Make Projection from a "PCA" Object

Similar to other transform methods, this function transforms values from a "PCA" object.

# S3 method for PCA
transform(
  model,
  data,
  key,
  features = NULL,
  n.components = NULL,
  scaling = NULL,
  ...
)

Arguments

model	PCA R6 model The model you want to transform
data	DataFrame DataFrame containting the data.
key	character Name of the ID column.
features	character of list of characters, optional Name of feature columns for prediction. If not provided, it defaults to all non-key columns of data.
n.components	integer, optional Number of components to be retained. The value range is from 1 to number of features. Defaults to number of features.
scaling	logical, optional If TRUE, scale variables to have unit variance before the analysis takes place. Defaults to FALSE.
...	Reserved parameter.

Value

DataFrame
Transformed variable values corresponding to each data point, structured as follows:

• ID column, with same name and type as data's ID column.

• Score columns, type DOUBLE, representing the component score values of each data point.

Examples

Perform the transformation on DataFrame data2 using "PCA" object pca:

> data2$Collect()
  ID X1 X2 X3 X4 X5 X6
1  1  2 32 10 54 38 20
2  2  9 57 20 25 48 19
3  3 12 24 28 35 30 20
4  4 15 42 27 36 61 27

Call the function:

> result <- transform(pca, data2, key="ID", n.components=4, scaling = TRUE)

Output:

> result[[1]]$Collect()
  ID COMPONENT_1 COMPONENT_2 COMPONENT_3 COMPONENT_4 COMPONENT_5 COMPONENT_6
1  1 -6.60374153  -12.352444   2.7068428   5.0252348          NA          NA
2  2 -4.06006881    1.910325  -0.9619991  -1.2077427          NA          NA
3  3 -6.33107883  -11.018845  12.5079870   2.3591360          NA          NA
4  4 -0.03122317   -4.423641   6.3617901  -0.5825786          NA          NA

See also

hanaml.PCA