R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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> x <- array(list(14
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+ ,14)
+ ,dim=c(5
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Month'
+ ,'Learning')
+ ,1:264))
> y <- array(NA,dim=c(5,264),dimnames=list(c('Happiness','Connected','Separate','Month','Learning'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Month Learning
1 14 41 38 9 13
2 18 39 32 9 16
3 11 30 35 9 19
4 12 31 33 9 15
5 16 34 37 9 14
6 18 35 29 9 13
7 14 39 31 9 19
8 14 34 36 9 15
9 15 36 35 9 14
10 15 37 38 9 15
11 17 38 31 9 16
12 19 36 34 9 16
13 10 38 35 9 16
14 16 39 38 9 16
15 18 33 37 9 17
16 14 32 33 9 15
17 14 36 32 9 15
18 17 38 38 9 20
19 14 39 38 9 18
20 16 32 32 9 16
21 18 32 33 9 16
22 11 31 31 9 16
23 14 39 38 9 19
24 12 37 39 9 16
25 17 39 32 9 17
26 9 41 32 9 17
27 16 36 35 9 16
28 14 33 37 9 15
29 15 33 33 9 16
30 11 34 33 9 14
31 16 31 31 9 15
32 13 27 32 9 12
33 17 37 31 9 14
34 15 34 37 9 16
35 14 34 30 9 14
36 16 32 33 9 10
37 9 29 31 9 10
38 15 36 33 9 14
39 17 29 31 9 16
40 13 35 33 9 16
41 15 37 32 9 16
42 16 34 33 9 14
43 16 38 32 9 20
44 12 35 33 9 14
45 15 38 28 9 14
46 11 37 35 9 11
47 15 38 39 9 14
48 15 33 34 9 15
49 17 36 38 9 16
50 13 38 32 9 14
51 16 32 38 9 16
52 14 32 30 9 14
53 11 32 33 9 12
54 12 34 38 9 16
55 12 32 32 9 9
56 15 37 35 9 14
57 16 39 34 9 16
58 15 29 34 9 16
59 12 37 36 9 15
60 12 35 34 9 16
61 8 30 28 9 12
62 13 38 34 9 16
63 11 34 35 9 16
64 14 31 35 9 14
65 15 34 31 9 16
66 10 35 37 10 17
67 11 36 35 10 18
68 12 30 27 10 18
69 15 39 40 10 12
70 15 35 37 10 16
71 14 38 36 10 10
72 16 31 38 10 14
73 15 34 39 10 18
74 15 38 41 10 18
75 13 34 27 10 16
76 12 39 30 10 17
77 17 37 37 10 16
78 13 34 31 10 16
79 15 28 31 10 13
80 13 37 27 10 16
81 15 33 36 10 16
82 15 35 37 10 16
83 16 37 33 10 15
84 15 32 34 10 15
85 14 33 31 10 16
86 15 38 39 10 14
87 14 33 34 10 16
88 13 29 32 10 16
89 7 33 33 10 15
90 17 31 36 10 12
91 13 36 32 10 17
92 15 35 41 10 16
93 14 32 28 10 15
94 13 29 30 10 13
95 16 39 36 10 16
96 12 37 35 10 16
97 14 35 31 10 16
98 17 37 34 10 16
99 15 32 36 10 14
100 17 38 36 10 16
101 12 37 35 10 16
102 16 36 37 10 20
103 11 32 28 10 15
104 15 33 39 10 16
105 9 40 32 10 13
106 16 38 35 10 17
107 15 41 39 10 16
108 10 36 35 10 16
109 10 43 42 10 12
110 15 30 34 10 16
111 11 31 33 10 16
112 13 32 41 10 17
113 14 32 33 10 13
114 18 37 34 10 12
115 16 37 32 10 18
116 14 33 40 10 14
117 14 34 40 10 14
118 14 33 35 10 13
119 14 38 36 10 16
120 12 33 37 10 13
121 14 31 27 10 16
122 15 38 39 10 13
123 15 37 38 10 16
124 15 36 31 10 15
125 13 31 33 10 16
126 17 39 32 10 15
127 17 44 39 10 17
128 19 33 36 10 15
129 15 35 33 10 12
130 13 32 33 10 16
131 9 28 32 10 10
132 15 40 37 10 16
133 15 27 30 10 12
134 15 37 38 10 14
135 16 32 29 10 15
136 11 28 22 10 13
137 14 34 35 10 15
138 11 30 35 10 11
139 15 35 34 10 12
140 13 31 35 10 11
141 15 32 34 10 16
142 16 30 37 10 15
143 14 30 35 10 17
144 15 31 23 10 16
145 16 40 31 10 10
146 16 32 27 10 18
147 11 36 36 10 13
148 12 32 31 10 16
149 9 35 32 10 13
150 16 38 39 10 10
151 13 42 37 10 15
152 16 34 38 10 16
153 12 35 39 10 16
154 9 38 34 9 14
155 13 33 31 10 10
156 13 36 32 10 17
157 14 32 37 10 13
158 19 33 36 10 15
159 13 34 32 10 16
160 12 32 38 10 12
161 13 34 36 10 13
162 10 27 26 11 13
163 14 31 26 11 12
164 16 38 33 11 17
165 10 34 39 11 15
166 11 24 30 11 10
167 14 30 33 11 14
168 12 26 25 11 11
169 9 34 38 11 13
170 9 27 37 11 16
171 11 37 31 11 12
172 16 36 37 11 16
173 9 41 35 11 12
174 13 29 25 11 9
175 16 36 28 11 12
176 13 32 35 11 15
177 9 37 33 11 12
178 12 30 30 11 12
179 16 31 31 11 14
180 11 38 37 11 12
181 14 36 36 11 16
182 13 35 30 11 11
183 15 31 36 11 19
184 14 38 32 11 15
185 16 22 28 11 8
186 13 32 36 11 16
187 14 36 34 11 17
188 15 39 31 11 12
189 13 28 28 11 11
190 11 32 36 11 11
191 11 32 36 11 14
192 14 38 40 11 16
193 15 32 33 11 12
194 11 35 37 11 16
195 15 32 32 11 13
196 12 37 38 11 15
197 14 34 31 11 16
198 14 33 37 11 16
199 8 33 33 11 14
200 13 26 32 11 16
201 9 30 30 11 16
202 15 24 30 11 14
203 17 34 31 11 11
204 13 34 32 11 12
205 15 33 34 11 15
206 15 34 36 11 15
207 14 35 37 11 16
208 16 35 36 11 16
209 13 36 33 11 11
210 16 34 33 11 15
211 9 34 33 11 12
212 16 41 44 11 12
213 11 32 39 11 15
214 10 30 32 11 15
215 11 35 35 11 16
216 15 28 25 11 14
217 17 33 35 11 17
218 14 39 34 11 14
219 8 36 35 11 13
220 15 36 39 11 15
221 11 35 33 11 13
222 16 38 36 11 14
223 10 33 32 11 15
224 15 31 32 11 12
225 9 34 36 11 13
226 16 32 36 11 8
227 19 31 32 11 14
228 12 33 34 11 14
229 8 34 33 11 11
230 11 34 35 11 12
231 14 34 30 11 13
232 9 33 38 11 10
233 15 32 34 11 16
234 13 41 33 11 18
235 16 34 32 11 13
236 11 36 31 11 11
237 12 37 30 11 4
238 13 36 27 11 13
239 10 29 31 11 16
240 11 37 30 11 10
241 12 27 32 11 12
242 8 35 35 11 12
243 12 28 28 11 10
244 12 35 33 11 13
245 15 37 31 11 15
246 11 29 35 11 12
247 13 32 35 11 14
248 14 36 32 11 10
249 10 19 21 11 12
250 12 21 20 11 12
251 15 31 34 11 11
252 13 33 32 11 10
253 13 36 34 11 12
254 13 33 32 11 16
255 12 37 33 11 12
256 12 34 33 11 14
257 9 35 37 11 16
258 9 31 32 11 14
259 15 37 34 11 13
260 10 35 30 11 4
261 14 27 30 11 15
262 15 34 38 11 11
263 7 40 36 11 11
264 14 29 32 11 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Month Learning
14.88902 0.03298 0.01882 -0.55981 0.17813
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6726 -1.5117 0.4099 1.6764 6.1501
