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(9
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+ ,dim=c(7
+ ,161)
+ ,dimnames=list(c('month'
+ ,'Part_of_team'
+ ,'Respect_of_coach'
+ ,'Respect_of_team'
+ ,'Be_on_different_team'
+ ,'Be_liked'
+ ,'Proudness')
+ ,1:161))
> y <- array(NA,dim=c(7,161),dimnames=list(c('month','Part_of_team','Respect_of_coach','Respect_of_team','Be_on_different_team','Be_liked','Proudness'),1:161))
> 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 = '2'
> #'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
Part_of_team month Respect_of_coach Respect_of_team Be_on_different_team
1 3 9 3 3 3
2 5 9 5 5 1
3 4 9 4 4 3
4 4 9 4 4 3
5 5 9 4 4 1
6 5 9 3 5 1
7 2 9 1 3 5
8 5 9 4 4 2
9 4 9 4 4 2
10 4 9 4 4 2
11 5 9 4 5 4
12 3 9 3 3 3
13 5 9 4 4 2
14 3 9 3 3 3
15 5 9 4 5 1
16 3 9 3 3 3
17 4 9 5 4 2
18 5 9 4 5 1
19 4 9 3 3 3
20 3 9 3 3 3
21 4 9 4 3 2
22 4 9 3 4 2
23 3 9 3 3 3
24 3 9 3 3 3
25 4 9 5 5 1
26 5 9 5 5 1
27 4 9 4 4 1
28 4 9 4 4 1
29 4 9 5 4 1
30 4 9 4 4 3
31 4 9 4 5 1
32 3 9 3 3 3
33 4 9 4 4 1
34 5 9 4 5 2
35 4 9 4 4 1
36 4 9 4 4 2
37 3 9 4 4 2
38 4 9 4 4 2
39 4 9 3 4 1
40 4 9 4 4 3
41 5 9 2 4 1
42 4 9 4 4 1
43 3 9 3 3 3
44 3 9 3 3 3
45 4 9 4 4 1
46 4 9 3 4 2
47 4 9 4 4 4
48 5 9 4 4 2
49 4 9 4 4 1
50 5 9 4 4 1
51 4 9 4 4 2
52 4 9 4 4 2
53 4 9 3 3 3
54 4 10 4 5 5
55 4 10 3 4 2
56 5 10 4 4 1
57 4 10 4 4 1
58 4 10 3 5 1
59 4 10 4 4 1
60 4 10 4 4 2
61 3 10 3 2 2
62 4 10 4 4 2
63 5 10 4 1 4
64 3 1 2 3 3
65 3 3 3 3 3
66 4 5 5 1 4
67 4 4 3 2 4
68 4 4 4 1 4
69 4 3 3 3 3
70 3 4 4 2 4
71 4 4 4 2 4
72 3 3 3 3 3
73 3 5 4 1 4
74 3 4 3 1 4
75 5 5 4 1 5
76 4 4 5 2 5
77 4 4 4 2 4
78 2 4 4 1 4
79 4 4 4 1 4
80 3 3 3 3 3
81 4 5 4 1 5
82 4 5 4 2 4
83 4 4 3 1 4
84 4 4 4 2 4
85 4 5 5 1 5
86 4 4 4 1 4
87 4 4 4 2 4
88 3 3 3 3 3
89 4 4 4 2 4
90 3 4 4 2 5
91 3 3 3 3 3
92 5 5 4 2 4
93 4 5 4 1 4
94 5 5 4 1 4
95 4 4 4 1 4
96 3 4 4 2 4
97 3 4 4 1 4
98 4 4 4 1 4
99 4 4 4 1 4
100 4 4 4 2 4
101 5 4 4 1 4
102 5 5 5 1 5
103 3 4 4 3 4
104 5 4 4 1 5
105 4 3 4 2 4
106 4 5 4 3 4
107 4 4 4 2 4
108 3 3 3 3 3
109 4 2 4 4 2
110 5 5 1 5 5
111 4 4 1 4 4
112 5 5 2 5 5
113 1 1 1 1 1
114 4 5 2 4 4
115 5 5 1 5 5
116 3 3 3 3 3
117 4 4 4 4 11
118 3 4 4 3 11
119 1 5 5 5 11
120 3 3 3 3 11
121 1 4 4 3 11
122 1 5 5 4 11
123 1 4 5 4 11
124 3 4 4 4 11
125 2 4 5 4 11
126 1 5 5 5 11
127 2 4 4 4 11
128 4 5 5 4 11
129 2 4 4 4 11
130 1 4 4 4 11
131 2 4 4 4 11
132 1 4 4 4 11
133 2 4 4 4 11
134 2 4 4 5 11
135 1 5 4 5 11
136 3 3 3 3 11
137 3 4 3 4 11
138 4 3 3 3 11
139 2 4 5 3 11
140 1 4 4 4 11
141 3 3 3 3 11
142 1 5 5 5 11
143 1 5 5 5 11
144 1 4 4 4 11
145 1 5 5 5 11
146 3 4 5 3 11
147 2 3 3 3 11
148 2 5 4 4 11
149 1 4 4 5 11
150 1 5 5 5 11
151 2 4 4 4 11
152 1 4 4 4 11
153 2 4 4 4 11
154 2 4 4 4 11
155 2 5 5 4 11
156 3 3 3 3 11
157 1 4 4 4 11
158 4 5 5 4 11
159 1 4 5 5 11
160 1 5 5 5 11
161 3 4 4 5 9
Be_liked Proudness
1 3 3
2 5 5
3 3 4
4 4 4
5 4 4
6 5 5
7 3 2
8 4 4
9 4 4
10 5 4
11 5 4
12 3 3
13 4 4
14 3 3
15 5 4
16 3 3
17 4 4
18 5 5
19 4 3
20 3 3
21 4 3
22 4 3
23 3 3
24 4 3
25 4 4
26 4 4
27 4 4
28 4 4
29 4 4
30 4 4
31 4 4
32 3 3
33 4 4
34 5 5
35 4 4
36 4 4
37 4 4
38 3 4
39 5 4
40 4 4
41 5 3
42 4 4
43 3 3
44 3 3
45 3 4
46 4 3
47 4 4
48 4 4
49 4 3
50 4 4
51 4 4
52 4 4
53 4 4
54 4 5
55 3 3
56 4 4
57 4 4
58 4 4
59 4 4
60 4 4
61 3 3
62 4 4
63 4 10
64 3 10
65 3 10
66 4 10
67 4 10
68 4 10
69 4 10
70 4 10
71 4 10
72 3 10
73 4 10
74 3 10
75 5 10
76 5 10
77 4 10
78 4 10
79 4 10
80 3 10
81 4 10
82 3 10
83 5 10
84 4 10
85 4 10
86 4 10
87 4 10
88 3 10
89 4 10
90 4 10
91 3 10
92 4 10
93 4 10
94 5 10
95 4 10
96 4 10
97 4 10
98 5 10
99 5 10
100 4 10
101 4 10
102 4 10
103 4 10
104 4 10
105 4 10
106 4 10
107 3 11
108 3 11
109 3 11
110 11 4
111 11 5
112 11 1
113 11 5
114 11 5
115 11 3
