R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(13
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+ ,dim=c(10
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'friends_knowning'
+ ,'Liked'
+ ,'friends_liked'
+ ,'Celebrity'
+ ,'friends_celeb'
+ ,'Sum'
+ ,'friends_sum')
+ ,1:156))
> y <- array(NA,dim=c(10,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','friends_knowning','Liked','friends_liked','Celebrity','friends_celeb','Sum','friends_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
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
Popularity FindingFriends KnowingPeople friends_knowning Liked
1 13 13 14 182 13
2 12 12 8 96 13
3 15 10 12 120 16
4 12 9 7 63 12
5 10 10 10 100 11
6 12 12 7 84 12
7 15 13 16 208 18
8 9 12 11 132 11
9 12 12 14 168 14
10 11 6 6 36 9
11 11 5 16 80 14
12 11 12 11 132 12
13 15 11 16 176 11
14 7 14 12 168 12
15 11 14 7 98 13
16 11 12 13 156 11
17 10 12 11 132 12
18 14 11 15 165 16
19 10 11 7 77 9
20 6 7 9 63 11
21 11 9 7 63 13
22 15 11 14 154 15
23 11 11 15 165 10
24 12 12 7 84 11
25 14 12 15 180 13
26 15 11 17 187 16
27 9 11 15 165 15
28 13 8 14 112 14
29 13 9 14 126 14
30 16 12 8 96 14
31 13 10 8 80 8
32 12 10 14 140 13
33 14 12 14 168 15
34 11 8 8 64 13
35 9 12 11 132 11
36 16 11 16 176 15
37 12 12 10 120 15
38 10 7 8 56 9
39 13 11 14 154 13
40 16 11 16 176 16
41 14 12 13 156 13
42 15 9 5 45 11
43 5 15 8 120 12
44 8 11 10 110 12
45 11 11 8 88 12
46 16 11 13 143 14
47 17 11 15 165 14
48 9 15 6 90 8
49 9 11 12 132 13
50 13 12 16 192 16
51 10 12 5 60 13
52 6 9 15 135 11
53 12 12 12 144 14
54 8 12 8 96 13
55 14 13 13 169 13
56 12 11 14 154 13
57 11 9 12 108 12
58 16 9 16 144 16
59 8 11 10 110 15
60 15 11 15 165 15
61 7 12 8 96 12
62 16 12 16 192 14
63 14 9 19 171 12
64 16 11 14 154 15
65 9 9 6 54 12
66 14 12 13 156 13
67 11 12 15 180 12
68 13 12 7 84 12
69 15 12 13 156 13
70 5 14 4 56 5
71 15 11 14 154 13
72 13 12 13 156 13
73 11 11 11 121 14
74 11 6 14 84 17
75 12 10 12 120 13
76 12 12 15 180 13
77 12 13 14 182 12
78 12 8 13 104 13
79 14 12 8 96 14
80 6 12 6 72 11
81 7 12 7 84 12
82 14 6 13 78 12
83 14 11 13 143 16
84 10 10 11 110 12
85 13 12 5 60 12
86 12 13 12 156 12
87 9 11 8 88 10
88 12 7 11 77 15
89 16 11 14 154 15
90 10 11 9 99 12
91 14 11 10 110 16
92 10 11 13 143 15
93 16 12 16 192 16
94 15 10 16 160 13
95 12 11 11 121 12
96 10 12 8 96 11
97 8 7 4 28 13
98 8 13 7 91 10
99 11 8 14 112 15
100 13 12 11 132 13
101 16 11 17 187 16
102 16 12 15 180 15
103 14 14 17 238 18
104 11 10 5 50 13
105 4 10 4 40 10
106 14 13 10 130 16
107 9 10 11 110 13
108 14 11 15 165 15
109 8 10 10 100 14
110 8 7 9 63 15
111 11 10 12 120 14
112 12 8 15 120 13
113 11 12 7 84 13
114 14 12 13 156 15
115 15 12 12 144 16
116 16 11 14 154 14
117 16 12 14 168 14
118 11 12 8 96 16
119 14 12 15 180 14
120 14 11 12 132 12
121 12 12 12 144 13
122 14 11 16 176 12
123 8 11 9 99 12
124 13 13 15 195 14
125 16 12 15 180 14
126 12 12 6 72 14
127 16 12 14 168 16
128 12 12 15 180 13
129 11 8 10 80 14
130 4 8 6 48 4
131 16 12 14 168 16
132 15 11 12 132 13
133 10 12 8 96 16
134 13 13 11 143 15
135 15 12 13 156 14
136 12 12 9 108 13
137 14 11 15 165 14
138 7 12 13 156 12
139 19 12 15 180 15
140 12 10 14 140 14
141 12 11 16 176 13
142 13 12 14 168 14
143 15 12 14 168 16
144 8 10 10 100 6
145 12 12 10 120 13
146 10 13 4 52 13
147 8 12 8 96 14
148 10 15 15 225 15
149 15 11 16 176 14
150 16 12 12 144 15
151 13 11 12 132 13
152 16 12 15 180 16
153 9 11 9 99 12
154 14 10 12 120 15
155 14 11 14 154 12
156 12 11 11 121 14
friends_liked Celebrity friends_celeb Sum friends_sum
1 169 3 39 2 26
2 156 5 60 1 12
3 160 6 60 0 0
4 108 6 54 3 27
5 110 5 50 3 30
6 144 3 36 1 12
7 234 8 104 3 39
8 132 4 48 1 12
9 168 4 48 4 48
