View source: R/estimate_npmetric_erf.R
estimate_npmetric_erf | R Documentation |
Estimate smoothed exposure-response function (ERF) for matched and weighted data set using non-parametric models.
estimate_npmetric_erf(
m_Y,
m_w,
counter_weight,
bw_seq,
w_vals,
nthread,
kernel_appr = "locpol"
)
m_Y |
A vector of outcome variable in the matched set. |
m_w |
A vector of continuous exposure variable in the matched set. |
counter_weight |
A vector of counter or weight variable in the matched set. |
bw_seq |
A vector of bandwidth values. |
w_vals |
A vector of values that you want to calculate the values of the ERF at. |
nthread |
The number of available cores. |
kernel_appr |
Internal kernel approach. Available options are |
Estimate Functions Using Local Polynomial kernel regression.
The function returns a gpsm_erf object. The object includes the following attributes:
params
m_Y
m_w
bw_seq
w_vals
erf
fcall
set.seed(697)
m_d <- generate_syn_data(sample_size = 200)
pseudo_pop <- generate_pseudo_pop(m_d[, c("id", "w")],
m_d[, c("id", "cf1","cf2","cf3",
"cf4","cf5","cf6")],
ci_appr = "matching",
pred_model = "sl",
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)
data <- merge(m_d[, c("id", "Y")], pseudo_pop$pseudo_pop, by = "id")
erf_obj <- estimate_npmetric_erf(data$Y,
data$w,
data$counter_weight,
bw_seq=seq(0.2,2,0.2),
w_vals = seq(2,20,0.5),
nthread = 1)
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