R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9
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+ ,0)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('month'
+ ,'Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'bestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('month','Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','bestfriend','secondbestfriend','thirdbestfriend'),1:156))
> 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 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Popularity month FindingFriends KnowingPeople Liked Celebrity bestfriend
1 13 9 13 14 13 3 1
2 12 9 12 8 13 5 1
3 15 9 10 12 16 6 0
4 12 9 9 7 12 6 2
5 10 9 10 10 11 5 0
6 12 9 12 7 12 3 0
7 15 9 13 16 18 8 1
8 9 9 12 11 11 4 1
9 12 9 12 14 14 4 4
10 11 9 6 6 9 4 0
11 11 9 5 16 14 6 0
12 11 9 12 11 12 6 2
13 15 9 11 16 11 5 0
14 7 9 14 12 12 4 1
15 11 9 14 7 13 6 0
16 11 9 12 13 11 4 0
17 10 9 12 11 12 6 1
18 14 9 11 15 16 6 2
19 10 9 11 7 9 4 1
20 6 9 7 9 11 4 1
21 11 9 9 7 13 2 0
22 15 9 11 14 15 7 1
23 11 9 11 15 10 5 1
24 12 9 12 7 11 4 2
25 14 9 12 15 13 6 1
26 15 9 11 17 16 6 1
27 9 9 11 15 15 7 1
28 13 9 8 14 14 5 2
29 13 9 9 14 14 6 0
30 16 9 12 8 14 4 1
31 13 9 10 8 8 4 0
32 12 9 10 14 13 7 1
33 14 9 12 14 15 7 1
34 11 9 8 8 13 4 0
35 9 9 12 11 11 4 1
36 16 9 11 16 15 6 2
37 12 9 12 10 15 6 1
38 10 9 7 8 9 5 1
39 13 9 11 14 13 6 1
40 16 9 11 16 16 7 1
41 14 9 12 13 13 6 0
42 15 9 9 5 11 3 1
43 5 9 15 8 12 3 1
44 8 9 11 10 12 4 1
45 11 9 11 8 12 6 0
46 16 9 11 13 14 7 2
47 17 9 11 15 14 5 1
48 9 9 15 6 8 4 0
49 9 9 11 12 13 5 0
50 13 9 12 16 16 6 1
51 10 9 12 5 13 6 1
52 6 9 9 15 11 6 0
53 12 9 12 12 14 5 0
54 8 9 12 8 13 4 0
55 14 9 13 13 13 5 0
56 12 9 11 14 13 5 1
57 11 10 9 12 12 4 0
58 16 10 9 16 16 6 0
59 8 10 11 10 15 2 1
60 15 10 11 15 15 8 0
61 7 10 12 8 12 3 0
62 16 10 12 16 14 6 2
63 14 10 9 19 12 6 0
64 16 10 11 14 15 6 0
65 9 10 9 6 12 5 1
66 14 10 12 13 13 5 2
67 11 10 12 15 12 6 3
68 13 10 12 7 12 5 1
69 15 10 12 13 13 6 1
70 5 10 14 4 5 2 2
71 15 10 11 14 13 5 1
72 13 10 12 13 13 5 1
73 11 10 11 11 14 5 2
74 11 10 6 14 17 6 1
75 12 10 10 12 13 6 0
76 12 10 12 15 13 6 1
77 12 10 13 14 12 5 1
78 12 10 8 13 13 5 0
79 14 10 12 8 14 4 2
80 6 10 12 6 11 2 1
81 7 10 12 7 12 4 0
82 14 10 6 13 12 6 3
83 14 10 11 13 16 6 1
84 10 10 10 11 12 5 1
85 13 10 12 5 12 3 3
86 12 10 13 12 12 6 2
87 9 10 11 8 10 4 1
88 12 10 7 11 15 5 0
89 16 10 11 14 15 8 1
90 10 10 11 9 12 4 2
91 14 10 11 10 16 6 1
92 10 10 11 13 15 6 1
93 16 10 12 16 16 7 0
94 15 10 10 16 13 6 2
95 12 10 11 11 12 5 1
96 10 10 12 8 11 4 0
97 8 10 7 4 13 6 0
98 8 10 13 7 10 3 1
99 11 10 8 14 15 5 1
100 13 10 12 11 13 6 1
101 16 10 11 17 16 7 1
102 16 10 12 15 15 7 1
103 14 10 14 17 18 6 0
104 11 10 10 5 13 3 0
105 4 10 10 4 10 2 1
106 14 10 13 10 16 8 2
107 9 10 10 11 13 3 1
108 14 10 11 15 15 8 1
109 8 10 10 10 14 3 0
110 8 10 7 9 15 4 0
111 11 10 10 12 14 5 1
112 12 10 8 15 13 7 1
113 11 10 12 7 13 6 0
114 14 10 12 13 15 6 0
115 15 10 12 12 16 7 2
116 16 10 11 14 14 6 2
117 16 10 12 14 14 6 0
118 11 10 12 8 16 6 1
119 14 10 12 15 14 6 0
120 14 10 11 12 12 4 2
121 12 10 12 12 13 4 1
122 14 10 11 16 12 5 0
123 8 10 11 9 12 4 1
124 13 10 13 15 14 6 1
125 16 10 12 15 14 6 2
126 12 10 12 6 14 5 0
127 16 10 12 14 16 8 2
128 12 10 12 15 13 6 0
129 11 10 8 10 14 5 1
130 4 10 8 6 4 4 0
131 16 10 12 14 16 8 3
132 15 10 11 12 13 6 1
133 10 10 12 8 16 4 0
134 13 10 13 11 15 6 0
135 15 10 12 13 14 6 0
136 12 10 12 9 13 4 0
137 14 10 11 15 14 6 0
138 7 10 12 13 12 3 1
139 19 10 12 15 15 6 1
140 12 10 10 14 14 5 2
141 12 10 11 16 13 4 1
142 13 10 12 14 14 6 0
143 15 10 12 14 16 4 0
144 8 10 10 10 6 4 2
145 12 10 12 10 13 4 1
146 10 10 13 4 13 6 0
147 8 10 12 8 14 5 1
148 10 10 15 15 15 6 2
149 15 10 11 16 14 6 2
150 16 10 12 12 15 8 0
151 13 10 11 12 13 7 1
152 16 10 12 15 16 7 2
153 9 10 11 9 12 4 0
154 14 10 10 12 15 6 1
155 14 10 11 14 12 6 2
156 12 10 11 11 14 2 1