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.88902 2.97392 5.007 1.03e-06 ***
Connected 0.03298 0.04435 0.744 0.45773
Separate 0.01882 0.04526 0.416 0.67783
Month -0.55981 0.19972 -2.803 0.00545 **
Learning 0.17813 0.06492 2.744 0.00649 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.393 on 259 degrees of freedom
Multiple R-squared: 0.09686, Adjusted R-squared: 0.08291
F-statistic: 6.944 on 4 and 259 DF, p-value: 2.523e-05
> 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,] 0.60427417 0.79145166 0.39572583
[2,] 0.43748179 0.87496357 0.56251821
[3,] 0.33156299 0.66312598 0.66843701
[4,] 0.23110472 0.46220944 0.76889528
[5,] 0.49842060 0.99684120 0.50157940
[6,] 0.77755521 0.44488958 0.22244479
[7,] 0.75289796 0.49420408 0.24710204
[8,] 0.88702733 0.22594534 0.11297267
[9,] 0.84689487 0.30621026 0.15310513
[10,] 0.81472126 0.37055747 0.18527874
[11,] 0.81523705 0.36952590 0.18476295
[12,] 0.77109704 0.45780592 0.22890296
[13,] 0.72823837 0.54352326 0.27176163
[14,] 0.76319154 0.47361692 0.23680846
[15,] 0.82485504 0.35028993 0.17514496
[16,] 0.78407336 0.43185328 0.21592664
[17,] 0.78583377 0.42833246 0.21416623
[18,] 0.74875818 0.50248363 0.25124182
[19,] 0.93612323 0.12775354 0.06387677
[20,] 0.92034834 0.15930332 0.07965166
[21,] 0.89730242 0.20539517 0.10269758
[22,] 0.86880324 0.26239352 0.13119676
[23,] 0.90246783 0.19506433 0.09753217
[24,] 0.88405779 0.23188442 0.11594221
[25,] 0.86395922 0.27208156 0.13604078
[26,] 0.85301004 0.29397993 0.14698996
[27,] 0.82154243 0.35691514 0.17845757
[28,] 0.78933612 0.42132776 0.21066388
[29,] 0.76305495 0.47389010 0.23694505
[30,] 0.87041715 0.25916570 0.12958285
[31,] 0.84226466 0.31547069 0.15773534
[32,] 0.84902676 0.30194648 0.15097324
[33,] 0.83190486 0.33619027 0.16809514
[34,] 0.79887620 0.40224759 0.20112380
[35,] 0.77831715 0.44336570 0.22168285
[36,] 0.74263075 0.51473849 0.25736925
[37,] 0.74146876 0.51706248 0.25853124
[38,] 0.70263273 0.59473455 0.29736727
[39,] 0.71531841 0.56936318 0.28468159
[40,] 0.68011579 0.63976842 0.31988421
[41,] 0.64029036 0.71941928 0.35970964
[42,] 0.64290274 0.71419453 0.35709726
[43,] 0.61408335 0.77183329 0.38591665
[44,] 0.58720758 0.82558484 0.41279242
[45,] 0.54287605 0.91424790 0.45712395
[46,] 0.55323347 0.89353305 0.44676653
[47,] 0.55504861 0.88990279 0.44495139
[48,] 0.51660121 0.96679759 0.48339879
[49,] 0.47728602 0.95457205 0.52271398
[50,] 0.44490132 0.88980263 0.55509868
[51,] 0.40510456 0.81020911 0.59489544
[52,] 0.40391326 0.80782652 0.59608674
[53,] 0.41035779 0.82071557 0.58964221
[54,] 0.58324051 0.83351898 0.41675949
[55,] 0.56224214 0.87551572 0.43775786
[56,] 0.60695685 0.78608630 0.39304315
[57,] 0.56689582 0.86620836 0.43310418
[58,] 0.52715410 0.94569179 0.47284590
[59,] 0.51667177 0.96665646 0.48332823
[60,] 0.49708252 0.99416504 0.50291748
[61,] 0.47287119 0.94574238 0.52712881
[62,] 0.53302508 0.93394983 0.46697492
[63,] 0.53072802 0.93854396 0.46927198
[64,] 0.51049772 0.97900455 0.48950228
[65,] 0.53666133 0.92667735 0.46333867
[66,] 0.50276496 0.99447007 0.49723504
[67,] 0.46435292 0.92870584 0.53564708
[68,] 0.42668944 0.85337889 0.57331056
[69,] 0.41053019 0.82106037 0.58946981
[70,] 0.43379101 0.86758202 0.56620899
[71,] 0.39785012 0.79570024 0.60214988
[72,] 0.39218735 0.78437470 0.60781265
[73,] 0.35814146 0.71628293 0.64185854
[74,] 0.32938255 0.65876509 0.67061745
[75,] 0.29939103 0.59878206 0.70060897
[76,] 0.29246974 0.58493948 0.70753026
[77,] 0.26844031 0.53688063 0.73155969
[78,] 0.23776726 0.47553452 0.76223274
[79,] 0.21226723 0.42453446 0.78773277
[80,] 0.18539420 0.37078840 0.81460580
[81,] 0.16286057 0.32572113 0.83713943
[82,] 0.37276381 0.74552762 0.62723619
[83,] 0.42194261 0.84388521 0.57805739
[84,] 0.39271703 0.78543407 0.60728297
[85,] 0.35945608 0.71891217 0.64054392
[86,] 0.32877591 0.65755182 0.67122409
[87,] 0.29586391 0.59172782 0.70413609
[88,] 0.27919393 0.55838787 0.72080607
[89,] 0.27424271 0.54848541 0.72575729
[90,] 0.24468755 0.48937510 0.75531245
[91,] 0.25761239 0.51522477 0.74238761
[92,] 0.23613886 0.47227772 0.76386114
[93,] 0.24237655 0.48475309 0.75762345
[94,] 0.23961083 0.47922166 0.76038917
[95,] 0.21800746 0.43601491 0.78199254
[96,] 0.21945941 0.43891883 0.78054059
[97,] 0.19593852 0.39187703 0.80406148
[98,] 0.27641985 0.55283971 0.72358015
[99,] 0.26011465 0.52022929 0.73988535
[100,] 0.23256138 0.46512276 0.76743862
[101,] 0.29247767 0.58495533 0.70752233
[102,] 0.35158173 0.70316346 0.64841827
[103,] 0.32525621 0.65051242 0.67474379
[104,] 0.33996293 0.67992586 0.66003707
[105,] 0.32029605 0.64059211 0.67970395
[106,] 0.29164354 0.58328707 0.70835646
[107,] 0.38139080 0.76278160 0.61860920
[108,] 0.36407884 0.72815768 0.63592116
[109,] 0.33157158 0.66314316 0.66842842
[110,] 0.30025355 0.60050710 0.69974645
[111,] 0.27116257 0.54232514 0.72883743
[112,] 0.24252241 0.48504482 0.75747759
[113,] 0.22663494 0.45326988 0.77336506
[114,] 0.20251119 0.40502239 0.79748881
[115,] 0.18427487 0.36854975 0.81572513
[116,] 0.16355766 0.32711531 0.83644234
[117,] 0.14779430 0.29558859 0.85220570
[118,] 0.13063112 0.26126224 0.86936888
[119,] 0.14208696 0.28417393 0.85791304
[120,] 0.13992254 0.27984509 0.86007746
[121,] 0.22573822 0.45147645 0.77426178
[122,] 0.21260562 0.42521125 0.78739438
[123,] 0.19057300 0.38114601 0.80942700
[124,] 0.22325904 0.44651807 0.77674096
[125,] 0.20042390 0.40084779 0.79957610
[126,] 0.19574288 0.39148575 0.80425712
