RegCombin (version 0.4.1)

regCombin_profile: Computing the DGM bounds for different values of epsilon, proportional to the data-driven selected one

Description

Computing the DGM bounds for different values of epsilon, proportional to the data-driven selected one

Usage

regCombin_profile(
  Ldata,
  Rdata,
  out_var,
  nc_var,
  c_var = NULL,
  constraint = NULL,
  nc_sign = NULL,
  c_sign = NULL,
  weights_x = NULL,
  weights_y = NULL,
  nbCores = 1,
  methods = c("DGM"),
  grid = 10,
  alpha = 0.05,
  eps_default = 0.5,
  R2bound = NULL,
  projections = FALSE,
  unchanged = FALSE,
  ties = FALSE,
  multipliers = c(0.25, 0.5, 1, 1.5, 2)
)

Value

a list containing, in order: - details: a list with all the detailled results of the estimation for the different multipliers. see "regCombin".

- Profile_point : a matrix with the profile of the bounds without constraints for different values of the multiplier.

- Profile_point_sign : a matrix with the profile of the bounds with constraints for different values of the multiplier.

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.

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.

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.

c_sign

if 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.

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.

nbCores

number of cores for the parallel computation. Default is 1.

methods

method used for the bounds: "DGM" (Default) and/or "Variance".

grid

the number of points for the grid search on epsilon. Default is 30. If NULL, then epsilon is taken fixed equal to eps_default.

alpha

the level of the confidence intervals. Default is 0.05.

eps_default

If grid =NULL, then epsilon is taken equal to eps_default.

R2bound

the lower bound on the R2 of the long regression if any. Default is NULL.

projections

if FALSE compute the identified set along some directions or the confidence regions. Default is FALSE

unchanged

Boolean indicating if the categories based on Xc must be kept unchanged (TRUE). Otherwise (FALSE), a thresholding approach is taken imposing that each value appears more than 10 times in both datasets and 0.01 per cent is the pooled one. Default is FALSE.

ties

Boolean indicating if there are ties in the dataset. Default is FALSE.

multipliers

different multipliers of our selected epsilon to compute the bounds. Default is 0.25,0.5,1,1.5,2.

Examples

### Simulating according to this DGP
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)


############# Estimation #############
profile = regCombin_profile(Ldata,Rdata,out_var,nc_var, multipliers = seq(0.1,3,length.out=3))