R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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'citation()' on how to cite R or R packages in publications.
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> source('/home/pw/wessanet/cretab')
>
>
> RC.capture <- function (expression, collapse = NULL) {
+ resultConn <- textConnection('RC.resultText', open = 'w', local=TRUE)
+ sink(resultConn)
+ on.exit(function() {
+ sink()
+ close(resultConn)
+ })
+ expression
+ on.exit(NULL)
+ sink()
+ close(resultConn)
+ return(paste(c(RC.resultText, ''), collapse = collapse, sep = ''))
+ }
> RC.texteval <- function (sourceText, collapse = NULL, echo = TRUE) {
+ sourceConn <- textConnection(sourceText, open = 'r')
+ on.exit(close(sourceConn))
+ result <- RC.capture(source(file = sourceConn, local = FALSE, echo = echo, print.eval = TRUE), collapse = collapse)
+ on.exit(NULL)
+ close(sourceConn)
+ res <- ''
+ for(i in 1:length(result)) {
+ if (result[i]!='') res <- paste(res,result[i],'
+ ',sep='')
+ }
+ return(res)
+ }
>
>
> myrfcuid = 'q0893053-2019'
>
> x <- array(list(10,10,10,10,21,36,1,0,8,8,9,15,22,32,1,1,8,6,12,14,17,33,1,1,9,10,14,14,21,39,1,1,5,8,6,8,19,34,1,0,10,10,13,19,23,39,1,1,8,7,12,17,21,36,1,1,9,10,13,18,22,33,1,1,8,6,6,10,11,30,1,0,7,7,12,15,20,39,1,0,10,9,10,16,18,37,1,0,10,6,9,12,16,37,1,0,9,7,12,13,18,35,1,1,4,6,7,10,13,32,1,0,4,4,10,14,17,36,1,1,8,6,11,15,20,36,1,1,9,8,15,20,20,41,1,1,10,9,10,9,15,36,1,1,8,8,12,12,18,37,1,0,5,6,10,13,15,29,1,0,10,6,12,16,19,39,1,1,8,10,11,12,19,37,1,0,7,8,11,14,19,32,1,1,8,8,12,15,20,36,1,1,8,7,15,19,20,43,1,1,9,4,12,16,16,30,1,0,8,9,11,16,18,33,1,0,6,8,9,14,17,28,1,1,8,10,11,14,18,30,1,1,8,8,11,14,13,28,1,0,5,6,9,13,20,39,0,1,9,7,15,18,21,34,1,1,8,8,12,15,17,34,1,0,8,5,9,15,19,29,1,0,8,10,12,15,20,32,1,0,6,2,12,13,15,33,1,0,6,6,9,14,15,27,1,0,9,7,9,15,19,35,1,1,8,5,11,14,18,38,1,1,9,8,12,19,22,40,1,1,10,7,12,16,20,34,1,1,8,7,12,16,18,34,0,0,8,10,12,12,14,26,1,0,7,7,6,10,15,39,1,0,7,6,11,11,17,34,1,1,10,10,12,13,16,39,1,1,8,6,9,14,17,26,1,1,7,5,11,11,15,30,1,1,10,8,9,11,17,34,1,1,7,8,10,16,18,34,1,1,7,5,10,9,16,29,1,0,9,8,9,16,18,41,1,0,9,10,12,19,22,43,1,0,8,7,11,13,16,31,1,0,6,7,9,15,16,33,1,0,8,7,9,14,20,34,1,0,9,7,12,15,18,30,1,1,2,2,6,11,16,23,0,0,6,4,10,14,16,29,1,0,8,6,12,15,20,35,1,1,8,7,11,17,21,40,0,1,7,9,14,16,18,27,0,0,8,9,8,13,15,30,1,0,6,4,9,15,18,27,1,0,10,9,10,14,18,29,1,0,10,9,10,15,20,33,1,0,10,8,10,14,18,32,1,0,8,7,11,12,16,33,1,0,8,9,10,12,19,36,1,1,7,7,12,15,20,34,1,1,10,6,14,17,22,45,1,1,5,7,10,13,18,30,0,0,3,2,8,5,8,22,0,1,2,3,8,7,13,24,0,1,3,4,7,10,13,25,0,1,4,5,11,15,18,26,0,1,2,2,6,9,12,27,0,0,6,6,9,9,16,27,0,0,8,8,12,15,21,35,1,0,8,5,12,14,20,36,1,0,5,4,12,11,18,32,0,0,10,10,9,18,22,35,1,1,9,10,15,20,23,35,1,1,8,10,15,20,23,36,1,1,9,9,13,16,21,37,1,1,8,5,9,15,16,33,1,1,5,5,12,14,14,25,1,0,7,7,9,13,18,35,1,1,9,10,15,18,22,37,1,1,8,9,11,14,20,36,1,0,4,8,11,12,18,35,1,1,7,8,6,9,12,29,1,1,8,8,14,19,17,35,1,1,7,8,11,13,15,31,1,0,7,8,8,12,18,30,1,1,9,7,10,14,18,37,1,0,6,6,10,6,15,36,1,1,7,8,9,14,16,35,1,0,4,2,8,11,15,32,1,0,6,5,9,11,16,34,1,1,10,4,10,14,19,37,1,0,9,9,11,12,19,36,1,1,10,10,14,19,23,39,1,1,8,6,12,13,20,37,1,0,4,4,9,14,18,31,0,0,8,10,13,17,21,40,1,1,5,6,8,12,19,38,1,0,8,7,12,16,18,35,0,1,9,7,14,15,19,38,0,1,8,8,9,15,17,32,1,0,4,6,10,15,21,41,1,1,8,5,12,16,19,28,1,0,10,6,12,15,24,40,1,1,6,7,9,12,12,25,1,0,7,6,9,13,15,28,1,0,10,9,12,14,18,37,1,1,9,9,15,17,19,37,1,1,8,7,12,14,22,40,1,1,3,6,11,14,19,26,0,0,8,7,8,14,16,30,1,0,7,7,11,15,19,32,1,0,7,8,11,11,18,31,1,0,8,7,10,11,18,28,1,0,8,8,12,16,19,34,1,1,7,7,9,12,21,39,1,0,7,4,11,12,19,33,0,1,9,10,15,19,22,43,1,0,9,8,14,18,23,37,0,1,9,8,6,16,17,31,1,0,4,2,9,16,18,31,0,1,6,6,9,13,19,34,1,0,6,4,8,11,15,32,1,1,6,4,7,10,14,27,0,0,8,9,10,14,18,34,1,0,3,2,6,14,17,28,0,0,8,6,9,14,19,32,0,0,8,7,9,16,16,39,0,1,6,4,7,10,14,28,0,1,10,10,11,16,20,39,1,0,2,3,9,7,16,32,0,0,9,7,12,16,18,36,0,1,6,4,9,15,16,31,0,1,6,8,10,17,21,39,0,0,5,4,11,11,16,23,0,0,4,5,7,11,14,25,0,0,7,6,12,10,16,32,1,0,5,5,8,13,19,32,0,1,8,9,13,14,19,36,0,1,6,6,11,13,19,39,0,0,9,8,11,13,18,31,0,1,6,4,12,12,16,32,1,0,4,4,11,10,14,28,0,1,7,8,12,15,19,34,0,0,2,4,3,6,11,28,0,1,8,10,10,15,18,38,1,1,9,8,13,15,18,35,1,1,6,5,10,11,16,32,1,0,5,3,6,14,20,26,0,1,7,7,11,14,18,32,0,1,8,6,12,16,20,28,1,1,4,5,9,12,16,31,1,0,9,5,10,15,18,33,0,1,9,9,15,20,19,38,1,0,9,2,9,12,19,38,0,1,7,7,6,9,15,36,0,0,5,7,9,13,17,31,1,1,7,5,15,15,21,36,0,0,9,9,15,19,24,43,1,1,8,4,9,11,16,37,1,1,6,5,11,11,13,28,0,1,9,9,9,17,21,35,0,1,8,7,11,15,16,34,1,1,7,6,10,14,17,40,1,1,7,8,9,15,17,31,1,0,7,7,6,11,18,41,0,0,8,6,12,12,18,35,1,0,10,8,13,15,23,38,1,1,6,6,12,16,20,37,0,0,6,7,12,16,20,31,0,0),dim=c(8,179),dimnames=list(c('Intention_to_Use','Relative_Advantage','Perceived_Usefulness','Perceived_Ease_of_Use','Information_Quality','System_Quality','groupB','genderB'),1:179))
> y <- array(NA,dim=c(8,179),dimnames=list(c('Intention_to_Use','Relative_Advantage','Perceived_Usefulness','Perceived_Ease_of_Use','Information_Quality','System_Quality','groupB','genderB'),1:179))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par6 = '12'
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par6 <- '12'
> par5 <- ''
> par4 <- ''
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Fri, 21 Jul 2017 20:18:06 +0200)
> #Author: root
> #To cite this work: Wessa P., (2017), Multiple Regression (v1.0.48) in Free Statistics Software (v$_version), Office for Research Development and Education, URL https://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> library(car)
Loading required package: carData
> library(MASS)
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> mywarning <- ''
> par6 <- as.numeric(par6)
> if(is.na(par6)) {
+ par6 <- 12
+ mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
+ }
> par1 <- as.numeric(par1)
> if(is.na(par1)) {
+ par1 <- 1
+ mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
+ }
> if (par4=='') par4 <- 0
> par4 <- as.numeric(par4)
> if (!is.numeric(par4)) par4 <- 0
> if (par5=='') par5 <- 0
