R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(11
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+ ,6)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Maand'
+ ,'Schoolprestaties'
+ ,'Sport'
+ ,'GoingOut'
+ ,'Relation'
+ ,'Family'
+ ,'Friends'
+ ,'Coach'
+ ,'Job')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Maand','Schoolprestaties','Sport','GoingOut','Relation','Family','Friends','Coach','Job'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Maand Schoolprestaties Sport GoingOut Relation Family Friends Coach Job
1 11 14 3 2 3 3 3 7 6
2 11 8 5 6 0 7 7 2 7
3 11 12 6 6 0 6 8 3 8
4 11 7 6 6 6 6 9 8 8
5 11 10 7 8 5 5 5 7 9
6 11 9 3 1 0 7 7 7 8
7 11 16 8 9 8 8 8 9 8
8 11 7 4 4 0 2 3 2 7
9 11 14 7 7 0 4 8 4 7
10 11 6 4 4 9 9 4 4 4
11 11 16 6 6 6 6 6 6 6
12 11 11 6 5 6 6 4 4 7
13 11 17 7 7 5 5 8 9 5
14 11 12 4 5 4 4 8 8 8
15 11 7 6 6 0 2 2 7 5
16 11 13 5 5 0 4 9 4 4
17 11 9 0 2 2 2 2 2 9
18 11 15 9 9 6 6 8 8 8
19 11 7 4 4 0 4 8 4 4
20 11 9 4 4 4 4 4 4 6
21 11 7 2 5 5 5 5 2 6
22 11 14 7 7 7 7 7 9 7
23 11 15 5 5 5 5 3 3 3
24 11 7 9 9 4 4 4 4 4
25 11 13 6 6 6 6 6 6 6
26 11 17 6 6 6 6 6 6 6
27 11 15 7 3 0 7 9 7 7
28 11 14 3 3 1 2 2 2 5
29 11 14 6 5 0 6 6 6 8
30 11 8 6 5 4 4 4 4 6
31 11 8 4 4 4 4 8 2 4
32 11 12 7 7 7 7 3 9 9
33 11 14 7 6 7 7 7 7 7
34 11 8 7 7 0 4 4 4 4
35 11 11 4 4 4 4 4 4 6
36 11 16 5 5 5 5 8 7 8
37 11 11 6 6 0 6 6 6 6
38 11 8 5 5 5 5 5 5 5
39 11 14 6 0 1 6 6 6 6
40 11 16 6 6 2 2 9 2 6
41 11 14 6 5 0 6 4 2 4
42 11 5 3 3 9 9 7 7 7
43 11 8 3 3 3 3 3 3 9
44 11 10 3 3 0 4 4 4 8
45 11 8 6 7 6 6 6 6 6
46 11 13 7 7 1 5 8 5 6
47 11 15 5 1 5 5 5 7 5
48 11 6 5 5 0 4 4 4 7
49 11 12 5 5 0 2 2 2 5
50 11 14 6 6 0 6 9 6 8
51 11 5 6 2 6 6 6 9 6
52 11 15 6 6 7 7 8 8 8
53 11 11 5 5 0 5 5 5 5
54 11 8 4 2 4 4 4 4 4
55 11 13 7 7 5 5 5 2 5
56 11 14 5 5 1 5 9 9 6
57 12 12 3 3 4 4 4 4 4
58 12 16 6 6 9 9 8 6 6
59 12 10 2 2 2 2 2 2 9
60 12 15 8 8 8 8 8 8 7
61 12 8 3 5 3 3 3 3 3
62 12 16 0 2 1 6 3 3 6
63 12 19 6 6 0 6 6 7 6
64 12 14 8 2 6 6 6 2 6
65 12 7 4 1 0 5 5 9 5
66 12 13 5 5 0 5 5 5 5
67 12 15 6 6 6 6 4 4 5
68 12 7 5 2 2 2 9 2 9
69 12 13 6 6 1 6 6 6 8
70 12 4 2 2 5 5 5 5 5
71 12 14 6 6 5 5 5 5 6
72 12 13 5 5 5 5 3 9 7
73 12 11 5 0 5 5 8 2 5
74 12 14 6 2 6 6 9 6 6
75 12 12 4 4 6 6 6 6 6
76 12 15 6 1 0 9 6 6 6
77 12 14 5 5 0 5 5 5 6
78 12 13 5 5 1 5 3 3 9
79 12 7 4 2 7 7 4 2 7
80 12 5 2 2 2 2 9 2 9
81 12 7 7 7 4 4 4 4 4
82 12 13 5 5 0 6 8 8 8
83 12 13 6 2 5 5 5 5 5
84 12 11 5 5 5 5 5 9 8
85 12 6 3 3 3 3 8 2 9
86 12 12 6 6 0 6 6 6 6
87 12 8 4 1 4 4 9 4 4
88 12 11 5 5 9 9 5 5 7
89 12 12 7 7 0 8 8 8 8
90 12 9 4 2 4 4 3 3 9
91 12 12 6 6 2 2 2 2 9
92 12 13 8 8 7 7 7 7 7
93 12 16 7 7 7 7 7 7 8
94 12 16 6 6 6 6 4 9 4
95 12 11 7 7 0 5 5 5 6
96 12 8 4 4 5 5 9 5 7
97 12 4 0 5 6 6 6 2 6
98 12 7 3 2 0 3 3 3 7
99 12 14 5 5 5 5 5 5 5
100 12 11 6 2 9 9 2 2 9
101 12 17 5 5 0 7 7 7 7
102 12 15 7 7 7 7 7 7 7
103 12 14 6 5 1 6 6 6 6
104 12 5 8 8 3 3 8 3 6
105 12 4 7 2 7 7 9 3 9
106 12 19 8 8 8 8 8 2 9
107 12 11 3 3 0 3 3 3 8
108 12 15 8 2 5 5 5 5 8
109 12 10 3 3 3 3 3 3 3
110 12 9 4 5 0 4 4 4 6
111 12 12 2 2 5 5 5 5 5
112 12 15 7 2 7 7 9 7 7
113 12 7 6 6 0 6 6 6 6
114 12 13 2 2 0 7 7 7 7
115 12 14 7 7 0 9 7 2 7
116 12 14 6 6 6 6 6 6 6
117 12 14 6 2 0 6 3 9 8
118 12 8 6 2 6 6 9 4 9
119 12 15 6 5 6 6 6 6 6
120 12 15 6 6 2 2 2 2 9
121 12 9 4 4 5 5 5 2 5
122 12 16 5 5 0 5 5 5 6
