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Data X:
149 0.5 0 0.67 0.67 0.5 1 0 1 2011 139 1 0.5 0.33 0.83 0.5 0.89 1 1 2011 148 1 0 0.67 1 0.4 0.89 0 1 2011 158 0 0 0 0.83 0.5 0.89 1 1 2011 128 1 1 0 0.67 0.7 0.89 1 1 2011 224 0.5 0.5 0 0 0.3 0.78 1 1 2011 159 0 0.5 0.67 0.83 0.4 0.89 0 1 2011 105 1 1 0.67 0.5 0.4 1 1 1 2011 159 0 0.5 0 0.83 0.7 0.89 1 1 2011 167 0.5 0.5 0.67 0.33 0.6 0.78 1 1 2011 165 0.5 0 1 0.5 0.6 1 1 1 2011 159 0.5 0.5 0 0.67 0.2 0.78 1 1 2011 119 0.5 0.5 0 1 0.4 0.89 1 1 2011 176 1 0 0.67 0.5 0.4 0.89 0 1 2011 54 0 0 0.33 0.67 0.5 0.89 0 1 2011 91 0.5 0 0.67 0.17 0.3 0.89 0 0 2011 163 0.5 0.5 0.33 0.83 0.4 0.89 1 1 2011 124 1 0.5 0.33 0.67 0.7 0.67 0 1 2011 137 1 0 0.33 0.67 0.5 1 1 0 2011 121 1 0 0 0.67 0.2 0.78 0 1 2011 153 0.5 0 0.67 0.5 0.3 0.78 1 1 2011 148 1 0 0.33 1 0.6 0.89 1 1 2011 221 1 0 0.33 0.83 0.6 0.78 0 1 2011 188 1 0 0.33 0.83 0.2 0.89 1 1 2011 149 0 1 0.67 1 0.7 0.89 1 1 2011 244 0 0 0 0.67 0.2 0.33 1 1 2011 148 1 1 0.33 1 1 1 1 0 2011 92 0.5 0 0.67 0.83 0.4 0.89 0 0 2011 150 1 0 1 1 0.4 0.89 1 1 2011 153 0.5 0 0.67 0.83 0.2 0.67 0 1 2011 94 1 0 0.33 0.67 0.4 0.56 0 1 2011 156 1 0.5 0 0.67 0.4 0.89 0 1 2011 132 0.5 0.5 0.67 1 0.7 0.89 1 1 2011 161 0.5 0 0.67 0.67 0.2 1 1 1 2011 105 0.5 0 1 1 0.6 0.78 1 1 2011 97 0.5 0.5 1 1 0.3 0.78 1 1 2011 151 0 0 0.33 0.5 0.3 0.33 0 1 2011 131 0 0.5 0 0.67 0.2 0.78 1 0 2011 166 0.5 0.5 0.67 0.83 0.5 0.89 1 1 2011 157 1 0.5 0.67 1 0.7 0.89 0 1 2011 111 0.5 0.5 0.67 1 0.6 0.78 1 1 2011 145 1 0.5 0.67 1 0.4 0.89 1 1 2011 162 1 0.5 0.33 1 0.6 0.89 1 1 2011 163 1 0 1 1 0.4 1 1 1 2011 59 1 0 0.67 0.83 0.3 0.67 1 0 2011 187 0.5 0.5 0.67 0.83 0.5 1 0 1 2011 109 1 0 0 0.5 0.2 0.89 1 1 2011 90 1 0.5 0 0.83 0.3 0.89 1 0 2011 105 1 0 0 0.17 0.5 0.89 0 1 2011 83 1 0.5 1 0.83 0.7 0.78 1 0 2011 116 0.5 1 0.67 1 0.4 0.89 1 0 2011 42 0.5 0 0 1 0.3 0.78 1 0 2011 148 1 1 0.67 0.67 0.2 0.78 1 1 2011 155 0.5 0 0 1 0.5 1 1 0 2011 125 0 0.5 0 1 0.4 0.78 1 1 2011 116 1 1 0.67 1 0.6 1 1 1 2011 128 1 0 1 0.83 0.4 0.78 0 0 2011 138 0.5 0 0 0.33 0.4 0.67 