[127,] 0.17851057 0.35702113 0.82148943
[128,] 0.17993863 0.35987727 0.82006137
[129,] 0.17275313 0.34550625 0.82724687
[130,] 0.15137115 0.30274231 0.84862885
[131,] 0.14432014 0.28864028 0.85567986
[132,] 0.13484058 0.26968116 0.86515942
[133,] 0.11658725 0.23317449 0.88341275
[134,] 0.10406673 0.20813346 0.89593327
[135,] 0.10498244 0.20996487 0.89501756
[136,] 0.09003632 0.18007264 0.90996368
[137,] 0.08190925 0.16381849 0.91809075
[138,] 0.09046392 0.18092785 0.90953608
[139,] 0.08804098 0.17608196 0.91195902
[140,] 0.08789749 0.17579499 0.91210251
[141,] 0.08018964 0.16037928 0.91981036
[142,] 0.11293684 0.22587368 0.88706316
[143,] 0.12537202 0.25074405 0.87462798
[144,] 0.11174802 0.22349605 0.88825198
[145,] 0.11180998 0.22361995 0.88819002
[146,] 0.10499544 0.20999088 0.89500456
[147,] 0.17185424 0.34370848 0.82814576
[148,] 0.15030098 0.30060196 0.84969902
[149,] 0.14009383 0.28018766 0.85990617
[150,] 0.12111728 0.24223456 0.87888272
[151,] 0.19849960 0.39699921 0.80150040
[152,] 0.17657378 0.35314757 0.82342622
[153,] 0.15887679 0.31775357 0.84112321
[154,] 0.13871177 0.27742353 0.86128823
[155,] 0.14525673 0.29051346 0.85474327
[156,] 0.13082865 0.26165729 0.86917135
[157,] 0.12691348 0.25382697 0.87308652
[158,] 0.14372165 0.28744331 0.85627835
[159,] 0.12745659 0.25491319 0.87254341
[160,] 0.11277099 0.22554198 0.88722901
[161,] 0.09700079 0.19400159 0.90299921
[162,] 0.12289252 0.24578504 0.87710748
[163,] 0.16278946 0.32557892 0.83721054
[164,] 0.15110494 0.30220989 0.84889506
[165,] 0.15559244 0.31118489 0.84440756
[166,] 0.18841572 0.37683144 0.81158428
[167,] 0.16930180 0.33860360 0.83069820
[168,] 0.19090070 0.38180140 0.80909930
[169,] 0.16647157 0.33294314 0.83352843
[170,] 0.19765457 0.39530914 0.80234543
[171,] 0.17374355 0.34748710 0.82625645
[172,] 0.18930730 0.37861459 0.81069270
[173,] 0.17674625 0.35349250 0.82325375
[174,] 0.15506475 0.31012950 0.84493525
[175,] 0.13441322 0.26882645 0.86558678
[176,] 0.12050002 0.24100003 0.87949998
[177,] 0.10455719 0.20911439 0.89544281
[178,] 0.14648770 0.29297540 0.85351230
[179,] 0.12573942 0.25147885 0.87426058
[180,] 0.10754184 0.21508369 0.89245816
[181,] 0.10436233 0.20872467 0.89563767
[182,] 0.08923569 0.17847138 0.91076431
[183,] 0.07923617 0.15847233 0.92076383
[184,] 0.07376456 0.14752912 0.92623544
[185,] 0.06165886 0.12331773 0.93834114
[186,] 0.06123665 0.12247330 0.93876335
[187,] 0.05984674 0.11969347 0.94015326
[188,] 0.05825723 0.11651447 0.94174277
[189,] 0.05008811 0.10017622 0.94991189
[190,] 0.04151265 0.08302529 0.95848735
[191,] 0.03389956 0.06779912 0.96610044
[192,] 0.06136584 0.12273169 0.93863416
[193,] 0.04990068 0.09980136 0.95009932
[194,] 0.07118051 0.14236103 0.92881949
[195,] 0.06943931 0.13887863 0.93056069
[196,] 0.11105494 0.22210988 0.88894506
[197,] 0.09314005 0.18628010 0.90685995
[198,] 0.08595888 0.17191777 0.91404112
[199,] 0.07890948 0.15781895 0.92109052
[200,] 0.06539882 0.13079764 0.93460118
[201,] 0.06836665 0.13673330 0.93163335
[202,] 0.05615215 0.11230429 0.94384785
[203,] 0.06252096 0.12504193 0.93747904
[204,] 0.07520962 0.15041925 0.92479038
[205,] 0.09219816 0.18439631 0.90780184
[206,] 0.08296734 0.16593469 0.91703266
[207,] 0.08856818 0.17713636 0.91143182
[208,] 0.08370908 0.16741816 0.91629092
[209,] 0.08008040 0.16016080 0.91991960
[210,] 0.10399726 0.20799452 0.89600274
[211,] 0.08998556 0.17997113 0.91001444
[212,] 0.14124140 0.28248280 0.85875860
[213,] 0.13520890 0.27041780 0.86479110
[214,] 0.11931222 0.23862444 0.88068778
[215,] 0.14197340 0.28394680 0.85802660
[216,] 0.14687825 0.29375649 0.85312175
[217,] 0.15108633 0.30217267 0.84891367
[218,] 0.17706956 0.35413913 0.82293044
[219,] 0.26764199 0.53528397 0.73235801
[220,] 0.58485672 0.83028656 0.41514328
[221,] 0.53275030 0.93449940 0.46724970
[222,] 0.62667862 0.74664277 0.37332138
[223,] 0.58309299 0.83381402 0.41690701
[224,] 0.54904732 0.90190536 0.45095268
[225,] 0.56618319 0.86763362 0.43381681
[226,] 0.56620341 0.86759319 0.43379659
[227,] 0.50797410 0.98405181 0.49202590
[228,] 0.60099168 0.79801664 0.39900832
[229,] 0.55118901 0.89762198 0.44881099
[230,] 0.49128334 0.98256668 0.50871666
[231,] 0.43738945 0.87477889 0.56261055
[232,] 0.44023508 0.88047017 0.55976492
[233,] 0.38134987 0.76269975 0.61865013
[234,] 0.31840388 0.63680776 0.68159612
[235,] 0.44814727 0.89629453 0.55185273
[236,] 0.37747833 0.75495667 0.62252167
[237,] 0.31114184 0.62228368 0.68885816
[238,] 0.34026178 0.68052357 0.65973822
[239,] 0.33776561 0.67553123 0.66223439
[240,] 0.26663868 0.53327737 0.73336132
[241,] 0.27570724 0.55141448 0.72429276
[242,] 0.29799138 0.59598276 0.70200862
[243,] 0.24160054 0.48320109 0.75839946
[244,] 0.19835355 0.39670709 0.80164645
[245,] 0.14095010 0.28190020 0.85904990
[246,] 0.10747938 0.21495877 0.89252062
[247,] 0.07082737 0.14165474 0.92917263
[248,] 0.04899021 0.09798042 0.95100979
[249,] 0.02503066 0.05006132 0.97496934
> postscript(file="/var/wessaorg/rcomp/tmp/1fv571384798135.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2x5cx1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3te4a1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4rpep1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wn5z1384798135.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
-0.23418414 3.41032622 -3.88368676 -2.16648403 1.83739943 4.13314275
7 8 9 10 11 12
-1.10525406 -0.32191121 0.80907800 0.54148577 2.46213528 4.47163254
13 14 15 16 17 18
