R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(41
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+ ,46)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('Connected','Separate','Learning','Software','Depression','Belonging','Belonging_Final'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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, 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
Software Connected Separate Learning Depression Belonging Belonging_Final
1 12 41 38 13 12 53 32
2 11 39 32 16 11 86 51
3 15 30 35 19 14 66 42
4 6 31 33 15 12 67 41
5 13 34 37 14 21 76 46
6 10 35 29 13 12 78 47
7 12 39 31 19 22 53 37
8 14 34 36 15 11 80 49
9 12 36 35 14 10 74 45
10 6 37 38 15 13 76 47
11 10 38 31 16 10 79 49
12 12 36 34 16 8 54 33
13 12 38 35 16 15 67 42
14 11 39 38 16 14 54 33
15 15 33 37 17 10 87 53
16 12 32 33 15 14 58 36
17 10 36 32 15 14 75 45
18 12 38 38 20 11 88 54
19 11 39 38 18 10 64 41
20 12 32 32 16 13 57 36
21 11 32 33 16 7 66 41
22 12 31 31 16 14 68 44
23 13 39 38 19 12 54 33
24 11 37 39 16 14 56 37
25 9 39 32 17 11 86 52
26 13 41 32 17 9 80 47
27 10 36 35 16 11 76 43
28 14 33 37 15 15 69 44
29 12 33 33 16 14 78 45
30 10 34 33 14 13 67 44
31 12 31 28 15 9 80 49
32 8 27 32 12 15 54 33
33 10 37 31 14 10 71 43
34 12 34 37 16 11 84 54
35 12 34 30 14 13 74 42
36 7 32 33 7 8 71 44
37 6 29 31 10 20 63 37
38 12 36 33 14 12 71 43
39 10 29 31 16 10 76 46
40 10 35 33 16 10 69 42
41 10 37 32 16 9 74 45
42 12 34 33 14 14 75 44
43 15 38 32 20 8 54 33
44 10 35 33 14 14 52 31
45 10 38 28 14 11 69 42
46 12 37 35 11 13 68 40
47 13 38 39 14 9 65 43
48 11 33 34 15 11 75 46
49 11 36 38 16 15 74 42
50 12 38 32 14 11 75 45
51 14 32 38 16 10 72 44
52 10 32 30 14 14 67 40
53 12 32 33 12 18 63 37
54 13 34 38 16 14 62 46
55 5 32 32 9 11 63 36
56 6 37 32 14 12 76 47
57 12 39 34 16 13 74 45
58 12 29 34 16 9 67 42
59 11 37 36 15 10 73 43
60 10 35 34 16 15 70 43
61 7 30 28 12 20 53 32
62 12 38 34 16 12 77 45
63 14 34 35 16 12 77 45
64 11 31 35 14 14 52 31
65 12 34 31 16 13 54 33
66 13 35 37 17 11 80 49
67 14 36 35 18 17 66 42
68 11 30 27 18 12 73 41
69 12 39 40 12 13 63 38
70 12 35 37 16 14 69 42
71 8 38 36 10 13 67 44
72 11 31 38 14 15 54 33
73 14 34 39 18 13 81 48
74 14 38 41 18 10 69 40
75 12 34 27 16 11 84 50
76 9 39 30 17 19 80 49
77 13 37 37 16 13 70 43
78 11 34 31 16 17 69 44
79 12 28 31 13 13 77 47
80 12 37 27 16 9 54 33
81 12 33 36 16 11 79 46
82 12 37 38 20 10 30 0
83 12 35 37 16 9 71 45
84 12 37 33 15 12 73 43
85 11 32 34 15 12 72 44
86 10 33 31 16 13 77 47
87 9 38 39 14 13 75 45
88 12 33 34 16 12 69 42
89 12 29 32 16 15 54 33
90 12 33 33 15 22 70 43
91 9 31 36 12 13 73 46
92 15 36 32 17 15 54 33
93 12 35 41 16 13 77 46
94 12 32 28 15 15 82 48
95 12 29 30 13 10 80 47
96 10 39 36 16 11 80 47
97 13 37 35 16 16 69 43
98 9 35 31 16 11 78 46
99 12 37 34 16 11 81 48
100 10 32 36 14 10 76 46
101 14 38 36 16 10 76 45
102 11 37 35 16 16 73 45
103 15 36 37 20 12 85 52
104 11 32 28 15 11 66 42
105 11 33 39 16 16 79 47
106 12 40 32 13 19 68 41
107 12 38 35 17 11 76 47
108 12 41 39 16 16 71 43
109 11 36 35 16 15 54 33
110 7 43 42 12 24 46 30
111 12 30 34 16 14 82 49
112 14 31 33 16 15 74 44
113 11 32 41 17 11 88 55
114 11 32 33 13 15 38 11
115 10 37 34 12 12 76 47
116 13 37 32 18 10 86 53
117 13 33 40 14 14 54 33
118 8 34 40 14 13 70 44
119 11 33 35 13 9 69 42
120 12 38 36 16 15 90 55
121 11 33 37 13 15 54 33
122 13 31 27 16 14 76 46
123 12 38 39 13 11 89 54
124 14 37 38 16 8 76 47
125 13 33 31 15 11 73 45
126 15 31 33 16 11 79 47
127 10 39 32 15 8 90 55
128 11 44 39 17 10 74 44
129 9 33 36 15 11 81 53
130 11 35 33 12 13 72 44
131 10 32 33 16 11 71 42
132 11 28 32 10 20 66 40
133 8 40 37 16 10 77 46
134 11 27 30 12 15 65 40
135 12 37 38 14 12 74 46
136 12 32 29 15 14 82 53
137 9 28 22 13 23 54 33
138 11 34 35 15 14 63 42
