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Proportion of Household Income Spent on Food

FoodExpenditure.Rd

Data on proportion of income spent on food for a random sample of 38 households in a large US city.

Usage

data("FoodExpenditure", package = "betareg")

Format

A data frame containing 38 observations on 3 variables.

food

household expenditures for food.

income

household income.

persons

number of persons living in household.

Source

Taken from Griffiths et al. (1993, Table 15.4).

References

Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. doi:10.18637/jss.v034.i02

Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.

Griffiths, W.E., Hill, R.C., and Judge, G.G. (1993). Learning and Practicing Econometrics New York: John Wiley and Sons.

See also

betareg

Examples

data("FoodExpenditure", package = "betareg")

## Ferrari and Cribari-Neto (2004)
## Section 4
fe_lin <- lm(I(food/income) ~ income + persons, data = FoodExpenditure)
library("lmtest")
#> Loading required package: zoo
#> 
#> Attaching package: ‘zoo’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.Date, as.Date.numeric
bptest(fe_lin)
#> 
#> 	studentized Breusch-Pagan test
#> 
#> data:  fe_lin
#> BP = 5.9348, df = 2, p-value = 0.05144
#> 

## Table 2
fe_beta <- betareg(I(food/income) ~ income + persons, data = FoodExpenditure)
summary(fe_beta)
#> 
#> Call:
#> betareg(formula = I(food/income) ~ income + persons, data = FoodExpenditure)
#> 
#> Quantile residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.5328 -0.4599  0.1698  0.6416  1.7733 
#> 
#> Coefficients (mean model with logit link):
#>              Estimate Std. Error z value Pr(>|z|)    
#> (Intercept) -0.622548   0.223854  -2.781 0.005418 ** 
#> income      -0.012299   0.003036  -4.052 5.09e-05 ***
#> persons      0.118462   0.035341   3.352 0.000802 ***
#> 
#> Phi coefficients (precision model with identity link):
#>       Estimate Std. Error z value Pr(>|z|)    
#> (phi)    35.61       8.08   4.407 1.05e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
#> 
#> Type of estimator: ML (maximum likelihood)
#> Log-likelihood: 45.33 on 4 Df
#> Pseudo R-squared: 0.3878
#> Number of iterations: 28 (BFGS) + 4 (Fisher scoring)