R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(2
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Gender'
+ ,'Age'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Gender','Age','Connected','Separate','Learning','Software','Happiness','Depression'),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 = '7'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '7'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Gender Age Connected Separate Learning Software Depression
1 14 2 7 41 38 13 12 12
2 18 2 5 39 32 16 11 11
3 11 2 5 30 35 19 15 14
4 12 1 5 31 33 15 6 12
5 16 2 8 34 37 14 13 21
6 18 2 6 35 29 13 10 12
7 14 2 5 39 31 19 12 22
8 14 2 6 34 36 15 14 11
9 15 2 5 36 35 14 12 10
10 15 2 4 37 38 15 6 13
11 17 1 6 38 31 16 10 10
12 19 2 5 36 34 16 12 8
13 10 1 5 38 35 16 12 15
14 16 2 6 39 38 16 11 14
15 18 2 7 33 37 17 15 10
16 14 1 6 32 33 15 12 14
17 14 1 7 36 32 15 10 14
18 17 2 6 38 38 20 12 11
19 14 1 8 39 38 18 11 10
20 16 2 7 32 32 16 12 13
21 18 1 5 32 33 16 11 7
22 11 2 5 31 31 16 12 14
23 14 2 7 39 38 19 13 12
24 12 2 7 37 39 16 11 14
25 17 1 5 39 32 17 9 11
26 9 2 4 41 32 17 13 9
27 16 1 10 36 35 16 10 11
28 14 2 6 33 37 15 14 15
29 15 2 5 33 33 16 12 14
30 11 1 5 34 33 14 10 13
31 16 2 5 31 28 15 12 9
32 13 1 5 27 32 12 8 15
33 17 2 6 37 31 14 10 10
34 15 2 5 34 37 16 12 11
35 14 1 5 34 30 14 12 13
36 16 1 5 32 33 7 7 8
37 9 1 5 29 31 10 6 20
38 15 1 5 36 33 14 12 12
39 17 2 5 29 31 16 10 10
40 13 1 5 35 33 16 10 10
41 15 1 5 37 32 16 10 9
42 16 2 7 34 33 14 12 14
43 16 1 5 38 32 20 15 8
44 12 1 6 35 33 14 10 14
45 12 2 7 38 28 14 10 11
46 11 2 7 37 35 11 12 13
47 15 2 5 38 39 14 13 9
48 15 2 5 33 34 15 11 11
49 17 2 4 36 38 16 11 15
50 13 1 5 38 32 14 12 11
51 16 2 4 32 38 16 14 10
52 14 1 5 32 30 14 10 14
53 11 1 5 32 33 12 12 18
54 12 2 7 34 38 16 13 14
55 12 1 5 32 32 9 5 11
56 15 2 5 37 32 14 6 12
57 16 2 6 39 34 16 12 13
58 15 2 4 29 34 16 12 9
59 12 1 6 37 36 15 11 10
60 12 2 6 35 34 16 10 15
61 8 1 5 30 28 12 7 20
62 13 1 7 38 34 16 12 12
63 11 2 6 34 35 16 14 12
64 14 2 8 31 35 14 11 14
65 15 2 7 34 31 16 12 13
66 10 1 5 35 37 17 13 11
67 11 2 6 36 35 18 14 17
68 12 1 6 30 27 18 11 12
69 15 2 5 39 40 12 12 13
70 15 1 5 35 37 16 12 14
71 14 1 5 38 36 10 8 13
72 16 2 5 31 38 14 11 15
73 15 2 4 34 39 18 14 13
74 15 1 6 38 41 18 14 10
75 13 1 6 34 27 16 12 11
76 12 2 6 39 30 17 9 19
77 17 2 6 37 37 16 13 13
78 13 2 7 34 31 16 11 17
79 15 1 5 28 31 13 12 13
80 13 1 7 37 27 16 12 9
81 15 1 6 33 36 16 12 11
82 16 1 5 37 38 20 12 10
83 15 2 5 35 37 16 12 9
84 16 1 4 37 33 15 12 12
85 15 2 8 32 34 15 11 12
86 14 2 8 33 31 16 10 13
87 15 1 5 38 39 14 9 13
88 14 2 5 33 34 16 12 12
89 13 2 6 29 32 16 12 15
90 7 2 4 33 33 15 12 22
91 17 2 5 31 36 12 9 13
92 13 2 5 36 32 17 15 15
93 15 2 5 35 41 16 12 13
94 14 2 5 32 28 15 12 15
95 13 2 6 29 30 13 12 10
96 16 2 6 39 36 16 10 11
97 12 2 5 37 35 16 13 16
98 14 2 6 35 31 16 9 11
99 17 1 5 37 34 16 12 11
100 15 1 7 32 36 14 10 10
101 17 2 5 38 36 16 14 10
102 12 1 6 37 35 16 11 16
103 16 2 6 36 37 20 15 12
104 11 1 6 32 28 15 11 11
105 15 2 4 33 39 16 11 16
106 9 1 5 40 32 13 12 19
107 16 2 5 38 35 17 12 11
108 15 1 7 41 39 16 12 16
109 10 1 6 36 35 16 11 15
110 10 2 9 43 42 12 7 24
111 15 2 6 30 34 16 12 14
112 11 2 6 31 33 16 14 15
113 13 2 5 32 41 17 11 11
114 14 1 6 32 33 13 11 15
115 18 2 5 37 34 12 10 12
116 16 1 8 37 32 18 13 10
117 14 2 7 33 40 14 13 14
118 14 2 5 34 40 14 8 13
119 14 2 7 33 35 13 11 9
120 14 2 6 38 36 16 12 15
121 12 2 6 33 37 13 11 15
122 14 2 9 31 27 16 13 14
123 15 2 7 38 39 13 12 11
124 15 2 6 37 38 16 14 8
125 15 2 5 33 31 15 13 11
