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CS594_PC_algorithm.R
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library(data.table)
library(caret)
library(dimRed)
library(pcalg)
#library(varimpact)
#Copula Estimate
## For details about the arguements and outputs, refer to function 'sbgcop.mcmc' in R package 'sbgcop',
## https://cran.r-project.org/web/packages/sbgcop/index.html.
copula.estimate <- function (Y, n0 = dim(Y)[2] + 1, S0 = diag(dim(Y)[2])/n0, nsamp = 100,
odens = max(1, round(nsamp/1000)), impute = any(is.na(Y)),
plugin.threshold = 100, plugin.marginal = (apply(Y, 2, function(x) {
length(unique(x))
}) > plugin.threshold), seed = NULL, verb = TRUE)
{
require(sbgcop)
ok_S0 <- all(eigen(S0)$val > 0) & dim(S0)[1] == dim(Y)[2] &
dim(S0)[2] == dim(Y)[2]
ok_n0 <- (n0 >= 0)
if (!ok_S0) {
stop("Error: S0 must be a positive definite p x p matrix \n")
}
if (!ok_n0) {
stop("Error: n0 must be positive \n")
}
vnames <- colnames(Y)
Y <- as.matrix(Y)
colnames(Y) <- vnames
n <- dim(Y)[1]
p <- dim(Y)[2]
set.seed(seed)
R <- NULL
for (j in 1:p) {
R <- cbind(R, match(Y[, j], sort(unique(Y[, j]))))
}
Rlevels <- apply(R, 2, max, na.rm = TRUE)
Ranks <- apply(Y, 2, rank, ties.method = "max", na.last = "keep")
N <- apply(!is.na(Ranks), 2, sum)
U <- t(t(Ranks)/(N + 1))
Z <- qnorm(U)
Zfill <- matrix(rnorm(n * p), n, p)
Z[is.na(Y)] <- Zfill[is.na(Y)]
S <- cov(Z)
Y.pmean <- Y
if (impute) {
Y.pmean <- matrix(0, nrow = n, ncol = p)
}
LPC <- NULL
C.psamp <- array(dim = c(p, p, floor(nsamp/odens)))
Y.imp <- NULL
if (impute) {
Y.imp <- array(dim = c(n, p, floor(nsamp/odens)))
}
dimnames(C.psamp) <- list(colnames(Y), colnames(Y),
1:floor(nsamp/odens))
for (ns in 1:nsamp) {
for (j in sample(1:p)) {
Sjc <- S[j, -j] %*% solve(S[-j, -j])
sdj <- sqrt(S[j, j] - S[j, -j] %*% solve(S[-j,
-j]) %*% S[-j, j])
muj <- Z[, -j] %*% t(Sjc)
if (!plugin.marginal[j]) {
for (r in 1:Rlevels[j]) {
ir <- (1:n)[R[, j] == r & !is.na(R[, j])]
lb <- suppressWarnings(max(Z[R[, j] == r -
1, j], na.rm = TRUE))
ub <- suppressWarnings(min(Z[R[, j] == r +
1, j], na.rm = TRUE))
Z[ir, j] <- qnorm(runif(length(ir), pnorm(lb,
muj[ir], sdj), pnorm(ub, muj[ir], sdj)),
muj[ir], sdj)
}
}
ir <- (1:n)[is.na(R[, j])]
Z[ir, j] <- rnorm(length(ir), muj[ir], sdj)
}
# relocate the mean to zero
# added by Ruifei Cui
Z = t( (t(Z)-apply(Z,2,mean)))
S <- solve(rwish(solve(S0 * n0 + t(Z) %*% Z), n0 +
n))
if (ns%%odens == 0) {
C <- S/(sqrt(diag(S)) %*% t(sqrt(diag(S))))
lpc <- ldmvnorm(Z %*% diag(1/sqrt(diag(S))),
C)
LPC <- c(LPC, lpc)
C.psamp[, , ns/odens] <- C
if (impute) {
Y.imp.s <- Y
for (j in 1:p) {
Y.imp.s[is.na(Y[, j]), j] <- quantile(Y[,
j], pnorm(Z[is.na(Y[, j]), j], 0, sqrt(S[j,
j])), na.rm = TRUE, type = 1)
}
Y.imp[, , ns/odens] <- Y.imp.s
