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(1
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('number'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final'
+ ,'Connected'
+ ,'Separate')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('number','Learning','Software','Happiness','Depression','Belonging','Belonging_Final','Connected','Separate'),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 = '2'
> 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
Learning number Software Happiness Depression Belonging Belonging_Final
1 13 1 12 14 12 53 32
2 16 2 11 18 11 86 51
3 19 3 15 11 14 66 42
4 15 4 6 12 12 67 41
5 14 5 13 16 21 76 46
6 13 6 10 18 12 78 47
7 19 7 12 14 22 53 37
8 15 8 14 14 11 80 49
9 14 9 12 15 10 74 45
10 15 10 6 15 13 76 47
11 16 11 10 17 10 79 49
12 16 12 12 19 8 54 33
13 16 13 12 10 15 67 42
14 16 14 11 16 14 54 33
15 17 15 15 18 10 87 53
16 15 16 12 14 14 58 36
17 15 17 10 14 14 75 45
18 20 18 12 17 11 88 54
19 18 19 11 14 10 64 41
20 16 20 12 16 13 57 36
21 16 21 11 18 7 66 41
22 16 22 12 11 14 68 44
23 19 23 13 14 12 54 33
24 16 24 11 12 14 56 37
25 17 25 9 17 11 86 52
26 17 26 13 9 9 80 47
27 16 27 10 16 11 76 43
28 15 28 14 14 15 69 44
29 16 29 12 15 14 78 45
30 14 30 10 11 13 67 44
31 15 31 12 16 9 80 49
32 12 32 8 13 15 54 33
33 14 33 10 17 10 71 43
34 16 34 12 15 11 84 54
35 14 35 12 14 13 74 42
36 7 36 7 16 8 71 44
37 10 37 6 9 20 63 37
38 14 38 12 15 12 71 43
39 16 39 10 17 10 76 46
40 16 40 10 13 10 69 42
41 16 41 10 15 9 74 45
42 14 42 12 16 14 75 44
43 20 43 15 16 8 54 33
44 14 44 10 12 14 52 31
45 14 45 10 12 11 69 42
46 11 46 12 11 13 68 40
47 14 47 13 15 9 65 43
48 15 48 11 15 11 75 46
49 16 49 11 17 15 74 42
50 14 50 12 13 11 75 45
51 16 51 14 16 10 72 44
52 14 52 10 14 14 67 40
53 12 53 12 11 18 63 37
54 16 54 13 12 14 62 46
55 9 55 5 12 11 63 36
56 14 56 6 15 12 76 47
57 16 57 12 16 13 74 45
58 16 58 12 15 9 67 42
59 15 59 11 12 10 73 43
60 16 60 10 12 15 70 43
61 12 61 7 8 20 53 32
62 16 62 12 13 12 77 45
63 16 63 14 11 12 77 45
64 14 64 11 14 14 52 31
65 16 65 12 15 13 54 33
66 17 66 13 10 11 80 49
67 18 67 14 11 17 66 42
68 18 68 11 12 12 73 41
69 12 69 12 15 13 63 38
70 16 70 12 15 14 69 42
71 10 71 8 14 13 67 44
72 14 72 11 16 15 54 33
73 18 73 14 15 13 81 48
74 18 74 14 15 10 69 40
75 16 75 12 13 11 84 50
76 17 76 9 12 19 80 49
77 16 77 13 17 13 70 43
78 16 78 11 13 17 69 44
79 13 79 12 15 13 77 47
80 16 80 12 13 9 54 33
81 16 81 12 15 11 79 46
82 20 82 12 16 10 30 0
83 16 83 12 15 9 71 45
84 15 84 12 16 12 73 43
85 15 85 11 15 12 72 44
86 16 86 10 14 13 77 47
87 14 87 9 15 13 75 45
88 16 88 12 14 12 69 42
89 16 89 12 13 15 54 33
90 15 90 12 7 22 70 43
91 12 91 9 17 13 73 46
92 17 92 15 13 15 54 33
93 16 93 12 15 13 77 46
94 15 94 12 14 15 82 48
95 13 95 12 13 10 80 47
96 16 96 10 16 11 80 47
97 16 97 13 12 16 69 43
98 16 98 9 14 11 78 46
99 16 99 12 17 11 81 48
100 14 100 10 15 10 76 46
101 16 101 14 17 10 76 45
102 16 102 11 12 16 73 45
103 20 103 15 16 12 85 52
104 15 104 11 11 11 66 42
105 16 105 11 15 16 79 47
106 13 106 12 9 19 68 41
107 17 107 12 16 11 76 47
108 16 108 12 15 16 71 43
109 16 109 11 10 15 54 33
110 12 110 7 10 24 46 30
111 16 111 12 15 14 82 49
112 16 112 14 11 15 74 44
113 17 113 11 13 11 88 55
114 13 114 11 14 15 38 11
115 12 115 10 18 12 76 47
116 18 116 13 16 10 86 53
117 14 117 13 14 14 54 33
118 14 118 8 14 13 70 44
119 13 119 11 14 9 69 42
120 16 120 12 14 15 90 55
121 13 121 11 12 15 54 33
122 16 122 13 14 14 76 46
123 13 123 12 15 11 89 54
124 16 124 14 15 8 76 47
125 15 125 13 15 11 73 45
126 16 126 15 13 11 79 47
127 15 127 10 17 8 90 55
128 17 128 11 17 10 74 44
129 15 129 9 19 11 81 53
130 12 130 11 15 13 72 44
131 16 131 10 13 11 71 42
132 10 132 11 9 20 66 40
133 16 133 8 15 10 77 46