10 54 4 24 0 0
11 70 6 30 3 15
12 144 6 72 2 24
13 121 5 55 4 44
14 168 4 56 3 42
15 182 6 84 1 14
16 132 4 48 1 12
17 144 6 72 2 24
18 176 6 66 3 33
19 99 4 44 1 11
20 77 4 28 1 7
21 117 2 18 2 18
22 165 7 77 3 33
23 110 5 55 4 44
24 132 4 48 2 24
25 156 6 72 1 12
26 176 6 66 2 22
27 165 7 77 2 22
28 112 5 40 4 32
29 126 6 54 2 18
30 168 4 48 3 36
31 80 4 40 3 30
32 130 7 70 3 30
33 180 7 84 4 48
34 104 4 32 2 16
35 132 4 48 2 24
36 165 6 66 4 44
37 180 6 72 3 36
38 63 5 35 4 28
39 143 6 66 2 22
40 176 7 77 5 55
41 156 6 72 3 36
42 99 3 27 1 9
43 180 3 45 1 15
44 132 4 44 1 11
45 132 6 66 2 22
46 154 7 77 3 33
47 154 5 55 9 99
48 120 4 60 0 0
49 143 5 55 0 0
50 192 6 72 2 24
51 156 6 72 2 24
52 99 6 54 3 27
53 168 5 60 1 12
54 156 4 48 2 24
55 169 5 65 0 0
56 143 5 55 5 55
57 108 4 36 2 18
58 144 6 54 4 36
59 165 2 22 3 33
60 165 8 88 0 0
61 144 3 36 0 0
62 168 6 72 4 48
63 108 6 54 1 9
64 165 6 66 1 11
65 108 5 45 4 36
66 156 5 60 2 24
67 144 6 72 4 48
68 144 5 60 1 12
69 156 6 72 4 48
70 70 2 28 2 28
71 143 5 55 5 55
72 156 5 60 4 48
73 154 5 55 4 44
74 102 6 36 4 24
75 130 6 60 4 40
76 156 6 72 3 36
77 156 5 65 3 39
78 104 5 40 3 24
79 168 4 48 2 24
80 132 2 24 1 12
81 144 4 48 1 12
82 72 6 36 5 30
83 176 6 66 4 44
84 120 5 50 2 20
85 144 3 36 3 36
86 156 6 78 2 26
87 110 4 44 2 22
88 105 5 35 2 14
89 165 8 88 2 22
90 132 4 44 3 33
91 176 6 66 2 22
92 165 6 66 3 33
93 192 7 84 4 48
94 130 6 60 3 30
95 132 5 55 3 33
96 132 4 48 0 0
97 91 6 42 1 7
98 130 3 39 2 26
99 120 5 40 2 16
100 156 6 72 3 36
101 176 7 77 4 44
102 180 7 84 4 48
103 252 6 84 1 14
104 130 3 30 2 20
105 100 2 20 2 20
106 208 8 104 3 39
107 130 3 30 3 30
108 165 8 88 3 33
109 140 3 30 1 10
110 105 4 28 1 7
111 140 5 50 1 10
112 104 7 56 1 8
113 156 6 72 0 0
114 180 6 72 1 12
115 192 7 84 3 36
116 154 6 66 3 33
117 168 6 72 0 0
118 192 6 72 2 24
119 168 6 72 5 60
120 132 4 44 2 22
121 156 4 48 3 36
122 132 5 55 3 33
123 132 4 44 5 55
124 182 6 78 4 52
125 168 6 72 4 48
126 168 5 60 0 0
127 192 8 96 3 36
128 156 6 72 0 0
129 112 5 40 2 16
130 32 4 32 0 0
131 192 8 96 6 72
132 143 6 66 3 33
133 192 4 48 1 12
134 195 6 78 6 78
135 168 6 72 2 24
136 156 4 48 1 12
137 154 6 66 3 33
138 144 3 36 1 12
139 180 6 72 2 24
140 140 5 50 4 40
141 143 4 44 1 11
142 168 6 72 2 24
143 192 4 48 0 0
144 60 4 40 5 50
145 156 4 48 2 24
146 169 6 78 1 13
147 168 5 60 1 12
148 225 6 90 4 60
149 154 6 66 3 33
150 180 8 96 0 0
151 143 7 77 3 33
152 192 7 84 3 36
153 132 4 44 0 0
154 150 6 60 2 20
155 132 6 66 5 55
156 154 2 22 2 22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople friends_knowning
7.13071 -0.50107 0.01444 0.01959
Liked friends_liked Celebrity friends_celeb
0.45337 -0.01193 -1.04257 0.14673
Sum friends_sum
1.14069 -0.08459
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.094934 -1.211309 -0.007295 1.355010 6.405574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.13071 5.61803 1.269 0.206
FindingFriends -0.50107 0.49329 -1.016 0.311
KnowingPeople 0.01444 0.39969 0.036 0.971
friends_knowning 0.01959 0.03578 0.548 0.585
Liked 0.45337 0.50207 0.903 0.368
friends_liked -0.01193 0.04708 -0.253 0.800
Celebrity -1.04257 1.20995 -0.862 0.390
friends_celeb 0.14673 0.10719 1.369 0.173
Sum 1.14069 0.84142 1.356 0.177
friends_sum -0.08459 0.07532 -1.123 0.263
Residual standard error: 2.094 on 146 degrees of freedom
Multiple R-squared: 0.5213, Adjusted R-squared: 0.4918
F-statistic: 17.66 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,] 0.7898568 0.42028649 0.21014325
[2,] 0.7942903 0.41141949 0.20570974
[3,] 0.7202811 0.55943784 0.27971892
[4,] 0.6523604 0.69527913 0.34763957
[5,] 0.5596470 0.88070596 0.44035298
[6,] 0.4616115 0.92322306 0.53838847
