R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(41
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> 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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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, 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
Software Connected Separate Learning Happiness Depression Belonging
1 12 41 38 13 14 12 53
2 11 39 32 16 18 11 86
3 15 30 35 19 11 14 66
4 6 31 33 15 12 12 67
5 13 34 37 14 16 21 76
6 10 35 29 13 18 12 78
7 12 39 31 19 14 22 53
8 14 34 36 15 14 11 80
9 12 36 35 14 15 10 74
10 6 37 38 15 15 13 76
11 10 38 31 16 17 10 79
12 12 36 34 16 19 8 54
13 12 38 35 16 10 15 67
14 11 39 38 16 16 14 54
15 15 33 37 17 18 10 87
16 12 32 33 15 14 14 58
17 10 36 32 15 14 14 75
18 12 38 38 20 17 11 88
19 11 39 38 18 14 10 64
20 12 32 32 16 16 13 57
21 11 32 33 16 18 7 66
22 12 31 31 16 11 14 68
23 13 39 38 19 14 12 54
24 11 37 39 16 12 14 56
25 9 39 32 17 17 11 86
26 13 41 32 17 9 9 80
27 10 36 35 16 16 11 76
28 14 33 37 15 14 15 69
29 12 33 33 16 15 14 78
30 10 34 33 14 11 13 67
31 12 31 28 15 16 9 80
32 8 27 32 12 13 15 54
33 10 37 31 14 17 10 71
34 12 34 37 16 15 11 84
35 12 34 30 14 14 13 74
36 7 32 33 7 16 8 71
37 6 29 31 10 9 20 63
38 12 36 33 14 15 12 71
39 10 29 31 16 17 10 76
40 10 35 33 16 13 10 69
41 10 37 32 16 15 9 74
42 12 34 33 14 16 14 75
43 15 38 32 20 16 8 54
44 10 35 33 14 12 14 52
45 10 38 28 14 12 11 69
46 12 37 35 11 11 13 68
47 13 38 39 14 15 9 65
48 11 33 34 15 15 11 75
49 11 36 38 16 17 15 74
50 12 38 32 14 13 11 75
51 14 32 38 16 16 10 72
52 10 32 30 14 14 14 67
53 12 32 33 12 11 18 63
54 13 34 38 16 12 14 62
55 5 32 32 9 12 11 63
56 6 37 32 14 15 12 76
57 12 39 34 16 16 13 74
58 12 29 34 16 15 9 67
59 11 37 36 15 12 10 73
60 10 35 34 16 12 15 70
61 7 30 28 12 8 20 53
62 12 38 34 16 13 12 77
63 14 34 35 16 11 12 77
64 11 31 35 14 14 14 52
65 12 34 31 16 15 13 54
66 13 35 37 17 10 11 80
67 14 36 35 18 11 17 66
68 11 30 27 18 12 12 73
69 12 39 40 12 15 13 63
70 12 35 37 16 15 14 69
71 8 38 36 10 14 13 67
72 11 31 38 14 16 15 54
73 14 34 39 18 15 13 81
74 14 38 41 18 15 10 69
75 12 34 27 16 13 11 84
76 9 39 30 17 12 19 80
77 13 37 37 16 17 13 70
78 11 34 31 16 13 17 69
79 12 28 31 13 15 13 77
80 12 37 27 16 13 9 54
81 12 33 36 16 15 11 79
82 12 37 38 20 16 10 30
83 12 35 37 16 15 9 71
84 12 37 33 15 16 12 73
85 11 32 34 15 15 12 72
86 10 33 31 16 14 13 77
87 9 38 39 14 15 13 75
88 12 33 34 16 14 12 69
89 12 29 32 16 13 15 54
90 12 33 33 15 7 22 70
91 9 31 36 12 17 13 73
92 15 36 32 17 13 15 54
93 12 35 41 16 15 13 77
94 12 32 28 15 14 15 82
95 12 29 30 13 13 10 80
96 10 39 36 16 16 11 80
97 13 37 35 16 12 16 69
98 9 35 31 16 14 11 78
99 12 37 34 16 17 11 81
100 10 32 36 14 15 10 76
101 14 38 36 16 17 10 76
102 11 37 35 16 12 16 73
103 15 36 37 20 16 12 85
104 11 32 28 15 11 11 66
105 11 33 39 16 15 16 79
106 12 40 32 13 9 19 68
107 12 38 35 17 16 11 76
108 12 41 39 16 15 16 71
109 11 36 35 16 10 15 54
110 7 43 42 12 10 24 46
111 12 30 34 16 15 14 82
112 14 31 33 16 11 15 74
113 11 32 41 17 13 11 88
114 11 32 33 13 14 15 38
115 10 37 34 12 18 12 76
116 13 37 32 18 16 10 86
117 13 33 40 14 14 14 54
118 8 34 40 14 14 13 70
119 11 33 35 13 14 9 69
120 12 38 36 16 14 15 90
121 11 33 37 13 12 15 54
122 13 31 27 16 14 14 76
123 12 38 39 13 15 11 89
124 14 37 38 16 15 8 76
125 13 33 31 15 15 11 73
126 15 31 33 16 13 11 79
127 10 39 32 15 17 8 90
128 11 44 39 17 17 10 74
129 9 33 36 15 19 11 81
130 11 35 33 12 15 13 72
131 10 32 33 16 13 11 71
132 11 28 32 10 9 20 66
133 8 40 37 16 15 10 77
134 11 27 30 12 15 15 65
135 12 37 38 14 15 12 74
136 12 32 29 15 16 14 82
137 9 28 22 13 11 23 54