1 1 2011 49 0 0 0.33 0.33 0.2 0.33 0 0 2011 96 1 0.5 0.67 1 0.9 1 1 0 2011 164 0.5 1 0.67 1 0.8 1 1 1 2011 162 1 0.5 0 0.83 0.8 0.78 0 1 2011 99 1 0.5 1 1 0.3 0.67 0 1 2011 202 0.5 0 0.67 0.83 0.2 1 1 1 2011 186 1 0.5 0 0.67 0.4 0.89 0 1 2011 66 1 0 1 0.83 0.2 0.89 1 0 2011 183 1 0.5 0.67 0.67 0.2 0.78 0 1 2011 214 1 0 0.67 0.83 0.1 1 1 1 2011 188 0 0.5 1 0.67 0.4 0.56 1 1 2011 104 0.5 0.5 0 1 0.5 0.67 0 0 2011 177 1 0.5 0.33 0.83 0.8 0.89 0 1 2011 126 0.5 0 0.67 0.67 0.4 0.89 0 1 2011 76 0.5 0.5 0.33 0.83 0.6 0.89 0 0 2011 99 1 0.5 0.67 0.83 0.5 0.89 1 0 2011 139 0 0 0 0.67 0.3 0.78 0 1 2011 162 0 0.5 0 0.33 0.4 1 0 1 2011 108 0.5 0.5 0.67 0.83 0.6 1 1 0 2011 159 0.5 0 0.33 1 0.4 0.89 0 1 2011 74 0 0 0 0.83 0.3 0.44 0 0 2011 110 1 1 0 0.83 0.8 0.78 1 1 2011 96 1 1 0.33 0.5 0.6 0.89 0 0 2011 116 0 0 0 0.5 0.3 0.67 0 0 2011 87 1 0.5 0.67 0.83 0.5 0.78 0 0 2011 97 1 0 0.33 1 0.4 0.78 1 0 2011 127 0 0 0.67 0.33 0.3 0.33 0 0 2011 106 0.5 0 0.33 1 0.7 0.89 1 0 2011 80 0.5 0.5 0.33 0.67 0.2 0.89 1 0 2011 74 1 0 1 0.83 0.4 0.89 0 0 2011 91 0.5 0.5 0.67 1 0.6 0.89 0 0 2011 133 1 0 0 0.83 0.6 0.56 0 0 2011 74 0.5 0.5 0.67 0.83 0.6 0.67 1 0 2011 114 1 0.5 0.33 1 0.4 0.67 1 0 2011 140 1 0 0 0.83 0.6 0.78 1 0 2011 95 1 0.5 0.33 1 0.5 0.78 0 0 2011 98 1 0 0 0.83 0.5 0.78 1 0 2011 121 1 0 0 0.67 0.6 0.89 0 0 2011 126 1 0.5 0.33 0.83 0.8 1 1 0 2011 98 0.5 1 0.67 0.83 0.5 0.89 1 0 2011 95 1 0.5 0.67 0.83 0.6 0.89 1 0 2011 110 1 0.5 0.67 0.83 0.4 0.78 1 0 2011 70 1 0.5 0.67 0.67 0.3 1 1 0 2011 102 0.5 0 1 0.83 0.3 0.78 0 0 2011 86 0 0 0 0 0.2 0.67 1 0 2011 130 0.5 0 0 0.83 0.4 0.78 1 0 2011 96 0.5 0 0 1 0.5 0.89 1 0 2011 102 0 0.5 0 0.17 0.3 0.67 0 0 2011 100 0 0.5 0 0.17 0.4 0.22 0 0 2011 94 0 0 1 0.5 0.5 0.44 0 0 2011 52 1 0 0.67 0.5 0.3 0.89 0 0 2011 98 0.5 0 0 1 0.5 0.67 0 0 2011 118 0.5 0 0.67 0.67 0.4 0.89 0 0 2011 99 1 0 0.67 0.83 0.4 0.67 1 0 2011 48 1 1 0 1 0.6 0.78 1 1 2012 50 1 1 0.67 1 0.3 0.78 1 1 2012 150 0.5 1 0.33 1 0.4 0.78 1 1 2012 154 1 1 1 1 0.3 1 1 1 2012 109 1 1 1 1 1 0.78 0 0 2012 68 0.5 0 0 1 0.4 0.67 1 0 