111,] -0.98921197 -3.02900582
[112,] 0.45155020 -0.98921197
[113,] 4.55664026 0.45155020
[114,] 2.18627479 4.55664026
[115,] -0.25448737 2.18627479
[116,] 0.54643932 -0.25448737
[117,] 0.34135965 0.54643932
[118,] 0.12607905 0.34135965
[119,] -1.76883089 0.12607905
[120,] 0.46175114 -1.76883089
[121,] 0.96079324 0.46175114
[122,] 0.69551783 0.96079324
[123,] 1.24137006 0.69551783
[124,] -1.18919116 1.24137006
[125,] 3.66683080 -1.18919116
[126,] 3.12097857 3.66683080
[127,] 5.60663506 3.12097857
[128,] 1.45155020 5.60663506
[129,] -0.38826447 1.45155020
[130,] -4.49335453 -0.38826447
[131,] 1.71172513 -4.49335453
[132,] 1.61683601 1.71172513
[133,] 1.01588851 1.61683601
[134,] 2.51174594 1.01588851
[135,] -1.62203116 2.51174594
[136,] 0.34135965 -1.62203116
[137,] -2.49845501 0.34135965
[138,] 1.87701094 -2.49845501
[139,] -0.33826967 1.87701094
[140,] 1.87701094 -0.33826967
[141,] 2.55153979 1.87701094
[142,] -0.29938170 2.55153979
[143,] 2.00250290 -0.29938170
[144,] 3.04229675 2.00250290
[145,] 2.46175114 3.04229675
[146,] -2.87392095 2.46175114
[147,] -1.59844460 -2.87392095
[148,] -4.17298386 -1.59844460
[149,] 2.76171993 -4.17298386
[150,] -1.08920156 2.76171993
[151,] 2.49644452 -1.08920156
[152,] -1.07809474 2.49644452
[153,] -4.12298906 -1.07809474
[154,] 0.04229675 -4.12298906
[155,] -1.13409589 0.04229675
[156,] 1.03209580 -1.13409589
[157,] 5.60663506 1.03209580
[158,] -0.49335453 5.60663506
[159,] -1.34337014 -0.49335453
[160,] -0.23317960 -1.34337014
[161,] -3.00259757 -0.23317960
[162,] 1.31777310 -3.00259757
[163,] 1.97099418 1.31777310
[164,] -3.71883609 1.97099418
[165,] -1.90260798 -3.71883609
[166,] 0.45155020 -1.90260798
[167,] -1.26787298 0.45155020
[168,] -4.98411149 -1.26787298
[169,] -4.92901622 -4.98411149
[170,] -1.47714724 -4.92901622
[171,] 2.07098378 -1.47714724
[172,] -4.49845501 2.07098378
[173,] 0.53305371 -4.49845501
[174,] 2.56684121 0.53305371
[175,] -0.65864035 2.56684121
[176,] -4.06789379 -0.65864035
[177,] -1.06279332 -4.06789379
[178,] 2.72192607 -1.06279332
[179,] -2.44846021 2.72192607
[180,] 0.12607905 -2.44846021
[181,] 0.41776270 0.12607905
[182,] 0.64552303 0.41776270
[183,] 0.34646013 0.64552303
[184,] 2.56684121 0.34646013
[185,] -0.87392095 2.56684121
[186,] 0.39645493 -0.87392095
[187,] 2.20248209 0.39645493
[188,] 0.36776790 2.20248209
[189,] -1.75262359 0.36776790
[190,] -2.87392095 -1.75262359
[191,] -0.09430203 -2.87392095
[192,] 2.73303290 -0.09430203
[193,] -2.76883089 2.73303290
[194,] 1.66683080 -2.76883089
[195,] -1.98411149 1.66683080
[196,] 0.88211141 -1.98411149
[197,] 0.07098378 0.88211141
[198,] -5.22807913 0.07098378
[199,] -0.65353987 -5.22807913
[200,] -4.54334933 -0.65353987
[201,] 1.77702134 -4.54334933
[202,] 3.88211141 1.77702134
[203,] -0.65353987 3.88211141
[204,] 1.87701094 -0.65353987
[205,] 1.28626438 1.87701094
[206,] 0.23116911 1.28626438
[207,] 2.12607905 0.23116911
[208,] -0.38826447 2.12607905
[209,] 2.61173553 -0.38826447
[210,] -4.70863514 2.61173553
[211,] 1.68531688 -4.70863514
[212,] -2.87902143 1.68531688
[213,] -3.01279852 -2.87902143
[214,] -2.81882568 -3.01279852
[215,] 2.05249770 -2.81882568
[216,] 3.34135965 2.05249770
[217,] 0.55664026 3.34135965
[218,] -5.17808433 0.55664026
[219,] 0.96079324 -5.17808433
[220,] -2.70863514 0.96079324