126 13 2 5 31 33 16 15 11
127 17 1 6 39 32 15 10 8
128 17 2 6 44 39 17 11 10
129 19 2 7 33 36 15 9 11
130 15 2 5 35 33 12 11 13
131 13 1 5 32 33 16 10 11
132 9 1 5 28 32 10 11 20
133 15 2 6 40 37 16 8 10
134 15 1 4 27 30 12 11 15
135 15 1 5 37 38 14 12 12
136 16 2 7 32 29 15 12 14
137 11 1 5 28 22 13 9 23
138 14 1 7 34 35 15 11 14
139 11 2 7 30 35 11 10 16
140 15 2 6 35 34 12 8 11
141 13 1 5 31 35 8 9 12
142 15 2 8 32 34 16 8 10
143 16 1 5 30 34 15 9 14
144 14 2 5 30 35 17 15 12
145 15 1 5 31 23 16 11 12
146 16 2 6 40 31 10 8 11
147 16 2 4 32 27 18 13 12
148 11 1 5 36 36 13 12 13
149 12 1 5 32 31 16 12 11
150 9 1 7 35 32 13 9 19
151 16 2 6 38 39 10 7 12
152 13 2 7 42 37 15 13 17
153 16 1 10 34 38 16 9 9
154 12 2 6 35 39 16 6 12
155 9 2 8 35 34 14 8 19
156 13 2 4 33 31 10 8 18
157 13 2 5 36 32 17 15 15
158 14 2 6 32 37 13 6 14
159 19 2 7 33 36 15 9 11
160 13 2 7 34 32 16 11 9
161 12 2 6 32 35 12 8 18
162 13 2 6 34 36 13 8 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Age Connected Separate Learning
15.5282459 0.9414064 -0.0007302 0.0264827 0.0331486 0.0616397
Software Depression
-0.0753992 -0.3986397
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0346 -1.2347 0.0971 1.2924 4.9565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.5282459 2.2779505 6.817 1.98e-10 ***
Gender 0.9414064 0.3284616 2.866 0.00474 **
Age -0.0007302 0.1338671 -0.005 0.99565
Connected 0.0264827 0.0500198 0.529 0.59726
Separate 0.0331486 0.0475053 0.698 0.48636
Learning 0.0616397 0.0839919 0.734 0.46414
Software -0.0753992 0.0860866 -0.876 0.38247
Depression -0.3986397 0.0498289 -8.000 2.78e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.929 on 154 degrees of freedom
Multiple R-squared: 0.3484, Adjusted R-squared: 0.3188
F-statistic: 11.76 on 7 and 154 DF, p-value: 5.983e-12
> 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,] 0.7661372 0.467725663 0.233862831
[2,] 0.8163287 0.367342509 0.183671254
[3,] 0.7454743 0.509051431 0.254525715
[4,] 0.6425267 0.714946595 0.357473298
[5,] 0.5705614 0.858877127 0.429438563
[6,] 0.6328512 0.734297516 0.367148758
[7,] 0.6054971 0.789005843 0.394502922
[8,] 0.5203118 0.959376463 0.479688232
[9,] 0.5249230 0.950153994 0.475076997
[10,] 0.5299910 0.940018038 0.470009019
[11,] 0.7279342 0.544131615 0.272065807
[12,] 0.8798030 0.240393981 0.120196990
[13,] 0.8864664 0.227067239 0.113533619
[14,] 0.9071620 0.185676071 0.092838036
[15,] 0.9011014 0.197797128 0.098898564
[16,] 0.9980705 0.003859088 0.001929544
[17,] 0.9981454 0.003709222 0.001854611
[18,] 0.9971363 0.005727303 0.002863651
[19,] 0.9957281 0.008543746 0.004271873
[20,] 0.9958507 0.008298544 0.004149272
[21,] 0.9939173 0.012165497 0.006082748
[22,] 0.9908995 0.018201032 0.009100516
[23,] 0.9873982 0.025203504 0.012601752
[24,] 0.9824037 0.035192676 0.017596338
[25,] 0.9769551 0.046089714 0.023044857
[26,] 0.9692471 0.061505824 0.030752912
[27,] 0.9743647 0.051270661 0.025635330
[28,] 0.9726499 0.054700156 0.027350078
[29,] 0.9657795 0.068440957 0.034220478
[30,] 0.9600390 0.079921944 0.039960972
[31,] 0.9470588 0.105882425 0.052941213
[32,] 0.9407109 0.118578240 0.059289120
[33,] 0.9303615 0.139276960 0.069638480
[34,] 0.9197391 0.160521709 0.080260855
[35,] 0.9756646 0.048670712 0.024335356
[36,] 0.9857880 0.028424042 0.014212021
[37,] 0.9813944 0.037211183 0.018605591
[38,] 0.9747650 0.050470086 0.025235043
[39,] 0.9865414 0.026917298 0.013458649
[40,] 0.9827004 0.034599248 0.017299624
[41,] 0.9776220 0.044756074 0.022378037
[42,] 0.9721557 0.055688557 0.027844279
[43,] 0.9631937 0.073612562 0.036806281
[44,] 0.9663218 0.067356391 0.033678196
