fixest object returned in sparse formatR/sparse_model_matrix.R
sparse_model_matrix.RdThis function creates the left-hand-side or the right-hand-side(s) of a femlm, feols or feglm estimation.
sparse_model_matrix(
object,
data,
type = "rhs",
sample = "estimation",
na.rm = FALSE,
collin.rm = NULL,
combine = TRUE,
...
)A fixest object. Obtained using the functions femlm, feols or feglm.
If missing (default) then the original data is obtained by evaluating the call.
Otherwise, it should be a data.frame.
Character vector or one sided formula, default is "rhs". Contains the type of matrix/data.frame to be returned. Possible values are: "lhs", "rhs", "fixef", "iv.rhs1" (1st stage RHS), "iv.rhs2" (2nd stage RHS), "iv.endo" (endogenous vars.), "iv.exo" (exogenous vars), "iv.inst" (instruments).
Character scalar equal to "estimation" (default) or "original". Only
used when data=NULL (i.e. the original data is requested). By default,
only the observations effectively used in the estimation are returned (it includes
the observations with NA values or the fully explained by the fixed-effects (FE), or
due to NAs in the weights).
If sample="original", all the observations are returned. In that case, if
you use na.rm=TRUE (which is not the default), you can withdraw the observations
with NA values (and keep the ones fully explained by the FEs).
Default is FALSE. Should observations with NAs be removed from the matrix?
Logical scalar. Whether to remove variables that were
found to be collinear during the estimation. Beware: it does not perform a
collinearity check and bases on the coef(object).
Default is TRUE if object is a fixest object, or FALSE if object is a formula.
Logical scalar, default is TRUE. Whether to combine each
resulting sparse matrix.
Not currently used.
It returns either a single sparse matrix a list of matrices,
depending whether combine is TRUE or FALSE.
The sparse matrix is of class dgCMatrix from the Matrix package.
See also the main estimation functions femlm, feols or feglm. formula.fixest, update.fixest, summary.fixest, vcov.fixest.
est = feols(wt ~ i(vs) + hp | cyl, mtcars)
sparse_model_matrix(est)
#> 32 x 2 sparse Matrix of class "dgCMatrix"
#> vs::1 hp
#> [1,] . 110
#> [2,] . 110
#> [3,] 1 93
#> [4,] 1 110
#> [5,] . 175
#> [6,] 1 105
#> [7,] . 245
#> [8,] 1 62
#> [9,] 1 95
#> [10,] 1 123
#> [11,] 1 123
#> [12,] . 180
#> [13,] . 180
#> [14,] . 180
#> [15,] . 205
#> [16,] . 215
#> [17,] . 230
#> [18,] 1 66
#> [19,] 1 52
#> [20,] 1 65
#> [21,] 1 97
#> [22,] . 150
#> [23,] . 150
#> [24,] . 245
#> [25,] . 175
#> [26,] 1 66
#> [27,] . 91
#> [28,] 1 113
#> [29,] . 264
#> [30,] . 175
#> [31,] . 335
#> [32,] 1 109
sparse_model_matrix(wt ~ i(vs) + hp | cyl, mtcars)
#> 32 x 3 sparse Matrix of class "dgCMatrix"
#> vs::0 vs::1 hp
#> [1,] 1 . 110
#> [2,] 1 . 110
#> [3,] . 1 93
#> [4,] . 1 110
#> [5,] 1 . 175
#> [6,] . 1 105
#> [7,] 1 . 245
#> [8,] . 1 62
#> [9,] . 1 95
#> [10,] . 1 123
#> [11,] . 1 123
#> [12,] 1 . 180
#> [13,] 1 . 180
#> [14,] 1 . 180
#> [15,] 1 . 205
#> [16,] 1 . 215
#> [17,] 1 . 230
#> [18,] . 1 66
#> [19,] . 1 52
#> [20,] . 1 65
#> [21,] . 1 97
#> [22,] 1 . 150
#> [23,] 1 . 150
#> [24,] 1 . 245
#> [25,] 1 . 175
#> [26,] . 1 66
#> [27,] 1 . 91
#> [28,] . 1 113
#> [29,] 1 . 264
#> [30,] 1 . 175
#> [31,] 1 . 335
#> [32,] . 1 109