> par5 <- as.numeric(par5)
> if (!is.numeric(par5)) par5 <- 0
> x <- na.omit(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'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'Seasonal Differences (s)'){
+ (n <- n - par6)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+par6,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'First and Seasonal Differences (s)'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ (n <- n - par6)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+par6,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if(par4 > 0) {
+ x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
+ for (i in 1:(n-par4)) {
+ for (j in 1:par4) {
+ x2[i,j] <- x[i+par4-j,par1]
+ }
+ }
+ x <- cbind(x[(par4+1):n,], x2)
+ n <- n - par4
+ }
> if(par5 > 0) {
+ x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
+ for (i in 1:(n-par5*par6)) {
+ for (j in 1:par5) {
+ x2[i,j] <- x[i+par5*par6-j*par6,par1]
+ }
+ }
+ x <- cbind(x[(par5*par6+1):n,], x2)
+ n <- n - par5*par6
+ }
> if (par2 == 'Include Seasonal Dummies'){
+ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
+ for (i in 1:(par6-1)){
+ x2[seq(i,n,par6),i] <- 1
+ }
+ x <- cbind(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[n,]))
[1] 8
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> print(x)
Intention_to_Use Relative_Advantage Perceived_Usefulness
1 10 10 10
2 8 8 9
3 8 6 12
4 9 10 14
5 5 8 6
6 10 10 13
7 8 7 12
8 9 10 13
9 8 6 6
10 7 7 12
11 10 9 10
12 10 6 9
13 9 7 12
14 4 6 7
15 4 4 10
16 8 6 11
17 9 8 15
18 10 9 10
19 8 8 12
20 5 6 10
21 10 6 12
22 8 10 11
23 7 8 11
24 8 8 12
25 8 7 15
26 9 4 12
27 8 9 11
28 6 8 9
29 8 10 11
30 8 8 11
31 5 6 9
32 9 7 15
33 8 8 12
34 8 5 9
35 8 10 12
36 6 2 12
37 6 6 9
38 9 7 9
39 8 5 11
40 9 8 12
41 10 7 12
42 8 7 12
43 8 10 12
44 7 7 6
45 7 6 11
46 10 10 12
47 8 6 9
48 7 5 11
49 10 8 9
50 7 8 10
51 7 5 10
52 9 8 9
53 9 10 12
54 8 7 11
55 6 7 9
56 8 7 9
57 9 7 12
58 2 2 6
59 6 4 10
60 8 6 12
61 8 7 11
62 7 9 14
63 8 9 8
64 6 4 9
65 10 9 10
66 10 9 10
67 10 8 10
68 8 7 11
69 8 9 10
70 7 7 12
71 10 6 14
72 5 7 10
73 3 2 8
74 2 3 8
75 3 4 7
76 4 5 11
77 2 2 6
78 6 6 9
79 8 8 12
80 8 5 12
81 5 4 12
82 10 10 9
83 9 10 15
84 8 10 15
85 9 9 13
86 8 5 9
87 5 5 12
88 7 7 9
89 9 10 15
90 8 9 11
91 4 8 11
92 7 8 6
93 8 8 14
94 7 8 11
95 7 8 8
96 9 7 10
97 6 6 10
98 7 8 9
99 4 2 8
100 6 5 9
101 10 4 10
102 9 9 11
103 10 10 14
104 8 6 12
105 4 4 9
106 8 10 13
107 5 6 8
108 8 7 12
109 9 7 14
110 8 8 9
111 4 6 10
112 8 5 12
113 10 6 12
114 6 7 9
115 7 6 9
116 10 9 12
117 9 9 15
118 8 7 12
119 3 6 11
120 8 7 8
121 7 7 11
122 7 8 11
123 8 7 10
124 8 8 12
125 7 7 9
126 7 4 11
127 9 10 15
128 9 8 14
129 9 8 6
130 4 2 9
131 6 6 9
132 6 4 8
133 6 4 7
134 8 9 10
135 3 2 6
136 8 6 9
137 8 7 9
138 6 4 7
139 10 10 11
140 2 3 9
141 9 7 12
142 6 4 9
143 6 8 10
144 5 4 11
145 4 5 7
146 7 6 12
147 5 5 8
148 8 9 13
149 6 6 11
150 9 8 11
151 6 4 12
152 4 4 11
153 7 8 12
154 2 4 3
155 8 10 10
156 9 8 13
157 6 5 10
158 5 3 6
159 7 7 11
160 8 6 12
161 4 5 9
162 9 5 10
163 9 9 15
164 9 2 9
165 7 7 6
166 5 7 9
167 7 5 15
168 9 9 15
169 8 4 9
170 6 5 11
171 9 9 9
172 8 7 11
173 7 6 10
174 7 8 9
175 7 7 6
176 8 6 12
177 10 8 13
178 6 6 12
179 6 7 12
Perceived_Ease_of_Use Information_Quality System_Quality groupB genderB
1 10 21 36 1 0
2 15 22 32 1 1
3 14 17 33 1 1
4 14 21 39 1 1
5 8 19 34 1 0
6 19 23 39 1 1
7 17 21 36 1 1
8 18 22 33 1 1
9 10 11 30 1 0
10 15 20 39 1 0
11 16 18 37 1 0
12 12 16 37 1 0
13 13 18 35 1 1
14 10 13 32 1 0
15 14 17 36 1 1
16 15 20 36 1 1
17 20 20 41 1 1
18 9 15 36 1 1
19 12 18 37 1 0
20 13 15 29 1 0
21 16 19 39 1 1
22 12 19 37 1 0
23 14 19 32 1 1
24 15 20 36 1 1
25 19 20 43 1 1
26 16 16 30 1 0
27 16 18 33 1 0
28 14 17 28 1 1
29 14 18 30 1 1
30 14 13 28 1 0
31 13 20 39 0 1
32 18 21 34 1 1
33 15 17 34 1 0
34 15 19 29 1 0
35 15 20 32 1 0
36 13 15 33 1 0
37 14 15 27 1 0
38 15 19 35 1 1
39 14 18 38 1 1
40 19 22 40 1 1
41 16 20 34 1 1
42 16 18 34 0 0
43 12 14 26 1 0
44 10 15 39 1 0
45 11 17 34 1 1
46 13 16 39 1 1
47 14 17 26 1 1
48 11 15 30 1 1
49 11 17 34 1 1
50 16 18 34 1 1
51 9 16 29 1 0
52 16 18 41 1 0
53 19 22 43 1 0
54 13 16 31 1 0
55 15 16 33 1 0
56 14 20 34 1 0
57 15 18 30 1 1
58 11 16 23 0 0
59 14 16 29 1 0
60 15 20 35 1 1
61 17 21 40 0 1
62 16 18 27 0 0
63 13 15 30 1 0
64 15 18 27 1 0
65 14 18 29 1 0
66 15 20 33 1 0
67 14 18 32 1 0
68 12 16 33 1 0
69 12 19 36 1 1
70 15 20 34 1 1
71 17 22 45 1 1
72 13 18 30 0 0
73 5 8 22 0 1
74 7 13 24 0 1
75 10 13 25 0 1
76 15 18 26 0 1
77 9 12 27 0 0
78 9 16 27 0 0
79 15 21 35 1 0
80 14 20 36 1 0
81 11 18 32 0 0
82 18 22 35 1 1
83 20 23 35 1 1
84 20 23 36 1 1
85 16 21 37 1 1
86 15 16 33 1 1
87 14 14 25 1 0
88 13 18 35 1 1
89 18 22 37 1 1
90 14 20 36 1 0
91 12 18 35 1 1
92 9 12 29 1 1
93 19 17 35 1 1
94 13 15 31 1 0
95 12 18 30 1 1
96 14 18 37 1 0
97 6 15 36 1 1
98 14 16 35 1 0
99 11 15 32 1 0
100 11 16 34 1 1
101 14 19 37 1 0
102 12 19 36 1 1
103 19 23 39 1 1
104 13 20 37 1 0
105 14 18 31 0 0
106 17 21 40 1 1
107 12 19 38 1 0
108 16 18 35 0 1
109 15 19 38 0 1
110 15 17 32 1 0
111 15 21 41 1 1
112 16 19 28 1 0
113 15 24 40 1 1
114 12 12 25 1 0
115 13 15 28 1 0
116 14 18 37 1 1
117 17 19 37 1 1
118 14 22 40 1 1
119 14 19 26 0 0
120 14 16 30 1 0
121 15 19 32 1 0
122 11 18 31 1 0
123 11 18 28 1 0
124 16 19 34 1 1
125 12 21 39 1 0
126 12 19 33 0 1
127 19 22 43 1 0
128 18 23 37 0 1
129 16 17 31 1 0
130 16 18 31 0 1
131 13 19 34 1 0
132 11 15 32 1 1
133 10 14 27 0 0
134 14 18 34 1 0
135 14 17 28 0 0
136 14 19 32 0 0
137 16 16 39 0 1
138 10 14 28 0 1
139 16 20 39 1 0
140 7 16 32 0 0
141 16 18 36 0 1
142 15 16 31 0 1
143 17 21 39 0 0
144 11 16 23 0 0
145 11 14 25 0 0
146 10 16 32 1 0
147 13 19 32 0 1
148 14 19 36 0 1
149 13 19 39 0 0
150 13 18 31 0 1
151 12 16 32 1 0
152 10 14 28 0 1
153 15 19 34 0 0
154 6 11 28 0 1
155 15 18 38 1 1
156 15 18 35 1 1
157 11 16 32 1 0
158 14 20 26 0 1
159 14 18 32 0 1
160 16 20 28 1 1
161 12 16 31 1 0
162 15 18 33 0 1
163 20 19 38 1 0
164 12 19 38 0 1
165 9 15 36 0 0
166 13 17 31 1 1