139 10 30 35 11 16 54 35
140 8 35 34 12 11 64 40
141 9 31 35 8 12 69 41
142 8 32 34 16 10 54 33
143 9 30 34 15 14 84 51
144 15 30 35 17 12 86 53
145 11 31 23 16 12 77 46
146 8 40 31 10 11 89 55
147 13 32 27 18 12 76 47
148 12 36 36 13 13 60 38
149 12 32 31 16 11 75 46
150 9 35 32 13 19 73 46
151 7 38 39 10 12 85 53
152 13 42 37 15 17 79 47
153 9 34 38 16 9 71 41
154 6 35 39 16 12 72 44
155 8 35 34 14 19 69 43
156 8 33 31 10 18 78 51
157 15 36 32 17 15 54 33
158 6 32 37 13 14 69 43
159 9 33 36 15 11 81 53
160 11 34 32 16 9 84 51
161 8 32 35 12 18 84 50
162 8 34 36 13 16 69 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
4.004067 -0.048300 0.030672 0.526837
Depression Belonging Belonging_Final
-0.008181 0.001537 -0.004676
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7459 -0.9337 0.2131 1.3610 3.2400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.004067 2.391114 1.675 0.096 .
Connected -0.048300 0.046880 -1.030 0.304
Separate 0.030672 0.043735 0.701 0.484
Learning 0.526837 0.066941 7.870 5.7e-13 ***
Depression -0.008181 0.049081 -0.167 0.868
Belonging 0.001537 0.043720 0.035 0.972
Belonging_Final -0.004676 0.063046 -0.074 0.941
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.822 on 155 degrees of freedom
Multiple R-squared: 0.3032, Adjusted R-squared: 0.2762
F-statistic: 11.24 on 6 and 155 DF, p-value: 2.106e-10
> 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,] 0.999928518 0.0001429645 7.148224e-05
[2,] 0.999783017 0.0004339661 2.169830e-04
[3,] 0.999427269 0.0011454616 5.727308e-04
[4,] 0.998675784 0.0026484323 1.324216e-03
[5,] 0.997976372 0.0040472564 2.023628e-03
[6,] 0.998060737 0.0038785260 1.939263e-03
[7,] 0.996721382 0.0065572365 3.278618e-03
[8,] 0.994718897 0.0105622053 5.281103e-03
[9,] 0.993650429 0.0126991425 6.349571e-03
[10,] 0.990420382 0.0191592365 9.579618e-03
[11,] 0.984529434 0.0309411330 1.547057e-02
[12,] 0.976056018 0.0478879647 2.394398e-02
[13,] 0.965150130 0.0696997395 3.484987e-02
[14,] 0.949082235 0.1018355307 5.091777e-02
[15,] 0.929337715 0.1413245708 7.066229e-02
[16,] 0.930182474 0.1396350524 6.981753e-02
[17,] 0.929344180 0.1413116406 7.065582e-02
[18,] 0.930902468 0.1381950643 6.909753e-02
[19,] 0.939300157 0.1213996866 6.069984e-02
[20,] 0.918006106 0.1639877885 8.199389e-02
[21,] 0.894222758 0.2115544833 1.057772e-01
[22,] 0.875523737 0.2489525259 1.244763e-01
[23,] 0.895320832 0.2093583368 1.046792e-01
[24,] 0.866065220 0.2678695599 1.339348e-01
[25,] 0.831461293 0.3370774136 1.685387e-01
[26,] 0.817445928 0.3651081436 1.825541e-01
[27,] 0.778012939 0.4439741229 2.219871e-01
[28,] 0.823426308 0.3531473837 1.765737e-01
[29,] 0.814219948 0.3715601035 1.857801e-01
[30,] 0.803880566 0.3922388679 1.961194e-01
[31,] 0.785166386 0.4296672276 2.148336e-01
[32,] 0.760587457 0.4788250861 2.394125e-01
[33,] 0.744132226 0.5117355474 2.558678e-01
[34,] 0.743250023 0.5134999532 2.567500e-01
[35,] 0.700864824 0.5982703530 2.991352e-01
[36,] 0.654801453 0.6903970930 3.451985e-01
[37,] 0.719392741 0.5612145187 2.806073e-01
[38,] 0.730469156 0.5390616880 2.695308e-01
[39,] 0.686159849 0.6276803012 3.138402e-01
[40,] 0.648910142 0.7021797167 3.510899e-01
[41,] 0.636319895 0.7273602094 3.636801e-01
[42,] 0.641075896 0.7178482070 3.589241e-01
[43,] 0.594589848 0.8108203042 4.054102e-01
[44,] 0.623310628 0.7533787449 3.766894e-01
[45,] 0.590441866 0.8191162688 4.095581e-01
[46,] 0.678571733 0.6428565337 3.214283e-01
[47,] 0.842584875 0.3148302492 1.574151e-01
[48,] 0.814686945 0.3706261100 1.853131e-01
[49,] 0.780543922 0.4389121560 2.194561e-01
[50,] 0.743380172 0.5132396562 2.566198e-01
[51,] 0.730455526 0.5390889489 2.695445e-01
[52,] 0.744471088 0.5110578232 2.555289e-01
[53,] 0.708002182 0.5839956358 2.919978e-01
[54,] 0.727660100 0.5446797994 2.723399e-01