134 12 134 11 15 15 65 40
135 14 135 12 15 12 74 46
136 15 136 12 16 14 82 53
137 13 137 9 11 23 54 33
138 15 138 11 14 14 63 42
139 11 139 10 11 16 54 35
140 12 140 8 15 11 64 40
141 8 141 9 13 12 69 41
142 16 142 8 15 10 54 33
143 15 143 9 16 14 84 51
144 17 144 15 14 12 86 53
145 16 145 11 15 12 77 46
146 10 146 8 16 11 89 55
147 18 147 13 16 12 76 47
148 13 148 12 11 13 60 38
149 16 149 12 12 11 75 46
150 13 150 9 9 19 73 46
151 10 151 7 16 12 85 53
152 15 152 13 13 17 79 47
153 16 153 9 16 9 71 41
154 16 154 6 12 12 72 44
155 14 155 8 9 19 69 43
156 10 156 8 13 18 78 51
157 17 157 15 13 15 54 33
158 13 158 6 14 14 69 43
159 15 159 9 19 11 81 53
160 16 160 11 13 9 84 51
161 12 161 8 12 18 84 50
162 13 162 8 13 16 69 46
Connected Separate
1 41 38
2 39 32
3 30 35
4 31 33
5 34 37
6 35 29
7 39 31
8 34 36
9 36 35
10 37 38
11 38 31
12 36 34
13 38 35
14 39 38
15 33 37
16 32 33
17 36 32
18 38 38
19 39 38
20 32 32
21 32 33
22 31 31
23 39 38
24 37 39
25 39 32
26 41 32
27 36 35
28 33 37
29 33 33
30 34 33
31 31 28
32 27 32
33 37 31
34 34 37
35 34 30
36 32 33
37 29 31
38 36 33
39 29 31
40 35 33
41 37 32
42 34 33
43 38 32
44 35 33
45 38 28
46 37 35
47 38 39
48 33 34
49 36 38
50 38 32
51 32 38
52 32 30
53 32 33
54 34 38
55 32 32
56 37 32
57 39 34
58 29 34
59 37 36
60 35 34
61 30 28
62 38 34
63 34 35
64 31 35
65 34 31
66 35 37
67 36 35
68 30 27
69 39 40
70 35 37
71 38 36
72 31 38
73 34 39
74 38 41
75 34 27
76 39 30
77 37 37
78 34 31
79 28 31
80 37 27
81 33 36
82 37 38
83 35 37
84 37 33
85 32 34
86 33 31
87 38 39
88 33 34
89 29 32
90 33 33
91 31 36
92 36 32
93 35 41
94 32 28
95 29 30
96 39 36
97 37 35
98 35 31
99 37 34
100 32 36
101 38 36
102 37 35
103 36 37
104 32 28
105 33 39
106 40 32
107 38 35
108 41 39
109 36 35
110 43 42
111 30 34
112 31 33
113 32 41
114 32 33
115 37 34
116 37 32
117 33 40
118 34 40
119 33 35
120 38 36
121 33 37
122 31 27
123 38 39
124 37 38
125 33 31
126 31 33
127 39 32
128 44 39
129 33 36
130 35 33
131 32 33
132 28 32
133 40 37
134 27 30
135 37 38
136 32 29
137 28 22
138 34 35
139 30 35
140 35 34
141 31 35
142 32 34
143 30 34
144 30 35
145 31 23
146 40 31
147 32 27
148 36 36
149 32 31
150 35 32
151 38 39
152 42 37
153 34 38
154 35 39
155 35 34
156 33 31
157 36 32
158 32 37
159 33 36
160 34 32
161 32 35
162 34 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) number Software Happiness
5.787328 -0.004224 0.530262 0.052244
Depression Belonging Belonging_Final Connected
-0.063589 0.042180 -0.056060 0.105729
Separate
-0.014531
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1062 -1.1796 0.2302 1.1291 4.1137
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.787328 2.599908 2.226 0.0275 *
number -0.004224 0.003255 -1.298 0.1963
Software 0.530262 0.069449 7.635 2.27e-12 ***
Happiness 0.052244 0.076429 0.684 0.4953
Depression -0.063589 0.056518 -1.125 0.2623
Belonging 0.042180 0.044706 0.943 0.3469
Belonging_Final -0.056060 0.063884 -0.878 0.3816
Connected 0.105729 0.047224 2.239 0.0266 *
Separate -0.014531 0.044961 -0.323 0.7470
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.846 on 153 degrees of freedom
Multiple R-squared: 0.3637, Adjusted R-squared: 0.3304
F-statistic: 10.93 on 8 and 153 DF, p-value: 3.96e-12
> 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,] 0.37252689 0.74505378 0.6274731
[2,] 0.59455334 0.81089331 0.4054467
[3,] 0.58579025 0.82841950 0.4142097
[4,] 0.46562012 0.93124025 0.5343799
[5,] 0.36316470 0.72632941 0.6368353
[6,] 0.29795101 0.59590203 0.7020490
[7,] 0.45680903 0.91361806 0.5431910
[8,] 0.37731035 0.75462070 0.6226897
[9,] 0.29792066 0.59584133 0.7020793
[10,] 0.23759449 0.47518898 0.7624055
[11,] 0.23396705 0.46793410 0.7660329
[12,] 0.35485203 0.70970407 0.6451480
[13,] 0.42918230 0.85836460 0.5708177
[14,] 0.37826802 0.75653604 0.6217320