167 15 21 36 0 0
168 19 24 43 1 1
169 11 16 37 1 1
170 11 13 28 0 1
171 17 21 35 0 1
172 15 16 34 1 1
173 14 17 40 1 1
174 15 17 31 1 0
175 11 18 41 0 0
176 12 18 35 1 0
177 15 23 38 1 1
178 16 20 37 0 0
179 16 20 31 0 0
> (k <- length(x[n,]))
[1] 8
> head(x)
Intention_to_Use Relative_Advantage Perceived_Usefulness
1 10 10 10
2 8 8 9
3 8 6 12
4 9 10 14
5 5 8 6
6 10 10 13
Perceived_Ease_of_Use Information_Quality System_Quality groupB genderB
1 10 21 36 1 0
2 15 22 32 1 1
3 14 17 33 1 1
4 14 21 39 1 1
5 8 19 34 1 0
6 19 23 39 1 1
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Relative_Advantage Perceived_Usefulness
-1.0284419 0.3282975 0.0917069
Perceived_Ease_of_Use Information_Quality System_Quality
0.1033088 0.0008306 0.0876004
groupB genderB
0.8950180 0.1881325
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.1003 -0.8381 -0.0893 0.7996 3.7741
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0284419 0.7824332 -1.314 0.190467
Relative_Advantage 0.3282975 0.0602457 5.449 1.74e-07 ***
Perceived_Usefulness 0.0917069 0.0591677 1.550 0.123003
Perceived_Ease_of_Use 0.1033088 0.0536559 1.925 0.055839 .
Information_Quality 0.0008306 0.0595849 0.014 0.988895
System_Quality 0.0876004 0.0288793 3.033 0.002796 **
groupB 0.8950180 0.2479348 3.610 0.000402 ***
genderB 0.1881325 0.2055850 0.915 0.361423
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.322 on 171 degrees of freedom
Multiple R-squared: 0.5644, Adjusted R-squared: 0.5465
F-statistic: 31.65 on 7 and 171 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.75826901 0.48346197 0.24173099
[2,] 0.89108349 0.21783302 0.10891651
[3,] 0.83520416 0.32959167 0.16479584
[4,] 0.97637186 0.04725629 0.02362814
[5,] 0.98245657 0.03508686 0.01754343
[6,] 0.97769039 0.04461922 0.02230961
[7,] 0.96753151 0.06493699 0.03246849
[8,] 0.95996255 0.08007490 0.04003745
[9,] 0.94212894 0.11574213 0.05787106
[10,] 0.93997949 0.12004103 0.06002051
[11,] 0.95567065 0.08865870 0.04432935
[12,] 0.94834430 0.10331139 0.05165570
[13,] 0.93275087 0.13449827 0.06724913
[14,] 0.90934450 0.18131100 0.09065550
[15,] 0.91419030 0.17161941 0.08580970
[16,] 0.97200464 0.05599073 0.02799536
[17,] 0.96286112 0.07427776 0.03713888
[18,] 0.96070711 0.07858577 0.03929289
[19,] 0.94651330 0.10697340 0.05348670
[20,] 0.92818086 0.14363828 0.07181914
[21,] 0.91255614 0.17488773 0.08744386
[22,] 0.89003308 0.21993385 0.10996692
[23,] 0.86225972 0.27548057 0.13774028
[24,] 0.87419031 0.25161939 0.12580969
[25,] 0.85094170 0.29811660 0.14905830
[26,] 0.81945978 0.36108043 0.18054022
[27,] 0.78737574 0.42524851 0.21262426
[28,] 0.78579271 0.42841458 0.21420729
[29,] 0.74726419 0.50547162 0.25273581
[30,] 0.70151722 0.59696556 0.29848278
[31,] 0.74722131 0.50555738 0.25277869
[32,] 0.75936410 0.48127181 0.24063590
[33,] 0.71687639 0.56624721 0.28312361
[34,] 0.67085553 0.65828893 0.32914447
[35,] 0.62254267 0.75491467 0.37745733
[36,] 0.58734264 0.82531471 0.41265736
[37,] 0.58734151 0.82531699 0.41265849
[38,] 0.53987067 0.92025866 0.46012933
[39,] 0.63164013 0.73671974 0.36835987
[40,] 0.62566595 0.74866810 0.37433405
[41,] 0.59416946 0.81166108 0.40583054
[42,] 0.55345751 0.89308499 0.44654249
[43,] 0.51956087 0.96087827 0.48043913
[44,] 0.47982080 0.95964160 0.52017920
[45,] 0.48895044 0.97790088 0.51104956
[46,] 0.45015732 0.90031465 0.54984268
[47,] 0.44098387 0.88196773 0.55901613
[48,] 0.43060190 0.86120379 0.56939810
[49,] 0.38647402 0.77294804 0.61352598
[50,] 0.34225898 0.68451795 0.65774102
[51,] 0.31816129 0.63632257 0.68183871
[52,] 0.27716597 0.55433194 0.72283403
[53,] 0.24331118 0.48662237 0.75668882
[54,] 0.20782250 0.41564500 0.79217750
[55,] 0.27438925 0.54877851 0.72561075
[56,] 0.30908774 0.61817548 0.69091226
[57,] 0.39182300 0.78364600 0.60817700
[58,] 0.35903669 0.71807337 0.64096331
[59,] 0.32152678 0.64305357 0.67847322
[60,] 0.30723547 0.61447093 0.69276453
[61,] 0.28979847 0.57959694 0.71020153
[62,] 0.27125924 0.54251848 0.72874076
[63,] 0.23542760 0.47085520 0.76457240
[64,] 0.24260312 0.48520623 0.75739688
[65,] 0.22761271 0.45522543 0.77238729
[66,] 0.22996002 0.45992004 0.77003998
[67,] 0.22234483 0.44468965 0.77765517
[68,] 0.21928835 0.43857669 0.78071165
[69,] 0.19224998 0.38449997 0.80775002
[70,] 0.17148452 0.34296904 0.82851548
[71,] 0.14534887 0.29069773 0.85465113
[72,] 0.13732685 0.27465371 0.86267315
[73,] 0.12500187 0.25000375 0.87499813
[74,] 0.15435614 0.30871229 0.84564386
[75,] 0.12953395 0.25906790 0.87046605
[76,] 0.12190451 0.24380901 0.87809549
[77,] 0.12569898 0.25139797 0.87430102
[78,] 0.10856347 0.21712694 0.89143653
[79,] 0.09603042 0.19206085 0.90396958
[80,] 0.08163426 0.16326853 0.91836574
[81,] 0.32120546 0.64241092 0.67879454
[82,] 0.28503171 0.57006343 0.71496829
[83,] 0.26754257 0.53508514 0.73245743
[84,] 0.24033802 0.48067605 0.75966198
[85,] 0.20878271 0.41756541 0.79121729
[86,] 0.20797447 0.41594895 0.79202553
[87,] 0.18716362 0.37432723 0.81283638
[88,] 0.16978231 0.33956463 0.83021769
[89,] 0.16680633 0.33361265 0.83319367
[90,] 0.14670622 0.29341244 0.85329378
[91,] 0.32087705 0.64175411 0.67912295
[92,] 0.28792978 0.57585956 0.71207022
[93,] 0.25110731 0.50221463 0.74889269
[94,] 0.22593822 0.45187645 0.77406178
[95,] 0.21458579 0.42917158 0.78541421
[96,] 0.24249189 0.48498377 0.75750811
[97,] 0.29255070 0.58510139 0.70744930
[98,] 0.27941567 0.55883135 0.72058433
[99,] 0.28997600 0.57995201 0.71002400
[100,] 0.25695243 0.51390486 0.74304757
[101,] 0.63599964 0.72800073 0.36400036
[102,] 0.64218684 0.71562632 0.35781316
[103,] 0.66573870 0.66852261 0.33426130
[104,] 0.62592468 0.74815065 0.37407532
[105,] 0.59819145 0.80361710 0.40180855
[106,] 0.58497720 0.83004560 0.41502280
[107,] 0.54342337 0.91315326 0.45657663
[108,] 0.50721776 0.98556449 0.49278224
[109,] 0.64891853 0.70216294 0.35108147
[110,] 0.65088646 0.69822709 0.34911354
[111,] 0.61009504 0.77980992 0.38990496
[112,] 0.56651409 0.86697182 0.43348591
[113,] 0.57972926 0.84054148 0.42027074
