Estimate Parametric Exposure Response Function
Source:R/estimate_pmetric_erf.R
estimate_pmetric_erf.Rd
Estimate a constant effect size for matched and weighted data set using parametric models
Arguments
- formula
a vector of outcome variable in matched set.
- family
a description of the error distribution (see ?gnm)
- data
dataset that formula is build upon (Note that there should be a
counter_weight
column in this data.)- ...
Additional parameters for further fine tuning the gnm model.
Examples
# \donttest{
m_d <- generate_syn_data(sample_size = 100)
pseudo_pop <- generate_pseudo_pop(m_d[, c("id", "w")],
m_d[, c("id", "cf1","cf2","cf3",
"cf4","cf5","cf6")],
ci_appr = "matching",
sl_lib = c("m_xgboost"),
params = list(xgb_nrounds=c(10,20,30),
xgb_eta=c(0.1,0.2,0.3)),
nthread = 1,
covar_bl_method = "absolute",
covar_bl_trs = 0.1,
covar_bl_trs_type= "mean",
max_attempt = 1,
dist_measure = "l1",
delta_n = 1,
scale = 0.5)
#> mean absolute correlation: 0.190532732398494| Covariate balance threshold: 0.1
#> mean absolute correlation: 0.149674153889077| Covariate balance threshold: 0.1
#> Covariate balance condition has not been met.
#> Best mean absolute correlation: 0.149674153889077| Covariate balance threshold: 0.1
data <- merge(m_d[, c("id", "Y")], pseudo_pop$pseudo_pop, by = "id")
outcome_m <- estimate_pmetric_erf(formula = Y ~ w,
family = gaussian,
data = data)
# }