[45,] 0.9677999 0.064400138 0.032200069
[46,] 0.9580313 0.083937416 0.041968708
[47,] 0.9526529 0.094694146 0.047347073
[48,] 0.9420002 0.115999618 0.057999809
[49,] 0.9514370 0.097126043 0.048563021
[50,] 0.9522714 0.095457157 0.047728578
[51,] 0.9620962 0.075807681 0.037903840
[52,] 0.9536763 0.092647333 0.046323667
[53,] 0.9756634 0.048673286 0.024336643
[54,] 0.9686536 0.062692707 0.031346353
[55,] 0.9607163 0.078567332 0.039283666
[56,] 0.9851199 0.029760259 0.014880129
[57,] 0.9843714 0.031257256 0.015628628
[58,] 0.9843242 0.031351674 0.015675837
[59,] 0.9802825 0.039435035 0.019717517
[60,] 0.9809511 0.038097896 0.019048948
[61,] 0.9757625 0.048474910 0.024237455
[62,] 0.9794412 0.041117656 0.020558828
[63,] 0.9735839 0.052832152 0.026416076
[64,] 0.9659829 0.068034133 0.034017067
[65,] 0.9595079 0.080984205 0.040492103
[66,] 0.9489467 0.102106585 0.051053292
[67,] 0.9579608 0.084078362 0.042039181
[68,] 0.9475834 0.104833218 0.052416609
[69,] 0.9484029 0.103194195 0.051597098
[70,] 0.9507662 0.098467672 0.049233836
[71,] 0.9394287 0.121142651 0.060571325
[72,] 0.9274438 0.145112389 0.072556194
[73,] 0.9161890 0.167621995 0.083810998
[74,] 0.9193295 0.161341040 0.080670520
[75,] 0.9016006 0.196798733 0.098399366
[76,] 0.8805438 0.238912321 0.119456161
[77,] 0.8624189 0.275162207 0.137581104
[78,] 0.8384990 0.323002018 0.161501009
[79,] 0.8090960 0.381807955 0.190903978
[80,] 0.8729173 0.254165417 0.127082709
[81,] 0.8940429 0.211914112 0.105957056
[82,] 0.8714348 0.257130477 0.128565238
[83,] 0.8469780 0.306043976 0.153021988
[84,] 0.8220970 0.355805981 0.177902990
[85,] 0.8258156 0.348368778 0.174184389
[86,] 0.7942693 0.411461336 0.205730668
[87,] 0.7711782 0.457643511 0.228821756
[88,] 0.7565495 0.486901029 0.243450514
[89,] 0.7839381 0.432123744 0.216061872
[90,] 0.7485673 0.502865417 0.251432709
[91,] 0.7297812 0.540437571 0.270218786
[92,] 0.6893463 0.621307473 0.310653737
[93,] 0.6652087 0.669582647 0.334791323
[94,] 0.7460088 0.507982429 0.253991214
[95,] 0.7574650 0.485070045 0.242535022
[96,] 0.7725160 0.454967931 0.227483965
[97,] 0.7373369 0.525326106 0.262663053
[98,] 0.7774392 0.445121521 0.222560761
[99,] 0.8152875 0.369425023 0.184712512
[100,] 0.7877888 0.424422448 0.212211224
[101,] 0.7768362 0.446327658 0.223163829
[102,] 0.7761064 0.447787290 0.223893645
[103,] 0.7765212 0.446957529 0.223478765
[104,] 0.7605452 0.478909570 0.239454785
[105,] 0.8238855 0.352228967 0.176114484
[106,] 0.7994375 0.401124999 0.200562499
[107,] 0.7747124 0.450575136 0.225287568
[108,] 0.7343224 0.531355273 0.265677637
[109,] 0.7289666 0.542066844 0.271033422
[110,] 0.6898810 0.620237923 0.310118961
[111,] 0.6539477 0.692104658 0.346052329
[112,] 0.6011089 0.797782131 0.398891066
[113,] 0.5478720 0.904255952 0.452127976
[114,] 0.5080905 0.983819028 0.491909514
[115,] 0.4511466 0.902293249 0.548853376
[116,] 0.4538593 0.907718693 0.546140653
[117,] 0.4090891 0.818178177 0.590910912
[118,] 0.3879207 0.775841386 0.612079307
[119,] 0.5529975 0.894004907 0.447002453
[120,] 0.4970314 0.994062894 0.502968553
[121,] 0.4737382 0.947476395 0.526261803
[122,] 0.4484085 0.896816908 0.551591546
[123,] 0.3901380 0.780275985 0.609862007
[124,] 0.3938428 0.787685651 0.606157174
[125,] 0.3692309 0.738461878 0.630769061
[126,] 0.3809542 0.761908315 0.619045843
[127,] 0.3585385 0.717076930 0.641461535
[128,] 0.3317572 0.663514351 0.668242825
[129,] 0.3029118 0.605823693 0.697088154
[130,] 0.2414811 0.482962125 0.758518938
[131,] 0.2133226 0.426645219 0.786677391
[132,] 0.1657813 0.331562547 0.834218727
[133,] 0.3087617 0.617523440 0.691238280
[134,] 0.2528955 0.505790949 0.747104526
