| DGM_bounds {RegCombin} | R Documentation |
This function compute the DGM bounds for all the different coefficients.
Description
This function compute the DGM bounds for all the different coefficients.
Usage
DGM_bounds(
Ldata,
Rdata,
values,
sam0,
refs0,
out_var,
nc_var,
c_var = NULL,
constraint = NULL,
nc_sign = NULL,
c_sign = NULL,
nbCores = 1,
eps_default = 0.5,
nb_pts = 1,
Bsamp = 1000,
grid = 30,
weights_x = NULL,
weights_y = NULL,
outside = FALSE,
meth = "adapt",
modeNA = FALSE,
version = "second",
version_sel = "second",
alpha = 0.05,
projections = FALSE,
R2bound = NULL,
values_sel = NULL,
ties = FALSE,
mult = NULL,
seed = 2131
)
Arguments
Ldata |
dataset containing (Y,Xc) where Y is the outcome, Xc are potential common regressors. |
Rdata |
dataset containing (Xnc,Xc) where Xnc are the non commonly observed regressors, Xc are potential common regressors. |
values |
the different unique points of support of the common regressor Xc. |
sam0 |
the directions q to compute the radial function. |
refs0 |
indicating the positions in the vector values corresponding to the components of betac. |
out_var |
label of the outcome variable Y. |
nc_var |
label of the non commonly observed regressors Xnc. |
c_var |
label of the commonly observed regressors Xc. |
constraint |
a vector indicating the different constraints in a vector of the size of X_c indicating the type of constraints, if any on f(X_c) : "concave", "concave", "nondecreasing", "nonincreasing", "nondecreasing_convex", "nondecreasing_concave", "nonincreasing_convex", "nonincreasing_concave", or NULL for none. Default is NULL, no contraints at all.#' @param nc_sign if sign restrictions on the non-commonly observed regressors Xnc: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints. |
nc_sign |
sign restrictions on the non-commonly observed regressors Xnc: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints. |
c_sign |
sign restrictions on the commonly observed regressors: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints. |
nbCores |
number of cores for the parallel computation. Default is 1. |
eps_default |
If grid =NULL, then epsilon is taken equal to eps_default. |
nb_pts |
the constant C in DGM for the epsilon_0, the lower bound on the grid for epsilon, taken equal to nb_pts*ln(n)/n. Default is 1 without regressors Xc, 3 with Xc. |
Bsamp |
the number of bootstrap/subsampling replications. Default is 1000. |
grid |
the number of points for the grid search on epsilon. Default is 30. If NULL, then epsilon is taken fixed equal to kp. |
weights_x |
the sampling weights for the dataset (Xnc,Xc). Default is NULL. |
weights_y |
the sampling weights for the dataset (Y,Xc). Default is NULL. |
outside |
if TRUE indicates that the parallel computing has been launched outside of the function. Default is FALSE. |
meth |
the method for the choice of epsilon, either "adapt", i.e. adapted to the direction or "min" the minimum over the directions. Default is "adapt". |
modeNA |
indicates if NA introduced if the interval is empty. Default is FALSE. |
version |
version of the computation of the ratio, "first" indicates no weights, no ties, same sizes of the two datasets; "second" otherwise. Default is "second". |
version_sel |
version of the selection of the epsilon, "first" indicates no weights, no ties, same sizes of the two datasets; "second" otherwise. Default is "second". |
alpha |
for the level of the confidence region. Default is 0.05. |
projections |
if FALSE compute the identified set along some directions or the confidence regions. Default is FALSE |
R2bound |
the lower bound on the R2 of the long regression if any. Default is NULL. |
values_sel |
the selected values of Xc for the conditioning. Default is NULL. |
ties |
Boolean indicating if there are ties in the dataset. Default is FALSE. |
mult |
a list of multipliers of our selected epsilon to look at the robustness of the point estimates with respect to it. Default is NULL |
seed |
set a seed to fix the subsampling replications |
Value
a list containing, in order: - ci : a list with all the information on the confidence intervals
* upper: upper bound of the confidence interval on the radial function S in the specified direction at level alpha, possibly with sign constraints
* lower: lower bound upper bound of the confidence interval on the radial function S, possibly with sign constraints
* unconstr: confidence interval on the radial function S, without sign constraints
* If common regressors, upper_agg, lower_agg, and unconstr_agg reports the same values but aggregated over the values of Xc (see the parameter theta0 in the paper)
* betac_ci: confidence intervals on each coefficients related to the common regressor, possibly with sign constraints
* betac_ci_unc: confidence intervals on each coefficients related to the common regressor without sign constraints
If projection is TRUE:
* support: confidence bound on the support function in each specified direction
- point : a list with all the information on the point estimates
* upper: the upper bounds on betanc, possibly with sign constraints
* lower: the lower bounds on betanc, possibly with sign constraints
* unconstr: bounds on betanc without sign constraints
* If common regressors, upper_agg, lower_agg, and unconstr_agg reports the same values but aggregated over the values of Xc (see the parameter theta0 in the paper)
* betac_pt: bounds on betanc, possibly with sign constraints
* betac_pt_unc: bounds on betanc without sign constraints If projection ==TRUE:
* support: point estimate of the support function in each specified direction
- epsilon : the values of the selected epsilon(q)
Examples
n=200
Xnc_x = rnorm(n,0,1.5)
Xnc_y = rnorm(n,0,1.5)
epsilon = rnorm(n,0,1)
## true value
beta0 =1
Y = Xnc_y*beta0 + epsilon
out_var = "Y"
nc_var = "Xnc"
# create the datasets
Ldata<- as.data.frame(Y)
colnames(Ldata) <- c(out_var)
Rdata <- as.data.frame(Xnc_x)
colnames(Rdata) <- c(nc_var)
values = NULL
s= NULL
refs0 = NULL
sam0 <- rbind(-1,1)
eps0 = 0
############# Estimation #############
output <- DGM_bounds(Ldata,Rdata,values,sam0,refs0,out_var,nc_var)