R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,2)
+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('NotPopular'
+ ,'Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'WeightedSum'
+ ,'Gender')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('NotPopular','Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','WeightedSum','Gender'),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 = '1'
> #'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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
NotPopular Popularity FindingFriends KnowingPeople Liked Celebrity
1 2 13 13 14 13 3
2 3 12 12 8 13 5
3 3 15 10 12 16 6
4 3 12 9 7 12 6
5 3 10 10 10 11 5
6 3 12 12 7 12 3
7 2 15 13 16 18 8
8 3 9 12 11 11 4
9 3 12 12 14 14 4
10 4 11 6 6 9 4
11 3 11 5 16 14 6
12 3 11 12 11 12 6
13 2 15 11 16 11 5
14 3 7 14 12 12 4
15 3 11 14 7 13 6
16 3 11 12 13 11 4
17 3 10 12 11 12 6
18 3 14 11 15 16 6
19 2 10 11 7 9 4
20 4 6 7 9 11 4
21 3 11 9 7 13 2
22 2 15 11 14 15 7
23 3 11 11 15 10 5
24 3 12 12 7 11 4
25 2 14 12 15 13 6
26 2 15 11 17 16 6
27 4 9 11 15 15 7
28 2 13 8 14 14 5
29 3 13 9 14 14 6
30 2 16 12 8 14 4
31 4 13 10 8 8 4
32 3 12 10 14 13 7
33 2 14 12 14 15 7
34 3 11 8 8 13 4
35 3 9 12 11 11 4
36 1 16 11 16 15 6
37 3 12 12 10 15 6
38 3 10 7 8 9 5
39 3 13 11 14 13 6
40 2 16 11 16 16 7
41 3 14 12 13 13 6
42 15 9 5 11 3 6
43 5 15 8 12 3 6
44 8 11 10 12 4 4
45 11 11 8 12 6 4
46 16 11 13 14 7 7
47 17 11 15 14 5 7
48 9 15 6 8 4 7
49 9 11 12 13 5 0
50 13 12 16 16 6 3
51 10 12 5 13 6 8
52 6 9 15 11 6 8
53 12 12 12 14 5 10
54 8 12 8 13 4 11
55 14 13 13 13 5 6
56 12 11 14 13 5 2
57 11 9 12 12 4 6
58 16 9 16 16 6 1
59 8 11 10 15 2 5
60 15 11 15 15 8 4
61 7 12 8 12 3 6
62 16 12 16 14 6 6
63 14 9 19 12 6 4
64 16 11 14 15 6 1
65 9 9 6 12 5 6
66 14 12 13 13 5 7
67 11 12 15 12 6 7
68 13 12 7 12 5 2
69 15 12 13 13 6 7
70 5 14 4 5 2 8
71 15 11 14 13 5 5
72 13 12 13 13 5 4
73 11 11 11 14 5 2
74 11 6 14 17 6 0
75 12 10 12 13 6 7
76 12 12 15 13 6 0
77 12 13 14 12 5 5
78 12 8 13 13 5 3
79 14 12 8 14 4 3
80 6 12 6 11 2 3
81 7 12 7 12 4 3
82 14 6 13 12 6 7
83 14 11 13 16 6 6
84 10 10 11 12 5 3
85 13 12 5 12 3 0
86 12 13 12 12 6 2
87 9 11 8 10 4 0
88 12 7 11 15 5 9
89 16 11 14 15 8 10
90 10 11 9 12 4 3
91 14 11 10 16 6 7
92 10 11 13 15 6 3
93 16 12 16 16 7 6
94 15 10 16 13 6 5
95 12 11 11 12 5 0
96 10 12 8 11 4 0
97 8 7 4 13 6 4
98 8 13 7 10 3 0
99 11 8 14 15 5 0
100 13 12 11 13 6 7
101 16 11 17 16 7 3
102 16 12 15 15 7 9
103 14 14 17 18 6 4
104 11 10 5 13 3 4
105 4 10 4 10 2 15
106 14 13 10 16 8 7
107 9 10 11 13 3 8
108 14 11 15 15 8 2
109 8 10 10 14 3 8
110 8 7 9 15 4 7
111 11 10 12 14 5 3
112 12 8 15 13 7 3
113 11 12 7 13 6 6
114 14 12 13 15 6 8
115 15 12 12 16 7 5
116 16 11 14 14 6 6
117 16 12 14 14 6 10
118 11 12 8 16 6 0
119 14 12 15 14 6 5
120 14 11 12 12 4 0
121 12 12 12 13 4 0
122 14 11 16 12 5 5
123 8 11 9 12 4 10
124 13 13 15 14 6 0
125 16 12 15 14 6 5
126 12 12 6 14 5 6
127 16 12 14 16 8 1
128 12 12 15 13 6 5
129 11 8 10 14 5 3
130 4 8 6 4 4 3
131 16 12 14 16 8 6
132 15 11 12 13 6 2
133 10 12 8 16 4 5
134 13 13 11 15 6 6
135 15 12 13 14 6 2
136 12 12 9 13 4 3
137 14 11 15 14 6 7
138 7 12 13 12 3 6
139 19 12 15 15 6 3
140 12 10 14 14 5 6
141 12 11 16 13 4 9
142 13 12 14 14 6 2
143 15 12 14 16 4 5
144 8 10 10 6 4 10
145 12 12 10 13 4 9
146 10 13 4 13 6 8
147 8 12 8 14 5 8
148 10 15 15 15 6 5
149 15 11 16 14 6 9
150 16 12 12 15 8 9
151 13 11 12 13 7 14
152 16 12 15 16 7 5
153 9 11 9 12 4 12
154 14 10 12 15 6 6
155 14 11 14 12 6 6
156 12 11 11 14 2 8
WeightedSum Gender
1 5 1
2 6 1
3 4 1
4 6 2
5 3 1
6 10 1
7 8 2
8 3 1
9 4 1
10 3 1
11 5 2
12 5 2
13 6 1
14 5 1
15 3 1
16 4 2
17 8 1
18 8 2
19 8 2
20 5 1
21 8 2
22 2 1
23 0 1
24 5 2
25 2 1
26 7 1
27 5 1
28 2 1
29 12 2
30 7 1
31 0 2
32 2 1
