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Data X:
149 2011 1 0.5 1 0.67 0.67 0 0 139 2011 1 0.5 0.89 0.83 0.33 0.5 1 148 2011 1 0.4 0.89 1 0.67 0 0 158 2011 1 0.5 0.89 0.83 0 0 1 128 2011 1 0.7 0.89 0.67 0 1 1 224 2011 1 0.3 0.78 0 0 0.5 1 159 2011 1 0.4 0.89 0.83 0.67 0.5 0 105 2011 1 0.4 1 0.5 0.67 1 1 159 2011 1 0.7 0.89 0.83 0 0.5 1 167 2011 1 0.6 0.78 0.33 0.67 0.5 1 165 2011 1 0.6 1 0.5 1 0 1 159 2011 1 0.2 0.78 0.67 0 0.5 1 119 2011 1 0.4 0.89 1 0 0.5 1 176 2011 1 0.4 0.89 0.5 0.67 0 0 54 2011 1 0.5 0.89 0.67 0.33 0 0 91 2011 0 0.3 0.89 0.17 0.67 0 0 163 2011 1 0.4 0.89 0.83 0.33 0.5 1 124 2011 1 0.7 0.67 0.67 0.33 0.5 0 137 2011 0 0.5 1 0.67 0.33 0 1 121 2011 1 0.2 0.78 0.67 0 0 0 153 2011 1 0.3 0.78 0.5 0.67 0 1 148 2011 1 0.6 0.89 1 0.33 0 1 221 2011 1 0.6 0.78 0.83 0.33 0 0 188 2011 1 0.2 0.89 0.83 0.33 0 1 149 2011 1 0.7 0.89 1 0.67 1 1 244 2011 1 0.2 0.33 0.67 0 0 1 148 2011 0 1 1 1 0.33 1 1 92 2011 0 0.4 0.89 0.83 0.67 0 0 150 2011 1 0.4 0.89 1 1 0 1 153 2011 1 0.2 0.67 0.83 0.67 0 0 94 2011 1 0.4 0.56 0.67 0.33 0 0 156 2011 1 0.4 0.89 0.67 0 0.5 0 132 2011 1 0.7 0.89 1 0.67 0.5 1 161 2011 1 0.2 1 0.67 0.67 0 1 105 2011 1 0.6 0.78 1 1 0 1 97 2011 1 0.3 0.78 1 1 0.5 1 151 2011 1 0.3 0.33 0.5 0.33 0 0 131 2011 0 0.2 0.78 0.67 0 0.5 1 166 2011 1 0.5 0.89 0.83 0.67 0.5 1 157 2011 1 0.7 0.89 1 0.67 0.5 0 111 2011 1 0.6 0.78 1 0.67 0.5 1 145 2011 1 0.4 0.89 1 0.67 0.5 1 162 2011 1 0.6 0.89 1 0.33 0.5 1 163 2011 1 0.4 1 1 1 0 1 59 2011 0 0.3 0.67 0.83 0.67 0 1 187 2011 1 0.5 1 0.83 0.67 0.5 0 109 2011 1 0.2 0.89 0.5 0 0 1 90 2011 0 0.3 0.89 0.83 0 0.5 1 105 2011 1 0.5 0.89 0.17 0 0 0 83 2011 0 0.7 0.78 0.83 1 0.5 1 116 2011 0 0.4 0.89 1 0.67 1 1 42 2011 0 0.3 0.78 1 0 0 1 148 2011 1 0.2 0.78 0.67 0.67 1 1 155 2011 0 0.5 1 1 0 0 1 125 2011 1 0.4 0.78 1 0 0.5 1 116 2011 1 0.6 1 1 0.67 1 1 128 2011 0 0.4 0.78 0.83 1 0 0 138 2011 1 0.4 0.67 0.33 0 0 1 49 2011 0 0.2 0.33 0.33 0.33 0 0 96 2011 0 0.9 1 1 0.67 0.5 1 164 2011 1 0.8 1 1 0.67 1 1 162 2011 1 0.8 0.78 0.83 0 0.5 0 99 2011 1 0.3 0.67 1 1 0.5 0 202 2011 1 0.2 1 0.83 0.67 0 1 186 2011 1 0.4 0.89 0.67 0 0.5 0 66 2011 0 0.2 0.89 0.83 1 0 1 183 2011 1 0.2 0.78 0.67 0.67 0.5 0 214 2011 1 0.1 1 0.83 0.67 0 1 188 2011 1 0.4 0.56 0.67 1 0.5 1 104 2011 0 0.5 0.67 1 0 0.5 0 177 2011 1 0.8 0.89 0.83 0.33 0.5 0 126 2011 1 0.4 0.89 0.67 0.67 0 0 76 2011 0 0.6 0.89 0.83 0.33 0.5 0 99 2011 0 0.5 0.89 0.83 0.67 0.5 1 139 2011 1 0.3 0.78 0.67 0 0 0 162 2011 1 0.4 1 0.33 0 0.5 0 108 2011 0 0.6 1 0.83 0.67 0.5 1 159 2011 1 0.4 0.89 1 0.33 0 0 74 2011 0 0.3 0.44 0.83 0 0 0 110 2011 1 0.8 0.78 0.83 0 1 1 96 2011 0 0.6 0.89 0.5 0.33 1 0 116 2011 0 0.3 