123 12 9 7 7 4 4 9 4 4
124 12 15 6 6 0 7 7 7 7
125 12 15 6 6 6 6 6 6 6
126 12 6 5 5 5 5 8 7 8
127 12 8 8 2 8 8 8 8 8
128 12 15 6 6 6 6 6 6 9
129 12 10 0 3 5 5 3 3 8
130 12 9 4 2 0 4 4 4 4
131 12 14 8 8 8 8 9 8 6
132 12 12 6 6 0 6 6 9 6
133 12 8 4 4 9 9 4 2 7
134 12 11 6 6 5 5 5 5 9
135 12 13 2 5 0 6 6 6 8
136 12 9 4 4 0 4 4 4 4
137 12 15 6 2 0 6 6 6 6
138 12 13 3 3 3 3 3 3 9
139 12 15 6 6 6 6 6 6 6
140 12 14 5 5 0 5 5 5 5
141 12 16 4 4 4 4 9 8 8
142 12 12 6 6 6 6 6 6 6
143 12 14 1 1 0 5 9 5 6
144 12 10 4 5 4 4 3 3 6
145 12 10 4 2 7 7 7 2 7
146 12 4 6 6 0 6 6 6 7
147 12 8 5 5 5 5 5 5 9
148 12 17 9 2 6 6 6 6 6
149 12 16 6 6 6 6 9 6 6
150 12 12 8 8 8 8 8 9 6
151 12 12 7 7 2 2 4 4 4
152 12 15 7 7 7 7 7 7 7
153 12 9 0 9 0 4 4 4 8
154 12 13 6 2 0 6 8 7 7
155 12 14 6 6 5 5 5 5 9
156 12 11 5 5 0 2 9 2 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Schoolprestaties Sport GoingOut
11.375227 0.014448 -0.011397 -0.029816
Relation Family Friends Coach
0.003637 0.037845 0.002299 -0.024865
Job
0.030111
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7891 -0.5513 0.2470 0.3619 0.6333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.375227 0.230388 49.374 <2e-16 ***
Schoolprestaties 0.014448 0.012219 1.182 0.239
Sport -0.011397 0.026820 -0.425 0.672
GoingOut -0.029816 0.021361 -1.396 0.165
Relation 0.003637 0.015045 0.242 0.809
Family 0.037845 0.028378 1.334 0.184
Friends 0.002299 0.020582 0.112 0.911
Coach -0.024865 0.021037 -1.182 0.239
Job 0.030111 0.024848 1.212 0.228
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4794 on 147 degrees of freedom
Multiple R-squared: 0.05876, Adjusted R-squared: 0.007532
F-statistic: 1.147 on 8 and 147 DF, p-value: 0.3355
> 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,] 1.489287e-44 2.978574e-44 1.000000e+00
[2,] 1.051792e-62 2.103584e-62 1.000000e+00
[3,] 4.788383e-72 9.576765e-72 1.000000e+00
[4,] 2.024158e-86 4.048315e-86 1.000000e+00
[5,] 0.000000e+00 0.000000e+00 1.000000e+00
[6,] 4.267250e-116 8.534500e-116 1.000000e+00
[7,] 8.626652e-144 1.725330e-143 1.000000e+00
[8,] 6.326651e-147 1.265330e-146 1.000000e+00
[9,] 8.347018e-167 1.669404e-166 1.000000e+00
[10,] 8.652543e-183 1.730509e-182 1.000000e+00
[11,] 3.678746e-187 7.357493e-187 1.000000e+00
[12,] 5.306043e-207 1.061209e-206 1.000000e+00
[13,] 1.106728e-230 2.213456e-230 1.000000e+00
[14,] 3.149216e-227 6.298431e-227 1.000000e+00
[15,] 2.446155e-246 4.892310e-246 1.000000e+00
[16,] 2.579341e-263 5.158681e-263 1.000000e+00
[17,] 5.253948e-271 1.050790e-270 1.000000e+00
[18,] 6.738310e-297 1.347662e-296 1.000000e+00
[19,] 5.095550e-300 1.019110e-299 1.000000e+00
[20,] 4.810865e-319 9.621731e-319 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 1.000000e+00 8.186630e-16 4.093315e-16
[46,] 1.000000e+00 0.000000e+00 0.000000e+00
[47,] 1.000000e+00 0.000000e+00 0.000000e+00
[48,] 1.000000e+00 0.000000e+00 0.000000e+00
[49,] 1.000000e+00 0.000000e+00 0.000000e+00
[50,] 1.000000e+00 0.000000e+00 0.000000e+00
[51,] 1.000000e+00 0.000000e+00 0.000000e+00
[52,] 1.000000e+00 0.000000e+00 0.000000e+00
[53,] 1.000000e+00 0.000000e+00 0.000000e+00
[54,] 1.000000e+00 0.000000e+00 0.000000e+00
[55,] 1.000000e+00 0.000000e+00 0.000000e+00
[56,] 1.000000e+00 0.000000e+00 0.000000e+00
[57,] 1.000000e+00 0.000000e+00 0.000000e+00
[58,] 1.000000e+00 0.000000e+00 0.000000e+00