33 3 1
34 0 2
35 9 2
36 2 2
37 3 1
38 1 2
39 10 2
40 1 1
41 4 1
42 1 5
43 1 4
44 2 3
45 2 2
46 1 2
47 2 3
48 2 4
49 1 2
50 2 3
51 1 4
52 1 3
53 1 4
54 1 3
55 2 3
56 1 4
57 1 2
58 1 4
59 2 2
60 2 4
61 1 2
62 1 3
63 1 2
64 2 3
65 1 3
66 2 3
67 2 3
68 2 2
69 1 4
70 1 2
71 1 3
72 2 4
73 2 2
74 1 3
75 1 3
76 1 3
77 1 3
78 2 2
79 2 4
80 2 4
81 2 2
82 1 2
83 1 4
84 1 3
85 2 3
86 2 3
87 1 3
88 1 2
89 1 4
90 1 3
91 1 4
92 2 2
93 1 2
94 1 3
95 1 3
96 1 3
97 2 4
98 1 2
99 2 3
100 2 2
101 1 2
102 2 3
103 1 3
104 1 5
105 2 2
106 1 4
107 2 3
108 1 4
109 1 4
110 1 3
111 1 3
112 1 3
113 1 3
114 1 2
115 1 2
116 2 2
117 1 2
118 1 1
119 2 2
120 1 3
121 1 3
122 1 4
123 1 3
124 1 2
125 1 3
126 2 2
127 1 3
128 1 3
129 2 5
130 2 2
131 1 2
132 2 3
133 2 1
134 2 3
135 2 3
136 1 3
137 1 4
138 2 1
139 2 2
140 1 4
141 1 2
142 1 2
143 1 4
144 2 3
145 1 3
146 1 4
147 2 1
148 2 2
149 2 2
150 2 3
151 1 2
152 1 3
153 1 3
154 2 3
155 2 3
156 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Popularity FindingFriends KnowingPeople Liked
0.41081 -0.01037 0.54903 0.43407 -0.60637
Celebrity WeightedSum Gender
-0.03010 -0.26723 1.05530
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.81060 -1.58855 -0.06435 1.81459 6.11949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.41081 1.79958 0.228 0.8197
Popularity -0.01037 0.11399 -0.091 0.9276
FindingFriends 0.54903 0.07024 7.816 9.34e-13 ***
KnowingPeople 0.43407 0.08911 4.871 2.82e-06 ***
Liked -0.60637 0.08409 -7.211 2.66e-11 ***
Celebrity -0.03010 0.07361 -0.409 0.6832
WeightedSum -0.26723 0.12977 -2.059 0.0412 *
Gender 1.05530 0.25144 4.197 4.65e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.546 on 148 degrees of freedom
Multiple R-squared: 0.7415, Adjusted R-squared: 0.7293
F-statistic: 60.66 on 7 and 148 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,] 2.040309e-03 4.080617e-03 0.997959691
[2,] 7.041232e-04 1.408246e-03 0.999295877
[3,] 9.114709e-05 1.822942e-04 0.999908853
[4,] 1.049019e-05 2.098038e-05 0.999989510
[5,] 1.267014e-06 2.534028e-06 0.999998733
[6,] 2.767347e-07 5.534693e-07 0.999999723
[7,] 4.716277e-08 9.432554e-08 0.999999953
[8,] 2.482134e-08 4.964269e-08 0.999999975
[9,] 6.741721e-08 1.348344e-07 0.999999933
[10,] 1.055137e-08 2.110274e-08 0.999999989
[11,] 1.828981e-09 3.657963e-09 0.999999998
[12,] 1.331547e-09 2.663094e-09 0.999999999
[13,] 3.239676e-10 6.479352e-10 1.000000000
[14,] 5.971553e-11 1.194311e-10 1.000000000
[15,] 1.898031e-11 3.796062e-11 1.000000000
[16,] 3.592508e-12 7.185015e-12 1.000000000
[17,] 2.908034e-12 5.816068e-12 1.000000000
[18,] 4.658183e-12 9.316366e-12 1.000000000
[19,] 1.106176e-12 2.212352e-12 1.000000000
[20,] 2.146801e-13 4.293601e-13 1.000000000
[21,] 1.030792e-12 2.061583e-12 1.000000000
[22,] 2.158876e-13 4.317751e-13 1.000000000
[23,] 7.702421e-14 1.540484e-13 1.000000000
[24,] 2.134338e-14 4.268677e-14 1.000000000
[25,] 4.068190e-15 8.136380e-15 1.000000000
[26,] 1.490293e-14 2.980586e-14 1.000000000
[27,] 3.427278e-15 6.854556e-15 1.000000000
[28,] 1.336026e-15 2.672051e-15 1.000000000
[29,] 4.877726e-16 9.755452e-16 1.000000000
[30,] 1.078425e-15 2.156850e-15 1.000000000
[31,] 2.025131e-14 4.050261e-14 1.000000000
[32,] 1.039119e-03 2.078239e-03 0.998960881
[33,] 5.952007e-03 1.190401e-02 0.994047993
[34,] 6.635851e-03 1.327170e-02 0.993364149
[35,] 8.922090e-02 1.784418e-01 0.910779103
[36,] 7.980231e-01 4.039537e-01 0.201976867
[37,] 9.354047e-01 1.291906e-01 0.064595297
[38,] 9.182741e-01 1.634517e-01 0.081725854
[39,] 9.280532e-01 1.438937e-01 0.071946828
[40,] 9.350042e-01 1.299917e-01 0.064995833
[41,] 9.181237e-01 1.637526e-01 0.081876278
[42,] 9.905281e-01 1.894379e-02 0.009471897
[43,] 9.870930e-01 2.581395e-02 0.012906973
[44,] 9.881653e-01 2.366945e-02 0.011834725