[15,] 0.31506904 0.63013808 0.6849310
[16,] 0.28982192 0.57964384 0.7101781
[17,] 0.39167262 0.78334525 0.6083274
[18,] 0.33742310 0.67484619 0.6625769
[19,] 0.44704706 0.89409411 0.5529529
[20,] 0.39830229 0.79660459 0.6016977
[21,] 0.36329444 0.72658888 0.6367056
[22,] 0.35324651 0.70649302 0.6467535
[23,] 0.32369414 0.64738829 0.6763059
[24,] 0.27940010 0.55880020 0.7205999
[25,] 0.84060385 0.31879231 0.1593962
[26,] 0.81159446 0.37681107 0.1884055
[27,] 0.79294662 0.41410676 0.2070534
[28,] 0.82659767 0.34680466 0.1734023
[29,] 0.81363379 0.37273242 0.1863662
[30,] 0.78445538 0.43108925 0.2155446
[31,] 0.75630106 0.48739788 0.2436989
[32,] 0.78703462 0.42593075 0.2129654
[33,] 0.74536748 0.50926505 0.2546325
[34,] 0.71543669 0.56912663 0.2845633
[35,] 0.86312766 0.27374468 0.1368723
[36,] 0.88890011 0.22219978 0.1110999
[37,] 0.86582543 0.26834913 0.1341746
[38,] 0.86267661 0.27464677 0.1373234
[39,] 0.85592157 0.28815685 0.1440784
[40,] 0.82764126 0.34471748 0.1723587
[41,] 0.79675090 0.40649821 0.2032491
[42,] 0.80936523 0.38126954 0.1906348
[43,] 0.77924745 0.44150510 0.2207525
[44,] 0.79888147 0.40223706 0.2011185
[45,] 0.77810054 0.44379892 0.2218995
[46,] 0.74052649 0.51894702 0.2594735
[47,] 0.72595473 0.54809054 0.2740453
[48,] 0.69536556 0.60926887 0.3046344
[49,] 0.70173103 0.59653794 0.2982690
[50,] 0.66724673 0.66550654 0.3327533
[51,] 0.63128013 0.73743975 0.3687199
[52,] 0.59856454 0.80287091 0.4014355
[53,] 0.55481437 0.89037126 0.4451856
[54,] 0.51797500 0.96405001 0.4820250
[55,] 0.49154662 0.98309324 0.5084534
[56,] 0.48652388 0.97304777 0.5134761
[57,] 0.62930541 0.74138919 0.3706946
[58,] 0.74015540 0.51968921 0.2598446
[59,] 0.70710079 0.58579842 0.2928992
[60,] 0.81306660 0.37386680 0.1869334
[61,] 0.78274138 0.43451723 0.2172586
[62,] 0.77673477 0.44653046 0.2232652
[63,] 0.76033478 0.47933045 0.2396652
[64,] 0.72272456 0.55455089 0.2772754
[65,] 0.76292163 0.47415674 0.2370784
[66,] 0.72573388 0.54853224 0.2742661
[67,] 0.70620757 0.58758487 0.2937924
[68,] 0.71396963 0.57206073 0.2860304
[69,] 0.67301308 0.65397384 0.3269869
[70,] 0.63602282 0.72795436 0.3639772
[71,] 0.76161163 0.47677675 0.2383884
[72,] 0.72672627 0.54654747 0.2732737
[73,] 0.69763862 0.60472275 0.3023614
[74,] 0.65672616 0.68654767 0.3432738
[75,] 0.64377225 0.71245550 0.3562277
[76,] 0.59979681 0.80040637 0.4002032
[77,] 0.55787669 0.88424662 0.4421233
[78,] 0.53543481 0.92913038 0.4645652
[79,] 0.49699174 0.99398347 0.5030083
[80,] 0.48411918 0.96823835 0.5158808
[81,] 0.44209327 0.88418655 0.5579067
[82,] 0.39912304 0.79824607 0.6008770
[83,] 0.35552304 0.71104607 0.6444770
[84,] 0.38190309 0.76380618 0.6180969
[85,] 0.34436742 0.68873483 0.6556326
[86,] 0.30295213 0.60590426 0.6970479
[87,] 0.29547242 0.59094484 0.7045276
[88,] 0.25502716 0.51005432 0.7449728
[89,] 0.22227707 0.44455414 0.7777229
[90,] 0.20041828 0.40083655 0.7995817
[91,] 0.18203992 0.36407985 0.8179601
[92,] 0.22263820 0.44527639 0.7773618
[93,] 0.18872811 0.37745622 0.8112719
[94,] 0.18150386 0.36300773 0.8184961
[95,] 0.19239290 0.38478580 0.8076071
[96,] 0.17268789 0.34537577 0.8273121
[97,] 0.15262964 0.30525929 0.8473704
[98,] 0.14862284 0.29724567 0.8513772
[99,] 0.14562268 0.29124537 0.8543773
[100,] 0.13201460 0.26402920 0.8679854
[101,] 0.11277409 0.22554819 0.8872259
[102,] 0.15178016 0.30356033 0.8482198
[103,] 0.14055447 0.28110893 0.8594455
[104,] 0.15820988 0.31641977 0.8417901
[105,] 0.16640335 0.33280670 0.8335966
[106,] 0.14180448 0.28360897 0.8581955
[107,] 0.14344368 0.28688736 0.8565563
[108,] 0.13083671 0.26167341 0.8691633
[109,] 0.14659080 0.29318160 0.8534092
[110,] 0.12013360 0.24026720 0.8798664
[111,] 0.10588923 0.21177845 0.8941108
[112,] 0.10223417 0.20446834 0.8977658
