R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(13
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+ ,0)
+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'bestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','bestfriend','secondbestfriend','thirdbestfriend'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Popularity FindingFriends KnowingPeople Liked Celebrity bestfriend
1 13 13 14 13 3 1
2 12 12 8 13 5 1
3 15 10 12 16 6 0
4 12 9 7 12 6 2
5 10 10 10 11 5 0
6 12 12 7 12 3 0
7 15 13 16 18 8 1
8 9 12 11 11 4 1
9 12 12 14 14 4 4
10 11 6 6 9 4 0
11 11 5 16 14 6 0
12 11 12 11 12 6 2
13 15 11 16 11 5 0
14 7 14 12 12 4 1
15 11 14 7 13 6 0
16 11 12 13 11 4 0
17 10 12 11 12 6 1
18 14 11 15 16 6 2
19 10 11 7 9 4 1
20 6 7 9 11 4 1
21 11 9 7 13 2 0
22 15 11 14 15 7 1
23 11 11 15 10 5 1
24 12 12 7 11 4 2
25 14 12 15 13 6 1
26 15 11 17 16 6 1
27 9 11 15 15 7 1
28 13 8 14 14 5 2
29 13 9 14 14 6 0
30 16 12 8 14 4 1
31 13 10 8 8 4 0
32 12 10 14 13 7 1
33 14 12 14 15 7 1
34 11 8 8 13 4 0
35 9 12 11 11 4 1
36 16 11 16 15 6 2
37 12 12 10 15 6 1
38 10 7 8 9 5 1
39 13 11 14 13 6 1
40 16 11 16 16 7 1
41 14 12 13 13 6 0
42 15 9 5 11 3 1
43 5 15 8 12 3 1
44 8 11 10 12 4 1
45 11 11 8 12 6 0
46 16 11 13 14 7 2
47 17 11 15 14 5 1
48 9 15 6 8 4 0
49 9 11 12 13 5 0
50 13 12 16 16 6 1
51 10 12 5 13 6 1
52 6 9 15 11 6 0
53 12 12 12 14 5 0
54 8 12 8 13 4 0
55 14 13 13 13 5 0
56 12 11 14 13 5 1
57 11 9 12 12 4 0
58 16 9 16 16 6 0
59 8 11 10 15 2 1
60 15 11 15 15 8 0
61 7 12 8 12 3 0
62 16 12 16 14 6 2
63 14 9 19 12 6 0
64 16 11 14 15 6 0
65 9 9 6 12 5 1
66 14 12 13 13 5 2
67 11 12 15 12 6 3
68 13 12 7 12 5 1
69 15 12 13 13 6 1
70 5 14 4 5 2 2
71 15 11 14 13 5 1
72 13 12 13 13 5 1
73 11 11 11 14 5 2
74 11 6 14 17 6 1
75 12 10 12 13 6 0
76 12 12 15 13 6 1
77 12 13 14 12 5 1
78 12 8 13 13 5 0
79 14 12 8 14 4 2
80 6 12 6 11 2 1
81 7 12 7 12 4 0
82 14 6 13 12 6 3
83 14 11 13 16 6 1
84 10 10 11 12 5 1
85 13 12 5 12 3 3
86 12 13 12 12 6 2
87 9 11 8 10 4 1
88 12 7 11 15 5 0
89 16 11 14 15 8 1
90 10 11 9 12 4 2
91 14 11 10 16 6 1
92 10 11 13 15 6 1
93 16 12 16 16 7 0
94 15 10 16 13 6 2
95 12 11 11 12 5 1
96 10 12 8 11 4 0
97 8 7 4 13 6 0
98 8 13 7 10 3 1
99 11 8 14 15 5 1
100 13 12 11 13 6 1
101 16 11 17 16 7 1
102 16 12 15 15 7 1
103 14 14 17 18 6 0
104 11 10 5 13 3 0
105 4 10 4 10 2 1
106 14 13 10 16 8 2
107 9 10 11 13 3 1
108 14 11 15 15 8 1
109 8 10 10 14 3 0
110 8 7 9 15 4 0
111 11 10 12 14 5 1
112 12 8 15 13 7 1
113 11 12 7 13 6 0
114 14 12 13 15 6 0
115 15 12 12 16 7 2
116 16 11 14 14 6 2
117 16 12 14 14 6 0
118 11 12 8 16 6 1
119 14 12 15 14 6 0
120 14 11 12 12 4 2
121 12 12 12 13 4 1
122 14 11 16 12 5 0
123 8 11 9 12 4 1
124 13 13 15 14 6 1
125 16 12 15 14 6 2
126 12 12 6 14 5 0
127 16 12 14 16 8 2
128 12 12 15 13 6 0
129 11 8 10 14 5 1
130 4 8 6 4 4 0
131 16 12 14 16 8 3
132 15 11 12 13 6 1
133 10 12 8 16 4 0
134 13 13 11 15 6 0
135 15 12 13 14 6 0
136 12 12 9 13 4 0
137 14 11 15 14 6 0
138 7 12 13 12 3 1
139 19 12 15 15 6 1
140 12 10 14 14 5 2
141 12 11 16 13 4 1
142 13 12 14 14 6 0
143 15 12 14 16 4 0
144 8 10 10 6 4 2
145 12 12 10 13 4 1
146 10 13 4 13 6 0
147 8 12 8 14 5 1
148 10 15 15 15 6 2
149 15 11 16 14 6 2
150 16 12 12 15 8 0
151 13 11 12 13 7 1
152 16 12 15 16 7 2
153 9 11 9 12 4 0
154 14 10 12 15 6 1
155 14 11 14 12 6 2
156 12 11 11 14 2 1
secondbestfriend thirdbestfriend
1 1 0
2 0 0
3 0 0
4 0 1
5 1 2
6 0 1
7 1 1
8 0 0
9 0 0
10 0 0
11 2 1
12 0 0
13 2 2
14 1 1
15 1 0
16 0 1
17 1 0
18 0 1
19 0 0
20 0 0
21 1 1
22 2 0
23 2 1
24 0 0
25 0 0
26 1 0
27 1 0
28 2 0
29 0 2
30 1 1
31 1 2
32 1 1
33 2 1
34 2 0
35 1 0
36 2 0
37 1 1
38 1 2
39 0 1
40 3 1
41 1 2