[45,] 9.916205e-01 1.675902e-02 0.008379512
[46,] 9.889352e-01 2.212954e-02 0.011064769
[47,] 9.857505e-01 2.849899e-02 0.014249497
[48,] 9.856109e-01 2.877813e-02 0.014389066
[49,] 9.858826e-01 2.823472e-02 0.014117362
[50,] 9.865616e-01 2.687688e-02 0.013438439
[51,] 9.840974e-01 3.180510e-02 0.015902552
[52,] 9.890293e-01 2.194132e-02 0.010970659
[53,] 9.868986e-01 2.620282e-02 0.013101410
[54,] 9.909576e-01 1.808478e-02 0.009042389
[55,] 9.875794e-01 2.484119e-02 0.012420594
[56,] 9.877500e-01 2.450002e-02 0.012250008
[57,] 9.875948e-01 2.481032e-02 0.012405160
[58,] 9.962069e-01 7.586293e-03 0.003793146
[59,] 9.958770e-01 8.245939e-03 0.004122969
[60,] 9.941583e-01 1.168346e-02 0.005841728
[61,] 9.948801e-01 1.023978e-02 0.005119892
[62,] 9.928614e-01 1.427726e-02 0.007138629
[63,] 9.905314e-01 1.893723e-02 0.009468614
[64,] 9.931298e-01 1.374041e-02 0.006870206
[65,] 9.907590e-01 1.848192e-02 0.009240960
[66,] 9.894737e-01 2.105254e-02 0.010526270
[67,] 9.859284e-01 2.814313e-02 0.014071567
[68,] 9.817973e-01 3.640548e-02 0.018202741
[69,] 9.850608e-01 2.987831e-02 0.014939154
[70,] 9.918200e-01 1.635995e-02 0.008179976
[71,] 9.911804e-01 1.763922e-02 0.008819608
[72,] 9.945172e-01 1.096553e-02 0.005482765
[73,] 9.923374e-01 1.532515e-02 0.007662574
[74,] 9.901874e-01 1.962520e-02 0.009812601
[75,] 9.973449e-01 5.310241e-03 0.002655121
[76,] 9.966073e-01 6.785472e-03 0.003392736
[77,] 9.952064e-01 9.587256e-03 0.004793628
[78,] 9.943707e-01 1.125857e-02 0.005629283
[79,] 9.935999e-01 1.280028e-02 0.006400138
[80,] 9.910679e-01 1.786422e-02 0.008932109
[81,] 9.884311e-01 2.313779e-02 0.011568895
[82,] 9.917434e-01 1.651324e-02 0.008256618
[83,] 9.924480e-01 1.510404e-02 0.007552018
[84,] 9.908409e-01 1.831813e-02 0.009159064
[85,] 9.877292e-01 2.454157e-02 0.012270785
[86,] 9.832825e-01 3.343504e-02 0.016717519
[87,] 9.799329e-01 4.013413e-02 0.020067063
[88,] 9.728492e-01 5.430156e-02 0.027150782
[89,] 9.748363e-01 5.032735e-02 0.025163673
[90,] 9.743492e-01 5.130169e-02 0.025650844
[91,] 9.712132e-01 5.757361e-02 0.028786804
[92,] 9.671988e-01 6.560231e-02 0.032801156
[93,] 9.730870e-01 5.382607e-02 0.026913035
[94,] 9.757590e-01 4.848196e-02 0.024240981
[95,] 9.731442e-01 5.371163e-02 0.026855814
[96,] 9.691189e-01 6.176226e-02 0.030881131
[97,] 9.669998e-01 6.600038e-02 0.033000190
[98,] 9.684758e-01 6.304848e-02 0.031524241
[99,] 9.792213e-01 4.155743e-02 0.020778713
[100,] 9.823114e-01 3.537729e-02 0.017688643
[101,] 9.791181e-01 4.176389e-02 0.020881946
[102,] 9.836998e-01 3.260031e-02 0.016300153
[103,] 9.787082e-01 4.258353e-02 0.021291763
[104,] 9.736869e-01 5.262614e-02 0.026313070
[105,] 9.717976e-01 5.640479e-02 0.028202393
[106,] 9.764563e-01 4.708732e-02 0.023543660
[107,] 9.845926e-01 3.081475e-02 0.015407373
[108,] 9.815111e-01 3.697783e-02 0.018488917
[109,] 9.740599e-01 5.188027e-02 0.025940134
[110,] 9.777952e-01 4.440965e-02 0.022204825
[111,] 9.686362e-01 6.272762e-02 0.031363808
[112,] 9.567569e-01 8.648613e-02 0.043243064
[113,] 9.500938e-01 9.981233e-02 0.049906167
[114,] 9.321240e-01 1.357520e-01 0.067875982
[115,] 9.271763e-01 1.456475e-01 0.072823744
[116,] 9.279717e-01 1.440566e-01 0.072028292
[117,] 9.081180e-01 1.837639e-01 0.091881968
[118,] 8.953206e-01 2.093587e-01 0.104679365
[119,] 9.491664e-01 1.016671e-01 0.050833557
[120,] 9.442981e-01 1.114038e-01 0.055701898
[121,] 9.283693e-01 1.432613e-01 0.071630656
[122,] 9.055250e-01 1.889501e-01 0.094475026
[123,] 8.723063e-01 2.553874e-01 0.127693704
[124,] 8.246571e-01 3.506858e-01 0.175342905
[125,] 7.718558e-01 4.562883e-01 0.228144170
[126,] 7.350770e-01 5.298461e-01 0.264923029
[127,] 6.779167e-01 6.441665e-01 0.322083261
[128,] 6.787175e-01 6.425650e-01 0.321282498