[114,] 0.53787259 0.92425483 0.46212741
[115,] 0.49646587 0.99293175 0.50353413
[116,] 0.49082854 0.98165708 0.50917146
[117,] 0.47588071 0.95176141 0.52411929
[118,] 0.44253552 0.88507105 0.55746448
[119,] 0.52870177 0.94259646 0.47129823
[120,] 0.54058650 0.91882699 0.45941350
[121,] 0.50281397 0.99437206 0.49718603
[122,] 0.45103605 0.90207210 0.54896395
[123,] 0.52192747 0.95614506 0.47807253
[124,] 0.48169983 0.96339965 0.51830017
[125,] 0.45758226 0.91516452 0.54241774
[126,] 0.54364553 0.91270894 0.45635447
[127,] 0.49704307 0.99408614 0.50295693
[128,] 0.49227954 0.98455909 0.50772046
[129,] 0.51171697 0.97656607 0.48828303
[130,] 0.67081985 0.65836030 0.32918015
[131,] 0.67021591 0.65956818 0.32978409
[132,] 0.61789256 0.76421489 0.38210744
[133,] 0.63962618 0.72074764 0.36037382
[134,] 0.59599485 0.80801030 0.40400515
[135,] 0.53931997 0.92136006 0.46068003
[136,] 0.50227915 0.99544170 0.49772085
[137,] 0.52321267 0.95357466 0.47678733
[138,] 0.46862774 0.93725548 0.53137226
[139,] 0.46502885 0.93005770 0.53497115
[140,] 0.55355323 0.89289354 0.44644677
[141,] 0.48688840 0.97377679 0.51311160
[142,] 0.49850913 0.99701826 0.50149087
[143,] 0.42835087 0.85670174 0.57164913
[144,] 0.59785817 0.80428367 0.40214183
[145,] 0.54634101 0.90731798 0.45365899
[146,] 0.48802106 0.97604212 0.51197894
[147,] 0.41230758 0.82461516 0.58769242
[148,] 0.38943232 0.77886463 0.61056768
[149,] 0.32858741 0.65717481 0.67141259
[150,] 0.28220985 0.56441969 0.71779015
[151,] 0.35388234 0.70776469 0.64611766
[152,] 0.40833383 0.81666766 0.59166617
[153,] 0.49032560 0.98065120 0.50967440
[154,] 0.53463937 0.93072126 0.46536063
[155,] 0.41992449 0.83984897 0.58007551
[156,] 0.94515719 0.10968563 0.05484281
[157,] 0.92465154 0.15069691 0.07534846
[158,] 0.82863452 0.34273095 0.17136548
> postscript(file="/home/pw/wessanet/rcomp/tmp/1gc5e1580202721.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/home/pw/wessanet/rcomp/tmp/2kzmy1580202721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/home/pw/wessanet/rcomp/tmp/3h6xj1580202721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> sresid <- studres(mylm)
> hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
> xfit<-seq(min(sresid),max(sresid),length=40)
> yfit<-dnorm(xfit)
> lines(xfit, yfit)
> grid()
> dev.off()
null device
1
> postscript(file="/home/pw/wessanet/rcomp/tmp/4dc0p1580202721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/home/pw/wessanet/rcomp/tmp/5qpop1580202721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqPlot(mylm, main='QQ Plot')
[1] 91 111
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 179
Frequency = 1
1 2 3 4 5
1.7292368606 0.1224331268 0.5237687890 -0.5017594260 -1.8638612089
6 7 8 9 10
0.0717422352 -0.3805785315 -0.2985161179 1.9431621567 -1.2477990138
11 12 13 14 15
1.3525727728 2.8440685001 1.1227487894 -2.3254065798 -2.8990236394
16 17 18 19 20
0.2468740127 -0.7310943756 1.9776940755 -0.0893081865 -1.6493135828
21 22 23 24 25
1.7898877662 -0.6550268827 -0.9551800898 -0.5014278371 -1.4746888216
26 27 28 29 30
2.4255103111 -0.3887325705 -1.4197037390 -0.4357437642 0.5883372533
31 32 33 34 35
-1.7308778068 0.4161928388 -0.1356029342 1.5607508969 -0.6194888529
36 37 38 39 40
0.1300611735 -0.4857147832 1.1904211707 0.5049406889 -0.2667257325
41 42 43 44 45
1.8987616014 0.9835731579 0.2210232290 -0.1768609905 -0.1621982878
46 47 48 49 50
0.7891159088 1.4120920105 0.5181618446 2.3646204343 -1.2444610580
51 52 53 54 55
1.0913886116 0.4221756123 -0.9979893979 0.7546507455 -1.4437539264
56 57 58 59 60
0.5686322655 1.3541330524 -1.3428888572 -0.0968579662 0.2427675339
61 62 63 64 65
0.2557447836 -0.2432328996 0.4616072646 0.0650797112 2.2599934304
66 67 68 69 70
1.8046219794 2.3254897912 0.6827588033 -0.3355546132 -0.9979295838
71 72 73 74 75
0.9750712824 -1.1726851690 -0.0003372785 -1.7146059667 -1.3487234285
76 77 78 79 80
-1.6521456120 -1.4833504973 0.9250167014 -0.2265255571 0.7749059290
81 82 83 84 85
-0.3397895223 0.8931105544 -0.8645787802 -1.9521791587 -0.1131719450
86 87 88 89 90
1.0247086035 -1.2565065409 -0.6021306386 -0.8323313466 -0.4465771986
91 92 93 94 95
-4.0105330347 0.2885133338 -1.0079847492 -0.5728161896 -0.2974105702
96 97 98 99 100
1.2157853948 -0.7274869914 -0.8439433649 -1.2088933891 -0.6496565159
101 102 103 104 105
3.1998473225 0.5727385295 -0.0199646222 0.4623168691 -1.2869950163
106 107 108 109 110
-1.8075793916 -2.1543167043 0.7078403168 1.3641037204 0.3147183948
111 112 113 114 115
-4.1002515836 1.2699218886 1.8014433971 -0.4297022096 0.5299936530
116 117 118 119 120
1.1876442251 -0.3982333523 -0.4218841622 -2.6898323919 1.0140628812
121 122 123 124 125
-0.5420589458 -0.3686902433 1.3141152458 -0.4287053338 -0.6635825585
126 127 128 129 130
1.3720451180 -1.2731099699 0.8101579139 1.5741305304 -1.0251501159
131 132 133 134 135
-0.9989308623 -0.0536208442 1.6633777159 -0.1780084621 -1.0916477552
136 137 138 139 140
1.9679790517 0.6342204969 1.3876448748 0.7557065400 -2.3214750728
141 142 143 144 145
1.6202399383 0.4232248285 -1.7051130143 0.5419818637 -0.5930278381
146 147 148 149 150
0.2135674503 -0.6968402425 0.0777251570 -0.7253284978 2.1315776363
151 152 153 154 155
-0.3364551868 -0.9791825545 -0.2422460846 -1.8298007535 -1.1481487498
156 157 158 159 160
0.4961268063 -0.3780301536 0.5646313598 0.2689659393 0.7526613687
161 162 163 164 165
-2.3020317325 2.8263585958 -0.6076277126 3.7740519326 1.0842669316
166 167 168 169 170
-2.2508985635 0.2906639530 -1.1346060584 1.4158398449 0.5900416956
171 172 173 174 175
1.2205653983 0.0970995178 -0.9060201460 -0.5976812267 0.4371557262
176 177 178 179
0.7424875631 1.2291728654 -0.9525916035 -0.7552868287
> postscript(file="/home/pw/wessanet/rcomp/tmp/6vv6k1580202721.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 = 179
Frequency = 1
lag(myerror, k = 1) myerror
0 1.7292368606 NA
1 0.1224331268 1.7292368606
2 0.5237687890 0.1224331268
3 -0.5017594260 0.5237687890
4 -1.8638612089 -0.5017594260
5 0.0717422352 -1.8638612089
