R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(13
+ ,15
+ ,9
+ ,42
+ ,12
+ ,12
+ ,18
+ ,9
+ ,51
+ ,15
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+ ,41
+ ,15
+ ,14
+ ,16
+ ,14
+ ,51
+ ,11
+ ,12
+ ,13
+ ,8
+ ,51
+ ,13)
+ ,dim=c(5
+ ,143)
+ ,dimnames=list(c('popularity'
+ ,'hapiness'
+ ,'doubsaboutactions'
+ ,'belonging'
+ ,'parentalexpectations')
+ ,1:143))
> y <- array(NA,dim=c(5,143),dimnames=list(c('popularity','hapiness','doubsaboutactions','belonging','parentalexpectations'),1:143))
> 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'
> #'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
belonging popularity hapiness doubsaboutactions parentalexpectations
1 42 13 15 9 12
2 51 12 18 9 15
3 42 15 11 9 14
4 46 12 16 8 10
5 41 10 12 14 10
6 49 12 17 14 9
7 47 15 15 15 18
8 33 9 19 11 11
9 47 11 18 8 12
10 42 11 10 14 11
11 32 11 14 9 15
12 53 15 18 6 17
13 41 7 18 14 14
14 41 11 14 8 24
15 33 11 14 11 7
16 37 10 12 16 18
17 43 14 16 11 11
18 33 6 13 13 14
19 49 11 16 7 18
20 42 15 14 9 12
21 43 11 9 15 11
22 37 12 9 16 5
23 43 14 17 10 12
24 42 15 13 14 11
25 43 9 15 12 10
26 46 13 17 6 11
27 33 13 16 4 15
28 42 16 12 12 16
29 40 13 11 14 14
30 44 12 16 13 8
31 42 14 17 9 13
32 52 11 17 14 18
33 44 9 16 14 17
34 45 16 13 10 10
35 46 12 12 14 13
36 36 10 12 8 11
37 45 13 16 8 12
38 49 16 14 10 12
39 43 14 12 9 12
40 43 15 12 9 9
41 37 5 14 11 18
42 32 8 8 15 7
43 45 11 15 9 14
44 45 16 14 9 16
45 45 17 11 10 12
46 45 9 13 8 17
47 31 9 14 8 12
48 33 13 15 14 9
49 44 10 16 10 12
50 49 6 10 11 9
51 44 12 11 9 13
52 41 8 12 12 10
53 44 14 14 13 10
54 38 12 15 14 11
55 33 11 16 15 13
56 47 16 9 11 13
57 37 8 11 9 13
58 48 15 15 8 6
59 40 7 15 7 7
60 50 16 13 10 13
61 54 14 17 10 21
62 43 16 17 10 11
63 54 9 15 9 9
64 44 14 13 13 18
65 47 11 15 11 9
66 33 13 13 8 9
67 45 15 15 10 15
68 33 5 10 14 9
69 44 15 15 11 11
70 47 13 14 10 14
71 45 11 15 16 14
72 43 11 16 11 8
73 43 12 7 16 12
74 33 12 13 6 8
75 46 12 15 11 11
76 47 14 13 14 17
77 47 6 16 9 16
78 0 7 16 9 11
79 43 14 12 11 13
80 46 13 15 12 11
81 36 12 14 20 8
82 42 9 11 11 11
83 44 12 14 12 13
84 47 16 15 9 13
85 41 10 9 10 15
86 47 14 15 14 15
87 46 10 17 8 12
88 47 16 16 10 12
89 46 15 14 8 15
90 46 12 15 7 12
91 36 10 16 11 21
92 30 8 10 14 24
93 48 8 17 8 11
94 45 11 15 14 12
95 49 13 15 10 15
96 55 16 13 9 17
97 11 14 14 16 12
98 52 11 16 8 16
99 33 4 11 12 13
100 47 14 18 8 15
101 33 9 14 16 11
102 44 14 14 13 15
103 42 8 14 13 12
104 55 8 14 8 14
105 42 11 15 9 12
106 46 12 14 11 20
107 46 14 15 9 17
108 47 15 15 8 12
109 33 16 12 14 11
110 53 16 19 7 11
111 42 14 13 11 9
112 44 12 15 11 12
113 55 14 17 10 11
114 40 8 9 14 8
115 46 16 15 10 12
116 53 12 16 9 15
117 44 12 17 8 10
118 35 11 11 14 14
119 40 4 15 12 16
120 44 16 11 12 18
121 46 15 15 6 6
122 45 10 17 16 16
123 53 13 14 8 11
124 45 15 12 13 20
125 48 12 14 12 10
126 46 14 15 11 16
127 55 7 16 12 15
128 47 19 16 9 14
129 43 12 14 11 7
130 38 12 11 16 9
131 40 8 14 10 12
132 47 12 13 13 12
133 47 10 13 11 13
134 42 8 14 11 17
135 53 10 16 9 11
136 43 14 16 11 11
137 44 16 12 12 14
138 42 13 11 10 13
139 51 16 13 13 12
140 54 9 15 9 11
141 41 14 13 14 15
142 51 14 16 14 11
143 51 12 13 8 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) popularity hapiness
30.4126 0.6165 0.5109
doubsaboutactions parentalexpectations
-0.4303 0.2265
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-41.5222 -2.0186 0.3845 3.1917 13.8626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.4126 5.9311 5.128 9.73e-07 ***
popularity 0.6165 0.1929 3.196 0.00173 **
hapiness 0.5109 0.2605 1.961 0.05187 .
