Colossus (version 1.2)

factorize_par: Splits a parameter into factors in parallel

Description

factorize_par uses user provided list of columns to define new parameter for each unique value and update the data.table. Not for interaction terms

Usage

factorize_par(df, col_list, verbose = 0, nthreads = as.numeric(detectCores()))

Value

returns a list with two named fields. df for the updated dataframe, and cols for the new column names

Arguments

df

a data.table containing the columns of interest

col_list

an array of column names that should have factor terms defined

verbose

integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0.

nthreads

number of threads to use, do not use more threads than available on your machine

See Also

Other Data Cleaning Functions: Check_Dupe_Columns(), Check_Trunc(), Check_Verbose(), Convert_Model_Eq(), Correct_Formula_Order(), Date_Shift(), Def_Control(), Def_Control_Guess(), Def_model_control(), Def_modelform_fix(), Event_Count_Gen(), Event_Time_Gen(), Joint_Multiple_Events(), Replace_Missing(), Time_Since(), factorize(), gen_time_dep(), interact_them()

Examples

library(data.table)
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 2, 1, 0, 1, 0)
df <- data.table::data.table("a" = a, "b" = b, "c" = c)
col_list <- c("c")
val <- factorize_par(df, col_list, FALSE, 2)
df <- val$df
new_col <- val$cols