secondbestfriend thirdbestfriend
1 1 0
2 0 0
3 0 0
4 0 1
5 1 2
6 0 1
7 1 1
8 0 0
9 0 0
10 0 0
11 2 1
12 0 0
13 2 2
14 1 1
15 1 0
16 0 1
17 1 0
18 0 1
19 0 0
20 0 0
21 1 1
22 2 0
23 2 1
24 0 0
25 0 0
26 1 0
27 1 0
28 2 0
29 0 2
30 1 1
31 1 2
32 1 1
33 2 1
34 2 0
35 1 0
36 2 0
37 1 1
38 1 2
39 0 1
40 3 1
41 1 2
42 0 0
43 0 0
44 0 0
45 1 1
46 0 1
47 4 4
48 0 0
49 0 0
50 0 1
51 1 0
52 2 1
53 1 0
54 1 1
55 0 0
56 2 2
57 0 2
58 3 1
59 2 0
60 0 0
61 0 0
62 2 0
63 1 0
64 0 1
65 2 1
66 0 0
67 1 0
68 0 0
69 2 1
70 0 0
71 2 2
72 3 0
73 0 2
74 2 1
75 3 1
76 1 1
77 0 2
78 1 2
79 0 0
80 0 0
81 1 0
82 1 1
83 2 1
84 1 0
85 0 0
86 0 0
87 1 0
88 0 2
89 0 1
90 0 1
91 1 0
92 1 1
93 3 1
94 1 0
95 1 1
96 0 0
97 0 1
98 1 0
99 1 0
100 0 2
101 1 2
102 1 2
103 0 1
104 1 1
105 0 1
106 1 0
107 1 1
108 1 1
109 1 0
110 1 0
111 0 0
112 0 0
113 0 0
114 1 0
115 1 0
116 1 0
117 0 0
118 1 0
119 4 1
120 0 0
121 1 1
122 0 3
123 2 2
124 1 2
125 0 2
126 0 0
127 0 1
128 0 0
129 1 0
130 0 0
131 2 1
132 0 2
133 1 0
134 2 4
135 2 0
136 1 0
137 3 0
138 0 0
139 1 0
140 1 1
141 0 0
142 1 1
143 0 0
144 1 2
145 0 1
146 1 0
147 0 0
148 2 0
149 0 1
150 0 0
151 1 1
152 1 0
153 0 0
154 0 1
155 1 2
156 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month FindingFriends KnowingPeople
0.29112 -0.05114 0.10028 0.21190
Liked Celebrity bestfriend secondbestfriend
0.38441 0.59165 0.31233 -0.02959
thirdbestfriend
0.40923
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.04034 -1.24538 -0.01831 1.37132 6.89133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.29112 3.56359 0.082 0.935001
month -0.05114 0.35484 -0.144 0.885611
FindingFriends 0.10028 0.09702 1.034 0.303007
KnowingPeople 0.21190 0.06384 3.319 0.001138 **
Liked 0.38441 0.09868 3.896 0.000148 ***
Celebrity 0.59165 0.15612 3.790 0.000219 ***
bestfriend 0.31233 0.21058 1.483 0.140152
secondbestfriend -0.02959 0.20144 -0.147 0.883429
thirdbestfriend 0.40923 0.21375 1.915 0.057496 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.096 on 147 degrees of freedom
Multiple R-squared: 0.5169, Adjusted R-squared: 0.4906
F-statistic: 19.66 on 8 and 147 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.2678513 0.53570267 0.732148665
[2,] 0.7315296 0.53694079 0.268470393
[3,] 0.9142176 0.17156478 0.085782392
[4,] 0.8664769 0.26704618 0.133523092
[5,] 0.8093256 0.38134878 0.190674389
[6,] 0.7396762 0.52064754 0.260323769
[7,] 0.6537385 0.69252297 0.346261487
[8,] 0.5735677 0.85286463 0.426432317
[9,] 0.7980923 0.40381549 0.201907747
[10,] 0.7415995 0.51680110 0.258400550
[11,] 0.7398475 0.52030498 0.260152489
[12,] 0.6738257 0.65234853 0.326174264
[13,] 0.6681045 0.66379102 0.331895511
[14,] 0.6320021 0.73599573 0.367997867
[15,] 0.5711288 0.85774232 0.428871162
[16,] 0.7820435 0.43591291 0.217956455
[17,] 0.7413396 0.51732072 0.258660362
[18,] 0.6832312 0.63353767 0.316768836
[19,] 0.7894184 0.42116314 0.210581572
[20,] 0.8471089 0.30578224 0.152891122
[21,] 0.8128609 0.37427829 0.187139147
[22,] 0.7686128 0.46277446 0.231387232
[23,] 0.7318356 0.53632879 0.268164396
[24,] 0.7063678 0.58726448 0.293632241
[25,] 0.7354872 0.52902551 0.264512756
[26,] 0.7124346 0.57513074 0.287565370
[27,] 0.6666290 0.66674200 0.333371002
[28,] 0.6176514 0.76469727 0.382348636
[29,] 0.5752150 0.84957008 0.424785040
[30,] 0.5279939 0.94401230 0.472006150
[31,] 0.8612218 0.27755644 0.138778219
[32,] 0.9665833 0.06683338 0.033416690
[33,] 0.9681958 0.06360847 0.031804234
[34,] 0.9592560 0.08148790 0.040743952
[35,] 0.9647205 0.07055896 0.035279482
[36,] 0.9717868 0.05642639 0.028213197
[37,] 0.9683610 0.06327801 0.031639007
[38,] 0.9646124 0.07077515 0.035387575
[39,] 0.9555287 0.08894260 0.044471300
[40,] 0.9493328 0.10133431 0.050667156
[41,] 0.9900910 0.01981803 0.009909017
[42,] 0.9864360 0.02712799 0.013563996
[43,] 0.9910223 0.01795539 0.008977694
[44,] 0.9930041 0.01399173 0.006995866
[45,] 0.9908837 0.01823253 0.009116266
