Error in checkMeasures(measures, learner) : object 'fbeta' not found - r

Question

I am doing an imbalanced classification task, so I want to use f-beta as performance measure. I used the library(mlr) to set measures=fbeta, which follows:
library(mlr)
#create tasks
## Create combined training data
train_data <- cbind(x_train, y_train)
valid_data <- cbind(x_valid,y_valid)
train_task_data <- rbind(train_data, valid_data)
size <- nrow(train_task_data)
train_ind <- seq_len(nrow(train_data))
validation_ind <- seq.int(max(train_ind) + 1, size)
## Create training task
train_task <- makeClassifTask(data = train_task_data, target = "DEFAULT", positive = 1)
testtask <- makeClassifTask(data = cbind(x_test,y_test),target = "DEFAULT")
#create learner
lrn <- makeLearner("classif.xgboost",predict.type = "response") ##predict.type = "prob"
lrn$par.vals <- list( objective="binary:logistic", eval_metric="logloss", nrounds=100L, eta=0.1)
#set parameter space
params <- makeParamSet( makeDiscreteParam("booster",values = c("gbtree","gblinear")),
makeIntegerParam("max_depth",lower = 9L,upper = 10L),
makeNumericParam("min_child_weight",lower = 9L,upper = 10L),
makeNumericParam("subsample",lower = 0.9,upper = 1),
makeNumericParam("colsample_bytree",lower = 0.9,upper = 1))
#search strategy
ctrl <- makeTuneControlRandom(maxit = 10L)
#set parallel backend
library(parallel)
library(parallelMap)
parallelStartSocket(cpus = detectCores())
mytune <- tuneParams(learner = lrn, task = train_task,
resampling = makeFixedHoldoutInstance(train_ind, validation_ind, size),
measures = fbeta, par.set = params, control = ctrl, show.info = T)
#parameter tuning
#set hyperparameters
lrn_tune <- setHyperPars(lrn,par.vals = mytune$x)
#train model
xgmodel <- train(learner = lrn_tune,task = train_task)
#predict model
xgpred <- predict(xgmodel,testtask)
confusionMatrix(xgpred$data$response,xgpred$data$truth)
However, this error is reported:
Error in checkMeasures(measures, learner) : object 'fbeta' not found
Besides, my dataset contains 150,000 instances, but based on the computed confusion matrix, they are less than 150,000.
> confusionMatrix(xgpred$data$response,xgpred$data$truth)
[,1] [,2]
[1,] 0 0
[2,] 0 149887
Update: I function to calculate f score is as follows, but I am not sure about it.
fbeta = makeMeasure(id = "fbeta", minimize = FALSE, best = 1, worst = 0,
properties = c("classif", "req.pred", "req.truth"),
name = "Fbeta measure",
note = "Defined as: (1+beta^2) * tp/ (beta^2 * sum(truth == positive) + sum(response == positive))",
fun = function(task, model, pred, feats, extra.args) {
beta = 1
beta = beta^2
truth = pred$data$truth
response = pred$data$response
positive = pred$task.desc$positive
(1+beta) * measureTP(truth, response, positive) /
(beta * sum(truth == positive) + sum(response == positive))
}
)