-4.61316141 1.29738118 3.33597986 -0.19946892 -0.31258429 1.61782679
19 20 21 22 23 24
-1.05888846 1.64122044 3.62239627 -3.30697050 -1.23702328 -2.65547322
25 26 27 28 29 30
2.23219140 -5.83377837 1.45280836 -0.30775050 0.58941138 -3.08730387
31 32 33 34 35 36
1.87116432 -0.48131584 2.85138981 0.48112979 -0.03083135 2.69120518
37 38 39 40 41 42
-4.17219181 0.84672635 2.75899928 -1.47655840 0.47629600 1.91269613
43 44 45 46 47 48
0.73077183 -2.12028876 0.87487744 -2.68950243 0.66781153 0.74872202
49 50 51 52 53 54
2.39633584 -1.20041925 1.52827540 0.03513842 -2.66506446 -2.53769438
55 56 57 58 59 60
-1.11183583 0.77609311 1.37267787 0.70252676 -2.42086588 -2.49538257
61 62 63 64 65 66
-5.50497381 -1.59433724 -3.48122186 -0.02599756 0.59407484 -4.17018037
67 68 69 70 71 72
-3.34365173 -1.99514901 1.53208165 1.00795445 0.99663287 2.47733946
73 74 75 76 77 78
0.64702135 0.47743345 -0.77081892 -2.17035071 2.94198467 -0.84611562
79 80 81 82 83 84
1.88619817 -0.86977359 1.09274840 1.00795445 2.19541619 1.34151645
85 86 87 88 89 90
0.18686927 1.22762107 0.13039675 -0.70001535 -6.67264426 3.87125745
91 92 93 94 95 96
-1.10904439 0.93265775 0.45446150 -0.12796255 1.89483907 -2.02036698
97 98 99 100 101 102
0.12089949 2.99845719 1.48200292 2.92782396 -2.02036698 1.26243028
103 104 105 106 107 108
-2.54553850 1.03627588 -4.52844467 1.76851331 0.77239677 -3.98738209
109 110 111 112 113 114
-3.63750625 1.22935141 -2.78480930 -1.14652240 0.71661026 4.71099647
115 116 117 118 119 120
1.67983590 0.37372134 0.34073645 0.64597703 -0.07217604 -1.39167132
121 122 123 124 125 126
0.32813574 1.40575589 0.92316050 1.26604942 -0.78480930 3.14827058
127 128 129 130 131 132
2.49530729 5.27088322 1.79579042 -0.81779419 -3.59822155 0.84303001
133 134 135 136 137 138
2.11614205 1.27943013 2.43563732 -1.94438427 0.25672250 -1.89879867
139 140 141 142 143 144
1.77696624 0.06821644 1.16338163 2.35101371 0.03239242 1.40343244
145 146 147 148 149 150
3.02478396 1.93888121 -2.47180181 -1.78014584 -4.36352023 2.94016035
151 152 153 154 155 156
-1.04480495 2.02211516 -2.02969390 -5.23806760 0.25567818 -1.10904439
157 158 159 160 161 162
0.64131357 5.27088322 -0.86493979 -1.19937579 -0.40583203 -2.42688653
163 164 165 166 167 168
1.61930873 2.36597120 -3.25876465 -0.86882411 1.16425476 -0.01880783
169 170 171 172 173 174
-3.88367084 -4.16835690 -1.67272147 2.53477910 -3.87995772 1.23850714
175 176 177 178 179 180
3.41673594 -0.11749818 -3.71036982 -0.42300308 3.16891822 -1.81865140
181 182 183 184 185 186
0.55360328 0.59020730 1.18412326 0.74106501 4.59106365 -0.31445717
187 188 189 190 191 192
0.41311680 2.26130876 0.85874987 -1.42378308 -1.95818753 0.41233680
193 194 195 196 197 198
2.45455463 -2.43223601 2.29524398 -1.33889514 0.71369392 0.63373377
199 200 201 202 203 204
-4.93469990 -0.04125115 -4.13554235 2.41863662 4.60436802 0.40740902
205 206 207 208 209 210
1.86834111 1.79770787 0.56776399 2.58658816 0.50074989 2.85418039
211 212 213 214 215 216
-3.61141515 2.95062472 -2.19279487 -2.99505588 -2.39458766 2.38081793
217 218 219 220 221 222
3.49324730 0.84856660 -4.89316809 1.67526557 -1.82253486 2.84390314
223 224 225 226 227 228
-3.09401054 2.50636369 -3.84602249 4.11062138 6.15009405 -0.95352407
229 230 231 232 233 234
-4.43328033 -1.64906350 1.26692255 -3.31628149 1.72319118 -0.91111828
235 236 237 238 239 240
3.22927420 -1.46160176 0.77118126 0.25742530 -3.12138164 -1.29762766
241 242 243 244 245 246
-0.36169676 -4.68204839 0.03688469 -0.82253486 1.79287408 -1.48413906
247 248 249 250 251 252
0.06063664 1.69770888 -1.89075174 0.06210266 2.64685016 0.79666355
253 254 255 256 257 258
0.30379090 -0.27214536 -0.71036982 -0.96768479 -4.43223601 -3.84990595
259 260 261 262 263 264
2.09267119 -1.16284897 1.14154713 2.47259880 -5.68766218 1.21606383
> postscript(file="/var/wessaorg/rcomp/tmp/6vyep1384798135.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 -0.23418414 NA
1 3.41032622 -0.23418414
2 -3.88368676 3.41032622
3 -2.16648403 -3.88368676
4 1.83739943 -2.16648403
5 4.13314275 1.83739943
6 -1.10525406 4.13314275
7 -0.32191121 -1.10525406
8 0.80907800 -0.32191121
9 0.54148577 0.80907800
10 2.46213528 0.54148577
11 4.47163254 2.46213528
12 -4.61316141 4.47163254
13 1.29738118 -4.61316141
14 3.33597986 1.29738118
15 -0.19946892 3.33597986
16 -0.31258429 -0.19946892
17 1.61782679 -0.31258429
18 -1.05888846 1.61782679
19 1.64122044 -1.05888846
20 3.62239627 1.64122044
21 -3.30697050 3.62239627
22 -1.23702328 -3.30697050
23 -2.65547322 -1.23702328
24 2.23219140 -2.65547322
25 -5.83377837 2.23219140
26 1.45280836 -5.83377837
27 -0.30775050 1.45280836
28 0.58941138 -0.30775050
29 -3.08730387 0.58941138
30 1.87116432 -3.08730387
31 -0.48131584 1.87116432
32 2.85138981 -0.48131584
33 0.48112979 2.85138981
34 -0.03083135 0.48112979
35 2.69120518 -0.03083135
36 -4.17219181 2.69120518
37 0.84672635 -4.17219181
38 2.75899928 0.84672635
39 -1.47655840 2.75899928
40 0.47629600 -1.47655840
41 1.91269613 0.47629600
42 0.73077183 1.91269613
43 -2.12028876 0.73077183
44 0.87487744 -2.12028876
45 -2.68950243 0.87487744
46 0.66781153 -2.68950243
47 0.74872202 0.66781153
48 2.39633584 0.74872202
49 -1.20041925 2.39633584
50 1.52827540 -1.20041925
51 0.03513842 1.52827540
52 -2.66506446 0.03513842
53 -2.53769438 -2.66506446
54 -1.11183583 -2.53769438
55 0.77609311 -1.11183583
56 1.37267787 0.77609311
57 0.70252676 1.37267787
58 -2.42086588 0.70252676
59 -2.49538257 -2.42086588
60 -5.50497381 -2.49538257
