R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(2
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+ ,13
+ ,8
+ ,13)
+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('gender'
+ ,'connected'
+ ,'separate'
+ ,'learning'
+ ,'software'
+ ,'happiness')
+ ,1:162))
> y <- array(NA,dim=c(6,162),dimnames=list(c('gender','connected','separate','learning','software','happiness'),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 = '5'
> #'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
> 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 gender connected separate learning happiness
1 12 2 41 38 13 14
2 11 2 39 32 16 18
3 15 2 30 35 19 11
4 6 1 31 33 15 12
5 13 2 34 37 14 16
6 10 2 35 29 13 18
7 12 2 39 31 19 14
8 14 2 34 36 15 14
9 12 2 36 35 14 15
10 6 2 37 38 15 15
11 10 1 38 31 16 17
12 12 2 36 34 16 19
13 12 1 38 35 16 10
14 11 2 39 38 16 16
15 15 2 33 37 17 18
16 12 1 32 33 15 14
17 10 1 36 32 15 14
18 12 2 38 38 20 17
19 11 1 39 38 18 14
20 12 2 32 32 16 16
21 11 1 32 33 16 18
22 12 2 31 31 16 11
23 13 2 39 38 19 14
24 11 2 37 39 16 12
25 9 1 39 32 17 17
26 13 2 41 32 17 9
27 10 1 36 35 16 16
28 14 2 33 37 15 14
29 12 2 33 33 16 15
30 10 1 34 33 14 11
31 12 2 31 28 15 16
32 8 1 27 32 12 13
33 10 2 37 31 14 17
34 12 2 34 37 16 15
35 12 1 34 30 14 14
36 7 1 32 33 7 16
37 6 1 29 31 10 9
38 12 1 36 33 14 15
39 10 2 29 31 16 17
40 10 1 35 33 16 13
41 10 1 37 32 16 15
42 12 2 34 33 14 16
43 15 1 38 32 20 16
44 10 1 35 33 14 12
45 10 2 38 28 14 12
46 12 2 37 35 11 11
47 13 2 38 39 14 15
48 11 2 33 34 15 15
49 11 2 36 38 16 17
50 12 1 38 32 14 13
51 14 2 32 38 16 16
52 10 1 32 30 14 14
53 12 1 32 33 12 11
54 13 2 34 38 16 12
55 5 1 32 32 9 12
56 6 2 37 32 14 15
57 12 2 39 34 16 16
58 12 2 29 34 16 15
59 11 1 37 36 15 12
60 10 2 35 34 16 12
61 7 1 30 28 12 8
62 12 1 38 34 16 13
63 14 2 34 35 16 11
64 11 2 31 35 14 14
65 12 2 34 31 16 15
66 13 1 35 37 17 10
67 14 2 36 35 18 11
68 11 1 30 27 18 12
69 12 2 39 40 12 15
70 12 1 35 37 16 15
71 8 1 38 36 10 14
72 11 2 31 38 14 16
73 14 2 34 39 18 15
74 14 1 38 41 18 15
75 12 1 34 27 16 13
76 9 2 39 30 17 12
77 13 2 37 37 16 17
78 11 2 34 31 16 13
79 12 1 28 31 13 15
80 12 1 37 27 16 13
81 12 1 33 36 16 15
82 12 1 37 38 20 16
83 12 2 35 37 16 15
84 12 1 37 33 15 16
85 11 2 32 34 15 15
86 10 2 33 31 16 14
87 9 1 38 39 14 15
88 12 2 33 34 16 14
89 12 2 29 32 16 13
90 12 2 33 33 15 7
91 9 2 31 36 12 17
92 15 2 36 32 17 13
93 12 2 35 41 16 15
94 12 2 32 28 15 14
95 12 2 29 30 13 13
96 10 2 39 36 16 16
97 13 2 37 35 16 12
98 9 2 35 31 16 14
99 12 1 37 34 16 17
100 10 1 32 36 14 15
101 14 2 38 36 16 17
102 11 1 37 35 16 12
103 15 2 36 37 20 16
104 11 1 32 28 15 11
105 11 2 33 39 16 15
106 12 1 40 32 13 9
107 12 2 38 35 17 16
108 12 1 41 39 16 15
109 11 1 36 35 16 10
110 7 2 43 42 12 10
111 12 2 30 34 16 15
112 14 2 31 33 16 11
113 11 2 32 41 17 13
114 11 1 32 33 13 14
115 10 2 37 34 12 18
116 13 1 37 32 18 16
117 13 2 33 40 14 14
118 8 2 34 40 14 14
119 11 2 33 35 13 14
120 12 2 38 36 16 14
121 11 2 33 37 13 12
122 13 2 31 27 16 14
123 12 2 38 39 13 15
124 14 2 37 38 16 15
125 13 2 33 31 15 15
126 15 2 31 33 16 13
127 10 1 39 32 15 17
128 11 2 44 39 17 17
129 9 2 33 36 15 19
130 11 2 35 33 12 15
131 10 1 32 33 16 13
132 11 1 28 32 10 9
133 8 2 40 37 16 15
134 11 1 27 30 12 15
135 12 1 37 38 14 15
136 12 2 32 29 15 16
137 9 1 28 22 13 11
138 11 1 34 35 15 14
139 10 2 30 35 11 11
140 8 2 35 34 12 15
141 9 1 31 35 8 13
142 8 2 32 34 16 15
143 9 1 30 34 15 16
144 15 2 30 35 17 14
145 11 1 31 23 16 15
146 8 2 40 31 10 16
147 13 2 32 27 18 16