2012 194 1 0.5 1 0.83 0.8 0.89 1 1 2012 158 1 1 0.67 1 0.3 0.89 0 1 2012 159 1 0 0.67 0.83 0.5 1 1 1 2012 67 0.5 0 0 1 0.4 0.78 0 1 2012 147 1 0 0.67 0.83 0.3 0.67 0 1 2012 39 1 0 1 0.83 0.5 0.89 1 1 2012 100 1 0 0.67 1 0.3 0.67 1 1 2012 111 1 0 0 0.67 0.3 0.67 1 1 2012 138 1 0 0 0.83 0.4 1 1 1 2012 101 0.5 0 0 1 0.3 0.67 1 1 2012 131 0.5 0.5 0.33 1 0.6 1 1 0 2012 101 1 1 0.67 0.83 0.6 0.89 1 1 2012 114 1 1 1 1 0.4 0.89 1 1 2012 165 0 0 0 1 0.4 1 0 1 2012 114 0.5 0 0.67 1 0.4 0.67 1 1 2012 111 1 0.5 0.67 0.67 0.3 0.44 1 1 2012 75 0 1 0.33 1 0.2 0.89 1 1 2012 82 1 0 0.67 0.83 0.5 0.56 1 1 2012 121 1 1 0.67 1 0.4 0.78 1 1 2012 32 0 0 0.67 1 0.4 1 1 1 2012 150 1 0 0.67 0.83 0.4 1 0 1 2012 117 0.5 0.5 0.67 0.67 0.3 0.89 1 1 2012 71 0.5 1 0.67 0.83 0.4 0.67 1 0 2012 165 1 0.5 0.33 1 0.2 0.89 1 1 2012 154 0 0 0 0 0 0.33 1 1 2012 126 1 0.5 0.67 1 0.4 0.89 1 1 2012 149 1 1 0 1 0.6 0.78 0 1 2012 145 0.5 0 0.67 0.67 0.4 1 0 1 2012 120 0.5 0 0 1 0.4 0.44 1 1 2012 109 0 0.5 0 0.83 0.4 0.67 0 1 2012 132 0 0.5 0 0.17 0.2 0.33 0 1 2012 172 1 1 1 0.83 0.4 0.89 1 1 2012 169 0.5 0 0 0.83 0.3 0.89 0 1 2012 114 0 1 0.67 0.83 0.6 1 1 1 2012 156 1 0 1 0.83 0.6 0.89 1 1 2012 172 1 0 0 0.83 0.4 0.89 0 1 2012 68 0.5 1 0.67 1 0.5 1 1 0 2012 89 1 0.5 0 0.83 0.4 0.89 1 0 2012 167 1 1 1 1 0.6 1 1 1 2012 113 1 0.5 0.67 0.83 0.6 0.78 0 1 2012 115 1 0.5 0.67 1 0.9 0.78 0 0 2012 78 0 0.5 0.67 0.83 0.4 0.67 0 0 2012 118 1 0.5 1 1 0.8 0.89 0 0 2012 87 1 0 1 0.83 0.5 0.67 1 0 2012 173 0 0 1 0.83 0.4 0.78 0 1 2012 2 0.5 1 0.67 1 0.4 0.89 1 1 2012 162 0.5 1 1 1 0.7 0.89 0 0 2012 49 1 1 0.33 1 0.4 0.78 1 0 2012 122 1 0.5 0.67 1 0.8 1 0 0 2012 96 0.5 1 1 1 0.4 1 1 0 2012 100 0.5 0 0.67 1 0.3 1 0 0 2012 82 1 0.5 0.67 1 0.5 0.67 0 0 2012 100 1 1 0.67 1 0.8 0.89 1 0 2012 115 0.5 0 0.33 0.83 0.4 1 0 0 2012 141 0 0.5 1 1 1 1 1 0 2012 165 1 1 0.67 1 0.5 0.89 1 1 2012 165 1 1 0.67 1 0.5 0.89 1 1 2012 110 1 0 0.33 1 0.3 0.89 1 0 2012 118 1 0.5 0.33 0.83 0.3 0.89 1 1 2012 158 1 0 0 0.5 0.3 0.89 0 1 2012 146 0.5 0.5 0.33 0.67 0.4 1 1 0 2012 49 1 0 0.33 1 0.5 0.67 