R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(13
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+ ,2)
+ ,dim=c(10
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'sum_friends'
+ ,'KnowingPeople'
+ ,'sum_know'
+ ,'Liked'
+ ,'sum_liked'
+ ,'Celebrity'
+ ,'sum_celeb'
+ ,'Sum')
+ ,1:156))
> y <- array(NA,dim=c(10,156),dimnames=list(c('Popularity','FindingFriends','sum_friends','KnowingPeople','sum_know','Liked','sum_liked','Celebrity','sum_celeb','Sum'),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
> 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 FindingFriends sum_friends KnowingPeople sum_know Liked
1 13 13 26 14 28 13
2 12 12 12 8 8 13
3 15 10 0 12 0 16
4 12 9 27 7 21 12
5 10 10 30 10 30 11
6 12 12 12 7 7 12
7 15 13 39 16 48 18
8 9 12 12 11 11 11
9 12 12 48 14 56 14
10 11 6 0 6 0 9
11 11 5 15 16 48 14
12 11 12 24 11 22 12
13 15 11 44 16 64 11
14 7 14 42 12 36 12
15 11 14 14 7 7 13
16 11 12 12 13 13 11
17 10 12 24 11 22 12
18 14 11 33 15 45 16
19 10 11 11 7 7 9
20 6 7 7 9 9 11
21 11 9 18 7 14 13
22 15 11 33 14 42 15
23 11 11 44 15 60 10
24 12 12 24 7 14 11
25 14 12 12 15 15 13
26 15 11 22 17 34 16
27 9 11 22 15 30 15
28 13 8 32 14 56 14
29 13 9 18 14 28 14
30 16 12 36 8 24 14
31 13 10 30 8 24 8
32 12 10 30 14 42 13
33 14 12 48 14 56 15
34 11 8 16 8 16 13
35 9 12 24 11 22 11
36 16 11 44 16 64 15
37 12 12 36 10 30 15
38 10 7 28 8 32 9
39 13 11 22 14 28 13
40 16 11 55 16 80 16
41 14 12 36 13 39 13
42 15 9 9 5 5 11
43 5 15 15 8 8 12
44 8 11 11 10 10 12
45 11 11 22 8 16 12
46 16 11 33 13 39 14
47 17 11 99 15 135 14
48 9 15 0 6 0 8
49 9 11 0 12 0 13
50 13 12 24 16 32 16
51 10 12 24 5 10 13
52 6 9 27 15 45 11
53 12 12 12 12 12 14
54 8 12 24 8 16 13
55 14 13 0 13 0 13
56 12 11 55 14 70 13
57 11 9 18 12 24 12
58 16 9 36 16 64 16
59 8 11 33 10 30 15
60 15 11 0 15 0 15
61 7 12 0 8 0 12
62 16 12 48 16 64 14
63 14 9 9 19 19 12
64 16 11 11 14 14 15
65 9 9 36 6 24 12
66 14 12 24 13 26 13
67 11 12 48 15 60 12
68 13 12 12 7 7 12
69 15 12 48 13 52 13
70 5 14 28 4 8 5
71 15 11 55 14 70 13
72 13 12 48 13 52 13
73 11 11 44 11 44 14
74 11 6 24 14 56 17
75 12 10 40 12 48 13
76 12 12 36 15 45 13
77 12 13 39 14 42 12
78 12 8 24 13 39 13
79 14 12 24 8 16 14
80 6 12 12 6 6 11
81 7 12 12 7 7 12
82 14 6 30 13 65 12
83 14 11 44 13 52 16
84 10 10 20 11 22 12
85 13 12 36 5 15 12
86 12 13 26 12 24 12
87 9 11 22 8 16 10
88 12 7 14 11 22 15
89 16 11 22 14 28 15
90 10 11 33 9 27 12
91 14 11 22 10 20 16
92 10 11 33 13 39 15
93 16 12 48 16 64 16
94 15 10 30 16 48 13
95 12 11 33 11 33 12
96 10 12 0 8 0 11
97 8 7 7 4 4 13
98 8 13 26 7 14 10
99 11 8 16 14 28 15
100 13 12 36 11 33 13
101 16 11 44 17 68 16
102 16 12 48 15 60 15
103 14 14 14 17 17 18
104 11 10 20 5 10 13
105 4 10 20 4 8 10
106 14 13 39 10 30 16
107 9 10 30 11 33 13
108 14 11 33 15 45 15
109 8 10 10 10 10 14
110 8 7 7 9 9 15
111 11 10 10 12 12 14
112 12 8 8 15 15 13
113 11 12 0 7 0 13
114 14 12 12 13 13 15
115 15 12 36 12 36 16
116 16 11 33 14 42 14
117 16 12 0 14 0 14
118 11 12 24 8 16 16
119 14 12 60 15 75 14
120 14 11 22 12 24 12
121 12 12 36 12 36 13
122 14 11 33 16 48 12
123 8 11 55 9 45 12
124 13 13 52 15 60 14
125 16 12 48 15 60 14
126 12 12 0 6 0 14
127 16 12 36 14 42 16
128 12 12 0 15 0 13
129 11 8 16 10 20 14
130 4 8 0 6 0 4
131 16 12 72 14 84 16
132 15 11 33 12 36 13
133 10 12 12 8 8 16
134 13 13 78 11 66 15
135 15 12 24 13 26 14
136 12 12 12 9 9 13
137 14 11 33 15 45 14
138 7 12 12 13 13 12
139 19 12 24 15 30 15
140 12 10 40 14 56 14
141 12 11 11 16 16 13
142 13 12 24 14 28 14
143 15 12 0 14 0 16
144 8 10 50 10 50 6
145 12 12 24 10 20 13
146 10 13 13 4 4 13
147 8 12 12 8 8 14
148 10 15 60 15 60 15
149 15 11 33 16 48 14
150 16 12 0 12 0 15
151 13 11 33 12 36 13
152 16 12 36 15 45 16
153 9 11 0 9 0 12
154 14 10 20 12 24 15
155 14 11 55 14 70 12
