eive (version 2.1)

eive.cga: Errors-in-variable estimation in linear regression with compact genetic algorithms

Description

Method performs a genetic search to find dummy variables that used in a two stage linear regression to reduce errors-in-variables bias in linear regression.

Usage

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

Arguments

dirtyx

Vector of values of independent variable measured with error

otherx

Matrix of other independent variables.

y

Vector of values of dependent variable

numdummies

Number of dummy variables used in algorithm. By default, it is 10.

popsize

Population size parameter used in CGA. By default it is 20.

Value

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.

Details

Algorithm performs a genetic search to separate mismeasured independent variable into clean and error parts.

Examples

# NOT RUN {
# 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 ##########################################

# }