R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(2
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+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'Concern_Mistakes'
+ ,'Doubts_actions'
+ ,'Parental_Expectations'
+ ,'Parental_Criticism'
+ ,'Personal_Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Gender','Concern_Mistakes','Doubts_actions','Parental_Expectations','Parental_Criticism','Personal_Standards','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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Gender Concern_Mistakes Doubts_actions Parental_Expectations
1 2 24 14 11
2 2 25 11 7
3 2 17 6 17
4 1 18 12 10
5 2 18 8 12
6 2 16 10 12
7 2 20 10 11
8 2 16 11 11
9 2 18 16 12
10 2 17 11 13
11 1 23 13 14
12 2 30 12 16
13 1 23 8 11
14 2 18 12 10
15 2 15 11 11
16 1 12 4 15
17 1 21 9 9
18 2 15 8 11
19 1 20 8 17
20 2 31 14 17
21 1 27 15 11
22 2 34 16 18
23 2 21 9 14
24 2 31 14 10
25 1 19 11 11
26 2 16 8 15
27 1 20 9 15
28 2 21 9 13
29 2 22 9 16
30 1 17 9 13
31 2 24 10 9
32 1 25 16 18
33 2 26 11 18
34 2 25 8 12
35 1 17 9 17
36 1 32 16 9
37 1 33 11 9
38 1 13 16 12
39 2 32 12 18
40 1 25 12 12
41 1 29 14 18
42 2 22 9 14
43 1 18 10 15
44 1 17 9 16
45 2 20 10 10
46 2 15 12 11
47 2 20 14 14
48 2 33 14 9
49 2 29 10 12
50 1 23 14 17
51 2 26 16 5
52 1 18 9 12
53 1 20 10 12
54 2 11 6 6
55 1 28 8 24
56 2 26 13 12
57 2 22 10 12
58 2 17 8 14
59 1 12 7 7
60 2 14 15 13
61 1 17 9 12
62 1 21 10 13
63 2 19 12 14
64 2 18 13 8
65 2 10 10 11
66 1 29 11 9
67 2 31 8 11
68 1 19 9 13
69 2 9 13 10
70 1 20 11 11
71 1 28 8 12
72 2 19 9 9
73 2 30 9 15
74 1 29 15 18
75 1 26 9 15
76 2 23 10 12
77 2 13 14 13
78 2 21 12 14
79 1 19 12 10
80 1 28 11 13
81 1 23 14 13
82 1 18 6 11
83 2 21 12 13
84 1 20 8 16
85 2 23 14 8
86 2 21 11 16
87 1 21 10 11
88 2 15 14 9
89 2 28 12 16
90 2 19 10 12
91 2 26 14 14
92 2 10 5 8
93 2 16 11 9
94 2 22 10 15
95 2 19 9 11
96 2 31 10 21
97 2 31 16 14
98 2 29 13 18
99 1 19 9 12
100 1 22 10 13
101 2 23 10 15
102 1 15 7 12
103 2 20 9 19
104 1 18 8 15
105 2 23 14 11
106 1 25 14 11
107 2 21 8 10
108 1 24 9 13
109 1 25 14 15
110 2 17 14 12
111 2 13 8 12
112 2 28 8 16
113 2 21 8 9
114 1 25 7 18
115 2 9 6 8
116 1 16 8 13
117 2 19 6 17
118 2 17 11 9
119 2 25 14 15
120 2 20 11 8
121 2 29 11 7
122 2 14 11 12
123 2 22 14 14
124 2 15 8 6
125 2 19 20 8
126 2 20 11 17
127 1 15 8 10
128 2 20 11 11
129 2 18 10 14
130 2 33 14 11
131 1 22 11 13
132 1 16 9 12
133 2 17 9 11
134 1 16 8 9
135 1 21 10 12
136 2 26 13 20
137 1 18 13 12
138 1 18 12 13
139 2 17 8 12
140 2 22 13 12
141 1 30 14 9
142 2 30 12 15
143 1 24 14 24
144 2 21 15 7
145 1 21 13 17
146 2 29 16 11
147 2 31 9 17
148 1 20 9 11
149 1 16 9 12
150 1 22 8 14
151 2 20 7 11
152 2 28 16 16
153 1 38 11 21
154 2 22 9 14
155 2 20 11 20
156 2 17 9 13
157 2 28 14 11
158 2 22 13 15
159 2 31 16 19
Parental_Criticism Personal_Standards Organization
1 12 24 26
2 8 25 23
3 8 30 25
4 8 19 23
5 9 22 19
6 7 22 29
7 4 25 25
8 11 23 21
9 7 17 22
10 7 21 25
11 12 19 24
12 10 19 18
13 10 15 22
14 8 16 15
15 8 23 22
16 4 27 28
17 9 22 20
18 8 14 12
19 7 22 24
20 11 23 20
21 9 23 21
22 11 21 20
23 13 19 21
24 8 18 23
25 8 20 28
26 9 23 24
27 6 25 24
28 9 19 24
29 9 24 23
30 6 22 23
31 6 25 29
32 16 26 24
33 5 29 18
34 7 32 25
35 9 25 21
36 6 29 26
37 6 28 22
38 5 17 22
39 12 28 22
40 7 29 23
41 10 26 30
42 9 25 23
43 8 14 17
44 5 25 23
45 8 26 23
46 8 20 25
47 10 18 24
48 6 32 24
49 8 25 23
50 7 25 21
51 4 23 24
52 8 21 24
53 8 20 28
54 4 15 16
55 20 30 20
56 8 24 29
57 8 26 27
58 6 24 22
59 4 22 28
60 8 14 16