116 11 4
117 5 4
118 5 5
119 3 3
120 4 3
121 5 5
122 4 3
123 4 5
124 4 4
125 5 5
126 4 4
127 5 4
128 4 4
129 4 4
130 4 4
131 4 4
132 3 2
133 4 4
134 5 5
135 3 3
136 4 3
137 3 3
138 4 2
139 3 4
140 3 3
141 5 5
142 5 4
143 5 4
144 5 4
145 5 4
146 4 3
147 4 5
148 4 4
149 5 4
150 5 5
151 4 4
152 4 4
153 4 5
154 5 5
155 3 3
156 4 4
157 5 4
158 5 4
159 5 5
160 5 5
161 3 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month Respect_of_coach
1.161281 0.211062 0.008344
Respect_of_team Be_on_different_team Be_liked
-0.042996 -0.119631 0.183773
Proudness
0.153490
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.007008 -0.571322 0.005321 0.271388 2.583357
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.161281 0.866303 1.341 0.182057
month 0.211062 0.062821 3.360 0.000984 ***
Respect_of_coach 0.008344 0.100456 0.083 0.933911
Respect_of_team -0.042996 0.078237 -0.550 0.583415
Be_on_different_team -0.119631 0.038220 -3.130 0.002091 **
Be_liked 0.183773 0.048226 3.811 0.000200 ***
Proudness 0.153490 0.052206 2.940 0.003788 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8102 on 154 degrees of freedom
Multiple R-squared: 0.5705, Adjusted R-squared: 0.5538
F-statistic: 34.09 on 6 and 154 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,] 2.610336e-01 5.220672e-01 0.7389664
[2,] 2.198417e-01 4.396834e-01 0.7801583
[3,] 1.189715e-01 2.379429e-01 0.8810285
[4,] 1.211675e-01 2.423350e-01 0.8788325
[5,] 6.687244e-02 1.337449e-01 0.9331276
[6,] 5.854411e-02 1.170882e-01 0.9414559
[7,] 3.211211e-02 6.422422e-02 0.9678879
[8,] 2.738136e-02 5.476271e-02 0.9726186
[9,] 1.655764e-02 3.311529e-02 0.9834424
[10,] 2.449370e-02 4.898740e-02 0.9755063
[11,] 1.430784e-02 2.861568e-02 0.9856922
[12,] 8.140221e-03 1.628044e-02 0.9918598
[13,] 4.200907e-03 8.401814e-03 0.9957991
[14,] 2.294758e-03 4.589515e-03 0.9977052
[15,] 1.605992e-03 3.211984e-03 0.9983940
[16,] 3.128362e-03 6.256725e-03 0.9968716
[17,] 2.257065e-03 4.514130e-03 0.9977429
[18,] 1.449004e-03 2.898008e-03 0.9985510
[19,] 8.819090e-04 1.763818e-03 0.9991181
[20,] 6.256738e-04 1.251348e-03 0.9993743
[21,] 3.266290e-04 6.532580e-04 0.9996734
[22,] 2.424967e-04 4.849933e-04 0.9997575
[23,] 1.334150e-04 2.668300e-04 0.9998666
[24,] 7.125349e-05 1.425070e-04 0.9999287
[25,] 3.936834e-05 7.873669e-05 0.9999606
[26,] 2.019112e-05 4.038224e-05 0.9999798
[27,] 9.933425e-06 1.986685e-05 0.9999901
[28,] 7.202518e-05 1.440504e-04 0.9999280
[29,] 4.240244e-05 8.480488e-05 0.9999576
[30,] 2.633717e-05 5.267434e-05 0.9999737
[31,] 1.331034e-05 2.662068e-05 0.9999867
[32,] 3.016428e-05 6.032855e-05 0.9999698
[33,] 1.611916e-05 3.223831e-05 0.9999839
[34,] 9.439090e-06 1.887818e-05 0.9999906
[35,] 5.672244e-06 1.134449e-05 0.9999943
[36,] 3.112652e-06 6.225303e-06 0.9999969
[37,] 1.547225e-06 3.094450e-06 0.9999985
[38,] 7.536457e-07 1.507291e-06 0.9999992
[39,] 2.093907e-06 4.187814e-06 0.9999979
[40,] 1.036089e-06 2.072177e-06 0.9999990
[41,] 1.919773e-06 3.839547e-06 0.9999981
[42,] 9.926678e-07 1.985336e-06 0.9999990
[43,] 5.068659e-07 1.013732e-06 0.9999995
[44,] 3.013236e-07 6.026472e-07 0.9999997
[45,] 1.449513e-07 2.899027e-07 0.9999999
[46,] 1.145095e-07 2.290190e-07 0.9999999
[47,] 9.174108e-08 1.834822e-07 0.9999999
[48,] 8.297435e-08 1.659487e-07 0.9999999
[49,] 7.077225e-08 1.415445e-07 0.9999999
[50,] 4.142162e-08 8.284323e-08 1.0000000
[51,] 2.055590e-08 4.111180e-08 1.0000000
[52,] 2.015199e-08 4.030398e-08 1.0000000
[53,] 1.127030e-08 2.254060e-08 1.0000000
[54,] 1.071147e-08 2.142294e-08 1.0000000
[55,] 8.046453e-09 1.609291e-08 1.0000000
[56,] 5.548377e-09 1.109675e-08 1.0000000
[57,] 2.552439e-09 5.104878e-09 1.0000000
[58,] 1.297145e-09 2.594290e-09 1.0000000
[59,] 6.332058e-10 1.266412e-09 1.0000000