148 12 1 36 36 13 11
149 12 1 32 31 16 12
150 9 1 35 32 13 9
151 7 2 38 39 10 16
152 13 2 42 37 15 13
153 9 1 34 38 16 16
154 6 2 35 39 16 12
155 8 2 35 34 14 9
156 8 2 33 31 10 13
157 15 2 36 32 17 13
158 6 2 32 37 13 14
159 9 2 33 36 15 19
160 11 2 34 32 16 13
161 8 2 32 35 12 12
162 8 2 34 36 13 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gender connected separate learning happiness
3.86486 0.45002 -0.04317 0.01850 0.52488 -0.03727
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9263 -0.9909 0.1542 1.2985 3.0493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.86486 1.88849 2.047 0.0424 *
gender 0.45002 0.30613 1.470 0.1436
connected -0.04317 0.04637 -0.931 0.3533
separate 0.01850 0.04439 0.417 0.6774
learning 0.52488 0.06555 8.007 2.53e-13 ***
happiness -0.03727 0.06320 -0.590 0.5563
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.803 on 156 degrees of freedom
Multiple R-squared: 0.3132, Adjusted R-squared: 0.2912
F-statistic: 14.23 on 5 and 156 DF, p-value: 1.817e-11
> 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,] 0.020594841 0.041189683 0.979405159
[2,] 0.974776205 0.050447590 0.025223795
[3,] 0.995903306 0.008193388 0.004096694
[4,] 0.991346133 0.017307735 0.008653867
[5,] 0.993037707 0.013924586 0.006962293
[6,] 0.987554722 0.024890555 0.012445278
[7,] 0.990866446 0.018267108 0.009133554
[8,] 0.990428337 0.019143326 0.009571663
[9,] 0.984215643 0.031568713 0.015784357
[10,] 0.979607501 0.040784998 0.020392499
[11,] 0.969513758 0.060972484 0.030486242
[12,] 0.954378240 0.091243520 0.045621760
[13,] 0.934653108 0.130693783 0.065346892
[14,] 0.909331782 0.181336437 0.090668218
[15,] 0.877189467 0.245621066 0.122810533
[16,] 0.850056851 0.299886297 0.149943149
[17,] 0.830388657 0.339222685 0.169611343
[18,] 0.806918446 0.386163107 0.193081554
[19,] 0.763703230 0.472593540 0.236296770
[20,] 0.768394990 0.463210021 0.231605010
[21,] 0.718421310 0.563157381 0.281578690
[22,] 0.663647804 0.672704391 0.336352196
[23,] 0.607459820 0.785080359 0.392540180
[24,] 0.600008105 0.799983790 0.399991895
[25,] 0.550114187 0.899771626 0.449885813
[26,] 0.492187574 0.984375149 0.507812426
[27,] 0.539526364 0.920947273 0.460473636
[28,] 0.482011638 0.964023276 0.517988362
[29,] 0.540257068 0.919485864 0.459742932
[30,] 0.586869378 0.826261243 0.413130622
[31,] 0.601418387 0.797163226 0.398581613
[32,] 0.558454623 0.883090753 0.441545377
[33,] 0.512275640 0.975448720 0.487724360
[34,] 0.475406907 0.950813814 0.524593093
[35,] 0.544718921 0.910562158 0.455281079
[36,] 0.493782206 0.987564413 0.506217794
[37,] 0.447288495 0.894576991 0.552711505
[38,] 0.488640707 0.977281415 0.511359293
[39,] 0.492138970 0.984277940 0.507861030
[40,] 0.445580556 0.891161111 0.554419444
[41,] 0.412510062 0.825020123 0.587489938
[42,] 0.429687128 0.859374257 0.570312872
[43,] 0.436009493 0.872018986 0.563990507
[44,] 0.389131053 0.778262106 0.610868947
[45,] 0.440678831 0.881357662 0.559321169
[46,] 0.401002795 0.802005589 0.598997205
[47,] 0.485522494 0.971044988 0.514477506
[48,] 0.745440073 0.509119855 0.254559927
[49,] 0.706866174 0.586267653 0.293133826
[50,] 0.664118657 0.671762686 0.335881343
[51,] 0.619811563 0.760376874 0.380188437
[52,] 0.624105590 0.751788821 0.375894410
[53,] 0.648443457 0.703113087 0.351556543
[54,] 0.616499820 0.767000361 0.383500180
[55,] 0.626412792 0.747174415 0.373587208
[56,] 0.581522597 0.836954806 0.418477403
[57,] 0.537104459 0.925791082 0.462895541
[58,] 0.502829739 0.994340522 0.497170261
[59,] 0.472682054 0.945364109 0.527317946
[60,] 0.451174580 0.902349160 0.548825420
