R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(23
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+ ,dim=c(10
+ ,148)
+ ,dimnames=list(c('I/ToKnow'
+ ,'I/Accomp.'
+ ,'I/Exp.Stimulation'
+ ,'E/Identified'
+ ,'E/Introjected'
+ ,'E/Ext.Regulation'
+ ,'Amotivation'
+ ,'gender'
+ ,'PE'
+ ,'PS')
+ ,1:148))
> y <- array(NA,dim=c(10,148),dimnames=list(c('I/ToKnow','I/Accomp.','I/Exp.Stimulation','E/Identified','E/Introjected','E/Ext.Regulation','Amotivation','gender','PE','PS'),1:148))
> 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 = '9'
> #'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
PE I/ToKnow I/Accomp. I/Exp.Stimulation E/Identified E/Introjected
1 6 23 13 14 22 11
2 5 20 12 7 20 22
3 20 26 26 22 25 23
4 12 19 16 12 23 21
5 11 17 18 15 20 19
6 12 17 12 9 22 12
7 11 21 18 20 18 24
8 9 18 20 10 22 21
9 13 16 18 12 23 21
10 9 26 24 23 28 26
11 14 20 17 10 19 18
12 12 14 19 11 26 21
13 18 22 12 20 27 22
14 9 23 25 11 23 26
15 15 25 23 22 27 20
16 12 24 22 19 23 20
17 12 24 23 20 23 26
18 12 16 16 16 19 27
19 15 16 16 12 21 27
20 11 20 15 14 25 16
21 13 20 24 14 22 26
22 10 15 18 9 13 20
23 17 22 23 19 12 25
24 13 20 18 17 20 16
25 17 20 19 14 24 20
26 15 24 17 19 23 20
27 13 27 22 20 25 24
28 17 25 22 20 28 24
29 21 13 8 9 24 22
30 12 15 12 10 18 18
31 12 19 22 6 19 21
32 15 20 16 15 24 17
33 8 11 12 9 22 15
34 15 28 28 24 28 28
35 16 21 15 11 24 23
36 9 25 17 4 28 19
37 13 22 16 12 21 15
38 11 24 24 22 25 26
39 9 21 27 16 23 20
40 15 15 10 14 17 11
41 9 22 20 13 27 17
42 15 18 17 13 18 16
43 14 23 20 10 23 21
44 8 20 16 12 18 18
45 11 23 16 13 28 17
46 14 24 22 16 28 21
47 14 19 19 18 22 18
48 12 16 11 10 23 16
49 15 18 11 12 22 13
50 11 28 28 9 28 28
51 11 18 12 7 23 25
52 9 21 22 16 26 24
53 8 15 15 12 20 15
54 13 18 19 15 20 21
55 12 24 12 15 28 11
56 24 23 18 8 28 27
57 11 20 21 14 22 23
58 11 20 21 13 21 21
59 16 24 15 18 21 16
60 12 17 12 11 19 20
61 18 26 25 12 21 21
62 12 18 12 12 21 10
63 14 26 25 24 28 18
64 16 21 17 11 23 20
65 24 20 26 5 27 21
66 13 25 24 17 23 24
67 11 9 18 9 23 26
68 14 23 20 20 23 23
69 16 20 17 17 26 22
70 12 19 11 14 23 13
71 21 26 27 23 27 27
72 11 13 14 10 20 24
73 6 21 22 19 28 19
74 9 14 19 5 19 17
75 14 26 19 16 24 16
76 16 23 18 19 26 20
77 18 19 9 5 20 8
78 9 25 22 15 25 16
79 13 21 17 18 25 17
80 17 24 23 20 27 23
81 11 20 16 17 22 18
82 16 22 23 19 25 24
83 11 20 13 11 26 17
84 11 23 21 12 21 20
85 11 21 17 13 23 22
86 20 16 15 7 24 22
87 10 20 16 8 24 20
88 12 16 19 15 20 18
89 11 25 19 13 22 21
90 14 18 16 18 25 23
91 12 25 23 19 27 28
92 12 21 19 12 22 19
93 12 18 17 12 20 22
94 10 21 20 17 24 17
95 12 22 25 17 25 25
96 10 22 22 11 28 22
97 10 19 18 11 20 21
98 13 18 16 17 22 15
99 12 24 18 5 17 20
100 13 23 15 8 20 25
101 9 22 19 17 23 21
102 14 19 23 18 22 24
103 14 17 20 17 22 23
104 12 22 24 17 23 22
105 18 24 17 10 25 14
106 17 24 20 8 28 11
107 12 20 11 9 24 22
108 15 19 20 13 25 22
109 8 19 8 14 25 6
110 8 20 22 5 21 15
111 12 22 20 16 25 26
112 10 25 23 22 23 26
113 18 21 11 15 20 20
114 15 21 22 14 26 26
115 16 18 10 8 21 15
116 11 17 19 10 24 25
117 10 25 26 18 24 22
118 7 23 22 18 25 20
119 17 15 12 9 20 18
120 7 22 13 15 25 23
121 14 20 19 9 11 22
122 12 23 19 15 24 23
123 15 26 21 21 23 17
124 13 16 11 9 24 20