[55,] 0.686900706 0.6261985876 3.130993e-01
[56,] 0.651447538 0.6971049239 3.485525e-01
[57,] 0.612754444 0.7744911126 3.872456e-01
[58,] 0.595535069 0.8089298617 4.044649e-01
[59,] 0.577478880 0.8450422410 4.225211e-01
[60,] 0.593810722 0.8123785557 4.061893e-01
[61,] 0.549657013 0.9006859748 4.503430e-01
[62,] 0.508395310 0.9832093810 4.916047e-01
[63,] 0.464781956 0.9295639125 5.352180e-01
[64,] 0.434661932 0.8693238645 5.653381e-01
[65,] 0.411724662 0.8234493242 5.882753e-01
[66,] 0.386552490 0.7731049791 6.134475e-01
[67,] 0.436809648 0.8736192957 5.631904e-01
[68,] 0.417559816 0.8351196314 5.824402e-01
[69,] 0.377102599 0.7542051977 6.228974e-01
[70,] 0.379005514 0.7580110275 6.209945e-01
[71,] 0.356230395 0.7124607905 6.437696e-01
[72,] 0.315738344 0.6314766883 6.842617e-01
[73,] 0.328954713 0.6579094251 6.710453e-01
[74,] 0.294029409 0.5880588171 7.059706e-01
[75,] 0.266399108 0.5327982157 7.336009e-01
[76,] 0.230189047 0.4603780944 7.698110e-01
[77,] 0.222289527 0.4445790539 7.777105e-01
[78,] 0.222903500 0.4458070006 7.770965e-01
[79,] 0.190515244 0.3810304871 8.094848e-01
[80,] 0.161648157 0.3232963140 8.383518e-01
[81,] 0.142859490 0.2857189800 8.571405e-01
[82,] 0.123835776 0.2476715520 8.761642e-01
[83,] 0.173889300 0.3477786001 8.261107e-01
[84,] 0.149476989 0.2989539780 8.505230e-01
[85,] 0.133958215 0.2679164298 8.660418e-01
[86,] 0.130862512 0.2617250240 8.691375e-01
[87,] 0.124934114 0.2498682289 8.750659e-01
[88,] 0.118107148 0.2362142955 8.818929e-01
[89,] 0.148147088 0.2962941769 8.518529e-01
[90,] 0.123285746 0.2465714924 8.767143e-01
[91,] 0.104838453 0.2096769064 8.951615e-01
[92,] 0.120136683 0.2402733659 8.798633e-01
[93,] 0.099257225 0.1985144499 9.007428e-01
[94,] 0.092443083 0.1848861662 9.075569e-01
[95,] 0.074417442 0.1488348838 9.255826e-01
[96,] 0.061869153 0.1237383062 9.381308e-01
[97,] 0.067454346 0.1349086919 9.325457e-01
[98,] 0.053313422 0.1066268442 9.466866e-01
[99,] 0.044541719 0.0890834381 9.554583e-01
[100,] 0.034694823 0.0693896457 9.653052e-01
[101,] 0.035908878 0.0718177560 9.640911e-01
[102,] 0.027314142 0.0546282838 9.726859e-01
[103,] 0.031997163 0.0639943259 9.680028e-01
[104,] 0.028172657 0.0563453145 9.718273e-01
[105,] 0.023130764 0.0462615276 9.768692e-01
[106,] 0.017382845 0.0347656908 9.826172e-01
[107,] 0.013229096 0.0264581921 9.867709e-01
[108,] 0.017882095 0.0357641904 9.821179e-01
[109,] 0.020655702 0.0413114045 9.793443e-01
[110,] 0.016124431 0.0322488628 9.838756e-01
[111,] 0.012330384 0.0246607689 9.876696e-01
[112,] 0.010011072 0.0200221447 9.899889e-01
[113,] 0.008222648 0.0164452960 9.917774e-01
[114,] 0.009737295 0.0194745905 9.902627e-01
[115,] 0.017254588 0.0345091760 9.827454e-01
[116,] 0.018289302 0.0365786037 9.817107e-01
[117,] 0.047964204 0.0959284075 9.520358e-01
[118,] 0.036605121 0.0732102422 9.633949e-01
[119,] 0.027667236 0.0553344721 9.723328e-01
[120,] 0.024338989 0.0486779781 9.756610e-01
[121,] 0.022261096 0.0445221930 9.777389e-01
[122,] 0.017494293 0.0349885862 9.825057e-01
[123,] 0.021755108 0.0435102160 9.782449e-01
[124,] 0.033464815 0.0669296292 9.665352e-01
[125,] 0.034862955 0.0697259108 9.651370e-01
[126,] 0.037126686 0.0742533719 9.628733e-01
[127,] 0.029666868 0.0593337353 9.703331e-01
[128,] 0.028352294 0.0567045874 9.716477e-01
[129,] 0.020920738 0.0418414754 9.790793e-01
[130,] 0.021808743 0.0436174861 9.781913e-01
[131,] 0.015640367 0.0312807338 9.843596e-01
[132,] 0.049490999 0.0989819976 9.505090e-01
[133,] 0.053669841 0.1073396826 9.463302e-01
[134,] 0.039695700 0.0793914008 9.603043e-01
[135,] 0.356443129 0.7128862590 6.435569e-01
[136,] 0.362051589 0.7241031777 6.379484e-01
[137,] 0.636151057 0.7276978870 3.638489e-01
[138,] 0.560504974 0.8789900529 4.394950e-01
[139,] 0.583618719 0.8327625611 4.163813e-01
[140,] 0.477550512 0.9551010236 5.224495e-01
