eive.cga {eive}

Performs CGA based errors-in-variables correction for a given set of variables. A single independent variable is supposed to be measured subject to error.

Description

Performs CGA based errors-in-variables correction for a given set of variables. A single independent variable is supposed to be measured subject to error.

Usage

eive.cga(dirtyx, otherx = NULL, y, numdummies = 10, popsize = 20)

Arguments

dirtyx

Vector of independent variable that is measured with error.

otherx

Matrix of other independent variables. If the model has a single independent variable, it is NULL by default.

y

Vector of response variable

numdummies

Number of dummy variables used in auxiliary regression.

popsize

Population size parameter for compact genetic algorithm. 1/popsize is the mutation rate.

Value

A list() of regression equations.

Slots

ols

lm object calculated using original values

eive

lm object calculated using the predicted variable by eive

proxy

lm object of proxy regression obtained by genetic search.

cleanedx

Error-free estimate of the x variable (dirtyx) that is measured with error.

measurementerror

Estimate of the measurement error.

Examples

# Creating an artificial data
# Loading required package
require("eive")

# Setting random number generator seed to 12345
# so each time the script runs, same numbers will
# be generated
set.seed(12345)

# Number of observations is set to 30
n <- 30

# Unobserved X values are drawn from a Normal distribution
# with mean 10 and variance 7
clean.x <- rnorm(n, mean = 10, sd = sqrt(7))

# Measurement error values are dranw from a Normal distribution
# with mean 0 and variance 3
delta.x <- rnorm(n, mean = 0, sd = sqrt(3))

# Error term of regression. Normally distributed with mean 0 and
# variance 5
e <- rnorm(n, mean = 0, sd = sqrt(5))

# Generating Y values using the linear model
# In this model, intercept is 20 and slope is 10.
y <- 20 + 10 * clean.x + e

# Generating observed X values by adding measurement errors
# to unobserved X
dirty.x <- clean.x + delta.x

# Performs a genetic search to find dummy variables that
# used in two stage least squares.
# Please un-comment the line below
# result <- eive.cga (dirtyx=dirty.x, y=y, numdummies=10)

# Print the result
# Please un-comment the line below
# print(result)

########################################### OUTPUT #############################################
# $ols
#
# Call:
# lm(formula = y ~ dirtyx)
#
# Coefficients:
# (Intercept)       dirtyx
#     63.590        5.533
#
#
# $eive
#
# Call:
# lm(formula = y ~ ols.proxy$fitted.values)
#
# Coefficients:
#            (Intercept)  ols.proxy$fitted.values
#                 23.863                    9.229
#
#
# $proxy
#
# Call:
# lm(formula = dirtyx ~ matrix(best, nrow = n))
#
# Coefficients:
#              (Intercept)   matrix(best, nrow = n)1   matrix(best, nrow = n)2
#                 12.9321                   -0.6252                   -1.9923
# matrix(best, nrow = n)3   matrix(best, nrow = n)4   matrix(best, nrow = n)5
#                  0.7537                   -0.7076                   -0.5247
# matrix(best, nrow = n)6   matrix(best, nrow = n)7   matrix(best, nrow = n)8
#                 -0.9196                   -2.0802                   -0.9246
# matrix(best, nrow = n)9  matrix(best, nrow = n)10
#                 -0.6164                    1.9694
######################################### END OF OUTPUT ##########################################