[135,] 0.2779661 0.555932237 0.722033882
[136,] 0.2094850 0.418969982 0.790515009
[137,] 0.3279721 0.655944138 0.672027931
[138,] 0.4945500 0.989100047 0.505449977
[139,] 0.4263359 0.852671886 0.573664057
[140,] 0.3030043 0.606008627 0.696995686
[141,] 0.2451037 0.490207364 0.754896318
> postscript(file="/var/fisher/rcomp/tmp/1kdv71354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2r9sk1354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3w8b51354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4hsxs1354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51adc1354802508.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
-0.86423094 2.72720745 -2.82129732 -2.06938987 4.95654326 3.44147351
7 8 9 10 11 12
3.03587275 -0.98440596 -0.49275139 0.06247433 2.25493647 2.61983850
13 14 15 16 17 18
-2.73439121 1.72496540 2.56313859 1.15453216 0.93168188 1.38436959
19 20 21 22 23 24
-1.05000575 1.78672520 2.22628530 -2.75646436 -1.10570433 -2.25448757
25 26 27 28 29 30
2.45617585 -7.03460879 1.57686822 0.60348690 1.12427318 -2.38696182
31 32 33 34 35 36
0.41142255 0.60132577 1.46329203 -0.23072277 0.86328221 0.87808513
37 38 39 40 41 42
-1.45281167 1.31223151 1.55114389 -1.73264286 -0.15109933 2.22253028
43 44 45 46 47 48
0.55421561 -1.01407458 -3.06437506 -3.13693558 -1.00155146 -0.11855396
49 50 51 52 53 54
3.20159264 -1.10622497 0.54052247 1.16408888 -0.06672038 -1.99109262
55 56 57 58 59 60
-2.16692494 -0.07490413 1.53431912 -0.79687333 -2.74728480 -1.71326918
61 62 63 64 65 66
-2.42772880 -0.89570123 -3.61425737 0.16101225 0.76690840 -4.30203950
67 68 69 70 71 72
-1.79730361 -1.65120866 0.58125617 1.88012002 0.50342205 2.45801556
73 74 75 76 77 78
0.52704831 0.10176833 -0.95710056 -0.22908575 2.56323799 0.28606795
79 80 81 82 83 84
2.05066936 -1.83309771 0.77104511 0.95288877 -1.05448481 2.22337895
85 86 87 88 89 90
0.30875911 -0.35667705 1.23281696 -0.70615474 -0.33727764 -3.62569993
91 92 93 94 95 96
2.69951424 -0.35882871 0.40747970 0.77677797 -2.07925999 0.51994428
97 98 99 100 101 102
-1.17527608 -1.28378143 2.73068129 0.37209928 1.44865377 -0.38393780
103 104 105 106 107 108
1.09532074 -2.95104329 1.64653179 -1.90843319 0.66800399 2.45366670
109 110 111 112 113 114
-2.75609480 -0.58001010 1.17130288 -2.27259318 -2.44739048 1.60105197
115 116 117 118 119 120
3.28367480 1.35264930 0.09237225 -0.71120650 -1.82424211 0.29178406
121 122 123 124 125 126
-1.49943135 0.45444999 -0.21657115 -1.38770980 0.13169008 -1.79248297
127 128 129 130 131 132
1.45966552 0.90320508 3.66581103 0.84382758 -1.25455515 -1.08248163
133 134 135 136 137 138
-1.08912501 2.89309020 1.12000606 2.34645020 2.10920565 0.96060075
139 140 141 142 143 144
-1.90643617 -0.21206767 -0.47801186 -0.77635747 2.94742117 -0.49529738
145 146 147 148 149 150
1.57745196 0.87824394 1.50375747 -2.32693482 -2.03745967 -2.00075690
151 152 153 154 155 156
0.98926134 0.08775324 0.65770934 -3.37652775 -3.14476903 0.85263998
157 158 159 160 161 162
-0.35882871 -0.24858428 3.66581103 -2.93619807 -0.37529041 -0.32032334
> postscript(file="/var/fisher/rcomp/tmp/6mzlf1354802508.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 -0.86423094 NA
1 2.72720745 -0.86423094
2 -2.82129732 2.72720745
3 -2.06938987 -2.82129732
4 4.95654326 -2.06938987
5 3.44147351 4.95654326
6 3.03587275 3.44147351
7 -0.98440596 3.03587275
8 -0.49275139 -0.98440596
9 0.06247433 -0.49275139
10 2.25493647 0.06247433
11 2.61983850 2.25493647
12 -2.73439121 2.61983850
13 1.72496540 -2.73439121
14 2.56313859 1.72496540
15 1.15453216 2.56313859
16 0.93168188 1.15453216
17 1.38436959 0.93168188
18 -1.05000575 1.38436959