[141,] 0.429632752 0.8592655040 5.703672e-01
[142,] 0.301969815 0.6039396297 6.980302e-01
[143,] 0.426203569 0.8524071376 5.737964e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1deaf1351697518.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d2ve1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vxdv1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4z5zx1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52qt41351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
2.128174991 -0.334949791 1.571019668 -5.234565342 2.397660049 0.146146914
7 8 9 10 11 12
-0.809551017 2.827569023 1.464012007 -5.075719092 -1.359353020 0.399270303
13 14 15 16 17 18
0.544571389 -0.529433188 2.694690749 0.820546076 -0.939622647 -1.663671787
19 20 21 22 23 24
-1.593787337 0.317737471 -0.752469238 0.328795678 -0.126304512 -0.641072841
25 26 27 28 29 30
-2.857109990 1.208967360 -1.593906892 2.774844398 0.353358956 -0.540619552
31 32 33 34 35 36
0.911683208 -1.809471905 -0.369742812 0.287294918 1.531285130 0.003585483
37 38 39 40 41 42
-2.582749838 1.536975251 -1.803469337 -1.582961860 -1.457526542 1.455266153
43 44 45 46 47 48
1.449867265 -0.521878650 -0.222849225 3.103203299 2.434222423 -0.165731684
49 50 51 52 53 54
-0.654801974 1.659271075 2.123521558 -0.555728155 2.430770935 1.277565865
55 56 57 58 59 60
-3.019989371 -4.373032018 0.610452384 0.091460681 -0.053012568 -1.569590286
61 62 63 64 65 66
-2.504120635 0.549361174 2.325490255 0.223578550 0.435590389 0.791523742
67 68 69 70 71 72
1.412197181 -1.688564870 2.517938503 0.327073785 -0.332089838 0.146022687
73 74 75 76 77 78
1.165191960 1.253536451 0.575308209 -2.735127714 1.418632006 -0.503299552
79 80 81 82 83 84
1.756422514 0.670454022 0.239940430 -1.883537470 0.297125191 1.055364659
85 86 87 88 89 90
-0.210592753 -1.582588428 -1.539070507 0.306128324 0.179781295 0.948584202
91 92 93 94 95 96
-0.723729869 2.991043032 0.202615901 1.001317570 1.806241119 -1.467121519
97 98 99 100 101 102
1.506055030 -2.508563760 0.500762218 -0.758256304 2.473192713 -0.490739754
103 104 105 106 107 108
1.273839265 -0.034873704 -0.806494871 2.340206194 -0.005438433 0.573492772
109 110 111 112 113 114
-0.574136032 -2.271501485 0.190345773 2.266411393 -1.460299868 0.796228249
115 116 117 118 119 120
0.619297380 0.515949498 2.173097652 -2.759933377 0.831423806 0.539344071
121 122 123 124 125 126
0.800130667 1.448540875 1.991975504 2.356540367 1.924681351 3.240032934
127 128 129 130 131 132
-0.820097612 -0.857463560 -2.203562103 1.553668938 -1.722754118 2.347697004
133 134 135 136 137 138
-3.457740155 1.267700771 1.441334088 0.985846796 -0.915843577 -0.123824317
139 140 141 142 143 144
0.787781862 -1.499774960 1.388873183 -3.777567150 -2.276538660 2.629033703
145 146 147 148 149 150
-0.446670072 -0.080863809 0.431482270 1.973500826 0.351147619 -0.885595133
151 152 153 154 155 156
-1.417862800 2.224564098 -2.800551969 -5.745889460 -2.481657071 -0.363496355
157 158 159 160 161 162
2.991043032 -4.232639374 -2.203562103 -0.589736401 -1.602054493 -2.074977250
> postscript(file="/var/wessaorg/rcomp/tmp/6l1101351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 2.128174991 NA
1 -0.334949791 2.128174991
2 1.571019668 -0.334949791
3 -5.234565342 1.571019668
4 2.397660049 -5.234565342
5 0.146146914 2.397660049
6 -0.809551017 0.146146914
7 2.827569023 -0.809551017
8 1.464012007 2.827569023
9 -5.075719092 1.464012007
10 -1.359353020 -5.075719092
11 0.399270303 -1.359353020
12 0.544571389 0.399270303
13 -0.529433188 0.544571389
14 2.694690749 -0.529433188
15 0.820546076 2.694690749
16 -0.939622647 0.820546076
17 -1.663671787 -0.939622647
18 -1.593787337 -1.663671787
19 0.317737471 -1.593787337
20 -0.752469238 0.317737471
21 0.328795678 -0.752469238
22 -0.126304512 0.328795678
23 -0.641072841 -0.126304512