[7,] 0.3668003 0.73360059 0.63319971
[8,] 0.7874859 0.42502818 0.21251409
[9,] 0.7466493 0.50670149 0.25335075
[10,] 0.7155049 0.56899025 0.28449513
[11,] 0.6495913 0.70081737 0.35040868
[12,] 0.6776320 0.64473600 0.32236800
[13,] 0.6120637 0.77587266 0.38793633
[14,] 0.5424018 0.91519648 0.45759824
[15,] 0.7794512 0.44109769 0.22054884
[16,] 0.7229351 0.55412987 0.27706494
[17,] 0.6736304 0.65273920 0.32636960
[18,] 0.8204146 0.35917086 0.17958543
[19,] 0.8636023 0.27279542 0.13639771
[20,] 0.8295369 0.34092624 0.17046312
[21,] 0.7938871 0.41222572 0.20611286
[22,] 0.7480112 0.50397758 0.25198879
[23,] 0.7267026 0.54659472 0.27329736
[24,] 0.7295595 0.54088096 0.27044048
[25,] 0.6969631 0.60607371 0.30303685
[26,] 0.7141915 0.57161696 0.28580848
[27,] 0.6703876 0.65922487 0.32961244
[28,] 0.6315973 0.73680545 0.36840272
[29,] 0.5998466 0.80030676 0.40015338
[30,] 0.8589059 0.28218823 0.14109412
[31,] 0.9440002 0.11199968 0.05599984
[32,] 0.9491198 0.10176031 0.05088015
[33,] 0.9338428 0.13231445 0.06615722
[34,] 0.9399473 0.12010544 0.06005272
[35,] 0.9347969 0.13040620 0.06520310
[36,] 0.9207704 0.15845919 0.07922960
[37,] 0.9190128 0.16197439 0.08098719
[38,] 0.9059598 0.18808036 0.09404018
[39,] 0.8948115 0.21037698 0.10518849
[40,] 0.9781575 0.04368506 0.02184253
[41,] 0.9711648 0.05767032 0.02883516
[42,] 0.9766734 0.04665324 0.02332662
[43,] 0.9785812 0.04283762 0.02141881
[44,] 0.9738782 0.05224360 0.02612180
[45,] 0.9658585 0.06828296 0.03414148
[46,] 0.9594752 0.08104965 0.04052482
[47,] 0.9722120 0.05557605 0.02778802
[48,] 0.9669890 0.06602208 0.03301104
[49,] 0.9664756 0.06704877 0.03352438
[50,] 0.9637196 0.07256083 0.03628041
[51,] 0.9751667 0.04966658 0.02483329
[52,] 0.9795250 0.04095001 0.02047501
[53,] 0.9799913 0.04001733 0.02000866
[54,] 0.9780335 0.04393293 0.02196647
[55,] 0.9810279 0.03794420 0.01897210
[56,] 0.9846394 0.03072129 0.01536065
[57,] 0.9832587 0.03348252 0.01674126
[58,] 0.9784648 0.04307038 0.02153519
[59,] 0.9784388 0.04312249 0.02156124
[60,] 0.9717362 0.05652753 0.02826377
[61,] 0.9672293 0.06554136 0.03277068
[62,] 0.9831713 0.03365731 0.01682865
[63,] 0.9786315 0.04273701 0.02136850
[64,] 0.9763568 0.04728634 0.02364317
[65,] 0.9693360 0.06132807 0.03066404
[66,] 0.9599078 0.08018448 0.04009224
[67,] 0.9748721 0.05025573 0.02512786
[68,] 0.9735243 0.05295143 0.02647571
[69,] 0.9797943 0.04041131 0.02020565
[70,] 0.9793872 0.04122557 0.02061278
[71,] 0.9726502 0.05469951 0.02734976
[72,] 0.9671766 0.06564681 0.03282340
[73,] 0.9897074 0.02058517 0.01029259
[74,] 0.9864938 0.02701237 0.01350619
[75,] 0.9818999 0.03620015 0.01810007
[76,] 0.9757554 0.04848919 0.02424459
[77,] 0.9703204 0.05935912 0.02967956
[78,] 0.9616353 0.07672942 0.03836471
[79,] 0.9534948 0.09301044 0.04650522
[80,] 0.9774567 0.04508662 0.02254331
[81,] 0.9699903 0.06001935 0.03000967
[82,] 0.9644199 0.07116027 0.03558014
[83,] 0.9537787 0.09244261 0.04622131
[84,] 0.9406498 0.11870041 0.05935021
[85,] 0.9277892 0.14442153 0.07221076
[86,] 0.9101137 0.17977266 0.08988633
[87,] 0.9071066 0.18578678 0.09289339
[88,] 0.8846071 0.23078584 0.11539292
[89,] 0.8606154 0.27876928 0.13938464
[90,] 0.8358716 0.32825687 0.16412843
[91,] 0.8757965 0.24840704 0.12420352
[92,] 0.9143596 0.17128081 0.08564040
[93,] 0.9250435 0.14991291 0.07495646
[94,] 0.9094358 0.18112846 0.09056423
[95,] 0.9058587 0.18828255 0.09414128
[96,] 0.9101649 0.17967028 0.08983514
[97,] 0.9088653 0.18226946 0.09113473
[98,] 0.9012212 0.19755767 0.09877884
[99,] 0.8860253 0.22794941 0.11397471
[100,] 0.8999151 0.20016989 0.10008494
[101,] 0.8717776 0.25644486 0.12822243
[102,] 0.8410652 0.31786957 0.15893478
[103,] 0.8028144 0.39437120 0.19718560