156 12 11 22 11 22 14
sum_liked Celebrity sum_celeb Sum
1 26 3 6 2
2 13 5 5 1
3 0 6 0 0
4 36 6 18 3
5 33 5 15 3
6 12 3 3 1
7 54 8 24 3
8 11 4 4 1
9 56 4 16 4
10 0 4 0 0
11 42 6 18 3
12 24 6 12 2
13 44 5 20 4
14 36 4 12 3
15 13 6 6 1
16 11 4 4 1
17 24 6 12 2
18 48 6 18 3
19 9 4 4 1
20 11 4 4 1
21 26 2 4 2
22 45 7 21 3
23 40 5 20 4
24 22 4 8 2
25 13 6 6 1
26 32 6 12 2
27 30 7 14 2
28 56 5 20 4
29 28 6 12 2
30 42 4 12 3
31 24 4 12 3
32 39 7 21 3
33 60 7 28 4
34 26 4 8 2
35 22 4 8 2
36 60 6 24 4
37 45 6 18 3
38 36 5 20 4
39 26 6 12 2
40 80 7 35 5
41 39 6 18 3
42 11 3 3 1
43 12 3 3 1
44 12 4 4 1
45 24 6 12 2
46 42 7 21 3
47 126 5 45 9
48 0 4 0 0
49 0 5 0 0
50 32 6 12 2
51 26 6 12 2
52 33 6 18 3
53 14 5 5 1
54 26 4 8 2
55 0 5 0 0
56 65 5 25 5
57 24 4 8 2
58 64 6 24 4
59 45 2 6 3
60 0 8 0 0
61 0 3 0 0
62 56 6 24 4
63 12 6 6 1
64 15 6 6 1
65 48 5 20 4
66 26 5 10 2
67 48 6 24 4
68 12 5 5 1
69 52 6 24 4
70 10 2 4 2
71 65 5 25 5
72 52 5 20 4
73 56 5 20 4
74 68 6 24 4
75 52 6 24 4
76 39 6 18 3
77 36 5 15 3
78 39 5 15 3
79 28 4 8 2
80 11 2 2 1
81 12 4 4 1
82 60 6 30 5
83 64 6 24 4
84 24 5 10 2
85 36 3 9 3
86 24 6 12 2
87 20 4 8 2
88 30 5 10 2
89 30 8 16 2
90 36 4 12 3
91 32 6 12 2
92 45 6 18 3
93 64 7 28 4
94 39 6 18 3
95 36 5 15 3
96 0 4 0 0
97 13 6 6 1
98 20 3 6 2
99 30 5 10 2
100 39 6 18 3
101 64 7 28 4
102 60 7 28 4
103 18 6 6 1
104 26 3 6 2
105 20 2 4 2
106 48 8 24 3
107 39 3 9 3
108 45 8 24 3
109 14 3 3 1
110 15 4 4 1
111 14 5 5 1
112 13 7 7 1
113 0 6 0 0
114 15 6 6 1
115 48 7 21 3
116 42 6 18 3
117 0 6 0 0
118 32 6 12 2
119 70 6 30 5
120 24 4 8 2
121 39 4 12 3
122 36 5 15 3
123 60 4 20 5
124 56 6 24 4
125 56 6 24 4
126 0 5 0 0
127 48 8 24 3
128 0 6 0 0
129 28 5 10 2
130 0 4 0 0
131 96 8 48 6
132 39 6 18 3
133 16 4 4 1
134 90 6 36 6
135 28 6 12 2
136 13 4 4 1
137 42 6 18 3
138 12 3 3 1
139 30 6 12 2
140 56 5 20 4
141 13 4 4 1
142 28 6 12 2
143 0 4 0 0
144 30 4 20 5
145 26 4 8 2
146 13 6 6 1
147 14 5 5 1
148 60 6 24 4
149 42 6 18 3
150 0 8 0 0
151 39 7 21 3
152 48 7 21 3
153 0 4 0 0
154 30 6 12 2
155 60 6 30 5
156 28 2 4 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends sum_friends KnowingPeople sum_know
-0.95652 0.12246 -0.00612 0.12990 0.03887
Liked sum_liked Celebrity sum_celeb Sum
0.42288 -0.02558 0.78243 -0.08246 0.57963
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.4348 -1.1801 0.0319 1.2285 7.0157
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.95652 2.45152 -0.390 0.69698
FindingFriends 0.12246 0.17725 0.691 0.49071
sum_friends -0.00612 0.06617 -0.092 0.92644
KnowingPeople 0.12990 0.11190 1.161 0.24759
sum_know 0.03887 0.04523 0.859 0.39160
Liked 0.42288 0.16963 2.493 0.01379 *
sum_liked -0.02558 0.06383 -0.401 0.68922
Celebrity 0.78243 0.29117 2.687 0.00804 **
sum_celeb -0.08246 0.11713 -0.704 0.48257
Sum 0.57963 0.88754 0.653 0.51474
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.11 on 146 degrees of freedom
Multiple R-squared: 0.5138, Adjusted R-squared: 0.4838
F-statistic: 17.14 on 9 and 146 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,] 0.1857188 0.37143766 0.814281171
[2,] 0.7760120 0.44797600 0.223987999
[3,] 0.6607512 0.67849759 0.339248793
[4,] 0.6146978 0.77060438 0.385302188
[5,] 0.5122675 0.97546497 0.487732483
[6,] 0.4162446 0.83248913 0.583755434
[7,] 0.3469406 0.69388113 0.653059434
[8,] 0.6806132 0.63877367 0.319386834
[9,] 0.6108025 0.77839498 0.389197488
[10,] 0.5579588 0.88408248 0.442041242
[11,] 0.5300091 0.93998173 0.469990863
[12,] 0.5241010 0.95179795 0.475898977
[13,] 0.5849373 0.83012549 0.415062744
[14,] 0.5224490 0.95510202 0.477551012
[15,] 0.7371403 0.52571946 0.262859731
[16,] 0.6838835 0.63223290 0.316116451
[17,] 0.6283808 0.74323849 0.371619243
[18,] 0.8194046 0.36119089 0.180595445
[19,] 0.8840706 0.23185886 0.115929432
