R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(41
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+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Belonging','Belonging_Final'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Software Connected Separate Learning Happiness Belonging Belonging_Final
1 12 41 38 13 14 53 32
2 11 39 32 16 18 86 51
3 15 30 35 19 11 66 42
4 6 31 33 15 12 67 41
5 13 34 37 14 16 76 46
6 10 35 29 13 18 78 47
7 12 39 31 19 14 53 37
8 14 34 36 15 14 80 49
9 12 36 35 14 15 74 45
10 6 37 38 15 15 76 47
11 10 38 31 16 17 79 49
12 12 36 34 16 19 54 33
13 12 38 35 16 10 67 42
14 11 39 38 16 16 54 33
15 15 33 37 17 18 87 53
16 12 32 33 15 14 58 36
17 10 36 32 15 14 75 45
18 12 38 38 20 17 88 54
19 11 39 38 18 14 64 41
20 12 32 32 16 16 57 36
21 11 32 33 16 18 66 41
22 12 31 31 16 11 68 44
23 13 39 38 19 14 54 33
24 11 37 39 16 12 56 37
25 9 39 32 17 17 86 52
26 13 41 32 17 9 80 47
27 10 36 35 16 16 76 43
28 14 33 37 15 14 69 44
29 12 33 33 16 15 78 45
30 10 34 33 14 11 67 44
31 12 31 28 15 16 80 49
32 8 27 32 12 13 54 33
33 10 37 31 14 17 71 43
34 12 34 37 16 15 84 54
35 12 34 30 14 14 74 42
36 7 32 33 7 16 71 44
37 6 29 31 10 9 63 37
38 12 36 33 14 15 71 43
39 10 29 31 16 17 76 46
40 10 35 33 16 13 69 42
41 10 37 32 16 15 74 45
42 12 34 33 14 16 75 44
43 15 38 32 20 16 54 33
44 10 35 33 14 12 52 31
45 10 38 28 14 12 69 42
46 12 37 35 11 11 68 40
47 13 38 39 14 15 65 43
48 11 33 34 15 15 75 46
49 11 36 38 16 17 74 42
50 12 38 32 14 13 75 45
51 14 32 38 16 16 72 44
52 10 32 30 14 14 67 40
53 12 32 33 12 11 63 37
54 13 34 38 16 12 62 46
55 5 32 32 9 12 63 36
56 6 37 32 14 15 76 47
57 12 39 34 16 16 74 45
58 12 29 34 16 15 67 42
59 11 37 36 15 12 73 43
60 10 35 34 16 12 70 43
61 7 30 28 12 8 53 32
62 12 38 34 16 13 77 45
63 14 34 35 16 11 77 45
64 11 31 35 14 14 52 31
65 12 34 31 16 15 54 33
66 13 35 37 17 10 80 49
67 14 36 35 18 11 66 42
68 11 30 27 18 12 73 41
69 12 39 40 12 15 63 38
70 12 35 37 16 15 69 42
71 8 38 36 10 14 67 44
72 11 31 38 14 16 54 33
73 14 34 39 18 15 81 48
74 14 38 41 18 15 69 40
75 12 34 27 16 13 84 50
76 9 39 30 17 12 80 49
77 13 37 37 16 17 70 43
78 11 34 31 16 13 69 44
79 12 28 31 13 15 77 47
80 12 37 27 16 13 54 33
81 12 33 36 16 15 79 46
82 12 37 38 20 16 30 0
83 12 35 37 16 15 71 45
84 12 37 33 15 16 73 43
85 11 32 34 15 15 72 44
86 10 33 31 16 14 77 47
87 9 38 39 14 15 75 45
88 12 33 34 16 14 69 42
89 12 29 32 16 13 54 33
90 12 33 33 15 7 70 43
91 9 31 36 12 17 73 46
92 15 36 32 17 13 54 33
93 12 35 41 16 15 77 46
94 12 32 28 15 14 82 48
95 12 29 30 13 13 80 47
96 10 39 36 16 16 80 47
97 13 37 35 16 12 69 43
98 9 35 31 16 14 78 46
99 12 37 34 16 17 81 48
100 10 32 36 14 15 76 46
101 14 38 36 16 17 76 45
102 11 37 35 16 12 73 45
103 15 36 37 20 16 85 52
104 11 32 28 15 11 66 42
105 11 33 39 16 15 79 47
106 12 40 32 13 9 68 41
107 12 38 35 17 16 76 47
108 12 41 39 16 15 71 43
109 11 36 35 16 10 54 33
110 7 43 42 12 10 46 30
111 12 30 34 16 15 82 49
112 14 31 33 16 11 74 44
113 11 32 41 17 13 88 55
114 11 32 33 13 14 38 11
115 10 37 34 12 18 76 47
116 13 37 32 18 16 86 53
117 13 33 40 14 14 54 33
118 8 34 40 14 14 70 44
119 11 33 35 13 14 69 42
120 12 38 36 16 14 90 55
121 11 33 37 13 12 54 33
122 13 31 27 16 14 76 46
123 12 38 39 13 15 89 54
124 14 37 38 16 15 76 47
125 13 33 31 15 15 73 45
126 15 31 33 16 13 79 47
127 10 39 32 15 17 90 55
128 11 44 39 17 17 74 44
129 9 33 36 15 19 81 53
130 11 35 33 12 15 72 44
131 10 32 33 16 13 71 42
132 11 28 32 10 9 66 40
133 8 40 37 16 15 77 46
134 11 27 30 12 15 65 40
135 12 37 38 14 15 74 46
136 12 32 29 15 16 82 53
137 9 28 22 13 11 54 33
138 11 34 35 15 14 63 42