doubsaboutactions -0.4303 0.2236 -1.924 0.05641 .
parentalexpectations 0.2265 0.1680 1.348 0.17975
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.815 on 138 degrees of freedom
Multiple R-squared: 0.1661, Adjusted R-squared: 0.1419
F-statistic: 6.873 on 4 and 138 DF, p-value: 4.486e-05
> 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,] 5.974406e-01 0.8051187586 0.4025593793
[2,] 4.757281e-01 0.9514561509 0.5242719245
[3,] 3.628645e-01 0.7257289727 0.6371355137
[4,] 3.533980e-01 0.7067959793 0.6466020104
[5,] 2.870549e-01 0.5741098910 0.7129450545
[6,] 2.625356e-01 0.5250711725 0.7374644138
[7,] 2.050084e-01 0.4100168684 0.7949915658
[8,] 2.328270e-01 0.4656540167 0.7671729917
[9,] 1.690068e-01 0.3380135878 0.8309932061
[10,] 1.376931e-01 0.2753861453 0.8623069273
[11,] 9.668122e-02 0.1933624301 0.9033187850
[12,] 1.002762e-01 0.2005523329 0.8997238336
[13,] 7.739960e-02 0.1547992080 0.9226003960
[14,] 8.335031e-02 0.1667006185 0.9166496908
[15,] 5.629322e-02 0.1125864300 0.9437067850
[16,] 4.323241e-02 0.0864648283 0.9567675859
[17,] 3.082940e-02 0.0616587902 0.9691706049
[18,] 2.466561e-02 0.0493312286 0.9753343857
[19,] 1.599877e-02 0.0319975377 0.9840012311
[20,] 4.515271e-02 0.0903054186 0.9548472907
[21,] 3.411033e-02 0.0682206579 0.9658896710
[22,] 2.309877e-02 0.0461975399 0.9769012300
[23,] 1.531093e-02 0.0306218664 0.9846890668
[24,] 1.180239e-02 0.0236047844 0.9881976078
[25,] 1.300859e-02 0.0260171767 0.9869914116
[26,] 8.521493e-03 0.0170429851 0.9914785074
[27,] 5.570720e-03 0.0111414406 0.9944292797
[28,] 4.818623e-03 0.0096372451 0.9951813775
[29,] 3.254694e-03 0.0065093870 0.9967453065
[30,] 2.125294e-03 0.0042505882 0.9978747059
[31,] 1.549016e-03 0.0030980327 0.9984509837
[32,] 9.888714e-04 0.0019777428 0.9990111286
[33,] 6.046413e-04 0.0012092826 0.9993953587
[34,] 3.745254e-04 0.0007490508 0.9996254746
[35,] 2.163793e-04 0.0004327585 0.9997836207
[36,] 1.531669e-04 0.0003063337 0.9998468331
[37,] 8.966313e-05 0.0001793263 0.9999103369
[38,] 5.012791e-05 0.0001002558 0.9999498721
[39,] 6.138571e-05 0.0001227714 0.9999386143
[40,] 1.064087e-04 0.0002128173 0.9998935913
[41,] 3.623866e-04 0.0007247733 0.9996376134
[42,] 2.456195e-04 0.0004912390 0.9997543805
[43,] 3.069584e-03 0.0061391676 0.9969304162
[44,] 2.251224e-03 0.0045024487 0.9977487756
[45,] 1.606148e-03 0.0032122956 0.9983938522
[46,] 1.040399e-03 0.0020807977 0.9989596012
[47,] 8.341408e-04 0.0016682817 0.9991658592
[48,] 1.463122e-03 0.0029262447 0.9985368777
[49,] 1.122182e-03 0.0022443642 0.9988778179
[50,] 7.618538e-04 0.0015237076 0.9992381462
[51,] 6.033128e-04 0.0012066256 0.9993966872
[52,] 3.993300e-04 0.0007986601 0.9996006700
[53,] 3.313649e-04 0.0006627297 0.9996686351
[54,] 3.458082e-04 0.0006916164 0.9996541918
[55,] 2.530789e-04 0.0005061579 0.9997469211
[56,] 1.183994e-03 0.0023679889 0.9988160055
[57,] 7.695008e-04 0.0015390016 0.9992304992
[58,] 6.587170e-04 0.0013174341 0.9993412830
[59,] 1.239655e-03 0.0024793101 0.9987603449
[60,] 8.259397e-04 0.0016518793 0.9991740603
[61,] 5.531756e-04 0.0011063512 0.9994468244
[62,] 3.560272e-04 0.0007120545 0.9996439728
[63,] 2.479773e-04 0.0004959547 0.9997520227
[64,] 1.778785e-04 0.0003557571 0.9998221215
[65,] 1.108316e-04 0.0002216632 0.9998891684
[66,] 9.555637e-05 0.0001911127 0.9999044436
