R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(66
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+ ,1)
+ ,dim=c(9
+ ,146)
+ ,dimnames=list(c('Groepsgevoel'
+ ,'InteractieGR_NV'
+ ,'InteractieGR_U'
+ ,'Vrienden_vinden'
+ ,'NVC'
+ ,'Uitingsangst'
+ ,'Geslacht'
+ ,'InteractieNV_U'
+ ,'Leeftijd')
+ ,1:146))
> y <- array(NA,dim=c(9,146),dimnames=list(c('Groepsgevoel','InteractieGR_NV','InteractieGR_U','Vrienden_vinden','NVC','Uitingsangst','Geslacht','InteractieNV_U','Leeftijd'),1:146))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Vrienden_vinden Groepsgevoel InteractieGR_NV InteractieGR_U NVC
1 5 66 4818 4488 73
2 12 54 3132 2916 58
3 11 82 5576 3362 68
4 6 61 3782 2989 62
5 12 65 4225 3185 65
6 11 77 6237 5544 81
7 12 66 4818 5148 73
8 7 66 4224 3828 64
9 8 66 4488 3828 68
10 13 48 2448 1104 51
11 12 57 3876 2223 68
12 13 80 4880 5040 61
13 12 60 4140 2760 69
14 12 70 5110 4060 73
15 11 85 5185 3315 61
16 12 59 3658 2596 62
17 12 72 4536 3528 63
18 12 70 4830 3990 69
19 11 74 3478 5624 47
20 13 70 4620 4410 66
21 9 51 2958 918 58
22 11 70 4410 2800 63
23 11 71 4899 4189 69
24 11 72 4248 4464 59
25 9 50 2950 3500 59
26 11 69 4347 4485 63
27 12 73 4745 4088 65
28 12 66 4290 2970 65
29 10 73 5183 4161 71
30 12 58 3480 2900 60
31 12 78 6318 3120 81
32 12 83 5561 4814 67
33 9 76 5016 3724 66
34 9 77 4774 3773 62
35 12 79 4977 2133 63
36 14 71 5183 3621 73
37 12 79 4345 5925 55
38 11 60 3540 3900 59
39 9 73 4672 3431 64
40 11 70 4410 3430 63
41 7 42 2688 2730 64
42 15 74 5402 4514 73
43 11 68 3672 3128 54
44 12 83 6308 5727 76
45 12 62 4588 3410 74
46 9 79 4977 6162 63
47 12 61 4453 3538 73
48 11 86 5762 2924 67
49 11 64 4352 4288 68
50 8 75 4950 3375 66
51 7 59 3658 4012 62
52 12 82 5822 4018 71
53 8 61 3843 1159 63
54 10 69 5175 4968 75
55 12 60 4620 3540 77
56 15 59 3658 2714 62
57 12 81 5994 4536 74
58 12 65 4355 2925 67
59 12 60 3360 3180 56
60 12 60 3600 4020 60
61 8 45 2610 3285 58
62 10 75 4875 3450 65
63 14 84 4116 5880 49
64 10 77 4697 2926 61
65 12 64 4224 3456 66
66 14 54 3456 2484 64
67 6 72 4680 3312 65
68 11 56 2576 2520 46
69 10 67 4355 3149 65
70 14 81 6561 2025 81
71 12 73 5256 4599 72
72 13 67 4355 3082 65
73 11 72 5328 4968 74
74 11 69 4071 2967 59
75 12 71 4899 3479 69
76 13 77 4466 3003 58
77 12 63 4473 4095 71
78 8 49 3871 2646 79
79 12 74 5032 3700 68
80 11 76 5016 3192 66
81 10 65 4030 2925 62
82 12 65 4485 3250 69
83 11 69 4347 3795 63
84 12 71 4402 2698 62
85 12 68 4148 2720 61
86 10 49 3185 2499 65
87 12 86 5504 4214 64
88 12 63 3528 2457 56
89 11 77 4312 4389 56
90 10 52 2496 1560 48
91 12 73 5402 3723 74
92 11 63 4347 3024 69
93 12 54 3348 3024 62
94 12 56 4088 3696 73
95 10 54 3456 3888 64
96 11 61 3477 1708 57
97 10 70 3990 3640 57
98 11 68 4080 3604 60
99 11 63 3843 4410 61
100 12 76 5472 4788 72
101 11 69 3933 3174 57
102 11 71 3621 3195 51
103 7 39 2457 2652 63
104 12 54 2916 2916 54
105 8 64 4608 3840 72
106 10 70 4340 3500 62
107 12 76 5168 5016 68
108 11 71 4402 3976 62
109 13 73 4599 3942 63
110 9 81 6237 5832 77
111 11 50 2850 1700 57
112 13 42 2394 1638 57
113 8 66 4026 4356 61
114 12 77 5005 2079 65
115 11 62 3906 3906 63
116 11 66 4356 4290 66
117 12 69 4692 4347 68
118 13 72 5184 3528 72
119 11 67 4556 2814 68
120 10 59 3481 3009 59
121 10 66 3696 3300 56
122 10 68 4216 4352 62
123 12 72 5184 4896 72
124 12 73 4964 4818 68
125 13 69 4623 4071 67
126 11 57 3078 1824 54
127 11 55 3795 3410 69
128 12 72 4392 3744 61
129 9 68 3740 2312 55
130 11 83 6225 5229 75
131 12 74 4070 3552 55
132 12 72 3528 3816 49
133 13 66 3564 2574 54
134 6 61 4026 3111 66