6 -0.3805785315 0.0717422352
7 -0.2985161179 -0.3805785315
8 1.9431621567 -0.2985161179
9 -1.2477990138 1.9431621567
10 1.3525727728 -1.2477990138
11 2.8440685001 1.3525727728
12 1.1227487894 2.8440685001
13 -2.3254065798 1.1227487894
14 -2.8990236394 -2.3254065798
15 0.2468740127 -2.8990236394
16 -0.7310943756 0.2468740127
17 1.9776940755 -0.7310943756
18 -0.0893081865 1.9776940755
19 -1.6493135828 -0.0893081865
20 1.7898877662 -1.6493135828
21 -0.6550268827 1.7898877662
22 -0.9551800898 -0.6550268827
23 -0.5014278371 -0.9551800898
24 -1.4746888216 -0.5014278371
25 2.4255103111 -1.4746888216
26 -0.3887325705 2.4255103111
27 -1.4197037390 -0.3887325705
28 -0.4357437642 -1.4197037390
29 0.5883372533 -0.4357437642
30 -1.7308778068 0.5883372533
31 0.4161928388 -1.7308778068
32 -0.1356029342 0.4161928388
33 1.5607508969 -0.1356029342
34 -0.6194888529 1.5607508969
35 0.1300611735 -0.6194888529
36 -0.4857147832 0.1300611735
37 1.1904211707 -0.4857147832
38 0.5049406889 1.1904211707
39 -0.2667257325 0.5049406889
40 1.8987616014 -0.2667257325
41 0.9835731579 1.8987616014
42 0.2210232290 0.9835731579
43 -0.1768609905 0.2210232290
44 -0.1621982878 -0.1768609905
45 0.7891159088 -0.1621982878
46 1.4120920105 0.7891159088
47 0.5181618446 1.4120920105
48 2.3646204343 0.5181618446
49 -1.2444610580 2.3646204343
50 1.0913886116 -1.2444610580
51 0.4221756123 1.0913886116
52 -0.9979893979 0.4221756123
53 0.7546507455 -0.9979893979
54 -1.4437539264 0.7546507455
55 0.5686322655 -1.4437539264
56 1.3541330524 0.5686322655
57 -1.3428888572 1.3541330524
58 -0.0968579662 -1.3428888572
59 0.2427675339 -0.0968579662
60 0.2557447836 0.2427675339
61 -0.2432328996 0.2557447836
62 0.4616072646 -0.2432328996
63 0.0650797112 0.4616072646
64 2.2599934304 0.0650797112
65 1.8046219794 2.2599934304
66 2.3254897912 1.8046219794
67 0.6827588033 2.3254897912
68 -0.3355546132 0.6827588033
69 -0.9979295838 -0.3355546132
70 0.9750712824 -0.9979295838
71 -1.1726851690 0.9750712824
72 -0.0003372785 -1.1726851690
73 -1.7146059667 -0.0003372785
74 -1.3487234285 -1.7146059667
75 -1.6521456120 -1.3487234285
76 -1.4833504973 -1.6521456120
77 0.9250167014 -1.4833504973
78 -0.2265255571 0.9250167014
79 0.7749059290 -0.2265255571
80 -0.3397895223 0.7749059290
81 0.8931105544 -0.3397895223
82 -0.8645787802 0.8931105544
83 -1.9521791587 -0.8645787802
84 -0.1131719450 -1.9521791587
85 1.0247086035 -0.1131719450
86 -1.2565065409 1.0247086035
87 -0.6021306386 -1.2565065409
88 -0.8323313466 -0.6021306386
89 -0.4465771986 -0.8323313466
90 -4.0105330347 -0.4465771986
91 0.2885133338 -4.0105330347
92 -1.0079847492 0.2885133338
93 -0.5728161896 -1.0079847492
94 -0.2974105702 -0.5728161896
95 1.2157853948 -0.2974105702
96 -0.7274869914 1.2157853948
97 -0.8439433649 -0.7274869914
98 -1.2088933891 -0.8439433649
99 -0.6496565159 -1.2088933891
100 3.1998473225 -0.6496565159
101 0.5727385295 3.1998473225
102 -0.0199646222 0.5727385295
103 0.4623168691 -0.0199646222
104 -1.2869950163 0.4623168691
105 -1.8075793916 -1.2869950163
106 -2.1543167043 -1.8075793916
107 0.7078403168 -2.1543167043
108 1.3641037204 0.7078403168
109 0.3147183948 1.3641037204
110 -4.1002515836 0.3147183948
111 1.2699218886 -4.1002515836
112 1.8014433971 1.2699218886
113 -0.4297022096 1.8014433971
114 0.5299936530 -0.4297022096
115 1.1876442251 0.5299936530
116 -0.3982333523 1.1876442251
117 -0.4218841622 -0.3982333523
118 -2.6898323919 -0.4218841622
119 1.0140628812 -2.6898323919
120 -0.5420589458 1.0140628812
121 -0.3686902433 -0.5420589458
122 1.3141152458 -0.3686902433
123 -0.4287053338 1.3141152458
124 -0.6635825585 -0.4287053338
125 1.3720451180 -0.6635825585
126 -1.2731099699 1.3720451180
127 0.8101579139 -1.2731099699
128 1.5741305304 0.8101579139
129 -1.0251501159 1.5741305304
130 -0.9989308623 -1.0251501159
131 -0.0536208442 -0.9989308623
132 1.6633777159 -0.0536208442
133 -0.1780084621 1.6633777159
134 -1.0916477552 -0.1780084621
135 1.9679790517 -1.0916477552
136 0.6342204969 1.9679790517
137 1.3876448748 0.6342204969
138 0.7557065400 1.3876448748
139 -2.3214750728 0.7557065400
140 1.6202399383 -2.3214750728
141 0.4232248285 1.6202399383
142 -1.7051130143 0.4232248285
143 0.5419818637 -1.7051130143
144 -0.5930278381 0.5419818637
145 0.2135674503 -0.5930278381
146 -0.6968402425 0.2135674503
147 0.0777251570 -0.6968402425
148 -0.7253284978 0.0777251570
149 2.1315776363 -0.7253284978
150 -0.3364551868 2.1315776363
151 -0.9791825545 -0.3364551868
152 -0.2422460846 -0.9791825545
153 -1.8298007535 -0.2422460846
154 -1.1481487498 -1.8298007535
155 0.4961268063 -1.1481487498
156 -0.3780301536 0.4961268063
157 0.5646313598 -0.3780301536
158 0.2689659393 0.5646313598
159 0.7526613687 0.2689659393
160 -2.3020317325 0.7526613687
161 2.8263585958 -2.3020317325
162 -0.6076277126 2.8263585958
163 3.7740519326 -0.6076277126
164 1.0842669316 3.7740519326
165 -2.2508985635 1.0842669316
166 0.2906639530 -2.2508985635
167 -1.1346060584 0.2906639530
168 1.4158398449 -1.1346060584
169 0.5900416956 1.4158398449
170 1.2205653983 0.5900416956
171 0.0970995178 1.2205653983
172 -0.9060201460 0.0970995178
173 -0.5976812267 -0.9060201460
174 0.4371557262 -0.5976812267
175 0.7424875631 0.4371557262
176 1.2291728654 0.7424875631
177 -0.9525916035 1.2291728654
178 -0.7552868287 -0.9525916035
179 NA -0.7552868287
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1224331268 1.7292368606
[2,] 0.5237687890 0.1224331268
[3,] -0.5017594260 0.5237687890
[4,] -1.8638612089 -0.5017594260
[5,] 0.0717422352 -1.8638612089
[6,] -0.3805785315 0.0717422352
[7,] -0.2985161179 -0.3805785315
[8,] 1.9431621567 -0.2985161179
[9,] -1.2477990138 1.9431621567
[10,] 1.3525727728 -1.2477990138
[11,] 2.8440685001 1.3525727728
[12,] 1.1227487894 2.8440685001
[13,] -2.3254065798 1.1227487894
[14,] -2.8990236394 -2.3254065798
[15,] 0.2468740127 -2.8990236394
[16,] -0.7310943756 0.2468740127
[17,] 1.9776940755 -0.7310943756
[18,] -0.0893081865 1.9776940755
[19,] -1.6493135828 -0.0893081865
[20,] 1.7898877662 -1.6493135828