[67,] 1.934194e-04 0.0003868387 0.9998065806
[68,] 1.334216e-04 0.0002668431 0.9998665784
[69,] 9.317158e-05 0.0001863432 0.9999068284
[70,] 8.305069e-05 0.0001661014 0.9999169493
[71,] 8.928294e-01 0.2143411221 0.1071705611
[72,] 8.680659e-01 0.2638682303 0.1319341152
[73,] 8.432995e-01 0.3134010548 0.1567005274
[74,] 8.259227e-01 0.3481545940 0.1740772970
[75,] 7.969297e-01 0.4061405816 0.2030702908
[76,] 7.608390e-01 0.4783220580 0.2391610290
[77,] 7.216377e-01 0.5567245737 0.2783622868
[78,] 6.784740e-01 0.6430520366 0.3215260183
[79,] 6.559920e-01 0.6880160569 0.3440080285
[80,] 6.330704e-01 0.7338591751 0.3669295876
[81,] 5.847996e-01 0.8304007042 0.4152003521
[82,] 5.424418e-01 0.9151164900 0.4575582450
[83,] 5.184790e-01 0.9630420731 0.4815210366
[84,] 5.844295e-01 0.8311409927 0.4155704964
[85,] 6.552897e-01 0.6894206464 0.3447103232
[86,] 6.406659e-01 0.7186681520 0.3593340760
[87,] 6.117959e-01 0.7764082071 0.3882041035
[88,] 5.706080e-01 0.8587839928 0.4293919964
[89,] 5.804746e-01 0.8390507759 0.4195253880
[90,] 9.989017e-01 0.0021966944 0.0010983472
[91,] 9.985384e-01 0.0029232140 0.0014616070
[92,] 9.989333e-01 0.0021334335 0.0010667168
[93,] 9.987764e-01 0.0024472210 0.0012236105
[94,] 9.991815e-01 0.0016369473 0.0008184737
[95,] 9.986498e-01 0.0027003712 0.0013501856
[96,] 9.980146e-01 0.0039707794 0.0019853897
[97,] 9.989317e-01 0.0021366610 0.0010683305
[98,] 9.989027e-01 0.0021946494 0.0010973247
[99,] 9.981775e-01 0.0036449494 0.0018224747
[100,] 9.972958e-01 0.0054083452 0.0027041726
[101,] 9.958298e-01 0.0083403909 0.0041701955
[102,] 9.980889e-01 0.0038221316 0.0019110658
[103,] 9.969682e-01 0.0060635688 0.0030317844
[104,] 9.955948e-01 0.0088104806 0.0044052403
[105,] 9.937999e-01 0.0124002759 0.0062001379
[106,] 9.937656e-01 0.0124687700 0.0062343850
[107,] 9.906914e-01 0.0186172225 0.0093086113
[108,] 9.861488e-01 0.0277024451 0.0138512225
[109,] 9.827397e-01 0.0345206261 0.0172603131
[110,] 9.858605e-01 0.0282789972 0.0141394986
[111,] 9.863375e-01 0.0273250776 0.0136625388
[112,] 9.892980e-01 0.0214040629 0.0107020314
[113,] 9.823392e-01 0.0353215235 0.0176607617
[114,] 9.792849e-01 0.0414302825 0.0207151413
[115,] 9.692654e-01 0.0614691612 0.0307345806
[116,] 9.663074e-01 0.0673851161 0.0336925580
[117,] 9.488822e-01 0.1022355571 0.0511177786
[118,] 9.248602e-01 0.1502796075 0.0751398037
[119,] 8.907473e-01 0.2185054907 0.1092527453
[120,] 9.124325e-01 0.1751350729 0.0875675364
[121,] 9.070292e-01 0.1859415470 0.0929707735
[122,] 9.002507e-01 0.1994986444 0.0997493222
[123,] 8.651571e-01 0.2696858149 0.1348429075
[124,] 9.376680e-01 0.1246640562 0.0623320281
[125,] 8.889479e-01 0.2221042984 0.1110521492
[126,] 8.130605e-01 0.3738789924 0.1869394962
[127,] 6.876724e-01 0.6246552133 0.3123276066
[128,] 5.321058e-01 0.9357883183 0.4678941591
> postscript(file="/var/www/html/rcomp/tmp/16cvb1292315507.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26cvb1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3z3uw1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4z3uw1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z3uw1292315507.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 = 143
Frequency = 1
1 2 3 4 5
-2.936986354 4.467187133 -2.579480632 1.191416006 2.049925181
6 7 8 9 10