0 1 2012 90 1 0.5 0.67 0.67 0.5 1 0 0 2012 121 0 0 0 1 0.4 0.89 0 0 2012 155 0 0.5 1 1 0.7 0.89 1 1 2012 104 0.5 0 0.33 0.5 0.5 0.89 0 0 2012 147 0 1 0.33 0.67 0.4 0.89 1 0 2012 110 1 0 1 0.67 0.7 1 0 0 2012 108 1 0 1 0.67 0.7 1 0 0 2012 113 1 0 1 0.67 0.7 1 0 0 2012 115 1 0 1 0.67 0.7 0.89 0 0 2012 61 0 0 0 0.67 0.7 0.89 1 0 2012 60 1 0.5 0.67 1 0.7 0.89 1 0 2012 109 0 0.5 0.33 0.67 0.1 0.33 1 0 2012 68 1 0.5 0.67 0.67 0.2 0.67 1 0 2012 111 1 0 0.33 0.33 0.3 0.56 0 0 2012 77 0.5 0 0.33 0.83 0.6 0.44 0 0 2012 73 1 1 1 1 0.8 1 1 0 2012 151 0.5 0.5 0.33 1 0.8 0.89 0 1 2012 89 0 0 0 0.17 0 0.33 0 0 2012 78 1 0 0.33 0.67 0.3 0.67 0 0 2012 110 1 0.5 0.33 0.83 0.6 0.67 0 0 2012 220 1 0 0.67 0.83 0.5 1 1 1 2012 65 0.5 0 0.33 1 0.7 0.78 1 0 2012 141 1 0.5 0 0.83 0.3 0.67 0 1 2012 117 0 0 0.67 1 0.3 1 0 0 2012 122 0.5 0 0.67 1 0.4 0.78 1 1 2012 63 1 0 1 0.83 0.4 0.89 0 0 2012 44 1 0 0 0.83 0.1 0.89 1 1 2012 52 1 0 0.67 1 0.5 0.89 1 0 2012 131 0 0 0 0 0 0 0 0 2012 101 0 0.5 0.33 1 0.4 0.67 1 0 2012 42 0.5 1 0.67 0.83 0.6 1 1 0 2012 152 1 0.5 0.33 1 0.4 1 1 1 2012 107 1 0.5 0 0.33 0.1 0.67 0 1 2012 77 1 0 0 0.83 0.3 0.89 0 0 2012 154 1 0 0.67 0.83 0.7 0.89 0 1 2012 103 1 0 0 0.17 0.3 0.56 1 1 2012 96 0 0.5 0.33 0.83 0.5 0.67 1 0 2012 175 1 1 0.67 0.83 0.3 1 1 1 2012 57 1 0.5 0.67 0.67 0.6 1 1 0 2012 112 1 0 1 1 0.9 1 0 0 2012 143 1 0.5 0 0.83 0.4 0.67 0 1 2012 49 0.5 0.5 0 1 0.3 0.44 0 0 2012 110 1 1 0.67 1 0.9 0.89 1 1 2012 131 0 0.5 0 1 0.5 0.44 1 1 2012 167 0.5 0.5 1 1 0.3 0.56 0 1 2012 56 0.5 0 0.67 0.83 0.6 0.89 0 0 2012 137 0.5 0 0.33 1 0.2 0.67 0 1 2012 86 1 0.5 1 0.83 0.4 0.89 1 0 2012 121 0.5 0.5 0.67 0.83 0.5 1 1 1 2012 149 0.5 0 0.67 0.83 0.4 0.78 0 1 2012 168 0 0 0 0 0 0.44 0 1 2012 140 1 0.5 0.33 1 0.2 0.89 0 1 2012 88 1 0.5 0.67 1 0.5 0.89 1 0 2012 168 0.5 0 0.67 1 0.3 0.89 1 1 2012 94 0 0 0 0 0 0.44 1 1 2012 51 1 0 1 0.83 0.5 1 1 1 2012 48 1 0 0.33 0.83 0.6 0.89 0 0 2012 145 0.5 0.5 0 0.83 0.3 0.67 1 1 2012 66 0 0 0 0 0 0.33 1 1 2012 85 0 0.5 0 0.67 0.3 0.78 1 0 2012 109 1 0.5 0.67 1 0.5 0.89 0 1 2012 63 1 