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 = '1'
> 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, 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
Connected Separate Learning Software Happiness Depression Belonging
1 41 38 13 12 14 12 53
2 39 32 16 11 18 11 86
3 30 35 19 15 11 14 66
4 31 33 15 6 12 12 67
5 34 37 14 13 16 21 76
6 35 29 13 10 18 12 78
7 39 31 19 12 14 22 53
8 34 36 15 14 14 11 80
9 36 35 14 12 15 10 74
10 37 38 15 6 15 13 76
11 38 31 16 10 17 10 79
12 36 34 16 12 19 8 54
13 38 35 16 12 10 15 67
14 39 38 16 11 16 14 54
15 33 37 17 15 18 10 87
16 32 33 15 12 14 14 58
17 36 32 15 10 14 14 75
18 38 38 20 12 17 11 88
19 39 38 18 11 14 10 64
20 32 32 16 12 16 13 57
21 32 33 16 11 18 7 66
22 31 31 16 12 11 14 68
23 39 38 19 13 14 12 54
24 37 39 16 11 12 14 56
25 39 32 17 9 17 11 86
26 41 32 17 13 9 9 80
27 36 35 16 10 16 11 76
28 33 37 15 14 14 15 69
29 33 33 16 12 15 14 78
30 34 33 14 10 11 13 67
31 31 28 15 12 16 9 80
32 27 32 12 8 13 15 54
33 37 31 14 10 17 10 71
34 34 37 16 12 15 11 84
35 34 30 14 12 14 13 74
36 32 33 7 7 16 8 71
37 29 31 10 6 9 20 63
38 36 33 14 12 15 12 71
39 29 31 16 10 17 10 76
40 35 33 16 10 13 10 69
41 37 32 16 10 15 9 74
42 34 33 14 12 16 14 75
43 38 32 20 15 16 8 54
44 35 33 14 10 12 14 52
45 38 28 14 10 12 11 69
46 37 35 11 12 11 13 68
47 38 39 14 13 15 9 65
48 33 34 15 11 15 11 75
49 36 38 16 11 17 15 74
50 38 32 14 12 13 11 75
51 32 38 16 14 16 10 72
52 32 30 14 10 14 14 67
53 32 33 12 12 11 18 63
54 34 38 16 13 12 14 62
55 32 32 9 5 12 11 63
56 37 32 14 6 15 12 76
57 39 34 16 12 16 13 74
58 29 34 16 12 15 9 67
59 37 36 15 11 12 10 73
60 35 34 16 10 12 15 70
61 30 28 12 7 8 20 53
62 38 34 16 12 13 12 77
63 34 35 16 14 11 12 77
64 31 35 14 11 14 14 52
65 34 31 16 12 15 13 54
66 35 37 17 13 10 11 80
67 36 35 18 14 11 17 66
68 30 27 18 11 12 12 73
69 39 40 12 12 15 13 63
70 35 37 16 12 15 14 69
71 38 36 10 8 14 13 67
72 31 38 14 11 16 15 54
73 34 39 18 14 15 13 81
74 38 41 18 14 15 10 69
75 34 27 16 12 13 11 84
76 39 30 17 9 12 19 80
77 37 37 16 13 17 13 70
78 34 31 16 11 13 17 69
79 28 31 13 12 15 13 77
80 37 27 16 12 13 9 54
81 33 36 16 12 15 11 79
82 37 38 20 12 16 10 30
83 35 37 16 12 15 9 71
84 37 33 15 12 16 12 73
85 32 34 15 11 15 12 72
86 33 31 16 10 14 13 77
87 38 39 14 9 15 13 75
88 33 34 16 12 14 12 69
89 29 32 16 12 13 15 54
90 33 33 15 12 7 22 70
91 31 36 12 9 17 13 73
92 36 32 17 15 13 15 54
93 35 41 16 12 15 13 77
94 32 28 15 12 14 15 82
95 29 30 13 12 13 10 80
96 39 36 16 10 16 11 80
97 37 35 16 13 12 16 69
98 35 31 16 9 14 11 78
99 37 34 16 12 17 11 81
100 32 36 14 10 15 10 76
101 38 36 16 14 17 10 76
102 37 35 16 11 12 16 73
103 36 37 20 15 16 12 85
104 32 28 15 11 11 11 66
105 33 39 16 11 15 16 79
106 40 32 13 12 9 19 68
107 38 35 17 12 16 11 76
108 41 39 16 12 15 16 71
109 36 35 16 11 10 15 54
110 43 42 12 7 10 24 46
111 30 34 16 12 15 14 82
112 31 33 16 14 11 15 74
113 32 41 17 11 13 11 88
114 32 33 13 11 14 15 38
115 37 34 12 10 18 12 76
116 37 32 18 13 16 10 86
117 33 40 14 13 14 14 54
118 34 40 14 8 14 13 70
119 33 35 13 11 14 9 69
120 38 36 16 12 14 15 90
121 33 37 13 11 12 15 54
122 31 27 16 13 14 14 76
123 38 39 13 12 15 11 89
124 37 38 16 14 15 8 76
125 33 31 15 13 15 11 73
126 31 33 16 15 13 11 79
127 39 32 15 10 17 8 90
128 44 39 17 11 17 10 74
129 33 36 15 9 19 11 81
130 35 33 12 11 15 13 72
131 32 33 16 10 13 11 71
132 28 32 10 11 9 20 66
133 40 37 16 8 15 10 77
134 27 30 12 11 15 15 65
135 37 38 14 12 15 12 74
136 32 29 15 12 16 14 82
137 28 22 13 9 11 23 54
138 34 35 15 11 14 14 63