[46,] 0.9874740 0.02505192 0.012525958
[47,] 0.9871492 0.02570159 0.012850794
[48,] 0.9902002 0.01959967 0.009799835
[49,] 0.9890190 0.02196209 0.010981045
[50,] 0.9888637 0.02227254 0.011136270
[51,] 0.9904261 0.01914784 0.009573918
[52,] 0.9888804 0.02223924 0.011119619
[53,] 0.9892955 0.02140893 0.010704463
[54,] 0.9887657 0.02246852 0.011234261
[55,] 0.9867137 0.02657264 0.013286318
[56,] 0.9884284 0.02314319 0.011571597
[57,] 0.9898264 0.02034716 0.010173582
[58,] 0.9892064 0.02158724 0.010793618
[59,] 0.9863757 0.02724852 0.013624262
[60,] 0.9864624 0.02707511 0.013537556
[61,] 0.9829213 0.03415746 0.017078732
[62,] 0.9841505 0.03169901 0.015849506
[63,] 0.9894996 0.02100077 0.010500387
[64,] 0.9859932 0.02801369 0.014006847
[65,] 0.9837312 0.03253756 0.016268778
[66,] 0.9790275 0.04194495 0.020972474
[67,] 0.9727438 0.05451234 0.027256170
[68,] 0.9789047 0.04219061 0.021095306
[69,] 0.9786668 0.04266633 0.021333166
[70,] 0.9802988 0.03940241 0.019701205
[71,] 0.9780727 0.04385470 0.021927348
[72,] 0.9708759 0.05824822 0.029124111
[73,] 0.9636129 0.07277417 0.036387085
[74,] 0.9844687 0.03106260 0.015531302
[75,] 0.9795302 0.04093965 0.020469824
[76,] 0.9729418 0.05411646 0.027058230
[77,] 0.9653188 0.06936236 0.034681182
[78,] 0.9565035 0.08699290 0.043496450
[79,] 0.9456790 0.10864199 0.054320995
[80,] 0.9360495 0.12790092 0.063950461
[81,] 0.9631666 0.07366670 0.036833350
[82,] 0.9539761 0.09204779 0.046023896
[83,] 0.9489604 0.10207929 0.051039646
[84,] 0.9363909 0.12721812 0.063609062
[85,] 0.9214250 0.15714999 0.078574995
[86,] 0.9174248 0.16515038 0.082575191
[87,] 0.8975948 0.20481039 0.102405193
[88,] 0.8881338 0.22373243 0.111866217
[89,] 0.8617929 0.27641429 0.138207143
[90,] 0.8326257 0.33474866 0.167374328
[91,] 0.8018491 0.39630173 0.198150863
[92,] 0.8302503 0.33949931 0.169749654
[93,] 0.8725283 0.25494342 0.127471712
[94,] 0.8794083 0.24118332 0.120591660
[95,] 0.8524916 0.29501688 0.147508440
[96,] 0.8286866 0.34262689 0.171313443
[97,] 0.8225861 0.35482789 0.177413945
[98,] 0.8125770 0.37484606 0.187423028
[99,] 0.8347472 0.33050558 0.165252788
[100,] 0.8205409 0.35891814 0.179459072
[101,] 0.8675320 0.26493605 0.132468024
[102,] 0.8344549 0.33109021 0.165545103
[103,] 0.8010762 0.39784760 0.198923802
[104,] 0.7600172 0.47996555 0.239982775
[105,] 0.7723196 0.45536077 0.227680386
[106,] 0.7820840 0.43583208 0.217916040
[107,] 0.7622000 0.47559996 0.237799981
[108,] 0.7165574 0.56688525 0.283442625
[109,] 0.8021962 0.39560751 0.197803756
[110,] 0.7680666 0.46386690 0.231933448
[111,] 0.7181076 0.56378470 0.281892351
[112,] 0.7167785 0.56644301 0.283221503
[113,] 0.6819367 0.63612657 0.318063285
[114,] 0.6551772 0.68964569 0.344822843
[115,] 0.6366250 0.72675005 0.363375024
[116,] 0.5703123 0.85937540 0.429687700
[117,] 0.5529379 0.89412415 0.447062075
[118,] 0.5415610 0.91687805 0.458439024
[119,] 0.5273172 0.94536566 0.472682832
[120,] 0.4582107 0.91642150 0.541789252
[121,] 0.4275904 0.85518083 0.572409583
[122,] 0.3913230 0.78264600 0.608677001
[123,] 0.3463124 0.69262483 0.653687585
[124,] 0.2987739 0.59754785 0.701226075
[125,] 0.2741180 0.54823603 0.725881983
[126,] 0.2413306 0.48266113 0.758669436
[127,] 0.2642180 0.52843596 0.735782019
[128,] 0.6435463 0.71290740 0.356453698
[129,] 0.6332559 0.73348826 0.366744128
[130,] 0.5260238 0.94795233 0.473976167
[131,] 0.5121257 0.97574865 0.487874324
[132,] 0.3780827 0.75616536 0.621917322
[133,] 0.2578906 0.51578124 0.742109381
> postscript(file="/var/www/rcomp/tmp/1czby1290508166.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2czby1290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3nqs11290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4nqs11290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5nqs11290508166.ps",horizontal=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 = 156
Frequency = 1
1 2 3 4 5
1.8438905899 1.0026796401 1.9231016058 0.5866039938 -0.9282941675