[129,] 9.182341e-01 1.635318e-01 0.081765885
[130,] 9.795592e-01 4.088160e-02 0.020440799
[131,] 9.620114e-01 7.597714e-02 0.037988572
[132,] 9.346055e-01 1.307890e-01 0.065394485
[133,] 8.801379e-01 2.397242e-01 0.119862122
[134,] 7.770201e-01 4.459599e-01 0.222979927
[135,] 6.778297e-01 6.443405e-01 0.322170268
> postscript(file="/var/www/html/rcomp/tmp/1m3811291297999.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xu741291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3xu741291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xu741291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5847p1291297999.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 = 156
Frequency = 1
1 2 3 4 5 6
-3.236437902 1.234088727 1.941728215 1.683685768 -0.571162071 2.070496001
7 8 9 10 11 12
-1.155101662 -2.143767104 -1.328534923 3.128779981 0.908286922 -1.977294826
13 14 15 16 17 18
-3.871095251 -2.555819391 -0.211847387 -3.779240152 -0.130691151 0.093717083
19 20 21 22 23 24
-1.779976604 2.972866606 2.693734571 -1.586170911 -4.688237169 -0.897201813
25 26 27 28 29 30
-3.822487214 -0.975988994 0.719206232 -0.626396889 1.471643336 1.119070837
31 32 33 34 35 36
-2.378088202 -1.280996636 -1.878345595 0.731080571 -1.595703917 -4.529342716
37 38 39 40 41 42
0.807104228 -0.858415865 -0.767239469 -2.104802223 -1.419887457 4.153042626
43 44 45 46 47 48
-6.810595193 -3.081449403 3.284646624 5.100797783 3.001928239 0.927455499
49 50 51 52 53 54
-2.339545611 -1.918909426 1.250619007 -6.347349764 -1.572831059 -2.463612913
55 56 57 58 59 60
1.524703180 -2.487994820 -0.351980931 -0.332747652 -4.511014041 1.241368195
61 62 63 64 65 66
-2.731118225 1.772313247 -0.042649328 2.542650271 0.493271924 1.544432483
67 68 69 70 71 72
-1.513183009 5.177475738 1.828280765 -0.021911661 1.657603256 -0.601165594
73 74 75 76 77 78
0.102838735 -3.674681216 0.411863188 -1.425188407 -0.887580378 0.437839100
79 80 81 82 83 84
2.073438155 -4.739023247 -1.398793950 3.310716672 -0.514411694 -1.331807365
85 86 87 88 89 90
3.947296861 0.993773579 -0.502872886 0.570758436 2.703776125 -0.829746993
91 92 93 94 95 96
1.162778139 -1.792822759 2.565832644 1.155542884 0.588261755 0.073425204
97 98 99 100 101 102
-0.105385407 -0.484174999 -3.124935449 3.304158034 1.916129112 2.851168858
103 104 105 106 107 108
-2.552468622 -0.764934256 -2.755832575 2.396262061 -3.560891287 -0.086059864
109 110 111 112 113 114
-5.768457195 -4.053049965 -1.748983837 -0.770001505 2.147653778 2.100826185
115 116 117 118 119 120
3.731850582 4.182523320 4.046071476 2.226390602 1.613764335 1.432861597
121 122 123 124 125 126
-0.990840313 -1.061678423 -2.619041473 0.206405394 2.291242140 3.978761783
127 128 129 130 131 132
3.064462830 -1.274684464 -2.515032384 -0.418663046 4.270262483 3.538957213
133 134 135 136 137 138
0.431380075 1.360986229 2.566225621 0.746551097 -0.714223479 -4.153744440
139 140 141 142 143 144
6.119489362 -2.812036547 -1.871127718 0.805265168 -1.295911632 -0.306775786
145 146 147 148 149 150
0.378126146 1.810020174 -0.003800293 -2.789194606 2.174766322 5.104628379
151 152 153 154 155 156
3.294606379 2.029465826 -1.558839897 1.780842092 1.995374399 -0.535667962
> postscript(file="/var/www/html/rcomp/tmp/6847p1291297999.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.236437902 NA
1 1.234088727 -3.236437902
2 1.941728215 1.234088727
3 1.683685768 1.941728215
4 -0.571162071 1.683685768
5 2.070496001 -0.571162071
6 -1.155101662 2.070496001
7 -2.143767104 -1.155101662
8 -1.328534923 -2.143767104
9 3.128779981 -1.328534923
10 0.908286922 3.128779981
11 -1.977294826 0.908286922
12 -3.871095251 -1.977294826
13 -2.555819391 -3.871095251
14 -0.211847387 -2.555819391
15 -3.779240152 -0.211847387
16 -0.130691151 -3.779240152
17 0.093717083 -0.130691151