61 -1.59433724 -5.50497381
62 -3.48122186 -1.59433724
63 -0.02599756 -3.48122186
64 0.59407484 -0.02599756
65 -4.17018037 0.59407484
66 -3.34365173 -4.17018037
67 -1.99514901 -3.34365173
68 1.53208165 -1.99514901
69 1.00795445 1.53208165
70 0.99663287 1.00795445
71 2.47733946 0.99663287
72 0.64702135 2.47733946
73 0.47743345 0.64702135
74 -0.77081892 0.47743345
75 -2.17035071 -0.77081892
76 2.94198467 -2.17035071
77 -0.84611562 2.94198467
78 1.88619817 -0.84611562
79 -0.86977359 1.88619817
80 1.09274840 -0.86977359
81 1.00795445 1.09274840
82 2.19541619 1.00795445
83 1.34151645 2.19541619
84 0.18686927 1.34151645
85 1.22762107 0.18686927
86 0.13039675 1.22762107
87 -0.70001535 0.13039675
88 -6.67264426 -0.70001535
89 3.87125745 -6.67264426
90 -1.10904439 3.87125745
91 0.93265775 -1.10904439
92 0.45446150 0.93265775
93 -0.12796255 0.45446150
94 1.89483907 -0.12796255
95 -2.02036698 1.89483907
96 0.12089949 -2.02036698
97 2.99845719 0.12089949
98 1.48200292 2.99845719
99 2.92782396 1.48200292
100 -2.02036698 2.92782396
101 1.26243028 -2.02036698
102 -2.54553850 1.26243028
103 1.03627588 -2.54553850
104 -4.52844467 1.03627588
105 1.76851331 -4.52844467
106 0.77239677 1.76851331
107 -3.98738209 0.77239677
108 -3.63750625 -3.98738209
109 1.22935141 -3.63750625
110 -2.78480930 1.22935141
111 -1.14652240 -2.78480930
112 0.71661026 -1.14652240
113 4.71099647 0.71661026
114 1.67983590 4.71099647
115 0.37372134 1.67983590
116 0.34073645 0.37372134
117 0.64597703 0.34073645
118 -0.07217604 0.64597703
119 -1.39167132 -0.07217604
120 0.32813574 -1.39167132
121 1.40575589 0.32813574
122 0.92316050 1.40575589
123 1.26604942 0.92316050
124 -0.78480930 1.26604942
125 3.14827058 -0.78480930
126 2.49530729 3.14827058
127 5.27088322 2.49530729
128 1.79579042 5.27088322
129 -0.81779419 1.79579042
130 -3.59822155 -0.81779419
131 0.84303001 -3.59822155
132 2.11614205 0.84303001
133 1.27943013 2.11614205
134 2.43563732 1.27943013
135 -1.94438427 2.43563732
136 0.25672250 -1.94438427
137 -1.89879867 0.25672250
138 1.77696624 -1.89879867
139 0.06821644 1.77696624
140 1.16338163 0.06821644
141 2.35101371 1.16338163
142 0.03239242 2.35101371
143 1.40343244 0.03239242
144 3.02478396 1.40343244
145 1.93888121 3.02478396
146 -2.47180181 1.93888121
147 -1.78014584 -2.47180181
148 -4.36352023 -1.78014584
149 2.94016035 -4.36352023
150 -1.04480495 2.94016035
151 2.02211516 -1.04480495
152 -2.02969390 2.02211516
153 -5.23806760 -2.02969390
154 0.25567818 -5.23806760
155 -1.10904439 0.25567818
156 0.64131357 -1.10904439
157 5.27088322 0.64131357
158 -0.86493979 5.27088322
159 -1.19937579 -0.86493979
160 -0.40583203 -1.19937579
161 -2.42688653 -0.40583203
162 1.61930873 -2.42688653
163 2.36597120 1.61930873
164 -3.25876465 2.36597120
165 -0.86882411 -3.25876465
166 1.16425476 -0.86882411
167 -0.01880783 1.16425476
168 -3.88367084 -0.01880783
169 -4.16835690 -3.88367084
170 -1.67272147 -4.16835690
171 2.53477910 -1.67272147
172 -3.87995772 2.53477910
173 1.23850714 -3.87995772
174 3.41673594 1.23850714
175 -0.11749818 3.41673594
176 -3.71036982 -0.11749818
177 -0.42300308 -3.71036982
178 3.16891822 -0.42300308
179 -1.81865140 3.16891822
180 0.55360328 -1.81865140
181 0.59020730 0.55360328
182 1.18412326 0.59020730
183 0.74106501 1.18412326
184 4.59106365 0.74106501
185 -0.31445717 4.59106365
186 0.41311680 -0.31445717
187 2.26130876 0.41311680
188 0.85874987 2.26130876
189 -1.42378308 0.85874987
190 -1.95818753 -1.42378308
191 0.41233680 -1.95818753
192 2.45455463 0.41233680
193 -2.43223601 2.45455463
194 2.29524398 -2.43223601
195 -1.33889514 2.29524398
196 0.71369392 -1.33889514
197 0.63373377 0.71369392
198 -4.93469990 0.63373377
199 -0.04125115 -4.93469990
200 -4.13554235 -0.04125115
201 2.41863662 -4.13554235
202 4.60436802 2.41863662
203 0.40740902 4.60436802
204 1.86834111 0.40740902
205 1.79770787 1.86834111
206 0.56776399 1.79770787
207 2.58658816 0.56776399
208 0.50074989 2.58658816
209 2.85418039 0.50074989
210 -3.61141515 2.85418039
211 2.95062472 -3.61141515
212 -2.19279487 2.95062472
213 -2.99505588 -2.19279487
214 -2.39458766 -2.99505588
215 2.38081793 -2.39458766
216 3.49324730 2.38081793
217 0.84856660 3.49324730
218 -4.89316809 0.84856660
219 1.67526557 -4.89316809
220 -1.82253486 1.67526557
221 2.84390314 -1.82253486
222 -3.09401054 2.84390314
223 2.50636369 -3.09401054
224 -3.84602249 2.50636369
225 4.11062138 -3.84602249
226 6.15009405 4.11062138
227 -0.95352407 6.15009405
228 -4.43328033 -0.95352407
229 -1.64906350 -4.43328033
230 1.26692255 -1.64906350
231 -3.31628149 1.26692255
232 1.72319118 -3.31628149
233 -0.91111828 1.72319118
234 3.22927420 -0.91111828
235 -1.46160176 3.22927420
236 0.77118126 -1.46160176
237 0.25742530 0.77118126
238 -3.12138164 0.25742530
239 -1.29762766 -3.12138164
240 -0.36169676 -1.29762766
241 -4.68204839 -0.36169676
242 0.03688469 -4.68204839
243 -0.82253486 0.03688469
244 1.79287408 -0.82253486
245 -1.48413906 1.79287408
246 0.06063664 -1.48413906
247 1.69770888 0.06063664
248 -1.89075174 1.69770888
249 0.06210266 -1.89075174
250 2.64685016 0.06210266
251 0.79666355 2.64685016
252 0.30379090 0.79666355
253 -0.27214536 0.30379090
254 -0.71036982 -0.27214536
255 -0.96768479 -0.71036982
256 -4.43223601 -0.96768479
257 -3.84990595 -4.43223601
258 2.09267119 -3.84990595
259 -1.16284897 2.09267119
260 1.14154713 -1.16284897
261 2.47259880 1.14154713
262 -5.68766218 2.47259880
263 1.21606383 -5.68766218
264 NA 1.21606383