61 9 24 25
62 6 24 24
63 7 24 28
64 9 24 24
65 5 19 23
66 5 31 30
67 8 22 24
68 8 27 21
69 6 19 25
70 8 25 25
71 7 20 22
72 7 21 23
73 9 27 26
74 11 23 23
75 6 25 25
76 8 20 21
77 6 21 25
78 9 22 24
79 8 23 29
80 6 25 22
81 10 25 27
82 8 17 26
83 8 19 22
84 10 25 24
85 5 19 27
86 7 20 24
87 5 26 24
88 8 23 29
89 14 27 22
90 7 17 21
91 8 17 24
92 6 19 24
93 5 17 23
94 6 22 20
95 10 21 27
96 12 32 26
97 9 21 25
98 12 21 21
99 7 18 21
100 8 18 19
101 10 23 21
102 6 19 21
103 10 20 16
104 10 21 22
105 10 20 29
106 5 17 15
107 7 18 17
108 10 19 15
109 11 22 21
110 6 15 21
111 7 14 19
112 12 18 24
113 11 24 20
114 11 35 17
115 11 29 23
116 5 21 24
117 8 25 14
118 6 20 19
119 9 22 24
120 4 13 13
121 4 26 22
122 7 17 16
123 11 25 19
124 6 20 25
125 7 19 25
126 8 21 23
127 4 22 24
128 8 24 26
129 9 21 26
130 8 26 25
131 11 24 18
132 8 16 21
133 5 23 26
134 4 18 23
135 8 16 23
136 10 26 22
137 6 19 20
138 9 21 13
139 9 21 24
140 13 22 15
141 9 23 14
142 10 29 22
143 20 21 10
144 5 21 24
145 11 23 22
146 6 27 24
147 9 25 19
148 7 21 20
149 9 10 13
150 10 20 20
151 9 26 22
152 8 24 24
153 7 29 29
154 6 19 12
155 13 24 20
156 6 19 21
157 8 24 24
158 10 22 22
159 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern_Mistakes Doubts_actions
1.5694368 -0.0015593 0.0220042
Parental_Expectations Parental_Criticism Personal_Standards
-0.0209205 0.0141086 -0.0003683
Organization
0.0000857
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7621 -0.5719 0.2955 0.3943 0.5789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5694368 0.3340928 4.698 5.84e-06 ***
Concern_Mistakes -0.0015593 0.0088352 -0.176 0.860
Doubts_actions 0.0220042 0.0159410 1.380 0.170
Parental_Expectations -0.0209205 0.0146776 -1.425 0.156
Parental_Criticism 0.0141086 0.0184635 0.764 0.446
Personal_Standards -0.0003683 0.0116041 -0.032 0.975
Organization 0.0000857 0.0113054 0.008 0.994
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4893 on 152 degrees of freedom
Multiple R-squared: 0.0319, Adjusted R-squared: -0.006318
F-statistic: 0.8347 on 6 and 152 DF, p-value: 0.5449
> 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,] 0.5890260 0.8219480 0.4109740
[2,] 0.5095587 0.9808826 0.4904413
[3,] 0.5113577 0.9772846 0.4886423
[4,] 0.3900311 0.7800622 0.6099689
[5,] 0.2901343 0.5802685 0.7098657
[6,] 0.2006866 0.4013731 0.7993134
[7,] 0.2828194 0.5656388 0.7171806
[8,] 0.3879658 0.7759316 0.6120342
[9,] 0.3609477 0.7218954 0.6390523
[10,] 0.3744467 0.7488933 0.6255533
[11,] 0.3000418 0.6000836 0.6999582
[12,] 0.5852525 0.8294949 0.4147475
[13,] 0.5194556 0.9610887 0.4805444
[14,] 0.5142037 0.9715926 0.4857963
[15,] 0.5347002 0.9305996 0.4652998
[16,] 0.5126597 0.9746806 0.4873403
[17,] 0.4847942 0.9695885 0.5152058
[18,] 0.5352238 0.9295525 0.4647762
[19,] 0.5518206 0.8963587 0.4481794
[20,] 0.5162877 0.9674246 0.4837123
[21,] 0.5412072 0.9175856 0.4587928
[22,] 0.5581010 0.8837981 0.4418990
[23,] 0.6556415 0.6887171 0.3443585
[24,] 0.6166579 0.7666842 0.3833421
[25,] 0.5744025 0.8511951 0.4255975
[26,] 0.6016759 0.7966482 0.3983241
[27,] 0.6898897 0.6202206 0.3101103
[28,] 0.7347759 0.5304483 0.2652241
[29,] 0.7514448 0.4971104 0.2485552
[30,] 0.7241465 0.5517071 0.2758535
[31,] 0.7437995 0.5124011 0.2562005
[32,] 0.7361051 0.5277898 0.2638949
[33,] 0.7170338 0.5659324 0.2829662
[34,] 0.7450565 0.5098870 0.2549435
[35,] 0.7372917 0.5254165 0.2627083
[36,] 0.7125626 0.5748747 0.2874374
[37,] 0.7053782 0.5892437 0.2946218
[38,] 0.6947573 0.6104854 0.3052427
[39,] 0.6692159 0.6615683 0.3307841