139 30 35 11 10 11 16 54
140 35 34 12 8 15 11 64
141 31 35 8 9 13 12 69
142 32 34 16 8 15 10 54
143 30 34 15 9 16 14 84
144 30 35 17 15 14 12 86
145 31 23 16 11 15 12 77
146 40 31 10 8 16 11 89
147 32 27 18 13 16 12 76
148 36 36 13 12 11 13 60
149 32 31 16 12 12 11 75
150 35 32 13 9 9 19 73
151 38 39 10 7 16 12 85
152 42 37 15 13 13 17 79
153 34 38 16 9 16 9 71
154 35 39 16 6 12 12 72
155 35 34 14 8 9 19 69
156 33 31 10 8 13 18 78
157 36 32 17 15 13 15 54
158 32 37 13 6 14 14 69
159 33 36 15 9 19 11 81
160 34 32 16 11 13 9 84
161 32 35 12 8 12 18 84
162 34 36 13 8 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) Separate Learning Software
18.26299 0.33603 0.32439 -0.13912
Happiness Depression Belonging Belonging_Final
0.03530 -0.02286 0.03491 -0.02499
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9723 -2.3208 -0.4044 2.2016 7.2036
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.26299 4.19225 4.356 2.40e-05 ***
Separate 0.33603 0.07061 4.759 4.45e-06 ***
Learning 0.32439 0.13336 2.432 0.0161 *
Software -0.13912 0.13723 -1.014 0.3123
Happiness 0.03530 0.12904 0.274 0.7848
Depression -0.02286 0.09549 -0.239 0.8111
Belonging 0.03491 0.07520 0.464 0.6431
Belonging_Final -0.02499 0.10808 -0.231 0.8174
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.12 on 154 degrees of freedom
Multiple R-squared: 0.1824, Adjusted R-squared: 0.1452
F-statistic: 4.907 on 7 and 154 DF, p-value: 5.026e-05
> 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,] 0.18586900 0.3717380 0.81413100
[2,] 0.87112652 0.2577470 0.12887348
[3,] 0.91778561 0.1644288 0.08221439
[4,] 0.87825497 0.2434901 0.12174503
[5,] 0.84097487 0.3180503 0.15902513
[6,] 0.86502129 0.2699574 0.13497871
[7,] 0.82169389 0.3566122 0.17830611
[8,] 0.78900626 0.4219875 0.21099374
[9,] 0.74270745 0.5145851 0.25729255
[10,] 0.77054743 0.4589051 0.22945257
[11,] 0.79025485 0.4194903 0.20974515
[12,] 0.74921776 0.5015645 0.25078224
[13,] 0.70594726 0.5881055 0.29405274
[14,] 0.63880086 0.7223983 0.36119914
[15,] 0.63326837 0.7334633 0.36673163
[16,] 0.77950350 0.4409930 0.22049650
[17,] 0.77310259 0.4537948 0.22689741
[18,] 0.72932247 0.5413551 0.27067753
[19,] 0.73839008 0.5232198 0.26160992
[20,] 0.68261029 0.6347794 0.31738971
[21,] 0.64513419 0.7097316 0.35486581
[22,] 0.80365321 0.3926936 0.19634679
[23,] 0.80125368 0.3974926 0.19874632
[24,] 0.76507462 0.4698508 0.23492538
[25,] 0.71755905 0.5648819 0.28244095
[26,] 0.67111032 0.6577794 0.32888968
[27,] 0.65907300 0.6818540 0.34092700
[28,] 0.62629488 0.7474102 0.37370512
[29,] 0.74423305 0.5115339 0.25576695
[30,] 0.69711129 0.6057774 0.30288871
[31,] 0.66721285 0.6655743 0.33278715
[32,] 0.61544864 0.7691027 0.38455136
[33,] 0.58612512 0.8277498 0.41387488
[34,] 0.53755541 0.9248892 0.46244459
[35,] 0.63473009 0.7305398 0.36526991
[36,] 0.64104055 0.7179189 0.35895945
[37,] 0.62376050 0.7524790 0.37623950
[38,] 0.59305005 0.8138999 0.40694995
[39,] 0.54534544 0.9093091 0.45465456
[40,] 0.57238705 0.8552259 0.42761295
[41,] 0.61886287 0.7622743 0.38113713
[42,] 0.57986742 0.8402652 0.42013258
[43,] 0.53497807 0.9300439 0.46502193
[44,] 0.48623419 0.9724684 0.51376581
[45,] 0.44130717 0.8826143 0.55869283
[46,] 0.43048665 0.8609733 0.56951335
[47,] 0.45797531 0.9159506 0.54202469
[48,] 0.59308368 0.8138326 0.40691632
[49,] 0.55395292 0.8920942 0.44604708
[50,] 0.50555940 0.9888812 0.49444060
[51,] 0.46902636 0.9380527 0.53097364
[52,] 0.45352326 0.9070465 0.54647674
[53,] 0.41981015 0.8396203 0.58018985
[54,] 0.42624721 0.8524944 0.57375279
[55,] 0.38074419 0.7614884 0.61925581