[60,] 3.252734e-10 6.505467e-10 1.0000000
[61,] 8.197807e-10 1.639561e-09 1.0000000
[62,] 4.061289e-10 8.122577e-10 1.0000000
[63,] 2.292113e-10 4.584226e-10 1.0000000
[64,] 7.471396e-10 1.494279e-09 1.0000000
[65,] 7.512816e-10 1.502563e-09 1.0000000
[66,] 1.181389e-09 2.362778e-09 1.0000000
[67,] 9.747262e-10 1.949452e-09 1.0000000
[68,] 5.081070e-10 1.016214e-09 1.0000000
[69,] 5.522125e-08 1.104425e-07 0.9999999
[70,] 3.858900e-08 7.717800e-08 1.0000000
[71,] 2.252185e-08 4.504370e-08 1.0000000
[72,] 1.531872e-08 3.063743e-08 1.0000000
[73,] 1.294255e-08 2.588509e-08 1.0000000
[74,] 7.610528e-09 1.522106e-08 1.0000000
[75,] 4.143481e-09 8.286962e-09 1.0000000
[76,] 2.136304e-09 4.272608e-09 1.0000000
[77,] 1.281591e-09 2.563182e-09 1.0000000
[78,] 6.619189e-10 1.323838e-09 1.0000000
[79,] 3.864299e-10 7.728598e-10 1.0000000
[80,] 1.954041e-10 3.908082e-10 1.0000000
[81,] 2.278819e-10 4.557638e-10 1.0000000
[82,] 1.361231e-10 2.722462e-10 1.0000000
[83,] 3.199308e-10 6.398616e-10 1.0000000
[84,] 2.142105e-10 4.284209e-10 1.0000000
[85,] 1.654181e-10 3.308363e-10 1.0000000
[86,] 8.916814e-11 1.783363e-10 1.0000000
[87,] 1.542692e-10 3.085383e-10 1.0000000
[88,] 3.380823e-10 6.761647e-10 1.0000000
[89,] 1.776317e-10 3.552633e-10 1.0000000
[90,] 9.405329e-11 1.881066e-10 1.0000000
[91,] 4.651466e-11 9.302932e-11 1.0000000
[92,] 2.375304e-10 4.750609e-10 1.0000000
[93,] 5.116884e-10 1.023377e-09 1.0000000
[94,] 7.725167e-10 1.545033e-09 1.0000000
[95,] 3.591111e-09 7.182223e-09 1.0000000
[96,] 2.860198e-09 5.720396e-09 1.0000000
[97,] 1.411829e-09 2.823659e-09 1.0000000
[98,] 8.847970e-10 1.769594e-09 1.0000000
[99,] 6.909727e-10 1.381945e-09 1.0000000
[100,] 1.786220e-07 3.572440e-07 0.9999998
[101,] 1.469344e-07 2.938687e-07 0.9999999
[102,] 1.957783e-07 3.915566e-07 0.9999998
[103,] 1.371255e-07 2.742510e-07 0.9999999
[104,] 9.133038e-05 1.826608e-04 0.9999087
[105,] 7.828378e-05 1.565676e-04 0.9999217
[106,] 1.071000e-04 2.142000e-04 0.9998929
[107,] 7.917115e-04 1.583423e-03 0.9992083
[108,] 3.447766e-03 6.895532e-03 0.9965522
[109,] 2.665239e-03 5.330478e-03 0.9973348
[110,] 6.378088e-03 1.275618e-02 0.9936219
[111,] 7.455399e-03 1.491080e-02 0.9925446
[112,] 7.463126e-02 1.492625e-01 0.9253687
[113,] 1.496024e-01 2.992049e-01 0.8503976
[114,] 1.649176e-01 3.298351e-01 0.8350824
[115,] 2.222167e-01 4.444335e-01 0.7777833
[116,] 1.970550e-01 3.941101e-01 0.8029450
[117,] 2.021790e-01 4.043579e-01 0.7978210
[118,] 1.665276e-01 3.330552e-01 0.8334724
[119,] 4.572960e-01 9.145920e-01 0.5427040
[120,] 4.013387e-01 8.026774e-01 0.5986613
[121,] 4.275283e-01 8.550565e-01 0.5724717
[122,] 3.698192e-01 7.396384e-01 0.6301808
[123,] 3.889246e-01 7.778492e-01 0.6110754
[124,] 3.299536e-01 6.599072e-01 0.6700464
[125,] 4.590483e-01 9.180966e-01 0.5409517
[126,] 4.039488e-01 8.078976e-01 0.5960512
[127,] 4.048850e-01 8.097700e-01 0.5951150
[128,] 5.751100e-01 8.497800e-01 0.4248900
[129,] 8.025130e-01 3.949740e-01 0.1974870
[130,] 8.257107e-01 3.485785e-01 0.1742893
[131,] 7.722613e-01 4.554773e-01 0.2277387
[132,] 7.227282e-01 5.545436e-01 0.2772718
[133,] 6.796633e-01 6.406734e-01 0.3203367
[134,] 6.259704e-01 7.480593e-01 0.3740296
[135,] 6.816557e-01 6.366887e-01 0.3183443
[136,] 6.241107e-01 7.517786e-01 0.3758893
[137,] 6.214065e-01 7.571869e-01 0.3785935
[138,] 5.467891e-01 9.064218e-01 0.4532109
[139,] 4.292167e-01 8.584334e-01 0.5707833
[140,] 8.257651e-01 3.484698e-01 0.1742349
[141,] 7.224485e-01 5.551031e-01 0.2775515
[142,] 8.566577e-01 2.866845e-01 0.1433423
> postscript(file="/var/www/html/rcomp/tmp/10cnw1290525413.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20cnw1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tl4h1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4tl4h1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tl4h1290525413.ps",horizontal=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 = 161