19 1.78672520 -1.05000575
20 2.22628530 1.78672520
21 -2.75646436 2.22628530
22 -1.10570433 -2.75646436
23 -2.25448757 -1.10570433
24 2.45617585 -2.25448757
25 -7.03460879 2.45617585
26 1.57686822 -7.03460879
27 0.60348690 1.57686822
28 1.12427318 0.60348690
29 -2.38696182 1.12427318
30 0.41142255 -2.38696182
31 0.60132577 0.41142255
32 1.46329203 0.60132577
33 -0.23072277 1.46329203
34 0.86328221 -0.23072277
35 0.87808513 0.86328221
36 -1.45281167 0.87808513
37 1.31223151 -1.45281167
38 1.55114389 1.31223151
39 -1.73264286 1.55114389
40 -0.15109933 -1.73264286
41 2.22253028 -0.15109933
42 0.55421561 2.22253028
43 -1.01407458 0.55421561
44 -3.06437506 -1.01407458
45 -3.13693558 -3.06437506
46 -1.00155146 -3.13693558
47 -0.11855396 -1.00155146
48 3.20159264 -0.11855396
49 -1.10622497 3.20159264
50 0.54052247 -1.10622497
51 1.16408888 0.54052247
52 -0.06672038 1.16408888
53 -1.99109262 -0.06672038
54 -2.16692494 -1.99109262
55 -0.07490413 -2.16692494
56 1.53431912 -0.07490413
57 -0.79687333 1.53431912
58 -2.74728480 -0.79687333
59 -1.71326918 -2.74728480
60 -2.42772880 -1.71326918
61 -0.89570123 -2.42772880
62 -3.61425737 -0.89570123
63 0.16101225 -3.61425737
64 0.76690840 0.16101225
65 -4.30203950 0.76690840
66 -1.79730361 -4.30203950
67 -1.65120866 -1.79730361
68 0.58125617 -1.65120866
69 1.88012002 0.58125617
70 0.50342205 1.88012002
71 2.45801556 0.50342205
72 0.52704831 2.45801556
73 0.10176833 0.52704831
74 -0.95710056 0.10176833
75 -0.22908575 -0.95710056
76 2.56323799 -0.22908575
77 0.28606795 2.56323799
78 2.05066936 0.28606795
79 -1.83309771 2.05066936
80 0.77104511 -1.83309771
81 0.95288877 0.77104511
82 -1.05448481 0.95288877
83 2.22337895 -1.05448481
84 0.30875911 2.22337895
85 -0.35667705 0.30875911
86 1.23281696 -0.35667705
87 -0.70615474 1.23281696
88 -0.33727764 -0.70615474
89 -3.62569993 -0.33727764
90 2.69951424 -3.62569993
91 -0.35882871 2.69951424
92 0.40747970 -0.35882871
93 0.77677797 0.40747970
94 -2.07925999 0.77677797
95 0.51994428 -2.07925999
96 -1.17527608 0.51994428
97 -1.28378143 -1.17527608
98 2.73068129 -1.28378143
99 0.37209928 2.73068129
100 1.44865377 0.37209928
101 -0.38393780 1.44865377
102 1.09532074 -0.38393780
103 -2.95104329 1.09532074
104 1.64653179 -2.95104329
105 -1.90843319 1.64653179
106 0.66800399 -1.90843319
107 2.45366670 0.66800399
108 -2.75609480 2.45366670
109 -0.58001010 -2.75609480
110 1.17130288 -0.58001010
111 -2.27259318 1.17130288
112 -2.44739048 -2.27259318
113 1.60105197 -2.44739048
114 3.28367480 1.60105197
115 1.35264930 3.28367480
116 0.09237225 1.35264930
117 -0.71120650 0.09237225
118 -1.82424211 -0.71120650
119 0.29178406 -1.82424211
120 -1.49943135 0.29178406
121 0.45444999 -1.49943135
122 -0.21657115 0.45444999
123 -1.38770980 -0.21657115
124 0.13169008 -1.38770980
125 -1.79248297 0.13169008
126 1.45966552 -1.79248297
127 0.90320508 1.45966552
128 3.66581103 0.90320508
129 0.84382758 3.66581103
130 -1.25455515 0.84382758
131 -1.08248163 -1.25455515
132 -1.08912501 -1.08248163
133 2.89309020 -1.08912501
134 1.12000606 2.89309020
135 2.34645020 1.12000606
136 2.10920565 2.34645020
137 0.96060075 2.10920565
138 -1.90643617 0.96060075
139 -0.21206767 -1.90643617
140 -0.47801186 -0.21206767
141 -0.77635747 -0.47801186
142 2.94742117 -0.77635747
143 -0.49529738 2.94742117
144 1.57745196 -0.49529738
145 0.87824394 1.57745196
146 1.50375747 0.87824394
147 -2.32693482 1.50375747
148 -2.03745967 -2.32693482
149 -2.00075690 -2.03745967
150 0.98926134 -2.00075690
151 0.08775324 0.98926134
152 0.65770934 0.08775324
153 -3.37652775 0.65770934
154 -3.14476903 -3.37652775
155 0.85263998 -3.14476903