42 0 0
43 0 0
44 0 0
45 1 1
46 0 1
47 4 4
48 0 0
49 0 0
50 0 1
51 1 0
52 2 1
53 1 0
54 1 1
55 0 0
56 2 2
57 0 2
58 3 1
59 2 0
60 0 0
61 0 0
62 2 0
63 1 0
64 0 1
65 2 1
66 0 0
67 1 0
68 0 0
69 2 1
70 0 0
71 2 2
72 3 0
73 0 2
74 2 1
75 3 1
76 1 1
77 0 2
78 1 2
79 0 0
80 0 0
81 1 0
82 1 1
83 2 1
84 1 0
85 0 0
86 0 0
87 1 0
88 0 2
89 0 1
90 0 1
91 1 0
92 1 1
93 3 1
94 1 0
95 1 1
96 0 0
97 0 1
98 1 0
99 1 0
100 0 2
101 1 2
102 1 2
103 0 1
104 1 1
105 0 1
106 1 0
107 1 1
108 1 1
109 1 0
110 1 0
111 0 0
112 0 0
113 0 0
114 1 0
115 1 0
116 1 0
117 0 0
118 1 0
119 4 1
120 0 0
121 1 1
122 0 3
123 2 2
124 1 2
125 0 2
126 0 0
127 0 1
128 0 0
129 1 0
130 0 0
131 2 1
132 0 2
133 1 0
134 2 4
135 2 0
136 1 0
137 3 0
138 0 0
139 1 0
140 1 1
141 0 0
142 1 1
143 0 0
144 1 2
145 0 1
146 1 0
147 0 0
148 2 0
149 0 1
150 0 0
151 1 1
152 1 0
153 0 0
154 0 1
155 1 2
156 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked
-0.17879 0.10025 0.21218 0.38229
Celebrity bestfriend secondbestfriend thirdbestfriend
0.59228 0.31037 -0.02887 0.40872
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0161 -1.2348 -0.0384 1.3723 6.9232
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.17879 1.43271 -0.125 0.900857
FindingFriends 0.10025 0.09669 1.037 0.301521
KnowingPeople 0.21218 0.06360 3.336 0.001073 **
Liked 0.38229 0.09726 3.931 0.000130 ***
Celebrity 0.59228 0.15554 3.808 0.000205 ***
bestfriend 0.31037 0.20944 1.482 0.140490
secondbestfriend -0.02887 0.20071 -0.144 0.885836
thirdbestfriend 0.40872 0.21301 1.919 0.056940 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.089 on 148 degrees of freedom
Multiple R-squared: 0.5168, Adjusted R-squared: 0.494
F-statistic: 22.61 on 7 and 148 DF, p-value: < 2.2e-16
> 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,] 0.2614611 0.52292226 0.738538871
[2,] 0.1388830 0.27776598 0.861117011
[3,] 0.6133160 0.77336806 0.386684031
[4,] 0.8647090 0.27058190 0.135290951
[5,] 0.8059182 0.38816355 0.194081777
[6,] 0.7401203 0.51975947 0.259879734
[7,] 0.6636767 0.67264668 0.336323342
[8,] 0.5733743 0.85325133 0.426625665
[9,] 0.4940434 0.98808676 0.505956621
[10,] 0.7440426 0.51191483 0.255957414
[11,] 0.6837722 0.63245556 0.316227778
[12,] 0.6851570 0.62968610 0.314843049
[13,] 0.6158861 0.76822788 0.384113942
[14,] 0.6129406 0.77411874 0.387059369
[15,] 0.5772857 0.84542858 0.422714292
[16,] 0.5164352 0.96712964 0.483564819
[17,] 0.7425610 0.51487790 0.257438950
[18,] 0.6997984 0.60040330 0.300201649
[19,] 0.6388799 0.72224011 0.361120056
[20,] 0.7566081 0.48678376 0.243391881
[21,] 0.8207687 0.35846262 0.179231309
[22,] 0.7846075 0.43078497 0.215392487
[23,] 0.7378283 0.52434346 0.262171732
[24,] 0.6987411 0.60251785 0.301258926
[25,] 0.6735364 0.65292710 0.326463551
[26,] 0.7045437 0.59091260 0.295456299
[27,] 0.6820522 0.63589564 0.317947819
[28,] 0.6343645 0.73127092 0.365635459
[29,] 0.5849390 0.83012193 0.415060964
[30,] 0.5418096 0.91638088 0.458190438
[31,] 0.4933899 0.98677982 0.506610089
[32,] 0.8201510 0.35969796 0.179848978
[33,] 0.9575032 0.08499361 0.042496807
[34,] 0.9615094 0.07698121 0.038490605
[35,] 0.9506264 0.09874714 0.049373571
[36,] 0.9543590 0.09128190 0.045640951
[37,] 0.9552881 0.08942373 0.044711863
[38,] 0.9460721 0.10785571 0.053927855
[39,] 0.9467459 0.10650816 0.053254080
[40,] 0.9410485 0.11790291 0.058951454
[41,] 0.9330309 0.13393820 0.066969098
[42,] 0.9891537 0.02169256 0.010846280
[43,] 0.9852450 0.02950999 0.014754997
[44,] 0.9901901 0.01961989 0.009809945
[45,] 0.9924166 0.01516672 0.007583359
[46,] 0.9903107 0.01937865 0.009689327
[47,] 0.9872027 0.02559463 0.012797317
[48,] 0.9866800 0.02664006 0.013320029