Frequency = 1
1 2 3 4 5 6
-0.609775261 0.545743613 0.271387536 0.087614754 0.848353598 0.562431539
7 8 9 10 11 12
-1.200336551 0.967984176 -0.032015824 -0.215788606 1.066468939 -0.609775261
13 14 15 16 17 18
0.967984176 -0.609775261 0.707577205 -0.609775261 -0.040359787 0.554087576
19 20 21 22 23 24
0.206451957 -0.609775261 0.078477416 0.129817768 -0.609775261 -0.793548043
25 26 27 28 29 30
-0.116993976 0.883006024 -0.151646402 -0.151646402 -0.159990365 0.087614754
31 32 33 34 35 36
-0.108650014 -0.609775261 -0.151646402 0.673718154 -0.151646402 -0.032015824
37 38 39 40 41 42
-1.032015824 0.151756958 -0.327075221 0.087614754 0.834758370 -0.151646402
43 44 45 46 47 48
-0.609775261 -0.609775261 0.032126380 0.129817768 0.207245332 0.967984176
49 50 51 52 53 54
0.001843227 0.848353598 -0.032015824 -0.032015824 0.052962329 0.005320877
55 56 57 58 59 60
0.102528756 0.637291804 -0.362708196 -0.311367845 -0.362708196 -0.243077618
61 62 63 64 65 66
-0.983464021 -0.243077618 -0.053743398 0.012635652 -0.417831899 -0.006778392
67 68 69 70 71 72
0.263967716 0.212627364 0.398395320 -0.744376247 0.255623753 -0.417831899
73 74 75 76 77 78
-0.998434429 -0.595255891 0.937423367 0.183137586 0.255623753 -1.787372636
79 80 81 82 83 84
0.212627364 -0.417831899 0.121196149 0.228334741 0.037198545 0.255623753
85 86 87 88 89 90
0.112852186 0.212627364 0.255623753 -0.417831899 0.255623753 -0.624745669
91 92 93 94 95 96
-0.417831899 1.044561959 0.001565571 0.817792789 0.212627364 -0.744376247
97 98 99 100 101 102
-0.787372636 0.028854583 0.028854583 0.255623753 1.212627364 1.112852186
103 104 105 106 107 108
-0.701379859 1.332257943 0.466685547 0.087558347 0.285906906 -0.571321527
109 110 111 112 113 114
0.554762114 0.952741890 -0.152312912 1.404866813 -3.007008430 -0.371718668
115 116 117 118 119 120
1.106231518 -0.967076381 1.916195566 0.719709549 -0.739178610 1.429867344
121 122 123 124 125 126
-1.280290451 -0.965947780 -1.061865244 1.099968348 -0.245638025 -1.076441020
127 128 129 130 131 132
-0.083804434 1.880562591 0.099968348 -0.900031652 0.099968348 -0.409279613
133 134 135 136 137 138
0.099968348 -0.194297674 -0.730834647 1.429867344 1.445574721 2.583356973
139 140 141 142 143 144
0.232400778 -0.562769242 0.939115305 -1.260213802 -1.260213802 -1.083804434
145 146 147 148 149 150
-1.260213802 1.202117625 0.122888087 -0.111093446 -1.040808046 -1.413703431
151 152 153 154 155 156
0.099968348 -0.900031652 -0.053521281 -0.237294063 0.217825002 1.276377716
157 158 159 160 161
-1.083804434 1.696789809 -1.202641637 -1.413703431 1.240965990
> postscript(file="/var/www/html/rcomp/tmp/63cm21290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.609775261 NA
1 0.545743613 -0.609775261
2 0.271387536 0.545743613
3 0.087614754 0.271387536
4 0.848353598 0.087614754
5 0.562431539 0.848353598
6 -1.200336551 0.562431539
7 0.967984176 -1.200336551
8 -0.032015824 0.967984176
9 -0.215788606 -0.032015824
10 1.066468939 -0.215788606
11 -0.609775261 1.066468939
12 0.967984176 -0.609775261
13 -0.609775261 0.967984176
14 0.707577205 -0.609775261
15 -0.609775261 0.707577205
16 -0.040359787 -0.609775261
17 0.554087576 -0.040359787
18 0.206451957 0.554087576
19 -0.609775261 0.206451957
20 0.078477416 -0.609775261
21 0.129817768 0.078477416
22 -0.609775261 0.129817768
23 -0.793548043 -0.609775261
24 -0.116993976 -0.793548043
25 0.883006024 -0.116993976
26 -0.151646402 0.883006024
27 -0.151646402 -0.151646402
28 -0.159990365 -0.151646402
29 0.087614754 -0.159990365
30 -0.108650014 0.087614754
31 -0.609775261 -0.108650014