[113,] 0.07995871 0.15991742 0.9200413
[114,] 0.06159197 0.12318394 0.9384080
[115,] 0.04657756 0.09315512 0.9534224
[116,] 0.03406131 0.06812262 0.9659387
[117,] 0.03239001 0.06478002 0.9676100
[118,] 0.03555068 0.07110136 0.9644493
[119,] 0.03527710 0.07055419 0.9647229
[120,] 0.03839262 0.07678524 0.9616074
[121,] 0.04082878 0.08165756 0.9591712
[122,] 0.08301303 0.16602607 0.9169870
[123,] 0.08634322 0.17268644 0.9136568
[124,] 0.06667566 0.13335132 0.9333243
[125,] 0.05456748 0.10913497 0.9454325
[126,] 0.03958577 0.07917154 0.9604142
[127,] 0.04726413 0.09452827 0.9527359
[128,] 0.03978563 0.07957127 0.9602144
[129,] 0.02662964 0.05325928 0.9733704
[130,] 0.54212318 0.91575363 0.4578768
[131,] 0.48190379 0.96380757 0.5180962
[132,] 0.40895642 0.81791285 0.5910436
[133,] 0.31988603 0.63977207 0.6801140
[134,] 0.23981511 0.47963023 0.7601849
[135,] 0.28481574 0.56963148 0.7151843
[136,] 0.31599543 0.63199086 0.6840046
[137,] 0.60346270 0.79307460 0.3965373
[138,] 0.61079470 0.77841060 0.3892053
[139,] 0.60896274 0.78207452 0.3910373
> postscript(file="/var/fisher/rcomp/tmp/1ey4j1352143006.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/2ydci1352143006.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/30vqt1352143006.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/42xor1352143006.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5t2ls1352143006.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.33889555 -0.27948382 2.49436721 2.85850083 -1.84416460 -2.17621733
7 8 9 10 11 12
3.71237887 -1.90822488 -2.15646080 2.18573021 0.55178531 -0.32360219
13 14 15 16 17 18
0.35521652 0.49430999 -0.63223768 -0.25610725 0.15868319 3.58682109
19 20 21 22 23 24
2.39224819 0.62036172 0.58404381 1.02932084 2.44911190 1.11139671
25 26 27 28 29 30
1.98650021 -0.07823587 0.79496435 -1.26545182 0.30178395 -0.18589518
31 32 33 34 35 36
-0.78127171 -0.43698691 -1.24847892 0.33599800 -1.83300119 -6.10618556
37 38 39 40 41 42
-1.20978741 -1.92142681 1.57998091 1.25888387 0.82632537 -1.73079621
43 44 45 46 47 48
2.13276941 -0.31722968 -0.99400587 -4.73337895 -2.47563205 -0.08133125
49 50 51 52 53 54
0.63163741 -2.11242593 -0.59700653 -0.24248214 -2.84356542 0.73171645
55 56 57 58 59 60
-2.69123700 1.22926510 -0.14689712 0.83958216 -0.41939898 1.74196643
61 62 63 64 65 66
0.40574565 -0.05344420 -0.56781041 -0.41551792 0.59506080 0.98481452
67 68 69 70 71 72
1.85136730 3.24299847 -3.88522271 0.53307623 -3.48823380 -0.35126839
73 74 75 76 77 78
1.38663238 0.86390962 0.24412275 3.02769510 -0.33327071 1.52304809
79 80 81 82 83 84
-1.89671473 0.13324301 0.38814705 3.37071559 0.35386728 -0.96944793
85 86 87 88 89 90
0.25869841 1.71697757 -0.24093659 0.70204134 1.47128156 0.71143281
91 92 93 94 95 96
-1.49159800 0.15306340 0.51157142 -0.27527864 -2.16220675 0.83929689
97 98 99 100 101 102
0.21631403 1.86105416 -0.06452277 -0.30239033 -1.21413758 1.24136167
103 104 105 106 107 108
2.68226391 0.53811292 1.43738695 -2.29863028 1.08515388 0.18716057
109 110 111 112 113 114
1.54624791 -0.22530751 1.03540462 0.18854267 2.46137969 -1.80620293
115 116 117 118 119 120
-2.77022854 1.50602074 -1.36320413 1.06480305 -1.81298311 0.37140886
121 122 123 124 125 126
-1.16130010 0.48130118 -2.89280485 -0.89276097 -0.83189140 -0.68414170
127 128 129 130 131 132
-0.30420370 0.92821990 1.28078123 -2.81934446 1.93969791 -3.29790373
133 134 135 136 137 138
2.02405060 -1.80201498 -1.50311608 0.02889162 0.83844335 0.73256847
139 140 141 142 143 144
-2.03892537 -1.18576630 -5.26111899 3.10565477 1.73688223 0.57913361
145 146 147 148 149 150
1.35925587 -3.99849049 2.30557109 -1.95693954 1.03391138 0.07606770
151 152 153 154 155 156
-2.99921314 -1.23715071 2.08412056 4.11367613 1.65705095 -2.37455825
157 158 159 160 161 162