24 -2.857109990 -0.641072841
25 1.208967360 -2.857109990
26 -1.593906892 1.208967360
27 2.774844398 -1.593906892
28 0.353358956 2.774844398
29 -0.540619552 0.353358956
30 0.911683208 -0.540619552
31 -1.809471905 0.911683208
32 -0.369742812 -1.809471905
33 0.287294918 -0.369742812
34 1.531285130 0.287294918
35 0.003585483 1.531285130
36 -2.582749838 0.003585483
37 1.536975251 -2.582749838
38 -1.803469337 1.536975251
39 -1.582961860 -1.803469337
40 -1.457526542 -1.582961860
41 1.455266153 -1.457526542
42 1.449867265 1.455266153
43 -0.521878650 1.449867265
44 -0.222849225 -0.521878650
45 3.103203299 -0.222849225
46 2.434222423 3.103203299
47 -0.165731684 2.434222423
48 -0.654801974 -0.165731684
49 1.659271075 -0.654801974
50 2.123521558 1.659271075
51 -0.555728155 2.123521558
52 2.430770935 -0.555728155
53 1.277565865 2.430770935
54 -3.019989371 1.277565865
55 -4.373032018 -3.019989371
56 0.610452384 -4.373032018
57 0.091460681 0.610452384
58 -0.053012568 0.091460681
59 -1.569590286 -0.053012568
60 -2.504120635 -1.569590286
61 0.549361174 -2.504120635
62 2.325490255 0.549361174
63 0.223578550 2.325490255
64 0.435590389 0.223578550
65 0.791523742 0.435590389
66 1.412197181 0.791523742
67 -1.688564870 1.412197181
68 2.517938503 -1.688564870
69 0.327073785 2.517938503
70 -0.332089838 0.327073785
71 0.146022687 -0.332089838
72 1.165191960 0.146022687
73 1.253536451 1.165191960
74 0.575308209 1.253536451
75 -2.735127714 0.575308209
76 1.418632006 -2.735127714
77 -0.503299552 1.418632006
78 1.756422514 -0.503299552
79 0.670454022 1.756422514
80 0.239940430 0.670454022
81 -1.883537470 0.239940430
82 0.297125191 -1.883537470
83 1.055364659 0.297125191
84 -0.210592753 1.055364659
85 -1.582588428 -0.210592753
86 -1.539070507 -1.582588428
87 0.306128324 -1.539070507
88 0.179781295 0.306128324
89 0.948584202 0.179781295
90 -0.723729869 0.948584202
91 2.991043032 -0.723729869
92 0.202615901 2.991043032
93 1.001317570 0.202615901
94 1.806241119 1.001317570
95 -1.467121519 1.806241119
96 1.506055030 -1.467121519
97 -2.508563760 1.506055030
98 0.500762218 -2.508563760
99 -0.758256304 0.500762218
100 2.473192713 -0.758256304
101 -0.490739754 2.473192713
102 1.273839265 -0.490739754
103 -0.034873704 1.273839265
104 -0.806494871 -0.034873704
105 2.340206194 -0.806494871
106 -0.005438433 2.340206194
107 0.573492772 -0.005438433
108 -0.574136032 0.573492772
109 -2.271501485 -0.574136032
110 0.190345773 -2.271501485
111 2.266411393 0.190345773
112 -1.460299868 2.266411393
113 0.796228249 -1.460299868
114 0.619297380 0.796228249
115 0.515949498 0.619297380
116 2.173097652 0.515949498
117 -2.759933377 2.173097652
118 0.831423806 -2.759933377
119 0.539344071 0.831423806
120 0.800130667 0.539344071
121 1.448540875 0.800130667
122 1.991975504 1.448540875
123 2.356540367 1.991975504
124 1.924681351 2.356540367
125 3.240032934 1.924681351
126 -0.820097612 3.240032934
127 -0.857463560 -0.820097612
128 -2.203562103 -0.857463560
129 1.553668938 -2.203562103
130 -1.722754118 1.553668938
131 2.347697004 -1.722754118
132 -3.457740155 2.347697004
133 1.267700771 -3.457740155
134 1.441334088 1.267700771
135 0.985846796 1.441334088
136 -0.915843577 0.985846796
137 -0.123824317 -0.915843577
138 0.787781862 -0.123824317
139 -1.499774960 0.787781862
140 1.388873183 -1.499774960
141 -3.777567150 1.388873183
142 -2.276538660 -3.777567150
143 2.629033703 -2.276538660
144 -0.446670072 2.629033703
145 -0.080863809 -0.446670072
146 0.431482270 -0.080863809
147 1.973500826 0.431482270
148 0.351147619 1.973500826
149 -0.885595133 0.351147619
150 -1.417862800 -0.885595133
151 2.224564098 -1.417862800
152 -2.800551969 2.224564098
153 -5.745889460 -2.800551969
154 -2.481657071 -5.745889460
155 -0.363496355 -2.481657071
156 2.991043032 -0.363496355
157 -4.232639374 2.991043032