[59,] 1.000000e+00 0.000000e+00 0.000000e+00
[60,] 1.000000e+00 0.000000e+00 0.000000e+00
[61,] 1.000000e+00 0.000000e+00 0.000000e+00
[62,] 1.000000e+00 0.000000e+00 0.000000e+00
[63,] 1.000000e+00 0.000000e+00 0.000000e+00
[64,] 1.000000e+00 0.000000e+00 0.000000e+00
[65,] 1.000000e+00 0.000000e+00 0.000000e+00
[66,] 1.000000e+00 0.000000e+00 0.000000e+00
[67,] 1.000000e+00 0.000000e+00 0.000000e+00
[68,] 1.000000e+00 0.000000e+00 0.000000e+00
[69,] 1.000000e+00 0.000000e+00 0.000000e+00
[70,] 1.000000e+00 0.000000e+00 0.000000e+00
[71,] 1.000000e+00 0.000000e+00 0.000000e+00
[72,] 1.000000e+00 0.000000e+00 0.000000e+00
[73,] 1.000000e+00 0.000000e+00 0.000000e+00
[74,] 1.000000e+00 0.000000e+00 0.000000e+00
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
[76,] 1.000000e+00 0.000000e+00 0.000000e+00
[77,] 1.000000e+00 0.000000e+00 0.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 4.835618e-315 2.417809e-315
[115,] 1.000000e+00 1.941219e-303 9.706095e-304
[116,] 1.000000e+00 1.786334e-286 8.931671e-287
[117,] 1.000000e+00 1.159753e-273 5.798767e-274
[118,] 1.000000e+00 1.621025e-263 8.105123e-264
[119,] 1.000000e+00 4.856014e-274 2.428007e-274
[120,] 1.000000e+00 3.183157e-227 1.591579e-227
[121,] 1.000000e+00 4.115114e-219 2.057557e-219
[122,] 1.000000e+00 7.588191e-204 3.794095e-204
[123,] 1.000000e+00 9.505722e-200 4.752861e-200
[124,] 1.000000e+00 6.755431e-176 3.377715e-176
[125,] 1.000000e+00 3.203952e-166 1.601976e-166
[126,] 1.000000e+00 1.811780e-147 9.058902e-148
[127,] 1.000000e+00 1.854270e-134 9.271352e-135
[128,] 1.000000e+00 1.137125e-116 5.685626e-117
[129,] 1.000000e+00 0.000000e+00 0.000000e+00
[130,] 1.000000e+00 1.453286e-87 7.266428e-88
[131,] 1.000000e+00 2.109891e-74 1.054946e-74
[132,] 1.000000e+00 1.355348e-59 6.776739e-60
[133,] 1.000000e+00 1.382227e-46 6.911133e-47
> postscript(file="/var/wessaorg/rcomp/tmp/1wi8x1321982081.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/2xh9g1321982081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ul9f1321982081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4f4ik1321982081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5n5hv1321982081.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.621640991 -0.696990464 -0.713089115 -0.540645899 -0.517258734 -0.789101859
7 8 9 10 11 12
-0.614239141 -0.555150940 -0.570108235 -0.700584242 -0.653290387 -0.686105813
13 14 15 16 17 18
-0.484938203 -0.580235361 -0.285880331 -0.550051328 -0.754464603 -0.530295280
19 20 21 22 23 24
-0.502274127 -0.596744214 -0.654334795 -0.582479105 -0.615934957 -0.301559980
25 26 27 28 29 30
-0.609945011 -0.667738846 -0.745060677 -0.638618255 -0.692609731 -0.529686010
31 32 33 34 35 36
-0.580999977 -0.604607071 -0.662024383 -0.383886071 -0.625641132 -0.692978546
37 38 39 40 41 42
-0.559225663 -0.529887978 -0.785105151 -0.593720485 -0.667024086 -0.749987090
43 44 45 46 47 48
-0.694926052 -0.698079864 -0.507886544 -0.542165549 -0.700562777 -0.527748851
49 50 51 52 53 54
-0.523658346 -0.669691784 -0.539028378 -0.695415744 -0.555047995 -0.581705584
55 56 57 58 59 60
-0.594298008 -0.541881127 0.378919968 0.217666042 0.253880511 0.375639851
61 62 63 64 65 66
0.545373849 0.109554129 0.350051220 0.179677200 0.471542570 0.416055088
67 68 69 70 71 72
0.346139044 0.315320385 0.348017830 0.404266978 0.394522971 0.441704238