[104,] 0.7922882 0.41542362 0.20771181
[105,] 0.8010632 0.39787357 0.19893679
[106,] 0.7928509 0.41429828 0.20714914
[107,] 0.7457259 0.50854819 0.25427409
[108,] 0.8158713 0.36825731 0.18412865
[109,] 0.7834726 0.43305479 0.21652740
[110,] 0.7396007 0.52079866 0.26039933
[111,] 0.7317013 0.53659749 0.26829874
[112,] 0.6771872 0.64562551 0.32281275
[113,] 0.6825766 0.63484681 0.31742341
[114,] 0.6522920 0.69541592 0.34770796
[115,] 0.5889595 0.82208107 0.41104053
[116,] 0.5521664 0.89566716 0.44783358
[117,] 0.4990352 0.99807048 0.50096476
[118,] 0.4449438 0.88988762 0.55505619
[119,] 0.3749814 0.74996284 0.62501858
[120,] 0.3753661 0.75073222 0.62463389
[121,] 0.3754716 0.75094317 0.62452842
[122,] 0.3350051 0.67001020 0.66499490
[123,] 0.2998500 0.59969994 0.70015003
[124,] 0.3010402 0.60208035 0.69895983
[125,] 0.2265020 0.45300398 0.77349801
[126,] 0.3514909 0.70298182 0.64850909
[127,] 0.6180005 0.76399905 0.38199952
[128,] 0.8133173 0.37336535 0.18668268
[129,] 0.7460326 0.50793474 0.25396737
[130,] 0.7195342 0.56093150 0.28046575
[131,] 0.6100578 0.77988447 0.38994224
> postscript(file="/var/www/html/freestat/rcomp/tmp/14qnf1293622594.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2eh4i1293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3eh4i1293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4eh4i1293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5p8ml1293622594.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
2.06067378 1.13624278 2.46191564 1.08520968 -0.90658378 3.13245119
7 8 9 10 11 12
-2.33481985 -1.27373775 -0.32981459 3.29630419 -1.23565699 -1.14600206
13 14 15 16 17 18
2.45999256 -3.93277528 -0.88603113 0.22717324 -2.14600206 -0.28207432
19 20 21 22 23 24
1.37591801 -3.54277687 1.23068423 0.69852985 -0.98791126 2.59883987
25 26 27 28 29 30
0.67120629 0.46821203 -5.32123134 -0.04201614 0.43719786 5.29305286
31 32 33 34 35 36
3.94111639 -1.26569045 -0.79470343 0.40219561 -1.39933816 1.59992566
37 38 39 40 41 42
-0.95270005 -0.28169210 0.12450058 0.49609865 0.91909448 6.40557404
43 44 45 46 47 48
-3.72145244 -2.28037055 -0.17364092 2.25062603 2.67255747 0.70629274
49 50 51 52 53 54
-2.42371993 -1.63458071 -0.95894904 -6.09493419 -0.17214927 -2.27113269
55 56 57 58 59 60
1.68191371 -0.93458124 0.06679315 1.60490889 -2.52420024 0.52765921
61 62 63 64 65 66
-1.99149290 1.73464652 1.55473522 2.69040442 -1.82536878 1.76291983
67 68 69 70 71 72
-2.39538092 2.69600132 1.79349407 0.36133623 2.06541876 0.51171900
73 74 75 76 77 78
-1.35667629 -3.02220841 -0.71499267 -1.57999453 -0.41196716 -0.04894314
79 80 81 82 83 84
3.41865328 -1.58956534 -2.58577375 1.38559920 -0.03236067 -1.15623547
85 86 87 88 89 90
4.38033938 -0.69764237 -0.38636945 0.14148952 1.33722911 -0.47080064
91 92 93 94 95 96
1.07787927 -3.50002568 0.39599353 1.73834780 0.49780243 0.60049619
97 98 99 100 101 102
-1.49370109 -0.16051736 -1.47202121 0.41818350 0.47633746 0.95575206
103 104 105 106 107 108
-2.20509030 1.62137180 -3.74128845 -1.12343187 -1.93557285 -1.10291463
109 110 111 112 113 114
-2.46972157 -3.02222686 -0.73996058 0.27404546 -0.20683723 0.54986725
115 116 117 118 119 120
0.51977197 2.59216558 2.73613719 -1.63822465 -0.14140939 3.04953325
121 122 123 124 125 126
0.60508886 1.34804011 -2.89118319 -1.18363979 1.98419103 1.45071818
127 128 129 130 131 132
0.30245802 -1.20319329 -0.42938309 -2.10612877 -0.07434322 2.37421423
133 134 135 136 137 138
-1.07617436 -0.48739702 1.73448087 1.60492321 0.36221312 -3.36481585
139 140 141 142 143 144
4.92517783 -1.04502783 0.01777096 -0.51506364 2.55215901 -1.40102189
145 146 147 148 149 150
1.22977829 -0.80182473 -3.17397124 -4.79372637 1.13226066 1.48856230
