Single Variable Risk Summaries for CMA Compute summaries of the risks associated with a change in a single variable in Z from a single level (quantile) to a second level (quantile), for the other variables in Z fixed to a specific level (quantile)

Source: R/SingVarRiskSummaries.CMA.R

Single Variable Risk Summaries for CMA Compute summaries of the risks associated with a change in a single variable in Z from a single level (quantile) to a second level (quantile), for the other variables in Z fixed to a specific level (quantile)

SingVarRiskSummaries.CMA(
  BKMRfits,
  e.y = NULL,
  e.y.names = NULL,
  which.z = 1:ncol(BKMRfits$Z),
  z.names = NULL,
  qs.diff = c(0.25, 0.75),
  q.fixed = c(0.25, 0.5, 0.75),
  alpha = 0.05,
  sel,
  seed = 122
)

Arguments

BKMRfits

An object containing the results returned by a the kmbayes function

e.y

effect modifier for the outcome variable

e.y.names

column name of the effect modifier for the outcome variable

which.z

vector indicating which variables (columns of Z) for which the summary should be computed

z.names

optional vector of names for the columns of z

qs.diff

vector indicating the two quantiles q_1 and q_2 at which to compute h(z_q2) -h(z_q1)

q.fixed

vector of quantiles at which to fix the remaining predictors in Z

alpha

1-confidence interval

sel

a vector selecting which iterations of the fit should be retained or inference

seed

the random seed to use to evaluate the code

Value

a data frame containing the (posterior mean) estimate and posterior standard deviation of the single predictor risk measures

Details

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_QuickStart.html

Examples

if (FALSE) {
library(causalbkmr)
dat <-  cma_sampledata(N=300, L=3, P=3, scenario=1, seed=7)

A <- cbind(dat$z1, dat$z2, dat$z3)
X <- cbind(dat$x3)
y  <- dat$y
m  <- dat$M

E.M <- NULL
E.Y <- dat$x2

Z.M <- cbind(A,E.M)
Z.Y <- cbind(A, E.Y)
Zm.Y <- cbind(Z.Y, m)

set.seed(1)
fit.y <- kmbayes(y=y, Z=Zm.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y,file="bkmr_y.RData")

set.seed(2)
fit.y.TE <- kmbayes(y=y, Z=Z.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y.TE,file="bkmr_y_TE.RData")

set.seed(3)
fit.m <- kmbayes(y=m, Z=Z.M, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.m,file="bkmr_m.RData")

X.predict <- matrix(colMeans(X),nrow=1)
astar <- c(apply(A, 2, quantile, probs=0.25))
a <- c(apply(A, 2, quantile, probs=0.75))

e.y10 = quantile(E.Y, probs=0.1)
e.y90 = quantile(E.Y, probs=0.9)

sel<-seq(2500,5000,by=5)


risks.singvar10 = SingVarRiskSummaries.CMA(BKMRfits = fit.y.TE, which.z = 1:3,
e.y=e.y10, e.y.names="E.Y",
sel=sel)
ggplot(risks.singvar10, aes(variable, est, ymin = est - 1.96*sd,
                            ymax = est + 1.96*sd, col = q.fixed)) +
  geom_pointrange(position = position_dodge(width = 0.75)) +
  coord_flip()

}