138 11 34 35 15 14 14 63
139 10 30 35 11 11 16 54
140 8 35 34 12 15 11 64
141 9 31 35 8 13 12 69
142 8 32 34 16 15 10 54
143 9 30 34 15 16 14 84
144 15 30 35 17 14 12 86
145 11 31 23 16 15 12 77
146 8 40 31 10 16 11 89
147 13 32 27 18 16 12 76
148 12 36 36 13 11 13 60
149 12 32 31 16 12 11 75
150 9 35 32 13 9 19 73
151 7 38 39 10 16 12 85
152 13 42 37 15 13 17 79
153 9 34 38 16 16 9 71
154 6 35 39 16 12 12 72
155 8 35 34 14 9 19 69
156 8 33 31 10 13 18 78
157 15 36 32 17 13 15 54
158 6 32 37 13 14 14 69
159 9 33 36 15 19 11 81
160 11 34 32 16 13 9 84
161 8 32 35 12 12 18 84
162 8 34 36 13 13 16 69
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
162 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
4.510457 -0.047650 0.033616 0.528536
Happiness Depression Belonging Belonging_Final
-0.040610 -0.022427 0.003910 -0.006264
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8598 -0.9782 0.2415 1.3473 3.1609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.510457 2.574743 1.752 0.0818 .
Connected -0.047650 0.047004 -1.014 0.3123
Separate 0.033616 0.044176 0.761 0.4478
Learning 0.528536 0.067169 7.869 5.9e-13 ***
Happiness -0.040610 0.075466 -0.538 0.5913
Depression -0.022427 0.055865 -0.401 0.6886
Belonging 0.003910 0.044042 0.089 0.9294
Belonging_Final -0.006264 0.063260 -0.099 0.9213
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.826 on 154 degrees of freedom
Multiple R-squared: 0.3045, Adjusted R-squared: 0.2729
F-statistic: 9.633 on 7 and 154 DF, p-value: 6.545e-10
> 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,] 0.999845278 0.0003094447 0.0001547224
[2,] 0.999738776 0.0005224477 0.0002612238
[3,] 0.999376209 0.0012475821 0.0006237911
[4,] 0.998978612 0.0020427766 0.0010213883
[5,] 0.998827712 0.0023445751 0.0011722875
[6,] 0.997888436 0.0042231285 0.0021115642
[7,] 0.996138794 0.0077224128 0.0038612064
[8,] 0.995239948 0.0095201046 0.0047600523
[9,] 0.992490905 0.0150181907 0.0075090953
[10,] 0.987331713 0.0253365733 0.0126682867
[11,] 0.980449244 0.0391015118 0.0195507559
[12,] 0.972111734 0.0557765320 0.0278882660
[13,] 0.958661872 0.0826762555 0.0413381278
[14,] 0.941455204 0.1170895920 0.0585447960
[15,] 0.939724832 0.1205503359 0.0602751679
[16,] 0.943808005 0.1123839895 0.0561919948
[17,] 0.944348473 0.1113030534 0.0556515267
[18,] 0.951029936 0.0979401277 0.0489700638
[19,] 0.932602662 0.1347946756 0.0673973378
[20,] 0.912464938 0.1750701236 0.0875350618
[21,] 0.895878222 0.2082435566 0.1041217783
[22,] 0.912752414 0.1744951724 0.0872475862
[23,] 0.886393476 0.2272130479 0.1136065240
[24,] 0.854862578 0.2902748439 0.1451374219
[25,] 0.840988187 0.3180236262 0.1590118131
[26,] 0.804102331 0.3917953385 0.1958976693
[27,] 0.845392430 0.3092151400 0.1546075700
[28,] 0.836623689 0.3267526214 0.1633763107
[29,] 0.826399834 0.3472003314 0.1736001657
[30,] 0.808340126 0.3833197471 0.1916598735
[31,] 0.784505302 0.4309893953 0.2154946976
[32,] 0.768456494 0.4630870111 0.2315435055
[33,] 0.766621269 0.4667574629 0.2333787314
[34,] 0.725509169 0.5489816620 0.2744908310
[35,] 0.682367104 0.6352657911 0.3176328955
[36,] 0.743492865 0.5130142703 0.2565071352
[37,] 0.752718108 0.4945637844 0.2472818922
[38,] 0.709722571 0.5805548584 0.2902774292
[39,] 0.672648204 0.6547035928 0.3273517964
[40,] 0.658340894 0.6833182120 0.3416591060
[41,] 0.662941817 0.6741163660 0.3370581830
[42,] 0.616677612 0.7666447768 0.3833223884
[43,] 0.642404077 0.7151918467 0.3575959233
[44,] 0.608556122 0.7828877565 0.3914438783
[45,] 0.698451747 0.6030965066 0.3015482533
[46,] 0.853549225 0.2929015502 0.1464507751
[47,] 0.827172042 0.3456559155 0.1728279577
[48,] 0.794109307 0.4117813866 0.2058906933
