R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9
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+ ,18)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Month'
+ ,'ConcernOverMistakes'
+ ,'DoubtsAboutActions'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Month','ConcernOverMistakes','DoubtsAboutActions','ParentalExpectations','ParentalCriticism','PersonalStandards','Organization'),1:159))
> 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 = '6'
> #'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
PersonalStandards Month ConcernOverMistakes DoubtsAboutActions
1 24 9 24 14
2 25 9 25 11
3 30 9 17 6
4 19 9 18 12
5 22 9 18 8
6 22 9 16 10
7 25 10 20 10
8 23 10 16 11
9 17 10 18 16
10 21 10 17 11
11 19 10 23 13
12 19 10 30 12
13 15 10 23 8
14 16 10 18 12
15 23 10 15 11
16 27 10 12 4
17 22 10 21 9
18 14 10 15 8
19 22 10 20 8
20 23 10 31 14
21 23 10 27 15
22 21 10 34 16
23 19 10 21 9
24 18 10 31 14
25 20 10 19 11
26 23 10 16 8
27 25 10 20 9
28 19 10 21 9
29 24 10 22 9
30 22 10 17 9
31 25 10 24 10
32 26 10 25 16
33 29 10 26 11
34 32 10 25 8
35 25 10 17 9
36 29 10 32 16
37 28 10 33 11
38 17 10 13 16
39 28 10 32 12
40 29 10 25 12
41 26 10 29 14
42 25 10 22 9
43 14 10 18 10
44 25 10 17 9
45 26 10 20 10
46 20 10 15 12
47 18 10 20 14
48 32 10 33 14
49 25 10 29 10
50 25 10 23 14
51 23 10 26 16
52 21 10 18 9
53 20 10 20 10
54 15 10 11 6
55 30 10 28 8
56 24 10 26 13
57 26 10 22 10
58 23 10 15 11
59 22 10 12 7
60 14 10 14 15
61 24 10 17 9
62 24 10 21 10
63 22 10 16 10
64 24 10 18 13
65 19 10 10 10
66 31 10 29 11
67 22 10 31 8
68 27 10 19 9
69 19 10 9 13
70 25 10 20 11
71 20 10 28 8
72 21 10 19 9
73 27 10 30 9
74 23 10 29 15
75 25 10 26 9
76 20 10 23 10
77 21 10 13 14
78 22 10 21 12
79 23 10 19 12
80 25 10 28 11
81 25 10 23 14
82 17 10 18 6
83 19 10 21 12
84 25 10 20 8
85 19 10 23 14
86 20 10 21 11
87 25 10 20 10
88 23 10 15 14
89 27 10 28 12
90 17 10 19 10
91 17 10 26 14
92 19 10 10 5
93 17 10 16 11
94 22 10 22 10
95 21 10 19 9
96 32 10 31 10
97 21 10 31 16
98 21 10 29 13
99 18 10 19 9
100 18 10 22 10
101 23 10 23 10
102 19 10 15 7
103 19 10 30 12
104 21 10 18 8
105 20 10 23 14
106 17 10 25 14
107 18 10 21 8
108 19 10 24 9
109 22 10 25 14
110 15 10 17 14
111 14 10 13 8
112 18 10 28 8
113 24 10 21 8
114 35 10 25 7
115 29 10 9 6
116 21 10 16 8
117 25 10 19 6
118 19 10 18 12
119 22 10 25 14
120 13 10 20 11
121 26 10 29 11
122 17 10 14 11
123 25 10 22 14
124 20 10 15 8
125 19 10 19 20
126 21 10 20 11
127 22 10 15 8
128 24 10 20 11
129 21 10 18 10
130 26 10 33 14
131 24 10 22 11
132 16 10 16 9
133 23 10 17 9
134 18 10 16 8
135 16 10 21 10
136 26 10 26 13
137 19 10 18 13
138 21 10 18 12
139 21 10 17 8
140 22 10 22 13
141 23 10 30 14
142 29 10 30 12
143 21 10 24 14
144 21 10 21 15
145 23 10 21 13
146 27 10 29 16
147 25 10 31 9
148 21 10 20 9
149 10 10 16 9
150 20 10 22 8
151 26 10 20 7
152 24 10 28 16
153 29 10 38 11
154 19 10 22 9
155 24 10 20 11
156 22 10 21 9
157 24 10 28 14
158 22 10 22 13
159 19 10 30 12
ParentalExpectations ParentalCriticism Organization
1 11 12 26
2 7 8 23
3 17 8 25
4 10 8 23
5 12 9 19
6 12 7 29
7 11 4 25
8 11 11 21
9 12 7 22
10 13 7 25
11 14 12 24
12 16 10 18
13 11 10 22
14 10 8 15
15 11 8 22
16 15 4 28
17 9 9 20
18 11 8 12
19 17 7 24
20 17 11 20
21 11 9 21
22 18 11 20
23 14 13 21
24 10 8 23
25 11 8 28
26 15 9 24
27 15 6 24
28 13 9 24
29 16 9 23
30 13 6 23
31 9 6 29
32 18 16 24
33 18 5 18
34 12 7 25
35 17 9 21
36 9 6 26
37 9 6 22
38 12 5 22
39 18 12 22
40 12 7 23
41 18 10 30
42 14 9 23
43 15 8 17
44 16 5 23
45 10 8 23
46 11 8 25
47 14 10 24
48 9 6 24
49 12 8 23
50 17 7 21
51 5 4 24
52 12 8 24
53 12 8 28
54 6 4 16
55 24 20 20
56 12 8 29
57 12 8 27
58 11 8 22
59 7 4 28
60 13 8 16
61 12 9 25
62 13 6 24
63 12 7 29
64 8 9 24
65 11 5 23
66 9 5 30
67 11 8 24
