This function can be used to predict test data using models generated by different machine learning algorithms

## Usage

```
test_models(
model_list,
split_df,
type = "prob",
save_confusionmatrix = FALSE,
file_path = NULL,
...
)
```

## Arguments

- model_list
A

`model_list`

object from performing`train_models`

.- split_df
A

`split_df`

object from performing`split_data`

.- type
Type of output. Set

`type`

as "prob" (default) to output class probabilities, and "raw" to output class predictions.- save_confusionmatrix
Logical. If

`TRUE`

, a tab-delimited text file ("Confusion_matrices.txt") with confusion matrices in the long-form data format will be saved in the directory specified by`file_path`

. See below for more details.- file_path
A string containing the directory path to save the file.

- ...
Additional arguments to be passed on to

`predict`

.

## Value

`prediction_list`

: If`type = "raw"`

, a list of factors containing class predictions for each method will be returned.

## Details

Setting

`type = "raw"`

is required to obtain confusion matrices.Setting

`type = "prob"`

(default) will output a list of probabilities that can be used to generate ROC curves using`roc_plot`

.

## 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 using the default list of machine learning (ML) algorithms
covid_model_list <- train_models(covid_split_df)
#>
#> Running svmRadial...
#>
#> 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!
# Test a list of models on a test data set and output class probabilities,
covid_prob_list <- test_models(model_list = covid_model_list, split_df = covid_split_df)
#>
#> Testing svmRadial...
#>
#> Testing rf...
#>
#> Testing glm...
#>
#> Testing xgbLinear...
#>
#> Testing naive_bayes...
#>
#> Done!
# }
if (FALSE) {
# Save confusion matrices in the working directory and output class predictions
covid_pred_list <- test_models(
model_list = covid_model_list,
split_df = covid_split_df,
type = "raw",
save_confusionmatrix = TRUE,
file_path = "."
)
}
```