[40,] 0.6464409 0.7071183 0.3535591
[41,] 0.6493989 0.7012021 0.3506011
[42,] 0.6238550 0.7522900 0.3761450
[43,] 0.6440705 0.7118590 0.3559295
[44,] 0.6502556 0.6994887 0.3497444
[45,] 0.6231041 0.7537917 0.3768959
[46,] 0.6459347 0.7081306 0.3540653
[47,] 0.6362760 0.7274480 0.3637240
[48,] 0.6206230 0.7587540 0.3793770
[49,] 0.6160241 0.7679519 0.3839759
[50,] 0.6393069 0.7213861 0.3606931
[51,] 0.6117290 0.7765420 0.3882710
[52,] 0.6322160 0.7355679 0.3677840
[53,] 0.6436715 0.7126570 0.3563285
[54,] 0.6438115 0.7123769 0.3561885
[55,] 0.6081287 0.7837425 0.3918713
[56,] 0.5915964 0.8168071 0.4084036
[57,] 0.6164140 0.7671721 0.3835860
[58,] 0.6029183 0.7941635 0.3970817
[59,] 0.6264095 0.7471810 0.3735905
[60,] 0.6007796 0.7984408 0.3992204
[61,] 0.6360421 0.7279157 0.3639579
[62,] 0.6469392 0.7061215 0.3530608
[63,] 0.6232935 0.7534129 0.3767065
[64,] 0.6216766 0.7566468 0.3783234
[65,] 0.6458205 0.7083589 0.3541795
[66,] 0.6473288 0.7053424 0.3526712
[67,] 0.6291368 0.7417263 0.3708632
[68,] 0.6073964 0.7852071 0.3926036
[69,] 0.5879614 0.8240772 0.4120386
[70,] 0.6396633 0.7206733 0.3603367
[71,] 0.6613590 0.6772819 0.3386410
[72,] 0.7210663 0.5578674 0.2789337
[73,] 0.7363973 0.5272054 0.2636027
[74,] 0.7174387 0.5651227 0.2825613
[75,] 0.7358786 0.5282429 0.2641214
[76,] 0.7082432 0.5835137 0.2917568
[77,] 0.7009101 0.5981799 0.2990899
[78,] 0.7382109 0.5235782 0.2617891
[79,] 0.7094049 0.5811903 0.2905951
[80,] 0.6850647 0.6298705 0.3149353
[81,] 0.6682671 0.6634657 0.3317329
[82,] 0.6448186 0.7103627 0.3551814
[83,] 0.6250811 0.7498378 0.3749189
[84,] 0.5971047 0.8057905 0.4028953
[85,] 0.5901812 0.8196375 0.4098188
[86,] 0.5620035 0.8759931 0.4379965
[87,] 0.5604413 0.8791174 0.4395587
[88,] 0.5262940 0.9474121 0.4737060
[89,] 0.5062877 0.9874247 0.4937123
[90,] 0.5276372 0.9447256 0.4723628
[91,] 0.5485986 0.9028029 0.4514014
[92,] 0.5293833 0.9412333 0.4706167
[93,] 0.5427947 0.9144107 0.4572053
[94,] 0.5561607 0.8876787 0.4438393
[95,] 0.5767323 0.8465354 0.4232677
[96,] 0.5352587 0.9294826 0.4647413
[97,] 0.5650820 0.8698361 0.4349180
[98,] 0.5539349 0.8921302 0.4460651
[99,] 0.5648733 0.8702534 0.4351267
[100,] 0.6205689 0.7588621 0.3794311
[101,] 0.5903569 0.8192863 0.4096431
[102,] 0.5900497 0.8199005 0.4099503
[103,] 0.5869327 0.8261346 0.4130673
[104,] 0.5574920 0.8850161 0.4425080
[105,] 0.6012811 0.7974377 0.3987189
[106,] 0.5554267 0.8891466 0.4445733
[107,] 0.5810210 0.8379580 0.4189790
[108,] 0.5858968 0.8282064 0.4141032
[109,] 0.5546221 0.8907558 0.4453779
[110,] 0.5169298 0.9661403 0.4830702
[111,] 0.5678668 0.8642664 0.4321332
[112,] 0.5339698 0.9320603 0.4660302
[113,] 0.5574198 0.8851605 0.4425802
[114,] 0.5094574 0.9810851 0.4905426
[115,] 0.4793814 0.9587627 0.5206186
[116,] 0.4205013 0.8410026 0.5794987
[117,] 0.4100016 0.8200031 0.5899984
[118,] 0.4345658 0.8691317 0.5654342
[119,] 0.3832668 0.7665336 0.6167332
[120,] 0.3510764 0.7021528 0.6489236
[121,] 0.3028302 0.6056603 0.6971698
[122,] 0.3527693 0.7055387 0.6472307
[123,] 0.3348748 0.6697496 0.6651252
[124,] 0.2964074 0.5928148 0.7035926
[125,] 0.2913436 0.5826872 0.7086564
[126,] 0.2949864 0.5899729 0.7050136
[127,] 0.2666750 0.5333499 0.7333250
[128,] 0.2960534 0.5921069 0.7039466
[129,] 0.3241350 0.6482701 0.6758650
[130,] 0.2767788 0.5535577 0.7232212
[131,] 0.2397549 0.4795097 0.7602451
[132,] 0.3473947 0.6947893 0.6526053
[133,] 0.2747190 0.5494381 0.7252810
[134,] 0.4320758 0.8641516 0.5679242
[135,] 0.3499079 0.6998157 0.6500921