125 10 22 21 16 24 21
126 16 22 25 15 26 23
127 11 25 27 10 27 25
128 7 14 21 4 21 25
129 15 18 14 12 20 21
130 18 16 16 14 18 22
131 11 22 16 14 23 18
132 13 17 19 18 20 18
133 11 27 24 19 24 18
134 13 21 18 16 20 21
135 12 15 16 7 21 21
136 11 24 20 12 28 25
137 11 22 19 18 24 24
138 13 16 20 13 25 24
139 8 25 27 21 23 28
140 12 24 24 24 24 24
141 9 23 23 17 22 22
142 14 20 20 12 25 22
143 18 18 20 12 20 20
144 15 22 20 10 24 25
145 9 18 15 14 19 13
146 11 20 17 14 25 21
147 17 22 16 13 25 23
148 12 23 20 17 26 18
E/Ext.Regulation Amotivation gender PS
1 23 8 1 15
2 24 4 2 23
3 24 7 2 26
4 21 4 2 19
5 21 4 2 19
6 19 5 2 16
7 12 15 1 23
8 21 5 1 22
9 25 7 2 19
10 27 4 2 24
11 21 4 1 19
12 27 7 1 25
13 20 8 1 23
14 16 4 2 31
15 26 8 1 29
16 24 4 2 18
17 25 5 2 17
18 25 16 1 22
19 27 7 1 21
20 23 4 2 24
21 22 6 1 22
22 10 4 1 16
23 25 5 2 22
24 18 4 1 21
25 21 4 1 25
26 20 6 1 22
27 18 4 1 24
28 25 4 1 25
29 28 4 1 29
30 27 8 1 19
31 20 5 2 29
32 20 4 1 25
33 20 10 2 19
34 27 4 2 27
35 23 4 1 25
36 23 4 2 23
37 22 5 2 24
38 26 5 1 25
39 21 4 1 23
40 17 6 1 22
41 27 4 2 32
42 16 4 2 22
43 26 4 1 18
44 17 4 1 19
45 24 4 2 23
46 23 4 2 24
47 20 6 1 19
48 10 4 1 16
49 21 5 1 23
50 25 4 1 17
51 28 4 1 17
52 25 5 2 28
53 20 10 2 24
54 20 10 1 21
55 27 4 1 14
56 26 4 1 21
57 19 4 2 20
58 26 8 1 25
59 20 4 2 20
60 22 14 1 17
61 19 4 2 26
62 23 5 2 17
63 28 4 2 17
64 22 8 2 24
65 27 4 2 30
66 14 4 1 25
67 25 5 1 15
68 22 8 1 25
69 24 7 1 18
70 23 4 1 20
71 25 4 1 32
72 28 9 2 14
73 28 4 1 20
74 16 4 2 25
75 25 5 1 25
76 21 4 1 25
77 27 4 1 35
78 21 6 2 29
79 22 6 1 25
80 26 4 2 21
81 21 6 1 21
82 24 4 1 24
83 24 6 1 26
84 23 4 1 24
85 26 8 2 20
86 21 5 1 24
87 24 8 1 18
88 23 7 1 17
89 21 4 2 22
90 20 6 1 22
91 22 4 1 22
92 26 5 1 24
93 23 6 1 32
94 23 4 2 19
95 22 4 2 21
96 25 4 2 23
97 21 8 2 18
98 21 9 1 19
99 25 4 1 22
100 26 12 2 27
101 21 4 1 21
102 24 8 1 20
103 21 8 2 21
104 23 4 1 20
105 24 4 1 29
106 24 4 1 30
107 24 15 1 10
108 25 3 1 23
109 28 8 1 29
110 18 4 2 19
111 28 5 1 26
112 22 4 1 22
113 28 3 1 26
114 22 11 1 27
115 24 6 1 19
116 27 4 2 24
117 21 5 2 26
118 26 4 2 22
119 24 16 1 23
120 25 8 1 25
121 20 4 2 19
122 21 4 1 20
123 23 4 1 25
124 23 5 1 14
125 19 8 2 19
126 22 4 1 27
127 15 4 2 21
128 24 4 2 21
129 18 8 2 14
130 18 8 1 21
131 23 4 1 23
132 17 18 1 18
133 19 4 2 20
134 21 5 2 19
135 12 4 2 15
136 25 4 2 23
137 25 4 1 26
138 24 7 1 21
139 24 4 2 13
140 24 6 2 24
141 22 4 2 17
142 22 4 1 21
143 21 6 1 28
144 23 5 1 22
145 21 4 1 18
146 24 8 1 27
147 22 6 1 25
148 25 5 2 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `I/ToKnow` `I/Accomp.`
8.539e+00 3.280e-02 -1.194e-01
`I/Exp.Stimulation` `E/Identified` `E/Introjected`
2.353e-02 -2.774e-03 1.304e-01
`E/Ext.Regulation` Amotivation gender
-1.115e-05 -5.609e-02 -1.309e+00
PS
2.241e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.05323 -2.03265 -0.05437 1.75130 11.24649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.539e+00 2.978e+00 2.868 0.00478 **
`I/ToKnow` 3.280e-02 1.057e-01 0.310 0.75682
`I/Accomp.` -1.194e-01 9.355e-02 -1.276 0.20407
`I/Exp.Stimulation` 2.353e-02 7.128e-02 0.330 0.74179
`E/Identified` -2.774e-03 1.022e-01 -0.027 0.97838
`E/Introjected` 1.304e-01 7.839e-02 1.664 0.09840 .