[20,] 0.8552166 0.28956673 0.144783363
[21,] 0.8174044 0.36519130 0.182595648
[22,] 0.7842951 0.43140980 0.215704901
[23,] 0.7693023 0.46139544 0.230697722
[24,] 0.7685744 0.46285125 0.231425625
[25,] 0.7341113 0.53177750 0.265888749
[26,] 0.6866217 0.62675659 0.313378294
[27,] 0.6443350 0.71133003 0.355665013
[28,] 0.5954739 0.80905222 0.404526109
[29,] 0.5601628 0.87967437 0.439837187
[30,] 0.8170261 0.36594783 0.182973914
[31,] 0.9611000 0.07779991 0.038899955
[32,] 0.9634312 0.07313751 0.036568753
[33,] 0.9519667 0.09606650 0.048033250
[34,] 0.9574979 0.08500429 0.042502147
[35,] 0.9499753 0.10004949 0.050024744
[36,] 0.9445517 0.11089652 0.055448260
[37,] 0.9428905 0.11421909 0.057109544
[38,] 0.9333769 0.13324619 0.066623097
[39,] 0.9290429 0.14191415 0.070957077
[40,] 0.9919177 0.01616459 0.008082297
[41,] 0.9888733 0.02225334 0.011126670
[42,] 0.9923467 0.01530661 0.007653305
[43,] 0.9947339 0.01053226 0.005266131
[44,] 0.9932735 0.01345298 0.006726488
[45,] 0.9907205 0.01855903 0.009279514
[46,] 0.9887277 0.02254461 0.011272303
[47,] 0.9922383 0.01552348 0.007761740
[48,] 0.9913994 0.01720124 0.008600622
[49,] 0.9913591 0.01728173 0.008640865
[50,] 0.9908139 0.01837214 0.009186069
[51,] 0.9892561 0.02148789 0.010743944
[52,] 0.9913373 0.01732547 0.008662734
[53,] 0.9892090 0.02158196 0.010790978
[54,] 0.9884016 0.02319690 0.011598448
[55,] 0.9888582 0.02228358 0.011141789
[56,] 0.9900786 0.01984287 0.009921434
[57,] 0.9896728 0.02065435 0.010327173
[58,] 0.9866115 0.02677703 0.013388516
[59,] 0.9862372 0.02752552 0.013762760
[60,] 0.9816028 0.03679438 0.018397190
[61,] 0.9781145 0.04377109 0.021885545
[62,] 0.9876115 0.02477701 0.012388504
[63,] 0.9839452 0.03210957 0.016054786
[64,] 0.9811483 0.03770350 0.018851748
[65,] 0.9748980 0.05020409 0.025102044
[66,] 0.9669834 0.06603313 0.033016566
[67,] 0.9778840 0.04423195 0.022115973
[68,] 0.9758805 0.04823893 0.024119466
[69,] 0.9812935 0.03741302 0.018706512
[70,] 0.9819489 0.03610219 0.018051097
[71,] 0.9762813 0.04743745 0.023718727
[72,] 0.9714320 0.05713608 0.028568042
[73,] 0.9918752 0.01624956 0.008124779
[74,] 0.9888870 0.02222608 0.011113039
[75,] 0.9849344 0.03013127 0.015065634
[76,] 0.9798892 0.04022168 0.020110839
[77,] 0.9745932 0.05081359 0.025406797
[78,] 0.9668617 0.06627659 0.033138293
[79,] 0.9593458 0.08130849 0.040654245
[80,] 0.9808523 0.03829547 0.019147735
[81,] 0.9747494 0.05050120 0.025250600
[82,] 0.9691450 0.06170992 0.030854960
[83,] 0.9600236 0.07995275 0.039976375
[84,] 0.9480110 0.10397800 0.051988999
[85,] 0.9511566 0.09768685 0.048843424
[86,] 0.9376782 0.12464362 0.062321808
[87,] 0.9349796 0.13004084 0.065020421
[88,] 0.9192021 0.16159587 0.080797933
[89,] 0.9054333 0.18913345 0.094566727
[90,] 0.8858674 0.22826530 0.114132648
[91,] 0.9112457 0.17750857 0.088754284
[92,] 0.9411389 0.11772219 0.058861093
[93,] 0.9418203 0.11635942 0.058179712
[94,] 0.9243149 0.15137014 0.075685070
[95,] 0.9182160 0.16356806 0.081784032
[96,] 0.9250449 0.14991022 0.074955112
[97,] 0.9165666 0.16686675 0.083433374
[98,] 0.9052094 0.18958122 0.094790611
[99,] 0.8845545 0.23089099 0.115445495
[100,] 0.8830947 0.23381057 0.116905283
[101,] 0.8528017 0.29439659 0.147198293
[102,] 0.8202025 0.35959492 0.179797459
[103,] 0.7803040 0.43939202 0.219696008
[104,] 0.7739980 0.45200403 0.226002014
[105,] 0.7838541 0.43229181 0.216145906
[106,] 0.7663660 0.46726791 0.233633955
[107,] 0.7170656 0.56586871 0.282934354
[108,] 0.7904971 0.41900575 0.209502873
[109,] 0.7565320 0.48693600 0.243468002
[110,] 0.7152155 0.56956898 0.284784491
[111,] 0.7137470 0.57250597 0.286252986
[112,] 0.6588712 0.68225765 0.341128826
[113,] 0.6584475 0.68310498 0.341552492
[114,] 0.6380858 0.72382844 0.361914218
[115,] 0.5733114 0.85337718 0.426688590
[116,] 0.6079598 0.78408036 0.392040180
