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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(46
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+ ,29.29916667)
+ ,dim=c(4
+ ,164)
+ ,dimnames=list(c('#logins'
+ ,'otaal#peer_reviews'
+ ,'totaal#karakterscompendium'
+ ,'AantalurenRFC')
+ ,1:164))
> y <- array(NA,dim=c(4,164),dimnames=list(c('#logins','otaal#peer_reviews','totaal#karakterscompendium','AantalurenRFC'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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
AantalurenRFC #logins otaal#peer_reviews totaal#karakterscompendium
1 47.38555556 46 26 95556
2 24.06138889 48 20 54565
3 31.48250000 37 24 63016
4 42.36388889 75 25 79774
5 23.94611111 31 15 31258
6 10.34916667 18 16 52491
7 85.01527778 79 20 91256
8 9.09722222 16 18 22807
9 32.36166667 38 19 77411
10 36.26083333 24 20 48821
11 44.96555556 65 30 52295
12 35.63166667 74 37 63262
13 28.43055556 43 23 50466
14 53.61777778 42 36 62932
15 39.32611111 55 29 38439
16 70.43305556 121 35 70817
17 50.30833333 42 24 105965
18 55.12000000 102 22 73795
19 31.62583333 36 19 82043
20 44.42777778 50 30 74349
21 46.33944444 48 27 82204
22 79.63194444 56 26 55709
23 25.46027778 19 15 37137
24 30.07722222 32 30 70780
25 40.65055556 77 28 55027
26 40.31722222 90 24 56699
27 44.92777778 81 21 65911
28 44.69583333 55 27 56316
29 29.69111111 34 21 26982
30 52.26388889 38 30 54628
31 52.61138889 53 30 96750
32 35.96777778 48 33 53009
33 56.67500000 63 30 64664
34 17.42527778 25 20 36990
35 67.67361111 56 27 85224
36 46.45972222 37 25 37048
37 73.48000000 83 30 59635
38 33.89555556 50 20 42051
39 22.49000000 26 8 26998
40 58.27638889 108 24 63717
41 62.27916667 55 25 55071
42 32.21416667 41 25 40001
43 38.38638889 49 21 54506
44 22.52944444 31 21 35838
45 25.86805556 49 21 50838
46 84.93222222 96 26 86997
47 21.88888889 42 26 33032
48 44.12083333 55 30 61704
49 61.59583333 70 34 117986
50 36.41888889 39 30 56733
51 35.75944444 53 18 55064
52 6.71888889 24 4 5950
53 71.57277778 209 31 84607
54 18.06361111 17 18 32551
55 27.24055556 58 14 31701
56 48.21861111 27 20 71170
57 50.01166667 58 36 101773
58 54.79611111 114 24 101653
59 58.90555556 75 26 81493
60 39.32833333 51 22 55901
61 68.08527778 86 31 109104
62 57.46638889 77 21 114425
63 40.47111111 62 31 36311
64 47.39861111 60 26 70027
65 39.46222222 39 24 73713
66 31.89444444 35 15 40671
67 31.51694444 86 19 89041
68 40.35694444 102 28 57231
69 41.94416667 49 24 68608
70 25.50333333 35 18 59155
71 33.00194444 33 25 55827
72 19.29750000 28 20 22618
73 35.17500000 44 25 58425
74 40.53000000 37 24 65724
75 27.33138889 33 23 56979
76 53.03500000 45 25 72369
77 55.22138889 57 20 79194
78 29.49805556 58 23 202316
79 24.81055556 36 22 44970
80 33.43388889 42 25 49319
81 27.44194444 30 18 36252
82 76.37583333 67 30 75741
83 36.88833333 53 22 38417
84 37.56972222 59 25 64102
85 22.48694444 25 8 56622
86 30.34361111 39 21 15430
87 26.84277778 36 22 72571
88 62.83083333 114 24 67271
89 47.57944444 54 30 43460
90 32.72638889 70 27 99501
91 37.10027778 51 24 28340
92 42.27583333 49 25 76013
93 31.11222222 42 21 37361
94 47.11472222 51 24 48204
95 52.07861111 51 24 76168
96 36.25916667 27 20 85168
97 39.53861111 29 20 125410
98 52.71222222 54 24 123328
99 56.00083333 92 40 83038
100 68.56500000 72 22 120087
101 43.31861111 63 31 91939
102 50.71694444 41 26 103646
103 29.54194444 111 20 29467
104 12.02416667 14 19 43750
105 35.41472222 45 15 34497
106 35.53611111 91 21 66477
107 41.39055556 29 22 71181
108 52.12583333 64 24 74482
109 20.58666667 32 19 174949
110 26.11277778 65 24 46765
111 49.06250000 42 23 90257
112 39.42583333 55 27 51370
113 6.37166667 10 1 1168
114 34.97972222 53 24 51360
115 17.18250000 25 11 25162
116 25.35833333 33 27 21067
117 70.86111111 66 22 58233
118 5.84833333 16 0 855