[136,] 0.6075374 0.7849253 0.3924626
[137,] 0.5447604 0.9104792 0.4552396
[138,] 0.4952285 0.9904570 0.5047715
[139,] 0.6113120 0.7773761 0.3886880
[140,] 0.6126116 0.7747768 0.3873884
> postscript(file="/var/wessaorg/rcomp/tmp/1awud1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2bhjl1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3c8fd1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4o2ea1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5roqz1323974619.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
0.22736287 0.26831241 0.57673448 -0.70405552 0.41314169 0.39337471
7 8 9 10 11 12
0.42246495 0.29506960 0.26322623 0.39382481 -0.69110087 0.41239107
13 14 15 16 17 18
-0.61692611 0.29552507 0.33575025 -0.37382263 -0.66703184 0.39930497
19 20 21 22 23 24
-0.45134833 0.37805583 -0.74757753 0.35890925 0.37994592 0.27183904
25 26 27 28 29 30
-0.65963157 0.47272447 -0.49998010 0.41520253 0.48145080 -0.54751845
31 32 33 34 35 36
0.35830132 -0.72416871 0.54422509 0.45544323 -0.50488562 -0.75951901
37 38 39 40 41 42
-0.64796405 -0.71635333 0.43210584 -0.63350729 -0.58978573 0.43997806
43 44 45 46 47 48
-0.55677183 -0.46954331 0.34564990 0.31238395 0.31006569 0.28732516
49 50 51 52 53 54
0.40115665 -0.57733366 0.17362140 -0.59555079 -0.61514747 0.38893320
55 56 57 58 59 60
-0.47255179 0.32958345 0.39026685 0.49622866 -0.60904123 0.28521430
61 62 63 64 65 66
-0.61019940 -0.56263439 0.39670767 0.21974656 0.39072449 -0.63967342
67 68 69 70 71 72
0.42617258 -0.57060388 0.28795197 -0.65597352 -0.54404159 0.35744119
73 74 75 76 77 78
0.47385283 -0.62640363 -0.49070979 0.39013041 0.33568333 0.37121535
79 80 81 82 83 84
-0.70153705 -0.57318357 -0.70385565 -0.55210333 0.36346979 -0.51348957
85 86 87 88 89 90
0.25987452 0.46254112 -0.58963025 0.22729661 0.35544199 0.39689665
91 92 93 94 95 96
0.34727049 0.42378979 0.33549854 0.48037214 0.35661380 0.53844709
97 98 99 100 101 102
0.29833774 0.40293102 -0.58073079 -0.59107365 0.42577987 -0.52848278
103 104 105 106 107 108
0.52611174 -0.53883068 0.25229026 -0.67395351 0.40289383 -0.59345676
109 110 111 112 113 114
-0.67359527 0.31913296 0.43061552 0.46818971 0.32749192 -0.45167302
115 116 117 118 119 120
0.33345238 -0.51341902 0.57895418 0.32439708 0.35436476 0.33430756
121 122 123 124 125 126
0.33143800 0.36752423 0.30208256 0.32401532 0.09356606 0.46824778
127 128 129 130 131 132
-0.56326307 0.34357246 0.41000609 0.29865346 -0.65310791 -0.60025400
133 134 135 136 137 138
0.42486027 -0.58401188 -0.61463296 0.47006714 -0.65574447 -0.65380886
139 140 141 142 143 144
0.41078555 0.25326641 -0.76213649 0.39481101 -0.61327249 0.21482482
145 146 147 148 149 150
-0.61570468 0.27707880 0.51711645 -0.59890132 -0.61588695 -0.55371081
151 152 153 154 155 156
0.41856027 0.35080004 -0.40346107 0.48103643 0.46182866 0.45154796
157 158 159
0.29020583 0.35775382 0.30313568
> postscript(file="/var/wessaorg/rcomp/tmp/65i371323974619.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 0.22736287 NA
1 0.26831241 0.22736287
2 0.57673448 0.26831241
3 -0.70405552 0.57673448
4 0.41314169 -0.70405552
5 0.39337471 0.41314169
6 0.42246495 0.39337471
7 0.29506960 0.42246495
8 0.26322623 0.29506960
9 0.39382481 0.26322623
10 -0.69110087 0.39382481
11 0.41239107 -0.69110087
12 -0.61692611 0.41239107
13 0.29552507 -0.61692611
14 0.33575025 0.29552507
15 -0.37382263 0.33575025
16 -0.66703184 -0.37382263
17 0.39930497 -0.66703184
18 -0.45134833 0.39930497
19 0.37805583 -0.45134833
20 -0.74757753 0.37805583