[49,] 0.9904729 0.01905426 0.009527128
[50,] 0.9885698 0.02286046 0.011430228
[51,] 0.9889989 0.02200224 0.011001120
[52,] 0.9901637 0.01967270 0.009836349
[53,] 0.9889803 0.02203945 0.011019724
[54,] 0.9902365 0.01952701 0.009763505
[55,] 0.9888001 0.02239980 0.011199902
[56,] 0.9873683 0.02526338 0.012631688
[57,] 0.9886331 0.02273383 0.011366915
[58,] 0.9906036 0.01879272 0.009396360
[59,] 0.9906699 0.01866014 0.009330070
[60,] 0.9877282 0.02454363 0.012271817
[61,] 0.9883790 0.02324192 0.011620958
[62,] 0.9856647 0.02867063 0.014335316
[63,] 0.9855911 0.02881772 0.014408859
[64,] 0.9900150 0.01996995 0.009984977
[65,] 0.9866018 0.02679635 0.013398173
[66,] 0.9843433 0.03131345 0.015656726
[67,] 0.9797632 0.04047359 0.020236796
[68,] 0.9736679 0.05266413 0.026332066
[69,] 0.9800620 0.03987600 0.019938002
[70,] 0.9796434 0.04071327 0.020356637
[71,] 0.9812641 0.03747171 0.018735855
[72,] 0.9791878 0.04162449 0.020812246
[73,] 0.9723500 0.05530002 0.027650012
[74,] 0.9654918 0.06901646 0.034508230
[75,] 0.9855485 0.02890304 0.014451522
[76,] 0.9809440 0.03811205 0.019056025
[77,] 0.9747809 0.05043811 0.025219056
[78,] 0.9676508 0.06469845 0.032349225
[79,] 0.9594274 0.08114517 0.040572585
[80,] 0.9492634 0.10147329 0.050736644
[81,] 0.9402980 0.11940402 0.059702008
[82,] 0.9662564 0.06748727 0.033743635
[83,] 0.9577096 0.08458072 0.042290359
[84,] 0.9531608 0.09367846 0.046839232
[85,] 0.9415400 0.11692000 0.058459998
[86,] 0.9276961 0.14460770 0.072303851
[87,] 0.9241705 0.15165893 0.075829464
[88,] 0.9057815 0.18843696 0.094218482
[89,] 0.8973089 0.20538219 0.102691093
[90,] 0.8726998 0.25460044 0.127300219
[91,] 0.8452521 0.30949576 0.154747882
[92,] 0.8161083 0.36778330 0.183891650
[93,] 0.8440160 0.31196805 0.155984027
[94,] 0.8844485 0.23110310 0.115551548
[95,] 0.8918024 0.21639515 0.108197577
[96,] 0.8671749 0.26565013 0.132825066
[97,] 0.8456961 0.30860782 0.154303912
[98,] 0.8408456 0.31830874 0.159154370
[99,] 0.8326763 0.33464736 0.167323679
[100,] 0.8545955 0.29080907 0.145404533
[101,] 0.8422002 0.31559953 0.157799767
[102,] 0.8861604 0.22767922 0.113839612
[103,] 0.8564507 0.28709853 0.143549263
[104,] 0.8256502 0.34869967 0.174349833
[105,] 0.7878217 0.42435661 0.212178305
[106,] 0.7993282 0.40134351 0.200671756
[107,] 0.8083615 0.38327703 0.191638513
[108,] 0.7908367 0.41832659 0.209163293
[109,] 0.7486765 0.50264691 0.251323456
[110,] 0.8295179 0.34096423 0.170482113
[111,] 0.7989138 0.40217239 0.201086195
[112,] 0.7531781 0.49364387 0.246821934
[113,] 0.7535118 0.49297641 0.246488203
[114,] 0.7221348 0.55573033 0.277865163
[115,] 0.6979772 0.60404556 0.302022779
[116,] 0.6816893 0.63662135 0.318310675
[117,] 0.6190851 0.76182987 0.380914933
[118,] 0.6040983 0.79180336 0.395901680
[119,] 0.5950871 0.80982574 0.404912872
[120,] 0.5839168 0.83216645 0.416083223
[121,] 0.5170329 0.96593426 0.482967130
[122,] 0.4881460 0.97629201 0.511853997
[123,] 0.4536106 0.90722123 0.546389386
[124,] 0.4094418 0.81888357 0.590558216
[125,] 0.3609713 0.72194255 0.639028723
[126,] 0.3372529 0.67450574 0.662747128
[127,] 0.3042139 0.60842780 0.695786101
[128,] 0.3352764 0.67055287 0.664723566
[129,] 0.7298269 0.54034629 0.270173147
[130,] 0.7275974 0.54480516 0.272402580
[131,] 0.6359700 0.72806004 0.364030018
[132,] 0.6347135 0.73057297 0.365286487
[133,] 0.5128198 0.97436037 0.487180186
[134,] 0.3956039 0.79120786 0.604396070
[135,] 0.3507750 0.70155000 0.649225001
> postscript(file="/var/fisher/rcomp/tmp/15glr1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2sjbh1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3paha1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4mvdo1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5zung1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5 6