[21,] -0.6550268827 1.7898877662
[22,] -0.9551800898 -0.6550268827
[23,] -0.5014278371 -0.9551800898
[24,] -1.4746888216 -0.5014278371
[25,] 2.4255103111 -1.4746888216
[26,] -0.3887325705 2.4255103111
[27,] -1.4197037390 -0.3887325705
[28,] -0.4357437642 -1.4197037390
[29,] 0.5883372533 -0.4357437642
[30,] -1.7308778068 0.5883372533
[31,] 0.4161928388 -1.7308778068
[32,] -0.1356029342 0.4161928388
[33,] 1.5607508969 -0.1356029342
[34,] -0.6194888529 1.5607508969
[35,] 0.1300611735 -0.6194888529
[36,] -0.4857147832 0.1300611735
[37,] 1.1904211707 -0.4857147832
[38,] 0.5049406889 1.1904211707
[39,] -0.2667257325 0.5049406889
[40,] 1.8987616014 -0.2667257325
[41,] 0.9835731579 1.8987616014
[42,] 0.2210232290 0.9835731579
[43,] -0.1768609905 0.2210232290
[44,] -0.1621982878 -0.1768609905
[45,] 0.7891159088 -0.1621982878
[46,] 1.4120920105 0.7891159088
[47,] 0.5181618446 1.4120920105
[48,] 2.3646204343 0.5181618446
[49,] -1.2444610580 2.3646204343
[50,] 1.0913886116 -1.2444610580
[51,] 0.4221756123 1.0913886116
[52,] -0.9979893979 0.4221756123
[53,] 0.7546507455 -0.9979893979
[54,] -1.4437539264 0.7546507455
[55,] 0.5686322655 -1.4437539264
[56,] 1.3541330524 0.5686322655
[57,] -1.3428888572 1.3541330524
[58,] -0.0968579662 -1.3428888572
[59,] 0.2427675339 -0.0968579662
[60,] 0.2557447836 0.2427675339
[61,] -0.2432328996 0.2557447836
[62,] 0.4616072646 -0.2432328996
[63,] 0.0650797112 0.4616072646
[64,] 2.2599934304 0.0650797112
[65,] 1.8046219794 2.2599934304
[66,] 2.3254897912 1.8046219794
[67,] 0.6827588033 2.3254897912
[68,] -0.3355546132 0.6827588033
[69,] -0.9979295838 -0.3355546132
[70,] 0.9750712824 -0.9979295838
[71,] -1.1726851690 0.9750712824
[72,] -0.0003372785 -1.1726851690
[73,] -1.7146059667 -0.0003372785
[74,] -1.3487234285 -1.7146059667
[75,] -1.6521456120 -1.3487234285
[76,] -1.4833504973 -1.6521456120
[77,] 0.9250167014 -1.4833504973
[78,] -0.2265255571 0.9250167014
[79,] 0.7749059290 -0.2265255571
[80,] -0.3397895223 0.7749059290
[81,] 0.8931105544 -0.3397895223
[82,] -0.8645787802 0.8931105544
[83,] -1.9521791587 -0.8645787802
[84,] -0.1131719450 -1.9521791587
[85,] 1.0247086035 -0.1131719450
[86,] -1.2565065409 1.0247086035
[87,] -0.6021306386 -1.2565065409
[88,] -0.8323313466 -0.6021306386
[89,] -0.4465771986 -0.8323313466
[90,] -4.0105330347 -0.4465771986
[91,] 0.2885133338 -4.0105330347
[92,] -1.0079847492 0.2885133338
[93,] -0.5728161896 -1.0079847492
[94,] -0.2974105702 -0.5728161896
[95,] 1.2157853948 -0.2974105702
[96,] -0.7274869914 1.2157853948
[97,] -0.8439433649 -0.7274869914
[98,] -1.2088933891 -0.8439433649
[99,] -0.6496565159 -1.2088933891
[100,] 3.1998473225 -0.6496565159
[101,] 0.5727385295 3.1998473225
[102,] -0.0199646222 0.5727385295
[103,] 0.4623168691 -0.0199646222
[104,] -1.2869950163 0.4623168691
[105,] -1.8075793916 -1.2869950163
[106,] -2.1543167043 -1.8075793916
[107,] 0.7078403168 -2.1543167043
[108,] 1.3641037204 0.7078403168
[109,] 0.3147183948 1.3641037204
[110,] -4.1002515836 0.3147183948
[111,] 1.2699218886 -4.1002515836
[112,] 1.8014433971 1.2699218886
[113,] -0.4297022096 1.8014433971
[114,] 0.5299936530 -0.4297022096
[115,] 1.1876442251 0.5299936530
[116,] -0.3982333523 1.1876442251
[117,] -0.4218841622 -0.3982333523
[118,] -2.6898323919 -0.4218841622
[119,] 1.0140628812 -2.6898323919
[120,] -0.5420589458 1.0140628812
[121,] -0.3686902433 -0.5420589458
[122,] 1.3141152458 -0.3686902433
[123,] -0.4287053338 1.3141152458
[124,] -0.6635825585 -0.4287053338
[125,] 1.3720451180 -0.6635825585
[126,] -1.2731099699 1.3720451180
[127,] 0.8101579139 -1.2731099699
[128,] 1.5741305304 0.8101579139
[129,] -1.0251501159 1.5741305304
[130,] -0.9989308623 -1.0251501159
[131,] -0.0536208442 -0.9989308623
[132,] 1.6633777159 -0.0536208442
[133,] -0.1780084621 1.6633777159
[134,] -1.0916477552 -0.1780084621
[135,] 1.9679790517 -1.0916477552
[136,] 0.6342204969 1.9679790517
[137,] 1.3876448748 0.6342204969
[138,] 0.7557065400 1.3876448748
[139,] -2.3214750728 0.7557065400
[140,] 1.6202399383 -2.3214750728
[141,] 0.4232248285 1.6202399383
[142,] -1.7051130143 0.4232248285
[143,] 0.5419818637 -1.7051130143
[144,] -0.5930278381 0.5419818637
[145,] 0.2135674503 -0.5930278381
[146,] -0.6968402425 0.2135674503
[147,] 0.0777251570 -0.6968402425
[148,] -0.7253284978 0.0777251570
[149,] 2.1315776363 -0.7253284978
[150,] -0.3364551868 2.1315776363
[151,] -0.9791825545 -0.3364551868
[152,] -0.2422460846 -0.9791825545
[153,] -1.8298007535 -0.2422460846
[154,] -1.1481487498 -1.8298007535
[155,] 0.4961268063 -1.1481487498
[156,] -0.3780301536 0.4961268063
[157,] 0.5646313598 -0.3780301536
[158,] 0.2689659393 0.5646313598
[159,] 0.7526613687 0.2689659393
[160,] -2.3020317325 0.7526613687
[161,] 2.8263585958 -2.3020317325
[162,] -0.6076277126 2.8263585958
[163,] 3.7740519326 -0.6076277126
[164,] 1.0842669316 3.7740519326
[165,] -2.2508985635 1.0842669316
[166,] 0.2906639530 -2.2508985635
[167,] -1.1346060584 0.2906639530
[168,] 1.4158398449 -1.1346060584
[169,] 0.5900416956 1.4158398449
[170,] 1.2205653983 0.5900416956
[171,] 0.0970995178 1.2205653983
[172,] -0.9060201460 0.0970995178
[173,] -0.5976812267 -0.9060201460
[174,] 0.4371557262 -0.5976812267
[175,] 0.7424875631 0.4371557262
[176,] 1.2291728654 0.7424875631
[177,] -0.9525916035 1.2291728654
[178,] -0.7552868287 -0.9525916035
> z <- as.data.frame(dum1)
> print(z)
lag(myerror, k = 1) myerror
1 0.1224331268 1.7292368606
2 0.5237687890 0.1224331268
3 -0.5017594260 0.5237687890
4 -1.8638612089 -0.5017594260
5 0.0717422352 -1.8638612089
6 -0.3805785315 0.0717422352
7 -0.2985161179 -0.3805785315
8 1.9431621567 -0.2985161179
9 -1.2477990138 1.9431621567
10 1.3525727728 -1.2477990138
11 2.8440685001 1.3525727728
12 1.1227487894 2.8440685001
13 -2.3254065798 1.1227487894
14 -2.8990236394 -2.3254065798
15 0.2468740127 -2.8990236394
16 -0.7310943756 0.2468740127
17 1.9776940755 -0.7310943756
18 -0.0893081865 1.9776940755
19 -1.6493135828 -0.0893081865
20 1.7898877662 -1.6493135828