139 10 30 35 11 11 54 35
140 8 35 34 12 15 64 40
141 9 31 35 8 13 69 41
142 8 32 34 16 15 54 33
143 9 30 34 15 16 84 51
144 15 30 35 17 14 86 53
145 11 31 23 16 15 77 46
146 8 40 31 10 16 89 55
147 13 32 27 18 16 76 47
148 12 36 36 13 11 60 38
149 12 32 31 16 12 75 46
150 9 35 32 13 9 73 46
151 7 38 39 10 16 85 53
152 13 42 37 15 13 79 47
153 9 34 38 16 16 71 41
154 6 35 39 16 12 72 44
155 8 35 34 14 9 69 43
156 8 33 31 10 13 78 51
157 15 36 32 17 13 54 33
158 6 32 37 13 14 69 43
159 9 33 36 15 19 81 53
160 11 34 32 16 13 84 51
161 8 32 35 12 12 84 50
162 8 34 36 13 13 69 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
3.885849 -0.047335 0.033582 0.532324
Happiness Belonging Belonging_Final
-0.026253 0.006650 -0.009209
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8146 -1.0337 0.2514 1.3194 3.2049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.885849 2.045906 1.899 0.0594 .
Connected -0.047335 0.046870 -1.010 0.3141
Separate 0.033582 0.044056 0.762 0.4471
Learning 0.532324 0.066323 8.026 2.33e-13 ***
Happiness -0.026253 0.066274 -0.396 0.6926
Belonging 0.006650 0.043392 0.153 0.8784
Belonging_Final -0.009209 0.062663 -0.147 0.8834
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.821 on 155 degrees of freedom
Multiple R-squared: 0.3038, Adjusted R-squared: 0.2768
F-statistic: 11.27 on 6 and 155 DF, p-value: 1.981e-10
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.999872162 0.0002556763 0.0001278381
[2,] 0.999621375 0.0007572495 0.0003786247
[3,] 0.999541509 0.0009169830 0.0004584915
[4,] 0.999047323 0.0019053538 0.0009526769
[5,] 0.998369085 0.0032618295 0.0016309148
[6,] 0.998012553 0.0039748935 0.0019874468
[7,] 0.996673254 0.0066534916 0.0033267458
[8,] 0.994066998 0.0118660049 0.0059330025
[9,] 0.992966908 0.0140661841 0.0070330920
[10,] 0.990044969 0.0199100616 0.0099550308
[11,] 0.983737343 0.0325253139 0.0162626570
[12,] 0.977440409 0.0451191814 0.0225595907
[13,] 0.967237188 0.0655256234 0.0327628117
[14,] 0.952620776 0.0947584479 0.0473792240
[15,] 0.935045104 0.1299097929 0.0649548965
[16,] 0.934814537 0.1303709257 0.0651854629
[17,] 0.934385619 0.1312287611 0.0656143805
[18,] 0.931913113 0.1361737745 0.0680868872
[19,] 0.940525537 0.1189489254 0.0594744627
[20,] 0.920091154 0.1598176924 0.0799088462
[21,] 0.899015498 0.2019690036 0.1009845018
[22,] 0.878862156 0.2422756882 0.1211378441
[23,] 0.896543604 0.2069127916 0.1034563958
[24,] 0.867228773 0.2655424536 0.1327712268
[25,] 0.832842239 0.3343155213 0.1671577607
[26,] 0.820130271 0.3597394590 0.1798697295
[27,] 0.782939786 0.4341204281 0.2170602140
[28,] 0.823466233 0.3530675347 0.1765337674
[29,] 0.814712767 0.3705744669 0.1852872335
[30,] 0.804181413 0.3916371731 0.1958185865
[31,] 0.784605710 0.4307885792 0.2153942896
[32,] 0.759294194 0.4814116129 0.2407058064
[33,] 0.743739794 0.5125204114 0.2562602057
[34,] 0.740541405 0.5189171894 0.2594585947
[35,] 0.697393139 0.6052137225 0.3026068613
[36,] 0.651912984 0.6961740314 0.3480870157
[37,] 0.719652413 0.5606951748 0.2803475874
[38,] 0.733501020 0.5329979602 0.2664989801
[39,] 0.689322225 0.6213555504 0.3106777752
[40,] 0.653654435 0.6926911309 0.3463455655
[41,] 0.641314602 0.7173707957 0.3586853979
[42,] 0.650106355 0.6997872894 0.3498936447
[43,] 0.603947264 0.7921054722 0.3960527361
[44,] 0.625722123 0.7485557533 0.3742778766
[45,] 0.592243611 0.8155127778 0.4077563889
[46,] 0.679838552 0.6403228969 0.3201614484
[47,] 0.841320808 0.3173583842 0.1586791921
[48,] 0.813528344 0.3729433122 0.1864716561
[49,] 0.779481746 0.4410365081 0.2205182540
[50,] 0.742315035 0.5153699300 0.2576849650
[51,] 0.730768767 0.5384624661 0.2692312331
