dlim  R Documentation 
Fit distributed lag interaction model
dlim(
y,
x,
modifiers,
z = NULL,
df_m,
df_l,
penalize = TRUE,
pen_fn = "ps",
mod_args = NULL,
lag_args = NULL,
fit_fn = "gam",
model_type = "standard",
ID = NULL,
...
)
y 
vector of response values (class " 
x 
matrix of exposure history (columns) for individuals (rows) (class " 
modifiers 
vector of modifying values (class " 
z 
matrix of covariates, not including the modifier (class " 
df_m 
degrees of freedom for modifier basis (class " 
df_l 
degrees of freedom for exposure time basis (class " 
penalize 

pen_fn 
if penalizing, can specify "ps" for penalized Bsplines or "cr" for cubic regression splines with penalties on second derivatives 
mod_args 
a list of additional arguments for the spline function (must be named by argument) 
lag_args 
a list of additional arguments for the spline function (must be named by argument) 
fit_fn 
specify "gam" to use the 
model_type 
"linear" for a DLIM with linear interaction, "quadratic" for a DLIM with quadratic interaction, "standard" for a DLIM with splines (class " 
ID 
group identifier for random intercept, only supported for penalized models 
... 
Other arguments to pass to model fitting function 
This function returns a list that is an object of class "dlim
" with the following components
cb 
crossbasis (class " 
fit 
model object (class " 
modifiers 
modifying values (class " 
call 
model call 
Type vignette('dlimOverview')
for a detailed description.
predict.dlim
plot_cumulative
plot_DLF
library(dlim)
data("ex_data")
dlim_fit < dlim(y = ex_data$y,
x = ex_data$exposure,
modifier = ex_data$modifier,
z = ex_data$z,
df_m = 10,
df_l = 10,
method = "REML")
dlim_pred < predict(dlim_fit,
newdata = 0.5,
type="CE")
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