21 -0.6550268827 1.7898877662
22 -0.9551800898 -0.6550268827
23 -0.5014278371 -0.9551800898
24 -1.4746888216 -0.5014278371
25 2.4255103111 -1.4746888216
26 -0.3887325705 2.4255103111
27 -1.4197037390 -0.3887325705
28 -0.4357437642 -1.4197037390
29 0.5883372533 -0.4357437642
30 -1.7308778068 0.5883372533
31 0.4161928388 -1.7308778068
32 -0.1356029342 0.4161928388
33 1.5607508969 -0.1356029342
34 -0.6194888529 1.5607508969
35 0.1300611735 -0.6194888529
36 -0.4857147832 0.1300611735
37 1.1904211707 -0.4857147832
38 0.5049406889 1.1904211707
39 -0.2667257325 0.5049406889
40 1.8987616014 -0.2667257325
41 0.9835731579 1.8987616014
42 0.2210232290 0.9835731579
43 -0.1768609905 0.2210232290
44 -0.1621982878 -0.1768609905
45 0.7891159088 -0.1621982878
46 1.4120920105 0.7891159088
47 0.5181618446 1.4120920105
48 2.3646204343 0.5181618446
49 -1.2444610580 2.3646204343
50 1.0913886116 -1.2444610580
51 0.4221756123 1.0913886116
52 -0.9979893979 0.4221756123
53 0.7546507455 -0.9979893979
54 -1.4437539264 0.7546507455
55 0.5686322655 -1.4437539264
56 1.3541330524 0.5686322655
57 -1.3428888572 1.3541330524
58 -0.0968579662 -1.3428888572
59 0.2427675339 -0.0968579662
60 0.2557447836 0.2427675339
61 -0.2432328996 0.2557447836
62 0.4616072646 -0.2432328996
63 0.0650797112 0.4616072646
64 2.2599934304 0.0650797112
65 1.8046219794 2.2599934304
66 2.3254897912 1.8046219794
67 0.6827588033 2.3254897912
68 -0.3355546132 0.6827588033
69 -0.9979295838 -0.3355546132
70 0.9750712824 -0.9979295838
71 -1.1726851690 0.9750712824
72 -0.0003372785 -1.1726851690
73 -1.7146059667 -0.0003372785
74 -1.3487234285 -1.7146059667
75 -1.6521456120 -1.3487234285
76 -1.4833504973 -1.6521456120
77 0.9250167014 -1.4833504973
78 -0.2265255571 0.9250167014
79 0.7749059290 -0.2265255571
80 -0.3397895223 0.7749059290
81 0.8931105544 -0.3397895223
82 -0.8645787802 0.8931105544
83 -1.9521791587 -0.8645787802
84 -0.1131719450 -1.9521791587
85 1.0247086035 -0.1131719450
86 -1.2565065409 1.0247086035
87 -0.6021306386 -1.2565065409
88 -0.8323313466 -0.6021306386
89 -0.4465771986 -0.8323313466
90 -4.0105330347 -0.4465771986
91 0.2885133338 -4.0105330347
92 -1.0079847492 0.2885133338
93 -0.5728161896 -1.0079847492
94 -0.2974105702 -0.5728161896
95 1.2157853948 -0.2974105702
96 -0.7274869914 1.2157853948
97 -0.8439433649 -0.7274869914
98 -1.2088933891 -0.8439433649
99 -0.6496565159 -1.2088933891
100 3.1998473225 -0.6496565159
101 0.5727385295 3.1998473225
102 -0.0199646222 0.5727385295
103 0.4623168691 -0.0199646222
104 -1.2869950163 0.4623168691
105 -1.8075793916 -1.2869950163
106 -2.1543167043 -1.8075793916
107 0.7078403168 -2.1543167043
108 1.3641037204 0.7078403168
109 0.3147183948 1.3641037204
110 -4.1002515836 0.3147183948
111 1.2699218886 -4.1002515836
112 1.8014433971 1.2699218886
113 -0.4297022096 1.8014433971
114 0.5299936530 -0.4297022096
115 1.1876442251 0.5299936530
116 -0.3982333523 1.1876442251
117 -0.4218841622 -0.3982333523
118 -2.6898323919 -0.4218841622
119 1.0140628812 -2.6898323919
120 -0.5420589458 1.0140628812
121 -0.3686902433 -0.5420589458
122 1.3141152458 -0.3686902433
123 -0.4287053338 1.3141152458
124 -0.6635825585 -0.4287053338
125 1.3720451180 -0.6635825585
126 -1.2731099699 1.3720451180
127 0.8101579139 -1.2731099699
128 1.5741305304 0.8101579139
129 -1.0251501159 1.5741305304
130 -0.9989308623 -1.0251501159
131 -0.0536208442 -0.9989308623
132 1.6633777159 -0.0536208442
133 -0.1780084621 1.6633777159
134 -1.0916477552 -0.1780084621
135 1.9679790517 -1.0916477552
136 0.6342204969 1.9679790517
137 1.3876448748 0.6342204969
138 0.7557065400 1.3876448748
139 -2.3214750728 0.7557065400
140 1.6202399383 -2.3214750728
141 0.4232248285 1.6202399383
142 -1.7051130143 0.4232248285
143 0.5419818637 -1.7051130143
144 -0.5930278381 0.5419818637
145 0.2135674503 -0.5930278381
146 -0.6968402425 0.2135674503
147 0.0777251570 -0.6968402425
148 -0.7253284978 0.0777251570
149 2.1315776363 -0.7253284978
150 -0.3364551868 2.1315776363
151 -0.9791825545 -0.3364551868
152 -0.2422460846 -0.9791825545
153 -1.8298007535 -0.2422460846
154 -1.1481487498 -1.8298007535
155 0.4961268063 -1.1481487498
156 -0.3780301536 0.4961268063
157 0.5646313598 -0.3780301536
158 0.2689659393 0.5646313598
159 0.7526613687 0.2689659393
160 -2.3020317325 0.7526613687
161 2.8263585958 -2.3020317325
162 -0.6076277126 2.8263585958
163 3.7740519326 -0.6076277126
164 1.0842669316 3.7740519326
165 -2.2508985635 1.0842669316
166 0.2906639530 -2.2508985635
167 -1.1346060584 0.2906639530
168 1.4158398449 -1.1346060584
169 0.5900416956 1.4158398449
170 1.2205653983 0.5900416956
171 0.0970995178 1.2205653983
172 -0.9060201460 0.0970995178
173 -0.5976812267 -0.9060201460
174 0.4371557262 -0.5976812267
175 0.7424875631 0.4371557262
176 1.2291728654 0.7424875631
177 -0.9525916035 1.2291728654
178 -0.7552868287 -0.9525916035
> 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="/home/pw/wessanet/rcomp/tmp/7cjhr1580202721.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="/home/pw/wessanet/rcomp/tmp/8jc8y1580202721.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="/home/pw/wessanet/rcomp/tmp/9t8wp1580202721.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="/home/pw/wessanet/rcomp/tmp/10gw6i1580202721.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
>
> 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, mywarning)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/11wdu61580202721.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/12hzkj1580202722.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
> 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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/13g6fq1580202722.tab")
> myr <- as.numeric(mysum$resid)
> myr
[1] 1.7292368606 0.1224331268 0.5237687890 -0.5017594260 -1.8638612089
[6] 0.0717422352 -0.3805785315 -0.2985161179 1.9431621567 -1.2477990138
[11] 1.3525727728 2.8440685001 1.1227487894 -2.3254065798 -2.8990236394
[16] 0.2468740127 -0.7310943756 1.9776940755 -0.0893081865 -1.6493135828