158 -2.203562103 -4.232639374
159 -0.589736401 -2.203562103
160 -1.602054493 -0.589736401
161 -2.074977250 -1.602054493
162 NA -2.074977250
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.334949791 2.128174991
[2,] 1.571019668 -0.334949791
[3,] -5.234565342 1.571019668
[4,] 2.397660049 -5.234565342
[5,] 0.146146914 2.397660049
[6,] -0.809551017 0.146146914
[7,] 2.827569023 -0.809551017
[8,] 1.464012007 2.827569023
[9,] -5.075719092 1.464012007
[10,] -1.359353020 -5.075719092
[11,] 0.399270303 -1.359353020
[12,] 0.544571389 0.399270303
[13,] -0.529433188 0.544571389
[14,] 2.694690749 -0.529433188
[15,] 0.820546076 2.694690749
[16,] -0.939622647 0.820546076
[17,] -1.663671787 -0.939622647
[18,] -1.593787337 -1.663671787
[19,] 0.317737471 -1.593787337
[20,] -0.752469238 0.317737471
[21,] 0.328795678 -0.752469238
[22,] -0.126304512 0.328795678
[23,] -0.641072841 -0.126304512
[24,] -2.857109990 -0.641072841
[25,] 1.208967360 -2.857109990
[26,] -1.593906892 1.208967360
[27,] 2.774844398 -1.593906892
[28,] 0.353358956 2.774844398
[29,] -0.540619552 0.353358956
[30,] 0.911683208 -0.540619552
[31,] -1.809471905 0.911683208
[32,] -0.369742812 -1.809471905
[33,] 0.287294918 -0.369742812
[34,] 1.531285130 0.287294918
[35,] 0.003585483 1.531285130
[36,] -2.582749838 0.003585483
[37,] 1.536975251 -2.582749838
[38,] -1.803469337 1.536975251
[39,] -1.582961860 -1.803469337
[40,] -1.457526542 -1.582961860
[41,] 1.455266153 -1.457526542
[42,] 1.449867265 1.455266153
[43,] -0.521878650 1.449867265
[44,] -0.222849225 -0.521878650
[45,] 3.103203299 -0.222849225
[46,] 2.434222423 3.103203299
[47,] -0.165731684 2.434222423
[48,] -0.654801974 -0.165731684
[49,] 1.659271075 -0.654801974
[50,] 2.123521558 1.659271075
[51,] -0.555728155 2.123521558
[52,] 2.430770935 -0.555728155
[53,] 1.277565865 2.430770935
[54,] -3.019989371 1.277565865
[55,] -4.373032018 -3.019989371
[56,] 0.610452384 -4.373032018
[57,] 0.091460681 0.610452384
[58,] -0.053012568 0.091460681
[59,] -1.569590286 -0.053012568
[60,] -2.504120635 -1.569590286
[61,] 0.549361174 -2.504120635
[62,] 2.325490255 0.549361174
[63,] 0.223578550 2.325490255
[64,] 0.435590389 0.223578550
[65,] 0.791523742 0.435590389
[66,] 1.412197181 0.791523742
[67,] -1.688564870 1.412197181
[68,] 2.517938503 -1.688564870
[69,] 0.327073785 2.517938503
[70,] -0.332089838 0.327073785
[71,] 0.146022687 -0.332089838
[72,] 1.165191960 0.146022687
[73,] 1.253536451 1.165191960
[74,] 0.575308209 1.253536451
[75,] -2.735127714 0.575308209
[76,] 1.418632006 -2.735127714
[77,] -0.503299552 1.418632006
[78,] 1.756422514 -0.503299552
[79,] 0.670454022 1.756422514
[80,] 0.239940430 0.670454022
[81,] -1.883537470 0.239940430
[82,] 0.297125191 -1.883537470
[83,] 1.055364659 0.297125191
[84,] -0.210592753 1.055364659
[85,] -1.582588428 -0.210592753
[86,] -1.539070507 -1.582588428
[87,] 0.306128324 -1.539070507
[88,] 0.179781295 0.306128324
[89,] 0.948584202 0.179781295
[90,] -0.723729869 0.948584202
[91,] 2.991043032 -0.723729869
[92,] 0.202615901 2.991043032
[93,] 1.001317570 0.202615901
[94,] 1.806241119 1.001317570
[95,] -1.467121519 1.806241119
[96,] 1.506055030 -1.467121519
[97,] -2.508563760 1.506055030
[98,] 0.500762218 -2.508563760
[99,] -0.758256304 0.500762218
[100,] 2.473192713 -0.758256304
[101,] -0.490739754 2.473192713
[102,] 1.273839265 -0.490739754
[103,] -0.034873704 1.273839265
[104,] -0.806494871 -0.034873704
[105,] 2.340206194 -0.806494871
[106,] -0.005438433 2.340206194
[107,] 0.573492772 -0.005438433
[108,] -0.574136032 0.573492772
[109,] -2.271501485 -0.574136032
[110,] 0.190345773 -2.271501485
[111,] 2.266411393 0.190345773
[112,] -1.460299868 2.266411393
[113,] 0.796228249 -1.460299868
[114,] 0.619297380 0.796228249
[115,] 0.515949498 0.619297380
[116,] 2.173097652 0.515949498
[117,] -2.759933377 2.173097652