0.42763287 1.51186660 1.40750560 1.13497586 -0.44647803 0.58985100
> postscript(file="/var/fisher/rcomp/tmp/6mdks1352143006.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 -3.33889555 NA
1 -0.27948382 -3.33889555
2 2.49436721 -0.27948382
3 2.85850083 2.49436721
4 -1.84416460 2.85850083
5 -2.17621733 -1.84416460
6 3.71237887 -2.17621733
7 -1.90822488 3.71237887
8 -2.15646080 -1.90822488
9 2.18573021 -2.15646080
10 0.55178531 2.18573021
11 -0.32360219 0.55178531
12 0.35521652 -0.32360219
13 0.49430999 0.35521652
14 -0.63223768 0.49430999
15 -0.25610725 -0.63223768
16 0.15868319 -0.25610725
17 3.58682109 0.15868319
18 2.39224819 3.58682109
19 0.62036172 2.39224819
20 0.58404381 0.62036172
21 1.02932084 0.58404381
22 2.44911190 1.02932084
23 1.11139671 2.44911190
24 1.98650021 1.11139671
25 -0.07823587 1.98650021
26 0.79496435 -0.07823587
27 -1.26545182 0.79496435
28 0.30178395 -1.26545182
29 -0.18589518 0.30178395
30 -0.78127171 -0.18589518
31 -0.43698691 -0.78127171
32 -1.24847892 -0.43698691
33 0.33599800 -1.24847892
34 -1.83300119 0.33599800
35 -6.10618556 -1.83300119
36 -1.20978741 -6.10618556
37 -1.92142681 -1.20978741
38 1.57998091 -1.92142681
39 1.25888387 1.57998091
40 0.82632537 1.25888387
41 -1.73079621 0.82632537
42 2.13276941 -1.73079621
43 -0.31722968 2.13276941
44 -0.99400587 -0.31722968
45 -4.73337895 -0.99400587
46 -2.47563205 -4.73337895
47 -0.08133125 -2.47563205
48 0.63163741 -0.08133125
49 -2.11242593 0.63163741
50 -0.59700653 -2.11242593
51 -0.24248214 -0.59700653
52 -2.84356542 -0.24248214
53 0.73171645 -2.84356542
54 -2.69123700 0.73171645
55 1.22926510 -2.69123700
56 -0.14689712 1.22926510
57 0.83958216 -0.14689712
58 -0.41939898 0.83958216
59 1.74196643 -0.41939898
60 0.40574565 1.74196643
61 -0.05344420 0.40574565
62 -0.56781041 -0.05344420
63 -0.41551792 -0.56781041
64 0.59506080 -0.41551792
65 0.98481452 0.59506080
66 1.85136730 0.98481452
67 3.24299847 1.85136730
68 -3.88522271 3.24299847
69 0.53307623 -3.88522271
70 -3.48823380 0.53307623
71 -0.35126839 -3.48823380
72 1.38663238 -0.35126839
73 0.86390962 1.38663238
74 0.24412275 0.86390962
75 3.02769510 0.24412275
76 -0.33327071 3.02769510
77 1.52304809 -0.33327071
78 -1.89671473 1.52304809
79 0.13324301 -1.89671473
80 0.38814705 0.13324301
81 3.37071559 0.38814705
82 0.35386728 3.37071559
83 -0.96944793 0.35386728
84 0.25869841 -0.96944793
85 1.71697757 0.25869841
86 -0.24093659 1.71697757
87 0.70204134 -0.24093659
88 1.47128156 0.70204134
89 0.71143281 1.47128156
90 -1.49159800 0.71143281
91 0.15306340 -1.49159800
92 0.51157142 0.15306340
93 -0.27527864 0.51157142
94 -2.16220675 -0.27527864
95 0.83929689 -2.16220675
96 0.21631403 0.83929689
97 1.86105416 0.21631403
98 -0.06452277 1.86105416
99 -0.30239033 -0.06452277
100 -1.21413758 -0.30239033
101 1.24136167 -1.21413758
102 2.68226391 1.24136167
103 0.53811292 2.68226391
104 1.43738695 0.53811292
105 -2.29863028 1.43738695
106 1.08515388 -2.29863028
107 0.18716057 1.08515388
108 1.54624791 0.18716057
109 -0.22530751 1.54624791
110 1.03540462 -0.22530751
111 0.18854267 1.03540462
112 2.46137969 0.18854267
113 -1.80620293 2.46137969
114 -2.77022854 -1.80620293
115 1.50602074 -2.77022854
116 -1.36320413 1.50602074
117 1.06480305 -1.36320413
118 -1.81298311 1.06480305
119 0.37140886 -1.81298311
120 -1.16130010 0.37140886
121 0.48130118 -1.16130010
122 -2.89280485 0.48130118
123 -0.89276097 -2.89280485
124 -0.83189140 -0.89276097
125 -0.68414170 -0.83189140
126 -0.30420370 -0.68414170
127 0.92821990 -0.30420370
128 1.28078123 0.92821990
129 -2.81934446 1.28078123
130 1.93969791 -2.81934446
131 -3.29790373 1.93969791
132 2.02405060 -3.29790373
133 -1.80201498 2.02405060
134 -1.50311608 -1.80201498
135 0.02889162 -1.50311608
136 0.83844335 0.02889162
137 0.73256847 0.83844335