`E/Ext.Regulation` -1.115e-05 8.352e-02 -0.000134 0.99989
Amotivation -5.609e-02 1.094e-01 -0.512 0.60913
gender -1.309e+00 5.821e-01 -2.248 0.02616 *
PS 2.241e-01 6.679e-02 3.355 0.00102 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.225 on 138 degrees of freedom
Multiple R-squared: 0.1475, Adjusted R-squared: 0.09192
F-statistic: 2.653 on 9 and 138 DF, p-value: 0.007232
> 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,] 0.9914485 0.01710308 0.008551542
[2,] 0.9915532 0.01689367 0.008446834
[3,] 0.9822003 0.03559941 0.017799707
[4,] 0.9660598 0.06788035 0.033940173
[5,] 0.9435817 0.11283660 0.056418300
[6,] 0.9161231 0.16775376 0.083876881
[7,] 0.9111971 0.17760589 0.088802947
[8,] 0.8748540 0.25029209 0.125146044
[9,] 0.8244685 0.35106307 0.175531533
[10,] 0.7756166 0.44876673 0.224383364
[11,] 0.7696187 0.46076254 0.230381272
[12,] 0.7184267 0.56314663 0.281573316
[13,] 0.7298005 0.54039890 0.270199452
[14,] 0.6738076 0.65238470 0.326192350
[15,] 0.6069881 0.78602384 0.393011920
[16,] 0.5645196 0.87096078 0.435480390
[17,] 0.5800928 0.83981434 0.419907172
[18,] 0.5141551 0.97168987 0.485844936
[19,] 0.4925874 0.98517472 0.507412640
[20,] 0.4278619 0.85572372 0.572138138
[21,] 0.3929369 0.78587379 0.607063104
[22,] 0.3381403 0.67628064 0.661859681
[23,] 0.3172569 0.63451380 0.682743101
[24,] 0.3408565 0.68171290 0.659143548
[25,] 0.2945373 0.58907453 0.705462736
[26,] 0.3770306 0.75406114 0.622969432
[27,] 0.3779722 0.75594436 0.622027820
[28,] 0.3284549 0.65690978 0.671545108
[29,] 0.3952065 0.79041302 0.604793489
[30,] 0.3859620 0.77192406 0.614037968
[31,] 0.3967084 0.79341677 0.603291616
[32,] 0.4513025 0.90260500 0.548697500
[33,] 0.3984565 0.79691308 0.601543458
[34,] 0.3711279 0.74225581 0.628872093
[35,] 0.3281352 0.65627042 0.671864792
[36,] 0.2820533 0.56410656 0.717946719
[37,] 0.2443608 0.48872156 0.755639222
[38,] 0.2405801 0.48116025 0.759419876
[39,] 0.2187091 0.43741828 0.781290862
[40,] 0.2536888 0.50737767 0.746311164
[41,] 0.2441427 0.48828539 0.755857304
[42,] 0.2081143 0.41622861 0.791885693
[43,] 0.1730589 0.34611779 0.826941107
[44,] 0.6459824 0.70803513 0.354017565
[45,] 0.5985615 0.80287694 0.401438468
[46,] 0.5686135 0.86277299 0.431386497
[47,] 0.5986467 0.80270651 0.401353255
[48,] 0.5547959 0.89040827 0.445204135
[49,] 0.6914975 0.61700504 0.308502520
[50,] 0.6620495 0.67590096 0.337950482
[51,] 0.6820246 0.63595071 0.317975355
[52,] 0.7103181 0.57936377 0.289681883
[53,] 0.9797138 0.04057241 0.020286203
[54,] 0.9750133 0.04997343 0.024986715
[55,] 0.9674894 0.06502119 0.032510596
[56,] 0.9574217 0.08515664 0.042578320
[57,] 0.9588901 0.08221981 0.041109907
[58,] 0.9478675 0.10426493 0.052132466
[59,] 0.9686612 0.06267754 0.031338770
[60,] 0.9595223 0.08095545 0.040477727
[61,] 0.9805113 0.03897747 0.019488737
[62,] 0.9820898 0.03582031 0.017910153
[63,] 0.9769385 0.04612300 0.023061498
[64,] 0.9724777 0.05504457 0.027522286
[65,] 0.9697455 0.06050908 0.030254542
[66,] 0.9703761 0.05924785 0.029623927
[67,] 0.9612159 0.07756825 0.038784126
[68,] 0.9864548 0.02709047 0.013545235
[69,] 0.9842424 0.03151524 0.015757618
[70,] 0.9836094 0.03278122 0.016390612
[71,] 0.9834917 0.03301669 0.016508345
[72,] 0.9816131 0.03677378 0.018386889
[73,] 0.9756646 0.04867074 0.024335371
[74,] 0.9880755 0.02384905 0.011924527
[75,] 0.9861562 0.02768754 0.013843771
[76,] 0.9809501 0.03809980 0.019049902
[77,] 0.9747187 0.05056266 0.025281330
[78,] 0.9658854 0.06822912 0.034114558
[79,] 0.9580030 0.08399398 0.041996991
[80,] 0.9467270 0.10654592 0.053272961
[81,] 0.9584770 0.08304599 0.041522995
[82,] 0.9463732 0.10725369 0.053626846
[83,] 0.9343012 0.13139763 0.065698816