73 74 75 76 77 78
0.196193896 0.249443611 0.322077528 0.120366098 0.371495370 0.246842692
79 80 81 82 83 84
0.168235239 0.310026943 0.616014101 0.355572229 0.319817995 0.435891079
85 86 87 88 89 90
0.297609269 0.426325878 0.376981199 0.200617790 0.376757582 0.230724518
91 92 93 94 95 96
0.389835433 0.423453209 0.308783614 0.486124606 0.497266666 0.359478909
97 98 99 100 101 102
0.322436015 0.360839967 0.383421270 -0.005351892 0.267479997 0.353343332
103 104 105 106 107 108
0.363975716 0.633320857 0.198915964 0.108436183 0.302751046 0.223380874
109 110 111 112 113 114
0.456844585 0.447620246 0.288679308 0.199663647 0.498568172 0.201634952
115 116 117 118 119 120
0.193239506 0.375606530 0.299433633 0.196071480 0.331341898 0.346490057
121 122 123 124 125 126
0.339856947 0.342598453 0.575620140 0.337589874 0.361158072 0.451506041
127 128 129 130 131 132
0.267770762 0.270824294 0.189134761 0.418394244 0.417900160 0.500919536
133 134 135 136 137 138
0.130455958 0.347534570 0.276251582 0.478026591 0.263715809 0.232831654
139 140 141 142 143 144
0.361158072 0.401606629 0.329855222 0.404503448 0.197445896 0.396058357
145 146 147 148 149 150
0.117991637 0.511802288 0.349666986 0.247186820 0.339811387 0.473961038
151 152 153 154 155 156
0.626734976 0.353343332 0.461075275 0.282767202 0.304189194 0.444582992
> postscript(file="/var/wessaorg/rcomp/tmp/6r89q1321982081.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 -0.621640991 NA
1 -0.696990464 -0.621640991
2 -0.713089115 -0.696990464
3 -0.540645899 -0.713089115
4 -0.517258734 -0.540645899
5 -0.789101859 -0.517258734
6 -0.614239141 -0.789101859
7 -0.555150940 -0.614239141
8 -0.570108235 -0.555150940
9 -0.700584242 -0.570108235
10 -0.653290387 -0.700584242
11 -0.686105813 -0.653290387
12 -0.484938203 -0.686105813
13 -0.580235361 -0.484938203
14 -0.285880331 -0.580235361
15 -0.550051328 -0.285880331
16 -0.754464603 -0.550051328
17 -0.530295280 -0.754464603
18 -0.502274127 -0.530295280
19 -0.596744214 -0.502274127
20 -0.654334795 -0.596744214
21 -0.582479105 -0.654334795
22 -0.615934957 -0.582479105
23 -0.301559980 -0.615934957
24 -0.609945011 -0.301559980
25 -0.667738846 -0.609945011
26 -0.745060677 -0.667738846
27 -0.638618255 -0.745060677
28 -0.692609731 -0.638618255
29 -0.529686010 -0.692609731
30 -0.580999977 -0.529686010
31 -0.604607071 -0.580999977
32 -0.662024383 -0.604607071
33 -0.383886071 -0.662024383
34 -0.625641132 -0.383886071
35 -0.692978546 -0.625641132
36 -0.559225663 -0.692978546
37 -0.529887978 -0.559225663
38 -0.785105151 -0.529887978
39 -0.593720485 -0.785105151
40 -0.667024086 -0.593720485
41 -0.749987090 -0.667024086
42 -0.694926052 -0.749987090
43 -0.698079864 -0.694926052
44 -0.507886544 -0.698079864
45 -0.542165549 -0.507886544
46 -0.700562777 -0.542165549
47 -0.527748851 -0.700562777
48 -0.523658346 -0.527748851
49 -0.669691784 -0.523658346
50 -0.539028378 -0.669691784
51 -0.695415744 -0.539028378
52 -0.555047995 -0.695415744
53 -0.581705584 -0.555047995
54 -0.594298008 -0.581705584
55 -0.541881127 -0.594298008
56 0.378919968 -0.541881127
57 0.217666042 0.378919968
58 0.253880511 0.217666042
59 0.375639851 0.253880511
60 0.545373849 0.375639851
61 0.109554129 0.545373849
62 0.350051220 0.109554129
63 0.179677200 0.350051220
64 0.471542570 0.179677200
65 0.416055088 0.471542570
66 0.346139044 0.416055088
