R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
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Type 'contributors()' for more information and
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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(3
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+ ,3)
+ ,dim=c(4
+ ,156)
+ ,dimnames=list(c('Poular'
+ ,'Friends'
+ ,'Considerfriends'
+ ,'FriendStudents')
+ ,1:156))
> y <- array(NA,dim=c(4,156),dimnames=list(c('Poular','Friends','Considerfriends','FriendStudents'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'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.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
Poular Friends Considerfriends FriendStudents
1 3 4 3 3
2 3 4 3 2
3 4 3 3 4
4 3 4 3 1
5 3 3 2 2
6 3 4 2 2
7 3 4 4 3
8 2 4 2 2
9 3 4 3 2
10 3 2 2 2
11 3 2 4 4
12 3 4 3 3
13 3 4 3 4
14 2 4 2 2
15 3 4 3 3
16 3 3 2 3
17 2 4 3 3
18 3 4 3 4
19 2 3 2 2
20 1 2 3 2
21 2 2 2 2
22 3 4 3 3
23 3 3 3 4
24 3 4 2 2
25 3 4 3 3
26 3 3 3 4
27 2 3 4 3
28 3 2 3 3
29 3 3 3 3
30 4 4 2 2
31 3 3 2 2
32 3 3 4 4
33 3 4 4 3
34 3 4 3 3
35 2 3 3 2
36 3 4 4 4
37 3 2 3 3
38 3 3 2 2
39 3 4 3 3
40 4 4 4 4
41 3 3 3 4
42 3 5 3 2
43 1 3 2 1
44 2 3 2 2
45 3 4 3 3
46 4 4 4 3
47 4 5 4 4
48 2 3 2 2
49 1 4 3 3
50 3 4 3 4
51 3 2 3 2
52 1 4 2 2
53 3 4 4 3
54 2 4 3 2
55 3 3 4 4
56 3 3 3 3
57 2 3 3 3
58 4 2 4 4
59 1 4 1 4
60 3 4 4 4
61 2 4 2 2
62 4 3 3 4
63 3 4 3 4
64 4 3 3 4
65 3 4 3 2
66 3 4 4 3
67 3 4 2 3
68 3 4 2 2
69 3 4 4 4
70 1 4 1 1
71 3 4 4 3
72 3 4 4 3
73 3 2 3 2
74 2 3 2 2
75 3 4 3 3
76 3 4 3 3
77 3 3 3 3
78 2 4 3 3
79 3 4 4 3
80 2 4 2 2
81 2 3 3 2
82 3 3 3 3
83 3 3 3 3
84 2 3 2 2
85 2 4 2 2
86 3 4 3 3
87 2 2 2 2
88 3 4 3 3
89 4 3 3 3
90 2 4 3 2
91 3 3 3 3
92 2 4 4 3
93 4 3 4 4
94 3 3 4 4
95 3 4 3 3
96 3 3 2 2
97 3 4 3 1
98 2 2 2 2
99 3 4 3 3
100 4 4 3 3
101 4 4 4 5
102 4 4 3 4
103 3 4 3 5
104 3 3 2 2
105 1 4 1 1
106 4 3 3 3
107 1 4 3 3
108 3 3 4 4
109 2 3 2 2
110 2 2 3 2
111 3 4 4 3
112 3 4 3 4
113 2 4 4 2
114 3 1 4 3
115 3 4 3 3
116 4 4 3 4
117 4 4 3 4
118 3 3 3 2
119 3 4 3 3
120 3 4 3 2
121 3 3 3 3
122 3 4 3 4
123 1 4 4 2
124 2 4 3 4
125 4 4 2 4
126 3 4 4 2
127 4 3 3 3
128 3 3 3 3
129 2 4 1 3
130 1 4 4 3
131 4 4 3 4
132 3 4 2 3
133 3 4 2 2
134 3 4 4 3
135 4 4 3 3
136 3 4 4 3
137 3 4 2 4
138 1 3 4 3
139 4 3 4 4
140 2 4 3 4
141 2 4 3 4
142 3 2 3 3
143 3 4 2 3
144 2 4 3 2
145 3 4 3 3
146 3 5 3 1
147 2 4 4 2
148 3 4 4 4
149 4 4 3 4
150 4 4 3 3
151 4 4 4 3
152 2 4 2 4
153 3 4 3 2
154 3 4 4 4
155 3 2 3 3
156 3 4 3 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Friends Considerfriends FriendStudents
1.350860 0.004249 0.151351 0.341802
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.9987 -0.3499 0.1527 0.4955 1.6458
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.350860 0.363066 3.721 0.000279 ***
Friends 0.004249 0.079078 0.054 0.957218
Considerfriends 0.151351 0.083278 1.817 0.071120 .