135 11 86 6278 5160 73
136 10 81 5103 5670 63
137 12 79 4819 3160 61
138 11 73 5402 4453 74
139 12 59 4779 2065 81
140 12 64 3968 2496 62
141 7 75 4800 2325 64
142 12 68 4216 2448 62
143 12 84 7140 4284 85
144 9 68 5032 3740 74
145 12 68 3468 4556 51
146 12 69 4554 2760 66
Uitingsangst Geslacht InteractieNV_U Leeftijd
1 68 0 4964 1
2 54 1 3132 1
3 41 1 2788 1
4 49 1 3038 1
5 49 1 3185 1
6 72 1 5832 1
7 78 1 5694 1
8 58 0 3712 1
9 58 1 3944 1
10 23 1 1173 1
11 39 1 2652 1
12 63 1 3843 1
13 46 1 3174 1
14 58 1 4234 1
15 39 0 2379 1
16 44 0 2728 1
17 49 1 3087 1
18 57 1 3933 1
19 76 0 3572 1
20 63 0 4158 1
21 18 1 1044 1
22 40 0 2520 1
23 59 1 4071 1
24 62 0 3658 1
25 70 1 4130 1
26 65 0 4095 1
27 56 0 3640 1
28 45 1 2925 1
29 57 0 4047 1
30 50 1 3000 1
31 40 0 3240 1
32 58 1 3886 1
33 49 0 3234 1
34 49 1 3038 1
35 27 1 1701 1
36 51 0 3723 1
37 75 0 4125 1
38 65 1 3835 1
39 47 1 3008 1
40 49 0 3087 1
41 65 1 4160 1
42 61 1 4453 1
43 46 1 2484 1
44 69 1 5244 1
45 55 0 4070 1
46 78 0 4914 1
47 58 0 4234 1
48 34 0 2278 1
49 67 0 4556 1
50 45 1 2970 1
51 68 0 4216 1
52 49 0 3479 1
53 19 1 1197 1
54 72 1 5400 1
55 59 1 4543 1
56 46 0 2852 1
57 56 1 4144 1
58 45 0 3015 1
59 53 0 2968 1
60 67 0 4020 1
61 73 0 4234 1
62 46 1 2990 1
63 70 0 3430 1
64 38 1 2318 1
65 54 0 3564 1
66 46 0 2944 1
67 46 0 2990 1
68 45 1 2070 1
69 47 0 3055 1
70 25 0 2025 1
71 63 1 4536 1
72 46 0 2990 1
73 69 0 5106 1
74 43 1 2537 1
75 49 1 3381 1
76 39 0 2262 1
77 65 1 4615 1
78 54 0 4266 1
79 50 0 3400 1
80 42 1 2772 1
81 45 0 2790 1
82 50 1 3450 1
83 55 0 3465 1
84 38 1 2356 1
85 40 1 2440 1
86 51 0 3315 1
87 49 1 3136 1
88 39 0 2184 1
89 57 0 3192 1
90 30 1 1440 1
91 51 1 3774 1
92 48 1 3312 1
93 56 1 3472 1
94 66 1 4818 1
95 72 1 4608 1
96 28 1 1596 1
97 52 1 2964 1
98 53 0 3180 1
99 70 0 4270 1
100 63 1 4536 1
101 46 1 2622 1
102 45 1 2295 1
103 68 1 4284 2
104 54 1 2916 1
105 60 1 4320 2
106 50 1 3100 1
107 66 1 4488 1
108 56 1 3472 1
109 54 0 3402 1
110 72 1 5544 1
111 34 1 1938 1
112 39 1 2223 1
113 66 1 4026 1
114 27 1 1755 1
115 63 1 3969 1
116 65 0 4290 1
117 63 1 4284 1
118 49 1 3528 1
119 42 1 2856 1
120 51 1 3009 1
121 50 1 2800 1
122 64 1 3968 1
123 68 0 4896 1
124 66 0 4488 1
125 59 1 3953 1
126 32 1 1728 1
127 62 0 4278 1
128 52 1 3172 1
129 34 1 1870 1
130 63 0 4725 1
131 48 1 2640 1
132 53 1 2597 1
133 39 0 2106 1
134 51 1 3366 1
135 60 1 4380 1
136 70 0 4410 1
137 40 0 2440 1
138 61 1 4514 1
139 35 0 2835 1
140 39 1 2418 1
141 31 1 1984 1
142 36 1 2232 1
143 51 1 4335 1
144 55 1 4070 1
145 67 1 3417 1
146 40 1 2640 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Groepsgevoel InteractieGR_NV InteractieGR_U
17.634600 -0.181160 0.001452 0.002178
NVC Uitingsangst Geslacht InteractieNV_U
0.038940 -0.004371 -0.146541 -0.002433
Leeftijd
-2.367466
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5435 -0.7982 0.2628 0.9940 3.9282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.634600 8.850847 1.992 0.0483 *
Groepsgevoel -0.181160 0.135777 -1.334 0.1843
InteractieGR_NV 0.001452 0.001962 0.740 0.4606
InteractieGR_U 0.002178 0.001092 1.994 0.0481 *
NVC 0.038940 0.145522 0.268 0.7894
Uitingsangst -0.004371 0.101495 -0.043 0.9657
Geslacht -0.146541 0.313971 -0.467 0.6414
InteractieNV_U -0.002433 0.001576 -1.544 0.1249
Leeftijd -2.367466 1.300442 -1.821 0.0709 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.731 on 137 degrees of freedom
Multiple R-squared: 0.1226, Adjusted R-squared: 0.07132
F-statistic: 2.392 on 8 and 137 DF, p-value: 0.01910
> 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,] 0.99793962 0.0041207595 0.0020603798
