transform.CATPCA.Rd
Similar to other transform methods, this function transforms values from a "CATPCA" object.
# S3 method for CATPCA
transform(
model,
data,
key,
features = NULL,
n.components = NULL,
thread.ratio = NULL,
ignore.unknown.category = NULL,
...
)
CATPCA R6 class
The model you want to transform the input data
DataFrame
DataFrame containting the data.
character
Name of the ID column.
character of list of characters, optional
Name of feature columns for prediction.
If not provided, it defaults to all non-key columns of data.
integer, optional
Number of components to be retained.
The value range is from 1 to number of given component loadings.
Defaults to the number of given components loadings.
double, optional
Specifies the ratio of total thread number available.
The value range is [0, 1].
0 means 1 thread, while 1 means all available threads.
Defaults to 1.0.
logical, optional
Specifies whether or not to ignore unknown category in data
during data transformation.
If set to FALSE, an error message shall be raised when any unknown category
is encountered; otherwise the unknown category is ignored with quantify 0.
Defaults to FALSE.
Reserved parameter.
DataFrame
Transformed components score values for all points in the input data,
structured as follows:
ID column, with same name and type as the ID column in data
.
COMPONENT_ID, type INTEGER, representing categorical PCA component IDs.
COMPONENT_SCORE, type DOUBLE, holding the component score values for
all points in data
.
In the following context we perform the transformation on a DataFrame
using "CATPCA" object cpc
.
Input data for transformation
> data2$Collect()
ID X1 X2 X3 X4 X5 X6
1 1 12 A 20 44 48 16
2 2 12 B 25 45 50 16
3 3 12 C 21 45 50 16
4 4 13 A 21 46 51 17
5 5 14 C 24 46 51 17
6 6 22 A 25 54 58 26
Call the function:
> result <- transform(cpc, data2,
key="ID", n.components=2,
thread.ratio = 0.5,
ignore.unknown.category=FALSE)
Output:
> result$Collect()
ID COMPONENT_ID COMPONENT_SCORE
1 1 1 2.73451825
2 2 1 1.05566374
3 3 1 1.91871148
4 4 1 1.76884062
5 5 1 1.01998751
6 6 1 -2.38612629
7 1 2 -0.74763131
8 2 2 1.70968717
9 3 2 -0.06488889
10 4 2 -0.76744182
11 5 2 0.55794762
12 6 2 -1.09488511