FriendStudents 0.341802 0.072283 4.729 5.12e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6987 on 152 degrees of freedom
Multiple R-squared: 0.2125, Adjusted R-squared: 0.1969
F-statistic: 13.67 on 3 and 152 DF, p-value: 6.097e-08
> 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,] 0.099707720 0.199415440 0.9002923
[2,] 0.184142139 0.368284279 0.8158579
[3,] 0.097224860 0.194449721 0.9027751
[4,] 0.083765895 0.167531790 0.9162341
[5,] 0.120747973 0.241495945 0.8792520
[6,] 0.069600622 0.139201243 0.9303994
[7,] 0.040757098 0.081514195 0.9592429
[8,] 0.059586586 0.119173171 0.9404134
[9,] 0.034313597 0.068627195 0.9656864
[10,] 0.019256519 0.038513038 0.9807435
[11,] 0.046676418 0.093352836 0.9533236
[12,] 0.028266294 0.056532589 0.9717337
[13,] 0.038003646 0.076007293 0.9619964
[14,] 0.280344394 0.560688788 0.7196556
[15,] 0.229403280 0.458806560 0.7705967
[16,] 0.176252537 0.352505074 0.8237475
[17,] 0.132468063 0.264936126 0.8675319
[18,] 0.109851137 0.219702274 0.8901489
[19,] 0.079752764 0.159505528 0.9202472
[20,] 0.056745984 0.113491967 0.9432540
[21,] 0.062669654 0.125339307 0.9373303
[22,] 0.053850322 0.107700644 0.9461497
[23,] 0.039935727 0.079871454 0.9600643
[24,] 0.108999833 0.217999667 0.8910002
[25,] 0.093394092 0.186788184 0.9066059
[26,] 0.071461373 0.142922747 0.9285386
[27,] 0.053512939 0.107025879 0.9464871
[28,] 0.038771081 0.077542162 0.9612289
[29,] 0.033080054 0.066160108 0.9669199
[30,] 0.024095625 0.048191249 0.9759044
[31,] 0.019759956 0.039519912 0.9802400
[32,] 0.016225597 0.032451193 0.9837744
[33,] 0.011176918 0.022353835 0.9888231
[34,] 0.014649431 0.029298862 0.9853506
[35,] 0.010331844 0.020663687 0.9896682
[36,] 0.007537580 0.015075159 0.9924624
[37,] 0.018876716 0.037753433 0.9811233
[38,] 0.016423521 0.032847043 0.9835765
[39,] 0.011624744 0.023249488 0.9883753
[40,] 0.020955254 0.041910509 0.9790447
[41,] 0.017757681 0.035515362 0.9822423
[42,] 0.014770407 0.029540814 0.9852296
[43,] 0.134356518 0.268713037 0.8656435
[44,] 0.113730990 0.227461981 0.8862690
[45,] 0.115440019 0.230880038 0.8845600
[46,] 0.237122356 0.474244711 0.7628776
[47,] 0.200080847 0.400161695 0.7999192
[48,] 0.188557814 0.377115627 0.8114422
[49,] 0.161450095 0.322900190 0.8385499
[50,] 0.135063355 0.270126711 0.8649366
[51,] 0.145690184 0.291380368 0.8543098
[52,] 0.157520479 0.315040957 0.8424795
[53,] 0.368002496 0.736004992 0.6319975
[54,] 0.334184411 0.668368823 0.6658156
[55,] 0.301291341 0.602582682 0.6987087
[56,] 0.332207429 0.664414858 0.6677926
[57,] 0.291883997 0.583767993 0.7081160
[58,] 0.316157893 0.632315785 0.6838421
[59,] 0.291693523 0.583387046 0.7083065
[60,] 0.253841543 0.507683085 0.7461585
[61,] 0.227225029 0.454450058 0.7727750
[62,] 0.222187210 0.444374420 0.7778128
[63,] 0.197089190 0.394178381 0.8029108
[64,] 0.212758090 0.425516180 0.7872419
[65,] 0.181261365 0.362522730 0.8187386
[66,] 0.152823870 0.305647740 0.8471761
[67,] 0.137759226 0.275518451 0.8622408
[68,] 0.119343487 0.238686974 0.8806565
[69,] 0.098502503 0.197005007 0.9014975
[70,] 0.080465899 0.160931799 0.9195341
[71,] 0.065072006 0.130144011 0.9349280
[72,] 0.072367478 0.144734956 0.9276325
[73,] 0.058001848 0.116003696 0.9419982
[74,] 0.048603029 0.097206059 0.9513970
[75,] 0.044047785 0.088095569 0.9559522
[76,] 0.034639199 0.069278398 0.9653608
[77,] 0.026941709 0.053883418 0.9730583
[78,] 0.022131116 0.044262233 0.9778689
[79,] 0.018089629 0.036179259 0.9819104
[80,] 0.013681358 0.027362716 0.9863186