[2,] 0.99619296 0.0076140774 0.0038070387
[3,] 0.99277666 0.0144466894 0.0072233447
[4,] 0.99363287 0.0127342530 0.0063671265
[5,] 0.99855475 0.0028905079 0.0014452540
[6,] 0.99711024 0.0057795195 0.0028897597
[7,] 0.99518528 0.0096294321 0.0048147161
[8,] 0.99196463 0.0160707472 0.0080353736
[9,] 0.99734564 0.0053087294 0.0026543647
[10,] 0.99878232 0.0024353621 0.0012176810
[11,] 0.99804492 0.0039101572 0.0019550786
[12,] 0.99656982 0.0068603580 0.0034301790
[13,] 0.99439502 0.0112099587 0.0056049794
[14,] 0.99157982 0.0168403538 0.0084201769
[15,] 0.98774130 0.0245174010 0.0122587005
[16,] 0.98452996 0.0309400822 0.0154700411
[17,] 0.97873378 0.0425324473 0.0212662236
[18,] 0.97068487 0.0586302614 0.0293151307
[19,] 0.96343275 0.0731345045 0.0365672522
[20,] 0.95005878 0.0998824372 0.0499412186
[21,] 0.93355008 0.1328998360 0.0664499180
[22,] 0.93628664 0.1274267231 0.0637133615
[23,] 0.94885333 0.1022933318 0.0511466659
[24,] 0.93589004 0.1282199185 0.0641099592
[25,] 0.96443130 0.0711373949 0.0355686975
[26,] 0.95212968 0.0957406343 0.0478703172
[27,] 0.93656218 0.1268756368 0.0634378184
[28,] 0.94369167 0.1126166564 0.0563083282
[29,] 0.92679535 0.1464092921 0.0732046460
[30,] 0.92768568 0.1446286312 0.0723143156
[31,] 0.96837999 0.0632400134 0.0316200067
[32,] 0.95743856 0.0851228768 0.0425614384
[33,] 0.94810237 0.1037952532 0.0518976266
[34,] 0.94714897 0.1057020613 0.0528510307
[35,] 0.96208548 0.0758290495 0.0379145247
[36,] 0.96095085 0.0780982997 0.0390491499
[37,] 0.94851892 0.1029621571 0.0514810786
[38,] 0.93634542 0.1273091591 0.0636545795
[39,] 0.96602009 0.0679598240 0.0339799120
[40,] 0.98303190 0.0339361959 0.0169680979
[41,] 0.97699047 0.0460190668 0.0230095334
[42,] 0.99114708 0.0177058355 0.0088529177
[43,] 0.98785909 0.0242818256 0.0121409128
[44,] 0.98753412 0.0249317568 0.0124658784
[45,] 0.99741643 0.0051671440 0.0025835720
[46,] 0.99627694 0.0074461248 0.0037230624
[47,] 0.99498214 0.0100357151 0.0050178575
[48,] 0.99395753 0.0120849306 0.0060424653
[49,] 0.99304824 0.0139035155 0.0069517578
[50,] 0.99385739 0.0122852152 0.0061426076
[51,] 0.99239984 0.0152003280 0.0076001640
[52,] 0.99303573 0.0139285468 0.0069642734
[53,] 0.99098097 0.0180380578 0.0090190289
[54,] 0.98855224 0.0228955135 0.0114477568
[55,] 0.99377639 0.0124472261 0.0062236130
[56,] 0.99984254 0.0003149249 0.0001574625
[57,] 0.99976166 0.0004766857 0.0002383429
[58,] 0.99971989 0.0005602198 0.0002801099
[59,] 0.99959375 0.0008125020 0.0004062510
[60,] 0.99946567 0.0010686596 0.0005343298
[61,] 0.99945578 0.0010884314 0.0005442157
[62,] 0.99915343 0.0016931375 0.0008465688
[63,] 0.99871700 0.0025659963 0.0012829981
[64,] 0.99823890 0.0035222062 0.0017611031
[65,] 0.99849166 0.0030166710 0.0015083355
[66,] 0.99847672 0.0030465684 0.0015232842
[67,] 0.99888086 0.0022382871 0.0011191435
[68,] 0.99835509 0.0032898268 0.0016449134
[69,] 0.99754068 0.0049186370 0.0024593185
[70,] 0.99718889 0.0056222235 0.0028111117
[71,] 0.99636666 0.0072666843 0.0036333422
[72,] 0.99479899 0.0104020212 0.0052010106
[73,] 0.99331270 0.0133745937 0.0066872969
[74,] 0.99156674 0.0168665274 0.0084332637
[75,] 0.99016478 0.0196704431 0.0098352215
[76,] 0.98748703 0.0250259480 0.0125129740
[77,] 0.98353010 0.0329397953 0.0164698977
[78,] 0.97780042 0.0443991525 0.0221995762
[79,] 0.97317359 0.0536528222 0.0268264111
[80,] 0.96663331 0.0667333852 0.0333666926
[81,] 0.95529608 0.0894078429 0.0447039214
[82,] 0.94959622 0.1008075522 0.0504037761
[83,] 0.96199711 0.0760057724 0.0380028862
[84,] 0.94942407 0.1011518648 0.0505759324