6 7 8 9 10
2.6853879760 -1.8694298031 -1.2725550165 -0.9984786544 3.4697648593
11 12 13 14 15
-2.0043488613 -1.1525974461 2.7296158701 -4.4490660940 -0.2357071966
16 17 18 19 20
0.2067463348 -1.8106758901 -0.8467791465 1.4441365537 -3.3473615464
21 22 23 24 25
2.2230549184 0.9386194493 -0.5771781899 2.2627092338 0.9277313171
26 27 28 29 30
0.4805768078 -5.3028672711 0.4948292650 -0.4500725737 4.8302763458
31 32 33 34 35
4.2403766389 -1.6311085758 -0.5708931556 1.3669539409 -1.2429679913
36 37 38 39 40
1.7941359840 -1.1612309436 0.2528211131 -0.1693239607 0.7507663083
41 42 43 44 45
0.8749846856 6.8913300590 -4.7304489089 -2.3447834919 -0.1715972466
46 47 48 49 50
1.7541837611 2.7166665471 0.9516625027 -2.4323057943 -1.8466231269
51 52 53 54 55
-0.9236847051 -6.0403434700 0.1125954283 -2.4729821369 2.1552368786
56 57 58 59 60
-0.9277332433 -0.0230223206 1.9064449042 -2.2043935119 0.4393657556
61 62 63 64 65
-2.0661419532 2.1294001319 1.1584335034 2.4253325741 -1.1872012966
66 67 68 69 70
1.6819826101 -2.2318077556 2.6501222991 2.0526069461 -0.7612705033
71 72 73 74 75
2.1234027338 1.0830782163 -1.9968137480 -3.0952478000 -0.1930141709
76 77 78 79 80
-1.4007794696 -0.7519219617 -0.0811136175 2.9487245560 -1.9786196507
81 82 83 84 85
-2.4163056889 1.3844307414 -0.0003351977 -0.9673318250 3.6325535403
86 87 88 89 90
-0.4136399801 -0.0714471259 -0.3554364093 0.9296971311 -0.8033161392
91 92 93 94 95
1.0150106519 -3.6455152363 1.0139580004 1.6847777251 0.5231555701
96 97 98 99 100
0.7266145773 -2.2857412364 -0.4684546063 -1.5556942387 0.0079984974
101 102 103 104 105
0.1215947509 0.8295223122 -1.6644239192 2.0060610141 -3.3790894308
106 107 108 109 110
-0.6811824229 -1.5776716884 -1.2526155391 -2.0286106599 -2.4919319596
111 112 113 114 115
-0.9776325186 -1.2116678982 -0.0136006128 0.9757742676 0.5869474586
116 117 118 119 120
2.6238913132 3.1186945337 -1.6614687734 0.6159091500 2.9702185640
121 122 123 124 125
0.4182205284 0.9279370214 -2.8410413471 -1.2946990611 1.4636581989
126 127 128 129 130
1.4055425520 0.1326767978 -0.7087981750 -0.3236884711 -1.7576218625
131 132 133 134 135
-0.1204836827 1.8963776180 -1.1658333304 -1.3080532886 2.3897682793
136 137 138 139 140
1.7754879340 1.0958347298 -3.4379749603 5.2396403462 -1.0934132038
141 142 143 144 145
0.0505473271 -0.2609522301 2.5331814728 -0.9881418629 0.8124328938
146 147 148 149 150
-0.4485933177 -3.3305913693 -4.3439436072 0.7612711085 1.9747860255
151 152 153 154 155
-0.2564520240 0.9512483728 -0.7694132886 0.6370762498 0.5742377084
156
1.9385267542
> postscript(file="/var/www/rcomp/tmp/6yh9m1290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.8438905899 NA
1 1.0026796401 1.8438905899
2 1.9231016058 1.0026796401
3 0.5866039938 1.9231016058
4 -0.9282941675 0.5866039938
5 2.6853879760 -0.9282941675
6 -1.8694298031 2.6853879760
7 -1.2725550165 -1.8694298031
8 -0.9984786544 -1.2725550165
9 3.4697648593 -0.9984786544
10 -2.0043488613 3.4697648593
11 -1.1525974461 -2.0043488613
12 2.7296158701 -1.1525974461
13 -4.4490660940 2.7296158701
14 -0.2357071966 -4.4490660940
15 0.2067463348 -0.2357071966
16 -1.8106758901 0.2067463348
17 -0.8467791465 -1.8106758901
18 1.4441365537 -0.8467791465
19 -3.3473615464 1.4441365537
20 2.2230549184 -3.3473615464
21 0.9386194493 2.2230549184
22 -0.5771781899 0.9386194493
23 2.2627092338 -0.5771781899
24 0.9277313171 2.2627092338
25 0.4805768078 0.9277313171
26 -5.3028672711 0.4805768078
27 0.4948292650 -5.3028672711
28 -0.4500725737 0.4948292650
29 4.8302763458 -0.4500725737
30 4.2403766389 4.8302763458
31 -1.6311085758 4.2403766389
32 -0.5708931556 -1.6311085758
33 1.3669539409 -0.5708931556
34 -1.2429679913 1.3669539409
35 1.7941359840 -1.2429679913
36 -1.1612309436 1.7941359840
37 0.2528211131 -1.1612309436
38 -0.1693239607 0.2528211131
39 0.7507663083 -0.1693239607
40 0.8749846856 0.7507663083
41 6.8913300590 0.8749846856