[84,] 0.9183741 0.16325178 0.081625891
[85,] 0.8990443 0.20191134 0.100955671
[86,] 0.8747250 0.25055003 0.125275014
[87,] 0.8633630 0.27327402 0.136637012
[88,] 0.8361835 0.32763293 0.163816463
[89,] 0.8670524 0.26589524 0.132947622
[90,] 0.8433492 0.31330154 0.156650772
[91,] 0.8462333 0.30753346 0.153766732
[92,] 0.8098209 0.38035819 0.190179097
[93,] 0.8198363 0.36032732 0.180163661
[94,] 0.8649169 0.27016612 0.135083061
[95,] 0.8335438 0.33291247 0.166456236
[96,] 0.8291964 0.34160717 0.170803584
[97,] 0.8716521 0.25669588 0.128347941
[98,] 0.8705742 0.25885152 0.129425761
[99,] 0.8431042 0.31379167 0.156895834
[100,] 0.8469771 0.30604583 0.153022913
[101,] 0.8579363 0.28412748 0.142063741
[102,] 0.8191581 0.36168382 0.180841912
[103,] 0.8207906 0.35841881 0.179209406
[104,] 0.7832191 0.43356175 0.216780875
[105,] 0.7509869 0.49802623 0.249013114
[106,] 0.7354076 0.52918472 0.264592361
[107,] 0.7585417 0.48291652 0.241458261
[108,] 0.9470959 0.10580826 0.052904128
[109,] 0.9260907 0.14781857 0.073909285
[110,] 0.9115793 0.17684135 0.088420676
[111,] 0.8848872 0.23022567 0.115112836
[112,] 0.8511171 0.29776570 0.148882851
[113,] 0.8112194 0.37756113 0.188780565
[114,] 0.8080623 0.38387537 0.191937683
[115,] 0.7642248 0.47155049 0.235775244
[116,] 0.8625510 0.27489809 0.137449045
[117,] 0.8549043 0.29019146 0.145095732
[118,] 0.8737770 0.25244607 0.126223036
[119,] 0.8112277 0.37754468 0.188772340
[120,] 0.7177237 0.56455266 0.282276328
[121,] 0.6087210 0.78255801 0.391279004
[122,] 0.6446005 0.71079903 0.355399517
[123,] 0.5131895 0.97362108 0.486810539
> postscript(file="/var/www/html/freestat/rcomp/tmp/17lis1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2iciv1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3iciv1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4iciv1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5s3hg1291992617.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 = 148
Frequency = 1
1 2 3 4 5 6
-5.04841195 -8.05323143 7.44775525 0.37447643 -0.13922001 1.93254275
7 8 9 10 11 12
-2.63391593 -3.99572556 1.87995804 -3.91756851 1.57964034 -1.55633194
13 14 15 16 17 18
3.51028954 -4.99994373 0.59444386 1.11655974 0.71002182 -1.72272404
19 20 21 22 23 24
1.09627124 -1.28747323 -0.27397877 -1.71880656 4.77866471 0.34972294
25 26 27 28 29 30
3.13279684 1.42678879 -1.17477035 2.67515581 5.00978463 -0.63160693
31 32 33 34 35 36
-0.96387126 1.14239101 -2.65371486 1.53771749 1.30180512 -3.13627448
37 38 39 40 41 42
0.98877525 -3.31342328 -3.54663071 2.16118559 -4.65012638 3.46909552
43 44 45 46 47 48
1.68332591 -4.58962656 -1.14098103 1.72610185 1.78342885 -0.06136088
49 50 51 52 53 54
1.70211412 -1.17715792 -2.33471788 -4.41253621 -3.62333993 0.26618766
55 56 57 58 59 60
0.79229051 10.05068844 -0.59621895 -2.51925044 4.37237621 -0.19419433
61 62 63 64 65 66
5.64514491 1.86318412 3.79028511 3.68616625 11.24648788 -1.02947534
67 68 69 70 71 72
-0.99647341 -0.15712647 3.30499502 -0.75880201 5.20603198 0.38091901
73 74 75 76 77 78
-6.39751644 -2.77256033 0.46675341 1.80288743 2.49686982 -3.64760701
79 80 81 82 83 84
-0.72665823 5.15999922 -2.03213851 2.13222872 -3.22793421 -2.46400271
85 86 87 88 89 90
-0.72538463 5.97053005 -2.29119314 0.45117473 -1.16275168 0.14202612
91 92 93 94 95 96
-2.03420349 -1.44783580 -3.72161945 -0.80676188 0.26852021 -1.99694922
97 98 99 100 101 102
-0.92311717 1.04117844 -1.25313039 -0.65591556 -4.24028519 1.36652706
103 104 105 106 107 108
2.31250198 -0.54970079 3.74600057 3.32673654 1.01277712 1.44255386
109 110 111 112 113 114
-5.99073150 -2.00031987 -2.80820324 -3.85504443 2.83021857 0.62558220
