robincar_linear: Estimate treatment-group-specific response means and...

In mbannick/RoboCar:

Estimate treatment-group-specific response means and (optionally) treatment group contrasts.

Description

Estimate treatment-group-specific response means and (optionally) treatment group contrasts.

Usage

robincar_linear(
  df,
  treat_col,
  response_col,
  strata_cols,
  covariate_cols,
  car_scheme = "simple",
  adj_method = "ANOVA",
  vcovHC = "HC0",
  covariate_to_include_strata = NULL,
  conf_level = 0.95,
  contrast_h = NULL,
  contrast_dh = NULL
)

Arguments

df	A data.frame with the required columns
treat_col	Name of column in df with treatment variable
response_col	Name of the column in df with response variable
strata_cols	Names of columns in df with strata variables
covariate_cols	Names of columns in df with covariate variables
car_scheme	Name of the type of covariate-adaptive randomization scheme
adj_method	Name of linear adjustment method to use
vcovHC	Type of heteroskedasticity-consistent variance estimates
covariate_to_include_strata	Whether to include strata variables in covariate adjustment. Defaults to F for ANOVA and ANCOVA; defaults to T for ANHECOVA. User may override by passing in this argument.
conf_level	Level for confidence intervals
contrast	An optional function to specify a desired contrast

Examples

data<-RobinCar:::data_sim
data$A<-as.factor(data$A)
fit.anova<-robincar_linear(df = data, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1", "x3"),
                           car_scheme="simple",
                           adj_method="ANOVA",
                           vcovHC="HC0")
fit.ancova<-robincar_linear(df = data, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1", "x3"),
                           car_scheme="simple",
                           adj_method="ANCOVA",
                           vcovHC="HC0")
fit.anhecova<-robincar_linear(df = data, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1", "x3"),
                           car_scheme="simple",
                           adj_method="ANHECOVA",
                           vcovHC="HC0")
                           
fit.anova<-robincar_linear(df = data, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1", "x3"),
                           car_scheme="simple",
                           adj_method="ANOVA",
                           vcovHC="HC0",
                           contrast_h="diff")
odds.ratio<-function(theta){
  theta0<-theta[1]
  theta1<-theta[2]
  theta2<-theta[3]
  OR01<-theta1/(1-theta1)/(theta0/(1-theta0))
  OR02<-theta2/(1-theta2)/(theta0/(1-theta0))
  return(c(OR01,OR02))
}
robincar_contrast(fit.anova$main, contrast_h=odds.ratio)
                           
n <- 1000
df <- data.frame(A=rbinom(n, size=1, prob=0.5),
                 y=rnorm(n),
                 x1=rnorm(n),
                 x2=rnorm(n),
                 x3=as.factor(rbinom(n, size=1, prob=0.5)),
                 z1=rbinom(n, size=1, prob=0.5),
                 z2=rbinom(n, size=1, prob=0.5))
df$A <- as.factor(df$A)

fit.ancova<-robincar_linear(df = df, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1", "x2"),
                           car_scheme="biased-coin",
                           adj_method="ANCOVA",
                           vcovHC="HC0")
fit.anhecova<-robincar_linear(df = df, 
                           response_col="y",
                           treat_col="A",
                           strata_cols=c("z1", "z2"),
                           covariate_cols=c("x1"),
                           car_scheme="biased-coin",
                           adj_method="ANHECOVA",
                           vcovHC="HC0")

mbannick/RoboCar documentation built on June 16, 2022, 6:56 p.m.