[61,] 0.468489314 0.936978628 0.531510686
[62,] 0.426593899 0.853187798 0.573406101
[63,] 0.382747576 0.765495152 0.617252424
[64,] 0.343839211 0.687678422 0.656160789
[65,] 0.316594370 0.633188739 0.683405630
[66,] 0.304277014 0.608554029 0.695722986
[67,] 0.291781103 0.583562206 0.708218897
[68,] 0.371745798 0.743491596 0.628254202
[69,] 0.353584970 0.707169940 0.646415030
[70,] 0.318427554 0.636855108 0.681572446
[71,] 0.338696279 0.677392558 0.661303721
[72,] 0.326413144 0.652826288 0.673586856
[73,] 0.289804176 0.579608353 0.710195824
[74,] 0.274515368 0.549030736 0.725484632
[75,] 0.240308662 0.480617325 0.759691338
[76,] 0.225391182 0.450782364 0.774608818
[77,] 0.193738105 0.387476211 0.806261895
[78,] 0.192445491 0.384890983 0.807554509
[79,] 0.181862480 0.363724960 0.818137520
[80,] 0.153127045 0.306254090 0.846872955
[81,] 0.127291171 0.254582342 0.872708829
[82,] 0.105627561 0.211255121 0.894372439
[83,] 0.091408520 0.182817041 0.908591480
[84,] 0.119930201 0.239860402 0.880069799
[85,] 0.103520409 0.207040818 0.896479591
[86,] 0.088098203 0.176196406 0.911901797
[87,] 0.084414273 0.168828546 0.915585727
[88,] 0.080414683 0.160829365 0.919585317
[89,] 0.071522334 0.143044667 0.928477666
[90,] 0.093697581 0.187395162 0.906302419
[91,] 0.079275297 0.158550594 0.920724703
[92,] 0.063961131 0.127922262 0.936038869
[93,] 0.078748886 0.157497772 0.921251114
[94,] 0.063501417 0.127002834 0.936498583
[95,] 0.059311265 0.118622529 0.940688735
[96,] 0.048646303 0.097292607 0.951353697
[97,] 0.040998442 0.081996884 0.959001558
[98,] 0.042956360 0.085912719 0.957043640
[99,] 0.033328029 0.066656058 0.966671971
[100,] 0.027950895 0.055901789 0.972049105
[101,] 0.021373766 0.042747532 0.978626234
[102,] 0.029565373 0.059130746 0.970434627
[103,] 0.022625746 0.045251492 0.977374254
[104,] 0.024418246 0.048836493 0.975581754
[105,] 0.021314863 0.042629726 0.978685137
[106,] 0.017402427 0.034804854 0.982597573
[107,] 0.013275229 0.026550458 0.986724771
[108,] 0.010314129 0.020628259 0.989685871
[109,] 0.014583483 0.029166966 0.985416517
[110,] 0.018110166 0.036220332 0.981889834
[111,] 0.014389779 0.028779558 0.985610221
[112,] 0.010793161 0.021586322 0.989206839
[113,] 0.008481310 0.016962620 0.991518690
[114,] 0.006870819 0.013741638 0.993129181
[115,] 0.008740841 0.017481681 0.991259159
[116,] 0.016172657 0.032345315 0.983827343
[117,] 0.017910247 0.035820494 0.982089753
[118,] 0.051392767 0.102785533 0.948607233
[119,] 0.041040728 0.082081457 0.958959272
[120,] 0.031782459 0.063564918 0.968217541
[121,] 0.027307612 0.054615223 0.972692388
[122,] 0.027072551 0.054145103 0.972927449
[123,] 0.022786213 0.045572427 0.977213787
[124,] 0.025244464 0.050488927 0.974755536
[125,] 0.040635664 0.081271329 0.959364336
[126,] 0.041148786 0.082297572 0.958851214
[127,] 0.041037249 0.082074498 0.958962751
[128,] 0.035628979 0.071257959 0.964371021
[129,] 0.033433881 0.066867763 0.966566119
[130,] 0.023658192 0.047316383 0.976341808
[131,] 0.027330392 0.054660784 0.972669608
[132,] 0.020026972 0.040053944 0.979973028
[133,] 0.042128310 0.084256619 0.957871690
[134,] 0.059233634 0.118467268 0.940766366
[135,] 0.042889590 0.085779180 0.957110410
[136,] 0.292644004 0.585288007 0.707355996
[137,] 0.342922471 0.685844941 0.657077529
[138,] 0.612260268 0.775479464 0.387739732
[139,] 0.595236999 0.809526003 0.404763001
[140,] 0.856549111 0.286901777 0.143450889
[141,] 0.804449346 0.391101308 0.195550654
[142,] 0.741740035 0.516519930 0.258259965
[143,] 0.624965321 0.750069358 0.375034679