6.488831713 2.052494779 -10.427368686 1.333042862 3.228678042
11 12 13 14 15
-11.872626173 2.873611718 -0.072114061 -5.341785169 -8.199709124
16 17 18 19 20
-2.901799277 -1.977296833 -5.331312241 2.565330698 -3.659140268
21 22 23 24 25
5.169889465 0.342894584 -3.145048350 -0.770196159 2.273122655
26 27 28 29 30
-1.023167253 -15.279010651 -2.869113246 -1.194922704 1.795986934
31 32 33 34 35
-4.801886592 8.066504459 2.037025017 0.118617749 5.137237710
36 37 38 39 40
-5.758403801 -0.878197777 3.154624285 -1.020780124 -0.957693436
41 42 43 44 45
-2.992448594 -2.563436100 0.843000646 -2.181834239 0.070830516
46 47 48 49 50
1.987975954 -11.390236850 -9.105875332 0.831999302 12.473531758
51 52 53 54 55
1.496660245 2.422396074 1.131666106 -3.942421926 -8.859583647
56 57 58 59 60
3.912948713 -3.037205378 2.758888874 -0.965680613 4.438997466
61 62 63 64 65
5.816090802 -4.151575445 12.208768306 -0.169741373 4.836297413
66 67 68 69 70
-10.665837670 -1.419375678 -1.619040203 -1.082917153 2.551144880
71 72 73 74 75
3.855087682 0.551924232 5.778940474 -10.683360277 2.766683630
76 77 78 79 80
3.487096870 4.961675154 -41.522157969 -0.386723923 2.580448184
81 82 83 84 85
-2.170099481 2.659937512 1.254814864 -0.013127231 0.728771943
86 87 88 89 90
2.918350509 1.460489731 0.132797736 -0.769058699 0.818950944
91 92 93 94 95
-8.776563398 -9.866742400 4.920097014 3.447571574 3.813691511
96 97 98 99 100
8.102538941 -31.030519638 6.448709035 -3.280176557 -1.196178204
101 102 103 104 105
-5.721311572 -0.001034365 2.377787484 12.773216556 -1.703919166
106 107 108 109 110
1.238736057 -0.686220420 0.399648309 -9.875816479 3.535703562
111 112 113 114 115
-0.991476821 0.540143536 9.081491744 4.268812383 -0.356288989
116 117 118 119 120
7.489013683 -1.319497269 -4.961855515 0.996550062 -0.811280160
121 122 123 124 125
-0.101707421 2.996714538 8.370168867 0.271558120 5.934435147
126 127 128 129 130
0.400915970 13.862576098 -2.600181383 1.183757282 -0.585092343
131 132 133 134 135
-0.913106960 5.422566381 5.568497180 0.384490717 9.628241248
136 137 138 139 140
-1.977296833 -0.416033057 -0.689575201 6.956432004 11.755688117
141 142 143
-2.059822942 7.313597611 7.044535547
> postscript(file="/var/www/html/rcomp/tmp/6sdtz1292315507.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.936986354 NA
1 4.467187133 -2.936986354
2 -2.579480632 4.467187133
3 1.191416006 -2.579480632
4 2.049925181 1.191416006
5 6.488831713 2.049925181
6 2.052494779 6.488831713
7 -10.427368686 2.052494779
8 1.333042862 -10.427368686
9 3.228678042 1.333042862
10 -11.872626173 3.228678042
11 2.873611718 -11.872626173
12 -0.072114061 2.873611718
13 -5.341785169 -0.072114061
14 -8.199709124 -5.341785169
15 -2.901799277 -8.199709124
16 -1.977296833 -2.901799277
17 -5.331312241 -1.977296833
18 2.565330698 -5.331312241
19 -3.659140268 2.565330698
20 5.169889465 -3.659140268
21 0.342894584 5.169889465
22 -3.145048350 0.342894584
23 -0.770196159 -3.145048350
24 2.273122655 -0.770196159
25 -1.023167253 2.273122655
26 -15.279010651 -1.023167253
27 -2.869113246 -15.279010651
28 -1.194922704 -2.869113246
29 1.795986934 -1.194922704
30 -4.801886592 1.795986934
31 8.066504459 -4.801886592
32 2.037025017 8.066504459
33 0.118617749 2.037025017
34 5.137237710 0.118617749
35 -5.758403801 5.137237710
36 -0.878197777 -5.758403801