68 13 8 21
69 10 6 25
70 11 8 25
71 12 7 22
72 9 7 23
73 15 9 26
74 18 11 23
75 15 6 25
76 12 8 21
77 13 6 25
78 14 9 24
79 10 8 29
80 13 6 22
81 13 10 27
82 11 8 26
83 13 8 22
84 16 10 24
85 8 5 27
86 16 7 24
87 11 4 25
88 9 8 29
89 16 14 22
90 12 7 21
91 14 8 24
92 8 6 24
93 9 5 23
94 15 6 20
95 11 10 27
96 21 12 26
97 14 9 25
98 18 12 21
99 12 7 21
100 13 8 19
101 15 10 21
102 12 6 21
103 16 10 18
104 15 10 22
105 11 10 29
106 11 5 15
107 10 7 17
108 13 10 15
109 15 11 21
110 12 6 21
111 12 7 19
112 16 12 24
113 9 11 20
114 18 11 17
115 8 11 23
116 13 5 24
117 17 8 14
118 10 8 23
119 15 9 24
120 8 4 13
121 7 4 22
122 12 7 16
123 14 11 19
124 6 6 25
125 8 7 25
126 17 8 23
127 10 4 24
128 11 8 26
129 14 9 26
130 11 8 25
131 13 11 18
132 12 8 21
133 11 5 26
134 9 4 23
135 12 8 23
136 20 10 22
137 12 6 20
138 13 9 13
139 12 9 24
140 12 13 15
141 9 9 14
142 15 10 22
143 24 20 10
144 7 5 24
145 17 11 22
146 11 6 24
147 17 9 19
148 11 7 20
149 12 9 13
150 14 10 20
151 11 9 22
152 16 8 24
153 21 7 29
154 14 6 12
155 20 13 20
156 9 9 20
157 11 8 24
158 15 10 22
159 16 10 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month ConcernOverMistakes
21.43570 -1.45277 0.31696
DoubtsAboutActions ParentalExpectations ParentalCriticism
-0.33319 0.19104 0.06274
Organization
0.40217
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5789 -2.4002 0.1011 2.0132 11.5346
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.43570 14.54484 1.474 0.14261
Month -1.45277 1.43617 -1.012 0.31336
ConcernOverMistakes 0.31696 0.05464 5.801 3.70e-08 ***
DoubtsAboutActions -0.33319 0.10732 -3.105 0.00227 **
ParentalExpectations 0.19104 0.10197 1.874 0.06291 .
ParentalCriticism 0.06274 0.13076 0.480 0.63205
Organization 0.40217 0.07173 5.607 9.46e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.397 on 152 degrees of freedom
Multiple R-squared: 0.3692, Adjusted R-squared: 0.3443
F-statistic: 14.83 on 6 and 152 DF, p-value: 2.650e-13
> 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,] 0.16935300 0.33870600 0.83064700
[2,] 0.48087024 0.96174048 0.51912976
[3,] 0.41059513 0.82119026 0.58940487
[4,] 0.83513768 0.32972465 0.16486232
[5,] 0.75925481 0.48149039 0.24074519
[6,] 0.74158908 0.51682183 0.25841092
[7,] 0.66206601 0.67586798 0.33793399
[8,] 0.60234259 0.79531483 0.39765741
[9,] 0.58606916 0.82786167 0.41393084
[10,] 0.50938112 0.98123776 0.49061888
[11,] 0.50608846 0.98782307 0.49391154
[12,] 0.50251073 0.99497855 0.49748927
[13,] 0.43305172 0.86610344 0.56694828
[14,] 0.37337830 0.74675660 0.62662170
[15,] 0.39211009 0.78422018 0.60788991
[16,] 0.36137265 0.72274531 0.63862735
[17,] 0.29959692 0.59919383 0.70040308
[18,] 0.25894413 0.51788825 0.74105587
[19,] 0.25074953 0.50149907 0.74925047
[20,] 0.20836293 0.41672585 0.79163707
[21,] 0.16334089 0.32668178 0.83665911
[22,] 0.13408087 0.26816174 0.86591913
[23,] 0.16960966 0.33921932 0.83039034
[24,] 0.28788730 0.57577460 0.71211270
[25,] 0.57526979 0.84946042 0.42473021
[26,] 0.55560488 0.88879024 0.44439512
[27,] 0.63295788 0.73408423 0.36704212
[28,] 0.63092656 0.73814688 0.36907344
[29,] 0.58520978 0.82958044 0.41479022
[30,] 0.55330673 0.89338654 0.44669327
[31,] 0.64879052 0.70241896 0.35120948
[32,] 0.61561053 0.76877894 0.38438947
[33,] 0.57341886 0.85316227 0.42658114
[34,] 0.65917887 0.68164227 0.34082113
[35,] 0.63573106 0.72853788 0.36426894
[36,] 0.67046806 0.65906389 0.32953194
[37,] 0.62124376 0.75751249 0.37875624
[38,] 0.61162240 0.77675519 0.38837760
[39,] 0.72811503 0.54376994 0.27188497
[40,] 0.68782845 0.62434309 0.31217155
[41,] 0.67736727 0.64526546 0.32263273
[42,] 0.64084871 0.71830258 0.35915129
[43,] 0.59771154 0.80457692 0.40228846
[44,] 0.62394347 0.75211306 0.37605653
[45,] 0.58589385 0.82821231 0.41410615
