Convert Dissimilarity Analysis Data Frame to Distance Matrix

Transforms a data frame resulting from distantia() into a dissimilarity matrix.

Usage

distantia_matrix(df = NULL)

Arguments

df

(required, data frame) Output of distantia() or distantia_aggregate(). Default: NULL

Value

numeric matrix

See also

Other dissimilarity_analysis: distantia_aggregate(), distantia_boxplot(), distantia_cluster_hclust(), distantia_cluster_kmeans(), distantia_plot(), distantia_to_sf()

Examples

#weekly covid prevalence in three California counties
#load as tsl
#subset 5 counties
#sum by month
tsl <- tsl_initialize(
  x = covid_prevalence,
  name_column = "name",
  time_column = "time"
) |>
  tsl_subset(
    names = 1:5
  ) |>
  tsl_aggregate(
    new_time = "months",
    method = sum
  )

if(interactive()){

  #plotting first three time series
  tsl_plot(
    tsl = tsl,
    guide_columns = 3
    )

  dev.off()

}

#dissimilarity analysis
#two combinations of arguments
distantia_df <- distantia(
  tsl = tsl,
  lock_step = c(TRUE, FALSE)
)

#to dissimilarity matrix
distantia_matrix <- distantia_matrix(
  df = distantia_df
)

#returns a list of matrices
lapply(
  X = distantia_matrix,
  FUN = class
  )
#> $`1`
#> [1] "matrix" "array" 
#> 
#> $`2`
#> [1] "matrix" "array" 
#> 

#these matrices have attributes tracing how they were generated
lapply(
  X = distantia_matrix,
  FUN = \(x) attributes(x)$distantia_args
)
#> $`1`
#>     distance diagonal weighted ignore_blocks lock_step group
#> 11 euclidean     TRUE     TRUE         FALSE      TRUE     1
#> 
#> $`2`
#>    distance diagonal weighted ignore_blocks lock_step group
#> 1 euclidean     TRUE     TRUE         FALSE     FALSE     2
#> 

#plot matrix
if(interactive()){

  #plot first matrix (default behavior of utils_matrix_plot())
  utils_matrix_plot(
    m = distantia_matrix
  )

  #plot second matrix
  utils_matrix_plot(
    m = distantia_matrix[[2]]
  )

}