[144,] 0.520600243 0.958799515 0.479399757
[145,] 0.363041248 0.726082495 0.636958752
> postscript(file="/var/wessaorg/rcomp/tmp/1m6am1323868384.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29ykn1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3l1ww1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42q6j1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5bkm61323868384.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
2.00046561 -0.40045768 1.31995720 -5.01304717 2.26641163 0.05700214
7 8 9 10 11 12
-1.10568513 2.68548956 1.35248683 -5.18472304 -1.01237952 0.47029461
13 14 15 16 17 18
0.65273976 -0.58599282 2.72312572 1.10466322 -0.70414715 -1.69143142
19 20 21 22 23 24
-1.26027813 0.22279682 -0.27114479 0.01177917 -0.23518195 -0.83991315
25 26 27 28 29 30
-2.51259030 0.82558320 -1.20999168 2.62381749 0.21020085 -0.39591479
31 32 33 34 35 36
0.77850623 -1.55531751 -0.45580460 0.17937519 1.77139065 0.37827112
37 38 39 40 41 42
-2.54978115 1.83950599 -1.85095226 -1.32797191 -1.14858945 1.34040982
43 44 45 46 47 48
1.83231699 -0.31547333 -0.54347811 2.82123487 2.36483368 -0.28341494
49 50 51 52 53 54
-0.67824145 1.86981271 2.11179954 -0.31495439 2.56750768 1.04906884
55 56 57 58 59 60
-2.80207258 -4.54884201 0.48800536 0.01901123 0.19048931 -1.83376046
61 62 63 64 65 66
-2.53814643 0.78304611 2.06729854 0.09935530 0.29037247 0.96133935
67 68 69 70 71 72
1.10387608 -1.51987367 2.43927416 0.67256778 -0.06738153 0.11839453
73 74 75 76 77 78
1.09260859 1.67831965 0.73985285 -3.11195594 1.38343062 -0.78416540
79 80 81 82 83 84
2.05600868 0.86937041 0.60472228 -1.32185281 0.22254771 1.39506369
85 86 87 88 89 90
-0.32658746 -1.79006899 -1.18514625 0.15443237 -0.01852755 0.43693313
91 92 93 94 95 96
-0.75756994 2.75879634 0.14854952 0.74714089 1.59312281 -1.54899373
97 98 99 100 101 102
1.23408504 -2.70372395 0.88894933 -0.38868273 2.44510268 -0.31589489
103 104 105 106 107 108
1.20345415 0.08535415 -0.90079642 2.33196576 -0.09855046 0.89460380
109 110 111 112 113 114
-0.43360528 -2.61137952 0.06218375 1.97478008 -1.58038966 1.15443073
115 116 117 118 119 120
0.57573321 0.83891198 2.09320260 -2.86362488 0.71058409 0.33329588
121 122 123 124 125 126
0.59904713 1.19758416 1.88971744 2.29039320 1.77208370 3.04931795
127 128 129 130 131 132
-0.46282279 -0.87624459 -2.17133829 1.39608091 -1.45748947 2.38854678
133 134 135 136 137 138
-3.56158969 1.55621946 1.79018078 0.80317921 -0.92657115 0.15400917
139 140 141 142 143 144
0.51902723 -1.62241864 1.66140895 -3.85147121 -1.92564350 2.48153151
145 146 147 148 149 150
-0.24112865 -0.26402096 0.26552704 2.15981537 0.54224069 -0.88389684
151 152 153 154 155 156
-1.49836237 1.97510123 -2.35183535 -5.92625819 -2.89579975 -0.67803540
157 158 159 160 161 162
2.75879634 -4.36958752 -2.17133829 -0.80266495 -1.88224255 -2.30201187
> postscript(file="/var/wessaorg/rcomp/tmp/66o3t1323868384.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 2.00046561 NA
1 -0.40045768 2.00046561
2 1.31995720 -0.40045768
3 -5.01304717 1.31995720
4 2.26641163 -5.01304717
5 0.05700214 2.26641163
6 -1.10568513 0.05700214
7 2.68548956 -1.10568513
8 1.35248683 2.68548956
9 -5.18472304 1.35248683
10 -1.01237952 -5.18472304
11 0.47029461 -1.01237952
12 0.65273976 0.47029461
13 -0.58599282 0.65273976
14 2.72312572 -0.58599282
15 1.10466322 2.72312572
16 -0.70414715 1.10466322
17 -1.69143142 -0.70414715
18 -1.26027813 -1.69143142
19 0.22279682 -1.26027813
20 -0.27114479 0.22279682
21 0.01177917 -0.27114479
22 -0.23518195 0.01177917
23 -0.83991315 -0.23518195
24 -2.51259030 -0.83991315