[52,] 0.750706869 0.4985862627 0.2492931313
[53,] 0.714777918 0.5704441643 0.2852220821
[54,] 0.731690715 0.5366185706 0.2683092853
[55,] 0.691380071 0.6172398570 0.3086199285
[56,] 0.656792296 0.6864154087 0.3432077044
[57,] 0.616649111 0.7667017785 0.3833508893
[58,] 0.595283473 0.8094330539 0.4047165270
[59,] 0.578678971 0.8426420577 0.4213210289
[60,] 0.597196128 0.8056077430 0.4028038715
[61,] 0.553301269 0.8933974624 0.4466987312
[62,] 0.511933076 0.9761338473 0.4880669236
[63,] 0.468986414 0.9379728274 0.5310135863
[64,] 0.437402454 0.8748049077 0.5625975461
[65,] 0.414740644 0.8294812877 0.5852593562
[66,] 0.389077069 0.7781541379 0.6109229310
[67,] 0.452782102 0.9055642036 0.5472178982
[68,] 0.438199618 0.8763992357 0.5618003821
[69,] 0.396697986 0.7933959719 0.6033020141
[70,] 0.400426182 0.8008523635 0.5995738183
[71,] 0.382429969 0.7648599378 0.6175700311
[72,] 0.340609597 0.6812191940 0.6593904030
[73,] 0.348875423 0.6977508462 0.6511245769
[74,] 0.312374626 0.6247492518 0.6876253741
[75,] 0.284704778 0.5694095552 0.7152952224
[76,] 0.247058082 0.4941161646 0.7529419177
[77,] 0.239523695 0.4790473900 0.7604763050
[78,] 0.241069048 0.4821380953 0.7589309523
[79,] 0.207199910 0.4143998208 0.7928000896
[80,] 0.176571123 0.3531422450 0.8234288775
[81,] 0.151926803 0.3038536053 0.8480731974
[82,] 0.131560684 0.2631213672 0.8684393164
[83,] 0.183196950 0.3663939000 0.8168030500
[84,] 0.158328212 0.3166564236 0.8416717882
[85,] 0.141355296 0.2827105919 0.8586447041
[86,] 0.137532918 0.2750658352 0.8624670824
[87,] 0.130636102 0.2612722038 0.8693638981
[88,] 0.121437702 0.2428754036 0.8785622982
[89,] 0.153374733 0.3067494651 0.8466252675
[90,] 0.128323062 0.2566461249 0.8716769376
[91,] 0.108839243 0.2176784863 0.8911607569
[92,] 0.129282972 0.2585659443 0.8707170279
[93,] 0.108216480 0.2164329596 0.8917835202
[94,] 0.101373640 0.2027472804 0.8986263598
[95,] 0.082572006 0.1651440119 0.9174279940
[96,] 0.069496973 0.1389939457 0.9305030271
[97,] 0.069539900 0.1390798003 0.9304600998
[98,] 0.055202597 0.1104051931 0.9447974034
[99,] 0.046010001 0.0920200029 0.9539899985
[100,] 0.036228085 0.0724561693 0.9637719154
[101,] 0.040979167 0.0819583341 0.9590208330
[102,] 0.031456426 0.0629128518 0.9685435741
[103,] 0.033969533 0.0679390655 0.9660304672
[104,] 0.030083511 0.0601670219 0.9699164891
[105,] 0.024190141 0.0483802818 0.9758098591
[106,] 0.018620234 0.0372404685 0.9813797658
[107,] 0.014229547 0.0284590933 0.9857704534
[108,] 0.020132639 0.0402652778 0.9798673611
[109,] 0.023238026 0.0464760520 0.9767619740
[110,] 0.018659195 0.0373183906 0.9813408047
[111,] 0.013898058 0.0277961162 0.9861019419
[112,] 0.011364906 0.0227298112 0.9886350944
[113,] 0.009291106 0.0185822125 0.9907088937
[114,] 0.010848466 0.0216969315 0.9891515342
[115,] 0.019576144 0.0391522876 0.9804238562
[116,] 0.021252754 0.0425055082 0.9787472459
[117,] 0.051284362 0.1025687231 0.9487156385
[118,] 0.039099368 0.0781987363 0.9609006318
[119,] 0.029939817 0.0598796336 0.9700601832
[120,] 0.025418254 0.0508365075 0.9745817462
[121,] 0.024014605 0.0480292092 0.9759853954
[122,] 0.019124146 0.0382482929 0.9808758536
[123,] 0.021788904 0.0435778070 0.9782110965
[124,] 0.032891445 0.0657828899 0.9671085550
[125,] 0.035420334 0.0708406683 0.9645796658
[126,] 0.038978383 0.0779567654 0.9610216173
[127,] 0.031965852 0.0639317046 0.9680341477
[128,] 0.029784438 0.0595688765 0.9702155617
[129,] 0.022193202 0.0443864050 0.9778067975
[130,] 0.022783629 0.0455672587 0.9772163706
[131,] 0.016119859 0.0322397171 0.9838801414
[132,] 0.046932374 0.0938647482 0.9530676259
[133,] 0.051496253 0.1029925058 0.9485037471