[46,] 0.64431952 0.71136097 0.35568048
[47,] 0.60876825 0.78246350 0.39123175
[48,] 0.57173432 0.85653136 0.42826568
[49,] 0.59084467 0.81831066 0.40915533
[50,] 0.54544035 0.90911930 0.45455965
[51,] 0.50492550 0.99014899 0.49507450
[52,] 0.47273744 0.94547489 0.52726256
[53,] 0.43234366 0.86468733 0.56765634
[54,] 0.39067881 0.78135761 0.60932119
[55,] 0.42755080 0.85510159 0.57244920
[56,] 0.38307311 0.76614621 0.61692689
[57,] 0.40759827 0.81519655 0.59240173
[58,] 0.46298287 0.92596574 0.53701713
[59,] 0.54722778 0.90554443 0.45277222
[60,] 0.50771854 0.98456292 0.49228146
[61,] 0.49241642 0.98483283 0.50758358
[62,] 0.55057193 0.89885614 0.44942807
[63,] 0.50413791 0.99172418 0.49586209
[64,] 0.46038579 0.92077158 0.53961421
[65,] 0.42415097 0.84830195 0.57584903
[66,] 0.38946991 0.77893982 0.61053009
[67,] 0.36172195 0.72344390 0.63827805
[68,] 0.33504324 0.67008648 0.66495676
[69,] 0.29418397 0.58836795 0.70581603
[70,] 0.25582648 0.51165296 0.74417352
[71,] 0.22710755 0.45421509 0.77289245
[72,] 0.20032922 0.40065845 0.79967078
[73,] 0.30021851 0.60043703 0.69978149
[74,] 0.27943466 0.55886931 0.72056534
[75,] 0.24660096 0.49320192 0.75339904
[76,] 0.24638145 0.49276289 0.75361855
[77,] 0.24004450 0.48008901 0.75995550
[78,] 0.23757250 0.47514501 0.76242750
[79,] 0.22002716 0.44005432 0.77997284
[80,] 0.20511856 0.41023713 0.79488144
[81,] 0.20809311 0.41618622 0.79190689
[82,] 0.29252097 0.58504194 0.70747903
[83,] 0.25427385 0.50854770 0.74572615
[84,] 0.23403807 0.46807613 0.76596193
[85,] 0.20202354 0.40404707 0.79797646
[86,] 0.18704057 0.37408114 0.81295943
[87,] 0.18927097 0.37854194 0.81072903
[88,] 0.19317405 0.38634809 0.80682595
[89,] 0.19290958 0.38581916 0.80709042
[90,] 0.18208541 0.36417083 0.81791459
[91,] 0.17409621 0.34819242 0.82590379
[92,] 0.14485623 0.28971246 0.85514377
[93,] 0.12105913 0.24211826 0.87894087
[94,] 0.13679885 0.27359769 0.86320115
[95,] 0.11427472 0.22854945 0.88572528
[96,] 0.12843277 0.25686553 0.87156723
[97,] 0.10688010 0.21376020 0.89311990
[98,] 0.09296525 0.18593051 0.90703475
[99,] 0.08229886 0.16459771 0.91770114
[100,] 0.06602874 0.13205748 0.93397126
[101,] 0.06482597 0.12965193 0.93517403
[102,] 0.07624016 0.15248033 0.92375984
[103,] 0.29142598 0.58285195 0.70857402
[104,] 0.27219169 0.54438337 0.72780831
[105,] 0.75534309 0.48931382 0.24465691
[106,] 0.94503057 0.10993887 0.05496943
[107,] 0.92902774 0.14194452 0.07097226
[108,] 0.96927200 0.06145599 0.03072800
[109,] 0.96146113 0.07707773 0.03853887
[110,] 0.95331715 0.09336569 0.04668285
[111,] 0.95623336 0.08753328 0.04376664
[112,] 0.95076883 0.09846233 0.04923117
[113,] 0.93380129 0.13239742 0.06619871
[114,] 0.94206889 0.11586222 0.05793111
[115,] 0.92169806 0.15660388 0.07830194
[116,] 0.91174368 0.17651265 0.08825632
[117,] 0.88536781 0.22926438 0.11463219
[118,] 0.88125043 0.23749914 0.11874957
[119,] 0.85117564 0.29764871 0.14882436
[120,] 0.81677978 0.36644044 0.18322022
[121,] 0.77970976 0.44058048 0.22029024
[122,] 0.76524206 0.46951588 0.23475794
[123,] 0.76790354 0.46419292 0.23209646
[124,] 0.73806329 0.52387342 0.26193671
[125,] 0.68110509 0.63778981 0.31889491
[126,] 0.83562714 0.32874573 0.16437286
[127,] 0.80682299 0.38635403 0.19317701
[128,] 0.74663557 0.50672886 0.25336443
[129,] 0.80579497 0.38841006 0.19420503
[130,] 0.75078163 0.49843674 0.24921837
[131,] 0.68783141 0.62433718 0.31216859
[132,] 0.63210796 0.73578408 0.36789204
[133,] 0.64812256 0.70375489 0.35187744
[134,] 0.63158808 0.73682384 0.36841192
[135,] 0.56561602 0.86876797 0.43438398
[136,] 0.45742226 0.91484453 0.54257774
[137,] 0.43621921 0.87243842 0.56378079
[138,] 0.39112389 0.78224777 0.60887611
[139,] 0.27164714 0.54329428 0.72835286
[140,] 0.75977185 0.48045629 0.24022815