[49,] 0.758373692 0.4832526168 0.2416263084
[50,] 0.746373390 0.5072532195 0.2536266098
[51,] 0.763250383 0.4734992337 0.2367496168
[52,] 0.727695308 0.5446093844 0.2723046922
[53,] 0.740549609 0.5189007822 0.2594503911
[54,] 0.700546428 0.5989071438 0.2994535719
[55,] 0.666018123 0.6679637537 0.3339818769
[56,] 0.624394020 0.7512119596 0.3756059798
[57,] 0.604952214 0.7900955712 0.3950477856
[58,] 0.589627700 0.8207445998 0.4103722999
[59,] 0.607092951 0.7858140970 0.3929070485
[60,] 0.563456388 0.8730872243 0.4365436122
[61,] 0.521856637 0.9562867264 0.4781433632
[62,] 0.479223504 0.9584470072 0.5207764964
[63,] 0.449122928 0.8982458565 0.5508770717
[64,] 0.424353393 0.8487067865 0.5756466067
[65,] 0.398045208 0.7960904156 0.6019547922
[66,] 0.448108124 0.8962162478 0.5518918761
[67,] 0.435551106 0.8711022121 0.5644488940
[68,] 0.393364937 0.7867298732 0.6066350634
[69,] 0.397659852 0.7953197032 0.6023401484
[70,] 0.374050417 0.7481008347 0.6259495826
[71,] 0.332350333 0.6647006662 0.6676496669
[72,] 0.340625559 0.6812511186 0.6593744407
[73,] 0.304154928 0.6083098551 0.6958450725
[74,] 0.277207745 0.5544154906 0.7227922547
[75,] 0.239892117 0.4797842331 0.7601078835
[76,] 0.231614217 0.4632284331 0.7683857834
[77,] 0.231049828 0.4620996566 0.7689501717
[78,] 0.197635371 0.3952707429 0.8023646285
[79,] 0.167954207 0.3359084147 0.8320457927
[80,] 0.146381775 0.2927635490 0.8536182255
[81,] 0.125881851 0.2517637021 0.8741181490
[82,] 0.177411368 0.3548227355 0.8225886323
[83,] 0.152960477 0.3059209535 0.8470395232
[84,] 0.137346008 0.2746920161 0.8626539920
[85,] 0.131068384 0.2621367689 0.8689316156
[86,] 0.123999259 0.2479985181 0.8760007409
[87,] 0.115807331 0.2316146627 0.8841926687
[88,] 0.148022240 0.2960444797 0.8519777601
[89,] 0.123547704 0.2470954089 0.8764522955
[90,] 0.105061237 0.2101224730 0.8949387635
[91,] 0.124138934 0.2482778682 0.8758610659
[92,] 0.103054477 0.2061089533 0.8969455234
[93,] 0.097777336 0.1955546719 0.9022226640
[94,] 0.080267680 0.1605353596 0.9197323202
[95,] 0.066599516 0.1331990317 0.9334004841
[96,] 0.067646941 0.1352938821 0.9323530589
[97,] 0.053522944 0.1070458875 0.9464770562
[98,] 0.046327117 0.0926542339 0.9536728830
[99,] 0.036640935 0.0732818700 0.9633590650
[100,] 0.037314732 0.0746294635 0.9626852682
[101,] 0.028712209 0.0574244174 0.9712877913
[102,] 0.031255693 0.0625113851 0.9687443075
[103,] 0.027998300 0.0559965990 0.9720017005
[104,] 0.022172992 0.0443459844 0.9778270078
[105,] 0.017174428 0.0343488567 0.9828255717
[106,] 0.012981819 0.0259636379 0.9870181810
[107,] 0.019785632 0.0395712641 0.9802143679
[108,] 0.022529452 0.0450589045 0.9774705477
[109,] 0.017159427 0.0343188540 0.9828405730
[110,] 0.013159168 0.0263183362 0.9868408319
[111,] 0.010813069 0.0216261371 0.9891869315
[112,] 0.008922272 0.0178445443 0.9910777279
[113,] 0.010438905 0.0208778103 0.9895610948
[114,] 0.016577812 0.0331556249 0.9834221876
[115,] 0.017426800 0.0348536008 0.9825731996
[116,] 0.041592193 0.0831843857 0.9584078072
[117,] 0.031386279 0.0627725587 0.9686137207
[118,] 0.023777081 0.0475541626 0.9762229187
[119,] 0.019779775 0.0395595501 0.9802202250
[120,] 0.018569876 0.0371397527 0.9814301236
[121,] 0.014772224 0.0295444476 0.9852277762
[122,] 0.017163682 0.0343273644 0.9828363178
[123,] 0.026394470 0.0527889403 0.9736055298
[124,] 0.029011930 0.0580238609 0.9709880696
[125,] 0.032115310 0.0642306191 0.9678846904
[126,] 0.026263825 0.0525276503 0.9737361748
[127,] 0.022350219 0.0447004387 0.9776497806
[128,] 0.016268610 0.0325372202 0.9837313899
[129,] 0.016480639 0.0329612777 0.9835193612
[130,] 0.011397730 0.0227954599 0.9886022700