25 0.82558320 -2.51259030
26 -1.20999168 0.82558320
27 2.62381749 -1.20999168
28 0.21020085 2.62381749
29 -0.39591479 0.21020085
30 0.77850623 -0.39591479
31 -1.55531751 0.77850623
32 -0.45580460 -1.55531751
33 0.17937519 -0.45580460
34 1.77139065 0.17937519
35 0.37827112 1.77139065
36 -2.54978115 0.37827112
37 1.83950599 -2.54978115
38 -1.85095226 1.83950599
39 -1.32797191 -1.85095226
40 -1.14858945 -1.32797191
41 1.34040982 -1.14858945
42 1.83231699 1.34040982
43 -0.31547333 1.83231699
44 -0.54347811 -0.31547333
45 2.82123487 -0.54347811
46 2.36483368 2.82123487
47 -0.28341494 2.36483368
48 -0.67824145 -0.28341494
49 1.86981271 -0.67824145
50 2.11179954 1.86981271
51 -0.31495439 2.11179954
52 2.56750768 -0.31495439
53 1.04906884 2.56750768
54 -2.80207258 1.04906884
55 -4.54884201 -2.80207258
56 0.48800536 -4.54884201
57 0.01901123 0.48800536
58 0.19048931 0.01901123
59 -1.83376046 0.19048931
60 -2.53814643 -1.83376046
61 0.78304611 -2.53814643
62 2.06729854 0.78304611
63 0.09935530 2.06729854
64 0.29037247 0.09935530
65 0.96133935 0.29037247
66 1.10387608 0.96133935
67 -1.51987367 1.10387608
68 2.43927416 -1.51987367
69 0.67256778 2.43927416
70 -0.06738153 0.67256778
71 0.11839453 -0.06738153
72 1.09260859 0.11839453
73 1.67831965 1.09260859
74 0.73985285 1.67831965
75 -3.11195594 0.73985285
76 1.38343062 -3.11195594
77 -0.78416540 1.38343062
78 2.05600868 -0.78416540
79 0.86937041 2.05600868
80 0.60472228 0.86937041
81 -1.32185281 0.60472228
82 0.22254771 -1.32185281
83 1.39506369 0.22254771
84 -0.32658746 1.39506369
85 -1.79006899 -0.32658746
86 -1.18514625 -1.79006899
87 0.15443237 -1.18514625
88 -0.01852755 0.15443237
89 0.43693313 -0.01852755
90 -0.75756994 0.43693313
91 2.75879634 -0.75756994
92 0.14854952 2.75879634
93 0.74714089 0.14854952
94 1.59312281 0.74714089
95 -1.54899373 1.59312281
96 1.23408504 -1.54899373
97 -2.70372395 1.23408504
98 0.88894933 -2.70372395
99 -0.38868273 0.88894933
100 2.44510268 -0.38868273
101 -0.31589489 2.44510268
102 1.20345415 -0.31589489
103 0.08535415 1.20345415
104 -0.90079642 0.08535415
105 2.33196576 -0.90079642
106 -0.09855046 2.33196576
107 0.89460380 -0.09855046
108 -0.43360528 0.89460380
109 -2.61137952 -0.43360528
110 0.06218375 -2.61137952
111 1.97478008 0.06218375
112 -1.58038966 1.97478008
113 1.15443073 -1.58038966
114 0.57573321 1.15443073
115 0.83891198 0.57573321
116 2.09320260 0.83891198
117 -2.86362488 2.09320260
118 0.71058409 -2.86362488
119 0.33329588 0.71058409
120 0.59904713 0.33329588
121 1.19758416 0.59904713
122 1.88971744 1.19758416
123 2.29039320 1.88971744
124 1.77208370 2.29039320
125 3.04931795 1.77208370
126 -0.46282279 3.04931795
127 -0.87624459 -0.46282279
128 -2.17133829 -0.87624459
129 1.39608091 -2.17133829
130 -1.45748947 1.39608091
131 2.38854678 -1.45748947
132 -3.56158969 2.38854678
133 1.55621946 -3.56158969
134 1.79018078 1.55621946
135 0.80317921 1.79018078
136 -0.92657115 0.80317921
137 0.15400917 -0.92657115
138 0.51902723 0.15400917
139 -1.62241864 0.51902723
140 1.66140895 -1.62241864
141 -3.85147121 1.66140895
142 -1.92564350 -3.85147121
143 2.48153151 -1.92564350
144 -0.24112865 2.48153151
145 -0.26402096 -0.24112865
146 0.26552704 -0.26402096
147 2.15981537 0.26552704
148 0.54224069 2.15981537
149 -0.88389684 0.54224069
150 -1.49836237 -0.88389684
151 1.97510123 -1.49836237
152 -2.35183535 1.97510123
153 -5.92625819 -2.35183535
154 -2.89579975 -5.92625819
155 -0.67803540 -2.89579975
156 2.75879634 -0.67803540
157 -4.36958752 2.75879634
158 -2.17133829 -4.36958752
159 -0.80266495 -2.17133829