> postscript(file="/var/www/html/freestat/rcomp/tmp/1w0f31292770614.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2w0f31292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/37sfo1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/47sfo1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/57sfo1292770614.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 = 159
Frequency = 1
1 2 3 4 5 6
-0.613848824 1.291243426 4.446241590 -2.729977548 0.101129248 -2.494787892
7 8 9 10 11 12
2.678070082 3.448603394 -1.861633892 -0.608158172 -3.946093143 -4.341584354
13 14 15 16 17 18
-8.109100489 -1.059863454 3.551606018 3.243949648 1.107157827 -2.426272259
19 20 21 22 23 24
-1.920586569 -1.050281732 1.420284211 -3.525821906 -3.501158462 -5.731297587
25 26 27 28 29 30
-3.129239054 0.603851967 1.857417661 -4.265674264 0.246416871 0.592541039
31 32 33 34 35 36
0.058166071 2.404444175 6.524670749 7.047649880 3.444507165 4.728128211
37 38 39 40 41 42
3.353905463 -0.151365938 1.908270528 6.184737141 -1.566351120 1.628496018
43 44 45 46 47 48
-5.485773401 3.082159812 4.422496583 -0.321711740 -3.536554785 7.549131335
49 50 51 52 53 54
0.187790092 3.334168540 2.323849952 -1.061021417 -3.970424812 -1.227339023
55 56 57 58 59 60
2.999554055 -1.274782052 1.797826450 3.551606018 0.771829007 -1.767753478
61 62 63 64 65 66
1.791031235 1.255725805 -1.042021393 3.973149746 0.589255426 4.467129736
67 68 69 70 71 72
-4.323631152 5.637485764 1.229741972 2.760307701 -4.696720553 -0.339955347
73 74 75 76 77 78
-0.304717654 -1.480721876 -0.446502350 -2.106121447 1.721976467 -0.457151067
79 80 81 82 83 84
-0.007180340 1.174540138 1.497103659 -6.673881513 -2.399037101 1.082240526
85 86 87 88 89 90
-3.234011009 -3.046942818 2.678070082 2.118068790 2.432709064 -3.775549579
91 92 93 94 95 96
-6.312831362 -0.968469748 -2.597229400 0.165361862 -2.518920771 3.377061423
97 98 99 100 101 102
-3.696155092 -3.405497651 -3.108737169 -3.175865528 0.195284847 -1.444540482
103 104 105 106 107 108
-4.341584354 -1.288465815 -3.925154093 -1.615025524 -2.085089002 -1.659771494
109 110 111 112 113 114
-0.168619472 -3.746144537 -4.735836298 -8.578903210 2.648495251 11.534622473
115 116 117 118 119 120
9.770157420 -0.763118914 4.688943845 -1.277211049 -1.249649835 -3.589602214
121 122 123 124 125 126
3.129293971 0.153273227 4.777632781 -0.573789248 0.711810821 -1.581592841
127 128 129 130 131 132
1.189695893 1.358139251 -1.976987365 0.639408753 3.371278033 -4.220598881
133 134 135 136 137 138
0.830852331 -2.534054678 -6.276541158 1.886605520 0.005877728 4.108617232
139 140 141 142 143 144
-1.139987906 3.309723150 3.333479132 4.240781422 1.288198467 1.130638691
145 146 147 148 149 150
0.981779716 4.101261744 -0.188576249 0.167512260 -7.065988778 -2.560923717
151 152 153 154 155 156
3.571325195 0.337547484 -2.401279026 0.240561441 1.738106321 1.107157827
157 158 159
0.626370171 0.109637762 -4.341584354
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ijer1292770614.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.613848824 NA
1 1.291243426 -0.613848824
2 4.446241590 1.291243426
3 -2.729977548 4.446241590
4 0.101129248 -2.729977548
5 -2.494787892 0.101129248
6 2.678070082 -2.494787892
7 3.448603394 2.678070082
8 -1.861633892 3.448603394
9 -0.608158172 -1.861633892
10 -3.946093143 -0.608158172
11 -4.341584354 -3.946093143
12 -8.109100489 -4.341584354
13 -1.059863454 -8.109100489
14 3.551606018 -1.059863454
15 3.243949648 3.551606018
16 1.107157827 3.243949648
17 -2.426272259 1.107157827
18 -1.920586569 -2.426272259
19 -1.050281732 -1.920586569
20 1.420284211 -1.050281732
21 -3.525821906 1.420284211
22 -3.501158462 -3.525821906
23 -5.731297587 -3.501158462
24 -3.129239054 -5.731297587
25 0.603851967 -3.129239054