151 152 153 154 155 156
-0.19727778 0.77113845 -0.84022681 1.20642478 0.81607047 1.77818230
> postscript(file="/var/www/html/freestat/rcomp/tmp/6p8ml1293622594.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 2.06067378 NA
1 1.13624278 2.06067378
2 2.46191564 1.13624278
3 1.08520968 2.46191564
4 -0.90658378 1.08520968
5 3.13245119 -0.90658378
6 -2.33481985 3.13245119
7 -1.27373775 -2.33481985
8 -0.32981459 -1.27373775
9 3.29630419 -0.32981459
10 -1.23565699 3.29630419
11 -1.14600206 -1.23565699
12 2.45999256 -1.14600206
13 -3.93277528 2.45999256
14 -0.88603113 -3.93277528
15 0.22717324 -0.88603113
16 -2.14600206 0.22717324
17 -0.28207432 -2.14600206
18 1.37591801 -0.28207432
19 -3.54277687 1.37591801
20 1.23068423 -3.54277687
21 0.69852985 1.23068423
22 -0.98791126 0.69852985
23 2.59883987 -0.98791126
24 0.67120629 2.59883987
25 0.46821203 0.67120629
26 -5.32123134 0.46821203
27 -0.04201614 -5.32123134
28 0.43719786 -0.04201614
29 5.29305286 0.43719786
30 3.94111639 5.29305286
31 -1.26569045 3.94111639
32 -0.79470343 -1.26569045
33 0.40219561 -0.79470343
34 -1.39933816 0.40219561
35 1.59992566 -1.39933816
36 -0.95270005 1.59992566
37 -0.28169210 -0.95270005
38 0.12450058 -0.28169210
39 0.49609865 0.12450058
40 0.91909448 0.49609865
41 6.40557404 0.91909448
42 -3.72145244 6.40557404
43 -2.28037055 -3.72145244
44 -0.17364092 -2.28037055
45 2.25062603 -0.17364092
46 2.67255747 2.25062603
47 0.70629274 2.67255747
48 -2.42371993 0.70629274
49 -1.63458071 -2.42371993
50 -0.95894904 -1.63458071
51 -6.09493419 -0.95894904
52 -0.17214927 -6.09493419
53 -2.27113269 -0.17214927
54 1.68191371 -2.27113269
55 -0.93458124 1.68191371
56 0.06679315 -0.93458124
57 1.60490889 0.06679315
58 -2.52420024 1.60490889
59 0.52765921 -2.52420024
60 -1.99149290 0.52765921
61 1.73464652 -1.99149290
62 1.55473522 1.73464652
63 2.69040442 1.55473522
64 -1.82536878 2.69040442
65 1.76291983 -1.82536878
66 -2.39538092 1.76291983
67 2.69600132 -2.39538092
68 1.79349407 2.69600132
69 0.36133623 1.79349407
70 2.06541876 0.36133623
71 0.51171900 2.06541876
72 -1.35667629 0.51171900
73 -3.02220841 -1.35667629
74 -0.71499267 -3.02220841
75 -1.57999453 -0.71499267
76 -0.41196716 -1.57999453
77 -0.04894314 -0.41196716
78 3.41865328 -0.04894314
79 -1.58956534 3.41865328
80 -2.58577375 -1.58956534
81 1.38559920 -2.58577375
82 -0.03236067 1.38559920
83 -1.15623547 -0.03236067
84 4.38033938 -1.15623547
85 -0.69764237 4.38033938
86 -0.38636945 -0.69764237
87 0.14148952 -0.38636945
88 1.33722911 0.14148952
89 -0.47080064 1.33722911
90 1.07787927 -0.47080064
91 -3.50002568 1.07787927
92 0.39599353 -3.50002568
93 1.73834780 0.39599353
94 0.49780243 1.73834780
95 0.60049619 0.49780243
96 -1.49370109 0.60049619
97 -0.16051736 -1.49370109
98 -1.47202121 -0.16051736
99 0.41818350 -1.47202121
100 0.47633746 0.41818350
101 0.95575206 0.47633746
102 -2.20509030 0.95575206
103 1.62137180 -2.20509030
104 -3.74128845 1.62137180
105 -1.12343187 -3.74128845
106 -1.93557285 -1.12343187
107 -1.10291463 -1.93557285
108 -2.46972157 -1.10291463
109 -3.02222686 -2.46972157
110 -0.73996058 -3.02222686
111 0.27404546 -0.73996058
112 -0.20683723 0.27404546
113 0.54986725 -0.20683723
114 0.51977197 0.54986725
115 2.59216558 0.51977197
116 2.73613719 2.59216558
117 -1.63822465 2.73613719
118 -0.14140939 -1.63822465
119 3.04953325 -0.14140939
120 0.60508886 3.04953325
121 1.34804011 0.60508886
122 -2.89118319 1.34804011
123 -1.18363979 -2.89118319
124 1.98419103 -1.18363979
125 1.45071818 1.98419103
126 0.30245802 1.45071818
127 -1.20319329 0.30245802
128 -0.42938309 -1.20319329
129 -2.10612877 -0.42938309
130 -0.07434322 -2.10612877
131 2.37421423 -0.07434322
132 -1.07617436 2.37421423