[81,] 0.011239348 0.022478697 0.9887607
[82,] 0.008337146 0.016674293 0.9916629
[83,] 0.013940601 0.027881203 0.9860594
[84,] 0.012332505 0.024665011 0.9876675
[85,] 0.009155764 0.018311528 0.9908442
[86,] 0.012743763 0.025487526 0.9872562
[87,] 0.012459602 0.024919205 0.9875404
[88,] 0.009771683 0.019543367 0.9902283
[89,] 0.007188675 0.014377351 0.9928113
[90,] 0.006649659 0.013299317 0.9933503
[91,] 0.007169277 0.014338554 0.9928307
[92,] 0.005718778 0.011437556 0.9942812
[93,] 0.004114477 0.008228954 0.9958855
[94,] 0.007156189 0.014312378 0.9928438
[95,] 0.005629016 0.011258032 0.9943710
[96,] 0.006399682 0.012799364 0.9936003
[97,] 0.005286634 0.010573269 0.9947134
[98,] 0.004784006 0.009568013 0.9952160
[99,] 0.006655351 0.013310703 0.9933446
[100,] 0.010951813 0.021903627 0.9890482
[101,] 0.054592218 0.109184436 0.9454078
[102,] 0.043833626 0.087667253 0.9561664
[103,] 0.039212963 0.078425927 0.9607870
[104,] 0.037076246 0.074152492 0.9629238
[105,] 0.028548450 0.057096900 0.9714515
[106,] 0.021419301 0.042838601 0.9785807
[107,] 0.020015554 0.040031107 0.9799844
[108,] 0.014617349 0.029234699 0.9853827
[109,] 0.010568553 0.021137106 0.9894314
[110,] 0.012113662 0.024227325 0.9878863
[111,] 0.014344218 0.028688437 0.9856558
[112,] 0.011000112 0.022000224 0.9889999
[113,] 0.007799507 0.015599014 0.9922005
[114,] 0.005952794 0.011905587 0.9940472
[115,] 0.004065077 0.008130154 0.9959349
[116,] 0.002722839 0.005445678 0.9972772
[117,] 0.012153546 0.024307092 0.9878465
[118,] 0.017936618 0.035873235 0.9820634
[119,] 0.021963567 0.043927134 0.9780364
[120,] 0.016157481 0.032314961 0.9838425
[121,] 0.025072939 0.050145877 0.9749271
[122,] 0.017942428 0.035884856 0.9820576
[123,] 0.019886370 0.039772740 0.9801136
[124,] 0.110662058 0.221324115 0.8893379
[125,] 0.126129657 0.252259314 0.8738703
[126,] 0.096779237 0.193558475 0.9032208
[127,] 0.076645851 0.153291702 0.9233541
[128,] 0.055253817 0.110507634 0.9447462
[129,] 0.081437153 0.162874306 0.9185628
[130,] 0.058204123 0.116408247 0.9417959
[131,] 0.040516862 0.081033724 0.9594831
[132,] 0.286707147 0.573414293 0.7132929
[133,] 0.289746661 0.579493323 0.7102533
[134,] 0.341146988 0.682293976 0.6588530
[135,] 0.450307508 0.900615017 0.5496925
[136,] 0.363274126 0.726548251 0.6367259
[137,] 0.284862182 0.569724365 0.7151378
[138,] 0.305020075 0.610040151 0.6949799
[139,] 0.219713365 0.439426730 0.7802866
[140,] 0.155540620 0.311081240 0.8444594
[141,] 0.364695392 0.729390783 0.6353046
[142,] 0.306309818 0.612619637 0.6936902
[143,] 0.463510207 0.927020414 0.5364898
> postscript(file="/var/www/html/rcomp/tmp/1gcu61290560674.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gcu61290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/394b91290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/494b91290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52dtu1290560674.ps",horizontal=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 = 156
Frequency = 1
1 2 3 4 5 6
0.152684490 0.494486094 0.815132015 0.836287698 0.650086522 0.645837394
7 8 9 10 11 12
0.001333190 -0.354162606 0.494486094 0.654335651 -0.331970156 0.152684490
13 14 15 16 17 18
-0.189117114 -0.354162606 0.152684490 0.308284918 -0.847315510 -0.189117114
19 20 21 22 23 24
-0.349913478 -1.497015649 -0.345664349 0.152684490 -0.184867985 0.645837394
25 26 27 28 29 30
0.152684490 -0.184867985 -0.994417681 0.161182747 0.156933619 1.645837394
31 32 33 34 35 36
0.650086522 -0.336219285 0.001333190 0.152684490 -0.501264777 -0.340468414