26 1.857417661 0.603851967
27 -4.265674264 1.857417661
28 0.246416871 -4.265674264
29 0.592541039 0.246416871
30 0.058166071 0.592541039
31 2.404444175 0.058166071
32 6.524670749 2.404444175
33 7.047649880 6.524670749
34 3.444507165 7.047649880
35 4.728128211 3.444507165
36 3.353905463 4.728128211
37 -0.151365938 3.353905463
38 1.908270528 -0.151365938
39 6.184737141 1.908270528
40 -1.566351120 6.184737141
41 1.628496018 -1.566351120
42 -5.485773401 1.628496018
43 3.082159812 -5.485773401
44 4.422496583 3.082159812
45 -0.321711740 4.422496583
46 -3.536554785 -0.321711740
47 7.549131335 -3.536554785
48 0.187790092 7.549131335
49 3.334168540 0.187790092
50 2.323849952 3.334168540
51 -1.061021417 2.323849952
52 -3.970424812 -1.061021417
53 -1.227339023 -3.970424812
54 2.999554055 -1.227339023
55 -1.274782052 2.999554055
56 1.797826450 -1.274782052
57 3.551606018 1.797826450
58 0.771829007 3.551606018
59 -1.767753478 0.771829007
60 1.791031235 -1.767753478
61 1.255725805 1.791031235
62 -1.042021393 1.255725805
63 3.973149746 -1.042021393
64 0.589255426 3.973149746
65 4.467129736 0.589255426
66 -4.323631152 4.467129736
67 5.637485764 -4.323631152
68 1.229741972 5.637485764
69 2.760307701 1.229741972
70 -4.696720553 2.760307701
71 -0.339955347 -4.696720553
72 -0.304717654 -0.339955347
73 -1.480721876 -0.304717654
74 -0.446502350 -1.480721876
75 -2.106121447 -0.446502350
76 1.721976467 -2.106121447
77 -0.457151067 1.721976467
78 -0.007180340 -0.457151067
79 1.174540138 -0.007180340
80 1.497103659 1.174540138
81 -6.673881513 1.497103659
82 -2.399037101 -6.673881513
83 1.082240526 -2.399037101
84 -3.234011009 1.082240526
85 -3.046942818 -3.234011009
86 2.678070082 -3.046942818
87 2.118068790 2.678070082
88 2.432709064 2.118068790
89 -3.775549579 2.432709064
90 -6.312831362 -3.775549579
91 -0.968469748 -6.312831362
92 -2.597229400 -0.968469748
93 0.165361862 -2.597229400
94 -2.518920771 0.165361862
95 3.377061423 -2.518920771
96 -3.696155092 3.377061423
97 -3.405497651 -3.696155092
98 -3.108737169 -3.405497651
99 -3.175865528 -3.108737169
100 0.195284847 -3.175865528
101 -1.444540482 0.195284847
102 -4.341584354 -1.444540482
103 -1.288465815 -4.341584354
104 -3.925154093 -1.288465815
105 -1.615025524 -3.925154093
106 -2.085089002 -1.615025524
107 -1.659771494 -2.085089002
108 -0.168619472 -1.659771494
109 -3.746144537 -0.168619472
110 -4.735836298 -3.746144537
111 -8.578903210 -4.735836298
112 2.648495251 -8.578903210
113 11.534622473 2.648495251
114 9.770157420 11.534622473
115 -0.763118914 9.770157420
116 4.688943845 -0.763118914
117 -1.277211049 4.688943845
118 -1.249649835 -1.277211049
119 -3.589602214 -1.249649835
120 3.129293971 -3.589602214
121 0.153273227 3.129293971
122 4.777632781 0.153273227
123 -0.573789248 4.777632781
124 0.711810821 -0.573789248
125 -1.581592841 0.711810821
126 1.189695893 -1.581592841
127 1.358139251 1.189695893
128 -1.976987365 1.358139251
129 0.639408753 -1.976987365
130 3.371278033 0.639408753
131 -4.220598881 3.371278033
132 0.830852331 -4.220598881
133 -2.534054678 0.830852331
134 -6.276541158 -2.534054678
135 1.886605520 -6.276541158
136 0.005877728 1.886605520
137 4.108617232 0.005877728
138 -1.139987906 4.108617232
139 3.309723150 -1.139987906
140 3.333479132 3.309723150
141 4.240781422 3.333479132
142 1.288198467 4.240781422
143 1.130638691 1.288198467
144 0.981779716 1.130638691
145 4.101261744 0.981779716
146 -0.188576249 4.101261744
147 0.167512260 -0.188576249
148 -7.065988778 0.167512260
149 -2.560923717 -7.065988778
150 3.571325195 -2.560923717
151 0.337547484 3.571325195
152 -2.401279026 0.337547484
153 0.240561441 -2.401279026
154 1.738106321 0.240561441
155 1.107157827 1.738106321
156 0.626370171 1.107157827
157 0.109637762 0.626370171