32 -0.151646402 -0.609775261
33 0.673718154 -0.151646402
34 -0.151646402 0.673718154
35 -0.032015824 -0.151646402
36 -1.032015824 -0.032015824
37 0.151756958 -1.032015824
38 -0.327075221 0.151756958
39 0.087614754 -0.327075221
40 0.834758370 0.087614754
41 -0.151646402 0.834758370
42 -0.609775261 -0.151646402
43 -0.609775261 -0.609775261
44 0.032126380 -0.609775261
45 0.129817768 0.032126380
46 0.207245332 0.129817768
47 0.967984176 0.207245332
48 0.001843227 0.967984176
49 0.848353598 0.001843227
50 -0.032015824 0.848353598
51 -0.032015824 -0.032015824
52 0.052962329 -0.032015824
53 0.005320877 0.052962329
54 0.102528756 0.005320877
55 0.637291804 0.102528756
56 -0.362708196 0.637291804
57 -0.311367845 -0.362708196
58 -0.362708196 -0.311367845
59 -0.243077618 -0.362708196
60 -0.983464021 -0.243077618
61 -0.243077618 -0.983464021
62 -0.053743398 -0.243077618
63 0.012635652 -0.053743398
64 -0.417831899 0.012635652
65 -0.006778392 -0.417831899
66 0.263967716 -0.006778392
67 0.212627364 0.263967716
68 0.398395320 0.212627364
69 -0.744376247 0.398395320
70 0.255623753 -0.744376247
71 -0.417831899 0.255623753
72 -0.998434429 -0.417831899
73 -0.595255891 -0.998434429
74 0.937423367 -0.595255891
75 0.183137586 0.937423367
76 0.255623753 0.183137586
77 -1.787372636 0.255623753
78 0.212627364 -1.787372636
79 -0.417831899 0.212627364
80 0.121196149 -0.417831899
81 0.228334741 0.121196149
82 0.037198545 0.228334741
83 0.255623753 0.037198545
84 0.112852186 0.255623753
85 0.212627364 0.112852186
86 0.255623753 0.212627364
87 -0.417831899 0.255623753
88 0.255623753 -0.417831899
89 -0.624745669 0.255623753
90 -0.417831899 -0.624745669
91 1.044561959 -0.417831899
92 0.001565571 1.044561959
93 0.817792789 0.001565571
94 0.212627364 0.817792789
95 -0.744376247 0.212627364
96 -0.787372636 -0.744376247
97 0.028854583 -0.787372636
98 0.028854583 0.028854583
99 0.255623753 0.028854583
100 1.212627364 0.255623753
101 1.112852186 1.212627364
102 -0.701379859 1.112852186
103 1.332257943 -0.701379859
104 0.466685547 1.332257943
105 0.087558347 0.466685547
106 0.285906906 0.087558347
107 -0.571321527 0.285906906
108 0.554762114 -0.571321527
109 0.952741890 0.554762114
110 -0.152312912 0.952741890
111 1.404866813 -0.152312912
112 -3.007008430 1.404866813
113 -0.371718668 -3.007008430
114 1.106231518 -0.371718668
115 -0.967076381 1.106231518
116 1.916195566 -0.967076381
117 0.719709549 1.916195566
118 -0.739178610 0.719709549
119 1.429867344 -0.739178610
120 -1.280290451 1.429867344
121 -0.965947780 -1.280290451
122 -1.061865244 -0.965947780
123 1.099968348 -1.061865244
124 -0.245638025 1.099968348
125 -1.076441020 -0.245638025
126 -0.083804434 -1.076441020
127 1.880562591 -0.083804434
128 0.099968348 1.880562591
129 -0.900031652 0.099968348
130 0.099968348 -0.900031652
131 -0.409279613 0.099968348
132 0.099968348 -0.409279613
133 -0.194297674 0.099968348
134 -0.730834647 -0.194297674
135 1.429867344 -0.730834647
136 1.445574721 1.429867344
137 2.583356973 1.445574721
138 0.232400778 2.583356973
139 -0.562769242 0.232400778
140 0.939115305 -0.562769242
141 -1.260213802 0.939115305
142 -1.260213802 -1.260213802
143 -1.083804434 -1.260213802
144 -1.260213802 -1.083804434
145 1.202117625 -1.260213802
146 0.122888087 1.202117625
147 -0.111093446 0.122888087
148 -1.040808046 -0.111093446
149 -1.413703431 -1.040808046
150 0.099968348 -1.413703431
151 -0.900031652 0.099968348
152 -0.053521281 -0.900031652
153 -0.237294063 -0.053521281
154 0.217825002 -0.237294063
155 1.276377716 0.217825002
156 -1.083804434 1.276377716
157 1.696789809 -1.083804434
158 -1.202641637 1.696789809
159 -1.413703431 -1.202641637
160 1.240965990 -1.413703431
161 NA 1.240965990