67 0.315320385 0.346139044
68 0.348017830 0.315320385
69 0.404266978 0.348017830
70 0.394522971 0.404266978
71 0.441704238 0.394522971
72 0.196193896 0.441704238
73 0.249443611 0.196193896
74 0.322077528 0.249443611
75 0.120366098 0.322077528
76 0.371495370 0.120366098
77 0.246842692 0.371495370
78 0.168235239 0.246842692
79 0.310026943 0.168235239
80 0.616014101 0.310026943
81 0.355572229 0.616014101
82 0.319817995 0.355572229
83 0.435891079 0.319817995
84 0.297609269 0.435891079
85 0.426325878 0.297609269
86 0.376981199 0.426325878
87 0.200617790 0.376981199
88 0.376757582 0.200617790
89 0.230724518 0.376757582
90 0.389835433 0.230724518
91 0.423453209 0.389835433
92 0.308783614 0.423453209
93 0.486124606 0.308783614
94 0.497266666 0.486124606
95 0.359478909 0.497266666
96 0.322436015 0.359478909
97 0.360839967 0.322436015
98 0.383421270 0.360839967
99 -0.005351892 0.383421270
100 0.267479997 -0.005351892
101 0.353343332 0.267479997
102 0.363975716 0.353343332
103 0.633320857 0.363975716
104 0.198915964 0.633320857
105 0.108436183 0.198915964
106 0.302751046 0.108436183
107 0.223380874 0.302751046
108 0.456844585 0.223380874
109 0.447620246 0.456844585
110 0.288679308 0.447620246
111 0.199663647 0.288679308
112 0.498568172 0.199663647
113 0.201634952 0.498568172
114 0.193239506 0.201634952
115 0.375606530 0.193239506
116 0.299433633 0.375606530
117 0.196071480 0.299433633
118 0.331341898 0.196071480
119 0.346490057 0.331341898
120 0.339856947 0.346490057
121 0.342598453 0.339856947
122 0.575620140 0.342598453
123 0.337589874 0.575620140
124 0.361158072 0.337589874
125 0.451506041 0.361158072
126 0.267770762 0.451506041
127 0.270824294 0.267770762
128 0.189134761 0.270824294
129 0.418394244 0.189134761
130 0.417900160 0.418394244
131 0.500919536 0.417900160
132 0.130455958 0.500919536
133 0.347534570 0.130455958
134 0.276251582 0.347534570
135 0.478026591 0.276251582
136 0.263715809 0.478026591
137 0.232831654 0.263715809
138 0.361158072 0.232831654
139 0.401606629 0.361158072
140 0.329855222 0.401606629
141 0.404503448 0.329855222
142 0.197445896 0.404503448
143 0.396058357 0.197445896
144 0.117991637 0.396058357
145 0.511802288 0.117991637
146 0.349666986 0.511802288
147 0.247186820 0.349666986
148 0.339811387 0.247186820
149 0.473961038 0.339811387
150 0.626734976 0.473961038
151 0.353343332 0.626734976
152 0.461075275 0.353343332
153 0.282767202 0.461075275
154 0.304189194 0.282767202
155 0.444582992 0.304189194
156 NA 0.444582992
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.696990464 -0.621640991
[2,] -0.713089115 -0.696990464
[3,] -0.540645899 -0.713089115
[4,] -0.517258734 -0.540645899
[5,] -0.789101859 -0.517258734
[6,] -0.614239141 -0.789101859
[7,] -0.555150940 -0.614239141
[8,] -0.570108235 -0.555150940
[9,] -0.700584242 -0.570108235
[10,] -0.653290387 -0.700584242
[11,] -0.686105813 -0.653290387
[12,] -0.484938203 -0.686105813
[13,] -0.580235361 -0.484938203
[14,] -0.285880331 -0.580235361
[15,] -0.550051328 -0.285880331
[16,] -0.754464603 -0.550051328
[17,] -0.530295280 -0.754464603
[18,] -0.502274127 -0.530295280
[19,] -0.596744214 -0.502274127
[20,] -0.654334795 -0.596744214
[21,] -0.582479105 -0.654334795
[22,] -0.615934957 -0.582479105
[23,] -0.301559980 -0.615934957
[24,] -0.609945011 -0.301559980
[25,] -0.667738846 -0.609945011
[26,] -0.745060677 -0.667738846