119 46.97027778 35 17 85903
120 8.72611111 19 8 14116
121 52.41694444 76 24 57637
122 38.20666667 35 31 94137
123 21.43500000 46 24 62147
124 20.71305556 29 20 62832
125 10.61500000 34 8 8773
126 25.26694444 25 22 63785
127 53.95111111 48 33 65196
128 37.57250000 38 33 73087
129 67.85333333 50 31 72631
130 56.04111111 65 33 86281
131 71.22277778 72 35 162365
132 38.65111111 23 21 56530
133 21.24166667 29 20 35606
134 52.63944444 194 24 70111
135 77.87055556 114 29 92046
136 14.16638889 15 20 63989
137 70.35388889 86 27 104911
138 28.67750000 50 24 43448
139 46.68305556 33 26 60029
140 35.76888889 50 26 38650
141 21.04055556 72 12 47261
142 69.23111111 81 21 73586
143 42.32388889 54 24 83042
144 48.12777778 63 21 37238
145 54.77694444 69 30 63958
146 18.75194444 39 32 78956
147 38.72472222 49 24 99518
148 51.49055556 67 29 111436
149 0.00000000 0 0 0
150 4.08000000 10 0 6023
151 0.02722222 1 0 0
152 0.12638889 2 0 0
153 0.00000000 0 0 0
154 0.00000000 0 0 0
155 38.30138889 58 20 42564
156 51.46888889 72 27 38885
157 0.00000000 0 0 0
158 0.05638889 4 0 0
159 1.99972222 5 0 1644
160 12.96111111 20 5 6179
161 4.87416667 5 1 3926
162 20.43527778 27 23 23238
163 0.26916667 2 0 0
164 29.29916667 33 16 49288
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `#logins`
-0.0992742 0.2469667
`otaal#peer_reviews` `totaal#karakterscompendium`
0.8049378 0.0001331
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.171 -6.711 0.099 5.888 37.557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.927e-02 2.355e+00 -0.042 0.966
`#logins` 2.470e-01 3.427e-02 7.206 2.14e-11 ***
`otaal#peer_reviews` 8.049e-01 1.359e-01 5.921 1.89e-08 ***
`totaal#karakterscompendium` 1.331e-04 3.291e-05 4.045 8.13e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.05 on 160 degrees of freedom
Multiple R-squared: 0.6786, Adjusted R-squared: 0.6726
F-statistic: 112.6 on 3 and 160 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.8007618 3.984765e-01 1.992382e-01
[2,] 0.7872238 4.255523e-01 2.127762e-01
[3,] 0.7282222 5.435556e-01 2.717778e-01
[4,] 0.8415322 3.169357e-01 1.584678e-01
[5,] 0.7889459 4.221081e-01 2.110541e-01
[6,] 0.7506922 4.986156e-01 2.493078e-01
[7,] 0.6682996 6.634009e-01 3.317004e-01
[8,] 0.8597866 2.804268e-01 1.402134e-01
[9,] 0.8243343 3.513313e-01 1.756657e-01
[10,] 0.7659645 4.680711e-01 2.340355e-01
[11,] 0.7015701 5.968598e-01 2.984299e-01
[12,] 0.6538600 6.922801e-01 3.461400e-01
[13,] 0.6084040 7.831920e-01 3.915960e-01
[14,] 0.5336196 9.327608e-01 4.663804e-01
[15,] 0.4594880 9.189761e-01 5.405120e-01
[16,] 0.9594478 8.110431e-02 4.055216e-02
[17,] 0.9480792 1.038417e-01 5.192084e-02
[18,] 0.9383359 1.233283e-01 6.166414e-02
[19,] 0.9284205 1.431589e-01 7.157946e-02
[20,] 0.9263289 1.473422e-01 7.367109e-02
[21,] 0.9055216 1.889567e-01 9.447837e-02
[22,] 0.8807678 2.384643e-01 1.192322e-01
[23,] 0.8570528 2.858944e-01 1.429472e-01
[24,] 0.8761479 2.477041e-01 1.238521e-01
[25,] 0.8438760 3.122479e-01 1.561240e-01
[26,] 0.8192973 3.614053e-01 1.807027e-01
[27,] 0.8037497 3.925005e-01 1.962503e-01
[28,] 0.7789852 4.420295e-01 2.210148e-01
[29,] 0.8371695 3.256611e-01 1.628305e-01
[30,] 0.8637167 2.725665e-01 1.362833e-01
[31,] 0.9138818 1.722363e-01 8.611817e-02
[32,] 0.8906896 2.186208e-01 1.093104e-01
[33,] 0.8728672 2.542657e-01 1.271328e-01
[34,] 0.8461643 3.076714e-01 1.538357e-01
[35,] 0.9060903 1.878195e-01 9.390975e-02
[36,] 0.8833913 2.332175e-01 1.166087e-01
[37,] 0.8566030 2.867939e-01 1.433970e-01
[38,] 0.8332065 3.335870e-01 1.667935e-01
[39,] 0.8288706 3.422587e-01 1.711294e-01
[40,] 0.9172559 1.654881e-01 8.274407e-02
[41,] 0.9178814 1.642373e-01 8.211863e-02
[42,] 0.8980177 2.039645e-01 1.019823e-01
[43,] 0.8826810 2.346380e-01 1.173190e-01
[44,] 0.8596143 2.807713e-01 1.403857e-01
[45,] 0.8318064 3.363871e-01 1.681936e-01