18 -1.779976604 0.093717083
19 2.972866606 -1.779976604
20 2.693734571 2.972866606
21 -1.586170911 2.693734571
22 -4.688237169 -1.586170911
23 -0.897201813 -4.688237169
24 -3.822487214 -0.897201813
25 -0.975988994 -3.822487214
26 0.719206232 -0.975988994
27 -0.626396889 0.719206232
28 1.471643336 -0.626396889
29 1.119070837 1.471643336
30 -2.378088202 1.119070837
31 -1.280996636 -2.378088202
32 -1.878345595 -1.280996636
33 0.731080571 -1.878345595
34 -1.595703917 0.731080571
35 -4.529342716 -1.595703917
36 0.807104228 -4.529342716
37 -0.858415865 0.807104228
38 -0.767239469 -0.858415865
39 -2.104802223 -0.767239469
40 -1.419887457 -2.104802223
41 4.153042626 -1.419887457
42 -6.810595193 4.153042626
43 -3.081449403 -6.810595193
44 3.284646624 -3.081449403
45 5.100797783 3.284646624
46 3.001928239 5.100797783
47 0.927455499 3.001928239
48 -2.339545611 0.927455499
49 -1.918909426 -2.339545611
50 1.250619007 -1.918909426
51 -6.347349764 1.250619007
52 -1.572831059 -6.347349764
53 -2.463612913 -1.572831059
54 1.524703180 -2.463612913
55 -2.487994820 1.524703180
56 -0.351980931 -2.487994820
57 -0.332747652 -0.351980931
58 -4.511014041 -0.332747652
59 1.241368195 -4.511014041
60 -2.731118225 1.241368195
61 1.772313247 -2.731118225
62 -0.042649328 1.772313247
63 2.542650271 -0.042649328
64 0.493271924 2.542650271
65 1.544432483 0.493271924
66 -1.513183009 1.544432483
67 5.177475738 -1.513183009
68 1.828280765 5.177475738
69 -0.021911661 1.828280765
70 1.657603256 -0.021911661
71 -0.601165594 1.657603256
72 0.102838735 -0.601165594
73 -3.674681216 0.102838735
74 0.411863188 -3.674681216
75 -1.425188407 0.411863188
76 -0.887580378 -1.425188407
77 0.437839100 -0.887580378
78 2.073438155 0.437839100
79 -4.739023247 2.073438155
80 -1.398793950 -4.739023247
81 3.310716672 -1.398793950
82 -0.514411694 3.310716672
83 -1.331807365 -0.514411694
84 3.947296861 -1.331807365
85 0.993773579 3.947296861
86 -0.502872886 0.993773579
87 0.570758436 -0.502872886
88 2.703776125 0.570758436
89 -0.829746993 2.703776125
90 1.162778139 -0.829746993
91 -1.792822759 1.162778139
92 2.565832644 -1.792822759
93 1.155542884 2.565832644
94 0.588261755 1.155542884
95 0.073425204 0.588261755
96 -0.105385407 0.073425204
97 -0.484174999 -0.105385407
98 -3.124935449 -0.484174999
99 3.304158034 -3.124935449
100 1.916129112 3.304158034
101 2.851168858 1.916129112
102 -2.552468622 2.851168858
103 -0.764934256 -2.552468622
104 -2.755832575 -0.764934256
105 2.396262061 -2.755832575
106 -3.560891287 2.396262061
107 -0.086059864 -3.560891287
108 -5.768457195 -0.086059864
109 -4.053049965 -5.768457195
110 -1.748983837 -4.053049965
111 -0.770001505 -1.748983837
112 2.147653778 -0.770001505
113 2.100826185 2.147653778
114 3.731850582 2.100826185
115 4.182523320 3.731850582
116 4.046071476 4.182523320
117 2.226390602 4.046071476
118 1.613764335 2.226390602
119 1.432861597 1.613764335
120 -0.990840313 1.432861597
121 -1.061678423 -0.990840313
122 -2.619041473 -1.061678423
123 0.206405394 -2.619041473
124 2.291242140 0.206405394
125 3.978761783 2.291242140
126 3.064462830 3.978761783
127 -1.274684464 3.064462830
128 -2.515032384 -1.274684464
129 -0.418663046 -2.515032384
130 4.270262483 -0.418663046
131 3.538957213 4.270262483
132 0.431380075 3.538957213
133 1.360986229 0.431380075
134 2.566225621 1.360986229
135 0.746551097 2.566225621
136 -0.714223479 0.746551097
137 -4.153744440 -0.714223479
138 6.119489362 -4.153744440
139 -2.812036547 6.119489362
140 -1.871127718 -2.812036547
141 0.805265168 -1.871127718
142 -1.295911632 0.805265168
143 -0.306775786 -1.295911632
144 0.378126146 -0.306775786
145 1.810020174 0.378126146
146 -0.003800293 1.810020174
147 -2.789194606 -0.003800293
148 2.174766322 -2.789194606
149 5.104628379 2.174766322
