library(neuralnet)
library(NeuralNetTools)
library(wooldridge)

data("wage1")
dim(wage1)

## [1] 526  24

model1=lm(wage~educ+exper,data=wage1)
summary(model1)

## 
## Call:
## lm(formula = wage ~ educ + exper, data = wage1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5532 -1.9801 -0.7071  1.2030 15.8370 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.39054    0.76657  -4.423 1.18e-05 ***
## educ         0.64427    0.05381  11.974  < 2e-16 ***
## exper        0.07010    0.01098   6.385 3.78e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.257 on 523 degrees of freedom
## Multiple R-squared:  0.2252, Adjusted R-squared:  0.2222 
## F-statistic: 75.99 on 2 and 523 DF,  p-value: < 2.2e-16

model=neuralnet(wage ~ educ+exper,data = wage1)
plot(model, rep = "best")

summary(model)

##                     Length Class      Mode    
## call                   3   -none-     call    
## response             526   -none-     numeric 
## covariate           1052   -none-     numeric 
## model.list             2   -none-     list    
## err.fct                1   -none-     function
## act.fct                1   -none-     function
## linear.output          1   -none-     logical 
## data                  24   data.frame list    
## exclude                0   -none-     NULL    
## net.result             1   -none-     list    
## weights                1   -none-     list    
## generalized.weights    1   -none-     list    
## startweights           1   -none-     list    
## result.matrix          8   -none-     numeric