[134,] 0.037663575 0.0753271498 0.9623364251
[135,] 0.363867473 0.7277349454 0.6361325273
[136,] 0.393634685 0.7872693709 0.6063653146
[137,] 0.679659562 0.6406808768 0.3203404384
[138,] 0.708464265 0.5830714698 0.2915357349
[139,] 0.930886729 0.1382265422 0.0691132711
[140,] 0.907871192 0.1842576159 0.0921288080
[141,] 0.837577232 0.3248455356 0.1624227678
[142,] 0.740544496 0.5189110076 0.2594555038
[143,] 0.615724824 0.7685503514 0.3842751757
> postscript(file="/var/wessaorg/rcomp/tmp/1ytih1351697233.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2os771351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vh4q1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4uzxy1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5aitf1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
2.16829547 -0.26132798 1.48129540 -5.26451635 2.36668598 0.26341852
7 8 9 10 11 12
-0.83919361 2.81646454 1.50635902 -5.07425987 -1.27319863 0.60280395
13 14 15 16 17 18
0.42404128 -0.46828067 2.76619552 0.84913222 -0.95812085 -1.65137893
19 20 21 22 23 24
-1.57826492 0.40954728 -0.58533702 0.26504897 -0.11775810 -0.67800858
25 26 27 28 29 30
-2.81069557 1.06780646 -1.56375491 2.76265606 0.34027037 -0.58881368
31 32 33 34 35 36
0.99562599 -1.78426645 -0.25793755 0.29625560 1.52572120 0.14744679
37 38 39 40 41 42
-2.71939593 1.57505681 -1.70688757 -1.58533989 -1.41020650 1.48924782
43 44 45 46 47 48
1.55658408 -0.53518923 -0.23702877 3.03950975 2.50813414 -0.13182775
49 50 51 52 53 54
-0.63415763 1.64261943 2.18196974 -0.54081365 2.34330171 1.25655001
55 56 57 58 59 60
-3.00910061 -4.34044131 0.64355104 0.16287612 -0.10273950 -1.64261656
61 62 63 64 65 66
-2.64175618 0.49750602 2.22207864 0.26081292 0.50386987 0.66055720
67 68 69 70 71 72
1.29762762 -1.74723039 2.55378884 0.33283627 -0.29216788 0.21768910
73 74 75 76 77 78
1.12913907 1.25744458 0.54273732 -2.86252067 1.48257051 -0.54709128
79 80 81 82 83 84
1.79280193 0.72769791 0.24208178 -1.83653907 0.34716305 1.10301985
85 86 87 88 89 90
-0.17762961 -1.59374885 -1.53995178 0.31266118 0.18110776 0.69735527
91 92 93 94 95 96
-0.63088383 2.98012707 0.18213983 0.96794465 1.80126182 -1.44509805
97 98 99 100 101 102
1.42512088 -2.51493903 0.55620918 -0.72065402 2.54200352 -0.58306243
103 104 105 106 107 108
1.26281394 -0.05966267 -0.84945638 2.17431691 0.03542760 0.54558810
109 110 111 112 113 114
-0.66705547 -2.41591845 0.17491883 2.15798144 -1.53496602 0.81655816
115 116 117 118 119 120
0.73580039 0.54526737 2.19268766 -2.76508320 0.87605017 0.46223060
121 122 123 124 125 126
0.77325285 1.44335274 1.98214889 2.39341632 1.97301127 3.20486291
127 128 129 130 131 132
-0.74502204 -0.80296761 -2.06941879 1.59492849 -1.74064496 2.20736322
133 134 135 136 137 138
-3.44685668 1.32671418 1.46215560 1.03291426 -0.98593695 -0.10136011
139 140 141 142 143 144
0.75522663 -1.42228735 1.40753757 -3.69154697 -2.26138679 2.59299469
145 146 147 148 149 150
-0.40271487 -0.02208833 0.48775505 1.92872998 0.31050208 -1.04956285
151 152 153 154 155 156
-1.37723415 2.12353895 -2.74433801 -5.81462050 -2.65007748 -0.39587253
157 158 159 160 161 162
2.98012707 -4.22924026 -2.06941879 -0.61596575 -1.71754954 -2.09961369
> postscript(file="/var/wessaorg/rcomp/tmp/6cthg1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 2.16829547 NA
1 -0.26132798 2.16829547
2 1.48129540 -0.26132798
3 -5.26451635 1.48129540
4 2.36668598 -5.26451635
5 0.26341852 2.36668598
6 -0.83919361 0.26341852
7 2.81646454 -0.83919361
8 1.50635902 2.81646454
9 -5.07425987 1.50635902
10 -1.27319863 -5.07425987
11 0.60280395 -1.27319863
12 0.42404128 0.60280395