[46,] 0.8004504 3.990992e-01 1.995496e-01
[47,] 0.8862269 2.275463e-01 1.137731e-01
[48,] 0.8649136 2.701729e-01 1.350864e-01
[49,] 0.8377646 3.244708e-01 1.622354e-01
[50,] 0.8518889 2.962221e-01 1.481111e-01
[51,] 0.8460840 3.078319e-01 1.539160e-01
[52,] 0.8433181 3.133638e-01 1.566819e-01
[53,] 0.8268794 3.462411e-01 1.731206e-01
[54,] 0.7955818 4.088364e-01 2.044182e-01
[55,] 0.7712036 4.575928e-01 2.287964e-01
[56,] 0.7480856 5.038288e-01 2.519144e-01
[57,] 0.7134949 5.730102e-01 2.865051e-01
[58,] 0.6740734 6.518532e-01 3.259266e-01
[59,] 0.6328796 7.342407e-01 3.671204e-01
[60,] 0.5998846 8.002308e-01 4.001154e-01
[61,] 0.6870012 6.259976e-01 3.129988e-01
[62,] 0.7090145 5.819709e-01 2.909855e-01
[63,] 0.6690451 6.619098e-01 3.309549e-01
[64,] 0.6412071 7.175858e-01 3.587929e-01
[65,] 0.6004628 7.990744e-01 3.995372e-01
[66,] 0.5678232 8.643537e-01 4.321768e-01
[67,] 0.5275720 9.448561e-01 4.724280e-01
[68,] 0.4848870 9.697740e-01 5.151130e-01
[69,] 0.4590632 9.181264e-01 5.409368e-01
[70,] 0.4623467 9.246935e-01 5.376533e-01
[71,] 0.4855661 9.711321e-01 5.144339e-01
[72,] 0.8169812 3.660375e-01 1.830188e-01
[73,] 0.8004838 3.990324e-01 1.995162e-01
[74,] 0.7705504 4.588991e-01 2.294496e-01
[75,] 0.7347985 5.304029e-01 2.652015e-01
[76,] 0.8684232 2.631536e-01 1.315768e-01
[77,] 0.8429668 3.140664e-01 1.570332e-01
[78,] 0.8210903 3.578194e-01 1.789097e-01
[79,] 0.7913613 4.172775e-01 2.086387e-01
[80,] 0.7583694 4.832612e-01 2.416306e-01
[81,] 0.7468308 5.063385e-01 2.531692e-01
[82,] 0.7238556 5.522888e-01 2.761444e-01
[83,] 0.6917531 6.164937e-01 3.082469e-01
[84,] 0.7663647 4.672706e-01 2.336353e-01
[85,] 0.7310267 5.379466e-01 2.689733e-01
[86,] 0.6917965 6.164070e-01 3.082035e-01
[87,] 0.6504763 6.990475e-01 3.495237e-01
[88,] 0.6369885 7.260229e-01 3.630115e-01
[89,] 0.6311803 7.376393e-01 3.688197e-01
[90,] 0.5886747 8.226506e-01 4.113253e-01
[91,] 0.5433124 9.133751e-01 4.566876e-01
[92,] 0.5021486 9.957029e-01 4.978514e-01
[93,] 0.4862614 9.725229e-01 5.137386e-01
[94,] 0.5556986 8.886028e-01 4.443014e-01
[95,] 0.5377550 9.244901e-01 4.622450e-01
[96,] 0.5073234 9.853533e-01 4.926766e-01
[97,] 0.5796253 8.407495e-01 4.203747e-01
[98,] 0.5893772 8.212456e-01 4.106228e-01
[99,] 0.5651922 8.696156e-01 4.348078e-01
[100,] 0.5768781 8.462439e-01 4.231219e-01
[101,] 0.5522788 8.954424e-01 4.477212e-01
[102,] 0.5260358 9.479284e-01 4.739642e-01
[103,] 0.7419790 5.160421e-01 2.580210e-01
[104,] 0.7712424 4.575152e-01 2.287576e-01
[105,] 0.7484764 5.030471e-01 2.515236e-01
[106,] 0.7085747 5.828506e-01 2.914253e-01
[107,] 0.6676576 6.646847e-01 3.323424e-01
[108,] 0.6264914 7.470171e-01 3.735086e-01
[109,] 0.5783407 8.433186e-01 4.216593e-01
[110,] 0.5415942 9.168117e-01 4.584058e-01
[111,] 0.8096586 3.806828e-01 1.903414e-01
[112,] 0.7737083 4.525833e-01 2.262917e-01
[113,] 0.7863689 4.272623e-01 2.136311e-01
[114,] 0.7510043 4.979913e-01 2.489957e-01
[115,] 0.7279152 5.441696e-01 2.720848e-01
[116,] 0.7053563 5.892874e-01 2.946437e-01
[117,] 0.7709307 4.581386e-01 2.290693e-01
[118,] 0.7749412 4.501175e-01 2.250588e-01
[119,] 0.7396951 5.206098e-01 2.603049e-01
[120,] 0.7184963 5.630073e-01 2.815037e-01
[121,] 0.6878719 6.242563e-01 3.121281e-01
[122,] 0.6709411 6.581177e-01 3.290589e-01
[123,] 0.7941119 4.117762e-01 2.058881e-01
[124,] 0.7520418 4.959165e-01 2.479582e-01
[125,] 0.7038656 5.922689e-01 2.961344e-01
[126,] 0.6850629 6.298741e-01 3.149371e-01
[127,] 0.6443181 7.113638e-01 3.556819e-01
[128,] 0.9835198 3.296041e-02 1.648020e-02
[129,] 0.9774269 4.514615e-02 2.257308e-02
[130,] 0.9712104 5.757912e-02 2.878956e-02
[131,] 0.9711736 5.765286e-02 2.882643e-02
[132,] 0.9688619 6.227620e-02 3.113810e-02
[133,] 0.9884476 2.310484e-02 1.155242e-02
[134,] 0.9815146 3.697083e-02 1.848541e-02
[135,] 1.0000000 9.627439e-08 4.813720e-08