[56,] 0.34471506 0.6894301 0.65528494
[57,] 0.30422393 0.6084479 0.69577607
[58,] 0.33489255 0.6697851 0.66510745
[59,] 0.34548637 0.6909727 0.65451363
[60,] 0.30602111 0.6120422 0.69397889
[61,] 0.33907245 0.6781449 0.66092755
[62,] 0.37791457 0.7558291 0.62208543
[63,] 0.37892515 0.7578503 0.62107485
[64,] 0.33540682 0.6708136 0.66459318
[65,] 0.30077186 0.6015437 0.69922814
[66,] 0.35630872 0.7126174 0.64369128
[67,] 0.31876745 0.6375349 0.68123255
[68,] 0.27825368 0.5565074 0.72174632
[69,] 0.34216587 0.6843317 0.65783413
[70,] 0.41780735 0.8356147 0.58219265
[71,] 0.40756816 0.8151363 0.59243184
[72,] 0.36294519 0.7258904 0.63705481
[73,] 0.32340506 0.6468101 0.67659494
[74,] 0.31179470 0.6235894 0.68820530
[75,] 0.29887653 0.5977531 0.70112347
[76,] 0.26508287 0.5301657 0.73491713
[77,] 0.23829348 0.4765870 0.76170652
[78,] 0.21300799 0.4260160 0.78699201
[79,] 0.25038402 0.5007680 0.74961598
[80,] 0.21643453 0.4328691 0.78356547
[81,] 0.23062928 0.4612586 0.76937072
[82,] 0.21606807 0.4321361 0.78393193
[83,] 0.20244245 0.4048849 0.79755755
[84,] 0.17223633 0.3444727 0.82776367
[85,] 0.17958214 0.3591643 0.82041786
[86,] 0.17549246 0.3509849 0.82450754
[87,] 0.16028905 0.3205781 0.83971095
[88,] 0.13704865 0.2740973 0.86295135
[89,] 0.12027011 0.2405402 0.87972989
[90,] 0.11916380 0.2383276 0.88083620
[91,] 0.10998703 0.2199741 0.89001297
[92,] 0.09652946 0.1930589 0.90347054
[93,] 0.07908564 0.1581713 0.92091436
[94,] 0.06490921 0.1298184 0.93509079
[95,] 0.07627202 0.1525440 0.92372798
[96,] 0.18270673 0.3654135 0.81729327
[97,] 0.16985914 0.3397183 0.83014086
[98,] 0.19379483 0.3875897 0.80620517
[99,] 0.18004061 0.3600812 0.81995939
[100,] 0.38072434 0.7614487 0.61927566
[101,] 0.45671509 0.9134302 0.54328491
[102,] 0.44020942 0.8804188 0.55979058
[103,] 0.56909554 0.8618089 0.43090446
[104,] 0.52229262 0.9554148 0.47770738
[105,] 0.50771968 0.9845606 0.49228032
[106,] 0.47208170 0.9441634 0.52791830
[107,] 0.44233225 0.8846645 0.55766775
[108,] 0.41830841 0.8366168 0.58169159
[109,] 0.37259855 0.7451971 0.62740145
[110,] 0.33640310 0.6728062 0.66359690
[111,] 0.29197286 0.5839457 0.70802714
[112,] 0.25094407 0.5018881 0.74905593
[113,] 0.21564562 0.4312912 0.78435438
[114,] 0.17844452 0.3568890 0.82155548
[115,] 0.14375868 0.2875174 0.85624132
[116,] 0.15344140 0.3068828 0.84655860
[117,] 0.17079555 0.3415911 0.82920445
[118,] 0.34962855 0.6992571 0.65037145
[119,] 0.32075097 0.6415019 0.67924903
[120,] 0.28400112 0.5680022 0.71599888
[121,] 0.25235948 0.5047190 0.74764052
[122,] 0.29471437 0.5894287 0.70528563
[123,] 0.36584161 0.7316832 0.63415839
[124,] 0.47618639 0.9523728 0.52381361
[125,] 0.42022457 0.8404491 0.57977543
[126,] 0.35813142 0.7162628 0.64186858
[127,] 0.31439193 0.6287839 0.68560807
[128,] 0.25438684 0.5087737 0.74561316
[129,] 0.30357203 0.6071441 0.69642797
[130,] 0.24858865 0.4971773 0.75141135
[131,] 0.47652294 0.9530459 0.52347706
[132,] 0.41498723 0.8299745 0.58501277
[133,] 0.44121221 0.8824244 0.55878779
[134,] 0.70276145 0.5944771 0.29723855
[135,] 0.61325535 0.7734893 0.38674465
[136,] 0.94428886 0.1114223 0.05571114
[137,] 0.92429656 0.1514069 0.07570344
[138,] 0.94648075 0.1070385 0.05351925
[139,] 0.94475297 0.1104941 0.05524703
[140,] 0.88242429 0.2351514 0.11757571
[141,] 0.76936792 0.4612642 0.23063208
> postscript(file="/var/wessaorg/rcomp/tmp/19wpe1352127789.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/26t3j1352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gh0s1352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4dvt91352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/518121352127789.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
