`prediction`

, a method for objects of class `nn`

, typically
produced by `neuralnet`

. In a first step, the dataframe will be
amended by a mean response, the mean of all responses corresponding to the
same covariate-vector. The calculated data.error is the error function
between the original response and the new mean response. In a second step,
all duplicate rows will be erased to get a quick overview of the data. To
obtain an overview of the results of the neural network and the glm objects,
the covariate matrix will be bound to the output of the neural network and
the fitted values of the glm object(if available) and will be reduced by all
duplicate rows.

`prediction(x, list.glm = NULL)`

x

neural network

list.glm

an optional list of glm objects

a list of the summaries of the repetitions of the neural networks, the data and the glm objects (if available).

# NOT RUN { Var1 <- rpois(100,0.5) Var2 <- rbinom(100,2,0.6) Var3 <- rbinom(100,1,0.5) SUM <- as.integer(abs(Var1+Var2+Var3+(rnorm(100)))) sum.data <- data.frame(Var1,Var2,Var3, SUM) print(net.sum <- neuralnet( SUM~Var1+Var2+Var3, sum.data, hidden=1, act.fct="tanh")) main <- glm(SUM~Var1+Var2+Var3, sum.data, family=poisson()) full <- glm(SUM~Var1*Var2*Var3, sum.data, family=poisson()) prediction(net.sum, list.glm=list(main=main, full=full)) # }