[221,] 2.44644972 -2.70863514
[222,] -3.65353987 2.44644972
[223,] 1.82701614 -3.65353987
[224,] -4.39336494 1.82701614
[225,] 3.08719107 -4.39336494
[226,] 5.66683080 3.08719107
[227,] -1.28317440 5.66683080
[228,] -5.22807913 -1.28317440
[229,] -2.33826967 -5.22807913
[230,] 1.41776270 -2.33826967
[231,] -4.66374082 1.41776270
[232,] 2.19738161 -4.66374082
[233,] -1.02900582 2.19738161
[234,] 2.66683080 -1.02900582
[235,] -2.27807393 2.66683080
[236,] -0.58223730 -2.27807393
[237,] -0.37806352 -0.58223730
[238,] -3.59844460 -0.37806352
[239,] -1.74242264 -3.59844460
[240,] -1.01279852 -1.74242264
[241,] -5.65864035 -1.01279852
[242,] 0.00850925 -5.65864035
[243,] -0.74752312 0.00850925
[244,] 1.72192607 -0.74752312
[245,] -2.17808433 1.72192607
[246,] -0.33826967 -2.17808433
[247,] 0.82701614 -0.33826967
[248,] -2.40675055 0.82701614
[249,] -0.51184061 -2.40675055
[250,] 2.03719628 -0.51184061
[251,] 0.14738682 2.03719628
[252,] 0.19738161 0.14738682
[253,] -0.17298386 0.19738161
[254,] -1.38826447 -0.17298386
[255,] -1.54844980 -1.38826447
[256,] -4.92901622 -1.54844980
[257,] -4.01279852 -4.92901622
[258,] 1.39645493 -4.01279852
[259,] -2.10168129 1.39645493
[260,] 0.61683601 -2.10168129
[261,] 1.01588851 0.61683601
[262,] -6.07299426 1.01588851
[263,] 0.82701614 -6.07299426
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.50664547 0.01588851
2 -3.29938170 4.50664547
3 -0.74752312 -3.29938170
4 1.91079844 -0.74752312
5 4.83211661 1.91079844
6 0.40155540 4.83211661
7 -0.19429163 0.40155540
8 1.18117432 -0.19429163
9 1.49644452 1.18117432
10 3.72192607 1.49644452
11 5.23626959 3.72192607
12 -3.81882568 5.23626959
13 2.17607384 -3.81882568
14 3.59042776 2.17607384
15 0.29136486 3.59042776
16 0.66683080 0.29136486
17 3.01588851 0.66683080
18 0.17607384 3.01588851
19 2.34646013 0.17607384
20 4.45155020 2.34646013
21 -2.59844460 4.45155020
22 -0.14429683 -2.59844460
23 -1.87902143 -0.14429683
24 3.34646013 -1.87902143
25 -4.81372521 3.34646013
26 2.50154499 -4.81372521
27 -0.24938690 2.50154499
28 1.29136486 -0.24938690
29 -2.38826447 1.29136486
30 2.40155540 -2.38826447
31 -0.01279852 2.40155540
32 3.72192607 -0.01279852
33 1.07098378 3.72192607
34 0.45665067 1.07098378
35 3.09229155 0.45665067
36 -4.11788859 3.09229155
37 1.29136486 -4.11788859
38 3.72192607 1.29136486
39 -0.38826447 3.72192607
40 1.66683080 -0.38826447
41 2.29136486 1.66683080
42 1.86590411 2.29136486
43 -1.38826447 1.86590411
44 1.88721188 -1.38826447
45 -2.81882568 1.88721188
46 0.80060790 -2.81882568
47 1.39645493 0.80060790
48 3.17607384 1.39645493
49 -0.65353987 3.17607384
50 1.69551783 -0.65353987
51 0.77702134 1.69551783
52 -2.70863514 0.77702134
53 -2.14429683 -2.70863514
54 -0.53224251 -2.14429683
55 2.14228634 -0.53224251
56 2.23626959 2.14228634
57 1.23626959 2.23626959
58 -1.71373562 1.23626959
59 -1.44335974 -1.71373562
60 -4.63223210 -1.44335974
61 -0.76373041 -4.63223210
62 -3.13919636 -0.76373041
63 0.34135965 -3.13919636
64 1.40155540 0.34135965
65 -4.08920156 1.40155540
66 -3.13919636 -4.08920156
67 -1.21787818 -3.13919636
68 0.90569797 -1.21787818
69 1.07098378 0.90569797
70 0.76682040 1.07098378
71 2.17607384 0.76682040
72 0.64042256 2.17607384
73 0.53023202 0.64042256
74 -0.37806352 0.53023202
75 -1.06279332 -0.37806352
76 2.91079844 -1.06279332