150 3.294606379 5.104628379
151 2.029465826 3.294606379
152 -1.558839897 2.029465826
153 1.780842092 -1.558839897
154 1.995374399 1.780842092
155 -0.535667962 1.995374399
156 NA -0.535667962
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.234088727 -3.236437902
[2,] 1.941728215 1.234088727
[3,] 1.683685768 1.941728215
[4,] -0.571162071 1.683685768
[5,] 2.070496001 -0.571162071
[6,] -1.155101662 2.070496001
[7,] -2.143767104 -1.155101662
[8,] -1.328534923 -2.143767104
[9,] 3.128779981 -1.328534923
[10,] 0.908286922 3.128779981
[11,] -1.977294826 0.908286922
[12,] -3.871095251 -1.977294826
[13,] -2.555819391 -3.871095251
[14,] -0.211847387 -2.555819391
[15,] -3.779240152 -0.211847387
[16,] -0.130691151 -3.779240152
[17,] 0.093717083 -0.130691151
[18,] -1.779976604 0.093717083
[19,] 2.972866606 -1.779976604
[20,] 2.693734571 2.972866606
[21,] -1.586170911 2.693734571
[22,] -4.688237169 -1.586170911
[23,] -0.897201813 -4.688237169
[24,] -3.822487214 -0.897201813
[25,] -0.975988994 -3.822487214
[26,] 0.719206232 -0.975988994
[27,] -0.626396889 0.719206232
[28,] 1.471643336 -0.626396889
[29,] 1.119070837 1.471643336
[30,] -2.378088202 1.119070837
[31,] -1.280996636 -2.378088202
[32,] -1.878345595 -1.280996636
[33,] 0.731080571 -1.878345595
[34,] -1.595703917 0.731080571
[35,] -4.529342716 -1.595703917
[36,] 0.807104228 -4.529342716
[37,] -0.858415865 0.807104228
[38,] -0.767239469 -0.858415865
[39,] -2.104802223 -0.767239469
[40,] -1.419887457 -2.104802223
[41,] 4.153042626 -1.419887457
[42,] -6.810595193 4.153042626
[43,] -3.081449403 -6.810595193
[44,] 3.284646624 -3.081449403
[45,] 5.100797783 3.284646624
[46,] 3.001928239 5.100797783
[47,] 0.927455499 3.001928239
[48,] -2.339545611 0.927455499
[49,] -1.918909426 -2.339545611
[50,] 1.250619007 -1.918909426
[51,] -6.347349764 1.250619007
[52,] -1.572831059 -6.347349764
[53,] -2.463612913 -1.572831059
[54,] 1.524703180 -2.463612913
[55,] -2.487994820 1.524703180
[56,] -0.351980931 -2.487994820
[57,] -0.332747652 -0.351980931
[58,] -4.511014041 -0.332747652
[59,] 1.241368195 -4.511014041
[60,] -2.731118225 1.241368195
[61,] 1.772313247 -2.731118225
[62,] -0.042649328 1.772313247
[63,] 2.542650271 -0.042649328
[64,] 0.493271924 2.542650271
[65,] 1.544432483 0.493271924
[66,] -1.513183009 1.544432483
[67,] 5.177475738 -1.513183009
[68,] 1.828280765 5.177475738
[69,] -0.021911661 1.828280765
[70,] 1.657603256 -0.021911661
[71,] -0.601165594 1.657603256
[72,] 0.102838735 -0.601165594
[73,] -3.674681216 0.102838735
[74,] 0.411863188 -3.674681216
[75,] -1.425188407 0.411863188
[76,] -0.887580378 -1.425188407
[77,] 0.437839100 -0.887580378
[78,] 2.073438155 0.437839100
[79,] -4.739023247 2.073438155
[80,] -1.398793950 -4.739023247
[81,] 3.310716672 -1.398793950
[82,] -0.514411694 3.310716672
[83,] -1.331807365 -0.514411694
[84,] 3.947296861 -1.331807365
[85,] 0.993773579 3.947296861
[86,] -0.502872886 0.993773579
[87,] 0.570758436 -0.502872886
[88,] 2.703776125 0.570758436
[89,] -0.829746993 2.703776125
[90,] 1.162778139 -0.829746993
[91,] -1.792822759 1.162778139
[92,] 2.565832644 -1.792822759
[93,] 1.155542884 2.565832644
[94,] 0.588261755 1.155542884
[95,] 0.073425204 0.588261755
[96,] -0.105385407 0.073425204
[97,] -0.484174999 -0.105385407
[98,] -3.124935449 -0.484174999
[99,] 3.304158034 -3.124935449
[100,] 1.916129112 3.304158034
[101,] 2.851168858 1.916129112
[102,] -2.552468622 2.851168858
[103,] -0.764934256 -2.552468622
[104,] -2.755832575 -0.764934256
[105,] 2.396262061 -2.755832575
[106,] -3.560891287 2.396262061
[107,] -0.086059864 -3.560891287
[108,] -5.768457195 -0.086059864
[109,] -4.053049965 -5.768457195
[110,] -1.748983837 -4.053049965
[111,] -0.770001505 -1.748983837
[112,] 2.147653778 -0.770001505