37 3.154624285 -0.878197777
38 -1.020780124 3.154624285
39 -0.957693436 -1.020780124
40 -2.992448594 -0.957693436
41 -2.563436100 -2.992448594
42 0.843000646 -2.563436100
43 -2.181834239 0.843000646
44 0.070830516 -2.181834239
45 1.987975954 0.070830516
46 -11.390236850 1.987975954
47 -9.105875332 -11.390236850
48 0.831999302 -9.105875332
49 12.473531758 0.831999302
50 1.496660245 12.473531758
51 2.422396074 1.496660245
52 1.131666106 2.422396074
53 -3.942421926 1.131666106
54 -8.859583647 -3.942421926
55 3.912948713 -8.859583647
56 -3.037205378 3.912948713
57 2.758888874 -3.037205378
58 -0.965680613 2.758888874
59 4.438997466 -0.965680613
60 5.816090802 4.438997466
61 -4.151575445 5.816090802
62 12.208768306 -4.151575445
63 -0.169741373 12.208768306
64 4.836297413 -0.169741373
65 -10.665837670 4.836297413
66 -1.419375678 -10.665837670
67 -1.619040203 -1.419375678
68 -1.082917153 -1.619040203
69 2.551144880 -1.082917153
70 3.855087682 2.551144880
71 0.551924232 3.855087682
72 5.778940474 0.551924232
73 -10.683360277 5.778940474
74 2.766683630 -10.683360277
75 3.487096870 2.766683630
76 4.961675154 3.487096870
77 -41.522157969 4.961675154
78 -0.386723923 -41.522157969
79 2.580448184 -0.386723923
80 -2.170099481 2.580448184
81 2.659937512 -2.170099481
82 1.254814864 2.659937512
83 -0.013127231 1.254814864
84 0.728771943 -0.013127231
85 2.918350509 0.728771943
86 1.460489731 2.918350509
87 0.132797736 1.460489731
88 -0.769058699 0.132797736
89 0.818950944 -0.769058699
90 -8.776563398 0.818950944
91 -9.866742400 -8.776563398
92 4.920097014 -9.866742400
93 3.447571574 4.920097014
94 3.813691511 3.447571574
95 8.102538941 3.813691511
96 -31.030519638 8.102538941
97 6.448709035 -31.030519638
98 -3.280176557 6.448709035
99 -1.196178204 -3.280176557
100 -5.721311572 -1.196178204
101 -0.001034365 -5.721311572
102 2.377787484 -0.001034365
103 12.773216556 2.377787484
104 -1.703919166 12.773216556
105 1.238736057 -1.703919166
106 -0.686220420 1.238736057
107 0.399648309 -0.686220420
108 -9.875816479 0.399648309
109 3.535703562 -9.875816479
110 -0.991476821 3.535703562
111 0.540143536 -0.991476821
112 9.081491744 0.540143536
113 4.268812383 9.081491744
114 -0.356288989 4.268812383
115 7.489013683 -0.356288989
116 -1.319497269 7.489013683
117 -4.961855515 -1.319497269
118 0.996550062 -4.961855515
119 -0.811280160 0.996550062
120 -0.101707421 -0.811280160
121 2.996714538 -0.101707421
122 8.370168867 2.996714538
123 0.271558120 8.370168867
124 5.934435147 0.271558120
125 0.400915970 5.934435147
126 13.862576098 0.400915970
127 -2.600181383 13.862576098
128 1.183757282 -2.600181383
129 -0.585092343 1.183757282
130 -0.913106960 -0.585092343
131 5.422566381 -0.913106960
132 5.568497180 5.422566381
133 0.384490717 5.568497180
134 9.628241248 0.384490717
135 -1.977296833 9.628241248
136 -0.416033057 -1.977296833
137 -0.689575201 -0.416033057
138 6.956432004 -0.689575201
139 11.755688117 6.956432004
140 -2.059822942 11.755688117
141 7.313597611 -2.059822942
142 7.044535547 7.313597611
143 NA 7.044535547
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.467187133 -2.936986354
[2,] -2.579480632 4.467187133
[3,] 1.191416006 -2.579480632
[4,] 2.049925181 1.191416006
[5,] 6.488831713 2.049925181
[6,] 2.052494779 6.488831713
[7,] -10.427368686 2.052494779