0 0 0.67 0.4 0.78 0 0 2012 102 0.5 0 0.67 0.83 0.5 0.78 1 0 2012 162 0.5 1 1 1 0.7 0.89 0 0 2012 86 1 0.5 0.67 1 0.8 0.78 1 0 2012 114 1 0.5 0.33 1 0.6 0.78 1 0 2012 164 0.5 0 0.33 0.83 0.4 0.67 0 1 2012 119 0 0.5 0.33 0.83 0.5 0.89 1 1 2012 126 1 0.5 0 1 0.5 0.89 0 1 2012 132 1 0 0.33 1 0.3 0.78 1 1 2012 142 1 0.5 0 1 0.6 1 1 1 2012 83 0.5 0 0.67 0.67 0.3 1 0 1 2012 94 0.5 0.5 1 0.83 0.6 0.78 1 0 2012 81 1 0 0.33 0.33 0.3 0.78 0 0 2012 166 1 1 0.67 1 0.7 0.89 1 1 2012 110 1 0 1 1 0.7 0.89 0 0 2012 64 1 0.5 1 0.67 0.6 0.67 1 0 2012 93 0 0.5 0.33 1 0.5 1 0 1 2012 104 0.5 0 0.33 0.83 0.5 0.67 0 0 2012 105 1 0 0 0.67 0.4 0.56 1 0 2012 49 1 1 0.33 1 0.4 0.78 1 0 2012 88 1 0 1 1 0.7 1 0 0 2012 95 0 0.5 0 0.17 0.2 0.67 1 0 2012 102 0.5 0 0.67 0.83 0.5 0.78 1 0 2012 99 0 0.5 0.67 0.83 0.4 0.56 0 0 2012 63 1 1 0.67 1 0.2 1 1 0 2012 76 0 0 0.67 0.67 0.5 0.89 0 0 2012 109 1 0 0 0.5 0.4 0.44 0 0 2012 117 1 1 1 0.67 0.7 1 1 0 2012 57 0 1 0.67 0.83 0.6 0.89 1 0 2012 120 0 0 0 0.83 0.4 0.78 0 0 2012 73 1 1 0.67 1 0.5 0.89 1 0 2012 91 0 0 0 0.17 0 0.11 0 0 2012 108 1 0.5 0.67 1 0.7 0.89 0 0 2012 105 1 0 0.67 0.67 0.4 0.89 1 0 2012 117 1 0 1 0.67 0.5 1 0 1 2012 119 0.5 0 0.67 0.83 0.6 0.89 0 0 2012 31 0.5 0.5 0.67 0.5 0.8 1 1 0 2012
Names of X columns:
LFM Estimation Probability_and_Sampling Proportionality_and_Ratio Graphical_Interpretation Algebraic_Reasoning Calculation gender group year

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



R Code
library(lattice) library(lmtest) 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-par512,par5), dimnames=list(1:(n-par512), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par512)) { for (j in 1:par5) { x2[i,j] <- x[i+par512-j12,par1] } } x <- cbind(x[(par512+1):n,], x2) n <- n - par512 } 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' } 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerrormyerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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') } }

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