156 -0.35882871 0.85263998
157 -0.24858428 -0.35882871
158 3.66581103 -0.24858428
159 -2.93619807 3.66581103
160 -0.37529041 -2.93619807
161 -0.32032334 -0.37529041
162 NA -0.32032334
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.72720745 -0.86423094
[2,] -2.82129732 2.72720745
[3,] -2.06938987 -2.82129732
[4,] 4.95654326 -2.06938987
[5,] 3.44147351 4.95654326
[6,] 3.03587275 3.44147351
[7,] -0.98440596 3.03587275
[8,] -0.49275139 -0.98440596
[9,] 0.06247433 -0.49275139
[10,] 2.25493647 0.06247433
[11,] 2.61983850 2.25493647
[12,] -2.73439121 2.61983850
[13,] 1.72496540 -2.73439121
[14,] 2.56313859 1.72496540
[15,] 1.15453216 2.56313859
[16,] 0.93168188 1.15453216
[17,] 1.38436959 0.93168188
[18,] -1.05000575 1.38436959
[19,] 1.78672520 -1.05000575
[20,] 2.22628530 1.78672520
[21,] -2.75646436 2.22628530
[22,] -1.10570433 -2.75646436
[23,] -2.25448757 -1.10570433
[24,] 2.45617585 -2.25448757
[25,] -7.03460879 2.45617585
[26,] 1.57686822 -7.03460879
[27,] 0.60348690 1.57686822
[28,] 1.12427318 0.60348690
[29,] -2.38696182 1.12427318
[30,] 0.41142255 -2.38696182
[31,] 0.60132577 0.41142255
[32,] 1.46329203 0.60132577
[33,] -0.23072277 1.46329203
[34,] 0.86328221 -0.23072277
[35,] 0.87808513 0.86328221
[36,] -1.45281167 0.87808513
[37,] 1.31223151 -1.45281167
[38,] 1.55114389 1.31223151
[39,] -1.73264286 1.55114389
[40,] -0.15109933 -1.73264286
[41,] 2.22253028 -0.15109933
[42,] 0.55421561 2.22253028
[43,] -1.01407458 0.55421561
[44,] -3.06437506 -1.01407458
[45,] -3.13693558 -3.06437506
[46,] -1.00155146 -3.13693558
[47,] -0.11855396 -1.00155146
[48,] 3.20159264 -0.11855396
[49,] -1.10622497 3.20159264
[50,] 0.54052247 -1.10622497
[51,] 1.16408888 0.54052247
[52,] -0.06672038 1.16408888
[53,] -1.99109262 -0.06672038
[54,] -2.16692494 -1.99109262
[55,] -0.07490413 -2.16692494
[56,] 1.53431912 -0.07490413
[57,] -0.79687333 1.53431912
[58,] -2.74728480 -0.79687333
[59,] -1.71326918 -2.74728480
[60,] -2.42772880 -1.71326918
[61,] -0.89570123 -2.42772880
[62,] -3.61425737 -0.89570123
[63,] 0.16101225 -3.61425737
[64,] 0.76690840 0.16101225
[65,] -4.30203950 0.76690840
[66,] -1.79730361 -4.30203950
[67,] -1.65120866 -1.79730361
[68,] 0.58125617 -1.65120866
[69,] 1.88012002 0.58125617
[70,] 0.50342205 1.88012002
[71,] 2.45801556 0.50342205
[72,] 0.52704831 2.45801556
[73,] 0.10176833 0.52704831
[74,] -0.95710056 0.10176833
[75,] -0.22908575 -0.95710056
[76,] 2.56323799 -0.22908575
[77,] 0.28606795 2.56323799
[78,] 2.05066936 0.28606795
[79,] -1.83309771 2.05066936
[80,] 0.77104511 -1.83309771
[81,] 0.95288877 0.77104511
[82,] -1.05448481 0.95288877
[83,] 2.22337895 -1.05448481
[84,] 0.30875911 2.22337895
[85,] -0.35667705 0.30875911
[86,] 1.23281696 -0.35667705
[87,] -0.70615474 1.23281696
[88,] -0.33727764 -0.70615474
[89,] -3.62569993 -0.33727764
[90,] 2.69951424 -3.62569993
[91,] -0.35882871 2.69951424
[92,] 0.40747970 -0.35882871
[93,] 0.77677797 0.40747970
[94,] -2.07925999 0.77677797
[95,] 0.51994428 -2.07925999
[96,] -1.17527608 0.51994428
[97,] -1.28378143 -1.17527608
[98,] 2.73068129 -1.28378143
[99,] 0.37209928 2.73068129
[100,] 1.44865377 0.37209928
[101,] -0.38393780 1.44865377
[102,] 1.09532074 -0.38393780
[103,] -2.95104329 1.09532074
[104,] 1.64653179 -2.95104329
[105,] -1.90843319 1.64653179
[106,] 0.66800399 -1.90843319
[107,] 2.45366670 0.66800399
[108,] -2.75609480 2.45366670
[109,] -0.58001010 -2.75609480
[110,] 1.17130288 -0.58001010
[111,] -2.27259318 1.17130288
[112,] -2.44739048 -2.27259318
[113,] 1.60105197 -2.44739048
[114,] 3.28367480 1.60105197
[115,] 1.35264930 3.28367480
[116,] 0.09237225 1.35264930