115 116 117 118 119 120
3.36568300 -1.79398357 -2.40986401 -4.71785930 3.94981694 -7.83674674
121 122 123 124 125 126
2.60668735 -1.26002205 1.39853867 0.94763128 -0.99405536 1.92605965
127 128 129 130 131 132
-0.42092415 -4.65172216 4.50491952 4.74900859 -2.58468810 1.74058861
133 134 135 136 137 138
0.07232356 1.50126508 1.51403376 -1.71613612 -3.77269110 -0.04738824
139 140 141 142 143 144
-2.28941624 -0.51359411 -1.72375938 0.93750114 3.79368538 1.35692013
145 146 147 148
-2.81142802 -3.45739876 2.45625560 0.61069872
> postscript(file="/var/www/html/freestat/rcomp/tmp/6s3hg1291992617.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 = 148
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.04841195 NA
1 -8.05323143 -5.04841195
2 7.44775525 -8.05323143
3 0.37447643 7.44775525
4 -0.13922001 0.37447643
5 1.93254275 -0.13922001
6 -2.63391593 1.93254275
7 -3.99572556 -2.63391593
8 1.87995804 -3.99572556
9 -3.91756851 1.87995804
10 1.57964034 -3.91756851
11 -1.55633194 1.57964034
12 3.51028954 -1.55633194
13 -4.99994373 3.51028954
14 0.59444386 -4.99994373
15 1.11655974 0.59444386
16 0.71002182 1.11655974
17 -1.72272404 0.71002182
18 1.09627124 -1.72272404
19 -1.28747323 1.09627124
20 -0.27397877 -1.28747323
21 -1.71880656 -0.27397877
22 4.77866471 -1.71880656
23 0.34972294 4.77866471
24 3.13279684 0.34972294
25 1.42678879 3.13279684
26 -1.17477035 1.42678879
27 2.67515581 -1.17477035
28 5.00978463 2.67515581
29 -0.63160693 5.00978463
30 -0.96387126 -0.63160693
31 1.14239101 -0.96387126
32 -2.65371486 1.14239101
33 1.53771749 -2.65371486
34 1.30180512 1.53771749
35 -3.13627448 1.30180512
36 0.98877525 -3.13627448
37 -3.31342328 0.98877525
38 -3.54663071 -3.31342328
39 2.16118559 -3.54663071
40 -4.65012638 2.16118559
41 3.46909552 -4.65012638
42 1.68332591 3.46909552
43 -4.58962656 1.68332591
44 -1.14098103 -4.58962656
45 1.72610185 -1.14098103
46 1.78342885 1.72610185
47 -0.06136088 1.78342885
48 1.70211412 -0.06136088
49 -1.17715792 1.70211412
50 -2.33471788 -1.17715792
51 -4.41253621 -2.33471788
52 -3.62333993 -4.41253621
53 0.26618766 -3.62333993
54 0.79229051 0.26618766
55 10.05068844 0.79229051
56 -0.59621895 10.05068844
57 -2.51925044 -0.59621895
58 4.37237621 -2.51925044
59 -0.19419433 4.37237621
60 5.64514491 -0.19419433
61 1.86318412 5.64514491
62 3.79028511 1.86318412
63 3.68616625 3.79028511
64 11.24648788 3.68616625
65 -1.02947534 11.24648788
66 -0.99647341 -1.02947534
67 -0.15712647 -0.99647341
68 3.30499502 -0.15712647
69 -0.75880201 3.30499502
70 5.20603198 -0.75880201
71 0.38091901 5.20603198
72 -6.39751644 0.38091901
73 -2.77256033 -6.39751644
74 0.46675341 -2.77256033
75 1.80288743 0.46675341
76 2.49686982 1.80288743
77 -3.64760701 2.49686982
78 -0.72665823 -3.64760701
79 5.15999922 -0.72665823
80 -2.03213851 5.15999922
81 2.13222872 -2.03213851
82 -3.22793421 2.13222872
83 -2.46400271 -3.22793421
84 -0.72538463 -2.46400271
85 5.97053005 -0.72538463
86 -2.29119314 5.97053005
87 0.45117473 -2.29119314
88 -1.16275168 0.45117473
89 0.14202612 -1.16275168
90 -2.03420349 0.14202612
91 -1.44783580 -2.03420349
92 -3.72161945 -1.44783580
93 -0.80676188 -3.72161945
94 0.26852021 -0.80676188
95 -1.99694922 0.26852021
96 -0.92311717 -1.99694922
97 1.04117844 -0.92311717
98 -1.25313039 1.04117844
99 -0.65591556 -1.25313039
100 -4.24028519 -0.65591556
101 1.36652706 -4.24028519
102 2.31250198 1.36652706
103 -0.54970079 2.31250198
104 3.74600057 -0.54970079
105 3.32673654 3.74600057
106 1.01277712 3.32673654
107 1.44255386 1.01277712
108 -5.99073150 1.44255386
109 -2.00031987 -5.99073150
110 -2.80820324 -2.00031987
111 -3.85504443 -2.80820324
112 2.83021857 -3.85504443