1.876784689 1.036717954 1.959696191 0.620587047 -0.900769782 2.717392576
7 8 9 10 11 12
-1.829161144 -1.242967610 -0.957498466 3.494420258 -1.974177002 -1.120181935
13 14 15 16 17 18
2.754742702 -4.417807303 -0.204648453 0.234308151 -1.780949396 -0.806562575
19 20 21 22 23 24
1.470607200 -3.317337107 2.257000214 0.972460043 -0.552422632 2.295400472
25 26 27 28 29 30
0.959156010 0.517026121 -5.268589941 0.529700216 -0.417276766 4.866844297
31 32 33 34 35 36
4.262756354 -1.600289014 -0.536515886 1.398104787 -1.214101172 1.830003978
37 38 39 40 41 42
-1.124371018 0.278577203 -0.137131010 0.785942171 0.905309145 6.923168835
43 44 45 46 47 48
-4.697191869 -2.312825356 -0.142503225 1.790115337 2.749964610 0.974440089
49 50 51 52 53 54
-2.401397446 -1.808632703 -0.890142097 -6.016122266 0.144922337 -2.440495630
55 56 57 58 59 60
2.185913644 -0.895844254 -0.043767477 1.889090761 -2.217420147 0.420632594
61 62 63 64 65 66
-2.086067722 2.112045269 1.132706388 2.408647144 -1.206853305 1.665434127
67 68 69 70 71 72
-2.250415777 2.631195472 2.032532731 -0.790236166 2.104155746 1.062399543
73 74 75 76 77 78
-2.009686568 -3.107310613 -0.215545820 -1.420700799 -0.771788523 -0.101403000
79 80 81 82 83 84
2.936335006 -1.997495632 -2.437294863 1.366744161 -0.014096506 -0.988166906
85 86 87 88 89 90
3.619384615 -0.432618163 -0.095003880 -0.370237612 0.913726793 -0.819731158
91 92 93 94 95 96
1.002310939 -3.660668972 0.996055588 1.665978169 0.502857164 0.703949126
97 98 99 100 101 102
-2.303918726 -0.491048573 -1.571094097 -0.009556303 0.107302500 0.813710882
103 104 105 106 107 108
-1.675543460 1.988837497 -3.399052450 -0.693114777 -1.594629875 -1.269590314
109 110 111 112 113 114
-2.045650956 -2.507280462 -0.993804836 -1.232110386 -0.033009527 0.958167693
115 116 117 118 119 120
0.575047940 2.607798604 3.099411682 -1.673574653 0.593970644 2.952441454
121 122 123 124 125 126
0.400404089 0.905992603 -2.860355428 -1.311970702 1.449049442 1.389157171
127 128 129 130 131 132
0.120814037 -0.730477890 -0.340065942 -1.794615241 -0.131819185 1.878512834
133 134 135 136 137 138
-1.178654302 -1.323744450 2.369328105 1.756044072 1.074080136 -3.457351549
139 140 141 142 143 144
5.223434502 -1.108394836 0.031779398 -0.280445127 2.519377987 -1.017754990
145 146 147 148 149 150
0.795904741 -0.467845134 -3.345576020 -4.358823206 0.745841827 1.956930548
151 152 153 154 155 156
-0.276174605 0.938497304 -0.790275711 0.622900817 0.554940055 1.923823842
> postscript(file="/var/fisher/rcomp/tmp/6pa1j1353440088.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.876784689 NA
1 1.036717954 1.876784689
2 1.959696191 1.036717954
3 0.620587047 1.959696191
4 -0.900769782 0.620587047
5 2.717392576 -0.900769782
6 -1.829161144 2.717392576
7 -1.242967610 -1.829161144
8 -0.957498466 -1.242967610
9 3.494420258 -0.957498466
10 -1.974177002 3.494420258
11 -1.120181935 -1.974177002
12 2.754742702 -1.120181935
13 -4.417807303 2.754742702
14 -0.204648453 -4.417807303
15 0.234308151 -0.204648453
16 -1.780949396 0.234308151
17 -0.806562575 -1.780949396
18 1.470607200 -0.806562575
19 -3.317337107 1.470607200
20 2.257000214 -3.317337107
21 0.972460043 2.257000214
22 -0.552422632 0.972460043
23 2.295400472 -0.552422632
24 0.959156010 2.295400472
25 0.517026121 0.959156010
26 -5.268589941 0.517026121
27 0.529700216 -5.268589941
28 -0.417276766 0.529700216
29 4.866844297 -0.417276766
30 4.262756354 4.866844297
31 -1.600289014 4.262756354
