contr.wec: Function calculates contrasts for a factor variable based on weighted effect coding.

Description

This function calculates contrasts for a factor variable based on weighted effect coding. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observations per group is different.

Usage

contr.wec(x, omitted)

Arguments

Factor variable

omitted

Label of the factor label that should be taken as the omitted category

Value

Returns a contrast matrix based on weighted effect coding.

References

Grotenhuis, M. Te, Pelzer, B., Schmidt-Catran, A., Nieuwenhuis, R., Konig, R., and Eisinga, R. (2016). When size matters: advantages of weighted effect coding in observational studies. International Journal of Public Health, online access: http://link.springer.com/article/10.1007/s00038-016-0901-1

Grotenhuis, M. Te, Pelzer, B., Schmidt-Catran, A., Nieuwenhuis, R., Konig, R., and Eisinga, R. (2016). Weighted effect coded interactions: a novel moderation regression analysis for observational studies. International Journal of Public Health, online access: http://link.springer.com/article/10.1007/s00038-016-0902-0

Sweeney, Robert E. and Ulveling, Edwin F. (1972) A Transformation for Simplifying the Interpretation of Coefficients of Binary Variables in Regression Analysis. The American Statistician, 26(5): 30-32.

Examples

Run this code

# NOT RUN {
data(BMI)


# Without controls
BMI$educ.wec.lowest <- BMI$educ.wec.highest <- BMI$educ

contrasts(BMI$educ.wec.lowest) <- contr.wec(BMI$education, omitted="lowest")
contrasts(BMI$educ.wec.highest) <- contr.wec(BMI$education, omitted="highest")

model.wec.lowest <- lm(BMI ~ educ.wec.lowest, data=BMI)
model.wec.highest <- lm(BMI ~ educ.wec.highest, data=BMI)
summary(model.wec.lowest)
summary(model.wec.highest)

# With Controls
BMI$sex.wec.female <- BMI$sex.wec.male <- BMI$sex
contrasts(BMI$sex.wec.female) <- contr.wec(BMI$sex, omitted="female")
contrasts(BMI$sex.wec.male) <- contr.wec(BMI$sex, omitted="male")

BMI$year.wec.2000 <- BMI$year.wec.2011 <- BMI$year
contrasts(BMI$year.wec.2000) <- contr.wec(BMI$year, omitted="2000")
contrasts(BMI$year.wec.2011) <- contr.wec(BMI$year, omitted="2011")

model.wec.lowest.controls <- lm(BMI ~ educ.wec.lowest +
  sex.wec.female + log_age + year.wec.2000,
  data=BMI)
model.wec.highest.controls <- lm(BMI ~ educ.wec.highest +
  sex.wec.male + log_age + year.wec.2011,
  data=BMI)
summary(model.wec.lowest.controls)
summary(model.wec.highest.controls)
# }