37 38 39 40 41 42
0.161182747 0.650086522 0.152684490 0.659531586 -0.184867985 0.490236965
43 44 45 46 47 48
-1.008111874 -0.349913478 0.152684490 1.001333190 0.655282458 -0.349913478
49 50 51 52 53 54
-1.847315510 -0.189117114 0.502984351 -1.354162606 0.001333190 -0.505513906
55 56 57 58 59 60
-0.336219285 0.156933619 -0.843066381 0.668029844 -1.886414514 -0.340468414
61 62 63 64 65 66
-0.354162606 0.815132015 -0.189117114 0.815132015 0.494486094 0.001333190
67 68 69 70 71 72
0.304035790 0.645837394 -0.340468414 -0.861009703 0.001333190 0.001333190
73 74 75 76 77 78
0.502984351 -0.349913478 0.152684490 0.152684490 0.156933619 -0.847315510
79 80 81 82 83 84
0.001333190 -0.354162606 -0.501264777 0.156933619 0.156933619 -0.349913478
85 86 87 88 89 90
-0.354162606 0.152684490 -0.345664349 0.152684490 1.156933619 -0.505513906
91 92 93 94 95 96
0.156933619 -0.998666810 0.663780715 -0.336219285 0.152684490 0.650086522
97 98 99 100 101 102
0.836287698 -0.345664349 0.152684490 1.152684490 0.317729982 0.810882886
103 104 105 106 107 108
-0.530918718 0.650086522 -0.861009703 1.156933619 -1.847315510 -0.336219285
109 110 111 112 113 114
-0.349913478 -0.497015649 0.001333190 -0.189117114 -0.656865206 0.014080576
115 116 117 118 119 120
0.152684490 0.810882886 0.810882886 0.498735223 0.152684490 0.494486094
121 122 123 124 125 126
0.156933619 -0.189117114 -1.656865206 -1.189117114 0.962234186 0.343134794
127 128 129 130 131 132
1.156933619 0.156933619 -0.544612911 -1.998666810 0.810882886 0.304035790
133 134 135 136 137 138
0.645837394 0.001333190 1.152684490 0.001333190 -0.037765814 -1.994417681
139 140 141 142 143 144
0.663780715 -1.189117114 -1.189117114 0.161182747 0.304035790 -0.505513906
145 146 147 148 149 150
0.152684490 0.832038569 -0.656865206 -0.340468414 0.810882886 1.152684490
151 152 153 154 155 156
1.001333190 -1.037765814 0.494486094 -0.340468414 0.161182747 0.152684490
> postscript(file="/var/www/html/rcomp/tmp/62dtu1290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 0.152684490 NA
1 0.494486094 0.152684490
2 0.815132015 0.494486094
3 0.836287698 0.815132015
4 0.650086522 0.836287698
5 0.645837394 0.650086522
6 0.001333190 0.645837394
7 -0.354162606 0.001333190
8 0.494486094 -0.354162606
9 0.654335651 0.494486094
10 -0.331970156 0.654335651
11 0.152684490 -0.331970156
12 -0.189117114 0.152684490
13 -0.354162606 -0.189117114
14 0.152684490 -0.354162606
15 0.308284918 0.152684490
16 -0.847315510 0.308284918
17 -0.189117114 -0.847315510
18 -0.349913478 -0.189117114
19 -1.497015649 -0.349913478
20 -0.345664349 -1.497015649
21 0.152684490 -0.345664349
22 -0.184867985 0.152684490
23 0.645837394 -0.184867985
24 0.152684490 0.645837394
25 -0.184867985 0.152684490
26 -0.994417681 -0.184867985
27 0.161182747 -0.994417681
28 0.156933619 0.161182747
29 1.645837394 0.156933619
30 0.650086522 1.645837394
31 -0.336219285 0.650086522
32 0.001333190 -0.336219285
33 0.152684490 0.001333190
34 -0.501264777 0.152684490
35 -0.340468414 -0.501264777
36 0.161182747 -0.340468414
37 0.650086522 0.161182747
38 0.152684490 0.650086522
39 0.659531586 0.152684490
40 -0.184867985 0.659531586
41 0.490236965 -0.184867985
42 -1.008111874 0.490236965
43 -0.349913478 -1.008111874
44 0.152684490 -0.349913478
45 1.001333190 0.152684490
46 0.655282458 1.001333190
47 -0.349913478 0.655282458
48 -1.847315510 -0.349913478
49 -0.189117114 -1.847315510
50 0.502984351 -0.189117114
51 -1.354162606 0.502984351
52 0.001333190 -1.354162606