32 -0.536515886 -1.600289014
33 1.398104787 -0.536515886
34 -1.214101172 1.398104787
35 1.830003978 -1.214101172
36 -1.124371018 1.830003978
37 0.278577203 -1.124371018
38 -0.137131010 0.278577203
39 0.785942171 -0.137131010
40 0.905309145 0.785942171
41 6.923168835 0.905309145
42 -4.697191869 6.923168835
43 -2.312825356 -4.697191869
44 -0.142503225 -2.312825356
45 1.790115337 -0.142503225
46 2.749964610 1.790115337
47 0.974440089 2.749964610
48 -2.401397446 0.974440089
49 -1.808632703 -2.401397446
50 -0.890142097 -1.808632703
51 -6.016122266 -0.890142097
52 0.144922337 -6.016122266
53 -2.440495630 0.144922337
54 2.185913644 -2.440495630
55 -0.895844254 2.185913644
56 -0.043767477 -0.895844254
57 1.889090761 -0.043767477
58 -2.217420147 1.889090761
59 0.420632594 -2.217420147
60 -2.086067722 0.420632594
61 2.112045269 -2.086067722
62 1.132706388 2.112045269
63 2.408647144 1.132706388
64 -1.206853305 2.408647144
65 1.665434127 -1.206853305
66 -2.250415777 1.665434127
67 2.631195472 -2.250415777
68 2.032532731 2.631195472
69 -0.790236166 2.032532731
70 2.104155746 -0.790236166
71 1.062399543 2.104155746
72 -2.009686568 1.062399543
73 -3.107310613 -2.009686568
74 -0.215545820 -3.107310613
75 -1.420700799 -0.215545820
76 -0.771788523 -1.420700799
77 -0.101403000 -0.771788523
78 2.936335006 -0.101403000
79 -1.997495632 2.936335006
80 -2.437294863 -1.997495632
81 1.366744161 -2.437294863
82 -0.014096506 1.366744161
83 -0.988166906 -0.014096506
84 3.619384615 -0.988166906
85 -0.432618163 3.619384615
86 -0.095003880 -0.432618163
87 -0.370237612 -0.095003880
88 0.913726793 -0.370237612
89 -0.819731158 0.913726793
90 1.002310939 -0.819731158
91 -3.660668972 1.002310939
92 0.996055588 -3.660668972
93 1.665978169 0.996055588
94 0.502857164 1.665978169
95 0.703949126 0.502857164
96 -2.303918726 0.703949126
97 -0.491048573 -2.303918726
98 -1.571094097 -0.491048573
99 -0.009556303 -1.571094097
100 0.107302500 -0.009556303
101 0.813710882 0.107302500
102 -1.675543460 0.813710882
103 1.988837497 -1.675543460
104 -3.399052450 1.988837497
105 -0.693114777 -3.399052450
106 -1.594629875 -0.693114777
107 -1.269590314 -1.594629875
108 -2.045650956 -1.269590314
109 -2.507280462 -2.045650956
110 -0.993804836 -2.507280462
111 -1.232110386 -0.993804836
112 -0.033009527 -1.232110386
113 0.958167693 -0.033009527
114 0.575047940 0.958167693
115 2.607798604 0.575047940
116 3.099411682 2.607798604
117 -1.673574653 3.099411682
118 0.593970644 -1.673574653
119 2.952441454 0.593970644
120 0.400404089 2.952441454
121 0.905992603 0.400404089
122 -2.860355428 0.905992603
123 -1.311970702 -2.860355428
124 1.449049442 -1.311970702
125 1.389157171 1.449049442
126 0.120814037 1.389157171
127 -0.730477890 0.120814037
128 -0.340065942 -0.730477890
129 -1.794615241 -0.340065942
130 -0.131819185 -1.794615241
131 1.878512834 -0.131819185
132 -1.178654302 1.878512834
133 -1.323744450 -1.178654302
134 2.369328105 -1.323744450
135 1.756044072 2.369328105
136 1.074080136 1.756044072
137 -3.457351549 1.074080136
138 5.223434502 -3.457351549
139 -1.108394836 5.223434502
140 0.031779398 -1.108394836
141 -0.280445127 0.031779398
142 2.519377987 -0.280445127
143 -1.017754990 2.519377987
144 0.795904741 -1.017754990
145 -0.467845134 0.795904741
146 -3.345576020 -0.467845134
147 -4.358823206 -3.345576020
148 0.745841827 -4.358823206
149 1.956930548 0.745841827
150 -0.276174605 1.956930548
151 0.938497304 -0.276174605
152 -0.790275711 0.938497304
153 0.622900817 -0.790275711
154 0.554940055 0.622900817
155 1.923823842 0.554940055
156 NA 1.923823842
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.036717954 1.876784689