[117,] -0.71120650 0.09237225
[118,] -1.82424211 -0.71120650
[119,] 0.29178406 -1.82424211
[120,] -1.49943135 0.29178406
[121,] 0.45444999 -1.49943135
[122,] -0.21657115 0.45444999
[123,] -1.38770980 -0.21657115
[124,] 0.13169008 -1.38770980
[125,] -1.79248297 0.13169008
[126,] 1.45966552 -1.79248297
[127,] 0.90320508 1.45966552
[128,] 3.66581103 0.90320508
[129,] 0.84382758 3.66581103
[130,] -1.25455515 0.84382758
[131,] -1.08248163 -1.25455515
[132,] -1.08912501 -1.08248163
[133,] 2.89309020 -1.08912501
[134,] 1.12000606 2.89309020
[135,] 2.34645020 1.12000606
[136,] 2.10920565 2.34645020
[137,] 0.96060075 2.10920565
[138,] -1.90643617 0.96060075
[139,] -0.21206767 -1.90643617
[140,] -0.47801186 -0.21206767
[141,] -0.77635747 -0.47801186
[142,] 2.94742117 -0.77635747
[143,] -0.49529738 2.94742117
[144,] 1.57745196 -0.49529738
[145,] 0.87824394 1.57745196
[146,] 1.50375747 0.87824394
[147,] -2.32693482 1.50375747
[148,] -2.03745967 -2.32693482
[149,] -2.00075690 -2.03745967
[150,] 0.98926134 -2.00075690
[151,] 0.08775324 0.98926134
[152,] 0.65770934 0.08775324
[153,] -3.37652775 0.65770934
[154,] -3.14476903 -3.37652775
[155,] 0.85263998 -3.14476903
[156,] -0.35882871 0.85263998
[157,] -0.24858428 -0.35882871
[158,] 3.66581103 -0.24858428
[159,] -2.93619807 3.66581103
[160,] -0.37529041 -2.93619807
[161,] -0.32032334 -0.37529041
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.72720745 -0.86423094
2 -2.82129732 2.72720745
3 -2.06938987 -2.82129732
4 4.95654326 -2.06938987
5 3.44147351 4.95654326
6 3.03587275 3.44147351
7 -0.98440596 3.03587275
8 -0.49275139 -0.98440596
9 0.06247433 -0.49275139
10 2.25493647 0.06247433
11 2.61983850 2.25493647
12 -2.73439121 2.61983850
13 1.72496540 -2.73439121
14 2.56313859 1.72496540
15 1.15453216 2.56313859
16 0.93168188 1.15453216
17 1.38436959 0.93168188
18 -1.05000575 1.38436959
19 1.78672520 -1.05000575
20 2.22628530 1.78672520
21 -2.75646436 2.22628530
22 -1.10570433 -2.75646436
23 -2.25448757 -1.10570433
24 2.45617585 -2.25448757
25 -7.03460879 2.45617585
26 1.57686822 -7.03460879
27 0.60348690 1.57686822
28 1.12427318 0.60348690
29 -2.38696182 1.12427318
30 0.41142255 -2.38696182
31 0.60132577 0.41142255
32 1.46329203 0.60132577
33 -0.23072277 1.46329203
34 0.86328221 -0.23072277
35 0.87808513 0.86328221
36 -1.45281167 0.87808513
37 1.31223151 -1.45281167
38 1.55114389 1.31223151
39 -1.73264286 1.55114389
40 -0.15109933 -1.73264286
41 2.22253028 -0.15109933
42 0.55421561 2.22253028
43 -1.01407458 0.55421561
44 -3.06437506 -1.01407458
45 -3.13693558 -3.06437506
46 -1.00155146 -3.13693558
47 -0.11855396 -1.00155146
48 3.20159264 -0.11855396
49 -1.10622497 3.20159264
50 0.54052247 -1.10622497
51 1.16408888 0.54052247
52 -0.06672038 1.16408888
53 -1.99109262 -0.06672038
54 -2.16692494 -1.99109262
55 -0.07490413 -2.16692494
56 1.53431912 -0.07490413
57 -0.79687333 1.53431912
58 -2.74728480 -0.79687333
59 -1.71326918 -2.74728480
60 -2.42772880 -1.71326918
61 -0.89570123 -2.42772880
62 -3.61425737 -0.89570123
63 0.16101225 -3.61425737
64 0.76690840 0.16101225
65 -4.30203950 0.76690840
66 -1.79730361 -4.30203950
67 -1.65120866 -1.79730361
68 0.58125617 -1.65120866
69 1.88012002 0.58125617
70 0.50342205 1.88012002
71 2.45801556 0.50342205
72 0.52704831 2.45801556
73 0.10176833 0.52704831
74 -0.95710056 0.10176833
75 -0.22908575 -0.95710056
76 2.56323799 -0.22908575
77 0.28606795 2.56323799
78 2.05066936 0.28606795
79 -1.83309771 2.05066936
80 0.77104511 -1.83309771
81 0.95288877 0.77104511
82 -1.05448481 0.95288877
83 2.22337895 -1.05448481
84 0.30875911 2.22337895
85 -0.35667705 0.30875911
86 1.23281696 -0.35667705