21 0.35890925 -0.74757753
22 0.37994592 0.35890925
23 0.27183904 0.37994592
24 -0.65963157 0.27183904
25 0.47272447 -0.65963157
26 -0.49998010 0.47272447
27 0.41520253 -0.49998010
28 0.48145080 0.41520253
29 -0.54751845 0.48145080
30 0.35830132 -0.54751845
31 -0.72416871 0.35830132
32 0.54422509 -0.72416871
33 0.45544323 0.54422509
34 -0.50488562 0.45544323
35 -0.75951901 -0.50488562
36 -0.64796405 -0.75951901
37 -0.71635333 -0.64796405
38 0.43210584 -0.71635333
39 -0.63350729 0.43210584
40 -0.58978573 -0.63350729
41 0.43997806 -0.58978573
42 -0.55677183 0.43997806
43 -0.46954331 -0.55677183
44 0.34564990 -0.46954331
45 0.31238395 0.34564990
46 0.31006569 0.31238395
47 0.28732516 0.31006569
48 0.40115665 0.28732516
49 -0.57733366 0.40115665
50 0.17362140 -0.57733366
51 -0.59555079 0.17362140
52 -0.61514747 -0.59555079
53 0.38893320 -0.61514747
54 -0.47255179 0.38893320
55 0.32958345 -0.47255179
56 0.39026685 0.32958345
57 0.49622866 0.39026685
58 -0.60904123 0.49622866
59 0.28521430 -0.60904123
60 -0.61019940 0.28521430
61 -0.56263439 -0.61019940
62 0.39670767 -0.56263439
63 0.21974656 0.39670767
64 0.39072449 0.21974656
65 -0.63967342 0.39072449
66 0.42617258 -0.63967342
67 -0.57060388 0.42617258
68 0.28795197 -0.57060388
69 -0.65597352 0.28795197
70 -0.54404159 -0.65597352
71 0.35744119 -0.54404159
72 0.47385283 0.35744119
73 -0.62640363 0.47385283
74 -0.49070979 -0.62640363
75 0.39013041 -0.49070979
76 0.33568333 0.39013041
77 0.37121535 0.33568333
78 -0.70153705 0.37121535
79 -0.57318357 -0.70153705
80 -0.70385565 -0.57318357
81 -0.55210333 -0.70385565
82 0.36346979 -0.55210333
83 -0.51348957 0.36346979
84 0.25987452 -0.51348957
85 0.46254112 0.25987452
86 -0.58963025 0.46254112
87 0.22729661 -0.58963025
88 0.35544199 0.22729661
89 0.39689665 0.35544199
90 0.34727049 0.39689665
91 0.42378979 0.34727049
92 0.33549854 0.42378979
93 0.48037214 0.33549854
94 0.35661380 0.48037214
95 0.53844709 0.35661380
96 0.29833774 0.53844709
97 0.40293102 0.29833774
98 -0.58073079 0.40293102
99 -0.59107365 -0.58073079
100 0.42577987 -0.59107365
101 -0.52848278 0.42577987
102 0.52611174 -0.52848278
103 -0.53883068 0.52611174
104 0.25229026 -0.53883068
105 -0.67395351 0.25229026
106 0.40289383 -0.67395351
107 -0.59345676 0.40289383
108 -0.67359527 -0.59345676
109 0.31913296 -0.67359527
110 0.43061552 0.31913296
111 0.46818971 0.43061552
112 0.32749192 0.46818971
113 -0.45167302 0.32749192
114 0.33345238 -0.45167302
115 -0.51341902 0.33345238
116 0.57895418 -0.51341902
117 0.32439708 0.57895418
118 0.35436476 0.32439708
119 0.33430756 0.35436476
120 0.33143800 0.33430756
121 0.36752423 0.33143800
122 0.30208256 0.36752423
123 0.32401532 0.30208256
124 0.09356606 0.32401532
125 0.46824778 0.09356606
126 -0.56326307 0.46824778
127 0.34357246 -0.56326307
128 0.41000609 0.34357246
129 0.29865346 0.41000609
130 -0.65310791 0.29865346
131 -0.60025400 -0.65310791
132 0.42486027 -0.60025400
133 -0.58401188 0.42486027
134 -0.61463296 -0.58401188
135 0.47006714 -0.61463296
136 -0.65574447 0.47006714
137 -0.65380886 -0.65574447
138 0.41078555 -0.65380886
139 0.25326641 0.41078555
140 -0.76213649 0.25326641
141 0.39481101 -0.76213649
142 -0.61327249 0.39481101
143 0.21482482 -0.61327249
144 -0.61570468 0.21482482
145 0.27707880 -0.61570468
146 0.51711645 0.27707880
147 -0.59890132 0.51711645
148 -0.61588695 -0.59890132
149 -0.55371081 -0.61588695
150 0.41856027 -0.55371081
151 0.35080004 0.41856027
152 -0.40346107 0.35080004
153 0.48103643 -0.40346107
154 0.46182866 0.48103643