[2,] 1.959696191 1.036717954
[3,] 0.620587047 1.959696191
[4,] -0.900769782 0.620587047
[5,] 2.717392576 -0.900769782
[6,] -1.829161144 2.717392576
[7,] -1.242967610 -1.829161144
[8,] -0.957498466 -1.242967610
[9,] 3.494420258 -0.957498466
[10,] -1.974177002 3.494420258
[11,] -1.120181935 -1.974177002
[12,] 2.754742702 -1.120181935
[13,] -4.417807303 2.754742702
[14,] -0.204648453 -4.417807303
[15,] 0.234308151 -0.204648453
[16,] -1.780949396 0.234308151
[17,] -0.806562575 -1.780949396
[18,] 1.470607200 -0.806562575
[19,] -3.317337107 1.470607200
[20,] 2.257000214 -3.317337107
[21,] 0.972460043 2.257000214
[22,] -0.552422632 0.972460043
[23,] 2.295400472 -0.552422632
[24,] 0.959156010 2.295400472
[25,] 0.517026121 0.959156010
[26,] -5.268589941 0.517026121
[27,] 0.529700216 -5.268589941
[28,] -0.417276766 0.529700216
[29,] 4.866844297 -0.417276766
[30,] 4.262756354 4.866844297
[31,] -1.600289014 4.262756354
[32,] -0.536515886 -1.600289014
[33,] 1.398104787 -0.536515886
[34,] -1.214101172 1.398104787
[35,] 1.830003978 -1.214101172
[36,] -1.124371018 1.830003978
[37,] 0.278577203 -1.124371018
[38,] -0.137131010 0.278577203
[39,] 0.785942171 -0.137131010
[40,] 0.905309145 0.785942171
[41,] 6.923168835 0.905309145
[42,] -4.697191869 6.923168835
[43,] -2.312825356 -4.697191869
[44,] -0.142503225 -2.312825356
[45,] 1.790115337 -0.142503225
[46,] 2.749964610 1.790115337
[47,] 0.974440089 2.749964610
[48,] -2.401397446 0.974440089
[49,] -1.808632703 -2.401397446
[50,] -0.890142097 -1.808632703
[51,] -6.016122266 -0.890142097
[52,] 0.144922337 -6.016122266
[53,] -2.440495630 0.144922337
[54,] 2.185913644 -2.440495630
[55,] -0.895844254 2.185913644
[56,] -0.043767477 -0.895844254
[57,] 1.889090761 -0.043767477
[58,] -2.217420147 1.889090761
[59,] 0.420632594 -2.217420147
[60,] -2.086067722 0.420632594
[61,] 2.112045269 -2.086067722
[62,] 1.132706388 2.112045269
[63,] 2.408647144 1.132706388
[64,] -1.206853305 2.408647144
[65,] 1.665434127 -1.206853305
[66,] -2.250415777 1.665434127
[67,] 2.631195472 -2.250415777
[68,] 2.032532731 2.631195472
[69,] -0.790236166 2.032532731
[70,] 2.104155746 -0.790236166
[71,] 1.062399543 2.104155746
[72,] -2.009686568 1.062399543
[73,] -3.107310613 -2.009686568
[74,] -0.215545820 -3.107310613
[75,] -1.420700799 -0.215545820
[76,] -0.771788523 -1.420700799
[77,] -0.101403000 -0.771788523
[78,] 2.936335006 -0.101403000
[79,] -1.997495632 2.936335006
[80,] -2.437294863 -1.997495632
[81,] 1.366744161 -2.437294863
[82,] -0.014096506 1.366744161
[83,] -0.988166906 -0.014096506
[84,] 3.619384615 -0.988166906
[85,] -0.432618163 3.619384615
[86,] -0.095003880 -0.432618163
[87,] -0.370237612 -0.095003880
[88,] 0.913726793 -0.370237612
[89,] -0.819731158 0.913726793
[90,] 1.002310939 -0.819731158
[91,] -3.660668972 1.002310939
[92,] 0.996055588 -3.660668972
[93,] 1.665978169 0.996055588
[94,] 0.502857164 1.665978169
[95,] 0.703949126 0.502857164
[96,] -2.303918726 0.703949126
[97,] -0.491048573 -2.303918726
[98,] -1.571094097 -0.491048573
[99,] -0.009556303 -1.571094097
[100,] 0.107302500 -0.009556303
[101,] 0.813710882 0.107302500
[102,] -1.675543460 0.813710882
[103,] 1.988837497 -1.675543460
[104,] -3.399052450 1.988837497
[105,] -0.693114777 -3.399052450
[106,] -1.594629875 -0.693114777
[107,] -1.269590314 -1.594629875
[108,] -2.045650956 -1.269590314
[109,] -2.507280462 -2.045650956
[110,] -0.993804836 -2.507280462
[111,] -1.232110386 -0.993804836
[112,] -0.033009527 -1.232110386
[113,] 0.958167693 -0.033009527
[114,] 0.575047940 0.958167693
[115,] 2.607798604 0.575047940
[116,] 3.099411682 2.607798604
[117,] -1.673574653 3.099411682