133 -0.48739702 -1.07617436
134 1.73448087 -0.48739702
135 1.60492321 1.73448087
136 0.36221312 1.60492321
137 -3.36481585 0.36221312
138 4.92517783 -3.36481585
139 -1.04502783 4.92517783
140 0.01777096 -1.04502783
141 -0.51506364 0.01777096
142 2.55215901 -0.51506364
143 -1.40102189 2.55215901
144 1.22977829 -1.40102189
145 -0.80182473 1.22977829
146 -3.17397124 -0.80182473
147 -4.79372637 -3.17397124
148 1.13226066 -4.79372637
149 1.48856230 1.13226066
150 -0.19727778 1.48856230
151 0.77113845 -0.19727778
152 -0.84022681 0.77113845
153 1.20642478 -0.84022681
154 0.81607047 1.20642478
155 1.77818230 0.81607047
156 NA 1.77818230
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.13624278 2.06067378
[2,] 2.46191564 1.13624278
[3,] 1.08520968 2.46191564
[4,] -0.90658378 1.08520968
[5,] 3.13245119 -0.90658378
[6,] -2.33481985 3.13245119
[7,] -1.27373775 -2.33481985
[8,] -0.32981459 -1.27373775
[9,] 3.29630419 -0.32981459
[10,] -1.23565699 3.29630419
[11,] -1.14600206 -1.23565699
[12,] 2.45999256 -1.14600206
[13,] -3.93277528 2.45999256
[14,] -0.88603113 -3.93277528
[15,] 0.22717324 -0.88603113
[16,] -2.14600206 0.22717324
[17,] -0.28207432 -2.14600206
[18,] 1.37591801 -0.28207432
[19,] -3.54277687 1.37591801
[20,] 1.23068423 -3.54277687
[21,] 0.69852985 1.23068423
[22,] -0.98791126 0.69852985
[23,] 2.59883987 -0.98791126
[24,] 0.67120629 2.59883987
[25,] 0.46821203 0.67120629
[26,] -5.32123134 0.46821203
[27,] -0.04201614 -5.32123134
[28,] 0.43719786 -0.04201614
[29,] 5.29305286 0.43719786
[30,] 3.94111639 5.29305286
[31,] -1.26569045 3.94111639
[32,] -0.79470343 -1.26569045
[33,] 0.40219561 -0.79470343
[34,] -1.39933816 0.40219561
[35,] 1.59992566 -1.39933816
[36,] -0.95270005 1.59992566
[37,] -0.28169210 -0.95270005
[38,] 0.12450058 -0.28169210
[39,] 0.49609865 0.12450058
[40,] 0.91909448 0.49609865
[41,] 6.40557404 0.91909448
[42,] -3.72145244 6.40557404
[43,] -2.28037055 -3.72145244
[44,] -0.17364092 -2.28037055
[45,] 2.25062603 -0.17364092
[46,] 2.67255747 2.25062603
[47,] 0.70629274 2.67255747
[48,] -2.42371993 0.70629274
[49,] -1.63458071 -2.42371993
[50,] -0.95894904 -1.63458071
[51,] -6.09493419 -0.95894904
[52,] -0.17214927 -6.09493419
[53,] -2.27113269 -0.17214927
[54,] 1.68191371 -2.27113269
[55,] -0.93458124 1.68191371
[56,] 0.06679315 -0.93458124
[57,] 1.60490889 0.06679315
[58,] -2.52420024 1.60490889
[59,] 0.52765921 -2.52420024
[60,] -1.99149290 0.52765921
[61,] 1.73464652 -1.99149290
[62,] 1.55473522 1.73464652
[63,] 2.69040442 1.55473522
[64,] -1.82536878 2.69040442
[65,] 1.76291983 -1.82536878
[66,] -2.39538092 1.76291983
[67,] 2.69600132 -2.39538092
[68,] 1.79349407 2.69600132
[69,] 0.36133623 1.79349407
[70,] 2.06541876 0.36133623
[71,] 0.51171900 2.06541876
[72,] -1.35667629 0.51171900
[73,] -3.02220841 -1.35667629
[74,] -0.71499267 -3.02220841
[75,] -1.57999453 -0.71499267
[76,] -0.41196716 -1.57999453
[77,] -0.04894314 -0.41196716
[78,] 3.41865328 -0.04894314
[79,] -1.58956534 3.41865328
[80,] -2.58577375 -1.58956534
[81,] 1.38559920 -2.58577375
[82,] -0.03236067 1.38559920
[83,] -1.15623547 -0.03236067
[84,] 4.38033938 -1.15623547
[85,] -0.69764237 4.38033938
[86,] -0.38636945 -0.69764237
[87,] 0.14148952 -0.38636945
[88,] 1.33722911 0.14148952
[89,] -0.47080064 1.33722911
[90,] 1.07787927 -0.47080064
[91,] -3.50002568 1.07787927
[92,] 0.39599353 -3.50002568
[93,] 1.73834780 0.39599353
[94,] 0.49780243 1.73834780
[95,] 0.60049619 0.49780243
[96,] -1.49370109 0.60049619
[97,] -0.16051736 -1.49370109
[98,] -1.47202121 -0.16051736
[99,] 0.41818350 -1.47202121
[100,] 0.47633746 0.41818350
[101,] 0.95575206 0.47633746
[102,] -2.20509030 0.95575206
[103,] 1.62137180 -2.20509030
[104,] -3.74128845 1.62137180