113 0.62558220 2.83021857
114 3.36568300 0.62558220
115 -1.79398357 3.36568300
116 -2.40986401 -1.79398357
117 -4.71785930 -2.40986401
118 3.94981694 -4.71785930
119 -7.83674674 3.94981694
120 2.60668735 -7.83674674
121 -1.26002205 2.60668735
122 1.39853867 -1.26002205
123 0.94763128 1.39853867
124 -0.99405536 0.94763128
125 1.92605965 -0.99405536
126 -0.42092415 1.92605965
127 -4.65172216 -0.42092415
128 4.50491952 -4.65172216
129 4.74900859 4.50491952
130 -2.58468810 4.74900859
131 1.74058861 -2.58468810
132 0.07232356 1.74058861
133 1.50126508 0.07232356
134 1.51403376 1.50126508
135 -1.71613612 1.51403376
136 -3.77269110 -1.71613612
137 -0.04738824 -3.77269110
138 -2.28941624 -0.04738824
139 -0.51359411 -2.28941624
140 -1.72375938 -0.51359411
141 0.93750114 -1.72375938
142 3.79368538 0.93750114
143 1.35692013 3.79368538
144 -2.81142802 1.35692013
145 -3.45739876 -2.81142802
146 2.45625560 -3.45739876
147 0.61069872 2.45625560
148 NA 0.61069872
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.05323143 -5.04841195
[2,] 7.44775525 -8.05323143
[3,] 0.37447643 7.44775525
[4,] -0.13922001 0.37447643
[5,] 1.93254275 -0.13922001
[6,] -2.63391593 1.93254275
[7,] -3.99572556 -2.63391593
[8,] 1.87995804 -3.99572556
[9,] -3.91756851 1.87995804
[10,] 1.57964034 -3.91756851
[11,] -1.55633194 1.57964034
[12,] 3.51028954 -1.55633194
[13,] -4.99994373 3.51028954
[14,] 0.59444386 -4.99994373
[15,] 1.11655974 0.59444386
[16,] 0.71002182 1.11655974
[17,] -1.72272404 0.71002182
[18,] 1.09627124 -1.72272404
[19,] -1.28747323 1.09627124
[20,] -0.27397877 -1.28747323
[21,] -1.71880656 -0.27397877
[22,] 4.77866471 -1.71880656
[23,] 0.34972294 4.77866471
[24,] 3.13279684 0.34972294
[25,] 1.42678879 3.13279684
[26,] -1.17477035 1.42678879
[27,] 2.67515581 -1.17477035
[28,] 5.00978463 2.67515581
[29,] -0.63160693 5.00978463
[30,] -0.96387126 -0.63160693
[31,] 1.14239101 -0.96387126
[32,] -2.65371486 1.14239101
[33,] 1.53771749 -2.65371486
[34,] 1.30180512 1.53771749
[35,] -3.13627448 1.30180512
[36,] 0.98877525 -3.13627448
[37,] -3.31342328 0.98877525
[38,] -3.54663071 -3.31342328
[39,] 2.16118559 -3.54663071
[40,] -4.65012638 2.16118559
[41,] 3.46909552 -4.65012638
[42,] 1.68332591 3.46909552
[43,] -4.58962656 1.68332591
[44,] -1.14098103 -4.58962656
[45,] 1.72610185 -1.14098103
[46,] 1.78342885 1.72610185
[47,] -0.06136088 1.78342885
[48,] 1.70211412 -0.06136088
[49,] -1.17715792 1.70211412
[50,] -2.33471788 -1.17715792
[51,] -4.41253621 -2.33471788
[52,] -3.62333993 -4.41253621
[53,] 0.26618766 -3.62333993
[54,] 0.79229051 0.26618766
[55,] 10.05068844 0.79229051
[56,] -0.59621895 10.05068844
[57,] -2.51925044 -0.59621895
[58,] 4.37237621 -2.51925044
[59,] -0.19419433 4.37237621
[60,] 5.64514491 -0.19419433
[61,] 1.86318412 5.64514491
[62,] 3.79028511 1.86318412
[63,] 3.68616625 3.79028511
[64,] 11.24648788 3.68616625
[65,] -1.02947534 11.24648788
[66,] -0.99647341 -1.02947534
[67,] -0.15712647 -0.99647341
[68,] 3.30499502 -0.15712647
[69,] -0.75880201 3.30499502
[70,] 5.20603198 -0.75880201
[71,] 0.38091901 5.20603198
[72,] -6.39751644 0.38091901
[73,] -2.77256033 -6.39751644
[74,] 0.46675341 -2.77256033
[75,] 1.80288743 0.46675341
[76,] 2.49686982 1.80288743
[77,] -3.64760701 2.49686982
[78,] -0.72665823 -3.64760701
[79,] 5.15999922 -0.72665823
[80,] -2.03213851 5.15999922
[81,] 2.13222872 -2.03213851
[82,] -3.22793421 2.13222872
[83,] -2.46400271 -3.22793421
[84,] -0.72538463 -2.46400271
[85,] 5.97053005 -0.72538463
[86,] -2.29119314 5.97053005
[87,] 0.45117473 -2.29119314
[88,] -1.16275168 0.45117473
[89,] 0.14202612 -1.16275168
[90,] -2.03420349 0.14202612
[91,] -1.44783580 -2.03420349
