# Function calculates contrasts for a factor variable based on weighted effect coding. 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. ```r contr.wec(x, omitted) ``` ## Arguments - `x`: Factor variable - `omitted`: Label of the factor label that should be taken as the omitted category ## Returns 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. ## Author(s) Rense Nieuwenhuis, Manfred te Grotenhuis, Ben Pelzer, Alexander Schmidt, Ruben Konig, Rob Eisinga ## See Also `wec.interact` ## Examples ```r 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) ```

contr.wec function