| Type: | Package |
| Title: | Variable Selection Methods Including an Exposure Variable |
| Version: | 1.0.3 |
| Author: | Alex Martinez [aut, cre], Andrew Chapple [aut] |
| Maintainer: | Alex Martinez <ahmartinez283@gmail.com> |
| Description: | Utilizes multiple variable selection methods to estimate Average Treatment Effect. |
| License: | GPL-2 |
| LinkingTo: | Rcpp, RcppArmadillo |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | yes |
| Packaged: | 2023-04-20 22:19:24 UTC; Alex |
| Repository: | CRAN |
| Date/Publication: | 2023-04-27 13:50:08 UTC |
Performs deviance-based backwards variable selection in logistic regression with an exposure.
Description
Returns the estimated Average Treatment Effect and estimated Relative Treatment Effect calculated by the optimal model chosen via backward selection including an exposure variable.
Usage
BACKWARD_EXPOSURE(Data)
Arguments
Data |
Data frame containing outcome variable (Y), exposure variable (E), and candidate covariates. |
Value
List containing (1) the estimated Average Treatment Effect, (2) estimated Relative Treatment Effect, (3) summary of the selected model, and (4) the first 6 rows of the data frame containing backward-selected covariates.
References
[1] **will contain our paper later**
Examples
###Generate data with n rows and p covariates, can be any number but we'll choose 750 rows
###and 7 covariates for this example
set.seed(3)
p = 7
n = 750
beta0 = rnorm(1, mean = 0, sd = 1)
betaE = rnorm(1, mean = 0, sd = 1)
beta0_E = rnorm(1, mean = 0, sd = 1)
betaX_E = c()
betaX_Y = c()
Y = rep(NA, n)
E = rep(NA, n)
pi0 = rep(NA, n)
pi1 = rep(NA, n)
data = data.frame(cbind(Y, E, pi0, pi1))
j = round(runif(1, 0, p))
for(i in 1:p){
betaX_Y[i] = rnorm(1, mean = 0, sd = 0.5)
betaX_E[i] = rnorm(1, mean = 0, sd = 0.5)
}
zeros = sample(1:p, j, replace = FALSE)
betaX_Y[zeros] = 0
betaX_E[zeros] = 0
mu = 0
sigma = 1
for(i in 1:p){
covar = rnorm(n, 0, 1)
data[,i+4] = covar
names(data)[i+4] = paste("X", i, sep = "")
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
for(i in 1:n){
p.event = beta0 + betaE + sum(betaX_Y*data[i,5:(p+4)])
p.noevent = beta0 + sum(betaX_Y*data[i,5:(p+4)])
pi0 = exp(p.noevent)/(1+exp(p.noevent))
pi1 = exp(p.event)/(1+exp(p.event))
data[i,3] = pi0
data[i,4] = pi1
if(data[i,2] == 1){
data[i,1] = rbinom(1, 1, prob = pi1)
}else{
data[i,1] = rbinom(1, 1, prob = pi0)
}
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
###Raw data includes pi0 and pi1 columns used to fill Y and E, so to test
###the function we'll remove these
testdata = data[,-c(3,4)]
BACKWARD_EXPOSURE(testdata)
Performs deviance-based forwards variable selection in logistic regression with an exposure
Description
Returns the estimated Average Treatment Effect and estimated Relative Treatment Effect calculated by the optimal model chosen via forward selection including an exposure variable.
Usage
FORWARD_EXPOSURE(Data)
Arguments
Data |
Data frame containing outcome variable (Y), exposure variable (E), and candidate covariates. |
Value
List containing (1) the estimated Average Treatment Effect, (2) summary of the selected model, and (3) the first 6 rows of the data frame containing forward-selected covariates.
References
[1] **will contain our paper later**
Examples
###Generate data with n rows and p covariates, can be any number but we'll choose 750 rows
###and 7 covariates for this example
set.seed(3)
p = 7
n = 750
beta0 = rnorm(1, mean = 0, sd = 1)
betaE = rnorm(1, mean = 0, sd = 1)
beta0_E = rnorm(1, mean = 0, sd = 1)
betaX_E = c()
betaX_Y = c()
Y = rep(NA, n)
E = rep(NA, n)
pi0 = rep(NA, n)
pi1 = rep(NA, n)
data = data.frame(cbind(Y, E, pi0, pi1))
j = round(runif(1, 0, p))
for(i in 1:p){
betaX_Y[i] = rnorm(1, mean = 0, sd = 0.5)
betaX_E[i] = rnorm(1, mean = 0, sd = 0.5)
}
zeros = sample(1:p, j, replace = FALSE)
betaX_Y[zeros] = 0
betaX_E[zeros] = 0
mu = 0
sigma = 1
for(i in 1:p){
covar = rnorm(n, 0, 1)
data[,i+4] = covar
names(data)[i+4] = paste("X", i, sep = "")
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
for(i in 1:n){
p.event = beta0 + betaE + sum(betaX_Y*data[i,5:(p+4)])
p.noevent = beta0 + sum(betaX_Y*data[i,5:(p+4)])
pi0 = exp(p.noevent)/(1+exp(p.noevent))
pi1 = exp(p.event)/(1+exp(p.event))
data[i,3] = pi0
data[i,4] = pi1
if(data[i,2] == 1){
data[i,1] = rbinom(1, 1, prob = pi1)
}else{
data[i,1] = rbinom(1, 1, prob = pi0)
}
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
###Raw data includes pi0 and pi1 columns used to fill Y and E, so to test
###the function we'll remove these
testdata = data[,-c(3,4)]
FORWARD_EXPOSURE(testdata)
Obtains likelihood
Description
Calculates likelihood from observed outcome data and given covariate data/parameters.