[21] 1.7898877662 -0.6550268827 -0.9551800898 -0.5014278371 -1.4746888216
[26] 2.4255103111 -0.3887325705 -1.4197037390 -0.4357437642 0.5883372533
[31] -1.7308778068 0.4161928388 -0.1356029342 1.5607508969 -0.6194888529
[36] 0.1300611735 -0.4857147832 1.1904211707 0.5049406889 -0.2667257325
[41] 1.8987616014 0.9835731579 0.2210232290 -0.1768609905 -0.1621982878
[46] 0.7891159088 1.4120920105 0.5181618446 2.3646204343 -1.2444610580
[51] 1.0913886116 0.4221756123 -0.9979893979 0.7546507455 -1.4437539264
[56] 0.5686322655 1.3541330524 -1.3428888572 -0.0968579662 0.2427675339
[61] 0.2557447836 -0.2432328996 0.4616072646 0.0650797112 2.2599934304
[66] 1.8046219794 2.3254897912 0.6827588033 -0.3355546132 -0.9979295838
[71] 0.9750712824 -1.1726851690 -0.0003372785 -1.7146059667 -1.3487234285
[76] -1.6521456120 -1.4833504973 0.9250167014 -0.2265255571 0.7749059290
[81] -0.3397895223 0.8931105544 -0.8645787802 -1.9521791587 -0.1131719450
[86] 1.0247086035 -1.2565065409 -0.6021306386 -0.8323313466 -0.4465771986
[91] -4.0105330347 0.2885133338 -1.0079847492 -0.5728161896 -0.2974105702
[96] 1.2157853948 -0.7274869914 -0.8439433649 -1.2088933891 -0.6496565159
[101] 3.1998473225 0.5727385295 -0.0199646222 0.4623168691 -1.2869950163
[106] -1.8075793916 -2.1543167043 0.7078403168 1.3641037204 0.3147183948
[111] -4.1002515836 1.2699218886 1.8014433971 -0.4297022096 0.5299936530
[116] 1.1876442251 -0.3982333523 -0.4218841622 -2.6898323919 1.0140628812
[121] -0.5420589458 -0.3686902433 1.3141152458 -0.4287053338 -0.6635825585
[126] 1.3720451180 -1.2731099699 0.8101579139 1.5741305304 -1.0251501159
[131] -0.9989308623 -0.0536208442 1.6633777159 -0.1780084621 -1.0916477552
[136] 1.9679790517 0.6342204969 1.3876448748 0.7557065400 -2.3214750728
[141] 1.6202399383 0.4232248285 -1.7051130143 0.5419818637 -0.5930278381
[146] 0.2135674503 -0.6968402425 0.0777251570 -0.7253284978 2.1315776363
[151] -0.3364551868 -0.9791825545 -0.2422460846 -1.8298007535 -1.1481487498
[156] 0.4961268063 -0.3780301536 0.5646313598 0.2689659393 0.7526613687
[161] -2.3020317325 2.8263585958 -0.6076277126 3.7740519326 1.0842669316
[166] -2.2508985635 0.2906639530 -1.1346060584 1.4158398449 0.5900416956
[171] 1.2205653983 0.0970995178 -0.9060201460 -0.5976812267 0.4371557262
[176] 0.7424875631 1.2291728654 -0.9525916035 -0.7552868287
> a <-table.start()
> a <- table.row.start(a)
> a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <- table.element(a,'Description',1,TRUE)
> a <- table.element(a,'Link',1,TRUE)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Histogram',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'QQ Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Spectral Analysis',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <- table.element(a,'Summary Statistics',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/14exzm1580202722.tab")
> if(n < 200) {
+ 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,formatC(signif(x[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/home/pw/wessanet/rcomp/tmp/15ie5f1580202722.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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/home/pw/wessanet/rcomp/tmp/1649161580202722.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,signif(numsignificant1,6))
+ a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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="/home/pw/wessanet/rcomp/tmp/178kzm1580202722.tab")
+ }
+ }
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
> a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'',sep='')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') > a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'',sep='')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') > a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'',sep='')) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/home/pw/wessanet/rcomp/tmp/18ohrd1580202722.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > vif <- vif(mylm) > a<-table.element(a,paste('
',RC.texteval('vif'),'',sep='')) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/home/pw/wessanet/rcomp/tmp/19yp681580202722.tab") > > try(system("convert /home/pw/wessanet/rcomp/tmp/1gc5e1580202721.ps /home/pw/wessanet/rcomp/tmp/1gc5e1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/1gc5e1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/2kzmy1580202721.ps /home/pw/wessanet/rcomp/tmp/2kzmy1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/2kzmy1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/3h6xj1580202721.ps /home/pw/wessanet/rcomp/tmp/3h6xj1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/3h6xj1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/4dc0p1580202721.ps /home/pw/wessanet/rcomp/tmp/4dc0p1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/4dc0p1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/5qpop1580202721.ps /home/pw/wessanet/rcomp/tmp/5qpop1580202721.png",intern=TRUE)) character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/6vv6k1580202721.ps /home/pw/wessanet/rcomp/tmp/6vv6k1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/6vv6k1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/7cjhr1580202721.ps /home/pw/wessanet/rcomp/tmp/7cjhr1580202721.png",intern=TRUE)) character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/8jc8y1580202721.ps /home/pw/wessanet/rcomp/tmp/8jc8y1580202721.png",intern=TRUE)) character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/9t8wp1580202721.ps /home/pw/wessanet/rcomp/tmp/9t8wp1580202721.png",intern=TRUE)) character(0) > try(system("convert /home/pw/wessanet/rcomp/tmp/10gw6i1580202721.ps /home/pw/wessanet/rcomp/tmp/10gw6i1580202721.png",intern=TRUE)) convert-im6.q16: profile 'icc': 'RGB ': RGB color space not permitted on grayscale PNG `/home/pw/wessanet/rcomp/tmp/10gw6i1580202721.png' @ warning/png.c/MagickPNGWarningHandler/1654. character(0) > > proc.time() user system elapsed 2.633 0.307 2.944