[105,] -1.12343187 -3.74128845
[106,] -1.93557285 -1.12343187
[107,] -1.10291463 -1.93557285
[108,] -2.46972157 -1.10291463
[109,] -3.02222686 -2.46972157
[110,] -0.73996058 -3.02222686
[111,] 0.27404546 -0.73996058
[112,] -0.20683723 0.27404546
[113,] 0.54986725 -0.20683723
[114,] 0.51977197 0.54986725
[115,] 2.59216558 0.51977197
[116,] 2.73613719 2.59216558
[117,] -1.63822465 2.73613719
[118,] -0.14140939 -1.63822465
[119,] 3.04953325 -0.14140939
[120,] 0.60508886 3.04953325
[121,] 1.34804011 0.60508886
[122,] -2.89118319 1.34804011
[123,] -1.18363979 -2.89118319
[124,] 1.98419103 -1.18363979
[125,] 1.45071818 1.98419103
[126,] 0.30245802 1.45071818
[127,] -1.20319329 0.30245802
[128,] -0.42938309 -1.20319329
[129,] -2.10612877 -0.42938309
[130,] -0.07434322 -2.10612877
[131,] 2.37421423 -0.07434322
[132,] -1.07617436 2.37421423
[133,] -0.48739702 -1.07617436
[134,] 1.73448087 -0.48739702
[135,] 1.60492321 1.73448087
[136,] 0.36221312 1.60492321
[137,] -3.36481585 0.36221312
[138,] 4.92517783 -3.36481585
[139,] -1.04502783 4.92517783
[140,] 0.01777096 -1.04502783
[141,] -0.51506364 0.01777096
[142,] 2.55215901 -0.51506364
[143,] -1.40102189 2.55215901
[144,] 1.22977829 -1.40102189
[145,] -0.80182473 1.22977829
[146,] -3.17397124 -0.80182473
[147,] -4.79372637 -3.17397124
[148,] 1.13226066 -4.79372637
[149,] 1.48856230 1.13226066
[150,] -0.19727778 1.48856230
[151,] 0.77113845 -0.19727778
[152,] -0.84022681 0.77113845
[153,] 1.20642478 -0.84022681
[154,] 0.81607047 1.20642478
[155,] 1.77818230 0.81607047
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.13624278 2.06067378
2 2.46191564 1.13624278
3 1.08520968 2.46191564
4 -0.90658378 1.08520968
5 3.13245119 -0.90658378
6 -2.33481985 3.13245119
7 -1.27373775 -2.33481985
8 -0.32981459 -1.27373775
9 3.29630419 -0.32981459
10 -1.23565699 3.29630419
11 -1.14600206 -1.23565699
12 2.45999256 -1.14600206
13 -3.93277528 2.45999256
14 -0.88603113 -3.93277528
15 0.22717324 -0.88603113
16 -2.14600206 0.22717324
17 -0.28207432 -2.14600206
18 1.37591801 -0.28207432
19 -3.54277687 1.37591801
20 1.23068423 -3.54277687
21 0.69852985 1.23068423
22 -0.98791126 0.69852985
23 2.59883987 -0.98791126
24 0.67120629 2.59883987
25 0.46821203 0.67120629
26 -5.32123134 0.46821203
27 -0.04201614 -5.32123134
28 0.43719786 -0.04201614
29 5.29305286 0.43719786
30 3.94111639 5.29305286
31 -1.26569045 3.94111639
32 -0.79470343 -1.26569045
33 0.40219561 -0.79470343
34 -1.39933816 0.40219561
35 1.59992566 -1.39933816
36 -0.95270005 1.59992566
37 -0.28169210 -0.95270005
38 0.12450058 -0.28169210
39 0.49609865 0.12450058
40 0.91909448 0.49609865
41 6.40557404 0.91909448
42 -3.72145244 6.40557404
43 -2.28037055 -3.72145244
44 -0.17364092 -2.28037055
45 2.25062603 -0.17364092
46 2.67255747 2.25062603
47 0.70629274 2.67255747
48 -2.42371993 0.70629274
49 -1.63458071 -2.42371993
50 -0.95894904 -1.63458071
51 -6.09493419 -0.95894904
52 -0.17214927 -6.09493419
53 -2.27113269 -0.17214927
54 1.68191371 -2.27113269
55 -0.93458124 1.68191371
56 0.06679315 -0.93458124
57 1.60490889 0.06679315
58 -2.52420024 1.60490889
59 0.52765921 -2.52420024
60 -1.99149290 0.52765921
61 1.73464652 -1.99149290
62 1.55473522 1.73464652
63 2.69040442 1.55473522
64 -1.82536878 2.69040442
65 1.76291983 -1.82536878
66 -2.39538092 1.76291983
67 2.69600132 -2.39538092
68 1.79349407 2.69600132
69 0.36133623 1.79349407
70 2.06541876 0.36133623
71 0.51171900 2.06541876
72 -1.35667629 0.51171900
73 -3.02220841 -1.35667629
74 -0.71499267 -3.02220841
75 -1.57999453 -0.71499267
76 -0.41196716 -1.57999453
77 -0.04894314 -0.41196716
78 3.41865328 -0.04894314
79 -1.58956534 3.41865328
80 -2.58577375 -1.58956534
81 1.38559920 -2.58577375
