plot.paths {paths}

Plot Method for paths Objects

Description

Plot point estimates and confidence intervals for each individual path-specific effect from a paths object.

Usage

## S3 method for class 'paths'
plot(
  x,
  mediator_names = NULL,
  estimator = c("pure", "hybrid", "both"),
  decomp = c("Type I", "Type II", "both"),
  horizontal = (decomp != "both"),
  ...
)

Arguments

x

an object of class paths returned by the paths function

mediator_names

a vector of character strings giving the labels for each mediator. It must contain as many elements as the number of mediators in the model. If not supplied, a set of default labels will be constructed from the fitted paths object.

estimator

either "pure", "hybrid", or "both", indicating whether the plot will display estimates obtained using the pure imputation estimator, the imputation-based weighting estimator, or both. Default is to show estimates from the pure imputation estimator.

decomp

either "Type I", "Type II", or "both", indicating whether the plot will display estimates obtained using Type I decomposition, Type II decomposition, or both Type I and Type II decompositions. Default is to show estimates from Type I decomposition.

horizontal

a logical variable indicating whether a horizontal plot should be used. Default is to use a horizontal plot when decomp != both.

...

additional arguments.

Value

a ggplot2 plot, which can be further customized by the user.

Examples

data(tatar)

m1 <- c("trust_g1", "victim_g1", "fear_g1")
m2 <- c("trust_g2", "victim_g2", "fear_g2")
m3 <- c("trust_g3", "victim_g3", "fear_g3")
mediators <- list(m1, m2, m3)

formula_m0 <- annex ~ kulak + prosoviet_pre + religiosity_pre + land_pre +
  orchard_pre + animals_pre + carriage_pre + otherprop_pre + violence
formula_m1 <- update(formula_m0,    ~ . + trust_g1 + victim_g1 + fear_g1)
formula_m2 <- update(formula_m1,    ~ . + trust_g2 + victim_g2 + fear_g2)
formula_m3 <- update(formula_m2,    ~ . + trust_g3 + victim_g3 + fear_g3)
formula_ps <- violence ~ kulak + prosoviet_pre + religiosity_pre +
  land_pre + orchard_pre + animals_pre + carriage_pre + otherprop_pre

####################################################
# Causal Paths Analysis using GLM
####################################################

# outcome models
glm_m0 <- glm(formula_m0, family = binomial("logit"), data = tatar)
glm_m1 <- glm(formula_m1, family = binomial("logit"), data = tatar)
glm_m2 <- glm(formula_m2, family = binomial("logit"), data = tatar)
glm_m3 <- glm(formula_m3, family = binomial("logit"), data = tatar)
glm_ymodels <- list(glm_m0, glm_m1, glm_m2, glm_m3)

# propensity score model
glm_ps <- glm(formula_ps, family = binomial("logit"), data = tatar)

# causal paths analysis using glm
# note: For illustration purposes only a small number of bootstrap replicates are used
paths_glm <- paths(a = "violence", y = "annex", m = mediators,
  glm_ymodels, ps_model = glm_ps, data = tatar, nboot = 3)


# plot total, direct, and path-specific effects
plot(paths_glm, mediator_names = c("G1 identity", "G2 identity", "G3 identity"),
     estimator = "both")