155 0.45154796 0.46182866
156 0.29020583 0.45154796
157 0.35775382 0.29020583
158 0.30313568 0.35775382
159 NA 0.30313568
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.26831241 0.22736287
[2,] 0.57673448 0.26831241
[3,] -0.70405552 0.57673448
[4,] 0.41314169 -0.70405552
[5,] 0.39337471 0.41314169
[6,] 0.42246495 0.39337471
[7,] 0.29506960 0.42246495
[8,] 0.26322623 0.29506960
[9,] 0.39382481 0.26322623
[10,] -0.69110087 0.39382481
[11,] 0.41239107 -0.69110087
[12,] -0.61692611 0.41239107
[13,] 0.29552507 -0.61692611
[14,] 0.33575025 0.29552507
[15,] -0.37382263 0.33575025
[16,] -0.66703184 -0.37382263
[17,] 0.39930497 -0.66703184
[18,] -0.45134833 0.39930497
[19,] 0.37805583 -0.45134833
[20,] -0.74757753 0.37805583
[21,] 0.35890925 -0.74757753
[22,] 0.37994592 0.35890925
[23,] 0.27183904 0.37994592
[24,] -0.65963157 0.27183904
[25,] 0.47272447 -0.65963157
[26,] -0.49998010 0.47272447
[27,] 0.41520253 -0.49998010
[28,] 0.48145080 0.41520253
[29,] -0.54751845 0.48145080
[30,] 0.35830132 -0.54751845
[31,] -0.72416871 0.35830132
[32,] 0.54422509 -0.72416871
[33,] 0.45544323 0.54422509
[34,] -0.50488562 0.45544323
[35,] -0.75951901 -0.50488562
[36,] -0.64796405 -0.75951901
[37,] -0.71635333 -0.64796405
[38,] 0.43210584 -0.71635333
[39,] -0.63350729 0.43210584
[40,] -0.58978573 -0.63350729
[41,] 0.43997806 -0.58978573
[42,] -0.55677183 0.43997806
[43,] -0.46954331 -0.55677183
[44,] 0.34564990 -0.46954331
[45,] 0.31238395 0.34564990
[46,] 0.31006569 0.31238395
[47,] 0.28732516 0.31006569
[48,] 0.40115665 0.28732516
[49,] -0.57733366 0.40115665
[50,] 0.17362140 -0.57733366
[51,] -0.59555079 0.17362140
[52,] -0.61514747 -0.59555079
[53,] 0.38893320 -0.61514747
[54,] -0.47255179 0.38893320
[55,] 0.32958345 -0.47255179
[56,] 0.39026685 0.32958345
[57,] 0.49622866 0.39026685
[58,] -0.60904123 0.49622866
[59,] 0.28521430 -0.60904123
[60,] -0.61019940 0.28521430
[61,] -0.56263439 -0.61019940
[62,] 0.39670767 -0.56263439
[63,] 0.21974656 0.39670767
[64,] 0.39072449 0.21974656
[65,] -0.63967342 0.39072449
[66,] 0.42617258 -0.63967342
[67,] -0.57060388 0.42617258
[68,] 0.28795197 -0.57060388
[69,] -0.65597352 0.28795197
[70,] -0.54404159 -0.65597352
[71,] 0.35744119 -0.54404159
[72,] 0.47385283 0.35744119
[73,] -0.62640363 0.47385283
[74,] -0.49070979 -0.62640363
[75,] 0.39013041 -0.49070979
[76,] 0.33568333 0.39013041
[77,] 0.37121535 0.33568333
[78,] -0.70153705 0.37121535
[79,] -0.57318357 -0.70153705
[80,] -0.70385565 -0.57318357
[81,] -0.55210333 -0.70385565
[82,] 0.36346979 -0.55210333
[83,] -0.51348957 0.36346979
[84,] 0.25987452 -0.51348957
[85,] 0.46254112 0.25987452
[86,] -0.58963025 0.46254112
[87,] 0.22729661 -0.58963025
[88,] 0.35544199 0.22729661
[89,] 0.39689665 0.35544199
[90,] 0.34727049 0.39689665
[91,] 0.42378979 0.34727049
[92,] 0.33549854 0.42378979
[93,] 0.48037214 0.33549854
[94,] 0.35661380 0.48037214
[95,] 0.53844709 0.35661380
[96,] 0.29833774 0.53844709
[97,] 0.40293102 0.29833774
[98,] -0.58073079 0.40293102
[99,] -0.59107365 -0.58073079
[100,] 0.42577987 -0.59107365
[101,] -0.52848278 0.42577987
[102,] 0.52611174 -0.52848278
[103,] -0.53883068 0.52611174
[104,] 0.25229026 -0.53883068
[105,] -0.67395351 0.25229026
[106,] 0.40289383 -0.67395351
[107,] -0.59345676 0.40289383
[108,] -0.67359527 -0.59345676
[109,] 0.31913296 -0.67359527
[110,] 0.43061552 0.31913296
[111,] 0.46818971 0.43061552
[112,] 0.32749192 0.46818971
[113,] -0.45167302 0.32749192
[114,] 0.33345238 -0.45167302
[115,] -0.51341902 0.33345238