Y.pmean <- ((ns/odens - 1)/(ns/odens)) * Y.pmean +
(1/(ns/odens)) * Y.imp.s
}
}
if (verb == TRUE & (ns%%(odens * 10)) == 0) {
cat(round(100 * ns/nsamp), "percent done ",
date(), "\n")
}
}
G.ps <- list(C.psamp = C.psamp, Y.pmean = Y.pmean, Y.impute = Y.imp,
LPC = LPC)
class(G.ps) <- "psgc"
return(G.ps)
}
setwd("C:/Users/WineGlass/Desktop/CS594_Causal_Inference/Term Project/Data_NHTS")
csv_files <- list.files(pattern = "csv$")
#Reading the data
data_ <- fread('trippub.csv', header = T, sep = ',')
#myvars <- c("TRVLCMIN", "TRPMILES", "GASPRICE")
data_1 <- data_[, .(R_AGE, R_SEX, EDUC, HHSIZE, URBRUR, HHFAMINC, TRIPPURP, GASPRICE, TRVLCMIN, TRPTRANS)]
s <- data_1[TRVLCMIN> 0 & GASPRICE > 0 & HHFAMINC > 0 & HHSIZE > 0 & TRIPPURP > 0 & R_AGE > 0& R_SEX > 0 & EDUC > 0 & TRPTRANS > 0]
s$AGEC[s$R_AGE <18] = 1
s$AGEC[s$R_AGE >=18 & s$R_AGE < 25] = 2
s$AGEC[s$R_AGE >=25 & s$R_AGE < 35] = 3
s$AGEC[s$R_AGE >=35 & s$R_AGE < 50] = 4
s$AGEC[s$R_AGE >=50 & s$R_AGE < 70] = 5
s$AGEC[s$R_AGE >= 70] = 6
dt = c(3, 4, 5, 6, 11, 16)
s1 <- s[TRPTRANS %in% dt]
s1 <- s1[TRPTRANS %in% c(3, 4,5,6), TRPTRANS := 0]
s1 <- s1[TRPTRANS %in% c(11,16), TRPTRANS := 1]
s1$TRIPPURP <- as.factor(s1$TRIPPURP)
s1$URBRUR <- as.factor(s1$URBRUR)
s1$R_SEX <- as.factor(s1$R_SEX)
s1$EDUC <- as.factor(s1$EDUC)
s1$HHFAMINC <- as.factor(s1$HHFAMINC)
s1$AGEC <- as.factor(s1$AGEC)
s1$HHSIZE <- as.numeric(s1$HHSIZE)
s1$TRVLCMIN <- as.numeric(s1$TRVLCMIN)
s1$TRPTRANS <- as.numeric(s1$TRPTRANS)
s1 <- s1[,!"R_AGE"]
s1 <- setcolorder(s1, c("AGEC", "R_SEX", "EDUC", "HHSIZE", "URBRUR", "HHFAMINC", "TRIPPURP", "GASPRICE", "TRVLCMIN", "TRPTRANS"))
s1 <- na.omit(s1)
#Y <- s1[,10]
#X <- s1[,1:9]
#vim<- varimpact(Y = s1[,10], data = s1[,1:9])
#s$TRIPPURP <- as.numeric(as.character(s$TRIPPURP))
x <- copula.estimate(s1)
j = 1:100
corr_mean <- 0
for (i in j)
{
m <- x$C.psamp[seq(i, length(x$C.psamp), 100)]
corr_mean[i] <- mean(m)
}
V <- colnames(s1)
q<-matrix(unlist(corr_mean), ncol = length(s1), byrow = TRUE)
rownames(q) <- V
colnames(q) <- V
constra <- diag(10) * 0
rownames(constra) <- colnames(s1)
colnames(constra) <- colnames(s1)
constra[c(2,11)] = 1
#suffStat <- list(C = cor(s), n = nrow(s))
suffStat <- list(C = q, n = nrow(s1))
pc.s1 <- pc(suffStat, indepTest = gaussCItest,
labels = V, alpha = 0.01, fixedGaps = constra, skel.method = "stable")
fci.s1 <- fci(suffStat, indepTest=gaussCItest, alpha = 0.01, labels = V, skel.method = "stable", maj.rule = TRUE)
if (require(Rgraphviz)) {
par(mfrow = c(1,2))
plot(fci.s1, main = "FIC")
plot(pc.s1, main = "Copula_PC")
}
#data(gmD)
#V <- colnames(gmD$x)
#suffStat <- list(dm = gmD$x, nlev = c(3,2,3,4,2), adaptDF = FALSE)
#pc.D <- pc(suffStat, indepTest = disCItest, alpha = 0.01, labels = V, verbose = TRUE)