Birthwt {grpreg}

Risk Factors Associated with Low Infant Birth Weight

Description

The Birthwt data contains 189 observations, 16 predictors, and an outcome, birthweight, available both as a continuous measure and a binary indicator for low birth weight. The data were collected at Baystate Medical Center, Springfield, Mass during 1986. This data frame is a reparameterization of the birthwt data frame from the MASS package.

Usage

data(Birthwt)

Format

The Birthwt object is a list containing four elements:

• bwt:Birth weight in kilograms

• low:Indicator of birth weight less than 2.5kg

• X:Matrix of predictors

• group:Vector describing how the columns of X are grouped

The matrix X contains the following columns:

• age1,age2,age3:Orthogonal polynomials of first, second, and third degree representing mother's age in years

• lwt1,lwt2,lwt3:Orthogonal polynomials of first, second, and third degree representing mother's weight in pounds at last menstrual period

• white,black:Indicator functions for mother's race; "other" is reference group

• smoke:Smoking status during pregnancy

• ptl1,ptl2m:Indicator functions for one or for two or more previous premature labors, respectively. No previous premature labors is the reference category.

• ht:History of hypertension

• ui:Presence of uterine irritability

• ftv1,ftv2,ftv3m:Indicator functions for one, for two, or for three or more physician visits during the first trimester, respectively. No visits is the reference category.

Source

MASS. R package. https://cran.r-project.org/package=MASS

References

• Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

• Hosmer, D.W. and Lemeshow, S. (1989) Applied Logistic Regression. New York: Wiley

See Also

birthwt, grpreg

Examples

data(Birthwt)
hist(Birthwt$bwt, xlab="Child's birth weight", main="")
table(Birthwt$low)
## See examples in ?birthwt (MASS package)
##   for more about the data set
## See examples in ?grpreg for use of this data set
##   with group penalized regression models