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Takes the output of the kmeans_user_item_tbl() function and applies the k-means algorithm to it using stats::kmeans()

Usage

kmeans_obj(.data, .centers = 5)

Arguments

.data

The data that gets passed from kmeans_user_item_tbl()

.centers

How many initial centers to start with

Value

A stats k-means object

Details

Uses the stats::kmeans() function and creates a wrapper around it.

Author

Steven P. Sanderson II, MPH

Examples

library(healthyR.data)
library(dplyr)

data_tbl <- healthyR_data%>%
   filter(ip_op_flag == "I") %>%
   filter(payer_grouping != "Medicare B") %>%
   filter(payer_grouping != "?") %>%
   select(service_line, payer_grouping) %>%
   mutate(record = 1) %>%
   as_tibble()

 kmeans_user_item_tbl(
   .data           = data_tbl
   , .row_input    = service_line
   , .col_input    =  payer_grouping
   , .record_input = record
 ) %>%
   kmeans_obj()
#> K-means clustering with 5 clusters of sizes 2, 5, 1, 3, 12
#> 
#> Cluster means:
#>   Blue Cross Commercial Compensation Exchange Plans        HMO   Medicaid
#> 1 0.27188303 0.05712358 0.0003293808    0.039065198 0.18065096 0.04246134
#> 2 0.13375082 0.03542694 0.0121998471    0.016160901 0.10724914 0.05150211
#> 3 0.00000000 0.00000000 0.0000000000    0.000000000 0.27272727 0.18181818
#> 4 0.07912806 0.02702478 0.0002914681    0.009301354 0.07723873 0.21428392
#> 5 0.07837450 0.02182129 0.0043244347    0.006202137 0.04493860 0.03684344
#>   Medicaid HMO Medicare A Medicare HMO    No Fault    Self Pay
#> 1   0.24760799 0.10958146   0.03584494 0.000000000 0.015452115
#> 2   0.13107693 0.35217108   0.11769769 0.008242686 0.034521844
#> 3   0.45454545 0.09090909   0.00000000 0.000000000 0.000000000
#> 4   0.28209782 0.23654904   0.04362913 0.002672067 0.027783628
#> 5   0.08001653 0.56250366   0.15152338 0.003475542 0.009976485
#> 
#> Clustering vector:
#>  [1] 4 1 5 5 5 5 2 2 5 5 1 5 4 2 5 2 5 4 2 5 5 3 5
#> 
#> Within cluster sum of squares by cluster:
#> [1] 0.03549821 0.02592247 0.00000000 0.04450884 0.09625399
#>  (between_SS / total_SS =  85.6 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"