42 -4.7304489089 6.8913300590
43 -2.3447834919 -4.7304489089
44 -0.1715972466 -2.3447834919
45 1.7541837611 -0.1715972466
46 2.7166665471 1.7541837611
47 0.9516625027 2.7166665471
48 -2.4323057943 0.9516625027
49 -1.8466231269 -2.4323057943
50 -0.9236847051 -1.8466231269
51 -6.0403434700 -0.9236847051
52 0.1125954283 -6.0403434700
53 -2.4729821369 0.1125954283
54 2.1552368786 -2.4729821369
55 -0.9277332433 2.1552368786
56 -0.0230223206 -0.9277332433
57 1.9064449042 -0.0230223206
58 -2.2043935119 1.9064449042
59 0.4393657556 -2.2043935119
60 -2.0661419532 0.4393657556
61 2.1294001319 -2.0661419532
62 1.1584335034 2.1294001319
63 2.4253325741 1.1584335034
64 -1.1872012966 2.4253325741
65 1.6819826101 -1.1872012966
66 -2.2318077556 1.6819826101
67 2.6501222991 -2.2318077556
68 2.0526069461 2.6501222991
69 -0.7612705033 2.0526069461
70 2.1234027338 -0.7612705033
71 1.0830782163 2.1234027338
72 -1.9968137480 1.0830782163
73 -3.0952478000 -1.9968137480
74 -0.1930141709 -3.0952478000
75 -1.4007794696 -0.1930141709
76 -0.7519219617 -1.4007794696
77 -0.0811136175 -0.7519219617
78 2.9487245560 -0.0811136175
79 -1.9786196507 2.9487245560
80 -2.4163056889 -1.9786196507
81 1.3844307414 -2.4163056889
82 -0.0003351977 1.3844307414
83 -0.9673318250 -0.0003351977
84 3.6325535403 -0.9673318250
85 -0.4136399801 3.6325535403
86 -0.0714471259 -0.4136399801
87 -0.3554364093 -0.0714471259
88 0.9296971311 -0.3554364093
89 -0.8033161392 0.9296971311
90 1.0150106519 -0.8033161392
91 -3.6455152363 1.0150106519
92 1.0139580004 -3.6455152363
93 1.6847777251 1.0139580004
94 0.5231555701 1.6847777251
95 0.7266145773 0.5231555701
96 -2.2857412364 0.7266145773
97 -0.4684546063 -2.2857412364
98 -1.5556942387 -0.4684546063
99 0.0079984974 -1.5556942387
100 0.1215947509 0.0079984974
101 0.8295223122 0.1215947509
102 -1.6644239192 0.8295223122
103 2.0060610141 -1.6644239192
104 -3.3790894308 2.0060610141
105 -0.6811824229 -3.3790894308
106 -1.5776716884 -0.6811824229
107 -1.2526155391 -1.5776716884
108 -2.0286106599 -1.2526155391
109 -2.4919319596 -2.0286106599
110 -0.9776325186 -2.4919319596
111 -1.2116678982 -0.9776325186
112 -0.0136006128 -1.2116678982
113 0.9757742676 -0.0136006128
114 0.5869474586 0.9757742676
115 2.6238913132 0.5869474586
116 3.1186945337 2.6238913132
117 -1.6614687734 3.1186945337
118 0.6159091500 -1.6614687734
119 2.9702185640 0.6159091500
120 0.4182205284 2.9702185640
121 0.9279370214 0.4182205284
122 -2.8410413471 0.9279370214
123 -1.2946990611 -2.8410413471
124 1.4636581989 -1.2946990611
125 1.4055425520 1.4636581989
126 0.1326767978 1.4055425520
127 -0.7087981750 0.1326767978
128 -0.3236884711 -0.7087981750
129 -1.7576218625 -0.3236884711
130 -0.1204836827 -1.7576218625
131 1.8963776180 -0.1204836827
132 -1.1658333304 1.8963776180
133 -1.3080532886 -1.1658333304
134 2.3897682793 -1.3080532886
135 1.7754879340 2.3897682793
136 1.0958347298 1.7754879340
137 -3.4379749603 1.0958347298
138 5.2396403462 -3.4379749603
139 -1.0934132038 5.2396403462
140 0.0505473271 -1.0934132038
141 -0.2609522301 0.0505473271
142 2.5331814728 -0.2609522301
143 -0.9881418629 2.5331814728
144 0.8124328938 -0.9881418629
145 -0.4485933177 0.8124328938
146 -3.3305913693 -0.4485933177
147 -4.3439436072 -3.3305913693
148 0.7612711085 -4.3439436072
149 1.9747860255 0.7612711085
150 -0.2564520240 1.9747860255
151 0.9512483728 -0.2564520240
152 -0.7694132886 0.9512483728
153 0.6370762498 -0.7694132886
154 0.5742377084 0.6370762498
155 1.9385267542 0.5742377084
156 NA 1.9385267542
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.0026796401 1.8438905899
[2,] 1.9231016058 1.0026796401
[3,] 0.5866039938 1.9231016058
[4,] -0.9282941675 0.5866039938
[5,] 2.6853879760 -0.9282941675
[6,] -1.8694298031 2.6853879760
[7,] -1.2725550165 -1.8694298031
[8,] -0.9984786544 -1.2725550165
[9,] 3.4697648593 -0.9984786544
[10,] -2.0043488613 3.4697648593
[11,] -1.1525974461 -2.0043488613
[12,] 2.7296158701 -1.1525974461
