muHVT (version 3.0.0)

mlayerHVT: Predict which cell and what level each point in the test dataset belongs to

Description

Predict which cell and what level each point in the test dataset belongs to

Usage

mlayerHVT(
  data,
  hvt_mapA,
  hvt_mapB,
  hvt_mapC,
  mad.threshold = 0.2,
  normalize = T,
  distance_metric = "L1_Norm",
  error_metric = "max",
  child.level = 1,
  line.width = c(0.6, 0.4, 0.2),
  color.vec = c("#141B41", "#6369D1", "#D8D2E1"),
  yVar = NULL,
  ...
)

Arguments

data

Data Frame. 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_mapA

A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data

hvt_mapB

A list of removed outlier rows using removedOutliers function

hvt_mapC

A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data without outlier(s)

mad.threshold

A numeric values indicating the permissible Mean Absolute Deviation

normalize

Logical. A logical value indicating if the columns in your dataset should be normalized. Default value is TRUE.

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

child.level

A number indicating the level for which the heat map is to be plotted.(Only used if hmap.cols is not NULL)

line.width

Vector. A line width vector

color.vec

Vector. A color vector

yVar

character. Name of the dependent variable(s)

...

color.vec and line.width can be passed from here

Author

Shubhra Prakash <[e-mail]>, Sangeet Moy Das <[e-mail]>, Shantanu Vaidya <[e-mail]>

See Also

HVT
hvtHmap

Examples

data(USArrests)

#Split in train and test
train <- USArrests[1:40,]
test <- USArrests[41:50,] 

hvt_mapA <- list()
hvt_mapA <- HVT(train, min_compression_perc = 70, quant.err = 0.2, 
                   distance_metric = "L1_Norm", error_metric = "mean",
                   projection.scale = 10, normalize = TRUE,
                   quant_method="kmeans")


identified_outlier_cells <- c(2, 10)
output_list <- removeOutliers(identified_outlier_cells, hvt_mapA)
hvt_mapB <- output_list[[1]]
dataset_without_outliers <- output_list[[2]] 


mapA_scale_summary = hvt_mapA[[3]]$scale_summary
hvt_mapC <- list()
hvt_mapC <- HVT(dataset_without_outliers, n_cells = 15, 
                 depth = 2, quant.err = 0.2, distance_metric = "L1_Norm",
                 error_metric = "max", quant_method = "kmeans",
                 projection.scale = 10, normalize = FALSE, scale_summary = mapA_scale_summary)

predictions <- list()
predictions <- mlayerHVT(test, hvt_mapA, hvt_mapB, hvt_mapC)