[92,] -3.72161945 -1.44783580
[93,] -0.80676188 -3.72161945
[94,] 0.26852021 -0.80676188
[95,] -1.99694922 0.26852021
[96,] -0.92311717 -1.99694922
[97,] 1.04117844 -0.92311717
[98,] -1.25313039 1.04117844
[99,] -0.65591556 -1.25313039
[100,] -4.24028519 -0.65591556
[101,] 1.36652706 -4.24028519
[102,] 2.31250198 1.36652706
[103,] -0.54970079 2.31250198
[104,] 3.74600057 -0.54970079
[105,] 3.32673654 3.74600057
[106,] 1.01277712 3.32673654
[107,] 1.44255386 1.01277712
[108,] -5.99073150 1.44255386
[109,] -2.00031987 -5.99073150
[110,] -2.80820324 -2.00031987
[111,] -3.85504443 -2.80820324
[112,] 2.83021857 -3.85504443
[113,] 0.62558220 2.83021857
[114,] 3.36568300 0.62558220
[115,] -1.79398357 3.36568300
[116,] -2.40986401 -1.79398357
[117,] -4.71785930 -2.40986401
[118,] 3.94981694 -4.71785930
[119,] -7.83674674 3.94981694
[120,] 2.60668735 -7.83674674
[121,] -1.26002205 2.60668735
[122,] 1.39853867 -1.26002205
[123,] 0.94763128 1.39853867
[124,] -0.99405536 0.94763128
[125,] 1.92605965 -0.99405536
[126,] -0.42092415 1.92605965
[127,] -4.65172216 -0.42092415
[128,] 4.50491952 -4.65172216
[129,] 4.74900859 4.50491952
[130,] -2.58468810 4.74900859
[131,] 1.74058861 -2.58468810
[132,] 0.07232356 1.74058861
[133,] 1.50126508 0.07232356
[134,] 1.51403376 1.50126508
[135,] -1.71613612 1.51403376
[136,] -3.77269110 -1.71613612
[137,] -0.04738824 -3.77269110
[138,] -2.28941624 -0.04738824
[139,] -0.51359411 -2.28941624
[140,] -1.72375938 -0.51359411
[141,] 0.93750114 -1.72375938
[142,] 3.79368538 0.93750114
[143,] 1.35692013 3.79368538
[144,] -2.81142802 1.35692013
[145,] -3.45739876 -2.81142802
[146,] 2.45625560 -3.45739876
[147,] 0.61069872 2.45625560
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.05323143 -5.04841195
2 7.44775525 -8.05323143
3 0.37447643 7.44775525
4 -0.13922001 0.37447643
5 1.93254275 -0.13922001
6 -2.63391593 1.93254275
7 -3.99572556 -2.63391593
8 1.87995804 -3.99572556
9 -3.91756851 1.87995804
10 1.57964034 -3.91756851
11 -1.55633194 1.57964034
12 3.51028954 -1.55633194
13 -4.99994373 3.51028954
14 0.59444386 -4.99994373
15 1.11655974 0.59444386
16 0.71002182 1.11655974
17 -1.72272404 0.71002182
18 1.09627124 -1.72272404
19 -1.28747323 1.09627124
20 -0.27397877 -1.28747323
21 -1.71880656 -0.27397877
22 4.77866471 -1.71880656
23 0.34972294 4.77866471
24 3.13279684 0.34972294
25 1.42678879 3.13279684
26 -1.17477035 1.42678879
27 2.67515581 -1.17477035
28 5.00978463 2.67515581
29 -0.63160693 5.00978463
30 -0.96387126 -0.63160693
31 1.14239101 -0.96387126
32 -2.65371486 1.14239101
33 1.53771749 -2.65371486
34 1.30180512 1.53771749
35 -3.13627448 1.30180512
36 0.98877525 -3.13627448
37 -3.31342328 0.98877525
38 -3.54663071 -3.31342328
39 2.16118559 -3.54663071
40 -4.65012638 2.16118559
41 3.46909552 -4.65012638
42 1.68332591 3.46909552
43 -4.58962656 1.68332591
44 -1.14098103 -4.58962656
45 1.72610185 -1.14098103
46 1.78342885 1.72610185
47 -0.06136088 1.78342885
48 1.70211412 -0.06136088
49 -1.17715792 1.70211412
50 -2.33471788 -1.17715792
51 -4.41253621 -2.33471788
52 -3.62333993 -4.41253621
53 0.26618766 -3.62333993
54 0.79229051 0.26618766
55 10.05068844 0.79229051
56 -0.59621895 10.05068844
57 -2.51925044 -0.59621895
58 4.37237621 -2.51925044
59 -0.19419433 4.37237621
60 5.64514491 -0.19419433
61 1.86318412 5.64514491
62 3.79028511 1.86318412
63 3.68616625 3.79028511
64 11.24648788 3.68616625
65 -1.02947534 11.24648788
66 -0.99647341 -1.02947534
67 -0.15712647 -0.99647341
68 3.30499502 -0.15712647
69 -0.75880201 3.30499502
70 5.20603198 -0.75880201
71 0.38091901 5.20603198
72 -6.39751644 0.38091901
73 -2.77256033 -6.39751644
74 0.46675341 -2.77256033