6.14966059 4.21219179 -5.42351961 -3.84693437 -1.01753078 2.25653501
7 8 9 10 11 12
4.90916856 -1.08947258 1.34405068 0.22559134 3.61595835 1.24273589
13 14 15 16 17 18
3.15553139 3.00257812 -3.24366174 -1.84782440 1.84136015 0.07802742
19 20 21 22 23 24
2.18375976 -1.89473459 -2.76693872 -2.54343370 2.33253675 0.83790570
25 26 27 28 29 30
3.66985992 6.54755912 0.28477612 -2.07495691 -1.68086525 0.16708643
31 32 33 34 35 36
-1.79579197 -5.97227048 3.39408690 -2.07812518 1.05312176 -0.41005838
37 38 39 40 41 42
-3.22443252 2.11659825 -5.35427969 0.25929606 2.40226639 0.01235697
43 44 45 46 47 48
3.14052248 1.35344541 5.64639747 3.61153421 2.38042603 -1.77048262
49 50 51 52 53 54
-0.48321172 4.41070149 -4.02504557 -1.00784036 -0.82687899 -1.53236138
55 56 57 58 59 60
-0.71187455 2.54330700 4.06459588 -5.82213390 1.63534592 0.06296478
61 62 63 64 65 66
-1.46650918 3.04289652 -0.94428745 -3.25010376 0.50636243 -1.07218798
67 68 69 70 71 72
0.83033778 -3.31778951 3.59036081 -0.78574151 4.07238879 -4.32578351
73 74 75 76 77 78
-3.12021565 0.35815444 1.25282364 4.83585534 1.24998900 0.28051524
79 80 81 82 83 84
-4.97359927 4.82964189 -2.76747286 -0.23415818 -0.89490680 2.68707791
85 86 87 88 89 90
-2.69286270 -1.18970017 1.61623588 -1.78806978 -4.71333612 -0.66181303
91 92 93 94 95 96
-3.70267532 2.37963936 -2.33207559 -0.68282812 -3.74029701 2.90906153
97 98 99 100 101 102
2.20207425 0.56554229 1.81414369 -3.31505882 2.49704982 1.83416113
103 104 105 106 107 108
-1.05565476 -0.39882099 -3.77537718 7.20363787 2.33860790 4.54308621
109 110 111 112 113 114
1.24535614 7.04430753 -5.05657985 -3.12388191 -5.92982087 -1.24186551
115 116 117 118 119 120
2.97059232 1.93939006 -2.67186313 -2.67403350 -1.35865163 2.20016906
121 122 123 124 125 126
-1.52415190 -1.39542504 2.04839810 0.89985326 -0.43930913 -3.24641360
127 128 129 130 131 132
4.32448769 6.79203925 -2.89653035 1.63919503 -2.78767020 -3.89462173
133 134 135 136 137 138
3.38697762 -5.16255418 1.40667633 -0.98735834 -1.54370154 -0.68361827
139 140 141 142 143 144
-3.23427880 1.01941532 -1.93605700 -3.12680832 -5.20469970 -5.34974322
145 146 147 148 149 150
-0.44548431 7.14302358 -1.13553890 1.85605857 -1.84174386 1.73667178
151 152 153 154 155 156
2.42818156 6.59278584 -2.78358194 -2.28710557 0.66577640 0.69306572
157 158 159 160 161 162
2.37963936 -3.58700774 -2.89653035 -0.58719028 -2.49901798 -0.81672054
> postscript(file="/var/wessaorg/rcomp/tmp/687v41352127789.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 6.14966059 NA
1 4.21219179 6.14966059
2 -5.42351961 4.21219179
3 -3.84693437 -5.42351961
4 -1.01753078 -3.84693437
5 2.25653501 -1.01753078
6 4.90916856 2.25653501
7 -1.08947258 4.90916856
8 1.34405068 -1.08947258
9 0.22559134 1.34405068
10 3.61595835 0.22559134
11 1.24273589 3.61595835
12 3.15553139 1.24273589
13 3.00257812 3.15553139
14 -3.24366174 3.00257812
15 -1.84782440 -3.24366174
16 1.84136015 -1.84782440
17 0.07802742 1.84136015
18 2.18375976 0.07802742
19 -1.89473459 2.18375976
20 -2.76693872 -1.89473459
21 -2.54343370 -2.76693872
22 2.33253675 -2.54343370
23 0.83790570 2.33253675
24 3.66985992 0.83790570
25 6.54755912 3.66985992
26 0.28477612 6.54755912
27 -2.07495691 0.28477612
28 -1.68086525 -2.07495691
29 0.16708643 -1.68086525
30 -1.79579197 0.16708643
31 -5.97227048 -1.79579197
32 3.39408690 -5.97227048
33 -2.07812518 3.39408690
34 1.05312176 -2.07812518