53 -0.505513906 0.001333190
54 -0.336219285 -0.505513906
55 0.156933619 -0.336219285
56 -0.843066381 0.156933619
57 0.668029844 -0.843066381
58 -1.886414514 0.668029844
59 -0.340468414 -1.886414514
60 -0.354162606 -0.340468414
61 0.815132015 -0.354162606
62 -0.189117114 0.815132015
63 0.815132015 -0.189117114
64 0.494486094 0.815132015
65 0.001333190 0.494486094
66 0.304035790 0.001333190
67 0.645837394 0.304035790
68 -0.340468414 0.645837394
69 -0.861009703 -0.340468414
70 0.001333190 -0.861009703
71 0.001333190 0.001333190
72 0.502984351 0.001333190
73 -0.349913478 0.502984351
74 0.152684490 -0.349913478
75 0.152684490 0.152684490
76 0.156933619 0.152684490
77 -0.847315510 0.156933619
78 0.001333190 -0.847315510
79 -0.354162606 0.001333190
80 -0.501264777 -0.354162606
81 0.156933619 -0.501264777
82 0.156933619 0.156933619
83 -0.349913478 0.156933619
84 -0.354162606 -0.349913478
85 0.152684490 -0.354162606
86 -0.345664349 0.152684490
87 0.152684490 -0.345664349
88 1.156933619 0.152684490
89 -0.505513906 1.156933619
90 0.156933619 -0.505513906
91 -0.998666810 0.156933619
92 0.663780715 -0.998666810
93 -0.336219285 0.663780715
94 0.152684490 -0.336219285
95 0.650086522 0.152684490
96 0.836287698 0.650086522
97 -0.345664349 0.836287698
98 0.152684490 -0.345664349
99 1.152684490 0.152684490
100 0.317729982 1.152684490
101 0.810882886 0.317729982
102 -0.530918718 0.810882886
103 0.650086522 -0.530918718
104 -0.861009703 0.650086522
105 1.156933619 -0.861009703
106 -1.847315510 1.156933619
107 -0.336219285 -1.847315510
108 -0.349913478 -0.336219285
109 -0.497015649 -0.349913478
110 0.001333190 -0.497015649
111 -0.189117114 0.001333190
112 -0.656865206 -0.189117114
113 0.014080576 -0.656865206
114 0.152684490 0.014080576
115 0.810882886 0.152684490
116 0.810882886 0.810882886
117 0.498735223 0.810882886
118 0.152684490 0.498735223
119 0.494486094 0.152684490
120 0.156933619 0.494486094
121 -0.189117114 0.156933619
122 -1.656865206 -0.189117114
123 -1.189117114 -1.656865206
124 0.962234186 -1.189117114
125 0.343134794 0.962234186
126 1.156933619 0.343134794
127 0.156933619 1.156933619
128 -0.544612911 0.156933619
129 -1.998666810 -0.544612911
130 0.810882886 -1.998666810
131 0.304035790 0.810882886
132 0.645837394 0.304035790
133 0.001333190 0.645837394
134 1.152684490 0.001333190
135 0.001333190 1.152684490
136 -0.037765814 0.001333190
137 -1.994417681 -0.037765814
138 0.663780715 -1.994417681
139 -1.189117114 0.663780715
140 -1.189117114 -1.189117114
141 0.161182747 -1.189117114
142 0.304035790 0.161182747
143 -0.505513906 0.304035790
144 0.152684490 -0.505513906
145 0.832038569 0.152684490
146 -0.656865206 0.832038569
147 -0.340468414 -0.656865206
148 0.810882886 -0.340468414
149 1.152684490 0.810882886
150 1.001333190 1.152684490
151 -1.037765814 1.001333190
152 0.494486094 -1.037765814
153 -0.340468414 0.494486094
154 0.161182747 -0.340468414
155 0.152684490 0.161182747
156 NA 0.152684490
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.494486094 0.152684490
[2,] 0.815132015 0.494486094
[3,] 0.836287698 0.815132015
[4,] 0.650086522 0.836287698
[5,] 0.645837394 0.650086522
[6,] 0.001333190 0.645837394
[7,] -0.354162606 0.001333190
[8,] 0.494486094 -0.354162606
[9,] 0.654335651 0.494486094
[10,] -0.331970156 0.654335651
[11,] 0.152684490 -0.331970156
[12,] -0.189117114 0.152684490
[13,] -0.354162606 -0.189117114
[14,] 0.152684490 -0.354162606
[15,] 0.308284918 0.152684490
[16,] -0.847315510 0.308284918
[17,] -0.189117114 -0.847315510