160 -1.88224255 -0.80266495
161 -2.30201187 -1.88224255
162 NA -2.30201187
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.40045768 2.00046561
[2,] 1.31995720 -0.40045768
[3,] -5.01304717 1.31995720
[4,] 2.26641163 -5.01304717
[5,] 0.05700214 2.26641163
[6,] -1.10568513 0.05700214
[7,] 2.68548956 -1.10568513
[8,] 1.35248683 2.68548956
[9,] -5.18472304 1.35248683
[10,] -1.01237952 -5.18472304
[11,] 0.47029461 -1.01237952
[12,] 0.65273976 0.47029461
[13,] -0.58599282 0.65273976
[14,] 2.72312572 -0.58599282
[15,] 1.10466322 2.72312572
[16,] -0.70414715 1.10466322
[17,] -1.69143142 -0.70414715
[18,] -1.26027813 -1.69143142
[19,] 0.22279682 -1.26027813
[20,] -0.27114479 0.22279682
[21,] 0.01177917 -0.27114479
[22,] -0.23518195 0.01177917
[23,] -0.83991315 -0.23518195
[24,] -2.51259030 -0.83991315
[25,] 0.82558320 -2.51259030
[26,] -1.20999168 0.82558320
[27,] 2.62381749 -1.20999168
[28,] 0.21020085 2.62381749
[29,] -0.39591479 0.21020085
[30,] 0.77850623 -0.39591479
[31,] -1.55531751 0.77850623
[32,] -0.45580460 -1.55531751
[33,] 0.17937519 -0.45580460
[34,] 1.77139065 0.17937519
[35,] 0.37827112 1.77139065
[36,] -2.54978115 0.37827112
[37,] 1.83950599 -2.54978115
[38,] -1.85095226 1.83950599
[39,] -1.32797191 -1.85095226
[40,] -1.14858945 -1.32797191
[41,] 1.34040982 -1.14858945
[42,] 1.83231699 1.34040982
[43,] -0.31547333 1.83231699
[44,] -0.54347811 -0.31547333
[45,] 2.82123487 -0.54347811
[46,] 2.36483368 2.82123487
[47,] -0.28341494 2.36483368
[48,] -0.67824145 -0.28341494
[49,] 1.86981271 -0.67824145
[50,] 2.11179954 1.86981271
[51,] -0.31495439 2.11179954
[52,] 2.56750768 -0.31495439
[53,] 1.04906884 2.56750768
[54,] -2.80207258 1.04906884
[55,] -4.54884201 -2.80207258
[56,] 0.48800536 -4.54884201
[57,] 0.01901123 0.48800536
[58,] 0.19048931 0.01901123
[59,] -1.83376046 0.19048931
[60,] -2.53814643 -1.83376046
[61,] 0.78304611 -2.53814643
[62,] 2.06729854 0.78304611
[63,] 0.09935530 2.06729854
[64,] 0.29037247 0.09935530
[65,] 0.96133935 0.29037247
[66,] 1.10387608 0.96133935
[67,] -1.51987367 1.10387608
[68,] 2.43927416 -1.51987367
[69,] 0.67256778 2.43927416
[70,] -0.06738153 0.67256778
[71,] 0.11839453 -0.06738153
[72,] 1.09260859 0.11839453
[73,] 1.67831965 1.09260859
[74,] 0.73985285 1.67831965
[75,] -3.11195594 0.73985285
[76,] 1.38343062 -3.11195594
[77,] -0.78416540 1.38343062
[78,] 2.05600868 -0.78416540
[79,] 0.86937041 2.05600868
[80,] 0.60472228 0.86937041
[81,] -1.32185281 0.60472228
[82,] 0.22254771 -1.32185281
[83,] 1.39506369 0.22254771
[84,] -0.32658746 1.39506369
[85,] -1.79006899 -0.32658746
[86,] -1.18514625 -1.79006899
[87,] 0.15443237 -1.18514625
[88,] -0.01852755 0.15443237
[89,] 0.43693313 -0.01852755
[90,] -0.75756994 0.43693313
[91,] 2.75879634 -0.75756994
[92,] 0.14854952 2.75879634
[93,] 0.74714089 0.14854952
[94,] 1.59312281 0.74714089
[95,] -1.54899373 1.59312281
[96,] 1.23408504 -1.54899373
[97,] -2.70372395 1.23408504
[98,] 0.88894933 -2.70372395
[99,] -0.38868273 0.88894933
[100,] 2.44510268 -0.38868273
[101,] -0.31589489 2.44510268
[102,] 1.20345415 -0.31589489
[103,] 0.08535415 1.20345415
[104,] -0.90079642 0.08535415
[105,] 2.33196576 -0.90079642
[106,] -0.09855046 2.33196576
[107,] 0.89460380 -0.09855046
[108,] -0.43360528 0.89460380
[109,] -2.61137952 -0.43360528
[110,] 0.06218375 -2.61137952
[111,] 1.97478008 0.06218375
[112,] -1.58038966 1.97478008
[113,] 1.15443073 -1.58038966
[114,] 0.57573321 1.15443073
[115,] 0.83891198 0.57573321
[116,] 2.09320260 0.83891198
[117,] -2.86362488 2.09320260
[118,] 0.71058409 -2.86362488
[119,] 0.33329588 0.71058409