35 -0.41005838 1.05312176
36 -3.22443252 -0.41005838
37 2.11659825 -3.22443252
38 -5.35427969 2.11659825
39 0.25929606 -5.35427969
40 2.40226639 0.25929606
41 0.01235697 2.40226639
42 3.14052248 0.01235697
43 1.35344541 3.14052248
44 5.64639747 1.35344541
45 3.61153421 5.64639747
46 2.38042603 3.61153421
47 -1.77048262 2.38042603
48 -0.48321172 -1.77048262
49 4.41070149 -0.48321172
50 -4.02504557 4.41070149
51 -1.00784036 -4.02504557
52 -0.82687899 -1.00784036
53 -1.53236138 -0.82687899
54 -0.71187455 -1.53236138
55 2.54330700 -0.71187455
56 4.06459588 2.54330700
57 -5.82213390 4.06459588
58 1.63534592 -5.82213390
59 0.06296478 1.63534592
60 -1.46650918 0.06296478
61 3.04289652 -1.46650918
62 -0.94428745 3.04289652
63 -3.25010376 -0.94428745
64 0.50636243 -3.25010376
65 -1.07218798 0.50636243
66 0.83033778 -1.07218798
67 -3.31778951 0.83033778
68 3.59036081 -3.31778951
69 -0.78574151 3.59036081
70 4.07238879 -0.78574151
71 -4.32578351 4.07238879
72 -3.12021565 -4.32578351
73 0.35815444 -3.12021565
74 1.25282364 0.35815444
75 4.83585534 1.25282364
76 1.24998900 4.83585534
77 0.28051524 1.24998900
78 -4.97359927 0.28051524
79 4.82964189 -4.97359927
80 -2.76747286 4.82964189
81 -0.23415818 -2.76747286
82 -0.89490680 -0.23415818
83 2.68707791 -0.89490680
84 -2.69286270 2.68707791
85 -1.18970017 -2.69286270
86 1.61623588 -1.18970017
87 -1.78806978 1.61623588
88 -4.71333612 -1.78806978
89 -0.66181303 -4.71333612
90 -3.70267532 -0.66181303
91 2.37963936 -3.70267532
92 -2.33207559 2.37963936
93 -0.68282812 -2.33207559
94 -3.74029701 -0.68282812
95 2.90906153 -3.74029701
96 2.20207425 2.90906153
97 0.56554229 2.20207425
98 1.81414369 0.56554229
99 -3.31505882 1.81414369
100 2.49704982 -3.31505882
101 1.83416113 2.49704982
102 -1.05565476 1.83416113
103 -0.39882099 -1.05565476
104 -3.77537718 -0.39882099
105 7.20363787 -3.77537718
106 2.33860790 7.20363787
107 4.54308621 2.33860790
108 1.24535614 4.54308621
109 7.04430753 1.24535614
110 -5.05657985 7.04430753
111 -3.12388191 -5.05657985
112 -5.92982087 -3.12388191
113 -1.24186551 -5.92982087
114 2.97059232 -1.24186551
115 1.93939006 2.97059232
116 -2.67186313 1.93939006
117 -2.67403350 -2.67186313
118 -1.35865163 -2.67403350
119 2.20016906 -1.35865163
120 -1.52415190 2.20016906
121 -1.39542504 -1.52415190
122 2.04839810 -1.39542504
123 0.89985326 2.04839810
124 -0.43930913 0.89985326
125 -3.24641360 -0.43930913
126 4.32448769 -3.24641360
127 6.79203925 4.32448769
128 -2.89653035 6.79203925
129 1.63919503 -2.89653035
130 -2.78767020 1.63919503
131 -3.89462173 -2.78767020
132 3.38697762 -3.89462173
133 -5.16255418 3.38697762
134 1.40667633 -5.16255418
135 -0.98735834 1.40667633
136 -1.54370154 -0.98735834
137 -0.68361827 -1.54370154
138 -3.23427880 -0.68361827
139 1.01941532 -3.23427880
140 -1.93605700 1.01941532
141 -3.12680832 -1.93605700
142 -5.20469970 -3.12680832
143 -5.34974322 -5.20469970
144 -0.44548431 -5.34974322
145 7.14302358 -0.44548431
146 -1.13553890 7.14302358
147 1.85605857 -1.13553890
148 -1.84174386 1.85605857
149 1.73667178 -1.84174386
150 2.42818156 1.73667178
151 6.59278584 2.42818156
152 -2.78358194 6.59278584
153 -2.28710557 -2.78358194
154 0.66577640 -2.28710557
155 0.69306572 0.66577640
156 2.37963936 0.69306572
157 -3.58700774 2.37963936
158 -2.89653035 -3.58700774
159 -0.58719028 -2.89653035
160 -2.49901798 -0.58719028
161 -0.81672054 -2.49901798
162 NA -0.81672054
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.21219179 6.14966059
[2,] -5.42351961 4.21219179
[3,] -3.84693437 -5.42351961
[4,] -1.01753078 -3.84693437