[85,] 0.93333930 0.1333214000 0.0666607000
[86,] 0.91969596 0.1606080811 0.0803040406
[87,] 0.90160601 0.1967879844 0.0983939922
[88,] 0.87879525 0.2424094910 0.1212047455
[89,] 0.86813789 0.2637242105 0.1318621053
[90,] 0.83539634 0.3292073287 0.1646036643
[91,] 0.79873608 0.4025278406 0.2012639203
[92,] 0.76161576 0.4767684825 0.2383842413
[93,] 0.72288446 0.5542310842 0.2771155421
[94,] 0.67279179 0.6544164130 0.3272082065
[95,] 0.63185293 0.7362941430 0.3681470715
[96,] 0.61304492 0.7739101600 0.3869550800
[97,] 0.55578338 0.8884332489 0.4442166245
[98,] 0.52729237 0.9454152601 0.4727076300
[99,] 0.51578024 0.9684395256 0.4842197628
[100,] 0.46033309 0.9206661766 0.5396669117
[101,] 0.44701870 0.8940374056 0.5529812972
[102,] 0.53878780 0.9224244094 0.4612122047
[103,] 0.49256343 0.9851268535 0.5074365732
[104,] 0.42854834 0.8570966740 0.5714516630
[105,] 0.36789381 0.7357876245 0.6321061877
[106,] 0.34274579 0.6854915720 0.6572542140
[107,] 0.37966050 0.7593210025 0.6203394988
[108,] 0.31849503 0.6369900616 0.6815049692
[109,] 0.27151090 0.5430218007 0.7284890996
[110,] 0.23091189 0.4618237711 0.7690881145
[111,] 0.19029813 0.3805962664 0.8097018668
[112,] 0.15237030 0.3047406002 0.8476296999
[113,] 0.11411947 0.2282389481 0.8858805260
[114,] 0.15483444 0.3096688740 0.8451655630
[115,] 0.11214092 0.2242818479 0.8878590760
[116,] 0.09143047 0.1828609411 0.9085695295
[117,] 0.07661206 0.1532241246 0.9233879377
[118,] 0.07689434 0.1537886725 0.9231056638
[119,] 0.04950371 0.0990074263 0.9504962869
[120,] 0.03865517 0.0773103440 0.9613448280
[121,] 0.02680978 0.0536195681 0.9731902160
[122,] 0.01379996 0.0275999285 0.9862000358
[123,] 0.22099726 0.4419945212 0.7790027394
> postscript(file="/var/www/html/rcomp/tmp/1s6fb1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2s6fb1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3lgww1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lgww1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lgww1291291447.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 = 146
Frequency = 1
1 2 3 4 5 6
-5.54350727 1.36464957 -0.36492866 -4.87604660 1.01962994 0.05461589
7 8 9 10 11 12
1.98587149 -3.98383072 -2.81174029 1.58651465 0.71425521 1.56500221
13 14 15 16 17 18
0.96702163 1.01579526 -0.02946046 0.87468116 0.92884246 0.99363037
19 20 21 22 23 24
-0.96252242 1.92789015 -2.81360863 -0.23089283 -0.01416006 -0.23582699
25 26 27 28 29 30
-0.90807197 -0.04804313 0.73899231 0.92446969 -1.29467402 1.20243038
31 32 33 34 35 36
-0.19695347 0.46122698 -2.37572982 -2.12461302 1.12578375 2.62638086
37 38 39 40 41 42
0.05905314 0.43642566 -2.11610079 -0.18372478 -2.44390615 3.87398495
43 44 45 46 47 48
0.19943859 0.39084698 1.14196831 -2.75295516 1.32915641 -0.33569387
49 50 51 52 53 54
0.40365528 -3.21445946 -3.48306976 0.30253482 -3.62945923 -0.41639079
55 56 57 58 59 60
1.64826650 3.92820191 0.42219802 0.74152106 1.07369738 1.36144671
61 62 63 64 65 66
-1.69360107 -1.17693032 1.91590431 -0.92958213 1.00839914 2.96242945
67 68 69 70 71 72
-5.28339896 0.24013385 -1.19997141 1.35618782 0.96930717 1.78338857
73 74 75 76 77 78
0.06890240 0.07315888 0.80920961 2.07644466 1.63166152 -2.23086297
79 80 81 82 83 84
0.62140135 -0.22551775 -1.13951031 0.99411564 -0.12222731 0.96166835
85 86 87 88 89 90
0.99105639 -0.69699910 0.64648167 0.97878024 -0.29855596 -0.95436907
91 92 93 94 95 96
0.68040030 -0.02030314 1.49623719 2.21167083 0.21434894 -0.04979253
97 98 99 100 101 102
-0.93734946 -0.08532054 0.28545608 0.78769822 0.12054818 0.32361286
103 104 105 106 107 108
