predictHVT {HVT} | R Documentation |
Predict which cell and what level each point in the test dataset belongs to
predictHVT(
data,
hvt.results.model,
child.level = 1,
mad.threshold = 0.2,
line.width = c(0.6, 0.4, 0.2),
color.vec = c("#141B41", "#6369D1", "#D8D2E1"),
normalize = TRUE,
seed = 300,
distance_metric = "L1_Norm",
error_metric = "max",
yVar = NULL,
...
)
data |
List. A dataframe containing test dataset. The dataframe should have atleast one variable used while training. The variables from this dataset can also be used to overlay as heatmap |
hvt.results.model |
A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data |
child.level |
A number indicating the level for which the heat map is to be plotted.(Only used if hmap.cols is not NULL) |
mad.threshold |
A numeric values indicating the permissible Mean Absolute Deviation |
line.width |
Vector. A line width vector |
color.vec |
Vector. A color vector |
normalize |
Logical. A logical value indicating if the columns in your dataset should be normalized. Default value is TRUE. |
seed |
Numeric. Random Seed. |
distance_metric |
character. The distance metric can be 'Euclidean" or "Manhattan". Euclidean is selected by default. |
error_metric |
character. The error metric can be "mean" or "max". mean is selected by default |
yVar |
character. Name of the dependent variable(s) |
... |
color.vec and line.width can be passed from here |
Dataframe containing scored predicted data, prediction plots and mean absolute deviation plots
Shubhra Prakash <shubhra.prakash@mu-sigma.com>, Sangeet Moy Das <sangeet.das@mu-sigma.com>
data(USArrests)
# Split in train and test
train <- USArrests[1:40, ]
test <- USArrests[41:50, ]
hvt.results <- list()
hvt.results <- HVT(train,
n_cells = 15, depth = 1, quant.err = 0.2,
distance_metric = "L1_Norm", error_metric = "mean",
projection.scale = 10, normalize = TRUE,
quant_method = "kmeans", diagnose = TRUE
)
predictions <- predictHVT(test, hvt.results, child.level = 2, mad.threshold = 0.2)
print(predictions$scoredPredictedData)