[136,] 0.9999999 1.153924e-07 5.769619e-08
[137,] 0.9999998 3.865891e-07 1.932946e-07
[138,] 0.9999994 1.250310e-06 6.251548e-07
[139,] 0.9999993 1.470600e-06 7.353001e-07
[140,] 1.0000000 3.795758e-10 1.897879e-10
[141,] 1.0000000 2.182661e-09 1.091331e-09
[142,] 1.0000000 3.547968e-09 1.773984e-09
[143,] 1.0000000 2.600605e-08 1.300303e-08
[144,] 0.9999999 1.001686e-07 5.008432e-08
[145,] 0.9999996 7.701908e-07 3.850954e-07
[146,] 0.9999973 5.337904e-06 2.668952e-06
[147,] 0.9999820 3.603107e-05 1.801554e-05
[148,] 0.9998862 2.275887e-04 1.137943e-04
[149,] 0.9999637 7.258015e-05 3.629007e-05
[150,] 0.9996426 7.148949e-04 3.574474e-04
[151,] 0.9971417 5.716592e-03 2.858296e-03
> postscript(file="/var/fisher/rcomp/tmp/19akb1352132022.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/2nxaq1352132022.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3fmut1352132022.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/414gk1352132022.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51dep1352132022.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 164
Frequency = 1
1 2 3 4 5 6
2.47610574 -11.05587780 -5.26283368 -6.80184660 0.15446291 -13.86326904
7 8 9 10 11 12
37.35794595 -12.27978998 -2.52212534 7.83537579 -2.09735353 -20.74837225
13 14 15 16 17 18
-7.32105555 5.98953834 -2.61776113 3.04977934 6.61103883 2.49686809
19 20 21 22 23 24
-3.38061089 -1.86633448 1.90846596 37.55703450 3.84965065 -11.29640467
25 26 27 28 29 30
-8.12974644 -8.67646253 -0.65464254 1.98215185 0.89813276 11.55852456
31 32 33 34 35 36
2.59448245 -9.40655478 8.45953312 -9.67227079 20.86489387 12.36616365
37 38 39 40 41 42
20.99463140 -0.04985177 6.13481707 3.90310744 21.34108819 -3.26034526
43 44 45 46 47 48
2.22507143 -6.70149436 -9.80499853 28.81376371 -13.70985378 -1.72488165
49 50 51 52 53 54
1.33392600 -4.81364779 0.95079593 -3.12081892 -16.15946202 -4.85743381
55 56 57 58 59 60
-2.47322495 16.07728134 -6.73833345 -6.10879571 8.70605871 1.68245018
61 62 63 64 65 66
7.46903846 6.41392667 -4.52813629 2.42990345 0.79903117 5.86192431
67 68 69 70 71 72
-16.76936812 -14.89090867 1.49085743 -5.40448558 -2.60350140 -6.62782947
73 74 75 76 77 78
-3.49291041 3.42419271 -6.81752901 12.26397668 14.60294918 -30.17146695
79 80 81 82 83 84
-7.67575457 -3.52794905 0.81767647 25.69799227 1.07588777 -5.55837852
85 86 87 88 89 90
2.43534927 1.85353609 -9.31762099 6.50266344 4.40923400 -19.44033523
91 92 93 94 95 96
1.51328304 0.03187465 -1.03808324 8.88354442 10.12502416 2.25450250
97 98 99 100 101 102
-0.31677333 3.74006338 -9.87188841 17.18874003 -9.33248697 5.96543310
103 104 105 106 107 108
-17.79331993 -12.45166342 7.73438312 -12.59131877 7.14395251 7.18611024
109 110 111 112 113 114
-25.79903083 -15.38438209 8.26110324 -2.62946467 3.04085866 -4.16549778
115 116 117 118 119 120
-1.09613181 -7.22993403 29.20030507 1.88232783 13.30685458 -4.18552621
121 122 123 124 125 126
6.75593203 -7.82194953 -17.41735749 -10.81230029 -5.28990773 -7.00727828
127 128 129 130 131 132
6.95451420 -8.00483525 20.98297366 2.03936489 3.75451837 8.64150405
133 134 135 136 137 138
-6.65951841 -23.82410495 14.21978898 -14.05544665 13.51554928 -8.67361936
139 140 141 142 143 144
9.71332547 -2.55342357 -12.59214187 22.62703830 -1.28562602 10.80754500
145 146 147 148 149 150
5.17365617 -27.04866767 -5.84315098 -3.13386370 0.09927421 0.90785975
151 152 153 154 155 156
-0.12047025 -0.26827027 0.09927421 0.09927421 2.31196043 6.87708967
157 158 159 160 161 162
0.09927421 -0.83220364 0.64532305 3.27384903 2.41106276 -7.74042937
163 164
-0.12549249 1.80859589
> postscript(file="/var/fisher/rcomp/tmp/651t61352132022.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 2.47610574 NA
1 -11.05587780 2.47610574
2 -5.26283368 -11.05587780
3 -6.80184660 -5.26283368
4 0.15446291 -6.80184660
