bang1 {R2MLwiN}

Sub-sample from the 1989 Bangladesh Fertility Survey

Description

A subset of data from the 1989 Bangladesh Fertility Survey, consisting of 1934 women across 60 districts.

Usage

bang1

Format

A data frame with 1934 observations on the following 11 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.

lc

Number of living children at time of survey; an ordered factor with 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; a factor with levels Rural and Urban.

educ

Woman's level of education; an ordered factor with levels None, Lower_primary, Upper_primary, Secondary_and_above.

hindu

Woman's religion; a factor with levels Muslim and Hindu.

d_illit

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

A column of ones. If included as an explanatory variable in a regression model (e.g. in MLwiN), its coefficient is the intercept.

Details

The bang1 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 Browne (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.

Browne, W. J. (2012) MCMC Estimation in MLwiN Version 2.26. University of Bristol: Centre for Multilevel Modelling.

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.

See Also

See mlmRev package for an alternative format of the same dataset, with fewer variables.

Examples

## Not run: 

data(bang1, package = "R2MLwiN")

bang1$denomb <- 1

# 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 <- logit(use, denomb) ~ 1 + age + C(lc, "contr.treatment") + urban +
#   (1 + urban | district)

# (mymodel <- runMLwiN(Formula = F1,
#                      D = "Binomial",
#                      estoptions = list(EstM = 1),
#                      data = bang1,
#                      allowcontrast = TRUE))

F1 <- logit(use, denomb) ~ 1 + age + lc + urban + (1 + urban | district)

(mymodel <- runMLwiN(Formula = F1,
                     D = "Binomial",
                     estoptions = list(EstM = 1),
                     data = bang1))

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


## End(Not run)