[117,] 0.6017894 0.79642127 0.398210635
[118,] 0.5734541 0.85309176 0.426545881
[119,] 0.4969080 0.99381601 0.503091997
[120,] 0.4759303 0.95186051 0.524069745
[121,] 0.4725166 0.94503312 0.527483439
[122,] 0.3974249 0.79484986 0.602575071
[123,] 0.3589824 0.71796470 0.641017649
[124,] 0.3602484 0.72049689 0.639751557
[125,] 0.2762863 0.55257263 0.723713687
[126,] 0.3388030 0.67760594 0.661197028
[127,] 0.6299650 0.74007008 0.370035042
[128,] 0.7911031 0.41779388 0.208896941
[129,] 0.7237014 0.55259723 0.276298616
[130,] 0.6841626 0.63167479 0.315837393
[131,] 0.7313374 0.53732511 0.268662554
> postscript(file="/var/www/rcomp/tmp/1tzec1293621609.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tzec1293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/348wx1293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/448wx1293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/548wx1293621609.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
1.772456186 0.965769736 1.712345358 1.191010250 -0.771373759 2.931792936
7 8 9 10 11 12
-1.590927225 -1.045942671 -0.831377991 3.506653666 -2.303355355 -0.975246968
13 14 15 16 17 18
2.205006684 -4.491880594 -0.798127909 0.616524056 -1.975246968 -0.373792579
19 20 21 22 23 24
1.540080702 -3.126686717 2.284297757 0.683797418 -1.189053703 2.462047050
25 26 27 28 29 30
1.084428074 0.402266522 -5.428257555 0.107958332 -0.010928553 5.010013494
31 32 33 34 35 36
4.295141284 -1.519791718 -0.509764677 0.951854407 -1.368481498 1.470097381
37 38 39 40 41 42
-0.899256158 0.521004468 0.140351700 0.549185895 1.053556992 7.015665964
43 44 45 46 47 48
-4.586007309 -2.158137645 -0.242125889 2.276448088 1.903971608 0.827356729
49 50 51 52 53 54
-2.359038459 -1.500326009 -1.101183735 -6.434817820 -0.106602955 -2.489044265
55 56 57 58 59 60
2.266131596 -1.177064803 0.382830780 1.345490770 -2.654908670 0.058195296
61 62 63 64 65 66
-1.974150665 1.692688925 1.155701284 2.574926954 -1.065968542 1.855277110
67 68 69 70 71 72
-2.380794987 2.531842519 1.869354815 -0.670092460 1.822935197 0.321958219
73 74 75 76 77 78
-1.329907047 -3.110404633 -0.649312183 -1.439438283 -0.415831741 0.005036863
79 80 81 82 83 84
3.139226146 -1.802159072 -2.768182273 1.531342935 0.005610323 -1.137279691
85 86 87 88 89 90
3.976873143 -0.293103773 -0.263630830 0.078205546 1.161856641 -0.440073281
91 92 93 94 95 96
0.855691481 -3.534644272 0.598932571 1.522273678 0.531870772 0.666299562
97 98 99 100 101 102
-2.477416246 -0.658930088 -1.654915533 0.546552268 0.411554364 1.204872185
103 104 105 106 107 108
-1.472325002 1.971819423 -3.087841733 -0.419635334 -1.640056116 -1.097760892
109 110 111 112 113 114
-2.136430192 -2.715911302 -0.873913882 -1.150168990 -0.614429664 0.627349054
115 116 117 118 119 120
0.726534862 2.565011122 3.053379685 -1.839268913 -0.258304558 3.162381444
121 122 123 124 125 126
0.370175974 1.299382582 -2.890775553 -1.119932868 1.978052063 0.875021245
127 128 129 130 131 132
0.698478915 -0.653638748 -0.452657396 -1.623861626 0.754208506 2.404159430
133 134 135 136 137 138
-1.526173494 -0.397453174 1.866029580 1.496978309 0.318513484 -3.080806881
139 140 141 142 143 144
5.079035717 -1.088011531 0.431956392 -0.341602557 2.772479232 -1.353139624
145 146 147 148 149 150
1.095691461 -1.175483464 -3.431536410 -4.636479206 1.072015847 1.325434297
151 152 153 154 155 156
-0.130901242 0.987041949 -0.764019873 0.922381464 0.747788979 1.861590284
> postscript(file="/var/www/rcomp/tmp/6eid01293621609.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 1.772456186 NA
1 0.965769736 1.772456186
2 1.712345358 0.965769736
3 1.191010250 1.712345358
4 -0.771373759 1.191010250
5 2.931792936 -0.771373759
6 -1.590927225 2.931792936
7 -1.045942671 -1.590927225
8 -0.831377991 -1.045942671
9 3.506653666 -0.831377991