[13,] -4.4490660940 2.7296158701
[14,] -0.2357071966 -4.4490660940
[15,] 0.2067463348 -0.2357071966
[16,] -1.8106758901 0.2067463348
[17,] -0.8467791465 -1.8106758901
[18,] 1.4441365537 -0.8467791465
[19,] -3.3473615464 1.4441365537
[20,] 2.2230549184 -3.3473615464
[21,] 0.9386194493 2.2230549184
[22,] -0.5771781899 0.9386194493
[23,] 2.2627092338 -0.5771781899
[24,] 0.9277313171 2.2627092338
[25,] 0.4805768078 0.9277313171
[26,] -5.3028672711 0.4805768078
[27,] 0.4948292650 -5.3028672711
[28,] -0.4500725737 0.4948292650
[29,] 4.8302763458 -0.4500725737
[30,] 4.2403766389 4.8302763458
[31,] -1.6311085758 4.2403766389
[32,] -0.5708931556 -1.6311085758
[33,] 1.3669539409 -0.5708931556
[34,] -1.2429679913 1.3669539409
[35,] 1.7941359840 -1.2429679913
[36,] -1.1612309436 1.7941359840
[37,] 0.2528211131 -1.1612309436
[38,] -0.1693239607 0.2528211131
[39,] 0.7507663083 -0.1693239607
[40,] 0.8749846856 0.7507663083
[41,] 6.8913300590 0.8749846856
[42,] -4.7304489089 6.8913300590
[43,] -2.3447834919 -4.7304489089
[44,] -0.1715972466 -2.3447834919
[45,] 1.7541837611 -0.1715972466
[46,] 2.7166665471 1.7541837611
[47,] 0.9516625027 2.7166665471
[48,] -2.4323057943 0.9516625027
[49,] -1.8466231269 -2.4323057943
[50,] -0.9236847051 -1.8466231269
[51,] -6.0403434700 -0.9236847051
[52,] 0.1125954283 -6.0403434700
[53,] -2.4729821369 0.1125954283
[54,] 2.1552368786 -2.4729821369
[55,] -0.9277332433 2.1552368786
[56,] -0.0230223206 -0.9277332433
[57,] 1.9064449042 -0.0230223206
[58,] -2.2043935119 1.9064449042
[59,] 0.4393657556 -2.2043935119
[60,] -2.0661419532 0.4393657556
[61,] 2.1294001319 -2.0661419532
[62,] 1.1584335034 2.1294001319
[63,] 2.4253325741 1.1584335034
[64,] -1.1872012966 2.4253325741
[65,] 1.6819826101 -1.1872012966
[66,] -2.2318077556 1.6819826101
[67,] 2.6501222991 -2.2318077556
[68,] 2.0526069461 2.6501222991
[69,] -0.7612705033 2.0526069461
[70,] 2.1234027338 -0.7612705033
[71,] 1.0830782163 2.1234027338
[72,] -1.9968137480 1.0830782163
[73,] -3.0952478000 -1.9968137480
[74,] -0.1930141709 -3.0952478000
[75,] -1.4007794696 -0.1930141709
[76,] -0.7519219617 -1.4007794696
[77,] -0.0811136175 -0.7519219617
[78,] 2.9487245560 -0.0811136175
[79,] -1.9786196507 2.9487245560
[80,] -2.4163056889 -1.9786196507
[81,] 1.3844307414 -2.4163056889
[82,] -0.0003351977 1.3844307414
[83,] -0.9673318250 -0.0003351977
[84,] 3.6325535403 -0.9673318250
[85,] -0.4136399801 3.6325535403
[86,] -0.0714471259 -0.4136399801
[87,] -0.3554364093 -0.0714471259
[88,] 0.9296971311 -0.3554364093
[89,] -0.8033161392 0.9296971311
[90,] 1.0150106519 -0.8033161392
[91,] -3.6455152363 1.0150106519
[92,] 1.0139580004 -3.6455152363
[93,] 1.6847777251 1.0139580004
[94,] 0.5231555701 1.6847777251
[95,] 0.7266145773 0.5231555701
[96,] -2.2857412364 0.7266145773
[97,] -0.4684546063 -2.2857412364
[98,] -1.5556942387 -0.4684546063
[99,] 0.0079984974 -1.5556942387
[100,] 0.1215947509 0.0079984974
[101,] 0.8295223122 0.1215947509
[102,] -1.6644239192 0.8295223122
[103,] 2.0060610141 -1.6644239192
[104,] -3.3790894308 2.0060610141
[105,] -0.6811824229 -3.3790894308
[106,] -1.5776716884 -0.6811824229
[107,] -1.2526155391 -1.5776716884
[108,] -2.0286106599 -1.2526155391
[109,] -2.4919319596 -2.0286106599
[110,] -0.9776325186 -2.4919319596
[111,] -1.2116678982 -0.9776325186
[112,] -0.0136006128 -1.2116678982
[113,] 0.9757742676 -0.0136006128
[114,] 0.5869474586 0.9757742676
[115,] 2.6238913132 0.5869474586
[116,] 3.1186945337 2.6238913132
[117,] -1.6614687734 3.1186945337
[118,] 0.6159091500 -1.6614687734
[119,] 2.9702185640 0.6159091500
[120,] 0.4182205284 2.9702185640
[121,] 0.9279370214 0.4182205284
[122,] -2.8410413471 0.9279370214
[123,] -1.2946990611 -2.8410413471
[124,] 1.4636581989 -1.2946990611
[125,] 1.4055425520 1.4636581989
[126,] 0.1326767978 1.4055425520
[127,] -0.7087981750 0.1326767978
[128,] -0.3236884711 -0.7087981750
[129,] -1.7576218625 -0.3236884711