[5,] 2.25653501 -1.01753078
[6,] 4.90916856 2.25653501
[7,] -1.08947258 4.90916856
[8,] 1.34405068 -1.08947258
[9,] 0.22559134 1.34405068
[10,] 3.61595835 0.22559134
[11,] 1.24273589 3.61595835
[12,] 3.15553139 1.24273589
[13,] 3.00257812 3.15553139
[14,] -3.24366174 3.00257812
[15,] -1.84782440 -3.24366174
[16,] 1.84136015 -1.84782440
[17,] 0.07802742 1.84136015
[18,] 2.18375976 0.07802742
[19,] -1.89473459 2.18375976
[20,] -2.76693872 -1.89473459
[21,] -2.54343370 -2.76693872
[22,] 2.33253675 -2.54343370
[23,] 0.83790570 2.33253675
[24,] 3.66985992 0.83790570
[25,] 6.54755912 3.66985992
[26,] 0.28477612 6.54755912
[27,] -2.07495691 0.28477612
[28,] -1.68086525 -2.07495691
[29,] 0.16708643 -1.68086525
[30,] -1.79579197 0.16708643
[31,] -5.97227048 -1.79579197
[32,] 3.39408690 -5.97227048
[33,] -2.07812518 3.39408690
[34,] 1.05312176 -2.07812518
[35,] -0.41005838 1.05312176
[36,] -3.22443252 -0.41005838
[37,] 2.11659825 -3.22443252
[38,] -5.35427969 2.11659825
[39,] 0.25929606 -5.35427969
[40,] 2.40226639 0.25929606
[41,] 0.01235697 2.40226639
[42,] 3.14052248 0.01235697
[43,] 1.35344541 3.14052248
[44,] 5.64639747 1.35344541
[45,] 3.61153421 5.64639747
[46,] 2.38042603 3.61153421
[47,] -1.77048262 2.38042603
[48,] -0.48321172 -1.77048262
[49,] 4.41070149 -0.48321172
[50,] -4.02504557 4.41070149
[51,] -1.00784036 -4.02504557
[52,] -0.82687899 -1.00784036
[53,] -1.53236138 -0.82687899
[54,] -0.71187455 -1.53236138
[55,] 2.54330700 -0.71187455
[56,] 4.06459588 2.54330700
[57,] -5.82213390 4.06459588
[58,] 1.63534592 -5.82213390
[59,] 0.06296478 1.63534592
[60,] -1.46650918 0.06296478
[61,] 3.04289652 -1.46650918
[62,] -0.94428745 3.04289652
[63,] -3.25010376 -0.94428745
[64,] 0.50636243 -3.25010376
[65,] -1.07218798 0.50636243
[66,] 0.83033778 -1.07218798
[67,] -3.31778951 0.83033778
[68,] 3.59036081 -3.31778951
[69,] -0.78574151 3.59036081
[70,] 4.07238879 -0.78574151
[71,] -4.32578351 4.07238879
[72,] -3.12021565 -4.32578351
[73,] 0.35815444 -3.12021565
[74,] 1.25282364 0.35815444
[75,] 4.83585534 1.25282364
[76,] 1.24998900 4.83585534
[77,] 0.28051524 1.24998900
[78,] -4.97359927 0.28051524
[79,] 4.82964189 -4.97359927
[80,] -2.76747286 4.82964189
[81,] -0.23415818 -2.76747286
[82,] -0.89490680 -0.23415818
[83,] 2.68707791 -0.89490680
[84,] -2.69286270 2.68707791
[85,] -1.18970017 -2.69286270
[86,] 1.61623588 -1.18970017
[87,] -1.78806978 1.61623588
[88,] -4.71333612 -1.78806978
[89,] -0.66181303 -4.71333612
[90,] -3.70267532 -0.66181303
[91,] 2.37963936 -3.70267532
[92,] -2.33207559 2.37963936
[93,] -0.68282812 -2.33207559
[94,] -3.74029701 -0.68282812
[95,] 2.90906153 -3.74029701
[96,] 2.20207425 2.90906153
[97,] 0.56554229 2.20207425
[98,] 1.81414369 0.56554229
[99,] -3.31505882 1.81414369
[100,] 2.49704982 -3.31505882
[101,] 1.83416113 2.49704982
[102,] -1.05565476 1.83416113
[103,] -0.39882099 -1.05565476
[104,] -3.77537718 -0.39882099
[105,] 7.20363787 -3.77537718
[106,] 2.33860790 7.20363787
[107,] 4.54308621 2.33860790
[108,] 1.24535614 4.54308621
[109,] 7.04430753 1.24535614
[110,] -5.05657985 7.04430753
[111,] -3.12388191 -5.05657985
[112,] -5.92982087 -3.12388191
[113,] -1.24186551 -5.92982087
[114,] 2.97059232 -1.24186551
[115,] 1.93939006 2.97059232
[116,] -2.67186313 1.93939006
[117,] -2.67403350 -2.67186313
[118,] -1.35865163 -2.67403350
[119,] 2.20016906 -1.35865163
[120,] -1.52415190 2.20016906
[121,] -1.39542504 -1.52415190
[122,] 2.04839810 -1.39542504
[123,] 0.89985326 2.04839810
[124,] -0.43930913 0.89985326
[125,] -3.24641360 -0.43930913
[126,] 4.32448769 -3.24641360