Usage
LIKE(Y, X, beta0, beta)
Arguments
Y |
Binary outcome vector. |
X |
Matrix of covariates. |
beta0 |
Intercept parameter. |
beta |
Vector of covariate parameters of length p. |
Value
Likelihood
Obtains posterior samples from an MCMC algorithm to perform variable selection.
Description
Performs posterior sampling from an MCMC algorithm to estimate average treatment effect and posterior probability of inclusion of candidate variables.
Usage
MCMC_LOGIT_KEEP(Y, Z, PIN, MAX_COV, SdBeta, NUM_REPS)
Arguments
Y |
Binary outcome vector. |
Z |
Matrix of covariates including binary exposure variable. |
PIN |
Prior probability of inclusion of candidate variables. |
MAX_COV |
Maximum number of covariates in desired model. |
SdBeta |
Prior standard deviation for generating distrubtion of proposal coefficients. |
NUM_REPS |
Number of MCMC iterations to perform. |
Value
List containing (1) the posterior distribution of the estimated Average Treatment Effect, (2) the posterior distributions of the intercept parameter, (3) the posterior distributions of the rest of the coefficients including the exposure coefficient, and (4) the posterior distribution for the indication of whether or not the variable was included in a given iteration's model.
Performs deviance-based stepwise variable selection in logistic regression with an exposure
Description
Returns the estimated Average Treatment Effect and estimated Relative Treatment Effect calculated by the optimal model chosen via stepwise selection including an exposure variable.
Usage
STEPWISE_EXPOSURE(Data)
Arguments
Data |
Data frame containing outcome variable (Y), exposure variable (E), and candidate covariates. |
Value
List containing (1) the estimated Average Treatment Effect, (2) summary of the selected model, and (3) the first 6 rows of the data frame containing stepwise-selected covariates.
References
[1] **will contain our paper later**
Examples
###Generate data with n rows and p covariates, can be any number but we'll choose 750 rows
###and 7 covariates for this example
set.seed(3)
p = 7
n = 750
beta0 = rnorm(1, mean = 0, sd = 1)
betaE = rnorm(1, mean = 0, sd = 1)
beta0_E = rnorm(1, mean = 0, sd = 1)
betaX_E = c()
betaX_Y = c()
Y = rep(NA, n)
E = rep(NA, n)
pi0 = rep(NA, n)
pi1 = rep(NA, n)
data = data.frame(cbind(Y, E, pi0, pi1))
j = round(runif(1, 0, p))
for(i in 1:p){
betaX_Y[i] = rnorm(1, mean = 0, sd = 0.5)
betaX_E[i] = rnorm(1, mean = 0, sd = 0.5)
}
zeros = sample(1:p, j, replace = FALSE)
betaX_Y[zeros] = 0
betaX_E[zeros] = 0
mu = 0
sigma = 1
for(i in 1:p){
covar = rnorm(n, 0, 1)
data[,i+4] = covar
names(data)[i+4] = paste("X", i, sep = "")
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
for(i in 1:n){
p.event = beta0 + betaE + sum(betaX_Y*data[i,5:(p+4)])
p.noevent = beta0 + sum(betaX_Y*data[i,5:(p+4)])
pi0 = exp(p.noevent)/(1+exp(p.noevent))
pi1 = exp(p.event)/(1+exp(p.event))
data[i,3] = pi0
data[i,4] = pi1
if(data[i,2] == 1){
data[i,1] = rbinom(1, 1, prob = pi1)
}else{
data[i,1] = rbinom(1, 1, prob = pi0)
}
}
for(i in 1:n){
p.event_E = beta0_E + sum(betaX_E*data[i,5:(p+4)])
pi1_E = exp(p.event_E)/(1+exp(p.event_E))
data[i,2] = rbinom(1, 1, prob = pi1_E)
}
###Raw data includes pi0 and pi1 columns used to fill Y and E, so to test
###the function we'll remove these
testdata = data[,-c(3,4)]
STEPWISE_EXPOSURE(testdata)