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.41032622 -0.23418414
[2,] -3.88368676 3.41032622
[3,] -2.16648403 -3.88368676
[4,] 1.83739943 -2.16648403
[5,] 4.13314275 1.83739943
[6,] -1.10525406 4.13314275
[7,] -0.32191121 -1.10525406
[8,] 0.80907800 -0.32191121
[9,] 0.54148577 0.80907800
[10,] 2.46213528 0.54148577
[11,] 4.47163254 2.46213528
[12,] -4.61316141 4.47163254
[13,] 1.29738118 -4.61316141
[14,] 3.33597986 1.29738118
[15,] -0.19946892 3.33597986
[16,] -0.31258429 -0.19946892
[17,] 1.61782679 -0.31258429
[18,] -1.05888846 1.61782679
[19,] 1.64122044 -1.05888846
[20,] 3.62239627 1.64122044
[21,] -3.30697050 3.62239627
[22,] -1.23702328 -3.30697050
[23,] -2.65547322 -1.23702328
[24,] 2.23219140 -2.65547322
[25,] -5.83377837 2.23219140
[26,] 1.45280836 -5.83377837
[27,] -0.30775050 1.45280836
[28,] 0.58941138 -0.30775050
[29,] -3.08730387 0.58941138
[30,] 1.87116432 -3.08730387
[31,] -0.48131584 1.87116432
[32,] 2.85138981 -0.48131584
[33,] 0.48112979 2.85138981
[34,] -0.03083135 0.48112979
[35,] 2.69120518 -0.03083135
[36,] -4.17219181 2.69120518
[37,] 0.84672635 -4.17219181
[38,] 2.75899928 0.84672635
[39,] -1.47655840 2.75899928
[40,] 0.47629600 -1.47655840
[41,] 1.91269613 0.47629600
[42,] 0.73077183 1.91269613
[43,] -2.12028876 0.73077183
[44,] 0.87487744 -2.12028876
[45,] -2.68950243 0.87487744
[46,] 0.66781153 -2.68950243
[47,] 0.74872202 0.66781153
[48,] 2.39633584 0.74872202
[49,] -1.20041925 2.39633584
[50,] 1.52827540 -1.20041925
[51,] 0.03513842 1.52827540
[52,] -2.66506446 0.03513842
[53,] -2.53769438 -2.66506446
[54,] -1.11183583 -2.53769438
[55,] 0.77609311 -1.11183583
[56,] 1.37267787 0.77609311
[57,] 0.70252676 1.37267787
[58,] -2.42086588 0.70252676
[59,] -2.49538257 -2.42086588
[60,] -5.50497381 -2.49538257
[61,] -1.59433724 -5.50497381
[62,] -3.48122186 -1.59433724
[63,] -0.02599756 -3.48122186
[64,] 0.59407484 -0.02599756
[65,] -4.17018037 0.59407484
[66,] -3.34365173 -4.17018037
[67,] -1.99514901 -3.34365173
[68,] 1.53208165 -1.99514901
[69,] 1.00795445 1.53208165
[70,] 0.99663287 1.00795445
[71,] 2.47733946 0.99663287
[72,] 0.64702135 2.47733946
[73,] 0.47743345 0.64702135
[74,] -0.77081892 0.47743345
[75,] -2.17035071 -0.77081892
[76,] 2.94198467 -2.17035071
[77,] -0.84611562 2.94198467
[78,] 1.88619817 -0.84611562
[79,] -0.86977359 1.88619817
[80,] 1.09274840 -0.86977359
[81,] 1.00795445 1.09274840
[82,] 2.19541619 1.00795445
[83,] 1.34151645 2.19541619
[84,] 0.18686927 1.34151645
[85,] 1.22762107 0.18686927
[86,] 0.13039675 1.22762107
[87,] -0.70001535 0.13039675
[88,] -6.67264426 -0.70001535
[89,] 3.87125745 -6.67264426
[90,] -1.10904439 3.87125745
[91,] 0.93265775 -1.10904439
[92,] 0.45446150 0.93265775
[93,] -0.12796255 0.45446150
[94,] 1.89483907 -0.12796255
[95,] -2.02036698 1.89483907
[96,] 0.12089949 -2.02036698
[97,] 2.99845719 0.12089949
[98,] 1.48200292 2.99845719
[99,] 2.92782396 1.48200292
[100,] -2.02036698 2.92782396
[101,] 1.26243028 -2.02036698
[102,] -2.54553850 1.26243028
[103,] 1.03627588 -2.54553850
[104,] -4.52844467 1.03627588
[105,] 1.76851331 -4.52844467
[106,] 0.77239677 1.76851331
[107,] -3.98738209 0.77239677
[108,] -3.63750625 -3.98738209
[109,] 1.22935141 -3.63750625
[110,] -2.78480930 1.22935141
[111,] -1.14652240 -2.78480930
[112,] 0.71661026 -1.14652240
[113,] 4.71099647 0.71661026
[114,] 1.67983590 4.71099647
[115,] 0.37372134 1.67983590
[116,] 0.34073645 0.37372134
[117,] 0.64597703 0.34073645
[118,] -0.07217604 0.64597703
[119,] -1.39167132 -0.07217604
[120,] 0.32813574 -1.39167132
[121,] 1.40575589 0.32813574
[122,] 0.92316050 1.40575589
[123,] 1.26604942 0.92316050
[124,] -0.78480930 1.26604942
[125,] 3.14827058 -0.78480930
[126,] 2.49530729 3.14827058
[127,] 5.27088322 2.49530729
[128,] 1.79579042 5.27088322
[129,] -0.81779419 1.79579042
[130,] -3.59822155 -0.81779419
[131,] 0.84303001 -3.59822155
[132,] 2.11614205 0.84303001
[133,] 1.27943013 2.11614205
[134,] 2.43563732 1.27943013
[135,] -1.94438427 2.43563732
[136,] 0.25672250 -1.94438427
[137,] -1.89879867 0.25672250
[138,] 1.77696624 -1.89879867
[139,] 0.06821644 1.77696624
[140,] 1.16338163 0.06821644
[141,] 2.35101371 1.16338163
[142,] 0.03239242 2.35101371
[143,] 1.40343244 0.03239242
[144,] 3.02478396 1.40343244
[145,] 1.93888121 3.02478396
[146,] -2.47180181 1.93888121
[147,] -1.78014584 -2.47180181
[148,] -4.36352023 -1.78014584
[149,] 2.94016035 -4.36352023
[150,] -1.04480495 2.94016035
[151,] 2.02211516 -1.04480495
[152,] -2.02969390 2.02211516
[153,] -5.23806760 -2.02969390
[154,] 0.25567818 -5.23806760
[155,] -1.10904439 0.25567818
[156,] 0.64131357 -1.10904439
[157,] 5.27088322 0.64131357
[158,] -0.86493979 5.27088322
[159,] -1.19937579 -0.86493979
[160,] -0.40583203 -1.19937579
[161,] -2.42688653 -0.40583203
[162,] 1.61930873 -2.42688653
[163,] 2.36597120 1.61930873
[164,] -3.25876465 2.36597120
[165,] -0.86882411 -3.25876465
[166,] 1.16425476 -0.86882411
[167,] -0.01880783 1.16425476
[168,] -3.88367084 -0.01880783
[169,] -4.16835690 -3.88367084
[170,] -1.67272147 -4.16835690
[171,] 2.53477910 -1.67272147
[172,] -3.87995772 2.53477910
[173,] 1.23850714 -3.87995772
[174,] 3.41673594 1.23850714
[175,] -0.11749818 3.41673594
[176,] -3.71036982 -0.11749818
[177,] -0.42300308 -3.71036982
[178,] 3.16891822 -0.42300308
[179,] -1.81865140 3.16891822
[180,] 0.55360328 -1.81865140
[181,] 0.59020730 0.55360328
[182,] 1.18412326 0.59020730
[183,] 0.74106501 1.18412326
[184,] 4.59106365 0.74106501
[185,] -0.31445717 4.59106365
[186,] 0.41311680 -0.31445717