10 -2.303355355 3.506653666
11 -0.975246968 -2.303355355
12 2.205006684 -0.975246968
13 -4.491880594 2.205006684
14 -0.798127909 -4.491880594
15 0.616524056 -0.798127909
16 -1.975246968 0.616524056
17 -0.373792579 -1.975246968
18 1.540080702 -0.373792579
19 -3.126686717 1.540080702
20 2.284297757 -3.126686717
21 0.683797418 2.284297757
22 -1.189053703 0.683797418
23 2.462047050 -1.189053703
24 1.084428074 2.462047050
25 0.402266522 1.084428074
26 -5.428257555 0.402266522
27 0.107958332 -5.428257555
28 -0.010928553 0.107958332
29 5.010013494 -0.010928553
30 4.295141284 5.010013494
31 -1.519791718 4.295141284
32 -0.509764677 -1.519791718
33 0.951854407 -0.509764677
34 -1.368481498 0.951854407
35 1.470097381 -1.368481498
36 -0.899256158 1.470097381
37 0.521004468 -0.899256158
38 0.140351700 0.521004468
39 0.549185895 0.140351700
40 1.053556992 0.549185895
41 7.015665964 1.053556992
42 -4.586007309 7.015665964
43 -2.158137645 -4.586007309
44 -0.242125889 -2.158137645
45 2.276448088 -0.242125889
46 1.903971608 2.276448088
47 0.827356729 1.903971608
48 -2.359038459 0.827356729
49 -1.500326009 -2.359038459
50 -1.101183735 -1.500326009
51 -6.434817820 -1.101183735
52 -0.106602955 -6.434817820
53 -2.489044265 -0.106602955
54 2.266131596 -2.489044265
55 -1.177064803 2.266131596
56 0.382830780 -1.177064803
57 1.345490770 0.382830780
58 -2.654908670 1.345490770
59 0.058195296 -2.654908670
60 -1.974150665 0.058195296
61 1.692688925 -1.974150665
62 1.155701284 1.692688925
63 2.574926954 1.155701284
64 -1.065968542 2.574926954
65 1.855277110 -1.065968542
66 -2.380794987 1.855277110
67 2.531842519 -2.380794987
68 1.869354815 2.531842519
69 -0.670092460 1.869354815
70 1.822935197 -0.670092460
71 0.321958219 1.822935197
72 -1.329907047 0.321958219
73 -3.110404633 -1.329907047
74 -0.649312183 -3.110404633
75 -1.439438283 -0.649312183
76 -0.415831741 -1.439438283
77 0.005036863 -0.415831741
78 3.139226146 0.005036863
79 -1.802159072 3.139226146
80 -2.768182273 -1.802159072
81 1.531342935 -2.768182273
82 0.005610323 1.531342935
83 -1.137279691 0.005610323
84 3.976873143 -1.137279691
85 -0.293103773 3.976873143
86 -0.263630830 -0.293103773
87 0.078205546 -0.263630830
88 1.161856641 0.078205546
89 -0.440073281 1.161856641
90 0.855691481 -0.440073281
91 -3.534644272 0.855691481
92 0.598932571 -3.534644272
93 1.522273678 0.598932571
94 0.531870772 1.522273678
95 0.666299562 0.531870772
96 -2.477416246 0.666299562
97 -0.658930088 -2.477416246
98 -1.654915533 -0.658930088
99 0.546552268 -1.654915533
100 0.411554364 0.546552268
101 1.204872185 0.411554364
102 -1.472325002 1.204872185
103 1.971819423 -1.472325002
104 -3.087841733 1.971819423
105 -0.419635334 -3.087841733
106 -1.640056116 -0.419635334
107 -1.097760892 -1.640056116
108 -2.136430192 -1.097760892
109 -2.715911302 -2.136430192
110 -0.873913882 -2.715911302
111 -1.150168990 -0.873913882
112 -0.614429664 -1.150168990
113 0.627349054 -0.614429664
114 0.726534862 0.627349054
115 2.565011122 0.726534862
116 3.053379685 2.565011122
117 -1.839268913 3.053379685
118 -0.258304558 -1.839268913
119 3.162381444 -0.258304558
120 0.370175974 3.162381444
121 1.299382582 0.370175974
122 -2.890775553 1.299382582
123 -1.119932868 -2.890775553
124 1.978052063 -1.119932868
125 0.875021245 1.978052063
126 0.698478915 0.875021245
127 -0.653638748 0.698478915
128 -0.452657396 -0.653638748
129 -1.623861626 -0.452657396
130 0.754208506 -1.623861626
131 2.404159430 0.754208506
132 -1.526173494 2.404159430
133 -0.397453174 -1.526173494
134 1.866029580 -0.397453174
135 1.496978309 1.866029580
136 0.318513484 1.496978309
137 -3.080806881 0.318513484
138 5.079035717 -3.080806881
139 -1.088011531 5.079035717
140 0.431956392 -1.088011531
141 -0.341602557 0.431956392
142 2.772479232 -0.341602557