13 -0.46828067 0.42404128
14 2.76619552 -0.46828067
15 0.84913222 2.76619552
16 -0.95812085 0.84913222
17 -1.65137893 -0.95812085
18 -1.57826492 -1.65137893
19 0.40954728 -1.57826492
20 -0.58533702 0.40954728
21 0.26504897 -0.58533702
22 -0.11775810 0.26504897
23 -0.67800858 -0.11775810
24 -2.81069557 -0.67800858
25 1.06780646 -2.81069557
26 -1.56375491 1.06780646
27 2.76265606 -1.56375491
28 0.34027037 2.76265606
29 -0.58881368 0.34027037
30 0.99562599 -0.58881368
31 -1.78426645 0.99562599
32 -0.25793755 -1.78426645
33 0.29625560 -0.25793755
34 1.52572120 0.29625560
35 0.14744679 1.52572120
36 -2.71939593 0.14744679
37 1.57505681 -2.71939593
38 -1.70688757 1.57505681
39 -1.58533989 -1.70688757
40 -1.41020650 -1.58533989
41 1.48924782 -1.41020650
42 1.55658408 1.48924782
43 -0.53518923 1.55658408
44 -0.23702877 -0.53518923
45 3.03950975 -0.23702877
46 2.50813414 3.03950975
47 -0.13182775 2.50813414
48 -0.63415763 -0.13182775
49 1.64261943 -0.63415763
50 2.18196974 1.64261943
51 -0.54081365 2.18196974
52 2.34330171 -0.54081365
53 1.25655001 2.34330171
54 -3.00910061 1.25655001
55 -4.34044131 -3.00910061
56 0.64355104 -4.34044131
57 0.16287612 0.64355104
58 -0.10273950 0.16287612
59 -1.64261656 -0.10273950
60 -2.64175618 -1.64261656
61 0.49750602 -2.64175618
62 2.22207864 0.49750602
63 0.26081292 2.22207864
64 0.50386987 0.26081292
65 0.66055720 0.50386987
66 1.29762762 0.66055720
67 -1.74723039 1.29762762
68 2.55378884 -1.74723039
69 0.33283627 2.55378884
70 -0.29216788 0.33283627
71 0.21768910 -0.29216788
72 1.12913907 0.21768910
73 1.25744458 1.12913907
74 0.54273732 1.25744458
75 -2.86252067 0.54273732
76 1.48257051 -2.86252067
77 -0.54709128 1.48257051
78 1.79280193 -0.54709128
79 0.72769791 1.79280193
80 0.24208178 0.72769791
81 -1.83653907 0.24208178
82 0.34716305 -1.83653907
83 1.10301985 0.34716305
84 -0.17762961 1.10301985
85 -1.59374885 -0.17762961
86 -1.53995178 -1.59374885
87 0.31266118 -1.53995178
88 0.18110776 0.31266118
89 0.69735527 0.18110776
90 -0.63088383 0.69735527
91 2.98012707 -0.63088383
92 0.18213983 2.98012707
93 0.96794465 0.18213983
94 1.80126182 0.96794465
95 -1.44509805 1.80126182
96 1.42512088 -1.44509805
97 -2.51493903 1.42512088
98 0.55620918 -2.51493903
99 -0.72065402 0.55620918
100 2.54200352 -0.72065402
101 -0.58306243 2.54200352
102 1.26281394 -0.58306243
103 -0.05966267 1.26281394
104 -0.84945638 -0.05966267
105 2.17431691 -0.84945638
106 0.03542760 2.17431691
107 0.54558810 0.03542760
108 -0.66705547 0.54558810
109 -2.41591845 -0.66705547
110 0.17491883 -2.41591845
111 2.15798144 0.17491883
112 -1.53496602 2.15798144
113 0.81655816 -1.53496602
114 0.73580039 0.81655816
115 0.54526737 0.73580039
116 2.19268766 0.54526737
117 -2.76508320 2.19268766
118 0.87605017 -2.76508320
119 0.46223060 0.87605017
120 0.77325285 0.46223060
121 1.44335274 0.77325285
122 1.98214889 1.44335274
123 2.39341632 1.98214889
124 1.97301127 2.39341632
125 3.20486291 1.97301127
126 -0.74502204 3.20486291
127 -0.80296761 -0.74502204
128 -2.06941879 -0.80296761
129 1.59492849 -2.06941879
130 -1.74064496 1.59492849
131 2.20736322 -1.74064496
132 -3.44685668 2.20736322
133 1.32671418 -3.44685668
134 1.46215560 1.32671418
135 1.03291426 1.46215560
136 -0.98593695 1.03291426
137 -0.10136011 -0.98593695
138 0.75522663 -0.10136011
139 -1.42228735 0.75522663
140 1.40753757 -1.42228735
141 -3.69154697 1.40753757
142 -2.26138679 -3.69154697
143 2.59299469 -2.26138679
144 -0.40271487 2.59299469
145 -0.02208833 -0.40271487
146 0.48775505 -0.02208833
147 1.92872998 0.48775505
148 0.31050208 1.92872998
149 -1.04956285 0.31050208
150 -1.37723415 -1.04956285
151 2.12353895 -1.37723415