[118,] 0.593970644 -1.673574653
[119,] 2.952441454 0.593970644
[120,] 0.400404089 2.952441454
[121,] 0.905992603 0.400404089
[122,] -2.860355428 0.905992603
[123,] -1.311970702 -2.860355428
[124,] 1.449049442 -1.311970702
[125,] 1.389157171 1.449049442
[126,] 0.120814037 1.389157171
[127,] -0.730477890 0.120814037
[128,] -0.340065942 -0.730477890
[129,] -1.794615241 -0.340065942
[130,] -0.131819185 -1.794615241
[131,] 1.878512834 -0.131819185
[132,] -1.178654302 1.878512834
[133,] -1.323744450 -1.178654302
[134,] 2.369328105 -1.323744450
[135,] 1.756044072 2.369328105
[136,] 1.074080136 1.756044072
[137,] -3.457351549 1.074080136
[138,] 5.223434502 -3.457351549
[139,] -1.108394836 5.223434502
[140,] 0.031779398 -1.108394836
[141,] -0.280445127 0.031779398
[142,] 2.519377987 -0.280445127
[143,] -1.017754990 2.519377987
[144,] 0.795904741 -1.017754990
[145,] -0.467845134 0.795904741
[146,] -3.345576020 -0.467845134
[147,] -4.358823206 -3.345576020
[148,] 0.745841827 -4.358823206
[149,] 1.956930548 0.745841827
[150,] -0.276174605 1.956930548
[151,] 0.938497304 -0.276174605
[152,] -0.790275711 0.938497304
[153,] 0.622900817 -0.790275711
[154,] 0.554940055 0.622900817
[155,] 1.923823842 0.554940055
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.036717954 1.876784689
2 1.959696191 1.036717954
3 0.620587047 1.959696191
4 -0.900769782 0.620587047
5 2.717392576 -0.900769782
6 -1.829161144 2.717392576
7 -1.242967610 -1.829161144
8 -0.957498466 -1.242967610
9 3.494420258 -0.957498466
10 -1.974177002 3.494420258
11 -1.120181935 -1.974177002
12 2.754742702 -1.120181935
13 -4.417807303 2.754742702
14 -0.204648453 -4.417807303
15 0.234308151 -0.204648453
16 -1.780949396 0.234308151
17 -0.806562575 -1.780949396
18 1.470607200 -0.806562575
19 -3.317337107 1.470607200
20 2.257000214 -3.317337107
21 0.972460043 2.257000214
22 -0.552422632 0.972460043
23 2.295400472 -0.552422632
24 0.959156010 2.295400472
25 0.517026121 0.959156010
26 -5.268589941 0.517026121
27 0.529700216 -5.268589941
28 -0.417276766 0.529700216
29 4.866844297 -0.417276766
30 4.262756354 4.866844297
31 -1.600289014 4.262756354
32 -0.536515886 -1.600289014
33 1.398104787 -0.536515886
34 -1.214101172 1.398104787
35 1.830003978 -1.214101172
36 -1.124371018 1.830003978
37 0.278577203 -1.124371018
38 -0.137131010 0.278577203
39 0.785942171 -0.137131010
40 0.905309145 0.785942171
41 6.923168835 0.905309145
42 -4.697191869 6.923168835
43 -2.312825356 -4.697191869
44 -0.142503225 -2.312825356
45 1.790115337 -0.142503225
46 2.749964610 1.790115337
47 0.974440089 2.749964610
48 -2.401397446 0.974440089
49 -1.808632703 -2.401397446
50 -0.890142097 -1.808632703
51 -6.016122266 -0.890142097
52 0.144922337 -6.016122266
53 -2.440495630 0.144922337
54 2.185913644 -2.440495630
55 -0.895844254 2.185913644
56 -0.043767477 -0.895844254
57 1.889090761 -0.043767477
58 -2.217420147 1.889090761
59 0.420632594 -2.217420147
60 -2.086067722 0.420632594
61 2.112045269 -2.086067722
62 1.132706388 2.112045269
63 2.408647144 1.132706388
64 -1.206853305 2.408647144
65 1.665434127 -1.206853305
66 -2.250415777 1.665434127
67 2.631195472 -2.250415777
68 2.032532731 2.631195472
69 -0.790236166 2.032532731
70 2.104155746 -0.790236166
71 1.062399543 2.104155746
72 -2.009686568 1.062399543
73 -3.107310613 -2.009686568
74 -0.215545820 -3.107310613
75 -1.420700799 -0.215545820
76 -0.771788523 -1.420700799
77 -0.101403000 -0.771788523
78 2.936335006 -0.101403000
79 -1.997495632 2.936335006
80 -2.437294863 -1.997495632
81 1.366744161 -2.437294863
82 -0.014096506 1.366744161
83 -0.988166906 -0.014096506
84 3.619384615 -0.988166906