158 -4.341584354 0.109637762
159 NA -4.341584354
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.291243426 -0.613848824
[2,] 4.446241590 1.291243426
[3,] -2.729977548 4.446241590
[4,] 0.101129248 -2.729977548
[5,] -2.494787892 0.101129248
[6,] 2.678070082 -2.494787892
[7,] 3.448603394 2.678070082
[8,] -1.861633892 3.448603394
[9,] -0.608158172 -1.861633892
[10,] -3.946093143 -0.608158172
[11,] -4.341584354 -3.946093143
[12,] -8.109100489 -4.341584354
[13,] -1.059863454 -8.109100489
[14,] 3.551606018 -1.059863454
[15,] 3.243949648 3.551606018
[16,] 1.107157827 3.243949648
[17,] -2.426272259 1.107157827
[18,] -1.920586569 -2.426272259
[19,] -1.050281732 -1.920586569
[20,] 1.420284211 -1.050281732
[21,] -3.525821906 1.420284211
[22,] -3.501158462 -3.525821906
[23,] -5.731297587 -3.501158462
[24,] -3.129239054 -5.731297587
[25,] 0.603851967 -3.129239054
[26,] 1.857417661 0.603851967
[27,] -4.265674264 1.857417661
[28,] 0.246416871 -4.265674264
[29,] 0.592541039 0.246416871
[30,] 0.058166071 0.592541039
[31,] 2.404444175 0.058166071
[32,] 6.524670749 2.404444175
[33,] 7.047649880 6.524670749
[34,] 3.444507165 7.047649880
[35,] 4.728128211 3.444507165
[36,] 3.353905463 4.728128211
[37,] -0.151365938 3.353905463
[38,] 1.908270528 -0.151365938
[39,] 6.184737141 1.908270528
[40,] -1.566351120 6.184737141
[41,] 1.628496018 -1.566351120
[42,] -5.485773401 1.628496018
[43,] 3.082159812 -5.485773401
[44,] 4.422496583 3.082159812
[45,] -0.321711740 4.422496583
[46,] -3.536554785 -0.321711740
[47,] 7.549131335 -3.536554785
[48,] 0.187790092 7.549131335
[49,] 3.334168540 0.187790092
[50,] 2.323849952 3.334168540
[51,] -1.061021417 2.323849952
[52,] -3.970424812 -1.061021417
[53,] -1.227339023 -3.970424812
[54,] 2.999554055 -1.227339023
[55,] -1.274782052 2.999554055
[56,] 1.797826450 -1.274782052
[57,] 3.551606018 1.797826450
[58,] 0.771829007 3.551606018
[59,] -1.767753478 0.771829007
[60,] 1.791031235 -1.767753478
[61,] 1.255725805 1.791031235
[62,] -1.042021393 1.255725805
[63,] 3.973149746 -1.042021393
[64,] 0.589255426 3.973149746
[65,] 4.467129736 0.589255426
[66,] -4.323631152 4.467129736
[67,] 5.637485764 -4.323631152
[68,] 1.229741972 5.637485764
[69,] 2.760307701 1.229741972
[70,] -4.696720553 2.760307701
[71,] -0.339955347 -4.696720553
[72,] -0.304717654 -0.339955347
[73,] -1.480721876 -0.304717654
[74,] -0.446502350 -1.480721876
[75,] -2.106121447 -0.446502350
[76,] 1.721976467 -2.106121447
[77,] -0.457151067 1.721976467
[78,] -0.007180340 -0.457151067
[79,] 1.174540138 -0.007180340
[80,] 1.497103659 1.174540138
[81,] -6.673881513 1.497103659
[82,] -2.399037101 -6.673881513
[83,] 1.082240526 -2.399037101
[84,] -3.234011009 1.082240526
[85,] -3.046942818 -3.234011009
[86,] 2.678070082 -3.046942818
[87,] 2.118068790 2.678070082
[88,] 2.432709064 2.118068790
[89,] -3.775549579 2.432709064
[90,] -6.312831362 -3.775549579
[91,] -0.968469748 -6.312831362
[92,] -2.597229400 -0.968469748
[93,] 0.165361862 -2.597229400
[94,] -2.518920771 0.165361862
[95,] 3.377061423 -2.518920771
[96,] -3.696155092 3.377061423
[97,] -3.405497651 -3.696155092
[98,] -3.108737169 -3.405497651
[99,] -3.175865528 -3.108737169
[100,] 0.195284847 -3.175865528
[101,] -1.444540482 0.195284847
[102,] -4.341584354 -1.444540482
[103,] -1.288465815 -4.341584354
[104,] -3.925154093 -1.288465815
[105,] -1.615025524 -3.925154093
[106,] -2.085089002 -1.615025524
[107,] -1.659771494 -2.085089002
[108,] -0.168619472 -1.659771494
[109,] -3.746144537 -0.168619472
[110,] -4.735836298 -3.746144537
[111,] -8.578903210 -4.735836298
[112,] 2.648495251 -8.578903210
[113,] 11.534622473 2.648495251
[114,] 9.770157420 11.534622473
[115,] -0.763118914 9.770157420
[116,] 4.688943845 -0.763118914
[117,] -1.277211049 4.688943845
[118,] -1.249649835 -1.277211049
