mlayerHVT: Predict which cell and what level each point in the test...

In muHVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmap for Data Analysis

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(s)

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)

muHVT documentation built on March 7, 2023, 6:38 p.m.