Description Usage Arguments References Examples
The lower bound root mean-square error and the upper bound root mean-square error proposed by Lima Neto and de Carvalho(2008) measure the differences between the predicted values and the observed values.
1 | RMSE(model)
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model |
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Lima Neto, E.A. and De Carvalho, F.A.T(2010), Constrained linear regression models for symbolic interval-valued variables Computational Statistics and Data Analysis, 54, 333-347
1 2 3 4 5 6 7 8 9 10 | set.seed(2017)
x1_L = rnorm(30, 3, 0.01) - rnorm(30, 0, 0.01)
x1_U = rnorm(30, 3, 0.01) + rnorm(30, 3, 0.01)
x2_L = runif(30, 1.5, 3) - runif(30, 0, 1)
x2_U = runif(30, 1.5, 3) + runif(30, 1, 2)
y_L = x1_L + x2_L
y_U = x1_U + x2_U
temp <- as.data.frame(cbind(y_L, y_U, x1_L, x1_U, x2_L, x2_U))
m1 <- imcmtn(cbind(y_L, y_U) ~ x1_L + x1_U + x2_L + x2_U, data = temp, b = 100)
RMSE(m1)
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