oes_prep: Prepare Model Output for OES Plots

In oesr: Methods for the Office of Evaluation Sciences

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
)

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).

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.

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.

Author(s)

Miles Williams

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)

oesr documentation built on Feb. 16, 2023, 8:23 p.m.