0.23901659 1.30835794 -0.23901659 -1.01305759 0.78457216 -0.02698996
109 110 111 112 113 114
1.75885596 -2.39289561 -0.25659655 1.80638096 -2.78377603 0.89392380
115 116 117 118 119 120
0.41596724 0.17769966 1.15465633 1.71084872 -0.23862038 -0.79000089
121 122 123 124 125 126
-0.86373249 -0.87735443 0.99722903 0.82181897 2.07185120 0.00764367
127 128 129 130 131 132
0.75635377 0.96534339 -1.70783324 -0.80096552 1.13480110 1.13522371
133 134 135 136 137 138
2.10331769 -4.84478726 -0.81234227 -1.76353053 0.90516781 -0.06485598
139 140 141 142 143 144
-0.11508806 0.91861960 -4.09285258 0.92209095 -0.13451073 -1.98760599
145 146
0.86491293 0.78773854
> postscript(file="/var/www/html/rcomp/tmp/6epvh1291291447.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 = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.54350727 NA
1 1.36464957 -5.54350727
2 -0.36492866 1.36464957
3 -4.87604660 -0.36492866
4 1.01962994 -4.87604660
5 0.05461589 1.01962994
6 1.98587149 0.05461589
7 -3.98383072 1.98587149
8 -2.81174029 -3.98383072
9 1.58651465 -2.81174029
10 0.71425521 1.58651465
11 1.56500221 0.71425521
12 0.96702163 1.56500221
13 1.01579526 0.96702163
14 -0.02946046 1.01579526
15 0.87468116 -0.02946046
16 0.92884246 0.87468116
17 0.99363037 0.92884246
18 -0.96252242 0.99363037
19 1.92789015 -0.96252242
20 -2.81360863 1.92789015
21 -0.23089283 -2.81360863
22 -0.01416006 -0.23089283
23 -0.23582699 -0.01416006
24 -0.90807197 -0.23582699
25 -0.04804313 -0.90807197
26 0.73899231 -0.04804313
27 0.92446969 0.73899231
28 -1.29467402 0.92446969
29 1.20243038 -1.29467402
30 -0.19695347 1.20243038
31 0.46122698 -0.19695347
32 -2.37572982 0.46122698
33 -2.12461302 -2.37572982
34 1.12578375 -2.12461302
35 2.62638086 1.12578375
36 0.05905314 2.62638086
37 0.43642566 0.05905314
38 -2.11610079 0.43642566
39 -0.18372478 -2.11610079
40 -2.44390615 -0.18372478
41 3.87398495 -2.44390615
42 0.19943859 3.87398495
43 0.39084698 0.19943859
44 1.14196831 0.39084698
45 -2.75295516 1.14196831
46 1.32915641 -2.75295516
47 -0.33569387 1.32915641
48 0.40365528 -0.33569387
49 -3.21445946 0.40365528
50 -3.48306976 -3.21445946
51 0.30253482 -3.48306976
52 -3.62945923 0.30253482
53 -0.41639079 -3.62945923
54 1.64826650 -0.41639079
55 3.92820191 1.64826650
56 0.42219802 3.92820191
57 0.74152106 0.42219802
58 1.07369738 0.74152106
59 1.36144671 1.07369738
60 -1.69360107 1.36144671
61 -1.17693032 -1.69360107
62 1.91590431 -1.17693032
63 -0.92958213 1.91590431
64 1.00839914 -0.92958213
65 2.96242945 1.00839914
66 -5.28339896 2.96242945
67 0.24013385 -5.28339896
68 -1.19997141 0.24013385
69 1.35618782 -1.19997141
70 0.96930717 1.35618782
71 1.78338857 0.96930717
72 0.06890240 1.78338857
73 0.07315888 0.06890240
74 0.80920961 0.07315888
75 2.07644466 0.80920961
76 1.63166152 2.07644466
77 -2.23086297 1.63166152
78 0.62140135 -2.23086297
79 -0.22551775 0.62140135
80 -1.13951031 -0.22551775
81 0.99411564 -1.13951031
82 -0.12222731 0.99411564
83 0.96166835 -0.12222731
84 0.99105639 0.96166835
85 -0.69699910 0.99105639
86 0.64648167 -0.69699910
87 0.97878024 0.64648167
88 -0.29855596 0.97878024
89 -0.95436907 -0.29855596
90 0.68040030 -0.95436907
91 -0.02030314 0.68040030
92 1.49623719 -0.02030314
93 2.21167083 1.49623719
94 0.21434894 2.21167083
95 -0.04979253 0.21434894
96 -0.93734946 -0.04979253
97 -0.08532054 -0.93734946
98 0.28545608 -0.08532054
99 0.78769822 0.28545608
100 0.12054818 0.78769822
101 0.32361286 0.12054818
102 0.23901659 0.32361286
103 1.30835794 0.23901659
104 -0.23901659 1.30835794
105 -1.01305759 -0.23901659
106 0.78457216 -1.01305759
107 -0.02698996 0.78457216