5 -13.86326904 0.15446291
6 37.35794595 -13.86326904
7 -12.27978998 37.35794595
8 -2.52212534 -12.27978998
9 7.83537579 -2.52212534
10 -2.09735353 7.83537579
11 -20.74837225 -2.09735353
12 -7.32105555 -20.74837225
13 5.98953834 -7.32105555
14 -2.61776113 5.98953834
15 3.04977934 -2.61776113
16 6.61103883 3.04977934
17 2.49686809 6.61103883
18 -3.38061089 2.49686809
19 -1.86633448 -3.38061089
20 1.90846596 -1.86633448
21 37.55703450 1.90846596
22 3.84965065 37.55703450
23 -11.29640467 3.84965065
24 -8.12974644 -11.29640467
25 -8.67646253 -8.12974644
26 -0.65464254 -8.67646253
27 1.98215185 -0.65464254
28 0.89813276 1.98215185
29 11.55852456 0.89813276
30 2.59448245 11.55852456
31 -9.40655478 2.59448245
32 8.45953312 -9.40655478
33 -9.67227079 8.45953312
34 20.86489387 -9.67227079
35 12.36616365 20.86489387
36 20.99463140 12.36616365
37 -0.04985177 20.99463140
38 6.13481707 -0.04985177
39 3.90310744 6.13481707
40 21.34108819 3.90310744
41 -3.26034526 21.34108819
42 2.22507143 -3.26034526
43 -6.70149436 2.22507143
44 -9.80499853 -6.70149436
45 28.81376371 -9.80499853
46 -13.70985378 28.81376371
47 -1.72488165 -13.70985378
48 1.33392600 -1.72488165
49 -4.81364779 1.33392600
50 0.95079593 -4.81364779
51 -3.12081892 0.95079593
52 -16.15946202 -3.12081892
53 -4.85743381 -16.15946202
54 -2.47322495 -4.85743381
55 16.07728134 -2.47322495
56 -6.73833345 16.07728134
57 -6.10879571 -6.73833345
58 8.70605871 -6.10879571
59 1.68245018 8.70605871
60 7.46903846 1.68245018
61 6.41392667 7.46903846
62 -4.52813629 6.41392667
63 2.42990345 -4.52813629
64 0.79903117 2.42990345
65 5.86192431 0.79903117
66 -16.76936812 5.86192431
67 -14.89090867 -16.76936812
68 1.49085743 -14.89090867
69 -5.40448558 1.49085743
70 -2.60350140 -5.40448558
71 -6.62782947 -2.60350140
72 -3.49291041 -6.62782947
73 3.42419271 -3.49291041
74 -6.81752901 3.42419271
75 12.26397668 -6.81752901
76 14.60294918 12.26397668
77 -30.17146695 14.60294918
78 -7.67575457 -30.17146695
79 -3.52794905 -7.67575457
80 0.81767647 -3.52794905
81 25.69799227 0.81767647
82 1.07588777 25.69799227
83 -5.55837852 1.07588777
84 2.43534927 -5.55837852
85 1.85353609 2.43534927
86 -9.31762099 1.85353609
87 6.50266344 -9.31762099
88 4.40923400 6.50266344
89 -19.44033523 4.40923400
90 1.51328304 -19.44033523
91 0.03187465 1.51328304
92 -1.03808324 0.03187465
93 8.88354442 -1.03808324
94 10.12502416 8.88354442
95 2.25450250 10.12502416
96 -0.31677333 2.25450250
97 3.74006338 -0.31677333
98 -9.87188841 3.74006338
99 17.18874003 -9.87188841
100 -9.33248697 17.18874003
101 5.96543310 -9.33248697
102 -17.79331993 5.96543310
103 -12.45166342 -17.79331993
104 7.73438312 -12.45166342
105 -12.59131877 7.73438312
106 7.14395251 -12.59131877
107 7.18611024 7.14395251
108 -25.79903083 7.18611024
109 -15.38438209 -25.79903083
110 8.26110324 -15.38438209
111 -2.62946467 8.26110324
112 3.04085866 -2.62946467
113 -4.16549778 3.04085866
114 -1.09613181 -4.16549778
115 -7.22993403 -1.09613181
116 29.20030507 -7.22993403
117 1.88232783 29.20030507
118 13.30685458 1.88232783
119 -4.18552621 13.30685458
120 6.75593203 -4.18552621
121 -7.82194953 6.75593203
122 -17.41735749 -7.82194953
123 -10.81230029 -17.41735749
124 -5.28990773 -10.81230029
125 -7.00727828 -5.28990773
126 6.95451420 -7.00727828
127 -8.00483525 6.95451420
128 20.98297366 -8.00483525
129 2.03936489 20.98297366
130 3.75451837 2.03936489
131 8.64150405 3.75451837
132 -6.65951841 8.64150405
133 -23.82410495 -6.65951841
134 14.21978898 -23.82410495
135 -14.05544665 14.21978898
136 13.51554928 -14.05544665
137 -8.67361936 13.51554928
138 9.71332547 -8.67361936
139 -2.55342357 9.71332547
140 -12.59214187 -2.55342357
141 22.62703830 -12.59214187
142 -1.28562602 22.62703830
143 10.80754500 -1.28562602
144 5.17365617 10.80754500
145 -27.04866767 5.17365617
146 -5.84315098 -27.04866767
