This function generates plots to visualize model performance

## Usage

```
performance_plot(
model_list,
type = "box",
text_size = 10,
palette = "viridis",
save = FALSE,
file_path = NULL,
file_name = "Performance_plot",
file_type = "pdf",
plot_width = 7,
plot_height = 7,
dpi = 80
)
```

## Arguments

- model_list
A

`model_list`

object from performing`train_models`

.- type
Type of plot to generate. Choices are "box" or "dot." Default is

`"box."`

for boxplots.- text_size
Text size for plot labels, axis labels etc. Default is

`10`

.- palette
Viridis color palette option for plots. Default is

`"viridis"`

. See`viridis`

for available options.- save
Logical. If

`TRUE`

saves a copy of the plot in the directory provided in`file_path`

.- file_path
A string containing the directory path to save the file.

- file_name
File name to save the plot. Default is

`"Performance_plot."`

- file_type
File type to save the plot. Default is

`"pdf"`

.- plot_width
Width of the plot. Default is

`7`

.- plot_height
Height of the plot. Default is

`7`

.- dpi
Plot resolution. Default is

`80`

.

## Details

The default metrics used for classification based models are "Accuracy" and "Kappa."

These metric types can be changed by providing additional arguments to the

`train_models`

function. See`train`

and`trainControl`

for more information.

## Examples

```
# \donttest{
## Create a model_df object
covid_model_df <- pre_process(covid_fit_df, covid_norm_df)
#> Total number of differentially expressed proteins (8) is less than n_top.
#> None of the proteins show high pair-wise correlation.
#>
#> No highly correlated proteins to be removed.
## Split the data frame into training and test data sets
covid_split_df <- split_data(covid_model_df)
## Fit models based on the default list of machine learning (ML) algorithms
covid_model_list <- train_models(covid_split_df)
#>
#> Running svmRadial...
#> Loading required package: ggplot2
#> Loading required package: lattice
#>
#> Running rf...
#>
#> Running glm...
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#>
#> Running xgbLinear...
#>
#> Running naive_bayes...
#> Done!
## Generate box plots to visualize performance of different ML algorithms
performance_plot(covid_model_list)
#> Using Resample as id variables
## Generate dot plots
performance_plot(covid_model_list, type = "dot")
#> Using Resample as id variables
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
## Change color palette
performance_plot(covid_model_list, type = "dot", palette = "inferno")
#> Using Resample as id variables
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
#> Warning: Removed 5 rows containing missing values (`geom_segment()`).
# }
```