[27,] -0.638618255 -0.745060677
[28,] -0.692609731 -0.638618255
[29,] -0.529686010 -0.692609731
[30,] -0.580999977 -0.529686010
[31,] -0.604607071 -0.580999977
[32,] -0.662024383 -0.604607071
[33,] -0.383886071 -0.662024383
[34,] -0.625641132 -0.383886071
[35,] -0.692978546 -0.625641132
[36,] -0.559225663 -0.692978546
[37,] -0.529887978 -0.559225663
[38,] -0.785105151 -0.529887978
[39,] -0.593720485 -0.785105151
[40,] -0.667024086 -0.593720485
[41,] -0.749987090 -0.667024086
[42,] -0.694926052 -0.749987090
[43,] -0.698079864 -0.694926052
[44,] -0.507886544 -0.698079864
[45,] -0.542165549 -0.507886544
[46,] -0.700562777 -0.542165549
[47,] -0.527748851 -0.700562777
[48,] -0.523658346 -0.527748851
[49,] -0.669691784 -0.523658346
[50,] -0.539028378 -0.669691784
[51,] -0.695415744 -0.539028378
[52,] -0.555047995 -0.695415744
[53,] -0.581705584 -0.555047995
[54,] -0.594298008 -0.581705584
[55,] -0.541881127 -0.594298008
[56,] 0.378919968 -0.541881127
[57,] 0.217666042 0.378919968
[58,] 0.253880511 0.217666042
[59,] 0.375639851 0.253880511
[60,] 0.545373849 0.375639851
[61,] 0.109554129 0.545373849
[62,] 0.350051220 0.109554129
[63,] 0.179677200 0.350051220
[64,] 0.471542570 0.179677200
[65,] 0.416055088 0.471542570
[66,] 0.346139044 0.416055088
[67,] 0.315320385 0.346139044
[68,] 0.348017830 0.315320385
[69,] 0.404266978 0.348017830
[70,] 0.394522971 0.404266978
[71,] 0.441704238 0.394522971
[72,] 0.196193896 0.441704238
[73,] 0.249443611 0.196193896
[74,] 0.322077528 0.249443611
[75,] 0.120366098 0.322077528
[76,] 0.371495370 0.120366098
[77,] 0.246842692 0.371495370
[78,] 0.168235239 0.246842692
[79,] 0.310026943 0.168235239
[80,] 0.616014101 0.310026943
[81,] 0.355572229 0.616014101
[82,] 0.319817995 0.355572229
[83,] 0.435891079 0.319817995
[84,] 0.297609269 0.435891079
[85,] 0.426325878 0.297609269
[86,] 0.376981199 0.426325878
[87,] 0.200617790 0.376981199
[88,] 0.376757582 0.200617790
[89,] 0.230724518 0.376757582
[90,] 0.389835433 0.230724518
[91,] 0.423453209 0.389835433
[92,] 0.308783614 0.423453209
[93,] 0.486124606 0.308783614
[94,] 0.497266666 0.486124606
[95,] 0.359478909 0.497266666
[96,] 0.322436015 0.359478909
[97,] 0.360839967 0.322436015
[98,] 0.383421270 0.360839967
[99,] -0.005351892 0.383421270
[100,] 0.267479997 -0.005351892
[101,] 0.353343332 0.267479997
[102,] 0.363975716 0.353343332
[103,] 0.633320857 0.363975716
[104,] 0.198915964 0.633320857
[105,] 0.108436183 0.198915964
[106,] 0.302751046 0.108436183
[107,] 0.223380874 0.302751046
[108,] 0.456844585 0.223380874
[109,] 0.447620246 0.456844585
[110,] 0.288679308 0.447620246
[111,] 0.199663647 0.288679308
[112,] 0.498568172 0.199663647
[113,] 0.201634952 0.498568172
[114,] 0.193239506 0.201634952
[115,] 0.375606530 0.193239506
[116,] 0.299433633 0.375606530
[117,] 0.196071480 0.299433633
[118,] 0.331341898 0.196071480
[119,] 0.346490057 0.331341898
[120,] 0.339856947 0.346490057
[121,] 0.342598453 0.339856947
[122,] 0.575620140 0.342598453
[123,] 0.337589874 0.575620140
[124,] 0.361158072 0.337589874
[125,] 0.451506041 0.361158072
[126,] 0.267770762 0.451506041
[127,] 0.270824294 0.267770762
[128,] 0.189134761 0.270824294
[129,] 0.418394244 0.189134761
[130,] 0.417900160 0.418394244
[131,] 0.500919536 0.417900160
[132,] 0.130455958 0.500919536
[133,] 0.347534570 0.130455958