[120,] 0.59904713 0.33329588
[121,] 1.19758416 0.59904713
[122,] 1.88971744 1.19758416
[123,] 2.29039320 1.88971744
[124,] 1.77208370 2.29039320
[125,] 3.04931795 1.77208370
[126,] -0.46282279 3.04931795
[127,] -0.87624459 -0.46282279
[128,] -2.17133829 -0.87624459
[129,] 1.39608091 -2.17133829
[130,] -1.45748947 1.39608091
[131,] 2.38854678 -1.45748947
[132,] -3.56158969 2.38854678
[133,] 1.55621946 -3.56158969
[134,] 1.79018078 1.55621946
[135,] 0.80317921 1.79018078
[136,] -0.92657115 0.80317921
[137,] 0.15400917 -0.92657115
[138,] 0.51902723 0.15400917
[139,] -1.62241864 0.51902723
[140,] 1.66140895 -1.62241864
[141,] -3.85147121 1.66140895
[142,] -1.92564350 -3.85147121
[143,] 2.48153151 -1.92564350
[144,] -0.24112865 2.48153151
[145,] -0.26402096 -0.24112865
[146,] 0.26552704 -0.26402096
[147,] 2.15981537 0.26552704
[148,] 0.54224069 2.15981537
[149,] -0.88389684 0.54224069
[150,] -1.49836237 -0.88389684
[151,] 1.97510123 -1.49836237
[152,] -2.35183535 1.97510123
[153,] -5.92625819 -2.35183535
[154,] -2.89579975 -5.92625819
[155,] -0.67803540 -2.89579975
[156,] 2.75879634 -0.67803540
[157,] -4.36958752 2.75879634
[158,] -2.17133829 -4.36958752
[159,] -0.80266495 -2.17133829
[160,] -1.88224255 -0.80266495
[161,] -2.30201187 -1.88224255
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.40045768 2.00046561
2 1.31995720 -0.40045768
3 -5.01304717 1.31995720
4 2.26641163 -5.01304717
5 0.05700214 2.26641163
6 -1.10568513 0.05700214
7 2.68548956 -1.10568513
8 1.35248683 2.68548956
9 -5.18472304 1.35248683
10 -1.01237952 -5.18472304
11 0.47029461 -1.01237952
12 0.65273976 0.47029461
13 -0.58599282 0.65273976
14 2.72312572 -0.58599282
15 1.10466322 2.72312572
16 -0.70414715 1.10466322
17 -1.69143142 -0.70414715
18 -1.26027813 -1.69143142
19 0.22279682 -1.26027813
20 -0.27114479 0.22279682
21 0.01177917 -0.27114479
22 -0.23518195 0.01177917
23 -0.83991315 -0.23518195
24 -2.51259030 -0.83991315
25 0.82558320 -2.51259030
26 -1.20999168 0.82558320
27 2.62381749 -1.20999168
28 0.21020085 2.62381749
29 -0.39591479 0.21020085
30 0.77850623 -0.39591479
31 -1.55531751 0.77850623
32 -0.45580460 -1.55531751
33 0.17937519 -0.45580460
34 1.77139065 0.17937519
35 0.37827112 1.77139065
36 -2.54978115 0.37827112
37 1.83950599 -2.54978115
38 -1.85095226 1.83950599
39 -1.32797191 -1.85095226
40 -1.14858945 -1.32797191
41 1.34040982 -1.14858945
42 1.83231699 1.34040982
43 -0.31547333 1.83231699
44 -0.54347811 -0.31547333
45 2.82123487 -0.54347811
46 2.36483368 2.82123487
47 -0.28341494 2.36483368
48 -0.67824145 -0.28341494
49 1.86981271 -0.67824145
50 2.11179954 1.86981271
51 -0.31495439 2.11179954
52 2.56750768 -0.31495439
53 1.04906884 2.56750768
54 -2.80207258 1.04906884
55 -4.54884201 -2.80207258
56 0.48800536 -4.54884201
57 0.01901123 0.48800536
58 0.19048931 0.01901123
59 -1.83376046 0.19048931
60 -2.53814643 -1.83376046
61 0.78304611 -2.53814643
62 2.06729854 0.78304611
63 0.09935530 2.06729854
64 0.29037247 0.09935530
65 0.96133935 0.29037247
66 1.10387608 0.96133935
67 -1.51987367 1.10387608
68 2.43927416 -1.51987367
69 0.67256778 2.43927416
70 -0.06738153 0.67256778
71 0.11839453 -0.06738153
72 1.09260859 0.11839453
73 1.67831965 1.09260859
74 0.73985285 1.67831965
75 -3.11195594 0.73985285
76 1.38343062 -3.11195594
77 -0.78416540 1.38343062
78 2.05600868 -0.78416540
79 0.86937041 2.05600868
80 0.60472228 0.86937041
81 -1.32185281 0.60472228
82 0.22254771 -1.32185281
83 1.39506369 0.22254771
84 -0.32658746 1.39506369
85 -1.79006899 -0.32658746
86 -1.18514625 -1.79006899
87 0.15443237 -1.18514625
