R Neural Networks - Weights -

i have error message when use weights in nnet() though package documentation says weights. however, if use wts works. suppose weight defaults 1 if don't enter value.

regardless of wts value enter nnet() return weights = 83 , how 83 weights assigned ?. please @ output below:

i suppose don't understand how these weights assigned

any appreciated. thank you.

attach(iris)  library(caret) set.seed(3456) trainindex <- createdatapartition(iris$species, p = .8,                               list = f,                               times = 1)  iristrain <- iris[ trainindex,] iristest  <- iris[-trainindex,]  irispred <- nnet(species ~ ., data=iristrain ,weights = 1, size=10) predicted <- predict(irispred,iristest,type="class") 

error when using weights (package documentations says use weights)

irispred <- nnet(species ~ ., data=iristrain ,weights = 1, size=10) error in model.frame.default(formula = species ~ ., data = iristrain,  :    variable lengths differ (found '(weights)') 

no error if use wts:

irispred <- nnet(species ~ ., data=iristrain ,wts = 1, size=10) # weights:  83 initial  value 148.744330  iter  10 value 20.508558 iter  20 value 7.683385 iter  30 value 5.719438 iter  40 value 3.831845 iter  50 value 3.524789 iter  60 value 3.461561 iter  70 value 3.352866 iter  80 value 3.061214 iter  90 value 3.049519 iter 100 value 3.001406 final  value 3.001406  stopped after 100 iterations  irispred$wts  [1]    0.46982680904   -1.08944343286   -0.85761073123   -2.05356837297   -1.56599897345  [6]  291.18284632141   31.85356741288   27.37999662827  -97.45738049129  -55.50299935575 [11]   -1.36175718738 .......up 83.  > irispred <- nnet(species ~ ., data=iristrain ,wts = 28, size=10) # weights:  83 initial  value 143.546315  iter  10 value 55.502915 iter  20 value 40.514073 iter  30 value 6.610363 iter  40 value 6.111119 iter  50 value 6.019070 iter  60 value 5.963004 iter  70 value 5.956329 iter  80 value 5.945786 iter  90 value 5.942088 iter 100 value 5.939509 final  value 5.939509  stopped after 100 iterations > irispred$wts  [1]   1.07816704818   1.89161925466   1.36901821472  -0.39336454435  -0.23879356006  [6]  -0.63780061885  -2.62845406757 ... 83