143 -1.353139624 2.772479232
144 1.095691461 -1.353139624
145 -1.175483464 1.095691461
146 -3.431536410 -1.175483464
147 -4.636479206 -3.431536410
148 1.072015847 -4.636479206
149 1.325434297 1.072015847
150 -0.130901242 1.325434297
151 0.987041949 -0.130901242
152 -0.764019873 0.987041949
153 0.922381464 -0.764019873
154 0.747788979 0.922381464
155 1.861590284 0.747788979
156 NA 1.861590284
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.965769736 1.772456186
[2,] 1.712345358 0.965769736
[3,] 1.191010250 1.712345358
[4,] -0.771373759 1.191010250
[5,] 2.931792936 -0.771373759
[6,] -1.590927225 2.931792936
[7,] -1.045942671 -1.590927225
[8,] -0.831377991 -1.045942671
[9,] 3.506653666 -0.831377991
[10,] -2.303355355 3.506653666
[11,] -0.975246968 -2.303355355
[12,] 2.205006684 -0.975246968
[13,] -4.491880594 2.205006684
[14,] -0.798127909 -4.491880594
[15,] 0.616524056 -0.798127909
[16,] -1.975246968 0.616524056
[17,] -0.373792579 -1.975246968
[18,] 1.540080702 -0.373792579
[19,] -3.126686717 1.540080702
[20,] 2.284297757 -3.126686717
[21,] 0.683797418 2.284297757
[22,] -1.189053703 0.683797418
[23,] 2.462047050 -1.189053703
[24,] 1.084428074 2.462047050
[25,] 0.402266522 1.084428074
[26,] -5.428257555 0.402266522
[27,] 0.107958332 -5.428257555
[28,] -0.010928553 0.107958332
[29,] 5.010013494 -0.010928553
[30,] 4.295141284 5.010013494
[31,] -1.519791718 4.295141284
[32,] -0.509764677 -1.519791718
[33,] 0.951854407 -0.509764677
[34,] -1.368481498 0.951854407
[35,] 1.470097381 -1.368481498
[36,] -0.899256158 1.470097381
[37,] 0.521004468 -0.899256158
[38,] 0.140351700 0.521004468
[39,] 0.549185895 0.140351700
[40,] 1.053556992 0.549185895
[41,] 7.015665964 1.053556992
[42,] -4.586007309 7.015665964
[43,] -2.158137645 -4.586007309
[44,] -0.242125889 -2.158137645
[45,] 2.276448088 -0.242125889
[46,] 1.903971608 2.276448088
[47,] 0.827356729 1.903971608
[48,] -2.359038459 0.827356729
[49,] -1.500326009 -2.359038459
[50,] -1.101183735 -1.500326009
[51,] -6.434817820 -1.101183735
[52,] -0.106602955 -6.434817820
[53,] -2.489044265 -0.106602955
[54,] 2.266131596 -2.489044265
[55,] -1.177064803 2.266131596
[56,] 0.382830780 -1.177064803
[57,] 1.345490770 0.382830780
[58,] -2.654908670 1.345490770
[59,] 0.058195296 -2.654908670
[60,] -1.974150665 0.058195296
[61,] 1.692688925 -1.974150665
[62,] 1.155701284 1.692688925
[63,] 2.574926954 1.155701284
[64,] -1.065968542 2.574926954
[65,] 1.855277110 -1.065968542
[66,] -2.380794987 1.855277110
[67,] 2.531842519 -2.380794987
[68,] 1.869354815 2.531842519
[69,] -0.670092460 1.869354815
[70,] 1.822935197 -0.670092460
[71,] 0.321958219 1.822935197
[72,] -1.329907047 0.321958219
[73,] -3.110404633 -1.329907047
[74,] -0.649312183 -3.110404633
[75,] -1.439438283 -0.649312183
[76,] -0.415831741 -1.439438283
[77,] 0.005036863 -0.415831741
[78,] 3.139226146 0.005036863
[79,] -1.802159072 3.139226146
[80,] -2.768182273 -1.802159072
[81,] 1.531342935 -2.768182273
[82,] 0.005610323 1.531342935
[83,] -1.137279691 0.005610323
[84,] 3.976873143 -1.137279691
[85,] -0.293103773 3.976873143
[86,] -0.263630830 -0.293103773
[87,] 0.078205546 -0.263630830
[88,] 1.161856641 0.078205546
[89,] -0.440073281 1.161856641
[90,] 0.855691481 -0.440073281
[91,] -3.534644272 0.855691481
[92,] 0.598932571 -3.534644272
[93,] 1.522273678 0.598932571
[94,] 0.531870772 1.522273678
[95,] 0.666299562 0.531870772
[96,] -2.477416246 0.666299562
[97,] -0.658930088 -2.477416246
[98,] -1.654915533 -0.658930088
[99,] 0.546552268 -1.654915533
[100,] 0.411554364 0.546552268
[101,] 1.204872185 0.411554364
[102,] -1.472325002 1.204872185
[103,] 1.971819423 -1.472325002
[104,] -3.087841733 1.971819423
[105,] -0.419635334 -3.087841733
[106,] -1.640056116 -0.419635334
[107,] -1.097760892 -1.640056116
[108,] -2.136430192 -1.097760892