[18,] -0.349913478 -0.189117114
[19,] -1.497015649 -0.349913478
[20,] -0.345664349 -1.497015649
[21,] 0.152684490 -0.345664349
[22,] -0.184867985 0.152684490
[23,] 0.645837394 -0.184867985
[24,] 0.152684490 0.645837394
[25,] -0.184867985 0.152684490
[26,] -0.994417681 -0.184867985
[27,] 0.161182747 -0.994417681
[28,] 0.156933619 0.161182747
[29,] 1.645837394 0.156933619
[30,] 0.650086522 1.645837394
[31,] -0.336219285 0.650086522
[32,] 0.001333190 -0.336219285
[33,] 0.152684490 0.001333190
[34,] -0.501264777 0.152684490
[35,] -0.340468414 -0.501264777
[36,] 0.161182747 -0.340468414
[37,] 0.650086522 0.161182747
[38,] 0.152684490 0.650086522
[39,] 0.659531586 0.152684490
[40,] -0.184867985 0.659531586
[41,] 0.490236965 -0.184867985
[42,] -1.008111874 0.490236965
[43,] -0.349913478 -1.008111874
[44,] 0.152684490 -0.349913478
[45,] 1.001333190 0.152684490
[46,] 0.655282458 1.001333190
[47,] -0.349913478 0.655282458
[48,] -1.847315510 -0.349913478
[49,] -0.189117114 -1.847315510
[50,] 0.502984351 -0.189117114
[51,] -1.354162606 0.502984351
[52,] 0.001333190 -1.354162606
[53,] -0.505513906 0.001333190
[54,] -0.336219285 -0.505513906
[55,] 0.156933619 -0.336219285
[56,] -0.843066381 0.156933619
[57,] 0.668029844 -0.843066381
[58,] -1.886414514 0.668029844
[59,] -0.340468414 -1.886414514
[60,] -0.354162606 -0.340468414
[61,] 0.815132015 -0.354162606
[62,] -0.189117114 0.815132015
[63,] 0.815132015 -0.189117114
[64,] 0.494486094 0.815132015
[65,] 0.001333190 0.494486094
[66,] 0.304035790 0.001333190
[67,] 0.645837394 0.304035790
[68,] -0.340468414 0.645837394
[69,] -0.861009703 -0.340468414
[70,] 0.001333190 -0.861009703
[71,] 0.001333190 0.001333190
[72,] 0.502984351 0.001333190
[73,] -0.349913478 0.502984351
[74,] 0.152684490 -0.349913478
[75,] 0.152684490 0.152684490
[76,] 0.156933619 0.152684490
[77,] -0.847315510 0.156933619
[78,] 0.001333190 -0.847315510
[79,] -0.354162606 0.001333190
[80,] -0.501264777 -0.354162606
[81,] 0.156933619 -0.501264777
[82,] 0.156933619 0.156933619
[83,] -0.349913478 0.156933619
[84,] -0.354162606 -0.349913478
[85,] 0.152684490 -0.354162606
[86,] -0.345664349 0.152684490
[87,] 0.152684490 -0.345664349
[88,] 1.156933619 0.152684490
[89,] -0.505513906 1.156933619
[90,] 0.156933619 -0.505513906
[91,] -0.998666810 0.156933619
[92,] 0.663780715 -0.998666810
[93,] -0.336219285 0.663780715
[94,] 0.152684490 -0.336219285
[95,] 0.650086522 0.152684490
[96,] 0.836287698 0.650086522
[97,] -0.345664349 0.836287698
[98,] 0.152684490 -0.345664349
[99,] 1.152684490 0.152684490
[100,] 0.317729982 1.152684490
[101,] 0.810882886 0.317729982
[102,] -0.530918718 0.810882886
[103,] 0.650086522 -0.530918718
[104,] -0.861009703 0.650086522
[105,] 1.156933619 -0.861009703
[106,] -1.847315510 1.156933619
[107,] -0.336219285 -1.847315510
[108,] -0.349913478 -0.336219285
[109,] -0.497015649 -0.349913478
[110,] 0.001333190 -0.497015649
[111,] -0.189117114 0.001333190
[112,] -0.656865206 -0.189117114
[113,] 0.014080576 -0.656865206
[114,] 0.152684490 0.014080576
[115,] 0.810882886 0.152684490
[116,] 0.810882886 0.810882886
[117,] 0.498735223 0.810882886
[118,] 0.152684490 0.498735223
[119,] 0.494486094 0.152684490
[120,] 0.156933619 0.494486094
[121,] -0.189117114 0.156933619
[122,] -1.656865206 -0.189117114
[123,] -1.189117114 -1.656865206
[124,] 0.962234186 -1.189117114
[125,] 0.343134794 0.962234186
[126,] 1.156933619 0.343134794
[127,] 0.156933619 1.156933619
[128,] -0.544612911 0.156933619
[129,] -1.998666810 -0.544612911