147 -3.13386370 -5.84315098
148 0.09927421 -3.13386370
149 0.90785975 0.09927421
150 -0.12047025 0.90785975
151 -0.26827027 -0.12047025
152 0.09927421 -0.26827027
153 0.09927421 0.09927421
154 2.31196043 0.09927421
155 6.87708967 2.31196043
156 0.09927421 6.87708967
157 -0.83220364 0.09927421
158 0.64532305 -0.83220364
159 3.27384903 0.64532305
160 2.41106276 3.27384903
161 -7.74042937 2.41106276
162 -0.12549249 -7.74042937
163 1.80859589 -0.12549249
164 NA 1.80859589
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.05587780 2.47610574
[2,] -5.26283368 -11.05587780
[3,] -6.80184660 -5.26283368
[4,] 0.15446291 -6.80184660
[5,] -13.86326904 0.15446291
[6,] 37.35794595 -13.86326904
[7,] -12.27978998 37.35794595
[8,] -2.52212534 -12.27978998
[9,] 7.83537579 -2.52212534
[10,] -2.09735353 7.83537579
[11,] -20.74837225 -2.09735353
[12,] -7.32105555 -20.74837225
[13,] 5.98953834 -7.32105555
[14,] -2.61776113 5.98953834
[15,] 3.04977934 -2.61776113
[16,] 6.61103883 3.04977934
[17,] 2.49686809 6.61103883
[18,] -3.38061089 2.49686809
[19,] -1.86633448 -3.38061089
[20,] 1.90846596 -1.86633448
[21,] 37.55703450 1.90846596
[22,] 3.84965065 37.55703450
[23,] -11.29640467 3.84965065
[24,] -8.12974644 -11.29640467
[25,] -8.67646253 -8.12974644
[26,] -0.65464254 -8.67646253
[27,] 1.98215185 -0.65464254
[28,] 0.89813276 1.98215185
[29,] 11.55852456 0.89813276
[30,] 2.59448245 11.55852456
[31,] -9.40655478 2.59448245
[32,] 8.45953312 -9.40655478
[33,] -9.67227079 8.45953312
[34,] 20.86489387 -9.67227079
[35,] 12.36616365 20.86489387
[36,] 20.99463140 12.36616365
[37,] -0.04985177 20.99463140
[38,] 6.13481707 -0.04985177
[39,] 3.90310744 6.13481707
[40,] 21.34108819 3.90310744
[41,] -3.26034526 21.34108819
[42,] 2.22507143 -3.26034526
[43,] -6.70149436 2.22507143
[44,] -9.80499853 -6.70149436
[45,] 28.81376371 -9.80499853
[46,] -13.70985378 28.81376371
[47,] -1.72488165 -13.70985378
[48,] 1.33392600 -1.72488165
[49,] -4.81364779 1.33392600
[50,] 0.95079593 -4.81364779
[51,] -3.12081892 0.95079593
[52,] -16.15946202 -3.12081892
[53,] -4.85743381 -16.15946202
[54,] -2.47322495 -4.85743381
[55,] 16.07728134 -2.47322495
[56,] -6.73833345 16.07728134
[57,] -6.10879571 -6.73833345
[58,] 8.70605871 -6.10879571
[59,] 1.68245018 8.70605871
[60,] 7.46903846 1.68245018
[61,] 6.41392667 7.46903846
[62,] -4.52813629 6.41392667
[63,] 2.42990345 -4.52813629
[64,] 0.79903117 2.42990345
[65,] 5.86192431 0.79903117
[66,] -16.76936812 5.86192431
[67,] -14.89090867 -16.76936812
[68,] 1.49085743 -14.89090867
[69,] -5.40448558 1.49085743
[70,] -2.60350140 -5.40448558
[71,] -6.62782947 -2.60350140
[72,] -3.49291041 -6.62782947
[73,] 3.42419271 -3.49291041
[74,] -6.81752901 3.42419271
[75,] 12.26397668 -6.81752901
[76,] 14.60294918 12.26397668
[77,] -30.17146695 14.60294918
[78,] -7.67575457 -30.17146695
[79,] -3.52794905 -7.67575457
[80,] 0.81767647 -3.52794905
[81,] 25.69799227 0.81767647
[82,] 1.07588777 25.69799227
[83,] -5.55837852 1.07588777
[84,] 2.43534927 -5.55837852
[85,] 1.85353609 2.43534927
[86,] -9.31762099 1.85353609
[87,] 6.50266344 -9.31762099
[88,] 4.40923400 6.50266344
[89,] -19.44033523 4.40923400
[90,] 1.51328304 -19.44033523
[91,] 0.03187465 1.51328304
[92,] -1.03808324 0.03187465
[93,] 8.88354442 -1.03808324
[94,] 10.12502416 8.88354442
[95,] 2.25450250 10.12502416
[96,] -0.31677333 2.25450250
[97,] 3.74006338 -0.31677333
[98,] -9.87188841 3.74006338
[99,] 17.18874003 -9.87188841
[100,] -9.33248697 17.18874003
[101,] 5.96543310 -9.33248697
[102,] -17.79331993 5.96543310
[103,] -12.45166342 -17.79331993
[104,] 7.73438312 -12.45166342
[105,] -12.59131877 7.73438312
[106,] 7.14395251 -12.59131877
[107,] 7.18611024 7.14395251
[108,] -25.79903083 7.18611024
[109,] -15.38438209 -25.79903083
[110,] 8.26110324 -15.38438209
[111,] -2.62946467 8.26110324
