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Estimating a Beta Regression with The Variable Dispersion in R

pkgs <- c('sas7bdat', 'betareg', 'lmtest')
lapply(pkgs, require, character.only = T)

df1 <- read.sas7bdat("lgd.sas7bdat")
df2 <- df1[which(df1$y < 1), ]

xvar <- paste("x", 1:7, sep = '', collapse = " + ")
fml1 <- as.formula(paste("y ~ ", xvar))
fml2 <- as.formula(paste("y ~ ", xvar, "|", xvar))

# FIT A BETA MODEL WITH THE FIXED PHI
beta1 <- betareg(fml1, data = df2)
summary(beta1)

# Coefficients (mean model with logit link):
#              Estimate Std. Error z value Pr(>|z|)    
# (Intercept) -1.500242   0.329670  -4.551 5.35e-06 ***
# x1           0.007516   0.026020   0.289 0.772680    
# x2           0.429756   0.135899   3.162 0.001565 ** 
# x3           0.099202   0.022285   4.452 8.53e-06 ***
# x4           2.465055   0.415657   5.931 3.02e-09 ***
# x5          -0.003687   0.001070  -3.446 0.000568 ***
# x6           0.007181   0.001821   3.943 8.06e-05 ***
# x7           0.128796   0.186715   0.690 0.490319    
#
# Phi coefficients (precision model with identity link):
#       Estimate Std. Error z value Pr(>|z|)    
# (phi)   3.6868     0.1421   25.95   <2e-16 ***

# FIT A BETA MODEL WITH THE VARIABLE PHI
beta2 <- betareg(fml2, data = df2)
summary(beta2)

# Coefficients (mean model with logit link):
#              Estimate Std. Error z value Pr(>|z|)    
# (Intercept) -1.996661   0.336445  -5.935 2.95e-09 ***
# x1           0.007033   0.026809   0.262 0.793072    
# x2           0.371098   0.135186   2.745 0.006049 ** 
# x3           0.133356   0.022624   5.894 3.76e-09 ***
# x4           2.951245   0.401493   7.351 1.97e-13 ***
# x5          -0.003475   0.001119  -3.105 0.001902 ** 
# x6           0.006528   0.001884   3.466 0.000529 ***
# x7           0.100274   0.190915   0.525 0.599424    
#
# Phi coefficients (precision model with log link):
#              Estimate Std. Error z value Pr(>|z|)    
# (Intercept) -0.454399   0.452302  -1.005 0.315072    
# x1           0.009119   0.035659   0.256 0.798150    
# x2           0.611049   0.188225   3.246 0.001169 ** 
# x3           0.092073   0.030678   3.001 0.002689 ** 
# x4           2.248399   0.579440   3.880 0.000104 ***
# x5          -0.002188   0.001455  -1.504 0.132704    
# x6          -0.000317   0.002519  -0.126 0.899847    
# x7          -0.166226   0.256199  -0.649 0.516457    

# LIKELIHOOD RATIO TEST TO COMPARE BOTH BETA MODELS
lrtest(beta1, beta2) 

# Likelihood ratio test
#
# Model 1: y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7
# Model 2: y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 | x1 + x2 + x3 + x4 + x5 + x6 + x7
#   #Df LogLik Df Chisq Pr(>Chisq)    
# 1   9 231.55                        
# 2  16 257.24  7 51.38  7.735e-09 ***
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Written by statcompute

October 19, 2014 at 5:25 pm

Posted in Econometrics, S+/R, Statistical Models

Tagged with ,

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