152 -2.74433801 2.12353895
153 -5.81462050 -2.74433801
154 -2.65007748 -5.81462050
155 -0.39587253 -2.65007748
156 2.98012707 -0.39587253
157 -4.22924026 2.98012707
158 -2.06941879 -4.22924026
159 -0.61596575 -2.06941879
160 -1.71754954 -0.61596575
161 -2.09961369 -1.71754954
162 NA -2.09961369
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.26132798 2.16829547
[2,] 1.48129540 -0.26132798
[3,] -5.26451635 1.48129540
[4,] 2.36668598 -5.26451635
[5,] 0.26341852 2.36668598
[6,] -0.83919361 0.26341852
[7,] 2.81646454 -0.83919361
[8,] 1.50635902 2.81646454
[9,] -5.07425987 1.50635902
[10,] -1.27319863 -5.07425987
[11,] 0.60280395 -1.27319863
[12,] 0.42404128 0.60280395
[13,] -0.46828067 0.42404128
[14,] 2.76619552 -0.46828067
[15,] 0.84913222 2.76619552
[16,] -0.95812085 0.84913222
[17,] -1.65137893 -0.95812085
[18,] -1.57826492 -1.65137893
[19,] 0.40954728 -1.57826492
[20,] -0.58533702 0.40954728
[21,] 0.26504897 -0.58533702
[22,] -0.11775810 0.26504897
[23,] -0.67800858 -0.11775810
[24,] -2.81069557 -0.67800858
[25,] 1.06780646 -2.81069557
[26,] -1.56375491 1.06780646
[27,] 2.76265606 -1.56375491
[28,] 0.34027037 2.76265606
[29,] -0.58881368 0.34027037
[30,] 0.99562599 -0.58881368
[31,] -1.78426645 0.99562599
[32,] -0.25793755 -1.78426645
[33,] 0.29625560 -0.25793755
[34,] 1.52572120 0.29625560
[35,] 0.14744679 1.52572120
[36,] -2.71939593 0.14744679
[37,] 1.57505681 -2.71939593
[38,] -1.70688757 1.57505681
[39,] -1.58533989 -1.70688757
[40,] -1.41020650 -1.58533989
[41,] 1.48924782 -1.41020650
[42,] 1.55658408 1.48924782
[43,] -0.53518923 1.55658408
[44,] -0.23702877 -0.53518923
[45,] 3.03950975 -0.23702877
[46,] 2.50813414 3.03950975
[47,] -0.13182775 2.50813414
[48,] -0.63415763 -0.13182775
[49,] 1.64261943 -0.63415763
[50,] 2.18196974 1.64261943
[51,] -0.54081365 2.18196974
[52,] 2.34330171 -0.54081365
[53,] 1.25655001 2.34330171
[54,] -3.00910061 1.25655001
[55,] -4.34044131 -3.00910061
[56,] 0.64355104 -4.34044131
[57,] 0.16287612 0.64355104
[58,] -0.10273950 0.16287612
[59,] -1.64261656 -0.10273950
[60,] -2.64175618 -1.64261656
[61,] 0.49750602 -2.64175618
[62,] 2.22207864 0.49750602
[63,] 0.26081292 2.22207864
[64,] 0.50386987 0.26081292
[65,] 0.66055720 0.50386987
[66,] 1.29762762 0.66055720
[67,] -1.74723039 1.29762762
[68,] 2.55378884 -1.74723039
[69,] 0.33283627 2.55378884
[70,] -0.29216788 0.33283627
[71,] 0.21768910 -0.29216788
[72,] 1.12913907 0.21768910
[73,] 1.25744458 1.12913907
[74,] 0.54273732 1.25744458
[75,] -2.86252067 0.54273732
[76,] 1.48257051 -2.86252067
[77,] -0.54709128 1.48257051
[78,] 1.79280193 -0.54709128
[79,] 0.72769791 1.79280193
[80,] 0.24208178 0.72769791
[81,] -1.83653907 0.24208178
[82,] 0.34716305 -1.83653907
[83,] 1.10301985 0.34716305
[84,] -0.17762961 1.10301985
[85,] -1.59374885 -0.17762961
[86,] -1.53995178 -1.59374885
[87,] 0.31266118 -1.53995178
[88,] 0.18110776 0.31266118
[89,] 0.69735527 0.18110776
[90,] -0.63088383 0.69735527
[91,] 2.98012707 -0.63088383
[92,] 0.18213983 2.98012707
[93,] 0.96794465 0.18213983
[94,] 1.80126182 0.96794465
[95,] -1.44509805 1.80126182
[96,] 1.42512088 -1.44509805
[97,] -2.51493903 1.42512088
[98,] 0.55620918 -2.51493903
[99,] -0.72065402 0.55620918
[100,] 2.54200352 -0.72065402
[101,] -0.58306243 2.54200352
[102,] 1.26281394 -0.58306243
[103,] -0.05966267 1.26281394
[104,] -0.84945638 -0.05966267
[105,] 2.17431691 -0.84945638
[106,] 0.03542760 2.17431691
[107,] 0.54558810 0.03542760
[108,] -0.66705547 0.54558810
[109,] -2.41591845 -0.66705547
[110,] 0.17491883 -2.41591845
[111,] 2.15798144 0.17491883
[112,] -1.53496602 2.15798144
[113,] 0.81655816 -1.53496602
[114,] 0.73580039 0.81655816