[112,] 3.04085866 -2.62946467
[113,] -4.16549778 3.04085866
[114,] -1.09613181 -4.16549778
[115,] -7.22993403 -1.09613181
[116,] 29.20030507 -7.22993403
[117,] 1.88232783 29.20030507
[118,] 13.30685458 1.88232783
[119,] -4.18552621 13.30685458
[120,] 6.75593203 -4.18552621
[121,] -7.82194953 6.75593203
[122,] -17.41735749 -7.82194953
[123,] -10.81230029 -17.41735749
[124,] -5.28990773 -10.81230029
[125,] -7.00727828 -5.28990773
[126,] 6.95451420 -7.00727828
[127,] -8.00483525 6.95451420
[128,] 20.98297366 -8.00483525
[129,] 2.03936489 20.98297366
[130,] 3.75451837 2.03936489
[131,] 8.64150405 3.75451837
[132,] -6.65951841 8.64150405
[133,] -23.82410495 -6.65951841
[134,] 14.21978898 -23.82410495
[135,] -14.05544665 14.21978898
[136,] 13.51554928 -14.05544665
[137,] -8.67361936 13.51554928
[138,] 9.71332547 -8.67361936
[139,] -2.55342357 9.71332547
[140,] -12.59214187 -2.55342357
[141,] 22.62703830 -12.59214187
[142,] -1.28562602 22.62703830
[143,] 10.80754500 -1.28562602
[144,] 5.17365617 10.80754500
[145,] -27.04866767 5.17365617
[146,] -5.84315098 -27.04866767
[147,] -3.13386370 -5.84315098
[148,] 0.09927421 -3.13386370
[149,] 0.90785975 0.09927421
[150,] -0.12047025 0.90785975
[151,] -0.26827027 -0.12047025
[152,] 0.09927421 -0.26827027
[153,] 0.09927421 0.09927421
[154,] 2.31196043 0.09927421
[155,] 6.87708967 2.31196043
[156,] 0.09927421 6.87708967
[157,] -0.83220364 0.09927421
[158,] 0.64532305 -0.83220364
[159,] 3.27384903 0.64532305
[160,] 2.41106276 3.27384903
[161,] -7.74042937 2.41106276
[162,] -0.12549249 -7.74042937
[163,] 1.80859589 -0.12549249
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.05587780 2.47610574
2 -5.26283368 -11.05587780
3 -6.80184660 -5.26283368
4 0.15446291 -6.80184660
5 -13.86326904 0.15446291
6 37.35794595 -13.86326904
7 -12.27978998 37.35794595
8 -2.52212534 -12.27978998
9 7.83537579 -2.52212534
10 -2.09735353 7.83537579
11 -20.74837225 -2.09735353
12 -7.32105555 -20.74837225
13 5.98953834 -7.32105555
14 -2.61776113 5.98953834
15 3.04977934 -2.61776113
16 6.61103883 3.04977934
17 2.49686809 6.61103883
18 -3.38061089 2.49686809
19 -1.86633448 -3.38061089
20 1.90846596 -1.86633448
21 37.55703450 1.90846596
22 3.84965065 37.55703450
23 -11.29640467 3.84965065
24 -8.12974644 -11.29640467
25 -8.67646253 -8.12974644
26 -0.65464254 -8.67646253
27 1.98215185 -0.65464254
28 0.89813276 1.98215185
29 11.55852456 0.89813276
30 2.59448245 11.55852456
31 -9.40655478 2.59448245
32 8.45953312 -9.40655478
33 -9.67227079 8.45953312
34 20.86489387 -9.67227079
35 12.36616365 20.86489387
36 20.99463140 12.36616365
37 -0.04985177 20.99463140
38 6.13481707 -0.04985177
39 3.90310744 6.13481707
40 21.34108819 3.90310744
41 -3.26034526 21.34108819
42 2.22507143 -3.26034526
43 -6.70149436 2.22507143
44 -9.80499853 -6.70149436
45 28.81376371 -9.80499853
46 -13.70985378 28.81376371
47 -1.72488165 -13.70985378
48 1.33392600 -1.72488165
49 -4.81364779 1.33392600
50 0.95079593 -4.81364779
51 -3.12081892 0.95079593
52 -16.15946202 -3.12081892
53 -4.85743381 -16.15946202
54 -2.47322495 -4.85743381
55 16.07728134 -2.47322495
56 -6.73833345 16.07728134
57 -6.10879571 -6.73833345
58 8.70605871 -6.10879571
59 1.68245018 8.70605871
60 7.46903846 1.68245018
61 6.41392667 7.46903846
62 -4.52813629 6.41392667
63 2.42990345 -4.52813629
64 0.79903117 2.42990345
65 5.86192431 0.79903117
66 -16.76936812 5.86192431
67 -14.89090867 -16.76936812
68 1.49085743 -14.89090867
69 -5.40448558 1.49085743
70 -2.60350140 -5.40448558
71 -6.62782947 -2.60350140
72 -3.49291041 -6.62782947
73 3.42419271 -3.49291041
74 -6.81752901 3.42419271
75 12.26397668 -6.81752901
76 14.60294918 12.26397668
77 -30.17146695 14.60294918
78 -7.67575457 -30.17146695
79 -3.52794905 -7.67575457
80 0.81767647 -3.52794905
81 25.69799227 0.81767647
82 1.07588777 25.69799227