88 -0.01852755 0.15443237
89 0.43693313 -0.01852755
90 -0.75756994 0.43693313
91 2.75879634 -0.75756994
92 0.14854952 2.75879634
93 0.74714089 0.14854952
94 1.59312281 0.74714089
95 -1.54899373 1.59312281
96 1.23408504 -1.54899373
97 -2.70372395 1.23408504
98 0.88894933 -2.70372395
99 -0.38868273 0.88894933
100 2.44510268 -0.38868273
101 -0.31589489 2.44510268
102 1.20345415 -0.31589489
103 0.08535415 1.20345415
104 -0.90079642 0.08535415
105 2.33196576 -0.90079642
106 -0.09855046 2.33196576
107 0.89460380 -0.09855046
108 -0.43360528 0.89460380
109 -2.61137952 -0.43360528
110 0.06218375 -2.61137952
111 1.97478008 0.06218375
112 -1.58038966 1.97478008
113 1.15443073 -1.58038966
114 0.57573321 1.15443073
115 0.83891198 0.57573321
116 2.09320260 0.83891198
117 -2.86362488 2.09320260
118 0.71058409 -2.86362488
119 0.33329588 0.71058409
120 0.59904713 0.33329588
121 1.19758416 0.59904713
122 1.88971744 1.19758416
123 2.29039320 1.88971744
124 1.77208370 2.29039320
125 3.04931795 1.77208370
126 -0.46282279 3.04931795
127 -0.87624459 -0.46282279
128 -2.17133829 -0.87624459
129 1.39608091 -2.17133829
130 -1.45748947 1.39608091
131 2.38854678 -1.45748947
132 -3.56158969 2.38854678
133 1.55621946 -3.56158969
134 1.79018078 1.55621946
135 0.80317921 1.79018078
136 -0.92657115 0.80317921
137 0.15400917 -0.92657115
138 0.51902723 0.15400917
139 -1.62241864 0.51902723
140 1.66140895 -1.62241864
141 -3.85147121 1.66140895
142 -1.92564350 -3.85147121
143 2.48153151 -1.92564350
144 -0.24112865 2.48153151
145 -0.26402096 -0.24112865
146 0.26552704 -0.26402096
147 2.15981537 0.26552704
148 0.54224069 2.15981537
149 -0.88389684 0.54224069
150 -1.49836237 -0.88389684
151 1.97510123 -1.49836237
152 -2.35183535 1.97510123
153 -5.92625819 -2.35183535
154 -2.89579975 -5.92625819
155 -0.67803540 -2.89579975
156 2.75879634 -0.67803540
157 -4.36958752 2.75879634
158 -2.17133829 -4.36958752
159 -0.80266495 -2.17133829
160 -1.88224255 -0.80266495
161 -2.30201187 -1.88224255
> 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/7p21k1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cngz1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9uql31323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10p6ra1323868384.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117n791323868384.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/12eetp1323868384.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/13yfw51323868384.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/1430zt1323868384.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/1521ef1323868384.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/16ouei1323868384.tab")
+ }
>
> try(system("convert tmp/1m6am1323868384.ps tmp/1m6am1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/29ykn1323868384.ps tmp/29ykn1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/3l1ww1323868384.ps tmp/3l1ww1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/42q6j1323868384.ps tmp/42q6j1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bkm61323868384.ps tmp/5bkm61323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/66o3t1323868384.ps tmp/66o3t1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p21k1323868384.ps tmp/7p21k1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cngz1323868384.ps tmp/8cngz1323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uql31323868384.ps tmp/9uql31323868384.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p6ra1323868384.ps tmp/10p6ra1323868384.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.828 0.545 5.386