[119,] -3.589602214 -1.249649835
[120,] 3.129293971 -3.589602214
[121,] 0.153273227 3.129293971
[122,] 4.777632781 0.153273227
[123,] -0.573789248 4.777632781
[124,] 0.711810821 -0.573789248
[125,] -1.581592841 0.711810821
[126,] 1.189695893 -1.581592841
[127,] 1.358139251 1.189695893
[128,] -1.976987365 1.358139251
[129,] 0.639408753 -1.976987365
[130,] 3.371278033 0.639408753
[131,] -4.220598881 3.371278033
[132,] 0.830852331 -4.220598881
[133,] -2.534054678 0.830852331
[134,] -6.276541158 -2.534054678
[135,] 1.886605520 -6.276541158
[136,] 0.005877728 1.886605520
[137,] 4.108617232 0.005877728
[138,] -1.139987906 4.108617232
[139,] 3.309723150 -1.139987906
[140,] 3.333479132 3.309723150
[141,] 4.240781422 3.333479132
[142,] 1.288198467 4.240781422
[143,] 1.130638691 1.288198467
[144,] 0.981779716 1.130638691
[145,] 4.101261744 0.981779716
[146,] -0.188576249 4.101261744
[147,] 0.167512260 -0.188576249
[148,] -7.065988778 0.167512260
[149,] -2.560923717 -7.065988778
[150,] 3.571325195 -2.560923717
[151,] 0.337547484 3.571325195
[152,] -2.401279026 0.337547484
[153,] 0.240561441 -2.401279026
[154,] 1.738106321 0.240561441
[155,] 1.107157827 1.738106321
[156,] 0.626370171 1.107157827
[157,] 0.109637762 0.626370171
[158,] -4.341584354 0.109637762
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.291243426 -0.613848824
2 4.446241590 1.291243426
3 -2.729977548 4.446241590
4 0.101129248 -2.729977548
5 -2.494787892 0.101129248
6 2.678070082 -2.494787892
7 3.448603394 2.678070082
8 -1.861633892 3.448603394
9 -0.608158172 -1.861633892
10 -3.946093143 -0.608158172
11 -4.341584354 -3.946093143
12 -8.109100489 -4.341584354
13 -1.059863454 -8.109100489
14 3.551606018 -1.059863454
15 3.243949648 3.551606018
16 1.107157827 3.243949648
17 -2.426272259 1.107157827
18 -1.920586569 -2.426272259
19 -1.050281732 -1.920586569
20 1.420284211 -1.050281732
21 -3.525821906 1.420284211
22 -3.501158462 -3.525821906
23 -5.731297587 -3.501158462
24 -3.129239054 -5.731297587
25 0.603851967 -3.129239054
26 1.857417661 0.603851967
27 -4.265674264 1.857417661
28 0.246416871 -4.265674264
29 0.592541039 0.246416871
30 0.058166071 0.592541039
31 2.404444175 0.058166071
32 6.524670749 2.404444175
33 7.047649880 6.524670749
34 3.444507165 7.047649880
35 4.728128211 3.444507165
36 3.353905463 4.728128211
37 -0.151365938 3.353905463
38 1.908270528 -0.151365938
39 6.184737141 1.908270528
40 -1.566351120 6.184737141
41 1.628496018 -1.566351120
42 -5.485773401 1.628496018
43 3.082159812 -5.485773401
44 4.422496583 3.082159812
45 -0.321711740 4.422496583
46 -3.536554785 -0.321711740
47 7.549131335 -3.536554785
48 0.187790092 7.549131335
49 3.334168540 0.187790092
50 2.323849952 3.334168540
51 -1.061021417 2.323849952
52 -3.970424812 -1.061021417
53 -1.227339023 -3.970424812
54 2.999554055 -1.227339023
55 -1.274782052 2.999554055
56 1.797826450 -1.274782052
57 3.551606018 1.797826450
58 0.771829007 3.551606018
59 -1.767753478 0.771829007
60 1.791031235 -1.767753478
61 1.255725805 1.791031235
62 -1.042021393 1.255725805
63 3.973149746 -1.042021393
64 0.589255426 3.973149746
65 4.467129736 0.589255426
66 -4.323631152 4.467129736
67 5.637485764 -4.323631152
68 1.229741972 5.637485764
69 2.760307701 1.229741972
70 -4.696720553 2.760307701
71 -0.339955347 -4.696720553
72 -0.304717654 -0.339955347
73 -1.480721876 -0.304717654
74 -0.446502350 -1.480721876
75 -2.106121447 -0.446502350
76 1.721976467 -2.106121447
77 -0.457151067 1.721976467
78 -0.007180340 -0.457151067
79 1.174540138 -0.007180340
80 1.497103659 1.174540138
81 -6.673881513 1.497103659
82 -2.399037101 -6.673881513
83 1.082240526 -2.399037101
84 -3.234011009 1.082240526
85 -3.046942818 -3.234011009
86 2.678070082 -3.046942818
87 2.118068790 2.678070082