108 1.75885596 -0.02698996
109 -2.39289561 1.75885596
110 -0.25659655 -2.39289561
111 1.80638096 -0.25659655
112 -2.78377603 1.80638096
113 0.89392380 -2.78377603
114 0.41596724 0.89392380
115 0.17769966 0.41596724
116 1.15465633 0.17769966
117 1.71084872 1.15465633
118 -0.23862038 1.71084872
119 -0.79000089 -0.23862038
120 -0.86373249 -0.79000089
121 -0.87735443 -0.86373249
122 0.99722903 -0.87735443
123 0.82181897 0.99722903
124 2.07185120 0.82181897
125 0.00764367 2.07185120
126 0.75635377 0.00764367
127 0.96534339 0.75635377
128 -1.70783324 0.96534339
129 -0.80096552 -1.70783324
130 1.13480110 -0.80096552
131 1.13522371 1.13480110
132 2.10331769 1.13522371
133 -4.84478726 2.10331769
134 -0.81234227 -4.84478726
135 -1.76353053 -0.81234227
136 0.90516781 -1.76353053
137 -0.06485598 0.90516781
138 -0.11508806 -0.06485598
139 0.91861960 -0.11508806
140 -4.09285258 0.91861960
141 0.92209095 -4.09285258
142 -0.13451073 0.92209095
143 -1.98760599 -0.13451073
144 0.86491293 -1.98760599
145 0.78773854 0.86491293
146 NA 0.78773854
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.36464957 -5.54350727
[2,] -0.36492866 1.36464957
[3,] -4.87604660 -0.36492866
[4,] 1.01962994 -4.87604660
[5,] 0.05461589 1.01962994
[6,] 1.98587149 0.05461589
[7,] -3.98383072 1.98587149
[8,] -2.81174029 -3.98383072
[9,] 1.58651465 -2.81174029
[10,] 0.71425521 1.58651465
[11,] 1.56500221 0.71425521
[12,] 0.96702163 1.56500221
[13,] 1.01579526 0.96702163
[14,] -0.02946046 1.01579526
[15,] 0.87468116 -0.02946046
[16,] 0.92884246 0.87468116
[17,] 0.99363037 0.92884246
[18,] -0.96252242 0.99363037
[19,] 1.92789015 -0.96252242
[20,] -2.81360863 1.92789015
[21,] -0.23089283 -2.81360863
[22,] -0.01416006 -0.23089283
[23,] -0.23582699 -0.01416006
[24,] -0.90807197 -0.23582699
[25,] -0.04804313 -0.90807197
[26,] 0.73899231 -0.04804313
[27,] 0.92446969 0.73899231
[28,] -1.29467402 0.92446969
[29,] 1.20243038 -1.29467402
[30,] -0.19695347 1.20243038
[31,] 0.46122698 -0.19695347
[32,] -2.37572982 0.46122698
[33,] -2.12461302 -2.37572982
[34,] 1.12578375 -2.12461302
[35,] 2.62638086 1.12578375
[36,] 0.05905314 2.62638086
[37,] 0.43642566 0.05905314
[38,] -2.11610079 0.43642566
[39,] -0.18372478 -2.11610079
[40,] -2.44390615 -0.18372478
[41,] 3.87398495 -2.44390615
[42,] 0.19943859 3.87398495
[43,] 0.39084698 0.19943859
[44,] 1.14196831 0.39084698
[45,] -2.75295516 1.14196831
[46,] 1.32915641 -2.75295516
[47,] -0.33569387 1.32915641
[48,] 0.40365528 -0.33569387
[49,] -3.21445946 0.40365528
[50,] -3.48306976 -3.21445946
[51,] 0.30253482 -3.48306976
[52,] -3.62945923 0.30253482
[53,] -0.41639079 -3.62945923
[54,] 1.64826650 -0.41639079
[55,] 3.92820191 1.64826650
[56,] 0.42219802 3.92820191
[57,] 0.74152106 0.42219802
[58,] 1.07369738 0.74152106
[59,] 1.36144671 1.07369738
[60,] -1.69360107 1.36144671
[61,] -1.17693032 -1.69360107
[62,] 1.91590431 -1.17693032
[63,] -0.92958213 1.91590431
[64,] 1.00839914 -0.92958213
[65,] 2.96242945 1.00839914
[66,] -5.28339896 2.96242945
[67,] 0.24013385 -5.28339896
[68,] -1.19997141 0.24013385
[69,] 1.35618782 -1.19997141
[70,] 0.96930717 1.35618782
[71,] 1.78338857 0.96930717
[72,] 0.06890240 1.78338857
[73,] 0.07315888 0.06890240
[74,] 0.80920961 0.07315888
[75,] 2.07644466 0.80920961
[76,] 1.63166152 2.07644466
[77,] -2.23086297 1.63166152
[78,] 0.62140135 -2.23086297
[79,] -0.22551775 0.62140135
[80,] -1.13951031 -0.22551775
[81,] 0.99411564 -1.13951031
[82,] -0.12222731 0.99411564
[83,] 0.96166835 -0.12222731
[84,] 0.99105639 0.96166835
[85,] -0.69699910 0.99105639
[86,] 0.64648167 -0.69699910