138 -2.03892537 0.73256847
139 -1.18576630 -2.03892537
140 -5.26111899 -1.18576630
141 3.10565477 -5.26111899
142 1.73688223 3.10565477
143 0.57913361 1.73688223
144 1.35925587 0.57913361
145 -3.99849049 1.35925587
146 2.30557109 -3.99849049
147 -1.95693954 2.30557109
148 1.03391138 -1.95693954
149 0.07606770 1.03391138
150 -2.99921314 0.07606770
151 -1.23715071 -2.99921314
152 2.08412056 -1.23715071
153 4.11367613 2.08412056
154 1.65705095 4.11367613
155 -2.37455825 1.65705095
156 0.42763287 -2.37455825
157 1.51186660 0.42763287
158 1.40750560 1.51186660
159 1.13497586 1.40750560
160 -0.44647803 1.13497586
161 0.58985100 -0.44647803
162 NA 0.58985100
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.27948382 -3.33889555
[2,] 2.49436721 -0.27948382
[3,] 2.85850083 2.49436721
[4,] -1.84416460 2.85850083
[5,] -2.17621733 -1.84416460
[6,] 3.71237887 -2.17621733
[7,] -1.90822488 3.71237887
[8,] -2.15646080 -1.90822488
[9,] 2.18573021 -2.15646080
[10,] 0.55178531 2.18573021
[11,] -0.32360219 0.55178531
[12,] 0.35521652 -0.32360219
[13,] 0.49430999 0.35521652
[14,] -0.63223768 0.49430999
[15,] -0.25610725 -0.63223768
[16,] 0.15868319 -0.25610725
[17,] 3.58682109 0.15868319
[18,] 2.39224819 3.58682109
[19,] 0.62036172 2.39224819
[20,] 0.58404381 0.62036172
[21,] 1.02932084 0.58404381
[22,] 2.44911190 1.02932084
[23,] 1.11139671 2.44911190
[24,] 1.98650021 1.11139671
[25,] -0.07823587 1.98650021
[26,] 0.79496435 -0.07823587
[27,] -1.26545182 0.79496435
[28,] 0.30178395 -1.26545182
[29,] -0.18589518 0.30178395
[30,] -0.78127171 -0.18589518
[31,] -0.43698691 -0.78127171
[32,] -1.24847892 -0.43698691
[33,] 0.33599800 -1.24847892
[34,] -1.83300119 0.33599800
[35,] -6.10618556 -1.83300119
[36,] -1.20978741 -6.10618556
[37,] -1.92142681 -1.20978741
[38,] 1.57998091 -1.92142681
[39,] 1.25888387 1.57998091
[40,] 0.82632537 1.25888387
[41,] -1.73079621 0.82632537
[42,] 2.13276941 -1.73079621
[43,] -0.31722968 2.13276941
[44,] -0.99400587 -0.31722968
[45,] -4.73337895 -0.99400587
[46,] -2.47563205 -4.73337895
[47,] -0.08133125 -2.47563205
[48,] 0.63163741 -0.08133125
[49,] -2.11242593 0.63163741
[50,] -0.59700653 -2.11242593
[51,] -0.24248214 -0.59700653
[52,] -2.84356542 -0.24248214
[53,] 0.73171645 -2.84356542
[54,] -2.69123700 0.73171645
[55,] 1.22926510 -2.69123700
[56,] -0.14689712 1.22926510
[57,] 0.83958216 -0.14689712
[58,] -0.41939898 0.83958216
[59,] 1.74196643 -0.41939898
[60,] 0.40574565 1.74196643
[61,] -0.05344420 0.40574565
[62,] -0.56781041 -0.05344420
[63,] -0.41551792 -0.56781041
[64,] 0.59506080 -0.41551792
[65,] 0.98481452 0.59506080
[66,] 1.85136730 0.98481452
[67,] 3.24299847 1.85136730
[68,] -3.88522271 3.24299847
[69,] 0.53307623 -3.88522271
[70,] -3.48823380 0.53307623
[71,] -0.35126839 -3.48823380
[72,] 1.38663238 -0.35126839
[73,] 0.86390962 1.38663238
[74,] 0.24412275 0.86390962
[75,] 3.02769510 0.24412275
[76,] -0.33327071 3.02769510
[77,] 1.52304809 -0.33327071
[78,] -1.89671473 1.52304809
[79,] 0.13324301 -1.89671473
[80,] 0.38814705 0.13324301
[81,] 3.37071559 0.38814705
[82,] 0.35386728 3.37071559
[83,] -0.96944793 0.35386728
[84,] 0.25869841 -0.96944793
[85,] 1.71697757 0.25869841
[86,] -0.24093659 1.71697757
[87,] 0.70204134 -0.24093659
[88,] 1.47128156 0.70204134
[89,] 0.71143281 1.47128156
[90,] -1.49159800 0.71143281
[91,] 0.15306340 -1.49159800
[92,] 0.51157142 0.15306340
[93,] -0.27527864 0.51157142
[94,] -2.16220675 -0.27527864
[95,] 0.83929689 -2.16220675
[96,] 0.21631403 0.83929689
[97,] 1.86105416 0.21631403
[98,] -0.06452277 1.86105416
[99,] -0.30239033 -0.06452277
[100,] -1.21413758 -0.30239033
[101,] 1.24136167 -1.21413758
[102,] 2.68226391 1.24136167
[103,] 0.53811292 2.68226391
[104,] 1.43738695 0.53811292
[105,] -2.29863028 1.43738695
[106,] 1.08515388 -2.29863028