[131,] 0.036256130 0.0725122591 0.9637438705
[132,] 0.038151955 0.0763039110 0.9618480445
[133,] 0.026984230 0.0539684593 0.9730157703
[134,] 0.295070500 0.5901409994 0.7049295003
[135,] 0.318911408 0.6378228168 0.6810885916
[136,] 0.637808547 0.7243829070 0.3621914535
[137,] 0.642779805 0.7144403909 0.3572201954
[138,] 0.911311887 0.1773762269 0.0886881134
[139,] 0.918488060 0.1630238798 0.0815119399
[140,] 0.839795699 0.3204086010 0.1602043005
[141,] 0.726040679 0.5479186425 0.2739593213
> postscript(file="/var/fisher/rcomp/tmp/1tm651355668184.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2l4zm1355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ctha1355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/47gn61355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/56w2u1355668184.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 = 162
Frequency = 1
1 2 3 4 5 6
2.12569892 -0.22352506 1.46600816 -5.31938397 2.57804867 0.30098194
7 8 9 10 11 12
-0.64991895 2.78079049 1.45483128 -5.05491381 -1.28575305 0.55199722
13 14 15 16 17 18
0.41072550 -0.42678267 2.78014858 0.85821179 -0.92766928 -1.61665290
19 20 21 22 23 24
-1.64377297 0.42599417 -0.66483607 0.23843686 -0.13846388 -0.70090324
25 26 27 28 29 30
-2.78640705 0.93130399 -1.55955156 2.80092449 0.39610758 -0.64728884
31 32 33 34 35 36
0.94313169 -1.78214942 -0.28263295 0.27492657 1.53548982 0.03243596
37 38 39 40 41 42
-2.65660110 1.56611962 -1.72166643 -1.66311945 -1.47616989 1.54690604
43 44 45 46 47 48
1.47855640 -0.55937882 -0.31320035 2.98507346 2.41590491 -0.15825917
49 50 51 52 53 54
-0.52852509 1.58827674 2.14847692 -0.52254068 2.39841169 1.22267452
55 56 57 58 59 60
-3.10500713 -4.34710989 0.68221650 0.08397566 -0.19011665 -1.62285665
61 62 63 64 65 66
-2.59800277 0.47857996 2.17314361 0.26400783 0.50723998 0.57531419
67 68 69 70 71 72
1.34772664 -1.77440772 2.55322077 0.37334446 -0.32156215 0.27151398
73 74 75 76 77 78
1.16962494 1.22252496 0.50481054 -2.73813906 1.52979111 -0.47402214
79 80 81 82 83 84
1.80470624 0.61372692 0.23033294 -1.83879496 0.27218069 1.11802348
85 86 87 88 89 90
-0.18427968 -1.58325998 -1.52096067 0.29342776 0.19900724 0.79793463
91 92 93 94 95 96
-0.60129001 3.00402323 0.21022845 1.03004036 1.72574190 -1.44080205
97 98 99 100 101 102
1.46516511 -2.54298706 0.57409234 -0.77094212 2.53284334 -0.53794780
103 104 105 106 107 108
1.30268242 -0.15651750 -0.75211612 2.23140572 0.03226779 0.63531194
109 110 111 112 113 114
-0.69011735 -2.26340022 0.22895540 2.17017174 -1.57396922 0.85931985
115 116 117 118 119 120
0.76455955 0.53298346 2.19593656 -2.77250065 0.77813905 0.53104482
121 122 123 124 125 126
0.76652751 1.47597603 1.96435363 2.30441552 1.94414472 3.16092410
127 128 129 130 131 132
-0.79346513 -0.80908194 -2.04266658 1.60032184 -1.79146350 2.26919366
133 134 135 136 137 138
-3.48433770 1.36713665 1.45275168 1.08653547 -0.82868087 -0.09568749
139 140 141 142 143 144
0.74222487 -1.47192204 1.34592579 -3.75618884 -2.19719169 2.59075450
145 146 147 148 149 150
-0.39771496 -0.03893797 0.50918358 1.86548861 0.24457290 -0.99507759
151 152 153 154 155 156
-1.37762520 2.21371155 -2.79353046 -5.85977299 -2.59399598 -0.31937445
157 158 159 160 161 162
3.00402323 -4.21834447 -2.04266658 -0.70185872 -1.62889376 -2.06639285
> postscript(file="/var/fisher/rcomp/tmp/6mnx81355668184.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 2.12569892 NA
1 -0.22352506 2.12569892
2 1.46600816 -0.22352506
3 -5.31938397 1.46600816
4 2.57804867 -5.31938397
5 0.30098194 2.57804867
6 -0.64991895 0.30098194
7 2.78079049 -0.64991895
8 1.45483128 2.78079049
9 -5.05491381 1.45483128
10 -1.28575305 -5.05491381
11 0.55199722 -1.28575305