[116,] 0.57895418 -0.51341902
[117,] 0.32439708 0.57895418
[118,] 0.35436476 0.32439708
[119,] 0.33430756 0.35436476
[120,] 0.33143800 0.33430756
[121,] 0.36752423 0.33143800
[122,] 0.30208256 0.36752423
[123,] 0.32401532 0.30208256
[124,] 0.09356606 0.32401532
[125,] 0.46824778 0.09356606
[126,] -0.56326307 0.46824778
[127,] 0.34357246 -0.56326307
[128,] 0.41000609 0.34357246
[129,] 0.29865346 0.41000609
[130,] -0.65310791 0.29865346
[131,] -0.60025400 -0.65310791
[132,] 0.42486027 -0.60025400
[133,] -0.58401188 0.42486027
[134,] -0.61463296 -0.58401188
[135,] 0.47006714 -0.61463296
[136,] -0.65574447 0.47006714
[137,] -0.65380886 -0.65574447
[138,] 0.41078555 -0.65380886
[139,] 0.25326641 0.41078555
[140,] -0.76213649 0.25326641
[141,] 0.39481101 -0.76213649
[142,] -0.61327249 0.39481101
[143,] 0.21482482 -0.61327249
[144,] -0.61570468 0.21482482
[145,] 0.27707880 -0.61570468
[146,] 0.51711645 0.27707880
[147,] -0.59890132 0.51711645
[148,] -0.61588695 -0.59890132
[149,] -0.55371081 -0.61588695
[150,] 0.41856027 -0.55371081
[151,] 0.35080004 0.41856027
[152,] -0.40346107 0.35080004
[153,] 0.48103643 -0.40346107
[154,] 0.46182866 0.48103643
[155,] 0.45154796 0.46182866
[156,] 0.29020583 0.45154796
[157,] 0.35775382 0.29020583
[158,] 0.30313568 0.35775382
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.26831241 0.22736287
2 0.57673448 0.26831241
3 -0.70405552 0.57673448
4 0.41314169 -0.70405552
5 0.39337471 0.41314169
6 0.42246495 0.39337471
7 0.29506960 0.42246495
8 0.26322623 0.29506960
9 0.39382481 0.26322623
10 -0.69110087 0.39382481
11 0.41239107 -0.69110087
12 -0.61692611 0.41239107
13 0.29552507 -0.61692611
14 0.33575025 0.29552507
15 -0.37382263 0.33575025
16 -0.66703184 -0.37382263
17 0.39930497 -0.66703184
18 -0.45134833 0.39930497
19 0.37805583 -0.45134833
20 -0.74757753 0.37805583
21 0.35890925 -0.74757753
22 0.37994592 0.35890925
23 0.27183904 0.37994592
24 -0.65963157 0.27183904
25 0.47272447 -0.65963157
26 -0.49998010 0.47272447
27 0.41520253 -0.49998010
28 0.48145080 0.41520253
29 -0.54751845 0.48145080
30 0.35830132 -0.54751845
31 -0.72416871 0.35830132
32 0.54422509 -0.72416871
33 0.45544323 0.54422509
34 -0.50488562 0.45544323
35 -0.75951901 -0.50488562
36 -0.64796405 -0.75951901
37 -0.71635333 -0.64796405
38 0.43210584 -0.71635333
39 -0.63350729 0.43210584
40 -0.58978573 -0.63350729
41 0.43997806 -0.58978573
42 -0.55677183 0.43997806
43 -0.46954331 -0.55677183
44 0.34564990 -0.46954331
45 0.31238395 0.34564990
46 0.31006569 0.31238395
47 0.28732516 0.31006569
48 0.40115665 0.28732516
49 -0.57733366 0.40115665
50 0.17362140 -0.57733366
51 -0.59555079 0.17362140
52 -0.61514747 -0.59555079
53 0.38893320 -0.61514747
54 -0.47255179 0.38893320
55 0.32958345 -0.47255179
56 0.39026685 0.32958345
57 0.49622866 0.39026685
58 -0.60904123 0.49622866
59 0.28521430 -0.60904123
60 -0.61019940 0.28521430
61 -0.56263439 -0.61019940
62 0.39670767 -0.56263439
63 0.21974656 0.39670767
64 0.39072449 0.21974656
65 -0.63967342 0.39072449
66 0.42617258 -0.63967342
67 -0.57060388 0.42617258
68 0.28795197 -0.57060388
69 -0.65597352 0.28795197
70 -0.54404159 -0.65597352
71 0.35744119 -0.54404159
72 0.47385283 0.35744119
73 -0.62640363 0.47385283
74 -0.49070979 -0.62640363
75 0.39013041 -0.49070979
76 0.33568333 0.39013041
77 0.37121535 0.33568333
78 -0.70153705 0.37121535
79 -0.57318357 -0.70153705
80 -0.70385565 -0.57318357
81 -0.55210333 -0.70385565
82 0.36346979 -0.55210333
83 -0.51348957 0.36346979
84 0.25987452 -0.51348957
85 0.46254112 0.25987452