[187,] 2.26130876 0.41311680
[188,] 0.85874987 2.26130876
[189,] -1.42378308 0.85874987
[190,] -1.95818753 -1.42378308
[191,] 0.41233680 -1.95818753
[192,] 2.45455463 0.41233680
[193,] -2.43223601 2.45455463
[194,] 2.29524398 -2.43223601
[195,] -1.33889514 2.29524398
[196,] 0.71369392 -1.33889514
[197,] 0.63373377 0.71369392
[198,] -4.93469990 0.63373377
[199,] -0.04125115 -4.93469990
[200,] -4.13554235 -0.04125115
[201,] 2.41863662 -4.13554235
[202,] 4.60436802 2.41863662
[203,] 0.40740902 4.60436802
[204,] 1.86834111 0.40740902
[205,] 1.79770787 1.86834111
[206,] 0.56776399 1.79770787
[207,] 2.58658816 0.56776399
[208,] 0.50074989 2.58658816
[209,] 2.85418039 0.50074989
[210,] -3.61141515 2.85418039
[211,] 2.95062472 -3.61141515
[212,] -2.19279487 2.95062472
[213,] -2.99505588 -2.19279487
[214,] -2.39458766 -2.99505588
[215,] 2.38081793 -2.39458766
[216,] 3.49324730 2.38081793
[217,] 0.84856660 3.49324730
[218,] -4.89316809 0.84856660
[219,] 1.67526557 -4.89316809
[220,] -1.82253486 1.67526557
[221,] 2.84390314 -1.82253486
[222,] -3.09401054 2.84390314
[223,] 2.50636369 -3.09401054
[224,] -3.84602249 2.50636369
[225,] 4.11062138 -3.84602249
[226,] 6.15009405 4.11062138
[227,] -0.95352407 6.15009405
[228,] -4.43328033 -0.95352407
[229,] -1.64906350 -4.43328033
[230,] 1.26692255 -1.64906350
[231,] -3.31628149 1.26692255
[232,] 1.72319118 -3.31628149
[233,] -0.91111828 1.72319118
[234,] 3.22927420 -0.91111828
[235,] -1.46160176 3.22927420
[236,] 0.77118126 -1.46160176
[237,] 0.25742530 0.77118126
[238,] -3.12138164 0.25742530
[239,] -1.29762766 -3.12138164
[240,] -0.36169676 -1.29762766
[241,] -4.68204839 -0.36169676
[242,] 0.03688469 -4.68204839
[243,] -0.82253486 0.03688469
[244,] 1.79287408 -0.82253486
[245,] -1.48413906 1.79287408
[246,] 0.06063664 -1.48413906
[247,] 1.69770888 0.06063664
[248,] -1.89075174 1.69770888
[249,] 0.06210266 -1.89075174
[250,] 2.64685016 0.06210266
[251,] 0.79666355 2.64685016
[252,] 0.30379090 0.79666355
[253,] -0.27214536 0.30379090
[254,] -0.71036982 -0.27214536
[255,] -0.96768479 -0.71036982
[256,] -4.43223601 -0.96768479
[257,] -3.84990595 -4.43223601
[258,] 2.09267119 -3.84990595
[259,] -1.16284897 2.09267119
[260,] 1.14154713 -1.16284897
[261,] 2.47259880 1.14154713
[262,] -5.68766218 2.47259880
[263,] 1.21606383 -5.68766218
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.41032622 -0.23418414
2 -3.88368676 3.41032622
3 -2.16648403 -3.88368676
4 1.83739943 -2.16648403
5 4.13314275 1.83739943
6 -1.10525406 4.13314275
7 -0.32191121 -1.10525406
8 0.80907800 -0.32191121
9 0.54148577 0.80907800
10 2.46213528 0.54148577
11 4.47163254 2.46213528
12 -4.61316141 4.47163254
13 1.29738118 -4.61316141
14 3.33597986 1.29738118
15 -0.19946892 3.33597986
16 -0.31258429 -0.19946892
17 1.61782679 -0.31258429
18 -1.05888846 1.61782679
19 1.64122044 -1.05888846
20 3.62239627 1.64122044
21 -3.30697050 3.62239627
22 -1.23702328 -3.30697050
23 -2.65547322 -1.23702328
24 2.23219140 -2.65547322
25 -5.83377837 2.23219140
26 1.45280836 -5.83377837
27 -0.30775050 1.45280836
28 0.58941138 -0.30775050
29 -3.08730387 0.58941138
30 1.87116432 -3.08730387
31 -0.48131584 1.87116432
32 2.85138981 -0.48131584
33 0.48112979 2.85138981
34 -0.03083135 0.48112979
35 2.69120518 -0.03083135
36 -4.17219181 2.69120518
37 0.84672635 -4.17219181
38 2.75899928 0.84672635
39 -1.47655840 2.75899928
40 0.47629600 -1.47655840
41 1.91269613 0.47629600
42 0.73077183 1.91269613
43 -2.12028876 0.73077183
44 0.87487744 -2.12028876
45 -2.68950243 0.87487744
46 0.66781153 -2.68950243
47 0.74872202 0.66781153
48 2.39633584 0.74872202
49 -1.20041925 2.39633584
50 1.52827540 -1.20041925
51 0.03513842 1.52827540
52 -2.66506446 0.03513842
53 -2.53769438 -2.66506446
54 -1.11183583 -2.53769438
55 0.77609311 -1.11183583
56 1.37267787 0.77609311
57 0.70252676 1.37267787
58 -2.42086588 0.70252676
59 -2.49538257 -2.42086588
60 -5.50497381 -2.49538257
61 -1.59433724 -5.50497381
62 -3.48122186 -1.59433724
63 -0.02599756 -3.48122186
64 0.59407484 -0.02599756
65 -4.17018037 0.59407484
66 -3.34365173 -4.17018037
67 -1.99514901 -3.34365173
68 1.53208165 -1.99514901
69 1.00795445 1.53208165
70 0.99663287 1.00795445
71 2.47733946 0.99663287
72 0.64702135 2.47733946
73 0.47743345 0.64702135
74 -0.77081892 0.47743345
75 -2.17035071 -0.77081892
76 2.94198467 -2.17035071
77 -0.84611562 2.94198467
78 1.88619817 -0.84611562
79 -0.86977359 1.88619817
80 1.09274840 -0.86977359
81 1.00795445 1.09274840
82 2.19541619 1.00795445
83 1.34151645 2.19541619
84 0.18686927 1.34151645
85 1.22762107 0.18686927
86 0.13039675 1.22762107
87 -0.70001535 0.13039675
88 -6.67264426 -0.70001535
89 3.87125745 -6.67264426
90 -1.10904439 3.87125745
91 0.93265775 -1.10904439
92 0.45446150 0.93265775
93 -0.12796255 0.45446150
94 1.89483907 -0.12796255
95 -2.02036698 1.89483907
96 0.12089949 -2.02036698
97 2.99845719 0.12089949
98 1.48200292 2.99845719
99 2.92782396 1.48200292
100 -2.02036698 2.92782396
101 1.26243028 -2.02036698
102 -2.54553850 1.26243028
103 1.03627588 -2.54553850
104 -4.52844467 1.03627588
105 1.76851331 -4.52844467
106 0.77239677 1.76851331
107 -3.98738209 0.77239677
108 -3.63750625 -3.98738209
109 1.22935141 -3.63750625
110 -2.78480930 1.22935141
111 -1.14652240 -2.78480930
112 0.71661026 -1.14652240
113 4.71099647 0.71661026
114 1.67983590 4.71099647
115 0.37372134 1.67983590
116 0.34073645 0.37372134
117 0.64597703 0.34073645
118 -0.07217604 0.64597703
119 -1.39167132 -0.07217604