[130,] -0.1204836827 -1.7576218625
[131,] 1.8963776180 -0.1204836827
[132,] -1.1658333304 1.8963776180
[133,] -1.3080532886 -1.1658333304
[134,] 2.3897682793 -1.3080532886
[135,] 1.7754879340 2.3897682793
[136,] 1.0958347298 1.7754879340
[137,] -3.4379749603 1.0958347298
[138,] 5.2396403462 -3.4379749603
[139,] -1.0934132038 5.2396403462
[140,] 0.0505473271 -1.0934132038
[141,] -0.2609522301 0.0505473271
[142,] 2.5331814728 -0.2609522301
[143,] -0.9881418629 2.5331814728
[144,] 0.8124328938 -0.9881418629
[145,] -0.4485933177 0.8124328938
[146,] -3.3305913693 -0.4485933177
[147,] -4.3439436072 -3.3305913693
[148,] 0.7612711085 -4.3439436072
[149,] 1.9747860255 0.7612711085
[150,] -0.2564520240 1.9747860255
[151,] 0.9512483728 -0.2564520240
[152,] -0.7694132886 0.9512483728
[153,] 0.6370762498 -0.7694132886
[154,] 0.5742377084 0.6370762498
[155,] 1.9385267542 0.5742377084
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.0026796401 1.8438905899
2 1.9231016058 1.0026796401
3 0.5866039938 1.9231016058
4 -0.9282941675 0.5866039938
5 2.6853879760 -0.9282941675
6 -1.8694298031 2.6853879760
7 -1.2725550165 -1.8694298031
8 -0.9984786544 -1.2725550165
9 3.4697648593 -0.9984786544
10 -2.0043488613 3.4697648593
11 -1.1525974461 -2.0043488613
12 2.7296158701 -1.1525974461
13 -4.4490660940 2.7296158701
14 -0.2357071966 -4.4490660940
15 0.2067463348 -0.2357071966
16 -1.8106758901 0.2067463348
17 -0.8467791465 -1.8106758901
18 1.4441365537 -0.8467791465
19 -3.3473615464 1.4441365537
20 2.2230549184 -3.3473615464
21 0.9386194493 2.2230549184
22 -0.5771781899 0.9386194493
23 2.2627092338 -0.5771781899
24 0.9277313171 2.2627092338
25 0.4805768078 0.9277313171
26 -5.3028672711 0.4805768078
27 0.4948292650 -5.3028672711
28 -0.4500725737 0.4948292650
29 4.8302763458 -0.4500725737
30 4.2403766389 4.8302763458
31 -1.6311085758 4.2403766389
32 -0.5708931556 -1.6311085758
33 1.3669539409 -0.5708931556
34 -1.2429679913 1.3669539409
35 1.7941359840 -1.2429679913
36 -1.1612309436 1.7941359840
37 0.2528211131 -1.1612309436
38 -0.1693239607 0.2528211131
39 0.7507663083 -0.1693239607
40 0.8749846856 0.7507663083
41 6.8913300590 0.8749846856
42 -4.7304489089 6.8913300590
43 -2.3447834919 -4.7304489089
44 -0.1715972466 -2.3447834919
45 1.7541837611 -0.1715972466
46 2.7166665471 1.7541837611
47 0.9516625027 2.7166665471
48 -2.4323057943 0.9516625027
49 -1.8466231269 -2.4323057943
50 -0.9236847051 -1.8466231269
51 -6.0403434700 -0.9236847051
52 0.1125954283 -6.0403434700
53 -2.4729821369 0.1125954283
54 2.1552368786 -2.4729821369
55 -0.9277332433 2.1552368786
56 -0.0230223206 -0.9277332433
57 1.9064449042 -0.0230223206
58 -2.2043935119 1.9064449042
59 0.4393657556 -2.2043935119
60 -2.0661419532 0.4393657556
61 2.1294001319 -2.0661419532
62 1.1584335034 2.1294001319
63 2.4253325741 1.1584335034
64 -1.1872012966 2.4253325741
65 1.6819826101 -1.1872012966
66 -2.2318077556 1.6819826101
67 2.6501222991 -2.2318077556
68 2.0526069461 2.6501222991
69 -0.7612705033 2.0526069461
70 2.1234027338 -0.7612705033
71 1.0830782163 2.1234027338
72 -1.9968137480 1.0830782163
73 -3.0952478000 -1.9968137480
74 -0.1930141709 -3.0952478000
75 -1.4007794696 -0.1930141709
76 -0.7519219617 -1.4007794696
77 -0.0811136175 -0.7519219617
78 2.9487245560 -0.0811136175
79 -1.9786196507 2.9487245560
80 -2.4163056889 -1.9786196507
81 1.3844307414 -2.4163056889
82 -0.0003351977 1.3844307414
83 -0.9673318250 -0.0003351977
84 3.6325535403 -0.9673318250
85 -0.4136399801 3.6325535403
86 -0.0714471259 -0.4136399801
87 -0.3554364093 -0.0714471259
88 0.9296971311 -0.3554364093
89 -0.8033161392 0.9296971311
90 1.0150106519 -0.8033161392
91 -3.6455152363 1.0150106519
92 1.0139580004 -3.6455152363
93 1.6847777251 1.0139580004
94 0.5231555701 1.6847777251
95 0.7266145773 0.5231555701
96 -2.2857412364 0.7266145773
97 -0.4684546063 -2.2857412364
98 -1.5556942387 -0.4684546063
99 0.0079984974 -1.5556942387