[107,] 0.18716057 1.08515388
[108,] 1.54624791 0.18716057
[109,] -0.22530751 1.54624791
[110,] 1.03540462 -0.22530751
[111,] 0.18854267 1.03540462
[112,] 2.46137969 0.18854267
[113,] -1.80620293 2.46137969
[114,] -2.77022854 -1.80620293
[115,] 1.50602074 -2.77022854
[116,] -1.36320413 1.50602074
[117,] 1.06480305 -1.36320413
[118,] -1.81298311 1.06480305
[119,] 0.37140886 -1.81298311
[120,] -1.16130010 0.37140886
[121,] 0.48130118 -1.16130010
[122,] -2.89280485 0.48130118
[123,] -0.89276097 -2.89280485
[124,] -0.83189140 -0.89276097
[125,] -0.68414170 -0.83189140
[126,] -0.30420370 -0.68414170
[127,] 0.92821990 -0.30420370
[128,] 1.28078123 0.92821990
[129,] -2.81934446 1.28078123
[130,] 1.93969791 -2.81934446
[131,] -3.29790373 1.93969791
[132,] 2.02405060 -3.29790373
[133,] -1.80201498 2.02405060
[134,] -1.50311608 -1.80201498
[135,] 0.02889162 -1.50311608
[136,] 0.83844335 0.02889162
[137,] 0.73256847 0.83844335
[138,] -2.03892537 0.73256847
[139,] -1.18576630 -2.03892537
[140,] -5.26111899 -1.18576630
[141,] 3.10565477 -5.26111899
[142,] 1.73688223 3.10565477
[143,] 0.57913361 1.73688223
[144,] 1.35925587 0.57913361
[145,] -3.99849049 1.35925587
[146,] 2.30557109 -3.99849049
[147,] -1.95693954 2.30557109
[148,] 1.03391138 -1.95693954
[149,] 0.07606770 1.03391138
[150,] -2.99921314 0.07606770
[151,] -1.23715071 -2.99921314
[152,] 2.08412056 -1.23715071
[153,] 4.11367613 2.08412056
[154,] 1.65705095 4.11367613
[155,] -2.37455825 1.65705095
[156,] 0.42763287 -2.37455825
[157,] 1.51186660 0.42763287
[158,] 1.40750560 1.51186660
[159,] 1.13497586 1.40750560
[160,] -0.44647803 1.13497586
[161,] 0.58985100 -0.44647803
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.27948382 -3.33889555
2 2.49436721 -0.27948382
3 2.85850083 2.49436721
4 -1.84416460 2.85850083
5 -2.17621733 -1.84416460
6 3.71237887 -2.17621733
7 -1.90822488 3.71237887
8 -2.15646080 -1.90822488
9 2.18573021 -2.15646080
10 0.55178531 2.18573021
11 -0.32360219 0.55178531
12 0.35521652 -0.32360219
13 0.49430999 0.35521652
14 -0.63223768 0.49430999
15 -0.25610725 -0.63223768
16 0.15868319 -0.25610725
17 3.58682109 0.15868319
18 2.39224819 3.58682109
19 0.62036172 2.39224819
20 0.58404381 0.62036172
21 1.02932084 0.58404381
22 2.44911190 1.02932084
23 1.11139671 2.44911190
24 1.98650021 1.11139671
25 -0.07823587 1.98650021
26 0.79496435 -0.07823587
27 -1.26545182 0.79496435
28 0.30178395 -1.26545182
29 -0.18589518 0.30178395
30 -0.78127171 -0.18589518
31 -0.43698691 -0.78127171
32 -1.24847892 -0.43698691
33 0.33599800 -1.24847892
34 -1.83300119 0.33599800
35 -6.10618556 -1.83300119
36 -1.20978741 -6.10618556
37 -1.92142681 -1.20978741
38 1.57998091 -1.92142681
39 1.25888387 1.57998091
40 0.82632537 1.25888387
41 -1.73079621 0.82632537
42 2.13276941 -1.73079621
43 -0.31722968 2.13276941
44 -0.99400587 -0.31722968
45 -4.73337895 -0.99400587
46 -2.47563205 -4.73337895
47 -0.08133125 -2.47563205
48 0.63163741 -0.08133125
49 -2.11242593 0.63163741
50 -0.59700653 -2.11242593
51 -0.24248214 -0.59700653
52 -2.84356542 -0.24248214
53 0.73171645 -2.84356542
54 -2.69123700 0.73171645
55 1.22926510 -2.69123700
56 -0.14689712 1.22926510
57 0.83958216 -0.14689712
58 -0.41939898 0.83958216
59 1.74196643 -0.41939898
60 0.40574565 1.74196643
61 -0.05344420 0.40574565
62 -0.56781041 -0.05344420
63 -0.41551792 -0.56781041
64 0.59506080 -0.41551792
65 0.98481452 0.59506080
66 1.85136730 0.98481452
67 3.24299847 1.85136730
68 -3.88522271 3.24299847
69 0.53307623 -3.88522271
70 -3.48823380 0.53307623
71 -0.35126839 -3.48823380
72 1.38663238 -0.35126839
73 0.86390962 1.38663238
74 0.24412275 0.86390962
75 3.02769510 0.24412275
76 -0.33327071 3.02769510
77 1.52304809 -0.33327071
78 -1.89671473 1.52304809
79 0.13324301 -1.89671473
80 0.38814705 0.13324301