120 0.32813574 -1.39167132
121 1.40575589 0.32813574
122 0.92316050 1.40575589
123 1.26604942 0.92316050
124 -0.78480930 1.26604942
125 3.14827058 -0.78480930
126 2.49530729 3.14827058
127 5.27088322 2.49530729
128 1.79579042 5.27088322
129 -0.81779419 1.79579042
130 -3.59822155 -0.81779419
131 0.84303001 -3.59822155
132 2.11614205 0.84303001
133 1.27943013 2.11614205
134 2.43563732 1.27943013
135 -1.94438427 2.43563732
136 0.25672250 -1.94438427
137 -1.89879867 0.25672250
138 1.77696624 -1.89879867
139 0.06821644 1.77696624
140 1.16338163 0.06821644
141 2.35101371 1.16338163
142 0.03239242 2.35101371
143 1.40343244 0.03239242
144 3.02478396 1.40343244
145 1.93888121 3.02478396
146 -2.47180181 1.93888121
147 -1.78014584 -2.47180181
148 -4.36352023 -1.78014584
149 2.94016035 -4.36352023
150 -1.04480495 2.94016035
151 2.02211516 -1.04480495
152 -2.02969390 2.02211516
153 -5.23806760 -2.02969390
154 0.25567818 -5.23806760
155 -1.10904439 0.25567818
156 0.64131357 -1.10904439
157 5.27088322 0.64131357
158 -0.86493979 5.27088322
159 -1.19937579 -0.86493979
160 -0.40583203 -1.19937579
161 -2.42688653 -0.40583203
162 1.61930873 -2.42688653
163 2.36597120 1.61930873
164 -3.25876465 2.36597120
165 -0.86882411 -3.25876465
166 1.16425476 -0.86882411
167 -0.01880783 1.16425476
168 -3.88367084 -0.01880783
169 -4.16835690 -3.88367084
170 -1.67272147 -4.16835690
171 2.53477910 -1.67272147
172 -3.87995772 2.53477910
173 1.23850714 -3.87995772
174 3.41673594 1.23850714
175 -0.11749818 3.41673594
176 -3.71036982 -0.11749818
177 -0.42300308 -3.71036982
178 3.16891822 -0.42300308
179 -1.81865140 3.16891822
180 0.55360328 -1.81865140
181 0.59020730 0.55360328
182 1.18412326 0.59020730
183 0.74106501 1.18412326
184 4.59106365 0.74106501
185 -0.31445717 4.59106365
186 0.41311680 -0.31445717
187 2.26130876 0.41311680
188 0.85874987 2.26130876
189 -1.42378308 0.85874987
190 -1.95818753 -1.42378308
191 0.41233680 -1.95818753
192 2.45455463 0.41233680
193 -2.43223601 2.45455463
194 2.29524398 -2.43223601
195 -1.33889514 2.29524398
196 0.71369392 -1.33889514
197 0.63373377 0.71369392
198 -4.93469990 0.63373377
199 -0.04125115 -4.93469990
200 -4.13554235 -0.04125115
201 2.41863662 -4.13554235
202 4.60436802 2.41863662
203 0.40740902 4.60436802
204 1.86834111 0.40740902
205 1.79770787 1.86834111
206 0.56776399 1.79770787
207 2.58658816 0.56776399
208 0.50074989 2.58658816
209 2.85418039 0.50074989
210 -3.61141515 2.85418039
211 2.95062472 -3.61141515
212 -2.19279487 2.95062472
213 -2.99505588 -2.19279487
214 -2.39458766 -2.99505588
215 2.38081793 -2.39458766
216 3.49324730 2.38081793
217 0.84856660 3.49324730
218 -4.89316809 0.84856660
219 1.67526557 -4.89316809
220 -1.82253486 1.67526557
221 2.84390314 -1.82253486
222 -3.09401054 2.84390314
223 2.50636369 -3.09401054
224 -3.84602249 2.50636369
225 4.11062138 -3.84602249
226 6.15009405 4.11062138
227 -0.95352407 6.15009405
228 -4.43328033 -0.95352407
229 -1.64906350 -4.43328033
230 1.26692255 -1.64906350
231 -3.31628149 1.26692255
232 1.72319118 -3.31628149
233 -0.91111828 1.72319118
234 3.22927420 -0.91111828
235 -1.46160176 3.22927420
236 0.77118126 -1.46160176
237 0.25742530 0.77118126
238 -3.12138164 0.25742530
239 -1.29762766 -3.12138164
240 -0.36169676 -1.29762766
241 -4.68204839 -0.36169676
242 0.03688469 -4.68204839
243 -0.82253486 0.03688469
244 1.79287408 -0.82253486
245 -1.48413906 1.79287408
246 0.06063664 -1.48413906
247 1.69770888 0.06063664
248 -1.89075174 1.69770888
249 0.06210266 -1.89075174
250 2.64685016 0.06210266
251 0.79666355 2.64685016
252 0.30379090 0.79666355
253 -0.27214536 0.30379090
254 -0.71036982 -0.27214536
255 -0.96768479 -0.71036982
256 -4.43223601 -0.96768479
257 -3.84990595 -4.43223601
258 2.09267119 -3.84990595
259 -1.16284897 2.09267119
260 1.14154713 -1.16284897
261 2.47259880 1.14154713
262 -5.68766218 2.47259880
263 1.21606383 -5.68766218
> 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/7cxzq1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/808y21384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9km1z1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10l3qq1384798135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11v5yo1384798135.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/1223zm1384798135.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/130vkn1384798135.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/14hheo1384798135.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/15tmvi1384798135.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/168w031384798135.tab")
+ }
>
> try(system("convert tmp/1fv571384798135.ps tmp/1fv571384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x5cx1384798135.ps tmp/2x5cx1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/3te4a1384798135.ps tmp/3te4a1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rpep1384798135.ps tmp/4rpep1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wn5z1384798135.ps tmp/5wn5z1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vyep1384798135.ps tmp/6vyep1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cxzq1384798135.ps tmp/7cxzq1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/808y21384798135.ps tmp/808y21384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/9km1z1384798135.ps tmp/9km1z1384798135.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l3qq1384798135.ps tmp/10l3qq1384798135.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.691 1.766 12.553