[87,] 0.97878024 0.64648167
[88,] -0.29855596 0.97878024
[89,] -0.95436907 -0.29855596
[90,] 0.68040030 -0.95436907
[91,] -0.02030314 0.68040030
[92,] 1.49623719 -0.02030314
[93,] 2.21167083 1.49623719
[94,] 0.21434894 2.21167083
[95,] -0.04979253 0.21434894
[96,] -0.93734946 -0.04979253
[97,] -0.08532054 -0.93734946
[98,] 0.28545608 -0.08532054
[99,] 0.78769822 0.28545608
[100,] 0.12054818 0.78769822
[101,] 0.32361286 0.12054818
[102,] 0.23901659 0.32361286
[103,] 1.30835794 0.23901659
[104,] -0.23901659 1.30835794
[105,] -1.01305759 -0.23901659
[106,] 0.78457216 -1.01305759
[107,] -0.02698996 0.78457216
[108,] 1.75885596 -0.02698996
[109,] -2.39289561 1.75885596
[110,] -0.25659655 -2.39289561
[111,] 1.80638096 -0.25659655
[112,] -2.78377603 1.80638096
[113,] 0.89392380 -2.78377603
[114,] 0.41596724 0.89392380
[115,] 0.17769966 0.41596724
[116,] 1.15465633 0.17769966
[117,] 1.71084872 1.15465633
[118,] -0.23862038 1.71084872
[119,] -0.79000089 -0.23862038
[120,] -0.86373249 -0.79000089
[121,] -0.87735443 -0.86373249
[122,] 0.99722903 -0.87735443
[123,] 0.82181897 0.99722903
[124,] 2.07185120 0.82181897
[125,] 0.00764367 2.07185120
[126,] 0.75635377 0.00764367
[127,] 0.96534339 0.75635377
[128,] -1.70783324 0.96534339
[129,] -0.80096552 -1.70783324
[130,] 1.13480110 -0.80096552
[131,] 1.13522371 1.13480110
[132,] 2.10331769 1.13522371
[133,] -4.84478726 2.10331769
[134,] -0.81234227 -4.84478726
[135,] -1.76353053 -0.81234227
[136,] 0.90516781 -1.76353053
[137,] -0.06485598 0.90516781
[138,] -0.11508806 -0.06485598
[139,] 0.91861960 -0.11508806
[140,] -4.09285258 0.91861960
[141,] 0.92209095 -4.09285258
[142,] -0.13451073 0.92209095
[143,] -1.98760599 -0.13451073
[144,] 0.86491293 -1.98760599
[145,] 0.78773854 0.86491293
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.36464957 -5.54350727
2 -0.36492866 1.36464957
3 -4.87604660 -0.36492866
4 1.01962994 -4.87604660
5 0.05461589 1.01962994
6 1.98587149 0.05461589
7 -3.98383072 1.98587149
8 -2.81174029 -3.98383072
9 1.58651465 -2.81174029
10 0.71425521 1.58651465
11 1.56500221 0.71425521
12 0.96702163 1.56500221
13 1.01579526 0.96702163
14 -0.02946046 1.01579526
15 0.87468116 -0.02946046
16 0.92884246 0.87468116
17 0.99363037 0.92884246
18 -0.96252242 0.99363037
19 1.92789015 -0.96252242
20 -2.81360863 1.92789015
21 -0.23089283 -2.81360863
22 -0.01416006 -0.23089283
23 -0.23582699 -0.01416006
24 -0.90807197 -0.23582699
25 -0.04804313 -0.90807197
26 0.73899231 -0.04804313
27 0.92446969 0.73899231
28 -1.29467402 0.92446969
29 1.20243038 -1.29467402
30 -0.19695347 1.20243038
31 0.46122698 -0.19695347
32 -2.37572982 0.46122698
33 -2.12461302 -2.37572982
34 1.12578375 -2.12461302
35 2.62638086 1.12578375
36 0.05905314 2.62638086
37 0.43642566 0.05905314
38 -2.11610079 0.43642566
39 -0.18372478 -2.11610079
40 -2.44390615 -0.18372478
41 3.87398495 -2.44390615
42 0.19943859 3.87398495
43 0.39084698 0.19943859
44 1.14196831 0.39084698
45 -2.75295516 1.14196831
46 1.32915641 -2.75295516
47 -0.33569387 1.32915641
48 0.40365528 -0.33569387
49 -3.21445946 0.40365528
50 -3.48306976 -3.21445946
51 0.30253482 -3.48306976
52 -3.62945923 0.30253482
53 -0.41639079 -3.62945923
54 1.64826650 -0.41639079
55 3.92820191 1.64826650
56 0.42219802 3.92820191
57 0.74152106 0.42219802
58 1.07369738 0.74152106
59 1.36144671 1.07369738
60 -1.69360107 1.36144671
61 -1.17693032 -1.69360107
62 1.91590431 -1.17693032
63 -0.92958213 1.91590431
64 1.00839914 -0.92958213
65 2.96242945 1.00839914
66 -5.28339896 2.96242945
67 0.24013385 -5.28339896
68 -1.19997141 0.24013385
69 1.35618782 -1.19997141