85 -0.432618163 3.619384615
86 -0.095003880 -0.432618163
87 -0.370237612 -0.095003880
88 0.913726793 -0.370237612
89 -0.819731158 0.913726793
90 1.002310939 -0.819731158
91 -3.660668972 1.002310939
92 0.996055588 -3.660668972
93 1.665978169 0.996055588
94 0.502857164 1.665978169
95 0.703949126 0.502857164
96 -2.303918726 0.703949126
97 -0.491048573 -2.303918726
98 -1.571094097 -0.491048573
99 -0.009556303 -1.571094097
100 0.107302500 -0.009556303
101 0.813710882 0.107302500
102 -1.675543460 0.813710882
103 1.988837497 -1.675543460
104 -3.399052450 1.988837497
105 -0.693114777 -3.399052450
106 -1.594629875 -0.693114777
107 -1.269590314 -1.594629875
108 -2.045650956 -1.269590314
109 -2.507280462 -2.045650956
110 -0.993804836 -2.507280462
111 -1.232110386 -0.993804836
112 -0.033009527 -1.232110386
113 0.958167693 -0.033009527
114 0.575047940 0.958167693
115 2.607798604 0.575047940
116 3.099411682 2.607798604
117 -1.673574653 3.099411682
118 0.593970644 -1.673574653
119 2.952441454 0.593970644
120 0.400404089 2.952441454
121 0.905992603 0.400404089
122 -2.860355428 0.905992603
123 -1.311970702 -2.860355428
124 1.449049442 -1.311970702
125 1.389157171 1.449049442
126 0.120814037 1.389157171
127 -0.730477890 0.120814037
128 -0.340065942 -0.730477890
129 -1.794615241 -0.340065942
130 -0.131819185 -1.794615241
131 1.878512834 -0.131819185
132 -1.178654302 1.878512834
133 -1.323744450 -1.178654302
134 2.369328105 -1.323744450
135 1.756044072 2.369328105
136 1.074080136 1.756044072
137 -3.457351549 1.074080136
138 5.223434502 -3.457351549
139 -1.108394836 5.223434502
140 0.031779398 -1.108394836
141 -0.280445127 0.031779398
142 2.519377987 -0.280445127
143 -1.017754990 2.519377987
144 0.795904741 -1.017754990
145 -0.467845134 0.795904741
146 -3.345576020 -0.467845134
147 -4.358823206 -3.345576020
148 0.745841827 -4.358823206
149 1.956930548 0.745841827
150 -0.276174605 1.956930548
151 0.938497304 -0.276174605
152 -0.790275711 0.938497304
153 0.622900817 -0.790275711
154 0.554940055 0.622900817
155 1.923823842 0.554940055
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7kob41353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8cxk81353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9nsqm1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/1030co1353440088.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11fbuy1353440088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/121gif1353440088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13brwb1353440088.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14t9fp1353440088.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15ytxh1353440088.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16iicq1353440088.tab")
+ }
>
> try(system("convert tmp/15glr1353440088.ps tmp/15glr1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sjbh1353440088.ps tmp/2sjbh1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/3paha1353440088.ps tmp/3paha1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mvdo1353440088.ps tmp/4mvdo1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zung1353440088.ps tmp/5zung1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pa1j1353440088.ps tmp/6pa1j1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kob41353440088.ps tmp/7kob41353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cxk81353440088.ps tmp/8cxk81353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nsqm1353440088.ps tmp/9nsqm1353440088.png",intern=TRUE))
character(0)
> try(system("convert tmp/1030co1353440088.ps tmp/1030co1353440088.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.108 1.326 9.447