[130,] 0.810882886 -1.998666810
[131,] 0.304035790 0.810882886
[132,] 0.645837394 0.304035790
[133,] 0.001333190 0.645837394
[134,] 1.152684490 0.001333190
[135,] 0.001333190 1.152684490
[136,] -0.037765814 0.001333190
[137,] -1.994417681 -0.037765814
[138,] 0.663780715 -1.994417681
[139,] -1.189117114 0.663780715
[140,] -1.189117114 -1.189117114
[141,] 0.161182747 -1.189117114
[142,] 0.304035790 0.161182747
[143,] -0.505513906 0.304035790
[144,] 0.152684490 -0.505513906
[145,] 0.832038569 0.152684490
[146,] -0.656865206 0.832038569
[147,] -0.340468414 -0.656865206
[148,] 0.810882886 -0.340468414
[149,] 1.152684490 0.810882886
[150,] 1.001333190 1.152684490
[151,] -1.037765814 1.001333190
[152,] 0.494486094 -1.037765814
[153,] -0.340468414 0.494486094
[154,] 0.161182747 -0.340468414
[155,] 0.152684490 0.161182747
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.494486094 0.152684490
2 0.815132015 0.494486094
3 0.836287698 0.815132015
4 0.650086522 0.836287698
5 0.645837394 0.650086522
6 0.001333190 0.645837394
7 -0.354162606 0.001333190
8 0.494486094 -0.354162606
9 0.654335651 0.494486094
10 -0.331970156 0.654335651
11 0.152684490 -0.331970156
12 -0.189117114 0.152684490
13 -0.354162606 -0.189117114
14 0.152684490 -0.354162606
15 0.308284918 0.152684490
16 -0.847315510 0.308284918
17 -0.189117114 -0.847315510
18 -0.349913478 -0.189117114
19 -1.497015649 -0.349913478
20 -0.345664349 -1.497015649
21 0.152684490 -0.345664349
22 -0.184867985 0.152684490
23 0.645837394 -0.184867985
24 0.152684490 0.645837394
25 -0.184867985 0.152684490
26 -0.994417681 -0.184867985
27 0.161182747 -0.994417681
28 0.156933619 0.161182747
29 1.645837394 0.156933619
30 0.650086522 1.645837394
31 -0.336219285 0.650086522
32 0.001333190 -0.336219285
33 0.152684490 0.001333190
34 -0.501264777 0.152684490
35 -0.340468414 -0.501264777
36 0.161182747 -0.340468414
37 0.650086522 0.161182747
38 0.152684490 0.650086522
39 0.659531586 0.152684490
40 -0.184867985 0.659531586
41 0.490236965 -0.184867985
42 -1.008111874 0.490236965
43 -0.349913478 -1.008111874
44 0.152684490 -0.349913478
45 1.001333190 0.152684490
46 0.655282458 1.001333190
47 -0.349913478 0.655282458
48 -1.847315510 -0.349913478
49 -0.189117114 -1.847315510
50 0.502984351 -0.189117114
51 -1.354162606 0.502984351
52 0.001333190 -1.354162606
53 -0.505513906 0.001333190
54 -0.336219285 -0.505513906
55 0.156933619 -0.336219285
56 -0.843066381 0.156933619
57 0.668029844 -0.843066381
58 -1.886414514 0.668029844
59 -0.340468414 -1.886414514
60 -0.354162606 -0.340468414
61 0.815132015 -0.354162606
62 -0.189117114 0.815132015
63 0.815132015 -0.189117114
64 0.494486094 0.815132015
65 0.001333190 0.494486094
66 0.304035790 0.001333190
67 0.645837394 0.304035790
68 -0.340468414 0.645837394
69 -0.861009703 -0.340468414
70 0.001333190 -0.861009703
71 0.001333190 0.001333190
72 0.502984351 0.001333190
73 -0.349913478 0.502984351
74 0.152684490 -0.349913478
75 0.152684490 0.152684490
76 0.156933619 0.152684490
77 -0.847315510 0.156933619
78 0.001333190 -0.847315510
79 -0.354162606 0.001333190
80 -0.501264777 -0.354162606
81 0.156933619 -0.501264777
82 0.156933619 0.156933619
83 -0.349913478 0.156933619
84 -0.354162606 -0.349913478
85 0.152684490 -0.354162606
86 -0.345664349 0.152684490
87 0.152684490 -0.345664349
88 1.156933619 0.152684490
89 -0.505513906 1.156933619
90 0.156933619 -0.505513906
91 -0.998666810 0.156933619
92 0.663780715 -0.998666810
93 -0.336219285 0.663780715
94 0.152684490 -0.336219285