12 0.41072550 0.55199722
13 -0.42678267 0.41072550
14 2.78014858 -0.42678267
15 0.85821179 2.78014858
16 -0.92766928 0.85821179
17 -1.61665290 -0.92766928
18 -1.64377297 -1.61665290
19 0.42599417 -1.64377297
20 -0.66483607 0.42599417
21 0.23843686 -0.66483607
22 -0.13846388 0.23843686
23 -0.70090324 -0.13846388
24 -2.78640705 -0.70090324
25 0.93130399 -2.78640705
26 -1.55955156 0.93130399
27 2.80092449 -1.55955156
28 0.39610758 2.80092449
29 -0.64728884 0.39610758
30 0.94313169 -0.64728884
31 -1.78214942 0.94313169
32 -0.28263295 -1.78214942
33 0.27492657 -0.28263295
34 1.53548982 0.27492657
35 0.03243596 1.53548982
36 -2.65660110 0.03243596
37 1.56611962 -2.65660110
38 -1.72166643 1.56611962
39 -1.66311945 -1.72166643
40 -1.47616989 -1.66311945
41 1.54690604 -1.47616989
42 1.47855640 1.54690604
43 -0.55937882 1.47855640
44 -0.31320035 -0.55937882
45 2.98507346 -0.31320035
46 2.41590491 2.98507346
47 -0.15825917 2.41590491
48 -0.52852509 -0.15825917
49 1.58827674 -0.52852509
50 2.14847692 1.58827674
51 -0.52254068 2.14847692
52 2.39841169 -0.52254068
53 1.22267452 2.39841169
54 -3.10500713 1.22267452
55 -4.34710989 -3.10500713
56 0.68221650 -4.34710989
57 0.08397566 0.68221650
58 -0.19011665 0.08397566
59 -1.62285665 -0.19011665
60 -2.59800277 -1.62285665
61 0.47857996 -2.59800277
62 2.17314361 0.47857996
63 0.26400783 2.17314361
64 0.50723998 0.26400783
65 0.57531419 0.50723998
66 1.34772664 0.57531419
67 -1.77440772 1.34772664
68 2.55322077 -1.77440772
69 0.37334446 2.55322077
70 -0.32156215 0.37334446
71 0.27151398 -0.32156215
72 1.16962494 0.27151398
73 1.22252496 1.16962494
74 0.50481054 1.22252496
75 -2.73813906 0.50481054
76 1.52979111 -2.73813906
77 -0.47402214 1.52979111
78 1.80470624 -0.47402214
79 0.61372692 1.80470624
80 0.23033294 0.61372692
81 -1.83879496 0.23033294
82 0.27218069 -1.83879496
83 1.11802348 0.27218069
84 -0.18427968 1.11802348
85 -1.58325998 -0.18427968
86 -1.52096067 -1.58325998
87 0.29342776 -1.52096067
88 0.19900724 0.29342776
89 0.79793463 0.19900724
90 -0.60129001 0.79793463
91 3.00402323 -0.60129001
92 0.21022845 3.00402323
93 1.03004036 0.21022845
94 1.72574190 1.03004036
95 -1.44080205 1.72574190
96 1.46516511 -1.44080205
97 -2.54298706 1.46516511
98 0.57409234 -2.54298706
99 -0.77094212 0.57409234
100 2.53284334 -0.77094212
101 -0.53794780 2.53284334
102 1.30268242 -0.53794780
103 -0.15651750 1.30268242
104 -0.75211612 -0.15651750
105 2.23140572 -0.75211612
106 0.03226779 2.23140572
107 0.63531194 0.03226779
108 -0.69011735 0.63531194
109 -2.26340022 -0.69011735
110 0.22895540 -2.26340022
111 2.17017174 0.22895540
112 -1.57396922 2.17017174
113 0.85931985 -1.57396922
114 0.76455955 0.85931985
115 0.53298346 0.76455955
116 2.19593656 0.53298346
117 -2.77250065 2.19593656
118 0.77813905 -2.77250065
119 0.53104482 0.77813905
120 0.76652751 0.53104482
121 1.47597603 0.76652751
122 1.96435363 1.47597603
123 2.30441552 1.96435363
124 1.94414472 2.30441552
125 3.16092410 1.94414472
126 -0.79346513 3.16092410
127 -0.80908194 -0.79346513
128 -2.04266658 -0.80908194
129 1.60032184 -2.04266658
130 -1.79146350 1.60032184
131 2.26919366 -1.79146350
132 -3.48433770 2.26919366
133 1.36713665 -3.48433770
134 1.45275168 1.36713665
135 1.08653547 1.45275168
136 -0.82868087 1.08653547
137 -0.09568749 -0.82868087
138 0.74222487 -0.09568749
139 -1.47192204 0.74222487
140 1.34592579 -1.47192204
141 -3.75618884 1.34592579
142 -2.19719169 -3.75618884
143 2.59075450 -2.19719169
144 -0.39771496 2.59075450
145 -0.03893797 -0.39771496
146 0.50918358 -0.03893797
147 1.86548861 0.50918358
148 0.24457290 1.86548861
149 -0.99507759 0.24457290
150 -1.37762520 -0.99507759
151 2.21371155 -1.37762520