[8,] 1.333042862 -10.427368686
[9,] 3.228678042 1.333042862
[10,] -11.872626173 3.228678042
[11,] 2.873611718 -11.872626173
[12,] -0.072114061 2.873611718
[13,] -5.341785169 -0.072114061
[14,] -8.199709124 -5.341785169
[15,] -2.901799277 -8.199709124
[16,] -1.977296833 -2.901799277
[17,] -5.331312241 -1.977296833
[18,] 2.565330698 -5.331312241
[19,] -3.659140268 2.565330698
[20,] 5.169889465 -3.659140268
[21,] 0.342894584 5.169889465
[22,] -3.145048350 0.342894584
[23,] -0.770196159 -3.145048350
[24,] 2.273122655 -0.770196159
[25,] -1.023167253 2.273122655
[26,] -15.279010651 -1.023167253
[27,] -2.869113246 -15.279010651
[28,] -1.194922704 -2.869113246
[29,] 1.795986934 -1.194922704
[30,] -4.801886592 1.795986934
[31,] 8.066504459 -4.801886592
[32,] 2.037025017 8.066504459
[33,] 0.118617749 2.037025017
[34,] 5.137237710 0.118617749
[35,] -5.758403801 5.137237710
[36,] -0.878197777 -5.758403801
[37,] 3.154624285 -0.878197777
[38,] -1.020780124 3.154624285
[39,] -0.957693436 -1.020780124
[40,] -2.992448594 -0.957693436
[41,] -2.563436100 -2.992448594
[42,] 0.843000646 -2.563436100
[43,] -2.181834239 0.843000646
[44,] 0.070830516 -2.181834239
[45,] 1.987975954 0.070830516
[46,] -11.390236850 1.987975954
[47,] -9.105875332 -11.390236850
[48,] 0.831999302 -9.105875332
[49,] 12.473531758 0.831999302
[50,] 1.496660245 12.473531758
[51,] 2.422396074 1.496660245
[52,] 1.131666106 2.422396074
[53,] -3.942421926 1.131666106
[54,] -8.859583647 -3.942421926
[55,] 3.912948713 -8.859583647
[56,] -3.037205378 3.912948713
[57,] 2.758888874 -3.037205378
[58,] -0.965680613 2.758888874
[59,] 4.438997466 -0.965680613
[60,] 5.816090802 4.438997466
[61,] -4.151575445 5.816090802
[62,] 12.208768306 -4.151575445
[63,] -0.169741373 12.208768306
[64,] 4.836297413 -0.169741373
[65,] -10.665837670 4.836297413
[66,] -1.419375678 -10.665837670
[67,] -1.619040203 -1.419375678
[68,] -1.082917153 -1.619040203
[69,] 2.551144880 -1.082917153
[70,] 3.855087682 2.551144880
[71,] 0.551924232 3.855087682
[72,] 5.778940474 0.551924232
[73,] -10.683360277 5.778940474
[74,] 2.766683630 -10.683360277
[75,] 3.487096870 2.766683630
[76,] 4.961675154 3.487096870
[77,] -41.522157969 4.961675154
[78,] -0.386723923 -41.522157969
[79,] 2.580448184 -0.386723923
[80,] -2.170099481 2.580448184
[81,] 2.659937512 -2.170099481
[82,] 1.254814864 2.659937512
[83,] -0.013127231 1.254814864
[84,] 0.728771943 -0.013127231
[85,] 2.918350509 0.728771943
[86,] 1.460489731 2.918350509
[87,] 0.132797736 1.460489731
[88,] -0.769058699 0.132797736
[89,] 0.818950944 -0.769058699
[90,] -8.776563398 0.818950944
[91,] -9.866742400 -8.776563398
[92,] 4.920097014 -9.866742400
[93,] 3.447571574 4.920097014
[94,] 3.813691511 3.447571574
[95,] 8.102538941 3.813691511
[96,] -31.030519638 8.102538941
[97,] 6.448709035 -31.030519638
[98,] -3.280176557 6.448709035
[99,] -1.196178204 -3.280176557
[100,] -5.721311572 -1.196178204
[101,] -0.001034365 -5.721311572
[102,] 2.377787484 -0.001034365
[103,] 12.773216556 2.377787484
[104,] -1.703919166 12.773216556
[105,] 1.238736057 -1.703919166
[106,] -0.686220420 1.238736057
[107,] 0.399648309 -0.686220420
[108,] -9.875816479 0.399648309
[109,] 3.535703562 -9.875816479
[110,] -0.991476821 3.535703562
[111,] 0.540143536 -0.991476821
[112,] 9.081491744 0.540143536