83 -5.55837852 1.07588777
84 2.43534927 -5.55837852
85 1.85353609 2.43534927
86 -9.31762099 1.85353609
87 6.50266344 -9.31762099
88 4.40923400 6.50266344
89 -19.44033523 4.40923400
90 1.51328304 -19.44033523
91 0.03187465 1.51328304
92 -1.03808324 0.03187465
93 8.88354442 -1.03808324
94 10.12502416 8.88354442
95 2.25450250 10.12502416
96 -0.31677333 2.25450250
97 3.74006338 -0.31677333
98 -9.87188841 3.74006338
99 17.18874003 -9.87188841
100 -9.33248697 17.18874003
101 5.96543310 -9.33248697
102 -17.79331993 5.96543310
103 -12.45166342 -17.79331993
104 7.73438312 -12.45166342
105 -12.59131877 7.73438312
106 7.14395251 -12.59131877
107 7.18611024 7.14395251
108 -25.79903083 7.18611024
109 -15.38438209 -25.79903083
110 8.26110324 -15.38438209
111 -2.62946467 8.26110324
112 3.04085866 -2.62946467
113 -4.16549778 3.04085866
114 -1.09613181 -4.16549778
115 -7.22993403 -1.09613181
116 29.20030507 -7.22993403
117 1.88232783 29.20030507
118 13.30685458 1.88232783
119 -4.18552621 13.30685458
120 6.75593203 -4.18552621
121 -7.82194953 6.75593203
122 -17.41735749 -7.82194953
123 -10.81230029 -17.41735749
124 -5.28990773 -10.81230029
125 -7.00727828 -5.28990773
126 6.95451420 -7.00727828
127 -8.00483525 6.95451420
128 20.98297366 -8.00483525
129 2.03936489 20.98297366
130 3.75451837 2.03936489
131 8.64150405 3.75451837
132 -6.65951841 8.64150405
133 -23.82410495 -6.65951841
134 14.21978898 -23.82410495
135 -14.05544665 14.21978898
136 13.51554928 -14.05544665
137 -8.67361936 13.51554928
138 9.71332547 -8.67361936
139 -2.55342357 9.71332547
140 -12.59214187 -2.55342357
141 22.62703830 -12.59214187
142 -1.28562602 22.62703830
143 10.80754500 -1.28562602
144 5.17365617 10.80754500
145 -27.04866767 5.17365617
146 -5.84315098 -27.04866767
147 -3.13386370 -5.84315098
148 0.09927421 -3.13386370
149 0.90785975 0.09927421
150 -0.12047025 0.90785975
151 -0.26827027 -0.12047025
152 0.09927421 -0.26827027
153 0.09927421 0.09927421
154 2.31196043 0.09927421
155 6.87708967 2.31196043
156 0.09927421 6.87708967
157 -0.83220364 0.09927421
158 0.64532305 -0.83220364
159 3.27384903 0.64532305
160 2.41106276 3.27384903
161 -7.74042937 2.41106276
162 -0.12549249 -7.74042937
163 1.80859589 -0.12549249
> 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/7p2hd1352132022.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/8bl9t1352132022.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/9mspp1352132022.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/10796x1352132022.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/118xot1352132022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1262af1352132022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13idzo1352132022.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14t6tl1352132022.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/15vnf01352132022.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/16u6lz1352132022.tab")
+ }
>
> try(system("convert tmp/19akb1352132022.ps tmp/19akb1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nxaq1352132022.ps tmp/2nxaq1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fmut1352132022.ps tmp/3fmut1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/414gk1352132022.ps tmp/414gk1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/51dep1352132022.ps tmp/51dep1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/651t61352132022.ps tmp/651t61352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p2hd1352132022.ps tmp/7p2hd1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bl9t1352132022.ps tmp/8bl9t1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mspp1352132022.ps tmp/9mspp1352132022.png",intern=TRUE))
character(0)
> try(system("convert tmp/10796x1352132022.ps tmp/10796x1352132022.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.775 1.087 8.861