152 -2.79353046 2.21371155
153 -5.85977299 -2.79353046
154 -2.59399598 -5.85977299
155 -0.31937445 -2.59399598
156 3.00402323 -0.31937445
157 -4.21834447 3.00402323
158 -2.04266658 -4.21834447
159 -0.70185872 -2.04266658
160 -1.62889376 -0.70185872
161 -2.06639285 -1.62889376
162 NA -2.06639285
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.22352506 2.12569892
[2,] 1.46600816 -0.22352506
[3,] -5.31938397 1.46600816
[4,] 2.57804867 -5.31938397
[5,] 0.30098194 2.57804867
[6,] -0.64991895 0.30098194
[7,] 2.78079049 -0.64991895
[8,] 1.45483128 2.78079049
[9,] -5.05491381 1.45483128
[10,] -1.28575305 -5.05491381
[11,] 0.55199722 -1.28575305
[12,] 0.41072550 0.55199722
[13,] -0.42678267 0.41072550
[14,] 2.78014858 -0.42678267
[15,] 0.85821179 2.78014858
[16,] -0.92766928 0.85821179
[17,] -1.61665290 -0.92766928
[18,] -1.64377297 -1.61665290
[19,] 0.42599417 -1.64377297
[20,] -0.66483607 0.42599417
[21,] 0.23843686 -0.66483607
[22,] -0.13846388 0.23843686
[23,] -0.70090324 -0.13846388
[24,] -2.78640705 -0.70090324
[25,] 0.93130399 -2.78640705
[26,] -1.55955156 0.93130399
[27,] 2.80092449 -1.55955156
[28,] 0.39610758 2.80092449
[29,] -0.64728884 0.39610758
[30,] 0.94313169 -0.64728884
[31,] -1.78214942 0.94313169
[32,] -0.28263295 -1.78214942
[33,] 0.27492657 -0.28263295
[34,] 1.53548982 0.27492657
[35,] 0.03243596 1.53548982
[36,] -2.65660110 0.03243596
[37,] 1.56611962 -2.65660110
[38,] -1.72166643 1.56611962
[39,] -1.66311945 -1.72166643
[40,] -1.47616989 -1.66311945
[41,] 1.54690604 -1.47616989
[42,] 1.47855640 1.54690604
[43,] -0.55937882 1.47855640
[44,] -0.31320035 -0.55937882
[45,] 2.98507346 -0.31320035
[46,] 2.41590491 2.98507346
[47,] -0.15825917 2.41590491
[48,] -0.52852509 -0.15825917
[49,] 1.58827674 -0.52852509
[50,] 2.14847692 1.58827674
[51,] -0.52254068 2.14847692
[52,] 2.39841169 -0.52254068
[53,] 1.22267452 2.39841169
[54,] -3.10500713 1.22267452
[55,] -4.34710989 -3.10500713
[56,] 0.68221650 -4.34710989
[57,] 0.08397566 0.68221650
[58,] -0.19011665 0.08397566
[59,] -1.62285665 -0.19011665
[60,] -2.59800277 -1.62285665
[61,] 0.47857996 -2.59800277
[62,] 2.17314361 0.47857996
[63,] 0.26400783 2.17314361
[64,] 0.50723998 0.26400783
[65,] 0.57531419 0.50723998
[66,] 1.34772664 0.57531419
[67,] -1.77440772 1.34772664
[68,] 2.55322077 -1.77440772
[69,] 0.37334446 2.55322077
[70,] -0.32156215 0.37334446
[71,] 0.27151398 -0.32156215
[72,] 1.16962494 0.27151398
[73,] 1.22252496 1.16962494
[74,] 0.50481054 1.22252496
[75,] -2.73813906 0.50481054
[76,] 1.52979111 -2.73813906
[77,] -0.47402214 1.52979111
[78,] 1.80470624 -0.47402214
[79,] 0.61372692 1.80470624
[80,] 0.23033294 0.61372692
[81,] -1.83879496 0.23033294
[82,] 0.27218069 -1.83879496
[83,] 1.11802348 0.27218069
[84,] -0.18427968 1.11802348
[85,] -1.58325998 -0.18427968
[86,] -1.52096067 -1.58325998
[87,] 0.29342776 -1.52096067
[88,] 0.19900724 0.29342776
[89,] 0.79793463 0.19900724
[90,] -0.60129001 0.79793463
[91,] 3.00402323 -0.60129001
[92,] 0.21022845 3.00402323
[93,] 1.03004036 0.21022845
[94,] 1.72574190 1.03004036
[95,] -1.44080205 1.72574190
[96,] 1.46516511 -1.44080205
[97,] -2.54298706 1.46516511
[98,] 0.57409234 -2.54298706
[99,] -0.77094212 0.57409234
[100,] 2.53284334 -0.77094212
[101,] -0.53794780 2.53284334
[102,] 1.30268242 -0.53794780
[103,] -0.15651750 1.30268242
[104,] -0.75211612 -0.15651750
[105,] 2.23140572 -0.75211612
[106,] 0.03226779 2.23140572
[107,] 0.63531194 0.03226779
[108,] -0.69011735 0.63531194
[109,] -2.26340022 -0.69011735
[110,] 0.22895540 -2.26340022
[111,] 2.17017174 0.22895540
[112,] -1.57396922 2.17017174
[113,] 0.85931985 -1.57396922
[114,] 0.76455955 0.85931985
[115,] 0.53298346 0.76455955