81 3.37071559 0.38814705
82 0.35386728 3.37071559
83 -0.96944793 0.35386728
84 0.25869841 -0.96944793
85 1.71697757 0.25869841
86 -0.24093659 1.71697757
87 0.70204134 -0.24093659
88 1.47128156 0.70204134
89 0.71143281 1.47128156
90 -1.49159800 0.71143281
91 0.15306340 -1.49159800
92 0.51157142 0.15306340
93 -0.27527864 0.51157142
94 -2.16220675 -0.27527864
95 0.83929689 -2.16220675
96 0.21631403 0.83929689
97 1.86105416 0.21631403
98 -0.06452277 1.86105416
99 -0.30239033 -0.06452277
100 -1.21413758 -0.30239033
101 1.24136167 -1.21413758
102 2.68226391 1.24136167
103 0.53811292 2.68226391
104 1.43738695 0.53811292
105 -2.29863028 1.43738695
106 1.08515388 -2.29863028
107 0.18716057 1.08515388
108 1.54624791 0.18716057
109 -0.22530751 1.54624791
110 1.03540462 -0.22530751
111 0.18854267 1.03540462
112 2.46137969 0.18854267
113 -1.80620293 2.46137969
114 -2.77022854 -1.80620293
115 1.50602074 -2.77022854
116 -1.36320413 1.50602074
117 1.06480305 -1.36320413
118 -1.81298311 1.06480305
119 0.37140886 -1.81298311
120 -1.16130010 0.37140886
121 0.48130118 -1.16130010
122 -2.89280485 0.48130118
123 -0.89276097 -2.89280485
124 -0.83189140 -0.89276097
125 -0.68414170 -0.83189140
126 -0.30420370 -0.68414170
127 0.92821990 -0.30420370
128 1.28078123 0.92821990
129 -2.81934446 1.28078123
130 1.93969791 -2.81934446
131 -3.29790373 1.93969791
132 2.02405060 -3.29790373
133 -1.80201498 2.02405060
134 -1.50311608 -1.80201498
135 0.02889162 -1.50311608
136 0.83844335 0.02889162
137 0.73256847 0.83844335
138 -2.03892537 0.73256847
139 -1.18576630 -2.03892537
140 -5.26111899 -1.18576630
141 3.10565477 -5.26111899
142 1.73688223 3.10565477
143 0.57913361 1.73688223
144 1.35925587 0.57913361
145 -3.99849049 1.35925587
146 2.30557109 -3.99849049
147 -1.95693954 2.30557109
148 1.03391138 -1.95693954
149 0.07606770 1.03391138
150 -2.99921314 0.07606770
151 -1.23715071 -2.99921314
152 2.08412056 -1.23715071
153 4.11367613 2.08412056
154 1.65705095 4.11367613
155 -2.37455825 1.65705095
156 0.42763287 -2.37455825
157 1.51186660 0.42763287
158 1.40750560 1.51186660
159 1.13497586 1.40750560
160 -0.44647803 1.13497586
161 0.58985100 -0.44647803
> 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/7dd8x1352143006.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/8v0so1352143006.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/9cyqt1352143006.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/10y1yq1352143006.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11p7c31352143006.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/124r6p1352143006.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13fau11352143006.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14ldgh1352143006.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/15bs3h1352143006.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/16dfos1352143006.tab")
+ }
>
> try(system("convert tmp/1ey4j1352143006.ps tmp/1ey4j1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ydci1352143006.ps tmp/2ydci1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/30vqt1352143006.ps tmp/30vqt1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/42xor1352143006.ps tmp/42xor1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t2ls1352143006.ps tmp/5t2ls1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mdks1352143006.ps tmp/6mdks1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dd8x1352143006.ps tmp/7dd8x1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v0so1352143006.ps tmp/8v0so1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cyqt1352143006.ps tmp/9cyqt1352143006.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y1yq1352143006.ps tmp/10y1yq1352143006.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.745 1.211 10.014