Calculates a cross-tabulation of observed and predicted classes.

## Arguments

- data
A data frame or a

`base::table()`

.- ...
Not used.

- truth
The column identifier for the true class results (that is a

`factor`

). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For`_vec()`

functions, a`factor`

vector.- estimate
The column identifier for the predicted class results (that is also

`factor`

). As with`truth`

this can be specified different ways but the primary method is to use an unquoted variable name. For`_vec()`

functions, a`factor`

vector.- dnn
A character vector of dimnames for the table.

- case_weights
The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in

`data`

. For`_vec()`

functions, a numeric vector,`hardhat::importance_weights()`

, or`hardhat::frequency_weights()`

.- x
A

`conf_mat`

object.

## Value

`conf_mat()`

produces an object with class `conf_mat`

. This contains the
table and other objects. `tidy.conf_mat()`

generates a tibble with columns
`name`

(the cell identifier) and `value`

(the cell count).

When used on a grouped data frame, `conf_mat()`

returns a tibble containing
columns for the groups along with `conf_mat`

, a list-column
where each element is a `conf_mat`

object.

## Details

For `conf_mat()`

objects, a `broom`

`tidy()`

method has been created
that collapses the cell counts by cell into a data frame for
easy manipulation.

There is also a `summary()`

method that computes various classification
metrics at once. See `summary.conf_mat()`

There is a `ggplot2::autoplot()`

method for quickly visualizing the matrix. Both a heatmap and mosaic type
is implemented.

The function requires that the factors have exactly the same levels.

## See also

`summary.conf_mat()`

for computing a large number of metrics from one
confusion matrix.

## Examples

```
library(dplyr)
data("hpc_cv")
# The confusion matrix from a single assessment set (i.e. fold)
cm <- hpc_cv %>%
filter(Resample == "Fold01") %>%
conf_mat(obs, pred)
cm
#> Truth
#> Prediction VF F M L
#> VF 166 33 8 1
#> F 11 71 24 7
#> M 0 3 5 3
#> L 0 1 4 10
# Now compute the average confusion matrix across all folds in
# terms of the proportion of the data contained in each cell.
# First get the raw cell counts per fold using the `tidy` method
library(tidyr)
cells_per_resample <- hpc_cv %>%
group_by(Resample) %>%
conf_mat(obs, pred) %>%
mutate(tidied = lapply(conf_mat, tidy)) %>%
unnest(tidied)
# Get the totals per resample
counts_per_resample <- hpc_cv %>%
group_by(Resample) %>%
summarize(total = n()) %>%
left_join(cells_per_resample, by = "Resample") %>%
# Compute the proportions
mutate(prop = value / total) %>%
group_by(name) %>%
# Average
summarize(prop = mean(prop))
counts_per_resample
#> # A tibble: 16 × 2
#> name prop
#> <chr> <dbl>
#> 1 cell_1_1 0.467
#> 2 cell_1_2 0.107
#> 3 cell_1_3 0.0185
#> 4 cell_1_4 0.00259
#> 5 cell_2_1 0.0407
#> 6 cell_2_2 0.187
#> 7 cell_2_3 0.0632
#> 8 cell_2_4 0.0173
#> 9 cell_3_1 0.00173
#> 10 cell_3_2 0.00692
#> 11 cell_3_3 0.0228
#> 12 cell_3_4 0.00807
#> 13 cell_4_1 0.000575
#> 14 cell_4_2 0.0104
#> 15 cell_4_3 0.0144
#> 16 cell_4_4 0.0320
# Now reshape these into a matrix
mean_cmat <- matrix(counts_per_resample$prop, byrow = TRUE, ncol = 4)
rownames(mean_cmat) <- levels(hpc_cv$obs)
colnames(mean_cmat) <- levels(hpc_cv$obs)
round(mean_cmat, 3)
#> VF F M L
#> VF 0.467 0.107 0.018 0.003
#> F 0.041 0.187 0.063 0.017
#> M 0.002 0.007 0.023 0.008
#> L 0.001 0.010 0.014 0.032
# The confusion matrix can quickly be visualized using autoplot()
library(ggplot2)
autoplot(cm, type = "mosaic")
autoplot(cm, type = "heatmap")
```