[113,] 2.100826185 2.147653778
[114,] 3.731850582 2.100826185
[115,] 4.182523320 3.731850582
[116,] 4.046071476 4.182523320
[117,] 2.226390602 4.046071476
[118,] 1.613764335 2.226390602
[119,] 1.432861597 1.613764335
[120,] -0.990840313 1.432861597
[121,] -1.061678423 -0.990840313
[122,] -2.619041473 -1.061678423
[123,] 0.206405394 -2.619041473
[124,] 2.291242140 0.206405394
[125,] 3.978761783 2.291242140
[126,] 3.064462830 3.978761783
[127,] -1.274684464 3.064462830
[128,] -2.515032384 -1.274684464
[129,] -0.418663046 -2.515032384
[130,] 4.270262483 -0.418663046
[131,] 3.538957213 4.270262483
[132,] 0.431380075 3.538957213
[133,] 1.360986229 0.431380075
[134,] 2.566225621 1.360986229
[135,] 0.746551097 2.566225621
[136,] -0.714223479 0.746551097
[137,] -4.153744440 -0.714223479
[138,] 6.119489362 -4.153744440
[139,] -2.812036547 6.119489362
[140,] -1.871127718 -2.812036547
[141,] 0.805265168 -1.871127718
[142,] -1.295911632 0.805265168
[143,] -0.306775786 -1.295911632
[144,] 0.378126146 -0.306775786
[145,] 1.810020174 0.378126146
[146,] -0.003800293 1.810020174
[147,] -2.789194606 -0.003800293
[148,] 2.174766322 -2.789194606
[149,] 5.104628379 2.174766322
[150,] 3.294606379 5.104628379
[151,] 2.029465826 3.294606379
[152,] -1.558839897 2.029465826
[153,] 1.780842092 -1.558839897
[154,] 1.995374399 1.780842092
[155,] -0.535667962 1.995374399
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.234088727 -3.236437902
2 1.941728215 1.234088727
3 1.683685768 1.941728215
4 -0.571162071 1.683685768
5 2.070496001 -0.571162071
6 -1.155101662 2.070496001
7 -2.143767104 -1.155101662
8 -1.328534923 -2.143767104
9 3.128779981 -1.328534923
10 0.908286922 3.128779981
11 -1.977294826 0.908286922
12 -3.871095251 -1.977294826
13 -2.555819391 -3.871095251
14 -0.211847387 -2.555819391
15 -3.779240152 -0.211847387
16 -0.130691151 -3.779240152
17 0.093717083 -0.130691151
18 -1.779976604 0.093717083
19 2.972866606 -1.779976604
20 2.693734571 2.972866606
21 -1.586170911 2.693734571
22 -4.688237169 -1.586170911
23 -0.897201813 -4.688237169
24 -3.822487214 -0.897201813
25 -0.975988994 -3.822487214
26 0.719206232 -0.975988994
27 -0.626396889 0.719206232
28 1.471643336 -0.626396889
29 1.119070837 1.471643336
30 -2.378088202 1.119070837
31 -1.280996636 -2.378088202
32 -1.878345595 -1.280996636
33 0.731080571 -1.878345595
34 -1.595703917 0.731080571
35 -4.529342716 -1.595703917
36 0.807104228 -4.529342716
37 -0.858415865 0.807104228
38 -0.767239469 -0.858415865
39 -2.104802223 -0.767239469
40 -1.419887457 -2.104802223
41 4.153042626 -1.419887457
42 -6.810595193 4.153042626
43 -3.081449403 -6.810595193
44 3.284646624 -3.081449403
45 5.100797783 3.284646624
46 3.001928239 5.100797783
47 0.927455499 3.001928239
48 -2.339545611 0.927455499
49 -1.918909426 -2.339545611
50 1.250619007 -1.918909426
51 -6.347349764 1.250619007
52 -1.572831059 -6.347349764
53 -2.463612913 -1.572831059
54 1.524703180 -2.463612913
55 -2.487994820 1.524703180
56 -0.351980931 -2.487994820
57 -0.332747652 -0.351980931
58 -4.511014041 -0.332747652
59 1.241368195 -4.511014041
60 -2.731118225 1.241368195
61 1.772313247 -2.731118225
62 -0.042649328 1.772313247
63 2.542650271 -0.042649328
64 0.493271924 2.542650271
65 1.544432483 0.493271924
66 -1.513183009 1.544432483
67 5.177475738 -1.513183009
68 1.828280765 5.177475738
69 -0.021911661 1.828280765
70 1.657603256 -0.021911661
71 -0.601165594 1.657603256
72 0.102838735 -0.601165594
73 -3.674681216 0.102838735
74 0.411863188 -3.674681216
75 -1.425188407 0.411863188
76 -0.887580378 -1.425188407
77 0.437839100 -0.887580378
78 2.073438155 0.437839100
79 -4.739023247 2.073438155
80 -1.398793950 -4.739023247
81 3.310716672 -1.398793950
82 -0.514411694 3.310716672
83 -1.331807365 -0.514411694
