causalreg (version 0.1.0)

cgam: Causal generalized additive model

Description

This function does a search for a causal submodel within the generalized additive model provided.

Usage

cgam(
  formula,
  family,
  data,
  alpha = 0.05,
  pval.approx = FALSE,
  B = 100,
  seed = 1,
  search = c("all", "stepwise"),
  ...
)

Value

A gam object of the selected causal submodel.

Arguments

formula

A formula object.

family

A distributional family object. Currently supported options are: binomial and poisson.

data

A data frame containing the variables in the model.

alpha

Significance level for statistical test.

pval.approx

If TRUE, chi-squared approximated p-values are calculated. Default is FALSE, in which case p-values are calculated via bootstrap.

B

Number of bootstrap sample when pval.approx=FALSE.

seed

Seed for generating bootstrap samples.

search

If search="stepwise", a greedy forward stepwise search is conducted. Default is search="all", in which case all possible submodels are considered.

...

Further arguments to be passed to the gam function.

Examples

##############################
#causal Poisson gam##########
n<-1000
set.seed(123)
X1<-rnorm(n,0,1)
Y<-rpois(n,exp(sin(X1)))
X2<-log(Y+1)+rnorm(n,0,0.5)
data<-data.frame(cbind(X1, X2, Y))
cm_all<-cgam(Y ~ s(X1)+s(X2),"poisson",data,pval.approx=TRUE,search="all")
cm_all$model.opt
cm_step<-cgam(Y ~ s(X1)+s(X2),"poisson",data,pval.approx=TRUE,search="stepwise")
cm_step$model.opt
# \donttest{
#bigger simulation with 7 covariates
set.seed(123)
n<-1000
X1<-rnorm(n=n,sd=sqrt(0.04))
X2<-X1+rnorm(n=n,sd=sqrt(0.04))
X3<-X1+X2+rnorm(n=n,sd=sqrt(0.04))
m<-sin(X2*5)+X3^3
Z<-m+rnorm(n=n,sd=sqrt(0.04))
X4<-X2+rnorm(n=n,sd=sqrt(0.04))
X5<-Z+rnorm(n=n,sd=sqrt(0.04))
X6<-Z+rnorm(n=n,sd=sqrt(0.04))
X7<-X6+rnorm(n=n,sd=sqrt(0.04))
Y<-qpois(pnorm(Z, mean = m, sd = sqrt(0.04)), lambda=exp(m))
dat<-data.frame(cbind(X1, X2, X3, X4, X5, X6, X7,Y))
fml<- Y~s(X1)+s(X2)+s(X3)+s(X4)+s(X5)+s(X6)+s(X7)
mod.all <-cgam(fml,"poisson",dat,pval.approx=TRUE,search="all")
mod.all$model.opt
mod.step <-cgam(fml,"poisson",dat,pval.approx=TRUE,search="stepwise")
mod.step$model.opt
####################################
#causal logistic gam################
n<-1000
set.seed(123)
X1<-rnorm(n,0,1)
Y<-rbinom(n,1,exp(X1)/(1+exp(X1)))
flip<-rbinom(n,1,0.1)
X2<-(1-flip)*Y+rnorm(n,0,0.3)
data<-data.frame(cbind(X1, X2, Y))
cm_all<-cgam(Y ~ s(X1)+s(X2),"binomial",data,pval.approx=FALSE,search="all")
cm_all$model.opt
cm_step<-cgam(Y ~ s(X1)+s(X2),"binomial",data,pval.approx=FALSE,search="stepwise")
cm_step$model.opt
# }