[115,] 0.54526737 0.73580039
[116,] 2.19268766 0.54526737
[117,] -2.76508320 2.19268766
[118,] 0.87605017 -2.76508320
[119,] 0.46223060 0.87605017
[120,] 0.77325285 0.46223060
[121,] 1.44335274 0.77325285
[122,] 1.98214889 1.44335274
[123,] 2.39341632 1.98214889
[124,] 1.97301127 2.39341632
[125,] 3.20486291 1.97301127
[126,] -0.74502204 3.20486291
[127,] -0.80296761 -0.74502204
[128,] -2.06941879 -0.80296761
[129,] 1.59492849 -2.06941879
[130,] -1.74064496 1.59492849
[131,] 2.20736322 -1.74064496
[132,] -3.44685668 2.20736322
[133,] 1.32671418 -3.44685668
[134,] 1.46215560 1.32671418
[135,] 1.03291426 1.46215560
[136,] -0.98593695 1.03291426
[137,] -0.10136011 -0.98593695
[138,] 0.75522663 -0.10136011
[139,] -1.42228735 0.75522663
[140,] 1.40753757 -1.42228735
[141,] -3.69154697 1.40753757
[142,] -2.26138679 -3.69154697
[143,] 2.59299469 -2.26138679
[144,] -0.40271487 2.59299469
[145,] -0.02208833 -0.40271487
[146,] 0.48775505 -0.02208833
[147,] 1.92872998 0.48775505
[148,] 0.31050208 1.92872998
[149,] -1.04956285 0.31050208
[150,] -1.37723415 -1.04956285
[151,] 2.12353895 -1.37723415
[152,] -2.74433801 2.12353895
[153,] -5.81462050 -2.74433801
[154,] -2.65007748 -5.81462050
[155,] -0.39587253 -2.65007748
[156,] 2.98012707 -0.39587253
[157,] -4.22924026 2.98012707
[158,] -2.06941879 -4.22924026
[159,] -0.61596575 -2.06941879
[160,] -1.71754954 -0.61596575
[161,] -2.09961369 -1.71754954
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.26132798 2.16829547
2 1.48129540 -0.26132798
3 -5.26451635 1.48129540
4 2.36668598 -5.26451635
5 0.26341852 2.36668598
6 -0.83919361 0.26341852
7 2.81646454 -0.83919361
8 1.50635902 2.81646454
9 -5.07425987 1.50635902
10 -1.27319863 -5.07425987
11 0.60280395 -1.27319863
12 0.42404128 0.60280395
13 -0.46828067 0.42404128
14 2.76619552 -0.46828067
15 0.84913222 2.76619552
16 -0.95812085 0.84913222
17 -1.65137893 -0.95812085
18 -1.57826492 -1.65137893
19 0.40954728 -1.57826492
20 -0.58533702 0.40954728
21 0.26504897 -0.58533702
22 -0.11775810 0.26504897
23 -0.67800858 -0.11775810
24 -2.81069557 -0.67800858
25 1.06780646 -2.81069557
26 -1.56375491 1.06780646
27 2.76265606 -1.56375491
28 0.34027037 2.76265606
29 -0.58881368 0.34027037
30 0.99562599 -0.58881368
31 -1.78426645 0.99562599
32 -0.25793755 -1.78426645
33 0.29625560 -0.25793755
34 1.52572120 0.29625560
35 0.14744679 1.52572120
36 -2.71939593 0.14744679
37 1.57505681 -2.71939593
38 -1.70688757 1.57505681
39 -1.58533989 -1.70688757
40 -1.41020650 -1.58533989
41 1.48924782 -1.41020650
42 1.55658408 1.48924782
43 -0.53518923 1.55658408
44 -0.23702877 -0.53518923
45 3.03950975 -0.23702877
46 2.50813414 3.03950975
47 -0.13182775 2.50813414
48 -0.63415763 -0.13182775
49 1.64261943 -0.63415763
50 2.18196974 1.64261943
51 -0.54081365 2.18196974
52 2.34330171 -0.54081365
53 1.25655001 2.34330171
54 -3.00910061 1.25655001
55 -4.34044131 -3.00910061
56 0.64355104 -4.34044131
57 0.16287612 0.64355104
58 -0.10273950 0.16287612
59 -1.64261656 -0.10273950
60 -2.64175618 -1.64261656
61 0.49750602 -2.64175618
62 2.22207864 0.49750602
63 0.26081292 2.22207864
64 0.50386987 0.26081292
65 0.66055720 0.50386987
66 1.29762762 0.66055720
67 -1.74723039 1.29762762
68 2.55378884 -1.74723039
69 0.33283627 2.55378884
70 -0.29216788 0.33283627
71 0.21768910 -0.29216788
72 1.12913907 0.21768910
73 1.25744458 1.12913907
74 0.54273732 1.25744458
75 -2.86252067 0.54273732
76 1.48257051 -2.86252067
77 -0.54709128 1.48257051
78 1.79280193 -0.54709128
79 0.72769791 1.79280193
80 0.24208178 0.72769791
81 -1.83653907 0.24208178
82 0.34716305 -1.83653907
83 1.10301985 0.34716305
84 -0.17762961 1.10301985