[127,] 6.79203925 4.32448769
[128,] -2.89653035 6.79203925
[129,] 1.63919503 -2.89653035
[130,] -2.78767020 1.63919503
[131,] -3.89462173 -2.78767020
[132,] 3.38697762 -3.89462173
[133,] -5.16255418 3.38697762
[134,] 1.40667633 -5.16255418
[135,] -0.98735834 1.40667633
[136,] -1.54370154 -0.98735834
[137,] -0.68361827 -1.54370154
[138,] -3.23427880 -0.68361827
[139,] 1.01941532 -3.23427880
[140,] -1.93605700 1.01941532
[141,] -3.12680832 -1.93605700
[142,] -5.20469970 -3.12680832
[143,] -5.34974322 -5.20469970
[144,] -0.44548431 -5.34974322
[145,] 7.14302358 -0.44548431
[146,] -1.13553890 7.14302358
[147,] 1.85605857 -1.13553890
[148,] -1.84174386 1.85605857
[149,] 1.73667178 -1.84174386
[150,] 2.42818156 1.73667178
[151,] 6.59278584 2.42818156
[152,] -2.78358194 6.59278584
[153,] -2.28710557 -2.78358194
[154,] 0.66577640 -2.28710557
[155,] 0.69306572 0.66577640
[156,] 2.37963936 0.69306572
[157,] -3.58700774 2.37963936
[158,] -2.89653035 -3.58700774
[159,] -0.58719028 -2.89653035
[160,] -2.49901798 -0.58719028
[161,] -0.81672054 -2.49901798
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.21219179 6.14966059
2 -5.42351961 4.21219179
3 -3.84693437 -5.42351961
4 -1.01753078 -3.84693437
5 2.25653501 -1.01753078
6 4.90916856 2.25653501
7 -1.08947258 4.90916856
8 1.34405068 -1.08947258
9 0.22559134 1.34405068
10 3.61595835 0.22559134
11 1.24273589 3.61595835
12 3.15553139 1.24273589
13 3.00257812 3.15553139
14 -3.24366174 3.00257812
15 -1.84782440 -3.24366174
16 1.84136015 -1.84782440
17 0.07802742 1.84136015
18 2.18375976 0.07802742
19 -1.89473459 2.18375976
20 -2.76693872 -1.89473459
21 -2.54343370 -2.76693872
22 2.33253675 -2.54343370
23 0.83790570 2.33253675
24 3.66985992 0.83790570
25 6.54755912 3.66985992
26 0.28477612 6.54755912
27 -2.07495691 0.28477612
28 -1.68086525 -2.07495691
29 0.16708643 -1.68086525
30 -1.79579197 0.16708643
31 -5.97227048 -1.79579197
32 3.39408690 -5.97227048
33 -2.07812518 3.39408690
34 1.05312176 -2.07812518
35 -0.41005838 1.05312176
36 -3.22443252 -0.41005838
37 2.11659825 -3.22443252
38 -5.35427969 2.11659825
39 0.25929606 -5.35427969
40 2.40226639 0.25929606
41 0.01235697 2.40226639
42 3.14052248 0.01235697
43 1.35344541 3.14052248
44 5.64639747 1.35344541
45 3.61153421 5.64639747
46 2.38042603 3.61153421
47 -1.77048262 2.38042603
48 -0.48321172 -1.77048262
49 4.41070149 -0.48321172
50 -4.02504557 4.41070149
51 -1.00784036 -4.02504557
52 -0.82687899 -1.00784036
53 -1.53236138 -0.82687899
54 -0.71187455 -1.53236138
55 2.54330700 -0.71187455
56 4.06459588 2.54330700
57 -5.82213390 4.06459588
58 1.63534592 -5.82213390
59 0.06296478 1.63534592
60 -1.46650918 0.06296478
61 3.04289652 -1.46650918
62 -0.94428745 3.04289652
63 -3.25010376 -0.94428745
64 0.50636243 -3.25010376
65 -1.07218798 0.50636243
66 0.83033778 -1.07218798
67 -3.31778951 0.83033778
68 3.59036081 -3.31778951
69 -0.78574151 3.59036081
70 4.07238879 -0.78574151
71 -4.32578351 4.07238879
72 -3.12021565 -4.32578351
73 0.35815444 -3.12021565
74 1.25282364 0.35815444
75 4.83585534 1.25282364
76 1.24998900 4.83585534
77 0.28051524 1.24998900
78 -4.97359927 0.28051524
79 4.82964189 -4.97359927
80 -2.76747286 4.82964189
81 -0.23415818 -2.76747286
82 -0.89490680 -0.23415818
83 2.68707791 -0.89490680
84 -2.69286270 2.68707791
85 -1.18970017 -2.69286270
86 1.61623588 -1.18970017
87 -1.78806978 1.61623588
88 -4.71333612 -1.78806978
89 -0.66181303 -4.71333612
90 -3.70267532 -0.66181303
91 2.37963936 -3.70267532
92 -2.33207559 2.37963936
93 -0.68282812 -2.33207559
94 -3.74029701 -0.68282812