88 2.432709064 2.118068790
89 -3.775549579 2.432709064
90 -6.312831362 -3.775549579
91 -0.968469748 -6.312831362
92 -2.597229400 -0.968469748
93 0.165361862 -2.597229400
94 -2.518920771 0.165361862
95 3.377061423 -2.518920771
96 -3.696155092 3.377061423
97 -3.405497651 -3.696155092
98 -3.108737169 -3.405497651
99 -3.175865528 -3.108737169
100 0.195284847 -3.175865528
101 -1.444540482 0.195284847
102 -4.341584354 -1.444540482
103 -1.288465815 -4.341584354
104 -3.925154093 -1.288465815
105 -1.615025524 -3.925154093
106 -2.085089002 -1.615025524
107 -1.659771494 -2.085089002
108 -0.168619472 -1.659771494
109 -3.746144537 -0.168619472
110 -4.735836298 -3.746144537
111 -8.578903210 -4.735836298
112 2.648495251 -8.578903210
113 11.534622473 2.648495251
114 9.770157420 11.534622473
115 -0.763118914 9.770157420
116 4.688943845 -0.763118914
117 -1.277211049 4.688943845
118 -1.249649835 -1.277211049
119 -3.589602214 -1.249649835
120 3.129293971 -3.589602214
121 0.153273227 3.129293971
122 4.777632781 0.153273227
123 -0.573789248 4.777632781
124 0.711810821 -0.573789248
125 -1.581592841 0.711810821
126 1.189695893 -1.581592841
127 1.358139251 1.189695893
128 -1.976987365 1.358139251
129 0.639408753 -1.976987365
130 3.371278033 0.639408753
131 -4.220598881 3.371278033
132 0.830852331 -4.220598881
133 -2.534054678 0.830852331
134 -6.276541158 -2.534054678
135 1.886605520 -6.276541158
136 0.005877728 1.886605520
137 4.108617232 0.005877728
138 -1.139987906 4.108617232
139 3.309723150 -1.139987906
140 3.333479132 3.309723150
141 4.240781422 3.333479132
142 1.288198467 4.240781422
143 1.130638691 1.288198467
144 0.981779716 1.130638691
145 4.101261744 0.981779716
146 -0.188576249 4.101261744
147 0.167512260 -0.188576249
148 -7.065988778 0.167512260
149 -2.560923717 -7.065988778
150 3.571325195 -2.560923717
151 0.337547484 3.571325195
152 -2.401279026 0.337547484
153 0.240561441 -2.401279026
154 1.738106321 0.240561441
155 1.107157827 1.738106321
156 0.626370171 1.107157827
157 0.109637762 0.626370171
158 -4.341584354 0.109637762
> 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/freestat/rcomp/tmp/7tadc1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8tadc1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9tadc1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/103kdf1292770614.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11p2b31292770614.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/freestat/rcomp/tmp/12al9r1292770614.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/freestat/rcomp/tmp/13ou7z1292770614.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/freestat/rcomp/tmp/14rd651292770614.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/freestat/rcomp/tmp/15dd4t1292770614.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/freestat/rcomp/tmp/16ywlz1292770614.tab")
+ }
>
> try(system("convert tmp/1w0f31292770614.ps tmp/1w0f31292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w0f31292770614.ps tmp/2w0f31292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/37sfo1292770614.ps tmp/37sfo1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/47sfo1292770614.ps tmp/47sfo1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/57sfo1292770614.ps tmp/57sfo1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ijer1292770614.ps tmp/6ijer1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tadc1292770614.ps tmp/7tadc1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tadc1292770614.ps tmp/8tadc1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tadc1292770614.ps tmp/9tadc1292770614.png",intern=TRUE))
character(0)
> try(system("convert tmp/103kdf1292770614.ps tmp/103kdf1292770614.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.356 2.816 7.932