[109,] -2.715911302 -2.136430192
[110,] -0.873913882 -2.715911302
[111,] -1.150168990 -0.873913882
[112,] -0.614429664 -1.150168990
[113,] 0.627349054 -0.614429664
[114,] 0.726534862 0.627349054
[115,] 2.565011122 0.726534862
[116,] 3.053379685 2.565011122
[117,] -1.839268913 3.053379685
[118,] -0.258304558 -1.839268913
[119,] 3.162381444 -0.258304558
[120,] 0.370175974 3.162381444
[121,] 1.299382582 0.370175974
[122,] -2.890775553 1.299382582
[123,] -1.119932868 -2.890775553
[124,] 1.978052063 -1.119932868
[125,] 0.875021245 1.978052063
[126,] 0.698478915 0.875021245
[127,] -0.653638748 0.698478915
[128,] -0.452657396 -0.653638748
[129,] -1.623861626 -0.452657396
[130,] 0.754208506 -1.623861626
[131,] 2.404159430 0.754208506
[132,] -1.526173494 2.404159430
[133,] -0.397453174 -1.526173494
[134,] 1.866029580 -0.397453174
[135,] 1.496978309 1.866029580
[136,] 0.318513484 1.496978309
[137,] -3.080806881 0.318513484
[138,] 5.079035717 -3.080806881
[139,] -1.088011531 5.079035717
[140,] 0.431956392 -1.088011531
[141,] -0.341602557 0.431956392
[142,] 2.772479232 -0.341602557
[143,] -1.353139624 2.772479232
[144,] 1.095691461 -1.353139624
[145,] -1.175483464 1.095691461
[146,] -3.431536410 -1.175483464
[147,] -4.636479206 -3.431536410
[148,] 1.072015847 -4.636479206
[149,] 1.325434297 1.072015847
[150,] -0.130901242 1.325434297
[151,] 0.987041949 -0.130901242
[152,] -0.764019873 0.987041949
[153,] 0.922381464 -0.764019873
[154,] 0.747788979 0.922381464
[155,] 1.861590284 0.747788979
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.965769736 1.772456186
2 1.712345358 0.965769736
3 1.191010250 1.712345358
4 -0.771373759 1.191010250
5 2.931792936 -0.771373759
6 -1.590927225 2.931792936
7 -1.045942671 -1.590927225
8 -0.831377991 -1.045942671
9 3.506653666 -0.831377991
10 -2.303355355 3.506653666
11 -0.975246968 -2.303355355
12 2.205006684 -0.975246968
13 -4.491880594 2.205006684
14 -0.798127909 -4.491880594
15 0.616524056 -0.798127909
16 -1.975246968 0.616524056
17 -0.373792579 -1.975246968
18 1.540080702 -0.373792579
19 -3.126686717 1.540080702
20 2.284297757 -3.126686717
21 0.683797418 2.284297757
22 -1.189053703 0.683797418
23 2.462047050 -1.189053703
24 1.084428074 2.462047050
25 0.402266522 1.084428074
26 -5.428257555 0.402266522
27 0.107958332 -5.428257555
28 -0.010928553 0.107958332
29 5.010013494 -0.010928553
30 4.295141284 5.010013494
31 -1.519791718 4.295141284
32 -0.509764677 -1.519791718
33 0.951854407 -0.509764677
34 -1.368481498 0.951854407
35 1.470097381 -1.368481498
36 -0.899256158 1.470097381
37 0.521004468 -0.899256158
38 0.140351700 0.521004468
39 0.549185895 0.140351700
40 1.053556992 0.549185895
41 7.015665964 1.053556992
42 -4.586007309 7.015665964
43 -2.158137645 -4.586007309
44 -0.242125889 -2.158137645
45 2.276448088 -0.242125889
46 1.903971608 2.276448088
47 0.827356729 1.903971608
48 -2.359038459 0.827356729
49 -1.500326009 -2.359038459
50 -1.101183735 -1.500326009
51 -6.434817820 -1.101183735
52 -0.106602955 -6.434817820
53 -2.489044265 -0.106602955
54 2.266131596 -2.489044265
55 -1.177064803 2.266131596
56 0.382830780 -1.177064803
57 1.345490770 0.382830780
58 -2.654908670 1.345490770
59 0.058195296 -2.654908670
60 -1.974150665 0.058195296
61 1.692688925 -1.974150665
62 1.155701284 1.692688925
63 2.574926954 1.155701284
64 -1.065968542 2.574926954
65 1.855277110 -1.065968542
66 -2.380794987 1.855277110
67 2.531842519 -2.380794987
68 1.869354815 2.531842519
69 -0.670092460 1.869354815
70 1.822935197 -0.670092460
71 0.321958219 1.822935197
72 -1.329907047 0.321958219
73 -3.110404633 -1.329907047
74 -0.649312183 -3.110404633
75 -1.439438283 -0.649312183
76 -0.415831741 -1.439438283
77 0.005036863 -0.415831741
78 3.139226146 0.005036863
79 -1.802159072 3.139226146