0.67 0.5 0 0 0 87 2011 0 0.5 0.78 0.83 0.67 0.5 0 97 2011 0 0.4 0.78 1 0.33 0 1 127 2011 0 0.3 0.33 0.33 0.67 0 0 106 2011 0 0.7 0.89 1 0.33 0 1 80 2011 0 0.2 0.89 0.67 0.33 0.5 1 74 2011 0 0.4 0.89 0.83 1 0 0 91 2011 0 0.6 0.89 1 0.67 0.5 0 133 2011 0 0.6 0.56 0.83 0 0 0 74 2011 0 0.6 0.67 0.83 0.67 0.5 1 114 2011 0 0.4 0.67 1 0.33 0.5 1 140 2011 0 0.6 0.78 0.83 0 0 1 95 2011 0 0.5 0.78 1 0.33 0.5 0 98 2011 0 0.5 0.78 0.83 0 0 1 121 2011 0 0.6 0.89 0.67 0 0 0 126 2011 0 0.8 1 0.83 0.33 0.5 1 98 2011 0 0.5 0.89 0.83 0.67 1 1 95 2011 0 0.6 0.89 0.83 0.67 0.5 1 110 2011 0 0.4 0.78 0.83 0.67 0.5 1 70 2011 0 0.3 1 0.67 0.67 0.5 1 102 2011 0 0.3 0.78 0.83 1 0 0 86 2011 0 0.2 0.67 0 0 0 1 130 2011 0 0.4 0.78 0.83 0 0 1 96 2011 0 0.5 0.89 1 0 0 1 102 2011 0 0.3 0.67 0.17 0 0.5 0 100 2011 0 0.4 0.22 0.17 0 0.5 0 94 2011 0 0.5 0.44 0.5 1 0 0 52 2011 0 0.3 0.89 0.5 0.67 0 0 98 2011 0 0.5 0.67 1 0 0 0 118 2011 0 0.4 0.89 0.67 0.67 0 0 99 2011 0 0.4 0.67 0.83 0.67 0 1 48 2012 1 0.6 0.78 1 0 1 1 50 2012 1 0.3 0.78 1 0.67 1 1 150 2012 1 0.4 0.78 1 0.33 1 1 154 2012 1 0.3 1 1 1 1 1 109 2012 0 1 0.78 1 1 1 0 68 2012 0 0.4 0.67 1 0 0 1 194 2012 1 0.8 0.89 0.83 1 0.5 1 158 2012 1 0.3 0.89 1 0.67 1 0 159 2012 1 0.5 1 0.83 0.67 0 1 67 2012 1 0.4 0.78 1 0 0 0 147 2012 1 0.3 0.67 0.83 0.67 0 0 39 2012 1 0.5 0.89 0.83 1 0 1 100 2012 1 0.3 0.67 1 0.67 0 1 111 2012 1 0.3 0.67 0.67 0 0 1 138 2012 1 0.4 1 0.83 0 0 1 101 2012 1 0.3 0.67 1 0 0 1 131 2012 0 0.6 1 1 0.33 0.5 1 101 2012 1 0.6 0.89 0.83 0.67 1 1 114 2012 1 0.4 0.89 1 1 1 1 165 2012 1 0.4 1 1 0 0 0 114 2012 1 0.4 0.67 1 0.67 0 1 111 2012 1 0.3 0.44 0.67 0.67 0.5 1 75 2012 1 0.2 0.89 1 0.33 1 1 82 2012 1 0.5 0.56 0.83 0.67 0 1 121 2012 1 0.4 0.78 1 0.67 1 1 32 2012 1 0.4 1 1 0.67 0 1 150 2012 1 0.4 1 0.83 0.67 0 0 117 2012 1 0.3 0.89 0.67 0.67 0.5 1 71 2012 0 0.4 0.67 0.83 0.67 1 1 165 2012 1 0.2 0.89 1 0.33 0.5 1 154 2012 1 0 0.33 0 0 0 1 126 2012 1 0.4 0.89 1 0.67 0.5 1 149 2012 1 0.6 0.78 1 0 1 0 145 2012 1 0.4 1 0.67 0.67 0 0 120 2012 1 0.4 0.44 1 0 0 1 109 2012 1 0.4 0.67 0.83 0 0.5 0 132 2012 1 0.2 0.33 0.17 0 0.5 0 172 2012 1 0.4 0.89 0.83 1 1 1 169 2012 1 0.3 0.89 0.83 0 0 0 114 2012 1 0.6 1 0.83 0.67 1 1 156 2012 1 0.6 0.89 0.83 1 0 1 172 2012 1 0.4 0.89 0.83 0 0 0 68 2012 0 0.5 1 1 0.67 1 1 89 2012 0 0.4 0.89 0.83 0 0.5 1 167 2012 1 0.6 1 1 1 1 1 113 2012 1 0.6 0.78 0.83 0.67 0.5 0 115 2012 0 0.9 0.78 1 0.67 0.5 0 78 2012 0 0.4 0.67 0.83 0.67 0.5 0 118 2012 0 0.8 0.89 1 1 0.5 0 87 2012 0 0.5 0.67 0.83 1 0 1 173 2012 1 0.4 0.78 0.83 1 0 0 2 2012 1 0.4 0.89 1 0.67 1 1 162 2012 0 0.7 0.89 1 1 1 0 49 2012 0 0.4 0.78 1 0.33 1 1 122 2012 0 0.8 1 1 0.67 0.5 0 96 2012 