Finding the precision, recall and the f1 in R

I want to run models on a loop via and then store the performance metrics into a table. I do not want to use the confusionMatrix function in caret, but I want to compute the precision, recall and f1 and then store those in a table. Please assist, edits to the code are welcome.
My attempt is below.

library(MASS) #will load our biopsy data
library(caret)
data("biopsy")
biopsy$ID<-NULL
names(biopsy)<-c('clump thickness','uniformity cell size','uniformity cell shape',
                 'marginal adhesion','single epithelial cell size','bare nuclei',
                 'bland chromatin','normal nuclei','mitosis','class')

sum(is.na(biopsy))
biopsy<-na.omit(biopsy)
sum(is.na(biopsy))
head(biopsy,5)
set.seed(123)
inTraining <- createDataPartition(biopsy$class, p = .75, list = FALSE)
training <- biopsy[ inTraining,]
testing  <- biopsy[-inTraining,]

# Run algorithms using 10-fold cross validation
control <- trainControl(method="repeatedcv", number=10,repeats = 5, verboseIter = F, classProbs = T)
#CHANGING THE CHARACTERS INTO FACTORS VARAIBLES
training<- as.data.frame(unclass(training),                     
                         stringsAsFactors = TRUE)

#CHANGING THE CHARACTERS INTO FACTORS VARAIBLES
testing <- as.data.frame(unclass(testing),                     
                         stringsAsFactors = TRUE)


models<-c("svmRadial","rf")
results_table <- data.frame(models = models, stringsAsFactors = F)

for (i in models){
  model_train<-train(class~., data=training, method=i,
                     trControl=control,metric="Accuracy")
  predictions<-predict(model_train, newdata=testing)
  precision_<-posPredValue(predictions,testing)
  recall_<-sensitivity(predictions,testing)
  f1<-(2*precision_*recall_)/(precision_+recall_)

  # put that in the results table
  results_table[i, "Precision"] <- precision_
  results_table[i, "Recall"] <- recall_
  results_table[i, "F1score"] <- f1
}

However I get an error which says Error in posPredValue.default(predictions, testing) :
inputs must be factors. i do not know where I went wrong and any edits to my code are welcome.

I know that I could get precision,recall, f1 by just using the code below (B), however this is a tutorial question where I am required not to use the code example below (B):

(B)

for (i in models){
  model_train<-train(class~., data=training, method=i,
                     trControl=control,metric="Accuracy")
  predictions<-predict(model_train, newdata=testing)
  print(confusionMatrix(predictions, testing$class,mode="prec_recall"))
}

A few things need to happen.

1. You have to change the function calls for posPredValue and sensitivity. For both, change testing to testing$class.

2. for the results_table, i is a word, not a value, so you're assigning results_table["rf", "Precision"] <- precision_ (This makes a new row, where the row name is "rf".)

Here is your for statement, with changes to those functions mentioned in 1) and a modification to address the issue in 2).

for (i in models){
  model_train <- train(class~., data = training, method = i,
                     trControl= control, metric = "Accuracy")
  assign("fit", model_train)
  predictions <- predict(model_train, newdata = testing)
  precision_ <-posPredValue(predictions, testing$class)
  recall_ <- sensitivity(predictions, testing$class)
  f1 <- (2*precision_ * recall_) / (precision_ + recall_)
  
  # put that in the results table
  results_table[results_table$models %in% i, "Precision"] <- precision_
  results_table[results_table$models %in% i, "Recall"] <- recall_
  results_table[results_table$models %in% i, "F1score"] <- f1
  }

This is what it looks like for me.

results_table
#      models Precision    Recall   F1score
# 1 svmRadial 0.9722222 0.9459459 0.9589041
# 2        rf 0.9732143 0.9819820 0.9775785