[116,] 2.19593656 0.53298346
[117,] -2.77250065 2.19593656
[118,] 0.77813905 -2.77250065
[119,] 0.53104482 0.77813905
[120,] 0.76652751 0.53104482
[121,] 1.47597603 0.76652751
[122,] 1.96435363 1.47597603
[123,] 2.30441552 1.96435363
[124,] 1.94414472 2.30441552
[125,] 3.16092410 1.94414472
[126,] -0.79346513 3.16092410
[127,] -0.80908194 -0.79346513
[128,] -2.04266658 -0.80908194
[129,] 1.60032184 -2.04266658
[130,] -1.79146350 1.60032184
[131,] 2.26919366 -1.79146350
[132,] -3.48433770 2.26919366
[133,] 1.36713665 -3.48433770
[134,] 1.45275168 1.36713665
[135,] 1.08653547 1.45275168
[136,] -0.82868087 1.08653547
[137,] -0.09568749 -0.82868087
[138,] 0.74222487 -0.09568749
[139,] -1.47192204 0.74222487
[140,] 1.34592579 -1.47192204
[141,] -3.75618884 1.34592579
[142,] -2.19719169 -3.75618884
[143,] 2.59075450 -2.19719169
[144,] -0.39771496 2.59075450
[145,] -0.03893797 -0.39771496
[146,] 0.50918358 -0.03893797
[147,] 1.86548861 0.50918358
[148,] 0.24457290 1.86548861
[149,] -0.99507759 0.24457290
[150,] -1.37762520 -0.99507759
[151,] 2.21371155 -1.37762520
[152,] -2.79353046 2.21371155
[153,] -5.85977299 -2.79353046
[154,] -2.59399598 -5.85977299
[155,] -0.31937445 -2.59399598
[156,] 3.00402323 -0.31937445
[157,] -4.21834447 3.00402323
[158,] -2.04266658 -4.21834447
[159,] -0.70185872 -2.04266658
[160,] -1.62889376 -0.70185872
[161,] -2.06639285 -1.62889376
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.22352506 2.12569892
2 1.46600816 -0.22352506
3 -5.31938397 1.46600816
4 2.57804867 -5.31938397
5 0.30098194 2.57804867
6 -0.64991895 0.30098194
7 2.78079049 -0.64991895
8 1.45483128 2.78079049
9 -5.05491381 1.45483128
10 -1.28575305 -5.05491381
11 0.55199722 -1.28575305
12 0.41072550 0.55199722
13 -0.42678267 0.41072550
14 2.78014858 -0.42678267
15 0.85821179 2.78014858
16 -0.92766928 0.85821179
17 -1.61665290 -0.92766928
18 -1.64377297 -1.61665290
19 0.42599417 -1.64377297
20 -0.66483607 0.42599417
21 0.23843686 -0.66483607
22 -0.13846388 0.23843686
23 -0.70090324 -0.13846388
24 -2.78640705 -0.70090324
25 0.93130399 -2.78640705
26 -1.55955156 0.93130399
27 2.80092449 -1.55955156
28 0.39610758 2.80092449
29 -0.64728884 0.39610758
30 0.94313169 -0.64728884
31 -1.78214942 0.94313169
32 -0.28263295 -1.78214942
33 0.27492657 -0.28263295
34 1.53548982 0.27492657
35 0.03243596 1.53548982
36 -2.65660110 0.03243596
37 1.56611962 -2.65660110
38 -1.72166643 1.56611962
39 -1.66311945 -1.72166643
40 -1.47616989 -1.66311945
41 1.54690604 -1.47616989
42 1.47855640 1.54690604
43 -0.55937882 1.47855640
44 -0.31320035 -0.55937882
45 2.98507346 -0.31320035
46 2.41590491 2.98507346
47 -0.15825917 2.41590491
48 -0.52852509 -0.15825917
49 1.58827674 -0.52852509
50 2.14847692 1.58827674
51 -0.52254068 2.14847692
52 2.39841169 -0.52254068
53 1.22267452 2.39841169
54 -3.10500713 1.22267452
55 -4.34710989 -3.10500713
56 0.68221650 -4.34710989
57 0.08397566 0.68221650
58 -0.19011665 0.08397566
59 -1.62285665 -0.19011665
60 -2.59800277 -1.62285665
61 0.47857996 -2.59800277
62 2.17314361 0.47857996
63 0.26400783 2.17314361
64 0.50723998 0.26400783
65 0.57531419 0.50723998
66 1.34772664 0.57531419
67 -1.77440772 1.34772664
68 2.55322077 -1.77440772
69 0.37334446 2.55322077
70 -0.32156215 0.37334446
71 0.27151398 -0.32156215
72 1.16962494 0.27151398
73 1.22252496 1.16962494
74 0.50481054 1.22252496
75 -2.73813906 0.50481054
76 1.52979111 -2.73813906
77 -0.47402214 1.52979111
78 1.80470624 -0.47402214
79 0.61372692 1.80470624
80 0.23033294 0.61372692
81 -1.83879496 0.23033294
82 0.27218069 -1.83879496
83 1.11802348 0.27218069
84 -0.18427968 1.11802348
85 -1.58325998 -0.18427968
86 -1.52096067 -1.58325998
87 0.29342776 -1.52096067