77 -0.43825926 2.91079844
78 1.40155540 -0.43825926
79 -0.37806352 1.40155540
80 1.12607905 -0.37806352
81 1.07098378 1.12607905
82 2.29136486 1.07098378
83 1.39645493 2.29136486
84 0.72192607 1.39645493
85 1.44134925 0.72192607
86 0.23626959 1.44134925
87 -0.65353987 0.23626959
88 -6.70863514 -0.65353987
89 3.60663506 -6.70863514
90 -1.13409589 3.60663506
91 0.85060270 -1.13409589
92 0.56684121 0.85060270
93 -0.54334933 0.56684121
94 2.44644972 -0.54334933
95 -1.97901102 2.44644972
96 0.88211141 -1.97901102
97 3.23626959 0.88211141
98 1.44644972 3.23626959
99 2.80570837 1.44644972
100 -1.65864035 2.80570837
101 1.59042776 -1.65864035
102 -2.27297345 1.59042776
103 1.12097857 -2.27297345
104 -4.65353987 1.12097857
105 2.18117432 -4.65353987
106 0.96079324 2.18117432
107 -3.65864035 0.96079324
108 -3.40356589 -3.65864035
109 1.23626959 -3.40356589
110 -3.02900582 1.23626959
111 -0.98921197 -3.02900582
112 0.45155020 -0.98921197
113 4.55664026 0.45155020
114 2.18627479 4.55664026
115 -0.25448737 2.18627479
116 0.54643932 -0.25448737
117 0.34135965 0.54643932
118 0.12607905 0.34135965
119 -1.76883089 0.12607905
120 0.46175114 -1.76883089
121 0.96079324 0.46175114
122 0.69551783 0.96079324
123 1.24137006 0.69551783
124 -1.18919116 1.24137006
125 3.66683080 -1.18919116
126 3.12097857 3.66683080
127 5.60663506 3.12097857
128 1.45155020 5.60663506
129 -0.38826447 1.45155020
130 -4.49335453 -0.38826447
131 1.71172513 -4.49335453
132 1.61683601 1.71172513
133 1.01588851 1.61683601
134 2.51174594 1.01588851
135 -1.62203116 2.51174594
136 0.34135965 -1.62203116
137 -2.49845501 0.34135965
138 1.87701094 -2.49845501
139 -0.33826967 1.87701094
140 1.87701094 -0.33826967
141 2.55153979 1.87701094
142 -0.29938170 2.55153979
143 2.00250290 -0.29938170
144 3.04229675 2.00250290
145 2.46175114 3.04229675
146 -2.87392095 2.46175114
147 -1.59844460 -2.87392095
148 -4.17298386 -1.59844460
149 2.76171993 -4.17298386
150 -1.08920156 2.76171993
151 2.49644452 -1.08920156
152 -1.07809474 2.49644452
153 -4.12298906 -1.07809474
154 0.04229675 -4.12298906
155 -1.13409589 0.04229675
156 1.03209580 -1.13409589
157 5.60663506 1.03209580
158 -0.49335453 5.60663506
159 -1.34337014 -0.49335453
160 -0.23317960 -1.34337014
161 -3.00259757 -0.23317960
162 1.31777310 -3.00259757
163 1.97099418 1.31777310
164 -3.71883609 1.97099418
165 -1.90260798 -3.71883609
166 0.45155020 -1.90260798
167 -1.26787298 0.45155020
168 -4.98411149 -1.26787298
169 -4.92901622 -4.98411149
170 -1.47714724 -4.92901622
171 2.07098378 -1.47714724
172 -4.49845501 2.07098378
173 0.53305371 -4.49845501
174 2.56684121 0.53305371
175 -0.65864035 2.56684121
176 -4.06789379 -0.65864035
177 -1.06279332 -4.06789379
178 2.72192607 -1.06279332
179 -2.44846021 2.72192607
180 0.12607905 -2.44846021
181 0.41776270 0.12607905
182 0.64552303 0.41776270
183 0.34646013 0.64552303
184 2.56684121 0.34646013
185 -0.87392095 2.56684121
186 0.39645493 -0.87392095
187 2.20248209 0.39645493
188 0.36776790 2.20248209
189 -1.75262359 0.36776790
190 -2.87392095 -1.75262359
191 -0.09430203 -2.87392095
192 2.73303290 -0.09430203
193 -2.76883089 2.73303290
194 1.66683080 -2.76883089
195 -1.98411149 1.66683080
196 0.88211141 -1.98411149
197 0.07098378 0.88211141
198 -5.22807913 0.07098378
199 -0.65353987 -5.22807913
200 -4.54334933 -0.65353987
