K-clustering
kclus(dataset, vars, fun = "kmeans", hc_init = TRUE, distance = "sq.euclidian", method = "ward.D", seed = 1234, nr_clus = 2, standardize = TRUE, lambda = NULL, data_filter = "", envir = parent.frame())
| dataset | Dataset |
|---|---|
| vars | Vector of variables to include in the analysis |
| fun | Use either "kmeans" or "kproto" for clustering |
| hc_init | Use centers from hclus as the starting point |
| distance | Distance for hclus |
| method | Method for hclus |
| seed | Random see to use for k-clustering if hc_init is FALSE |
| nr_clus | Number of clusters to extract |
| standardize | Standardize data (TRUE or FALSE) |
| lambda | Parameter > 0 to trade off between Euclidean distance of numeric variables and simple matching coefficient between categorical variables. Also a vector of variable specific factors is possible where the order must correspond to the order of the variables in the data. In this case all variables' distances will be multiplied by their corresponding lambda value. |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
| envir | Environment to extract data from |
A list of all variables used in kclus as an object of class kclus
See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant
summary.kclus to summarize results
plot.kclus to plot results
store.kclus to add cluster membership to the selected dataset
#> List of 19 #> $ nr_obs : int 20 #> $ clus_means :'data.frame': 3 obs. of 6 variables: #> ..$ v1: num [1:3] 5.75 1.67 3.5 #> ..$ v2: num [1:3] 3.62 3 5.83 #> ..$ v3: num [1:3] 6 1.83 3.33 #> ..$ v4: num [1:3] 3.12 3.5 6 #> ..$ v5: num [1:3] 1.88 5.5 3.5 #> ..$ v6: num [1:3] 3.88 3.33 6 #> $ clus_names : chr [1:3] "Cluster 1" "Cluster 2" "Cluster 3" #> $ km_out :List of 9 #> ..$ cluster : int [1:20] 1 2 1 3 2 1 1 1 2 3 ... #> ..$ centers : num [1:3, 1:6] 1 -1.149 -0.184 -0.337 -0.78 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : chr [1:3] "1" "2" "3" #> .. .. ..$ : chr [1:6] "v1" "v2" "v3" "v4" ... #> ..$ totss : num 114 #> ..$ withinss : num [1:3] 11.98 7.72 10.02 #> ..$ tot.withinss: num 29.7 #> ..$ betweenss : num 84.3 #> ..$ size : int [1:3] 8 6 6 #> ..$ iter : int 1 #> ..$ ifault : int 0 #> ..- attr(*, "class")= chr "kmeans" #> $ clus_var : int [1:20] 1 2 1 3 2 1 1 1 2 3 ... #> $ center_calc:function (x, prop = FALSE) #> $ max_freq :function (x) #> $ df_name : chr "shopping" #> $ dataset :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 20 obs. of 6 variables: #> ..$ v1: int [1:20] 6 2 7 4 1 6 5 7 2 3 ... #> ..$ v2: int [1:20] 4 3 2 6 3 4 3 3 4 5 ... #> ..$ v3: int [1:20] 7 1 6 4 2 6 6 7 3 3 ... #> ..$ v4: int [1:20] 3 4 4 5 2 3 3 4 3 6 ... #> ..$ v5: int [1:20] 2 5 1 3 6 3 3 1 6 4 ... #> ..$ v6: int [1:20] 3 4 3 6 4 4 4 4 3 6 ... #> ..- attr(*, "description")= chr "## Shopping attitudes\n\n### Description\n\n20 consumers were asked to respond to six questions to determine th"| __truncated__ #> $ vars : chr [1:6] "v1" "v2" "v3" "v4" ... #> $ fun : chr "kmeans" #> $ hc_init : logi TRUE #> $ distance : chr "sq.euclidian" #> $ method : chr "ward.D" #> $ seed : num 1234 #> $ nr_clus : num 3 #> $ standardize: logi TRUE #> $ lambda : NULL #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:2] "kclus" "list"