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.545743613 -0.609775261
[2,] 0.271387536 0.545743613
[3,] 0.087614754 0.271387536
[4,] 0.848353598 0.087614754
[5,] 0.562431539 0.848353598
[6,] -1.200336551 0.562431539
[7,] 0.967984176 -1.200336551
[8,] -0.032015824 0.967984176
[9,] -0.215788606 -0.032015824
[10,] 1.066468939 -0.215788606
[11,] -0.609775261 1.066468939
[12,] 0.967984176 -0.609775261
[13,] -0.609775261 0.967984176
[14,] 0.707577205 -0.609775261
[15,] -0.609775261 0.707577205
[16,] -0.040359787 -0.609775261
[17,] 0.554087576 -0.040359787
[18,] 0.206451957 0.554087576
[19,] -0.609775261 0.206451957
[20,] 0.078477416 -0.609775261
[21,] 0.129817768 0.078477416
[22,] -0.609775261 0.129817768
[23,] -0.793548043 -0.609775261
[24,] -0.116993976 -0.793548043
[25,] 0.883006024 -0.116993976
[26,] -0.151646402 0.883006024
[27,] -0.151646402 -0.151646402
[28,] -0.159990365 -0.151646402
[29,] 0.087614754 -0.159990365
[30,] -0.108650014 0.087614754
[31,] -0.609775261 -0.108650014
[32,] -0.151646402 -0.609775261
[33,] 0.673718154 -0.151646402
[34,] -0.151646402 0.673718154
[35,] -0.032015824 -0.151646402
[36,] -1.032015824 -0.032015824
[37,] 0.151756958 -1.032015824
[38,] -0.327075221 0.151756958
[39,] 0.087614754 -0.327075221
[40,] 0.834758370 0.087614754
[41,] -0.151646402 0.834758370
[42,] -0.609775261 -0.151646402
[43,] -0.609775261 -0.609775261
[44,] 0.032126380 -0.609775261
[45,] 0.129817768 0.032126380
[46,] 0.207245332 0.129817768
[47,] 0.967984176 0.207245332
[48,] 0.001843227 0.967984176
[49,] 0.848353598 0.001843227
[50,] -0.032015824 0.848353598
[51,] -0.032015824 -0.032015824
[52,] 0.052962329 -0.032015824
[53,] 0.005320877 0.052962329
[54,] 0.102528756 0.005320877
[55,] 0.637291804 0.102528756
[56,] -0.362708196 0.637291804
[57,] -0.311367845 -0.362708196
[58,] -0.362708196 -0.311367845
[59,] -0.243077618 -0.362708196
[60,] -0.983464021 -0.243077618
[61,] -0.243077618 -0.983464021
[62,] -0.053743398 -0.243077618
[63,] 0.012635652 -0.053743398
[64,] -0.417831899 0.012635652
[65,] -0.006778392 -0.417831899
[66,] 0.263967716 -0.006778392
[67,] 0.212627364 0.263967716
[68,] 0.398395320 0.212627364
[69,] -0.744376247 0.398395320
[70,] 0.255623753 -0.744376247
[71,] -0.417831899 0.255623753
[72,] -0.998434429 -0.417831899
[73,] -0.595255891 -0.998434429
[74,] 0.937423367 -0.595255891
[75,] 0.183137586 0.937423367
[76,] 0.255623753 0.183137586
[77,] -1.787372636 0.255623753
[78,] 0.212627364 -1.787372636
[79,] -0.417831899 0.212627364
[80,] 0.121196149 -0.417831899
[81,] 0.228334741 0.121196149
[82,] 0.037198545 0.228334741
[83,] 0.255623753 0.037198545
[84,] 0.112852186 0.255623753
[85,] 0.212627364 0.112852186
[86,] 0.255623753 0.212627364
[87,] -0.417831899 0.255623753
[88,] 0.255623753 -0.417831899
[89,] -0.624745669 0.255623753
[90,] -0.417831899 -0.624745669
[91,] 1.044561959 -0.417831899
[92,] 0.001565571 1.044561959
[93,] 0.817792789 0.001565571
[94,] 0.212627364 0.817792789
[95,] -0.744376247 0.212627364
[96,] -0.787372636 -0.744376247
[97,] 0.028854583 -0.787372636
[98,] 0.028854583 0.028854583
[99,] 0.255623753 0.028854583
[100,] 1.212627364 0.255623753
[101,] 1.112852186 1.212627364
[102,] -0.701379859 1.112852186
[103,] 1.332257943 -0.701379859
[104,] 0.466685547 1.332257943
[105,] 0.087558347 0.466685547
[106,] 0.285906906 0.087558347
[107,] -0.571321527 0.285906906
[108,] 0.554762114 -0.571321527
[109,] 0.952741890 0.554762114
[110,] -0.152312912 0.952741890
[111,] 1.404866813 -0.152312912
[112,] -3.007008430 1.404866813
[113,] -0.371718668 -3.007008430
[114,] 1.106231518 -0.371718668
[115,] -0.967076381 1.106231518
[116,] 1.916195566 -0.967076381
[117,] 0.719709549 1.916195566
[118,] -0.739178610 0.719709549
[119,] 1.429867344 -0.739178610
[120,] -1.280290451 1.429867344