0 0.4 1 1 1 1 1 100 2012 0 0.3 1 1 0.67 0 0 82 2012 0 0.5 0.67 1 0.67 0.5 0 100 2012 0 0.8 0.89 1 0.67 1 1 115 2012 0 0.4 1 0.83 0.33 0 0 141 2012 0 1 1 1 1 0.5 1 165 2012 1 0.5 0.89 1 0.67 1 1 165 2012 1 0.5 0.89 1 0.67 1 1 110 2012 0 0.3 0.89 1 0.33 0 1 118 2012 1 0.3 0.89 0.83 0.33 0.5 1 158 2012 1 0.3 0.89 0.5 0 0 0 146 2012 0 0.4 1 0.67 0.33 0.5 1 49 2012 1 0.5 0.67 1 0.33 0 0 90 2012 0 0.5 1 0.67 0.67 0.5 0 121 2012 0 0.4 0.89 1 0 0 0 155 2012 1 0.7 0.89 1 1 0.5 1 104 2012 0 0.5 0.89 0.5 0.33 0 0 147 2012 0 0.4 0.89 0.67 0.33 1 1 110 2012 0 0.7 1 0.67 1 0 0 108 2012 0 0.7 1 0.67 1 0 0 113 2012 0 0.7 1 0.67 1 0 0 115 2012 0 0.7 0.89 0.67 1 0 0 61 2012 0 0.7 0.89 0.67 0 0 1 60 2012 0 0.7 0.89 1 0.67 0.5 1 109 2012 0 0.1 0.33 0.67 0.33 0.5 1 68 2012 0 0.2 0.67 0.67 0.67 0.5 1 111 2012 0 0.3 0.56 0.33 0.33 0 0 77 2012 0 0.6 0.44 0.83 0.33 0 0 73 2012 0 0.8 1 1 1 1 1 151 2012 1 0.8 0.89 1 0.33 0.5 0 89 2012 0 0 0.33 0.17 0 0 0 78 2012 0 0.3 0.67 0.67 0.33 0 0 110 2012 0 0.6 0.67 0.83 0.33 0.5 0 220 2012 1 0.5 1 0.83 0.67 0 1 65 2012 0 0.7 0.78 1 0.33 0 1 141 2012 1 0.3 0.67 0.83 0 0.5 0 117 2012 0 0.3 1 1 0.67 0 0 122 2012 1 0.4 0.78 1 0.67 0 1 63 2012 0 0.4 0.89 0.83 1 0 0 44 2012 1 0.1 0.89 0.83 0 0 1 52 2012 0 0.5 0.89 1 0.67 0 1 131 2012 0 0 0 0 0 0 0 101 2012 0 0.4 0.67 1 0.33 0.5 1 42 2012 0 0.6 1 0.83 0.67 1 1 152 2012 1 0.4 1 1 0.33 0.5 1 107 2012 1 0.1 0.67 0.33 0 0.5 0 77 2012 0 0.3 0.89 0.83 0 0 0 154 2012 1 0.7 0.89 0.83 0.67 0 0 103 2012 1 0.3 0.56 0.17 0 0 1 96 2012 0 0.5 0.67 0.83 0.33 0.5 1 175 2012 1 0.3 1 0.83 0.67 1 1 57 2012 0 0.6 1 0.67 0.67 0.5 1 112 2012 0 0.9 1 1 1 0 0 143 2012 1 0.4 0.67 0.83 0 0.5 0 49 2012 0 0.3 0.44 1 0 0.5 0 110 2012 1 0.9 0.89 1 0.67 1 1 131 2012 1 0.5 0.44 1 0 0.5 1 167 2012 1 0.3 0.56 1 1 0.5 0 56 2012 0 0.6 0.89 0.83 0.67 0 0 137 2012 1 0.2 0.67 1 0.33 0 0 86 2012 0 0.4 0.89 0.83 1 0.5 1 121 2012 1 0.5 1 0.83 0.67 0.5 1 149 2012 1 0.4 0.78 0.83 0.67 0 0 168 2012 1 0 0.44 0 0 0 0 140 2012 1 0.2 0.89 1 0.33 0.5 0 88 2012 0 0.5 0.89 1 0.67 0.5 1 168 2012 1 0.3 0.89 1 0.67 0 1 94 2012 1 0 0.44 0 0 0 1 51 2012 1 0.5 1 0.83 1 0 1 48 2012 0 0.6 0.89 0.83 0.33 0 0 145 2012 1 0.3 0.67 0.83 0 0.5 1 66 2012 1 0 0.33 0 0 0 1 85 2012 0 0.3 0.78 0.67 0 0.5 1 109 2012 1 0.5 0.89 1 0.67 0.5 0 63 2012 0 0.4 0.78 0.67 0 0 0 102 2012 0 0.5 0.78 0.83 0.67 0 1 162 2012 0 0.7 0.89 1 1 1 0 86 2012 0 0.8 0.78 1 0.67 0.5 1 114 2012 0 0.6 0.78 1 0.33 0.5 1 164 2012 1 0.4 0.67 0.83 0.33 0 0 119 2012 1 0.5 0.89 0.83 0.33 0.5 1 126 2012 1 0.5 0.89 1 0 0.5 0 132 2012 1 0.3 0.78 1 0.33 0 1 142 2012 1 0.6 1 1 0 0.5 1 83 2012 1 0.3 1 0.67 0.67 0 0 94 2012 0 0.6 0.78 0.83 1 0.5 1 81 2012 