PAsso: an R Package for Assessing Partial Association between Ordinal Variables

if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("XiaoruiZhu/PAsso")

library(PAsso)

PAsso_1 <- PAsso(responses = c("PreVote.num", "PID"),
                 adjustments = c("income.num", "age", "edu.year"),
                 data = ANES2016,
                 uni.model = "probit",
                 method = c("kendall")
                )
                
# Print the partial association matrix only
print(PAsso_1, 5)

# Provide partial association matrix, marginal association matrix, and summary of models' coefficients
summary(PAsso_1, 4)

# Plot partial association regression plot: residuals
plot(PAsso_1)

# Retrieve residuals
test_resids <- residuals(PAsso_1, draw = 1)
head(test_resids)
dim(test_resids)

# Association analysis between three ordinal responses ----------

PAsso_2 <- PAsso(responses = c("PreVote.num", "PID", "selfLR"),
                adjustments = c("income.num", "age", "edu.year"),
                data = ANES2016,
                uni.model <- "probit",
                method = c("kendall"),
                resids.type = "surrogate")
                
# Compare marginal correlation and partial correlation.
summary(PAsso_2, digits=4)
plot(PAsso_2)

# test function: Conduct inference based on object of "PAsso.test" class ----------------------------
library(progress); #library(doParallel)

system.time(Pcor_SR_test1 <- test(object = PAsso_2, boot_SE=100, H0=0, parallel=F))
print(Pcor_SR_test1, digits=6)
print(PAsso_2, 6)

# diagnostic.plot function -----------------------------------------------------
check_qq <- diagnostic.plot(object = PAsso_2, output = "qq")

check_fitted <- diagnostic.plot(object = PAsso_2, output = "fitted")

check_covar <- diagnostic.plot(object = PAsso_2, output = "covariate")

# general association measure and 3-D plot for VOTE and PID ------------------
library("copula")
library("plotly")

# Draw all pairs
testPlots <- plot3D(PAsso_1)
testPlots$`PreVote.num v.s. PID`

# Draw just one pair
testPlots2 <- plot3D(object = PAsso_2, y1 = "selfLR", y2 = "PID")
testPlots2

# "PAsso" advanced using of the function: Input a few models directly ------------------------------

fit_vote <- glm(PreVote.num ~ income.num + age + edu.year, data = nes2016,
               family = binomial(link = "probit"))
summary(fit_vote)

fit_PID <- polr(as.factor(PID) ~ income.num + age + edu.year, data = nes2016,
               method = "probit", Hess = TRUE)

summary(fit_PID)

system.time(PAsso_adv1 <- PAsso(fitted.models=list(fit_vote, fit_PID),
                                association = c("partial"),
                                method = c("kendall"),
                                resids.type = "surrogate")
)

# Partial association coefficients (Parts of Table 7 in paper)
print(PAsso_adv1, digits = 3)
summary(PAsso_adv1, digits = 3)