[113,] 4.268812383 9.081491744
[114,] -0.356288989 4.268812383
[115,] 7.489013683 -0.356288989
[116,] -1.319497269 7.489013683
[117,] -4.961855515 -1.319497269
[118,] 0.996550062 -4.961855515
[119,] -0.811280160 0.996550062
[120,] -0.101707421 -0.811280160
[121,] 2.996714538 -0.101707421
[122,] 8.370168867 2.996714538
[123,] 0.271558120 8.370168867
[124,] 5.934435147 0.271558120
[125,] 0.400915970 5.934435147
[126,] 13.862576098 0.400915970
[127,] -2.600181383 13.862576098
[128,] 1.183757282 -2.600181383
[129,] -0.585092343 1.183757282
[130,] -0.913106960 -0.585092343
[131,] 5.422566381 -0.913106960
[132,] 5.568497180 5.422566381
[133,] 0.384490717 5.568497180
[134,] 9.628241248 0.384490717
[135,] -1.977296833 9.628241248
[136,] -0.416033057 -1.977296833
[137,] -0.689575201 -0.416033057
[138,] 6.956432004 -0.689575201
[139,] 11.755688117 6.956432004
[140,] -2.059822942 11.755688117
[141,] 7.313597611 -2.059822942
[142,] 7.044535547 7.313597611
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.467187133 -2.936986354
2 -2.579480632 4.467187133
3 1.191416006 -2.579480632
4 2.049925181 1.191416006
5 6.488831713 2.049925181
6 2.052494779 6.488831713
7 -10.427368686 2.052494779
8 1.333042862 -10.427368686
9 3.228678042 1.333042862
10 -11.872626173 3.228678042
11 2.873611718 -11.872626173
12 -0.072114061 2.873611718
13 -5.341785169 -0.072114061
14 -8.199709124 -5.341785169
15 -2.901799277 -8.199709124
16 -1.977296833 -2.901799277
17 -5.331312241 -1.977296833
18 2.565330698 -5.331312241
19 -3.659140268 2.565330698
20 5.169889465 -3.659140268
21 0.342894584 5.169889465
22 -3.145048350 0.342894584
23 -0.770196159 -3.145048350
24 2.273122655 -0.770196159
25 -1.023167253 2.273122655
26 -15.279010651 -1.023167253
27 -2.869113246 -15.279010651
28 -1.194922704 -2.869113246
29 1.795986934 -1.194922704
30 -4.801886592 1.795986934
31 8.066504459 -4.801886592
32 2.037025017 8.066504459
33 0.118617749 2.037025017
34 5.137237710 0.118617749
35 -5.758403801 5.137237710
36 -0.878197777 -5.758403801
37 3.154624285 -0.878197777
38 -1.020780124 3.154624285
39 -0.957693436 -1.020780124
40 -2.992448594 -0.957693436
41 -2.563436100 -2.992448594
42 0.843000646 -2.563436100
43 -2.181834239 0.843000646
44 0.070830516 -2.181834239
45 1.987975954 0.070830516
46 -11.390236850 1.987975954
47 -9.105875332 -11.390236850
48 0.831999302 -9.105875332
49 12.473531758 0.831999302
50 1.496660245 12.473531758
51 2.422396074 1.496660245
52 1.131666106 2.422396074
53 -3.942421926 1.131666106
54 -8.859583647 -3.942421926
55 3.912948713 -8.859583647
56 -3.037205378 3.912948713
57 2.758888874 -3.037205378
58 -0.965680613 2.758888874
59 4.438997466 -0.965680613
60 5.816090802 4.438997466
61 -4.151575445 5.816090802
62 12.208768306 -4.151575445
63 -0.169741373 12.208768306
64 4.836297413 -0.169741373
65 -10.665837670 4.836297413
66 -1.419375678 -10.665837670
67 -1.619040203 -1.419375678
68 -1.082917153 -1.619040203
69 2.551144880 -1.082917153
70 3.855087682 2.551144880
71 0.551924232 3.855087682
72 5.778940474 0.551924232
73 -10.683360277 5.778940474
74 2.766683630 -10.683360277
75 3.487096870 2.766683630
76 4.961675154 3.487096870
77 -41.522157969 4.961675154
78 -0.386723923 -41.522157969
79 2.580448184 -0.386723923
80 -2.170099481 2.580448184
81 2.659937512 -2.170099481
82 1.254814864 2.659937512