84 3.947296861 -1.331807365
85 0.993773579 3.947296861
86 -0.502872886 0.993773579
87 0.570758436 -0.502872886
88 2.703776125 0.570758436
89 -0.829746993 2.703776125
90 1.162778139 -0.829746993
91 -1.792822759 1.162778139
92 2.565832644 -1.792822759
93 1.155542884 2.565832644
94 0.588261755 1.155542884
95 0.073425204 0.588261755
96 -0.105385407 0.073425204
97 -0.484174999 -0.105385407
98 -3.124935449 -0.484174999
99 3.304158034 -3.124935449
100 1.916129112 3.304158034
101 2.851168858 1.916129112
102 -2.552468622 2.851168858
103 -0.764934256 -2.552468622
104 -2.755832575 -0.764934256
105 2.396262061 -2.755832575
106 -3.560891287 2.396262061
107 -0.086059864 -3.560891287
108 -5.768457195 -0.086059864
109 -4.053049965 -5.768457195
110 -1.748983837 -4.053049965
111 -0.770001505 -1.748983837
112 2.147653778 -0.770001505
113 2.100826185 2.147653778
114 3.731850582 2.100826185
115 4.182523320 3.731850582
116 4.046071476 4.182523320
117 2.226390602 4.046071476
118 1.613764335 2.226390602
119 1.432861597 1.613764335
120 -0.990840313 1.432861597
121 -1.061678423 -0.990840313
122 -2.619041473 -1.061678423
123 0.206405394 -2.619041473
124 2.291242140 0.206405394
125 3.978761783 2.291242140
126 3.064462830 3.978761783
127 -1.274684464 3.064462830
128 -2.515032384 -1.274684464
129 -0.418663046 -2.515032384
130 4.270262483 -0.418663046
131 3.538957213 4.270262483
132 0.431380075 3.538957213
133 1.360986229 0.431380075
134 2.566225621 1.360986229
135 0.746551097 2.566225621
136 -0.714223479 0.746551097
137 -4.153744440 -0.714223479
138 6.119489362 -4.153744440
139 -2.812036547 6.119489362
140 -1.871127718 -2.812036547
141 0.805265168 -1.871127718
142 -1.295911632 0.805265168
143 -0.306775786 -1.295911632
144 0.378126146 -0.306775786
145 1.810020174 0.378126146
146 -0.003800293 1.810020174
147 -2.789194606 -0.003800293
148 2.174766322 -2.789194606
149 5.104628379 2.174766322
150 3.294606379 5.104628379
151 2.029465826 3.294606379
152 -1.558839897 2.029465826
153 1.780842092 -1.558839897
154 1.995374399 1.780842092
155 -0.535667962 1.995374399
> 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/html/rcomp/tmp/70voa1291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80voa1291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tm5v1291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tm5v1291297999.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11f5401291297999.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/html/rcomp/tmp/120ok61291297999.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/html/rcomp/tmp/13ppzi1291297999.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/html/rcomp/tmp/14zgzl1291297999.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/html/rcomp/tmp/15lgxr1291297999.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/html/rcomp/tmp/16hqv01291297999.tab")
+ }
>
> try(system("convert tmp/1m3811291297999.ps tmp/1m3811291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xu741291297999.ps tmp/2xu741291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xu741291297999.ps tmp/3xu741291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xu741291297999.ps tmp/4xu741291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/5847p1291297999.ps tmp/5847p1291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/6847p1291297999.ps tmp/6847p1291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/70voa1291297999.ps tmp/70voa1291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/80voa1291297999.ps tmp/80voa1291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tm5v1291297999.ps tmp/9tm5v1291297999.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tm5v1291297999.ps tmp/10tm5v1291297999.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.197 1.786 9.082