Update Response Accounting for Selected Predictors

update_y updates the response accounting for the selected predictors in linear models, and selected or removed predictors in generalized linear models.

update_y(y, X, family, vars, update_y_thresh = NULL)

Arguments

y

Response. If family = "normal", a numeric vector. If family = "binomial", a numeric/integer/logical vector with values in {0,1}.

family

Model family; one of c("normal","binomial"). Determines which engine is called (update_y_LM, update_y_GLM).

X

Predictor matrix. Can be a base matrix or something as.matrix() can coerce. No missing values are allowed.

vars

Character vector containing the names of predictors that need to be accounted for. They must appear in X.

update_y_thresh

Numeric scalar threshold used only family = "binomial". When \(|y - fitted\_y| > update\_y\_thresh\), y is kept, else y replaced by the rounded value of \(fitted_y\), which is the fitted probability from the logistic model. The default value is 0.5.

Value

Returns the updated response vector.

Author

Nilotpal Sanyal <nsanyal@utep.edu>, Padmore N. Prempeh <pprempeh@albany.edu>

Examples

# Simulate continuous data
set.seed(123)
n <- 100
p <- 150
X <- matrix(rnorm(n * p), n, p)
colnames(X) <- paste0("V", 1:p)
y <- X[,1] + 0.5 * X[,2] + rnorm(n)
update_y(y = y, X = X, family = "normal", vars = c("V1","V4"))
#>           1           2           3           4           5           6 
#> -1.59862166 -0.33644302 -0.84792451  0.91750191  0.26170677  1.49935928 
#>           7           8           9          10          11          12 
#> -1.75492925 -0.09430317  0.29054672  0.93731582  0.47309448 -1.08727736 
#>          13          14          15          16          17          18 
#> -0.11190232 -0.09686395  0.52997059 -0.08881610  0.89866451  0.73425980 
#>          19          20          21          22          23          24 
#> -0.50852445 -0.14688709  1.80022226 -2.48579611 -0.05175838  1.74883938 
#>          25          26          27          28          29          30 
#>  0.63209248 -1.04741718  0.45238278 -0.68760999 -2.02531954 -0.40485644 
#>          31          32          33          34          35          36 
#> -1.47729999 -0.58445736  0.51910850  0.65578067 -0.90410896  0.60663777 
#>          37          38          39          40          41          42 
#> -0.69638978  1.01918253 -0.46919980 -0.94121368  1.30826006 -0.57818400 
#>          43          44          45          46          47          48 
#> -1.79119259 -0.44887340 -0.22290510  0.13157632  0.00347607 -0.57949953 
#>          49          50          51          52          53          54 
#>  2.04821867  0.16114766  0.78927157  1.00553923 -1.18679206  1.58930587 
#>          55          56          57          58          59          60 
#> -0.57605265  1.15647333  1.60202371 -1.43408889  0.14526039 -0.10207504 
#>          61          62          63          64          65          66 
#> -0.26228995 -0.05982090 -0.64578051  1.16864863 -0.87074835  1.15271169 
#>          67          68          69          70          71          72 
#>  0.77931919 -1.36751043  2.04792441  1.45383582 -0.01324524  2.09113996 
#>          73          74          75          76          77          78 
#> -1.03193597 -0.46872210 -2.77089039 -1.06448436 -0.44336872  1.16287738 
#>          79          80          81          82          83          84 
#>  1.59798127  0.04755143 -0.66022489  0.23181967  0.84349509 -1.04422504 
#>          85          86          87          88          89          90 
#> -0.16946596 -0.53139685  0.16640201  0.10477904  0.66961606  0.25861698 
#>          91          92          93          94          95          96 
#> -0.43119447 -1.47147398  1.98506800  0.30713562  0.45539893  0.17870718 
#>          97          98          99         100 
#> -0.71970294 -0.28267033 -1.14786871  0.20435594