paths (version 0.1.1)

summary.paths: Summarizing Output from Causal Paths Analysis

Description

Function to report results from causal paths analysis. Report point estimates and standard errors for the total effect, direct effect, and each individual indirect effect, separately for Type I and Type II decompositions.

Usage

# S3 method for paths
summary(object, ...)
# S3 method for summary.paths
print(x, ...)

Arguments

object

an object of class paths returned by the paths function.

...

additional arguments to be passed to printCoefmat for the print method

x

an object of class summary.paths

Value

An object of class summary.paths, which is a list containing the call, varnames, formulas, classes, args, ps_formula, ps_class, ps_args, nboot, conf_level components from the paths object, plus

nobs

number of observations in data

estimates

a list containing four matrices, corresponding to effect estimates obtained using the pure imputation estimator and the imputation-based weighting estimator, each with Type I and Type II decompositions. Each matrix contains the point estimates, standard errors, and confidence intervals of the total effect, direct effect, and each individual indirect effect for the corresponding decomposition. The elements in each matrix are extracted from the paths object.

Details

print.summary.paths tries to smartly format the point estimates and confidence intervals, and provides 'significance stars' through the printCoefmat function.

It also prints out the names of the treatment, outcome, mediator variables as well as pretreatment covariates, which are extracted from the formulas argument of the call to paths so that users can verify if the model formulas have been correctly specified.

See Also

paths, print.paths, plot.paths

Examples

# NOT RUN {
# **For illustration purposes a small number of bootstrap replicates are used**

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
summary(paths_glm)

# }