Deprecated. `HCPC_tab(data, row_vars, clust, wt)`, and `clust_tab()` given a data frame first,
are the former form of clust_tab, which now takes the analysis first and reads the
active variables, the weights and the rows from it: `clust_tab(res.mca, data, clust)`.
Usage
HCPC_tab(
data,
row_vars = character(),
clust,
wt,
excl = NA,
color = "difference",
pct = "col",
row_tot = "% of population",
cleannames = TRUE,
...
)Arguments
- data
A data frame.
- row_vars
<tidy-select> The variables to describe the clusters with. Numeric ones become mean rows.
- clust
The variable with the clusters, as a bare name or a string, or the clusters themselves. Rows without a cluster are left out.
- wt
The weight variable. Leave empty for unweighted results.
- excl
The levels not to show, matched exactly by name; their individuals still count in the percentages. `NA`, the default, hides the missing values (and the levels named `<VAR>.NA`); `excl = NULL` shows every level.
- color
The colour measure, see
tab. With `"difference"` (the default), percentages are coloured by their difference with the whole population, and means by their difference in standard deviations — but not both in one table, which a single ladder cannot grade: there, means stay uncoloured, and `"ratio"` colours every row.- pct
`"col"` (default) reads each cluster as a distribution: of the people in this cluster, what percentage are in this level. `"row"` reads each level as a distribution across clusters.
- row_tot
The name of the row giving each cluster's share of the population.
- cleannames
Set to
FALSEto keep the level and cluster names as they are, prefix numbers like"1-"and text in parentheses included.- ...
Additional arguments to pass to
tab.