201 1.77702134 -4.54334933
202 3.88211141 1.77702134
203 -0.65353987 3.88211141
204 1.87701094 -0.65353987
205 1.28626438 1.87701094
206 0.23116911 1.28626438
207 2.12607905 0.23116911
208 -0.38826447 2.12607905
209 2.61173553 -0.38826447
210 -4.70863514 2.61173553
211 1.68531688 -4.70863514
212 -2.87902143 1.68531688
213 -3.01279852 -2.87902143
214 -2.81882568 -3.01279852
215 2.05249770 -2.81882568
216 3.34135965 2.05249770
217 0.55664026 3.34135965
218 -5.17808433 0.55664026
219 0.96079324 -5.17808433
220 -2.70863514 0.96079324
221 2.44644972 -2.70863514
222 -3.65353987 2.44644972
223 1.82701614 -3.65353987
224 -4.39336494 1.82701614
225 3.08719107 -4.39336494
226 5.66683080 3.08719107
227 -1.28317440 5.66683080
228 -5.22807913 -1.28317440
229 -2.33826967 -5.22807913
230 1.41776270 -2.33826967
231 -4.66374082 1.41776270
232 2.19738161 -4.66374082
233 -1.02900582 2.19738161
234 2.66683080 -1.02900582
235 -2.27807393 2.66683080
236 -0.58223730 -2.27807393
237 -0.37806352 -0.58223730
238 -3.59844460 -0.37806352
239 -1.74242264 -3.59844460
240 -1.01279852 -1.74242264
241 -5.65864035 -1.01279852
242 0.00850925 -5.65864035
243 -0.74752312 0.00850925
244 1.72192607 -0.74752312
245 -2.17808433 1.72192607
246 -0.33826967 -2.17808433
247 0.82701614 -0.33826967
248 -2.40675055 0.82701614
249 -0.51184061 -2.40675055
250 2.03719628 -0.51184061
251 0.14738682 2.03719628
252 0.19738161 0.14738682
253 -0.17298386 0.19738161
254 -1.38826447 -0.17298386
255 -1.54844980 -1.38826447
256 -4.92901622 -1.54844980
257 -4.01279852 -4.92901622
258 1.39645493 -4.01279852
259 -2.10168129 1.39645493
260 0.61683601 -2.10168129
261 1.01588851 0.61683601
262 -6.07299426 1.01588851
263 0.82701614 -6.07299426
> 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/7ex0v1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8breo1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9gmov1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10aybi1384685343.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1194h81384685343.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/12nkrb1384685343.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/13t3s91384685344.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/14ni0o1384685344.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/15vqzr1384685344.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/16rcqr1384685344.tab")
+ }
>
> try(system("convert tmp/194fk1384685343.ps tmp/194fk1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/29gxm1384685343.ps tmp/29gxm1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/3088g1384685343.ps tmp/3088g1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uur91384685343.ps tmp/4uur91384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/59qkw1384685343.ps tmp/59qkw1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z0xq1384685343.ps tmp/6z0xq1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ex0v1384685343.ps tmp/7ex0v1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/8breo1384685343.ps tmp/8breo1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gmov1384685343.ps tmp/9gmov1384685343.png",intern=TRUE))
character(0)
> try(system("convert tmp/10aybi1384685343.ps tmp/10aybi1384685343.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.908 1.574 11.472