oesr (version 0.1.0)

oes_prep: Prepare Model Output for OES Plots

Description

Prepare output of linear modeling object into a tidy data table to feed into OES plotting function

Usage

oes_prep(
  model,
  treatment_vars = NULL,
  treatment_arms = NULL,
  scale = c("response", "percentage"),
  treatment_labels,
  control_label,
  alpha_level = 0.05
)

Value

A tibble of \(T+1\) rows and 8 columns, where \(T\) is the number of treatment conditions specified via treatment_vars or treatment_arms.

Arguments

model

An object of class lm or lm_robust

treatment_vars

An optional character vector of treatment arm names. One of treatment_vars or treatment_arms must be supplied.

treatment_arms

An optional numeric value indicating the number of treatment arms. (Not required if treatment_vars is given explicitly.) One of treatment_vars or treatment_arms must be supplied.

scale

String indicating the \(y\)-axis scale. Default is 'response'. For binary outcomes, it may be desirable to set to "percentage".

treatment_labels

Optional vector of string labels providing treatment condition(s)

control_label

Optional string providing control condition label

alpha_level

The level at which to reject the null hypothesis for adding asterisks to plots. Set to 0.05 by default. This value also determines the size of the confidence intervals (alpha_level = 0.05 corresponds to 95% confidence intervals).

Author

Miles Williams

Details

oes_prep() takes a linear modeling output object (from lm() or lm_robust()) and returns a tidy tibble of estimates, confidence bounds, and related quantities ready for oes_plot to plot. Functionality for lm_lin() objects is in development.

Examples

data(df_oes)

# Single binary treatment:
fit <- lm(y1 ~ x1, df_oes)

# Multiple treatment conditions:
fit2 <- lm(y2 ~ x2, df_oes)

# Using HC2 SE's from lm_robust():
fit_robust <- estimatr::lm_robust(y1 ~ x1, df_oes)
fit_robust2 <- estimatr::lm_robust(y2 ~ x2, df_oes)

# Using covariates and lm():
fit_covars <- lm(y2 ~ x2 + z1 + z2 + z3, df_oes)

# Using covariates and lm_robust():
fit_covars_robust <- estimatr::lm_robust(y2 ~ x2 + z1 + z2 + z3, df_oes)

# Example specifying number of treatment arms:
oes_prep(fit, treatment_arms = 1)

# Example specifying name of treatment variable:
oes_prep(fit, treatment_vars = "x1")

# Example reporting outcomes as percentages:
oes_prep(fit, treatment_vars = "x1", scale = "percentage")

# Example specifying several treatment arms, labels, etc.:
oes_prep(fit2, treatment_arms = 3,
  treatment_labels = c(
    "Email",
    "Email +\nReward",
    "Email +\nRisk"),
  control_label = "Status Quo",
  scale = "percentage")

# Examples with lm_robust():
oes_prep(fit_robust, treatment_arms = 1)
oes_prep(fit_robust2, treatment_arms = 3)

# Examples with covariates:
oes_prep(fit_covars, treatment_arms = 3)
oes_prep(fit_covars_robust, treatment_arms = 3)