87 -0.70615474 1.23281696
88 -0.33727764 -0.70615474
89 -3.62569993 -0.33727764
90 2.69951424 -3.62569993
91 -0.35882871 2.69951424
92 0.40747970 -0.35882871
93 0.77677797 0.40747970
94 -2.07925999 0.77677797
95 0.51994428 -2.07925999
96 -1.17527608 0.51994428
97 -1.28378143 -1.17527608
98 2.73068129 -1.28378143
99 0.37209928 2.73068129
100 1.44865377 0.37209928
101 -0.38393780 1.44865377
102 1.09532074 -0.38393780
103 -2.95104329 1.09532074
104 1.64653179 -2.95104329
105 -1.90843319 1.64653179
106 0.66800399 -1.90843319
107 2.45366670 0.66800399
108 -2.75609480 2.45366670
109 -0.58001010 -2.75609480
110 1.17130288 -0.58001010
111 -2.27259318 1.17130288
112 -2.44739048 -2.27259318
113 1.60105197 -2.44739048
114 3.28367480 1.60105197
115 1.35264930 3.28367480
116 0.09237225 1.35264930
117 -0.71120650 0.09237225
118 -1.82424211 -0.71120650
119 0.29178406 -1.82424211
120 -1.49943135 0.29178406
121 0.45444999 -1.49943135
122 -0.21657115 0.45444999
123 -1.38770980 -0.21657115
124 0.13169008 -1.38770980
125 -1.79248297 0.13169008
126 1.45966552 -1.79248297
127 0.90320508 1.45966552
128 3.66581103 0.90320508
129 0.84382758 3.66581103
130 -1.25455515 0.84382758
131 -1.08248163 -1.25455515
132 -1.08912501 -1.08248163
133 2.89309020 -1.08912501
134 1.12000606 2.89309020
135 2.34645020 1.12000606
136 2.10920565 2.34645020
137 0.96060075 2.10920565
138 -1.90643617 0.96060075
139 -0.21206767 -1.90643617
140 -0.47801186 -0.21206767
141 -0.77635747 -0.47801186
142 2.94742117 -0.77635747
143 -0.49529738 2.94742117
144 1.57745196 -0.49529738
145 0.87824394 1.57745196
146 1.50375747 0.87824394
147 -2.32693482 1.50375747
148 -2.03745967 -2.32693482
149 -2.00075690 -2.03745967
150 0.98926134 -2.00075690
151 0.08775324 0.98926134
152 0.65770934 0.08775324
153 -3.37652775 0.65770934
154 -3.14476903 -3.37652775
155 0.85263998 -3.14476903
156 -0.35882871 0.85263998
157 -0.24858428 -0.35882871
158 3.66581103 -0.24858428
159 -2.93619807 3.66581103
160 -0.37529041 -2.93619807
161 -0.32032334 -0.37529041
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/78bk41354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8qp921354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9sbsn1354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10nr3v1354802508.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11kp6k1354802508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12l5xs1354802508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/138w2i1354802508.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14rz921354802508.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15p2du1354802508.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16jydd1354802508.tab")
+ }
>
> try(system("convert tmp/1kdv71354802508.ps tmp/1kdv71354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r9sk1354802508.ps tmp/2r9sk1354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w8b51354802508.ps tmp/3w8b51354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hsxs1354802508.ps tmp/4hsxs1354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/51adc1354802508.ps tmp/51adc1354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mzlf1354802508.ps tmp/6mzlf1354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/78bk41354802508.ps tmp/78bk41354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qp921354802508.ps tmp/8qp921354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sbsn1354802508.ps tmp/9sbsn1354802508.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nr3v1354802508.ps tmp/10nr3v1354802508.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.287 1.495 9.779