# conf_mat

##### Confusion Matrix for Categorical Data

Calculates a cross-tabulation of observed and predicted classes.

##### Usage

`conf_mat(data, ...)`# S3 method for data.frame
conf_mat(data, truth, estimate, dnn = c("Prediction", "Truth"), ...)

# S3 method for conf_mat
tidy(x, ...)

autoplot.conf_mat(object, type = "mosaic", ...)

##### Arguments

- data
A data frame or a

`base::table()`

.- ...
Options to pass to

`base::table()`

(not including`dnn`

). This argument is not currently used for the`tidy`

method.- 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.

- x
A

`conf_mat`

object.- object
The

`conf_mat`

data frame returned from`conf_mat()`

.- type
Type of plot desired, must be "mosaic" or "heatmap", defaults to "mosaic".

##### 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.

##### 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.

##### See Also

`summary.conf_mat()`

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

##### Examples

```
# NOT RUN {
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
# 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(purrr)
library(tidyr)
cells_per_resample <- hpc_cv %>%
group_by(Resample) %>%
conf_mat(obs, pred) %>%
mutate(tidied = map(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
# 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)
# The confusion matrix can quickly be visualized using autoplot()
library(ggplot2)
autoplot(cm, type = "mosaic")
autoplot(cm, type = "heatmap")
# }
```

*Documentation reproduced from package yardstick, version 0.0.6, License: GPL-2*