[121,] -0.965947780 -1.280290451
[122,] -1.061865244 -0.965947780
[123,] 1.099968348 -1.061865244
[124,] -0.245638025 1.099968348
[125,] -1.076441020 -0.245638025
[126,] -0.083804434 -1.076441020
[127,] 1.880562591 -0.083804434
[128,] 0.099968348 1.880562591
[129,] -0.900031652 0.099968348
[130,] 0.099968348 -0.900031652
[131,] -0.409279613 0.099968348
[132,] 0.099968348 -0.409279613
[133,] -0.194297674 0.099968348
[134,] -0.730834647 -0.194297674
[135,] 1.429867344 -0.730834647
[136,] 1.445574721 1.429867344
[137,] 2.583356973 1.445574721
[138,] 0.232400778 2.583356973
[139,] -0.562769242 0.232400778
[140,] 0.939115305 -0.562769242
[141,] -1.260213802 0.939115305
[142,] -1.260213802 -1.260213802
[143,] -1.083804434 -1.260213802
[144,] -1.260213802 -1.083804434
[145,] 1.202117625 -1.260213802
[146,] 0.122888087 1.202117625
[147,] -0.111093446 0.122888087
[148,] -1.040808046 -0.111093446
[149,] -1.413703431 -1.040808046
[150,] 0.099968348 -1.413703431
[151,] -0.900031652 0.099968348
[152,] -0.053521281 -0.900031652
[153,] -0.237294063 -0.053521281
[154,] 0.217825002 -0.237294063
[155,] 1.276377716 0.217825002
[156,] -1.083804434 1.276377716
[157,] 1.696789809 -1.083804434
[158,] -1.202641637 1.696789809
[159,] -1.413703431 -1.202641637
[160,] 1.240965990 -1.413703431
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.545743613 -0.609775261
2 0.271387536 0.545743613
3 0.087614754 0.271387536
4 0.848353598 0.087614754
5 0.562431539 0.848353598
6 -1.200336551 0.562431539
7 0.967984176 -1.200336551
8 -0.032015824 0.967984176
9 -0.215788606 -0.032015824
10 1.066468939 -0.215788606
11 -0.609775261 1.066468939
12 0.967984176 -0.609775261
13 -0.609775261 0.967984176
14 0.707577205 -0.609775261
15 -0.609775261 0.707577205
16 -0.040359787 -0.609775261
17 0.554087576 -0.040359787
18 0.206451957 0.554087576
19 -0.609775261 0.206451957
20 0.078477416 -0.609775261
21 0.129817768 0.078477416
22 -0.609775261 0.129817768
23 -0.793548043 -0.609775261
24 -0.116993976 -0.793548043
25 0.883006024 -0.116993976
26 -0.151646402 0.883006024
27 -0.151646402 -0.151646402
28 -0.159990365 -0.151646402
29 0.087614754 -0.159990365
30 -0.108650014 0.087614754
31 -0.609775261 -0.108650014
32 -0.151646402 -0.609775261
33 0.673718154 -0.151646402
34 -0.151646402 0.673718154
35 -0.032015824 -0.151646402
36 -1.032015824 -0.032015824
37 0.151756958 -1.032015824
38 -0.327075221 0.151756958
39 0.087614754 -0.327075221
40 0.834758370 0.087614754
41 -0.151646402 0.834758370
42 -0.609775261 -0.151646402
43 -0.609775261 -0.609775261
44 0.032126380 -0.609775261
45 0.129817768 0.032126380
46 0.207245332 0.129817768
47 0.967984176 0.207245332
48 0.001843227 0.967984176
49 0.848353598 0.001843227
50 -0.032015824 0.848353598
51 -0.032015824 -0.032015824
52 0.052962329 -0.032015824
53 0.005320877 0.052962329
54 0.102528756 0.005320877
55 0.637291804 0.102528756
56 -0.362708196 0.637291804
57 -0.311367845 -0.362708196
58 -0.362708196 -0.311367845
59 -0.243077618 -0.362708196
60 -0.983464021 -0.243077618
61 -0.243077618 -0.983464021
62 -0.053743398 -0.243077618
63 0.012635652 -0.053743398
64 -0.417831899 0.012635652
65 -0.006778392 -0.417831899
66 0.263967716 -0.006778392
67 0.212627364 0.263967716
68 0.398395320 0.212627364
69 -0.744376247 0.398395320
70 0.255623753 -0.744376247
71 -0.417831899 0.255623753
72 -0.998434429 -0.417831899
73 -0.595255891 -0.998434429
74 0.937423367 -0.595255891
75 0.183137586 0.937423367
76 0.255623753 0.183137586
77 -1.787372636 0.255623753
78 0.212627364 -1.787372636
79 -0.417831899 0.212627364
80 0.121196149 -0.417831899
81 0.228334741 0.121196149
82 0.037198545 0.228334741
83 0.255623753 0.037198545
84 0.112852186 0.255623753
85 0.212627364 0.112852186