86 -0.58963025 0.46254112
87 0.22729661 -0.58963025
88 0.35544199 0.22729661
89 0.39689665 0.35544199
90 0.34727049 0.39689665
91 0.42378979 0.34727049
92 0.33549854 0.42378979
93 0.48037214 0.33549854
94 0.35661380 0.48037214
95 0.53844709 0.35661380
96 0.29833774 0.53844709
97 0.40293102 0.29833774
98 -0.58073079 0.40293102
99 -0.59107365 -0.58073079
100 0.42577987 -0.59107365
101 -0.52848278 0.42577987
102 0.52611174 -0.52848278
103 -0.53883068 0.52611174
104 0.25229026 -0.53883068
105 -0.67395351 0.25229026
106 0.40289383 -0.67395351
107 -0.59345676 0.40289383
108 -0.67359527 -0.59345676
109 0.31913296 -0.67359527
110 0.43061552 0.31913296
111 0.46818971 0.43061552
112 0.32749192 0.46818971
113 -0.45167302 0.32749192
114 0.33345238 -0.45167302
115 -0.51341902 0.33345238
116 0.57895418 -0.51341902
117 0.32439708 0.57895418
118 0.35436476 0.32439708
119 0.33430756 0.35436476
120 0.33143800 0.33430756
121 0.36752423 0.33143800
122 0.30208256 0.36752423
123 0.32401532 0.30208256
124 0.09356606 0.32401532
125 0.46824778 0.09356606
126 -0.56326307 0.46824778
127 0.34357246 -0.56326307
128 0.41000609 0.34357246
129 0.29865346 0.41000609
130 -0.65310791 0.29865346
131 -0.60025400 -0.65310791
132 0.42486027 -0.60025400
133 -0.58401188 0.42486027
134 -0.61463296 -0.58401188
135 0.47006714 -0.61463296
136 -0.65574447 0.47006714
137 -0.65380886 -0.65574447
138 0.41078555 -0.65380886
139 0.25326641 0.41078555
140 -0.76213649 0.25326641
141 0.39481101 -0.76213649
142 -0.61327249 0.39481101
143 0.21482482 -0.61327249
144 -0.61570468 0.21482482
145 0.27707880 -0.61570468
146 0.51711645 0.27707880
147 -0.59890132 0.51711645
148 -0.61588695 -0.59890132
149 -0.55371081 -0.61588695
150 0.41856027 -0.55371081
151 0.35080004 0.41856027
152 -0.40346107 0.35080004
153 0.48103643 -0.40346107
154 0.46182866 0.48103643
155 0.45154796 0.46182866
156 0.29020583 0.45154796
157 0.35775382 0.29020583
158 0.30313568 0.35775382
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7byls1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8uru51323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ooiz1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1062ki1323974619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11a0wy1323974619.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122a9t1323974619.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13yzs31323974619.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14iwbu1323974619.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15uwmp1323974619.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16ohuc1323974619.tab")
+ }
>
> try(system("convert tmp/1awud1323974619.ps tmp/1awud1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bhjl1323974619.ps tmp/2bhjl1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c8fd1323974619.ps tmp/3c8fd1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o2ea1323974619.ps tmp/4o2ea1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/5roqz1323974619.ps tmp/5roqz1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/65i371323974619.ps tmp/65i371323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/7byls1323974619.ps tmp/7byls1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uru51323974619.ps tmp/8uru51323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ooiz1323974619.ps tmp/9ooiz1323974619.png",intern=TRUE))
character(0)
> try(system("convert tmp/1062ki1323974619.ps tmp/1062ki1323974619.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.923 0.675 5.631