[134,] 0.276251582 0.347534570
[135,] 0.478026591 0.276251582
[136,] 0.263715809 0.478026591
[137,] 0.232831654 0.263715809
[138,] 0.361158072 0.232831654
[139,] 0.401606629 0.361158072
[140,] 0.329855222 0.401606629
[141,] 0.404503448 0.329855222
[142,] 0.197445896 0.404503448
[143,] 0.396058357 0.197445896
[144,] 0.117991637 0.396058357
[145,] 0.511802288 0.117991637
[146,] 0.349666986 0.511802288
[147,] 0.247186820 0.349666986
[148,] 0.339811387 0.247186820
[149,] 0.473961038 0.339811387
[150,] 0.626734976 0.473961038
[151,] 0.353343332 0.626734976
[152,] 0.461075275 0.353343332
[153,] 0.282767202 0.461075275
[154,] 0.304189194 0.282767202
[155,] 0.444582992 0.304189194
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.696990464 -0.621640991
2 -0.713089115 -0.696990464
3 -0.540645899 -0.713089115
4 -0.517258734 -0.540645899
5 -0.789101859 -0.517258734
6 -0.614239141 -0.789101859
7 -0.555150940 -0.614239141
8 -0.570108235 -0.555150940
9 -0.700584242 -0.570108235
10 -0.653290387 -0.700584242
11 -0.686105813 -0.653290387
12 -0.484938203 -0.686105813
13 -0.580235361 -0.484938203
14 -0.285880331 -0.580235361
15 -0.550051328 -0.285880331
16 -0.754464603 -0.550051328
17 -0.530295280 -0.754464603
18 -0.502274127 -0.530295280
19 -0.596744214 -0.502274127
20 -0.654334795 -0.596744214
21 -0.582479105 -0.654334795
22 -0.615934957 -0.582479105
23 -0.301559980 -0.615934957
24 -0.609945011 -0.301559980
25 -0.667738846 -0.609945011
26 -0.745060677 -0.667738846
27 -0.638618255 -0.745060677
28 -0.692609731 -0.638618255
29 -0.529686010 -0.692609731
30 -0.580999977 -0.529686010
31 -0.604607071 -0.580999977
32 -0.662024383 -0.604607071
33 -0.383886071 -0.662024383
34 -0.625641132 -0.383886071
35 -0.692978546 -0.625641132
36 -0.559225663 -0.692978546
37 -0.529887978 -0.559225663
38 -0.785105151 -0.529887978
39 -0.593720485 -0.785105151
40 -0.667024086 -0.593720485
41 -0.749987090 -0.667024086
42 -0.694926052 -0.749987090
43 -0.698079864 -0.694926052
44 -0.507886544 -0.698079864
45 -0.542165549 -0.507886544
46 -0.700562777 -0.542165549
47 -0.527748851 -0.700562777
48 -0.523658346 -0.527748851
49 -0.669691784 -0.523658346
50 -0.539028378 -0.669691784
51 -0.695415744 -0.539028378
52 -0.555047995 -0.695415744
53 -0.581705584 -0.555047995
54 -0.594298008 -0.581705584
55 -0.541881127 -0.594298008
56 0.378919968 -0.541881127
57 0.217666042 0.378919968
58 0.253880511 0.217666042
59 0.375639851 0.253880511
60 0.545373849 0.375639851
61 0.109554129 0.545373849
62 0.350051220 0.109554129
63 0.179677200 0.350051220
64 0.471542570 0.179677200
65 0.416055088 0.471542570
66 0.346139044 0.416055088
67 0.315320385 0.346139044
68 0.348017830 0.315320385
69 0.404266978 0.348017830
70 0.394522971 0.404266978
71 0.441704238 0.394522971
72 0.196193896 0.441704238
73 0.249443611 0.196193896
74 0.322077528 0.249443611
75 0.120366098 0.322077528
76 0.371495370 0.120366098
77 0.246842692 0.371495370
78 0.168235239 0.246842692
79 0.310026943 0.168235239
80 0.616014101 0.310026943
81 0.355572229 0.616014101
82 0.319817995 0.355572229
83 0.435891079 0.319817995
84 0.297609269 0.435891079
85 0.426325878 0.297609269
86 0.376981199 0.426325878
87 0.200617790 0.376981199
88 0.376757582 0.200617790
89 0.230724518 0.376757582
90 0.389835433 0.230724518
91 0.423453209 0.389835433
92 0.308783614 0.423453209
93 0.486124606 0.308783614