[118,] 0.831423806 -2.759933377
[119,] 0.539344071 0.831423806
[120,] 0.800130667 0.539344071
[121,] 1.448540875 0.800130667
[122,] 1.991975504 1.448540875
[123,] 2.356540367 1.991975504
[124,] 1.924681351 2.356540367
[125,] 3.240032934 1.924681351
[126,] -0.820097612 3.240032934
[127,] -0.857463560 -0.820097612
[128,] -2.203562103 -0.857463560
[129,] 1.553668938 -2.203562103
[130,] -1.722754118 1.553668938
[131,] 2.347697004 -1.722754118
[132,] -3.457740155 2.347697004
[133,] 1.267700771 -3.457740155
[134,] 1.441334088 1.267700771
[135,] 0.985846796 1.441334088
[136,] -0.915843577 0.985846796
[137,] -0.123824317 -0.915843577
[138,] 0.787781862 -0.123824317
[139,] -1.499774960 0.787781862
[140,] 1.388873183 -1.499774960
[141,] -3.777567150 1.388873183
[142,] -2.276538660 -3.777567150
[143,] 2.629033703 -2.276538660
[144,] -0.446670072 2.629033703
[145,] -0.080863809 -0.446670072
[146,] 0.431482270 -0.080863809
[147,] 1.973500826 0.431482270
[148,] 0.351147619 1.973500826
[149,] -0.885595133 0.351147619
[150,] -1.417862800 -0.885595133
[151,] 2.224564098 -1.417862800
[152,] -2.800551969 2.224564098
[153,] -5.745889460 -2.800551969
[154,] -2.481657071 -5.745889460
[155,] -0.363496355 -2.481657071
[156,] 2.991043032 -0.363496355
[157,] -4.232639374 2.991043032
[158,] -2.203562103 -4.232639374
[159,] -0.589736401 -2.203562103
[160,] -1.602054493 -0.589736401
[161,] -2.074977250 -1.602054493
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.334949791 2.128174991
2 1.571019668 -0.334949791
3 -5.234565342 1.571019668
4 2.397660049 -5.234565342
5 0.146146914 2.397660049
6 -0.809551017 0.146146914
7 2.827569023 -0.809551017
8 1.464012007 2.827569023
9 -5.075719092 1.464012007
10 -1.359353020 -5.075719092
11 0.399270303 -1.359353020
12 0.544571389 0.399270303
13 -0.529433188 0.544571389
14 2.694690749 -0.529433188
15 0.820546076 2.694690749
16 -0.939622647 0.820546076
17 -1.663671787 -0.939622647
18 -1.593787337 -1.663671787
19 0.317737471 -1.593787337
20 -0.752469238 0.317737471
21 0.328795678 -0.752469238
22 -0.126304512 0.328795678
23 -0.641072841 -0.126304512
24 -2.857109990 -0.641072841
25 1.208967360 -2.857109990
26 -1.593906892 1.208967360
27 2.774844398 -1.593906892
28 0.353358956 2.774844398
29 -0.540619552 0.353358956
30 0.911683208 -0.540619552
31 -1.809471905 0.911683208
32 -0.369742812 -1.809471905
33 0.287294918 -0.369742812
34 1.531285130 0.287294918
35 0.003585483 1.531285130
36 -2.582749838 0.003585483
37 1.536975251 -2.582749838
38 -1.803469337 1.536975251
39 -1.582961860 -1.803469337
40 -1.457526542 -1.582961860
41 1.455266153 -1.457526542
42 1.449867265 1.455266153
43 -0.521878650 1.449867265
44 -0.222849225 -0.521878650
45 3.103203299 -0.222849225
46 2.434222423 3.103203299
47 -0.165731684 2.434222423
48 -0.654801974 -0.165731684
49 1.659271075 -0.654801974
50 2.123521558 1.659271075
51 -0.555728155 2.123521558
52 2.430770935 -0.555728155
53 1.277565865 2.430770935
54 -3.019989371 1.277565865
55 -4.373032018 -3.019989371
56 0.610452384 -4.373032018
57 0.091460681 0.610452384
58 -0.053012568 0.091460681
59 -1.569590286 -0.053012568
60 -2.504120635 -1.569590286
61 0.549361174 -2.504120635
62 2.325490255 0.549361174
63 0.223578550 2.325490255
64 0.435590389 0.223578550
65 0.791523742 0.435590389
66 1.412197181 0.791523742
67 -1.688564870 1.412197181
68 2.517938503 -1.688564870
69 0.327073785 2.517938503
70 -0.332089838 0.327073785
71 0.146022687 -0.332089838
72 1.165191960 0.146022687
73 1.253536451 1.165191960
74 0.575308209 1.253536451
75 -2.735127714 0.575308209
76 1.418632006 -2.735127714
77 -0.503299552 1.418632006
78 1.756422514 -0.503299552
79 0.670454022 1.756422514
80 0.239940430 0.670454022
81 -1.883537470 0.239940430
82 0.297125191 -1.883537470
83 1.055364659 0.297125191
84 -0.210592753 1.055364659