82 -0.03236067 1.38559920
83 -1.15623547 -0.03236067
84 4.38033938 -1.15623547
85 -0.69764237 4.38033938
86 -0.38636945 -0.69764237
87 0.14148952 -0.38636945
88 1.33722911 0.14148952
89 -0.47080064 1.33722911
90 1.07787927 -0.47080064
91 -3.50002568 1.07787927
92 0.39599353 -3.50002568
93 1.73834780 0.39599353
94 0.49780243 1.73834780
95 0.60049619 0.49780243
96 -1.49370109 0.60049619
97 -0.16051736 -1.49370109
98 -1.47202121 -0.16051736
99 0.41818350 -1.47202121
100 0.47633746 0.41818350
101 0.95575206 0.47633746
102 -2.20509030 0.95575206
103 1.62137180 -2.20509030
104 -3.74128845 1.62137180
105 -1.12343187 -3.74128845
106 -1.93557285 -1.12343187
107 -1.10291463 -1.93557285
108 -2.46972157 -1.10291463
109 -3.02222686 -2.46972157
110 -0.73996058 -3.02222686
111 0.27404546 -0.73996058
112 -0.20683723 0.27404546
113 0.54986725 -0.20683723
114 0.51977197 0.54986725
115 2.59216558 0.51977197
116 2.73613719 2.59216558
117 -1.63822465 2.73613719
118 -0.14140939 -1.63822465
119 3.04953325 -0.14140939
120 0.60508886 3.04953325
121 1.34804011 0.60508886
122 -2.89118319 1.34804011
123 -1.18363979 -2.89118319
124 1.98419103 -1.18363979
125 1.45071818 1.98419103
126 0.30245802 1.45071818
127 -1.20319329 0.30245802
128 -0.42938309 -1.20319329
129 -2.10612877 -0.42938309
130 -0.07434322 -2.10612877
131 2.37421423 -0.07434322
132 -1.07617436 2.37421423
133 -0.48739702 -1.07617436
134 1.73448087 -0.48739702
135 1.60492321 1.73448087
136 0.36221312 1.60492321
137 -3.36481585 0.36221312
138 4.92517783 -3.36481585
139 -1.04502783 4.92517783
140 0.01777096 -1.04502783
141 -0.51506364 0.01777096
142 2.55215901 -0.51506364
143 -1.40102189 2.55215901
144 1.22977829 -1.40102189
145 -0.80182473 1.22977829
146 -3.17397124 -0.80182473
147 -4.79372637 -3.17397124
148 1.13226066 -4.79372637
149 1.48856230 1.13226066
150 -0.19727778 1.48856230
151 0.77113845 -0.19727778
152 -0.84022681 0.77113845
153 1.20642478 -0.84022681
154 0.81607047 1.20642478
155 1.77818230 0.81607047
> 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/freestat/rcomp/tmp/7iz361293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8iz361293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9br281293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10br281293622594.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11erje1293622594.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/freestat/rcomp/tmp/12haz21293622594.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/freestat/rcomp/tmp/13w1xt1293622594.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/freestat/rcomp/tmp/14zkvh1293622594.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/freestat/rcomp/tmp/152lu51293622594.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/freestat/rcomp/tmp/16o3sb1293622594.tab")
+ }
>
> try(system("convert tmp/14qnf1293622594.ps tmp/14qnf1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eh4i1293622594.ps tmp/2eh4i1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/3eh4i1293622594.ps tmp/3eh4i1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/4eh4i1293622594.ps tmp/4eh4i1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/5p8ml1293622594.ps tmp/5p8ml1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p8ml1293622594.ps tmp/6p8ml1293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/7iz361293622594.ps tmp/7iz361293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/8iz361293622594.ps tmp/8iz361293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/9br281293622594.ps tmp/9br281293622594.png",intern=TRUE))
character(0)
> try(system("convert tmp/10br281293622594.ps tmp/10br281293622594.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.210 2.717 6.567