make_yx: This function is meant to set up a response variable and a...

In divoRce: Diagnosing Separation Phenomena in Categorical Response Models

This function is meant to set up a response variable and a design matrix from a formula-data combination for the pre-fit separation check functions.

Description

This function is meant to set up a response variable and a design matrix from a formula-data combination for the pre-fit separation check functions.

Usage

make_yx(formula, data, contrasts = NULL)

Arguments

formula	An object of class ‘"formula"’ (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’ in glm.
data	Either a standard data frame, list or environment (or object coercible by as.data.frame to a data frame) containing variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which make\_yx is called. Alternatively, data can be a data frame or matrix containing rational numbers as per the definition in rcdd (i.e. columns are characters, the entries are either integer numbers or ratios of integer numbers, e.g. "1", or "-234/19008". This is checked internally; see the Details for what happens when this structure is discovered,
contrasts	contrasts: an optional list. See the contrasts.arg of model.matrix.default. Only effective for standard data frames.

Details

For standard data frames and formulas this function returns a list with the first element being the response variable (as specified by the left hand side of the formula) and the second element being a design matrix corresponding to the right hand side of the formula and the given optional contrasts. The design matrix is created in exactly the same way as in glm. For a data frame/matrix given as rational numbers in the rcdd definition, it returns a list with the first element being the response variable in rational format (as specified by the left hand side of the formula) and the second element being the character matrix in rational format corresponding to the right hand side of the formula. Note that in the latter the formula does not get expanded and is taken literally, so e.g. variables in formula must match exactly with the column names in data, or factors need to be converted to dummies before that (wouldn't be possible in the rational format in any other way anyway).

Value

A list with two elements, $y being the response variable (as specified by the left hand side of the formula) and the second element $X being a design matrix corresponding to the right hand side of the formula.

Examples

## standard data frame
data(nsduh2019)
frml <- her_lifetime ~ alc_agefirst + demog_age_cat6 + demog_sex
mk <- make_yx(frml,nsduh2019)
str(mk)

## rational structure
yr <- as.character(as.numeric(mk$y))
Xr <- apply(mk$X,2,as.character)
datr1 <- data.frame(yr,Xr) #data frame with rational data
frmlr1 <- yr~X.Intercept.+ alc_agefirst + demog_sexFemale
mkr1 <- make_yx(frmlr1,datr1)
str(mkr1)

datr2 <- cbind(yr,Xr) #character matrix with rational data  
frmlr2 <- yr~(Intercept) + alc_agefirst #note the intercept column is labeled differently here 
mkr2 <- make_yx(frmlr2,datr2)
str(mkr2)

divoRce documentation built on April 28, 2026, 3:01 a.m.