94 0.497266666 0.486124606
95 0.359478909 0.497266666
96 0.322436015 0.359478909
97 0.360839967 0.322436015
98 0.383421270 0.360839967
99 -0.005351892 0.383421270
100 0.267479997 -0.005351892
101 0.353343332 0.267479997
102 0.363975716 0.353343332
103 0.633320857 0.363975716
104 0.198915964 0.633320857
105 0.108436183 0.198915964
106 0.302751046 0.108436183
107 0.223380874 0.302751046
108 0.456844585 0.223380874
109 0.447620246 0.456844585
110 0.288679308 0.447620246
111 0.199663647 0.288679308
112 0.498568172 0.199663647
113 0.201634952 0.498568172
114 0.193239506 0.201634952
115 0.375606530 0.193239506
116 0.299433633 0.375606530
117 0.196071480 0.299433633
118 0.331341898 0.196071480
119 0.346490057 0.331341898
120 0.339856947 0.346490057
121 0.342598453 0.339856947
122 0.575620140 0.342598453
123 0.337589874 0.575620140
124 0.361158072 0.337589874
125 0.451506041 0.361158072
126 0.267770762 0.451506041
127 0.270824294 0.267770762
128 0.189134761 0.270824294
129 0.418394244 0.189134761
130 0.417900160 0.418394244
131 0.500919536 0.417900160
132 0.130455958 0.500919536
133 0.347534570 0.130455958
134 0.276251582 0.347534570
135 0.478026591 0.276251582
136 0.263715809 0.478026591
137 0.232831654 0.263715809
138 0.361158072 0.232831654
139 0.401606629 0.361158072
140 0.329855222 0.401606629
141 0.404503448 0.329855222
142 0.197445896 0.404503448
143 0.396058357 0.197445896
144 0.117991637 0.396058357
145 0.511802288 0.117991637
146 0.349666986 0.511802288
147 0.247186820 0.349666986
148 0.339811387 0.247186820
149 0.473961038 0.339811387
150 0.626734976 0.473961038
151 0.353343332 0.626734976
152 0.461075275 0.353343332
153 0.282767202 0.461075275
154 0.304189194 0.282767202
155 0.444582992 0.304189194
> 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/7sunz1321982081.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/8jfhg1321982081.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/9yqdv1321982081.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/107nc11321982081.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/118lhn1321982081.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12p08d1321982081.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/135p8w1321982081.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14yhnq1321982081.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/15bapv1321982081.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/16q7bo1321982081.tab")
+ }
>
> try(system("convert tmp/1wi8x1321982081.ps tmp/1wi8x1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xh9g1321982081.ps tmp/2xh9g1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ul9f1321982081.ps tmp/3ul9f1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f4ik1321982081.ps tmp/4f4ik1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n5hv1321982081.ps tmp/5n5hv1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r89q1321982081.ps tmp/6r89q1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sunz1321982081.ps tmp/7sunz1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jfhg1321982081.ps tmp/8jfhg1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yqdv1321982081.ps tmp/9yqdv1321982081.png",intern=TRUE))
character(0)
> try(system("convert tmp/107nc11321982081.ps tmp/107nc11321982081.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.308 0.510 6.938