ctoc: Categorical variables into Continuous features
In corkscrew: Preprocessor for Data Modeling

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/ctoc.R

Converting categorical variables into continuous features using the mean of the response variable for the respective categories without using the index record.

1	ctoc(y, x, data, min.obs)

`y`	Response variable (categorical or continuous).
`x`	Predictor variables in the dataframe which are categorical and need to be converted into continuous.
`data`	Name of the dataframe.
`min.obs`	The minimum number of observations within a category in a categorical variable to get converted into a continuous feature. All the categories which have observations less than the min.obs will form a different category.

This function is only for categorical variables.

Returns a dataframe with converted features without replacing the original ones.

Santhosh Sasanapuri

apply.ctoc, tbin, apply.tbin.

data(ChickWeight)
# Converting the "Chick" variable into factor from ord.factor for demonstration purposes.
ChickWeight$Chick <- as.factor(as.numeric(ChickWeight$Chick))
# Returns a dataframe with two added columns for "Chick" and "Diet"
head(ctoc(y = "weight", x = c("Chick","Diet"), data = ChickWeight, min.obs = 12))

Loading required package: ggplot2
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: RColorBrewer
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Attaching package: 'igraph'

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    decompose, spectrum

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  Diet Chick weight Time Chick_cont Diet_cont
1    1     1     39    0   78.29730  102.9361
2    1     1     35    2   78.40541  102.9543
3    1    10     43    0   90.72727  102.9178
4    1    10     48    2   90.27273  102.8950
5    1    10     55    4   89.63636  102.8630
6    1    10     62    6   89.00000  102.8311