75 1.80288743 0.46675341
76 2.49686982 1.80288743
77 -3.64760701 2.49686982
78 -0.72665823 -3.64760701
79 5.15999922 -0.72665823
80 -2.03213851 5.15999922
81 2.13222872 -2.03213851
82 -3.22793421 2.13222872
83 -2.46400271 -3.22793421
84 -0.72538463 -2.46400271
85 5.97053005 -0.72538463
86 -2.29119314 5.97053005
87 0.45117473 -2.29119314
88 -1.16275168 0.45117473
89 0.14202612 -1.16275168
90 -2.03420349 0.14202612
91 -1.44783580 -2.03420349
92 -3.72161945 -1.44783580
93 -0.80676188 -3.72161945
94 0.26852021 -0.80676188
95 -1.99694922 0.26852021
96 -0.92311717 -1.99694922
97 1.04117844 -0.92311717
98 -1.25313039 1.04117844
99 -0.65591556 -1.25313039
100 -4.24028519 -0.65591556
101 1.36652706 -4.24028519
102 2.31250198 1.36652706
103 -0.54970079 2.31250198
104 3.74600057 -0.54970079
105 3.32673654 3.74600057
106 1.01277712 3.32673654
107 1.44255386 1.01277712
108 -5.99073150 1.44255386
109 -2.00031987 -5.99073150
110 -2.80820324 -2.00031987
111 -3.85504443 -2.80820324
112 2.83021857 -3.85504443
113 0.62558220 2.83021857
114 3.36568300 0.62558220
115 -1.79398357 3.36568300
116 -2.40986401 -1.79398357
117 -4.71785930 -2.40986401
118 3.94981694 -4.71785930
119 -7.83674674 3.94981694
120 2.60668735 -7.83674674
121 -1.26002205 2.60668735
122 1.39853867 -1.26002205
123 0.94763128 1.39853867
124 -0.99405536 0.94763128
125 1.92605965 -0.99405536
126 -0.42092415 1.92605965
127 -4.65172216 -0.42092415
128 4.50491952 -4.65172216
129 4.74900859 4.50491952
130 -2.58468810 4.74900859
131 1.74058861 -2.58468810
132 0.07232356 1.74058861
133 1.50126508 0.07232356
134 1.51403376 1.50126508
135 -1.71613612 1.51403376
136 -3.77269110 -1.71613612
137 -0.04738824 -3.77269110
138 -2.28941624 -0.04738824
139 -0.51359411 -2.28941624
140 -1.72375938 -0.51359411
141 0.93750114 -1.72375938
142 3.79368538 0.93750114
143 1.35692013 3.79368538
144 -2.81142802 1.35692013
145 -3.45739876 -2.81142802
146 2.45625560 -3.45739876
147 0.61069872 2.45625560
> 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/freestat/rcomp/tmp/7luy11291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8luy11291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9dmxm1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10dmxm1291992617.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11rwg41291992618.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/freestat/rcomp/tmp/12vffs1291992618.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/freestat/rcomp/tmp/132gu41291992618.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/freestat/rcomp/tmp/14c7b71291992618.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/freestat/rcomp/tmp/15yqav1291992618.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/freestat/rcomp/tmp/16ch7m1291992618.tab")
+ }
>
> try(system("convert tmp/17lis1291992617.ps tmp/17lis1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/2iciv1291992617.ps tmp/2iciv1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/3iciv1291992617.ps tmp/3iciv1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/4iciv1291992617.ps tmp/4iciv1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s3hg1291992617.ps tmp/5s3hg1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/6s3hg1291992617.ps tmp/6s3hg1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/7luy11291992617.ps tmp/7luy11291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/8luy11291992617.ps tmp/8luy11291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dmxm1291992617.ps tmp/9dmxm1291992617.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dmxm1291992617.ps tmp/10dmxm1291992617.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.908 2.704 6.447