88 0.19900724 0.29342776
89 0.79793463 0.19900724
90 -0.60129001 0.79793463
91 3.00402323 -0.60129001
92 0.21022845 3.00402323
93 1.03004036 0.21022845
94 1.72574190 1.03004036
95 -1.44080205 1.72574190
96 1.46516511 -1.44080205
97 -2.54298706 1.46516511
98 0.57409234 -2.54298706
99 -0.77094212 0.57409234
100 2.53284334 -0.77094212
101 -0.53794780 2.53284334
102 1.30268242 -0.53794780
103 -0.15651750 1.30268242
104 -0.75211612 -0.15651750
105 2.23140572 -0.75211612
106 0.03226779 2.23140572
107 0.63531194 0.03226779
108 -0.69011735 0.63531194
109 -2.26340022 -0.69011735
110 0.22895540 -2.26340022
111 2.17017174 0.22895540
112 -1.57396922 2.17017174
113 0.85931985 -1.57396922
114 0.76455955 0.85931985
115 0.53298346 0.76455955
116 2.19593656 0.53298346
117 -2.77250065 2.19593656
118 0.77813905 -2.77250065
119 0.53104482 0.77813905
120 0.76652751 0.53104482
121 1.47597603 0.76652751
122 1.96435363 1.47597603
123 2.30441552 1.96435363
124 1.94414472 2.30441552
125 3.16092410 1.94414472
126 -0.79346513 3.16092410
127 -0.80908194 -0.79346513
128 -2.04266658 -0.80908194
129 1.60032184 -2.04266658
130 -1.79146350 1.60032184
131 2.26919366 -1.79146350
132 -3.48433770 2.26919366
133 1.36713665 -3.48433770
134 1.45275168 1.36713665
135 1.08653547 1.45275168
136 -0.82868087 1.08653547
137 -0.09568749 -0.82868087
138 0.74222487 -0.09568749
139 -1.47192204 0.74222487
140 1.34592579 -1.47192204
141 -3.75618884 1.34592579
142 -2.19719169 -3.75618884
143 2.59075450 -2.19719169
144 -0.39771496 2.59075450
145 -0.03893797 -0.39771496
146 0.50918358 -0.03893797
147 1.86548861 0.50918358
148 0.24457290 1.86548861
149 -0.99507759 0.24457290
150 -1.37762520 -0.99507759
151 2.21371155 -1.37762520
152 -2.79353046 2.21371155
153 -5.85977299 -2.79353046
154 -2.59399598 -5.85977299
155 -0.31937445 -2.59399598
156 3.00402323 -0.31937445
157 -4.21834447 3.00402323
158 -2.04266658 -4.21834447
159 -0.70185872 -2.04266658
160 -1.62889376 -0.70185872
161 -2.06639285 -1.62889376
> 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/fisher/rcomp/tmp/7bail1355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8r21h1355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9bt0t1355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10dd361355668184.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11c0qx1355668184.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/fisher/rcomp/tmp/12vhfe1355668184.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/fisher/rcomp/tmp/133u0v1355668184.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/fisher/rcomp/tmp/14zsba1355668184.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/fisher/rcomp/tmp/15up5r1355668184.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/fisher/rcomp/tmp/160s821355668185.tab")
+ }
>
> try(system("convert tmp/1tm651355668184.ps tmp/1tm651355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l4zm1355668184.ps tmp/2l4zm1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ctha1355668184.ps tmp/3ctha1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/47gn61355668184.ps tmp/47gn61355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/56w2u1355668184.ps tmp/56w2u1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mnx81355668184.ps tmp/6mnx81355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bail1355668184.ps tmp/7bail1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r21h1355668184.ps tmp/8r21h1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bt0t1355668184.ps tmp/9bt0t1355668184.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dd361355668184.ps tmp/10dd361355668184.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.240 1.670 9.906