83 -0.013127231 1.254814864
84 0.728771943 -0.013127231
85 2.918350509 0.728771943
86 1.460489731 2.918350509
87 0.132797736 1.460489731
88 -0.769058699 0.132797736
89 0.818950944 -0.769058699
90 -8.776563398 0.818950944
91 -9.866742400 -8.776563398
92 4.920097014 -9.866742400
93 3.447571574 4.920097014
94 3.813691511 3.447571574
95 8.102538941 3.813691511
96 -31.030519638 8.102538941
97 6.448709035 -31.030519638
98 -3.280176557 6.448709035
99 -1.196178204 -3.280176557
100 -5.721311572 -1.196178204
101 -0.001034365 -5.721311572
102 2.377787484 -0.001034365
103 12.773216556 2.377787484
104 -1.703919166 12.773216556
105 1.238736057 -1.703919166
106 -0.686220420 1.238736057
107 0.399648309 -0.686220420
108 -9.875816479 0.399648309
109 3.535703562 -9.875816479
110 -0.991476821 3.535703562
111 0.540143536 -0.991476821
112 9.081491744 0.540143536
113 4.268812383 9.081491744
114 -0.356288989 4.268812383
115 7.489013683 -0.356288989
116 -1.319497269 7.489013683
117 -4.961855515 -1.319497269
118 0.996550062 -4.961855515
119 -0.811280160 0.996550062
120 -0.101707421 -0.811280160
121 2.996714538 -0.101707421
122 8.370168867 2.996714538
123 0.271558120 8.370168867
124 5.934435147 0.271558120
125 0.400915970 5.934435147
126 13.862576098 0.400915970
127 -2.600181383 13.862576098
128 1.183757282 -2.600181383
129 -0.585092343 1.183757282
130 -0.913106960 -0.585092343
131 5.422566381 -0.913106960
132 5.568497180 5.422566381
133 0.384490717 5.568497180
134 9.628241248 0.384490717
135 -1.977296833 9.628241248
136 -0.416033057 -1.977296833
137 -0.689575201 -0.416033057
138 6.956432004 -0.689575201
139 11.755688117 6.956432004
140 -2.059822942 11.755688117
141 7.313597611 -2.059822942
142 7.044535547 7.313597611
> 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/73mak1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83mak1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93mak1292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vvs51292315507.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ze8b1292315507.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/12kwph1292315507.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/138g721292315508.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/14cg5q1292315508.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/15485b1292315508.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/16ih221292315508.tab")
+ }
>
> try(system("convert tmp/16cvb1292315507.ps tmp/16cvb1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/26cvb1292315507.ps tmp/26cvb1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z3uw1292315507.ps tmp/3z3uw1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z3uw1292315507.ps tmp/4z3uw1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z3uw1292315507.ps tmp/5z3uw1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sdtz1292315507.ps tmp/6sdtz1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/73mak1292315507.ps tmp/73mak1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/83mak1292315507.ps tmp/83mak1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/93mak1292315507.ps tmp/93mak1292315507.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vvs51292315507.ps tmp/10vvs51292315507.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.767 1.771 8.186