85 -1.582588428 -0.210592753
86 -1.539070507 -1.582588428
87 0.306128324 -1.539070507
88 0.179781295 0.306128324
89 0.948584202 0.179781295
90 -0.723729869 0.948584202
91 2.991043032 -0.723729869
92 0.202615901 2.991043032
93 1.001317570 0.202615901
94 1.806241119 1.001317570
95 -1.467121519 1.806241119
96 1.506055030 -1.467121519
97 -2.508563760 1.506055030
98 0.500762218 -2.508563760
99 -0.758256304 0.500762218
100 2.473192713 -0.758256304
101 -0.490739754 2.473192713
102 1.273839265 -0.490739754
103 -0.034873704 1.273839265
104 -0.806494871 -0.034873704
105 2.340206194 -0.806494871
106 -0.005438433 2.340206194
107 0.573492772 -0.005438433
108 -0.574136032 0.573492772
109 -2.271501485 -0.574136032
110 0.190345773 -2.271501485
111 2.266411393 0.190345773
112 -1.460299868 2.266411393
113 0.796228249 -1.460299868
114 0.619297380 0.796228249
115 0.515949498 0.619297380
116 2.173097652 0.515949498
117 -2.759933377 2.173097652
118 0.831423806 -2.759933377
119 0.539344071 0.831423806
120 0.800130667 0.539344071
121 1.448540875 0.800130667
122 1.991975504 1.448540875
123 2.356540367 1.991975504
124 1.924681351 2.356540367
125 3.240032934 1.924681351
126 -0.820097612 3.240032934
127 -0.857463560 -0.820097612
128 -2.203562103 -0.857463560
129 1.553668938 -2.203562103
130 -1.722754118 1.553668938
131 2.347697004 -1.722754118
132 -3.457740155 2.347697004
133 1.267700771 -3.457740155
134 1.441334088 1.267700771
135 0.985846796 1.441334088
136 -0.915843577 0.985846796
137 -0.123824317 -0.915843577
138 0.787781862 -0.123824317
139 -1.499774960 0.787781862
140 1.388873183 -1.499774960
141 -3.777567150 1.388873183
142 -2.276538660 -3.777567150
143 2.629033703 -2.276538660
144 -0.446670072 2.629033703
145 -0.080863809 -0.446670072
146 0.431482270 -0.080863809
147 1.973500826 0.431482270
148 0.351147619 1.973500826
149 -0.885595133 0.351147619
150 -1.417862800 -0.885595133
151 2.224564098 -1.417862800
152 -2.800551969 2.224564098
153 -5.745889460 -2.800551969
154 -2.481657071 -5.745889460
155 -0.363496355 -2.481657071
156 2.991043032 -0.363496355
157 -4.232639374 2.991043032
158 -2.203562103 -4.232639374
159 -0.589736401 -2.203562103
160 -1.602054493 -0.589736401
161 -2.074977250 -1.602054493
> 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/77clb1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/86az31351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9249b1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10gokd1351697518.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wgw71351697518.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/1261t81351697518.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/13z9pq1351697518.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/14jzio1351697518.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/15i1fn1351697518.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/16ml2i1351697518.tab")
+ }
>
> try(system("convert tmp/1deaf1351697518.ps tmp/1deaf1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d2ve1351697518.ps tmp/2d2ve1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vxdv1351697518.ps tmp/3vxdv1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z5zx1351697518.ps tmp/4z5zx1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/52qt41351697518.ps tmp/52qt41351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l1101351697518.ps tmp/6l1101351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/77clb1351697518.ps tmp/77clb1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/86az31351697518.ps tmp/86az31351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/9249b1351697518.ps tmp/9249b1351697518.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gokd1351697518.ps tmp/10gokd1351697518.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.502 1.675 13.488