95 2.90906153 -3.74029701
96 2.20207425 2.90906153
97 0.56554229 2.20207425
98 1.81414369 0.56554229
99 -3.31505882 1.81414369
100 2.49704982 -3.31505882
101 1.83416113 2.49704982
102 -1.05565476 1.83416113
103 -0.39882099 -1.05565476
104 -3.77537718 -0.39882099
105 7.20363787 -3.77537718
106 2.33860790 7.20363787
107 4.54308621 2.33860790
108 1.24535614 4.54308621
109 7.04430753 1.24535614
110 -5.05657985 7.04430753
111 -3.12388191 -5.05657985
112 -5.92982087 -3.12388191
113 -1.24186551 -5.92982087
114 2.97059232 -1.24186551
115 1.93939006 2.97059232
116 -2.67186313 1.93939006
117 -2.67403350 -2.67186313
118 -1.35865163 -2.67403350
119 2.20016906 -1.35865163
120 -1.52415190 2.20016906
121 -1.39542504 -1.52415190
122 2.04839810 -1.39542504
123 0.89985326 2.04839810
124 -0.43930913 0.89985326
125 -3.24641360 -0.43930913
126 4.32448769 -3.24641360
127 6.79203925 4.32448769
128 -2.89653035 6.79203925
129 1.63919503 -2.89653035
130 -2.78767020 1.63919503
131 -3.89462173 -2.78767020
132 3.38697762 -3.89462173
133 -5.16255418 3.38697762
134 1.40667633 -5.16255418
135 -0.98735834 1.40667633
136 -1.54370154 -0.98735834
137 -0.68361827 -1.54370154
138 -3.23427880 -0.68361827
139 1.01941532 -3.23427880
140 -1.93605700 1.01941532
141 -3.12680832 -1.93605700
142 -5.20469970 -3.12680832
143 -5.34974322 -5.20469970
144 -0.44548431 -5.34974322
145 7.14302358 -0.44548431
146 -1.13553890 7.14302358
147 1.85605857 -1.13553890
148 -1.84174386 1.85605857
149 1.73667178 -1.84174386
150 2.42818156 1.73667178
151 6.59278584 2.42818156
152 -2.78358194 6.59278584
153 -2.28710557 -2.78358194
154 0.66577640 -2.28710557
155 0.69306572 0.66577640
156 2.37963936 0.69306572
157 -3.58700774 2.37963936
158 -2.89653035 -3.58700774
159 -0.58719028 -2.89653035
160 -2.49901798 -0.58719028
161 -0.81672054 -2.49901798
> 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/wessaorg/rcomp/tmp/70ww41352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8l22h1352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xizd1352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/102v3c1352127789.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11jc031352127789.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/wessaorg/rcomp/tmp/12vubu1352127790.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/wessaorg/rcomp/tmp/1352t21352127790.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/wessaorg/rcomp/tmp/14r7d01352127790.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/wessaorg/rcomp/tmp/15sayp1352127790.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/wessaorg/rcomp/tmp/16s16g1352127790.tab")
+ }
>
> try(system("convert tmp/19wpe1352127789.ps tmp/19wpe1352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/26t3j1352127789.ps tmp/26t3j1352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gh0s1352127789.ps tmp/3gh0s1352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dvt91352127789.ps tmp/4dvt91352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/518121352127789.ps tmp/518121352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/687v41352127789.ps tmp/687v41352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/70ww41352127789.ps tmp/70ww41352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l22h1352127789.ps tmp/8l22h1352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xizd1352127789.ps tmp/9xizd1352127789.png",intern=TRUE))
character(0)
> try(system("convert tmp/102v3c1352127789.ps tmp/102v3c1352127789.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.168 0.902 9.062