Error in plot.nn: weights were not calculated - r

Question

I've gotten an error message when attempting to plot a neural network. I was able to run the code fine at first then it stopped. I do not get an error message when the neuralnet() function is run. Any help would be appreciated. I predicting the loan default.
library(neuralnet)
library(plyr)
CreditCardnn <- read.csv("https://raw.githubusercontent.com/621-Group2/Final-Project/master/UCI_Credit_Card.csv")
#Normalize dataset
maxValue <- apply(CreditCardnn, 2, max)
minValue <- apply(CreditCardnn, 2, min)
CreditCardnn <- as.data.frame(scale(CreditCardnn, center = minValue, scale = maxValue - minValue))
#Rename to target variable
colnames(CreditCardnn)[25] <- "target"
smp <- floor(0.70 * nrow(CreditCardnn))
set.seed(4784)
CreditCardnn$ID <- NULL
train_index <- sample(seq_len(nrow(CreditCardnn)), size = smp, replace = FALSE)
train_nn <- CreditCardnn[train_index, ]
test_nn <- CreditCardnn[-train_index, ]
allVars <- colnames(CreditCardnn)
predictorVars <- allVars[!allVars%in%'target']
predictorVars <- paste(predictorVars, collapse = "+")
f <- as.formula(paste("target~", predictorVars, collapse = "+"))
nueralModel <- neuralnet(formula = f, hidden = c(4,2), linear.output = T, data = train_nn)
plot(nueralModel)
Which gives the following error:
Error in plot.nn(nueralModel) : weights were not calculated

Answer 1

Before the error you report, most probably you also got a warning:
# your data preparation code verbatim here
> nueralModel <- neuralnet(formula = f, hidden = c(4,2), linear.output = T, data = train_nn)
Warning message:
algorithm did not converge in 1 of 1 repetition(s) within the stepmax
This message is important, effectively warning you that your neural network did not converge. Given this message, the error further downstream, when you try to plot the network, is actually expected:
> plot(nueralModel)
Error in plot.nn(nueralModel) : weights were not calculated
Looking more closely into your code & data, it turns out that the problem lies in your choice for linear.output = T in fitting your neural network; from the docs:
linear.output logical. If act.fct should not be applied to the output neurons set linear output to TRUE, otherwise to FALSE.
Keeping a linear output in the final layer of a neural network is normally used in regression settings only; in classification settings, such as yours, the correct choice is to apply the activation function to the output neuron(s) as well. Hence, trying the same code as yours but with linear.output = F, we get:
> nueralModel <- neuralnet(formula = f, hidden = c(4,2), linear.output = F, data = train_nn) # no warning this time
> plot(nueralModel)
And here is the result of the plot:

Answer 2

Try increasing stepmax. Ex. set stepmax = 1e6 or higher. It takes longer time for higher stepmax but you can try:
nueralModel <- neuralnet(formula = f, hidden = c(4,2), linear.output = F, data = train_nn, stepmax = 1e6)