80 -2.768182273 -1.802159072
81 1.531342935 -2.768182273
82 0.005610323 1.531342935
83 -1.137279691 0.005610323
84 3.976873143 -1.137279691
85 -0.293103773 3.976873143
86 -0.263630830 -0.293103773
87 0.078205546 -0.263630830
88 1.161856641 0.078205546
89 -0.440073281 1.161856641
90 0.855691481 -0.440073281
91 -3.534644272 0.855691481
92 0.598932571 -3.534644272
93 1.522273678 0.598932571
94 0.531870772 1.522273678
95 0.666299562 0.531870772
96 -2.477416246 0.666299562
97 -0.658930088 -2.477416246
98 -1.654915533 -0.658930088
99 0.546552268 -1.654915533
100 0.411554364 0.546552268
101 1.204872185 0.411554364
102 -1.472325002 1.204872185
103 1.971819423 -1.472325002
104 -3.087841733 1.971819423
105 -0.419635334 -3.087841733
106 -1.640056116 -0.419635334
107 -1.097760892 -1.640056116
108 -2.136430192 -1.097760892
109 -2.715911302 -2.136430192
110 -0.873913882 -2.715911302
111 -1.150168990 -0.873913882
112 -0.614429664 -1.150168990
113 0.627349054 -0.614429664
114 0.726534862 0.627349054
115 2.565011122 0.726534862
116 3.053379685 2.565011122
117 -1.839268913 3.053379685
118 -0.258304558 -1.839268913
119 3.162381444 -0.258304558
120 0.370175974 3.162381444
121 1.299382582 0.370175974
122 -2.890775553 1.299382582
123 -1.119932868 -2.890775553
124 1.978052063 -1.119932868
125 0.875021245 1.978052063
126 0.698478915 0.875021245
127 -0.653638748 0.698478915
128 -0.452657396 -0.653638748
129 -1.623861626 -0.452657396
130 0.754208506 -1.623861626
131 2.404159430 0.754208506
132 -1.526173494 2.404159430
133 -0.397453174 -1.526173494
134 1.866029580 -0.397453174
135 1.496978309 1.866029580
136 0.318513484 1.496978309
137 -3.080806881 0.318513484
138 5.079035717 -3.080806881
139 -1.088011531 5.079035717
140 0.431956392 -1.088011531
141 -0.341602557 0.431956392
142 2.772479232 -0.341602557
143 -1.353139624 2.772479232
144 1.095691461 -1.353139624
145 -1.175483464 1.095691461
146 -3.431536410 -1.175483464
147 -4.636479206 -3.431536410
148 1.072015847 -4.636479206
149 1.325434297 1.072015847
150 -0.130901242 1.325434297
151 0.987041949 -0.130901242
152 -0.764019873 0.987041949
153 0.922381464 -0.764019873
154 0.747788979 0.922381464
155 1.861590284 0.747788979
> 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/7eid01293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8p9c31293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9p9c31293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/100ib61293621609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113jsu1293621609.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/1271801293621609.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/13kb681293621609.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/14otne1293621609.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/15zlmh1293621609.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/16vcj81293621609.tab")
+ }
>
> try(system("convert tmp/1tzec1293621609.ps tmp/1tzec1293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tzec1293621609.ps tmp/2tzec1293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/348wx1293621609.ps tmp/348wx1293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/448wx1293621609.ps tmp/448wx1293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/548wx1293621609.ps tmp/548wx1293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eid01293621609.ps tmp/6eid01293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/7eid01293621609.ps tmp/7eid01293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p9c31293621609.ps tmp/8p9c31293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p9c31293621609.ps tmp/9p9c31293621609.png",intern=TRUE))
character(0)
> try(system("convert tmp/100ib61293621609.ps tmp/100ib61293621609.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.050 1.630 6.694