70 0.96930717 1.35618782
71 1.78338857 0.96930717
72 0.06890240 1.78338857
73 0.07315888 0.06890240
74 0.80920961 0.07315888
75 2.07644466 0.80920961
76 1.63166152 2.07644466
77 -2.23086297 1.63166152
78 0.62140135 -2.23086297
79 -0.22551775 0.62140135
80 -1.13951031 -0.22551775
81 0.99411564 -1.13951031
82 -0.12222731 0.99411564
83 0.96166835 -0.12222731
84 0.99105639 0.96166835
85 -0.69699910 0.99105639
86 0.64648167 -0.69699910
87 0.97878024 0.64648167
88 -0.29855596 0.97878024
89 -0.95436907 -0.29855596
90 0.68040030 -0.95436907
91 -0.02030314 0.68040030
92 1.49623719 -0.02030314
93 2.21167083 1.49623719
94 0.21434894 2.21167083
95 -0.04979253 0.21434894
96 -0.93734946 -0.04979253
97 -0.08532054 -0.93734946
98 0.28545608 -0.08532054
99 0.78769822 0.28545608
100 0.12054818 0.78769822
101 0.32361286 0.12054818
102 0.23901659 0.32361286
103 1.30835794 0.23901659
104 -0.23901659 1.30835794
105 -1.01305759 -0.23901659
106 0.78457216 -1.01305759
107 -0.02698996 0.78457216
108 1.75885596 -0.02698996
109 -2.39289561 1.75885596
110 -0.25659655 -2.39289561
111 1.80638096 -0.25659655
112 -2.78377603 1.80638096
113 0.89392380 -2.78377603
114 0.41596724 0.89392380
115 0.17769966 0.41596724
116 1.15465633 0.17769966
117 1.71084872 1.15465633
118 -0.23862038 1.71084872
119 -0.79000089 -0.23862038
120 -0.86373249 -0.79000089
121 -0.87735443 -0.86373249
122 0.99722903 -0.87735443
123 0.82181897 0.99722903
124 2.07185120 0.82181897
125 0.00764367 2.07185120
126 0.75635377 0.00764367
127 0.96534339 0.75635377
128 -1.70783324 0.96534339
129 -0.80096552 -1.70783324
130 1.13480110 -0.80096552
131 1.13522371 1.13480110
132 2.10331769 1.13522371
133 -4.84478726 2.10331769
134 -0.81234227 -4.84478726
135 -1.76353053 -0.81234227
136 0.90516781 -1.76353053
137 -0.06485598 0.90516781
138 -0.11508806 -0.06485598
139 0.91861960 -0.11508806
140 -4.09285258 0.91861960
141 0.92209095 -4.09285258
142 -0.13451073 0.92209095
143 -1.98760599 -0.13451073
144 0.86491293 -1.98760599
145 0.78773854 0.86491293
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7epvh1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oyck1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oyck1291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zpu41291291447.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/112qaa1291291447.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1269rg1291291447.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/132io71291291447.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/145jnv1291291447.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/159jm11291291447.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16c22p1291291447.tab")
+ }
>
> try(system("convert tmp/1s6fb1291291447.ps tmp/1s6fb1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s6fb1291291447.ps tmp/2s6fb1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lgww1291291447.ps tmp/3lgww1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lgww1291291447.ps tmp/4lgww1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lgww1291291447.ps tmp/5lgww1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/6epvh1291291447.ps tmp/6epvh1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/7epvh1291291447.ps tmp/7epvh1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oyck1291291447.ps tmp/8oyck1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oyck1291291447.ps tmp/9oyck1291291447.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zpu41291291447.ps tmp/10zpu41291291447.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.088 1.786 10.064