apply Kernel Density Estimation analysis

# S3 method for KDE
predict(
  model,
  data = NULL,
  key = NULL,
  features = NULL,
  thread.ratio = NULL,
  stat.info = NULL
)

Format

S3 methods

Arguments

model: R6Class object
A 'KDE' object.
data: DataFrame
DataFrame containting the data points whose density value need to be evaluated.
key: character, optional
Name of the ID column.
Defaults to the first column if not provided.
features: character or list of characters, optional
Names of features columns.
If is not provided, it defaults to all non-key columns of data.
thread.ratio: double, optional
Controls the proportion of available threads that can be used by this function.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads.
Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored.
Defaults to 0.
stat.info: logical, optional
If TRUE, return a DataFrame with statistics information.
Defaults to FALSE.

Value

Returns a list of DataFrames.

DataFrame 1
Evaluated log density value of the data points, structured as follows:
- ID: id.
- DENSITY_VALUE: log Density value.

DataFrame 2
Statistics information, structured as follows:
COMMUNALITIES: DataFrame
- TEST_ID: ID of evaluated test data point.
- FITTING_IDS: Fitting IDs.

Examples

Input DataFrame data.df.fit and data.eval.df.fit:


> data.df.fit$Collect()
 ID        X1          X2
1 0 -2.1029683 -1.4283269
2 1 -2.1029683  0.7197969
3 2 -2.1029683  2.8679208
4 3 -0.6094340 -1.4283269
5 4 -0.6094340  0.7197969
6 5 -0.6094340  2.8679208
7 6  0.8841004 -1.4283269
8 7  0.8841004  0.7197969
9 8  0.8841004  2.8679208
> data.eval.df.fit$Collect()
 ID         X1          X2
1 0 -0.4257698 -1.39613035
2 1  0.8841004  1.38149350
3 2  0.1341262 -0.03222389
4 3  0.8455036  2.86792078
5 4  0.2884408  1.51333705
6 5 -0.6667847  1.24498042
7 6 -2.1029683 -1.42832694
8 7  0.7699024 -0.47300711
9 8  0.2102913  0.32843074
10 9 0.4823225 -0.43796174

Call the function:

estimation <- hanaml.KDE(data = data.df.fit,
                         leaf.size = 10,
                         algorithm = "kd-tree",
                         bandwidth = 0.68129,
                         distance.level = "euclidean",
                         kernel = "gaussian")

eval.result <- predict(estimation,
                       data = data.eval.df.fit,
                       stat.info = TRUE)

Output:


> eval.result[[1]]$Collect()
  ID DENSITY_VALUE
1  0     -3.852755
2  1     -4.586453
3  2     -6.110158
4  3     -3.275507
5  4     -2.888267
6  5     -4.107246
7  6     -3.387239
8  7     -2.732173
9  8     -3.554738

apply Kernel Density Estimation analysis

Format

Arguments

Value

Examples

See also