bang: Sub-sample from the 1989 Bangladesh Fertility Survey (see Huq...
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bang	R Documentation

Sub-sample from the 1989 Bangladesh Fertility Survey (see Huq & Cleveland, 1990)

Description

A subset of data from the 1989 Bangladesh Fertility Survey, consisting of 2867 women across 61 districts.

Usage

bang

Format

A data frame with 2867 observations on the following 12 variables:

woman: Identifying code for each woman (level 1 unit).
district: Identifying code for each district (level 2 unit).
use: Contraceptive use status at time of survey; a factor with levels Not_using and Using.
use4: Contraceptive use status and method (a factor with levels: Sterilization, Modern_reversible_method, Traditional_method, Not_using_contraception).
lc: Number of living children at time of survey; a factor with ordered levels None, One_child, Two_children, Three_plus.
age: Age of woman at time of survey (in years), centred on sample mean of 30 years.
urban: Type of region of residence; levels are Rural and Urban.
educ: Woman's level of education (a factor with ordered levels None, Lower_primary, Upper_primary, Secondary_and_above.
hindu: Woman's religion; levels are Muslim and Hindu.
d_lit: Proportion of women in district who are literate.
d_pray: Proportion of Muslim women in district who pray every day (a measure of religiosity).
cons: Constant of ones.

Details

The bang dataset is one of the sample datasets provided with the multilevel-modelling software package MLwiN (Rasbash et al., 2009), and is a subset of data from the 1989 Bangladesh Fertility Survey (Huq and Cleland, 1990) used by Rasbash et al. (2012) as an example when fitting logistic models for binary and binomial responses. The full sample was analysed in Amin et al. (1997).

Source

Amin, S., Diamond, I., Steele, F. (1997) Contraception and religiosity in Bangladesh. In: G. W. Jones, J. C. Caldwell, R. M. Douglas, R. M. D'Souza (eds) The Continuing Demographic Transition, 268–289. Oxford: Oxford University Press.

Huq, N. M., Cleland, J. (1990) Bangladesh fertility survey, 1989. Dhaka: National Institute of Population Research and Training (NIPORT).

Rasbash, J., Charlton, C., Browne, W.J., Healy, M. and Cameron, B. (2009) MLwiN Version 2.1. Centre for Multilevel Modelling, University of Bristol.

Rasbash, J., Steele, F., Browne, W.J. and Goldstein, H. (2012) A User's Guide to MLwiN Version 2.26. Centre for Multilevel Modelling, University of Bristol.

Examples


## Not run: 

data(bang, package = "R2MLwiN")

bang$use4 <- relevel(bang$use4, 4)

# Change contrasts if wish to avoid warning indicating that, by default,
# specified contrasts for ordered predictors will be ignored by runMLwiN
# (they will be fitted as "contr.treatment" regardless of this setting). To
# enable specified contrasts, set allowcontrast to TRUE (this will be the
# default in future package releases).
my_contrasts <- options("contrasts")$contrasts
options(contrasts = c(unordered = "contr.treatment",
                      ordered = "contr.treatment"))

# As an alternative to changing contrasts, can instead use C() to specify
# contrasts for ordered predictors in formula object, e.g.:

# F1 <- log(use4, cons) ~ 1 + C(lc, "contr.treatment") + (1 | district)

# (mymodel <- runMLwiN(Formula = F1, 
#                      D = "Unordered Multinomial",
#                      estoptions = list(EstM = 1, nonlinear = c(1, 2)),
#                      data = bang,
#                      allowcontrast = TRUE))

F1 <- log(use4, cons) ~ 1 + lc + (1 | district)

(mymodel <- runMLwiN(Formula = F1, 
                     D = "Unordered Multinomial",
                     estoptions = list(EstM = 1, nonlinear = c(1, 2)),
                     data = bang))

# Change contrasts back to pre-existing:
options(contrasts = my_contrasts)


## End(Not run)

R2MLwiN documentation built on May 29, 2024, 2:10 a.m.