0 0.3 0.78 0.33 0.33 0 0 166 2012 1 0.7 0.89 1 0.67 1 1 110 2012 0 0.7 0.89 1 1 0 0 64 2012 0 0.6 0.67 0.67 1 0.5 1 93 2012 1 0.5 1 1 0.33 0.5 0 104 2012 0 0.5 0.67 0.83 0.33 0 0 105 2012 0 0.4 0.56 0.67 0 0 1 49 2012 0 0.4 0.78 1 0.33 1 1 88 2012 0 0.7 1 1 1 0 0 95 2012 0 0.2 0.67 0.17 0 0.5 1 102 2012 0 0.5 0.78 0.83 0.67 0 1 99 2012 0 0.4 0.56 0.83 0.67 0.5 0 63 2012 0 0.2 1 1 0.67 1 1 76 2012 0 0.5 0.89 0.67 0.67 0 0 109 2012 0 0.4 0.44 0.5 0 0 0 117 2012 0 0.7 1 0.67 1 1 1 57 2012 0 0.6 0.89 0.83 0.67 1 1 120 2012 0 0.4 0.78 0.83 0 0 0 73 2012 0 0.5 0.89 1 0.67 1 1 91 2012 0 0 0.11 0.17 0 0 0 108 2012 0 0.7 0.89 1 0.67 0.5 0 105 2012 0 0.4 0.89 0.67 0.67 0 1 117 2012 1 0.5 1 0.67 1 0 0 119 2012 0 0.6 0.89 0.83 0.67 0 0 31 2012 0 0.8 1 0.5 0.67 0.5 1
Names of X columns:
LFM year group Algebraic_Reasoning Calculation Graphical_Interpretation Proportionality_and_Ratio Probability_and_Sampling gender
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' 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 (par5=='') par5 <- 0 par5 <- as.numeric(par5) 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=12)'){ (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s=12)'){ (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 - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*12)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*12-j*12,par1] } } x <- cbind(x[(par5*12+1):n,], x2) n <- n - par5*12 } 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,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(x) (k <- length(x[n,])) head(x) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, 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='mytable1.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<br />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='mytable2.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='mytable3.tab') myr <- as.numeric(mysum$resid) myr if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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='mytable4.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='mytable5.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='mytable6.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('<pre>',RC.texteval('reset_test_fitted'),'</pre>',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('<pre>',RC.texteval('reset_test_regressors'),'</pre>',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('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.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('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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