85 -1.59374885 -0.17762961
86 -1.53995178 -1.59374885
87 0.31266118 -1.53995178
88 0.18110776 0.31266118
89 0.69735527 0.18110776
90 -0.63088383 0.69735527
91 2.98012707 -0.63088383
92 0.18213983 2.98012707
93 0.96794465 0.18213983
94 1.80126182 0.96794465
95 -1.44509805 1.80126182
96 1.42512088 -1.44509805
97 -2.51493903 1.42512088
98 0.55620918 -2.51493903
99 -0.72065402 0.55620918
100 2.54200352 -0.72065402
101 -0.58306243 2.54200352
102 1.26281394 -0.58306243
103 -0.05966267 1.26281394
104 -0.84945638 -0.05966267
105 2.17431691 -0.84945638
106 0.03542760 2.17431691
107 0.54558810 0.03542760
108 -0.66705547 0.54558810
109 -2.41591845 -0.66705547
110 0.17491883 -2.41591845
111 2.15798144 0.17491883
112 -1.53496602 2.15798144
113 0.81655816 -1.53496602
114 0.73580039 0.81655816
115 0.54526737 0.73580039
116 2.19268766 0.54526737
117 -2.76508320 2.19268766
118 0.87605017 -2.76508320
119 0.46223060 0.87605017
120 0.77325285 0.46223060
121 1.44335274 0.77325285
122 1.98214889 1.44335274
123 2.39341632 1.98214889
124 1.97301127 2.39341632
125 3.20486291 1.97301127
126 -0.74502204 3.20486291
127 -0.80296761 -0.74502204
128 -2.06941879 -0.80296761
129 1.59492849 -2.06941879
130 -1.74064496 1.59492849
131 2.20736322 -1.74064496
132 -3.44685668 2.20736322
133 1.32671418 -3.44685668
134 1.46215560 1.32671418
135 1.03291426 1.46215560
136 -0.98593695 1.03291426
137 -0.10136011 -0.98593695
138 0.75522663 -0.10136011
139 -1.42228735 0.75522663
140 1.40753757 -1.42228735
141 -3.69154697 1.40753757
142 -2.26138679 -3.69154697
143 2.59299469 -2.26138679
144 -0.40271487 2.59299469
145 -0.02208833 -0.40271487
146 0.48775505 -0.02208833
147 1.92872998 0.48775505
148 0.31050208 1.92872998
149 -1.04956285 0.31050208
150 -1.37723415 -1.04956285
151 2.12353895 -1.37723415
152 -2.74433801 2.12353895
153 -5.81462050 -2.74433801
154 -2.65007748 -5.81462050
155 -0.39587253 -2.65007748
156 2.98012707 -0.39587253
157 -4.22924026 2.98012707
158 -2.06941879 -4.22924026
159 -0.61596575 -2.06941879
160 -1.71754954 -0.61596575
161 -2.09961369 -1.71754954
> 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/7rdn31351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/883k81351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9b2qq1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pj0n1351697233.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118d3w1351697233.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/12m8sy1351697233.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/13drs01351697233.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/143wns1351697233.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/15f2el1351697233.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/16tbla1351697233.tab")
+ }
>
> try(system("convert tmp/1ytih1351697233.ps tmp/1ytih1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/2os771351697233.ps tmp/2os771351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vh4q1351697233.ps tmp/3vh4q1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uzxy1351697233.ps tmp/4uzxy1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aitf1351697233.ps tmp/5aitf1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cthg1351697233.ps tmp/6cthg1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rdn31351697233.ps tmp/7rdn31351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/883k81351697233.ps tmp/883k81351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b2qq1351697233.ps tmp/9b2qq1351697233.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pj0n1351697233.ps tmp/10pj0n1351697233.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.963 1.172 9.349