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Added offset option for count models as "exposure_col" #17

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Added offset option for count models as "exposure_col" #17

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3038829
Added offset for count models.
mbannick Mar 6, 2024
b4f5f1c
Updated docs
mbannick Mar 6, 2024
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5 changes: 5 additions & 0 deletions R/Data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -176,6 +176,11 @@ validate.RoboDataTTE <- function(data, ref_arm){
if(!is.null(data$event)){
data$event <- as.vector(data$event[[1]])
}
if(ncol(data$exposure) == 0){
data$exposure <- NULL
} else {
data$exposure <- as.vector(data$exposure[[1]])
}
if(ncol(data$strata) == 0){
data$strata <- NULL
}
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3 changes: 3 additions & 0 deletions R/adjust-glm.R
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Expand Up @@ -12,6 +12,9 @@ predictions.GLMModel <- function(model, data, mod){
if(!is.null(dmat)){
df <- cbind(df, dmat)
}
if(!is.null(data$exposure)){
df <- cbind(df, exposure=data$exposure)
}
preds <- stats::predict(mod, newdata=df, type="response")
return(preds)
}
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37 changes: 32 additions & 5 deletions R/adjust-linear.R
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Expand Up @@ -35,7 +35,12 @@ linmod.ANOVA <- function(model, data, family=stats::gaussian, center=TRUE){
treat=data$treat,
response=data$response
)
mod <- fitmod(family=family, formula=response ~ 0 + treat, data=df)
if(is.null(data$exposure)){
mod <- fitmod(family=family, formula=response ~ 0 + treat, data=df)
} else {
df <- cbind(df, exposure=data$exposure)
mod <- fitmod(family=family, formula=response ~ 0 + treat + offset(exposure), data=df)
}
return(mod)
}

Expand All @@ -48,10 +53,21 @@ linmod.ANCOVA <- function(model, data, family=stats::gaussian, center=TRUE){
dmat <- get.dmat(data, model$adj_vars)
if(center) dmat <- .center.dmat(dmat)
df <- cbind(df, dmat)
if(!is.null(data$exposure)){
df <- cbind(df, exposure=data$exposure)
}
if(center){
mod <- fitmod(family=family, formula=response ~ 0 + treat + ., data=df)
if(is.null(data$exposure)){
mod <- fitmod(family=family, formula=response ~ 0 + treat + ., data=df)
} else {
mod <- fitmod(family=family, formula=response ~ 0 + treat + . - exposure + offset(exposure), data=df)
}
} else {
mod <- fitmod(family=family, formula=response ~ 1 + treat + ., data=df)
if(is.null(data$exposure)){
mod <- fitmod(family=family, formula=response ~ 1 + treat + ., data=df)
} else {
mod <- fitmod(family=family, formula=response ~ 1 + treat + . - exposure + offset(exposure), data=df)
}
}
return(mod)
}
Expand All @@ -65,10 +81,21 @@ linmod.ANHECOVA <- function(model, data, family=stats::gaussian, center=TRUE){
if(center) dmat <- .center.dmat(dmat)
df <- cbind(df, dmat)

if(!is.null(data$exposure)){
df <- cbind(df, exposure=data$exposure)
}
if(center){
mod <- fitmod(family=family, response ~ 0 + treat:., data=df)
if(is.null(data$exposure)){
mod <- fitmod(family=family, response ~ 0 + treat:., data=df)
} else {
mod <- fitmod(family=family, response ~ 0 + treat:(. - exposure) + offset(exposure), data=df)
}
} else {
mod <- fitmod(family=family, response ~ 1 + treat:., data=df)
if(is.null(data$exposure)){
mod <- fitmod(family=family, response ~ 1 + treat:., data=df)
} else {
mod <- fitmod(family=family, response ~ 1 + treat:(. - exposure) + offset(exposure), data=df)
}
}
return(mod)
}
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3 changes: 2 additions & 1 deletion R/robincar-SL.R
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Expand Up @@ -92,7 +92,8 @@ robincar_SL <- function(df,
treat_col=treat_col,
response_col=response_col,
strata_cols=strata_cols,
covariate_cols=covariate_cols
covariate_cols=covariate_cols,
exposure_col=NULL
)
validate(data)

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3 changes: 2 additions & 1 deletion R/robincar-covhr.R
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Expand Up @@ -36,7 +36,8 @@ robincar_covhr <- function(df,
response_col=response_col,
event_col=event_col,
strata_cols=strata_cols,
covariate_cols=covariate_cols
covariate_cols=covariate_cols,
exposure_col=NULL
)
validate(data, ref_arm)

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6 changes: 4 additions & 2 deletions R/robincar-glm.R
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Expand Up @@ -20,13 +20,14 @@
#' If you wish to use a negative binomial working model with an unknown dispersion parameter, then use `g_family="nb"`.
#' @param g_accuracy Level of accuracy to check prediction un-biasedness.
#' @param formula An optional formula to use for adjustment specified using as.formula("..."). This overrides strata_cols and covariate_cols.
#' @param exposure_col An offset variable to use for Poisson or negative binomial working models
#'
#' @export
robincar_glm <- function(df,
treat_col, response_col, strata_cols=NULL, covariate_cols=NULL,
car_scheme="simple", adj_method="heterogeneous", # vcovHC="HC0",
covariate_to_include_strata=NULL,
g_family=stats::gaussian, g_accuracy=7, formula=NULL,
g_family=stats::gaussian, g_accuracy=7, formula=NULL, exposure_col=NULL,
contrast_h=NULL, contrast_dh=NULL){

.check.car_scheme(car_scheme)
Expand All @@ -42,7 +43,8 @@ robincar_glm <- function(df,
response_col=response_col,
strata_cols=strata_cols,
covariate_cols=covariate_cols,
formula=formula
formula=formula,
exposure_col=exposure_col
)
validate(data)

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3 changes: 2 additions & 1 deletion R/robincar-tte.R
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Expand Up @@ -38,7 +38,8 @@ robincar_tte <- function(df,
response_col=response_col,
event_col=event_col,
strata_cols=strata_cols,
covariate_cols=covariate_cols
covariate_cols=covariate_cols,
exposure_col=NULL
)
validate(data, ref_arm)

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3 changes: 3 additions & 0 deletions man/robincar_glm.Rd
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35 changes: 35 additions & 0 deletions tests/testthat/test-glm.R
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Expand Up @@ -159,6 +159,41 @@ test_that("GLM full function -- NEGATIVE binomial, permuted block", {

})

test_that("GLM full function -- NEGATIVE binomial, permuted block w offset", {

DATA2$offset1 <- DATA2$y + rbinom(n=nrow(DATA2), size=10, prob=0.5)

# Known dispersion parameter
non <- robincar_glm(
df=DATA2,
response_col="y",
treat_col="A",
strata_cols=c("z1"),
covariate_cols=c("x1"),
car_scheme="permuted-block",
g_family=negative.binomial(1),
g_accuracy=7,
adj_method="heterogeneous",
covariate_to_include_strata=TRUE,
exposure_col="offset1")
expect_equal(class(non), "GLMModelResult")

# Known dispersion parameter
non <- robincar_glm(
df=DATA2,
response_col="y",
treat_col="A",
strata_cols=c("z1"),
covariate_cols=c("x1"),
car_scheme="permuted-block",
g_family="nb",
g_accuracy=7,
adj_method="heterogeneous",
covariate_to_include_strata=TRUE)
expect_equal(class(non), "GLMModelResult")

})

test_that("GLM Settings", {
non <- robincar_glm(
df=DATA2,
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