oes_prep {oesr} | R Documentation |
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 |
treatment_vars |
An optional character vector of treatment arm names. One
of |
treatment_arms |
An optional numeric value indicating the number of treatment
arms. (Not required if treatment_vars is given explicitly.) One
of |
scale |
String indicating the |
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 ( |
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)