100 0.1215947509 0.0079984974
101 0.8295223122 0.1215947509
102 -1.6644239192 0.8295223122
103 2.0060610141 -1.6644239192
104 -3.3790894308 2.0060610141
105 -0.6811824229 -3.3790894308
106 -1.5776716884 -0.6811824229
107 -1.2526155391 -1.5776716884
108 -2.0286106599 -1.2526155391
109 -2.4919319596 -2.0286106599
110 -0.9776325186 -2.4919319596
111 -1.2116678982 -0.9776325186
112 -0.0136006128 -1.2116678982
113 0.9757742676 -0.0136006128
114 0.5869474586 0.9757742676
115 2.6238913132 0.5869474586
116 3.1186945337 2.6238913132
117 -1.6614687734 3.1186945337
118 0.6159091500 -1.6614687734
119 2.9702185640 0.6159091500
120 0.4182205284 2.9702185640
121 0.9279370214 0.4182205284
122 -2.8410413471 0.9279370214
123 -1.2946990611 -2.8410413471
124 1.4636581989 -1.2946990611
125 1.4055425520 1.4636581989
126 0.1326767978 1.4055425520
127 -0.7087981750 0.1326767978
128 -0.3236884711 -0.7087981750
129 -1.7576218625 -0.3236884711
130 -0.1204836827 -1.7576218625
131 1.8963776180 -0.1204836827
132 -1.1658333304 1.8963776180
133 -1.3080532886 -1.1658333304
134 2.3897682793 -1.3080532886
135 1.7754879340 2.3897682793
136 1.0958347298 1.7754879340
137 -3.4379749603 1.0958347298
138 5.2396403462 -3.4379749603
139 -1.0934132038 5.2396403462
140 0.0505473271 -1.0934132038
141 -0.2609522301 0.0505473271
142 2.5331814728 -0.2609522301
143 -0.9881418629 2.5331814728
144 0.8124328938 -0.9881418629
145 -0.4485933177 0.8124328938
146 -3.3305913693 -0.4485933177
147 -4.3439436072 -3.3305913693
148 0.7612711085 -4.3439436072
149 1.9747860255 0.7612711085
150 -0.2564520240 1.9747860255
151 0.9512483728 -0.2564520240
152 -0.7694132886 0.9512483728
153 0.6370762498 -0.7694132886
154 0.5742377084 0.6370762498
155 1.9385267542 0.5742377084
> 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/www/rcomp/tmp/78qq71290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/88qq71290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/98qq71290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10j08a1290508166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$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/www/rcomp/tmp/11mi6y1290508166.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/128j541290508166.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, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13ma2v1290508166.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,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/147b1j1290508166.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,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15bui71290508166.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,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ 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,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ 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,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ 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/www/rcomp/tmp/16wugc1290508166.tab")
+ }
> try(system("convert tmp/1czby1290508166.ps tmp/1czby1290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/2czby1290508166.ps tmp/2czby1290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nqs11290508166.ps tmp/3nqs11290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nqs11290508166.ps tmp/4nqs11290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nqs11290508166.ps tmp/5nqs11290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yh9m1290508166.ps tmp/6yh9m1290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/78qq71290508166.ps tmp/78qq71290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/88qq71290508166.ps tmp/88qq71290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/98qq71290508166.ps tmp/98qq71290508166.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j08a1290508166.ps tmp/10j08a1290508166.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.680 2.260 7.939