95 0.650086522 0.152684490
96 0.836287698 0.650086522
97 -0.345664349 0.836287698
98 0.152684490 -0.345664349
99 1.152684490 0.152684490
100 0.317729982 1.152684490
101 0.810882886 0.317729982
102 -0.530918718 0.810882886
103 0.650086522 -0.530918718
104 -0.861009703 0.650086522
105 1.156933619 -0.861009703
106 -1.847315510 1.156933619
107 -0.336219285 -1.847315510
108 -0.349913478 -0.336219285
109 -0.497015649 -0.349913478
110 0.001333190 -0.497015649
111 -0.189117114 0.001333190
112 -0.656865206 -0.189117114
113 0.014080576 -0.656865206
114 0.152684490 0.014080576
115 0.810882886 0.152684490
116 0.810882886 0.810882886
117 0.498735223 0.810882886
118 0.152684490 0.498735223
119 0.494486094 0.152684490
120 0.156933619 0.494486094
121 -0.189117114 0.156933619
122 -1.656865206 -0.189117114
123 -1.189117114 -1.656865206
124 0.962234186 -1.189117114
125 0.343134794 0.962234186
126 1.156933619 0.343134794
127 0.156933619 1.156933619
128 -0.544612911 0.156933619
129 -1.998666810 -0.544612911
130 0.810882886 -1.998666810
131 0.304035790 0.810882886
132 0.645837394 0.304035790
133 0.001333190 0.645837394
134 1.152684490 0.001333190
135 0.001333190 1.152684490
136 -0.037765814 0.001333190
137 -1.994417681 -0.037765814
138 0.663780715 -1.994417681
139 -1.189117114 0.663780715
140 -1.189117114 -1.189117114
141 0.161182747 -1.189117114
142 0.304035790 0.161182747
143 -0.505513906 0.304035790
144 0.152684490 -0.505513906
145 0.832038569 0.152684490
146 -0.656865206 0.832038569
147 -0.340468414 -0.656865206
148 0.810882886 -0.340468414
149 1.152684490 0.810882886
150 1.001333190 1.152684490
151 -1.037765814 1.001333190
152 0.494486094 -1.037765814
153 -0.340468414 0.494486094
154 0.161182747 -0.340468414
155 0.152684490 0.161182747
> 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/www/html/rcomp/tmp/7v4ax1290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v4ax1290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95vr01290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105vr01290560674.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/111n791290560674.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/www/html/rcomp/tmp/12ueoc1290560674.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/www/html/rcomp/tmp/131fl51290560674.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/www/html/rcomp/tmp/14u7281290560674.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/www/html/rcomp/tmp/15x71w1290560674.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/www/html/rcomp/tmp/16thzn1290560674.tab")
+ }
>
> try(system("convert tmp/1gcu61290560674.ps tmp/1gcu61290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gcu61290560674.ps tmp/2gcu61290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/394b91290560674.ps tmp/394b91290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/494b91290560674.ps tmp/494b91290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/52dtu1290560674.ps tmp/52dtu1290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/62dtu1290560674.ps tmp/62dtu1290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v4ax1290560674.ps tmp/7v4ax1290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v4ax1290560674.ps tmp/8v4ax1290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/95vr01290560674.ps tmp/95vr01290560674.png",intern=TRUE))
character(0)
> try(system("convert tmp/105vr01290560674.ps tmp/105vr01290560674.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.800 1.681 9.214