86 0.255623753 0.212627364
87 -0.417831899 0.255623753
88 0.255623753 -0.417831899
89 -0.624745669 0.255623753
90 -0.417831899 -0.624745669
91 1.044561959 -0.417831899
92 0.001565571 1.044561959
93 0.817792789 0.001565571
94 0.212627364 0.817792789
95 -0.744376247 0.212627364
96 -0.787372636 -0.744376247
97 0.028854583 -0.787372636
98 0.028854583 0.028854583
99 0.255623753 0.028854583
100 1.212627364 0.255623753
101 1.112852186 1.212627364
102 -0.701379859 1.112852186
103 1.332257943 -0.701379859
104 0.466685547 1.332257943
105 0.087558347 0.466685547
106 0.285906906 0.087558347
107 -0.571321527 0.285906906
108 0.554762114 -0.571321527
109 0.952741890 0.554762114
110 -0.152312912 0.952741890
111 1.404866813 -0.152312912
112 -3.007008430 1.404866813
113 -0.371718668 -3.007008430
114 1.106231518 -0.371718668
115 -0.967076381 1.106231518
116 1.916195566 -0.967076381
117 0.719709549 1.916195566
118 -0.739178610 0.719709549
119 1.429867344 -0.739178610
120 -1.280290451 1.429867344
121 -0.965947780 -1.280290451
122 -1.061865244 -0.965947780
123 1.099968348 -1.061865244
124 -0.245638025 1.099968348
125 -1.076441020 -0.245638025
126 -0.083804434 -1.076441020
127 1.880562591 -0.083804434
128 0.099968348 1.880562591
129 -0.900031652 0.099968348
130 0.099968348 -0.900031652
131 -0.409279613 0.099968348
132 0.099968348 -0.409279613
133 -0.194297674 0.099968348
134 -0.730834647 -0.194297674
135 1.429867344 -0.730834647
136 1.445574721 1.429867344
137 2.583356973 1.445574721
138 0.232400778 2.583356973
139 -0.562769242 0.232400778
140 0.939115305 -0.562769242
141 -1.260213802 0.939115305
142 -1.260213802 -1.260213802
143 -1.083804434 -1.260213802
144 -1.260213802 -1.083804434
145 1.202117625 -1.260213802
146 0.122888087 1.202117625
147 -0.111093446 0.122888087
148 -1.040808046 -0.111093446
149 -1.413703431 -1.040808046
150 0.099968348 -1.413703431
151 -0.900031652 0.099968348
152 -0.053521281 -0.900031652
153 -0.237294063 -0.053521281
154 0.217825002 -0.237294063
155 1.276377716 0.217825002
156 -1.083804434 1.276377716
157 1.696789809 -1.083804434
158 -1.202641637 1.696789809
159 -1.413703431 -1.202641637
160 1.240965990 -1.413703431
> 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/7w43n1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8w43n1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w43n1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107v2p1290525413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sdjv1290525413.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/12vwzj1290525413.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/132fev1290525413.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/14vody1290525413.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/15ypum1290525413.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/16vyav1290525413.tab")
+ }
>
> try(system("convert tmp/10cnw1290525413.ps tmp/10cnw1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/20cnw1290525413.ps tmp/20cnw1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tl4h1290525413.ps tmp/3tl4h1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tl4h1290525413.ps tmp/4tl4h1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tl4h1290525413.ps tmp/5tl4h1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/63cm21290525413.ps tmp/63cm21290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w43n1290525413.ps tmp/7w43n1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/8w43n1290525413.ps tmp/8w43n1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w43n1290525413.ps tmp/9w43n1290525413.png",intern=TRUE))
character(0)
> try(system("convert tmp/107v2p1290525413.ps tmp/107v2p1290525413.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.163 1.779 9.817