Neural Networks using nnet
nn( dataset, rvar, evar, type = "classification", lev = "", size = 1, decay = 0.5, wts = "None", seed = NA, check = "standardize", form, data_filter = "", envir = parent.frame() )
| dataset | Dataset |
|---|---|
| rvar | The response variable in the model |
| evar | Explanatory variables in the model |
| type | Model type (i.e., "classification" or "regression") |
| lev | The level in the response variable defined as _success_ |
| size | Number of units (nodes) in the hidden layer |
| decay | Parameter decay |
| wts | Weights to use in estimation |
| seed | Random seed to use as the starting point |
| check | Optional estimation parameters ("standardize" is the default) |
| form | Optional formula to use instead of rvar and evar |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
| envir | Environment to extract data from |
A list with all variables defined in nn as an object of class nn
See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant
summary.nn to summarize results
plot.nn to plot results
predict.nn for prediction
#> Neural Network #> Activation function : Logistic (classification) #> Data : titanic #> Response variable : survived #> Level : Yes in survived #> Explanatory variables: pclass, sex #> Network size : 1 #> Parameter decay : 0.5 #> Network : 3-1-1 with 6 weights #> Nr obs : 1,043 #> Weights : #> b->h1 i1->h1 i2->h1 i3->h1 #> -1.93 0.90 2.46 3.12 #> b->o h1->o #> 2.94 -4.73#> List of 20 #> $ coefnames : chr [1:3] "pclass|2nd" "pclass|3rd" "sex|male" #> $ model :List of 21 #> ..$ n : num [1:3] 3 1 1 #> ..$ nunits : int 6 #> ..$ nconn : num [1:7] 0 0 0 0 0 4 6 #> ..$ conn : num [1:6] 0 1 2 3 0 4 #> ..$ nsunits : int 6 #> ..$ decay : num 0.5 #> ..$ entropy : logi TRUE #> ..$ softmax : logi FALSE #> ..$ censored : logi FALSE #> ..$ value : num 515 #> ..$ wts : num [1:6] -1.931 0.903 2.463 3.119 2.941 ... #> ..$ convergence : int 0 #> ..$ fitted.values: num [1:1043, 1] 0.912 0.336 0.912 0.336 0.912 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : chr [1:1043] "1" "2" "3" "4" ... #> .. .. ..$ : NULL #> ..$ residuals : Named num [1:1043] 0.0877 0.6639 -0.9123 -0.3361 -0.9123 ... #> .. ..- attr(*, "names")= chr [1:1043] "1" "2" "3" "4" ... #> ..$ lev : chr [1:2] "0" "1" #> ..$ call : language nnet.formula(formula = survived ~ ., data = list(survived = c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE| __truncated__ ... #> ..$ terms :Classes 'terms', 'formula' language survived ~ pclass + sex #> .. .. ..- attr(*, "variables")= language list(survived, pclass, sex) #> .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 #> .. .. .. ..- attr(*, "dimnames")=List of 2 #> .. .. .. .. ..$ : chr [1:3] "survived" "pclass" "sex" #> .. .. .. .. ..$ : chr [1:2] "pclass" "sex" #> .. .. ..- attr(*, "term.labels")= chr [1:2] "pclass" "sex" #> .. .. ..- attr(*, "order")= int [1:2] 1 1 #> .. .. ..- attr(*, "intercept")= int 1 #> .. .. ..- attr(*, "response")= int 1 #> .. .. ..- attr(*, ".Environment")=<environment: 0x12141108> #> .. .. ..- attr(*, "predvars")= language list(survived, pclass, sex) #> .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "logical" "factor" "factor" #> .. .. .. ..- attr(*, "names")= chr [1:3] "survived" "pclass" "sex" #> ..$ coefnames : chr [1:3] "pclass2nd" "pclass3rd" "sexmale" #> ..$ contrasts :List of 2 #> .. ..$ pclass: chr "contr.treatment" #> .. ..$ sex : chr "contr.treatment" #> ..$ xlevels :List of 2 #> .. ..$ pclass: chr [1:3] "1st" "2nd" "3rd" #> .. ..$ sex : chr [1:2] "female" "male" #> ..$ model : tibble [1,043 × 3] (S3: tbl_df/tbl/data.frame) #> .. ..$ survived: logi [1:1043] TRUE TRUE FALSE FALSE FALSE TRUE ... #> .. ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> .. ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> .. ..- attr(*, "description")= chr "## Titanic\n\nThis dataset describes the survival status of individual passengers on the Titanic. The titanic d"| __truncated__ #> ..- attr(*, "class")= chr [1:2] "nnet.formula" "nnet" #> $ nninput :List of 11 #> ..$ formula:Class 'formula' language survived ~ . #> .. .. ..- attr(*, ".Environment")=<environment: 0x12141108> #> ..$ rang : num 0.1 #> ..$ size : num 1 #> ..$ decay : num 0.5 #> ..$ weights: NULL #> ..$ maxit : num 10000 #> ..$ linout : logi FALSE #> ..$ entropy: logi TRUE #> ..$ skip : logi FALSE #> ..$ trace : logi FALSE #> ..$ data : tibble [1,043 × 3] (S3: tbl_df/tbl/data.frame) #> .. ..$ survived: logi [1:1043] TRUE TRUE FALSE FALSE FALSE TRUE ... #> .. ..$ pclass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ... #> .. ..$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ... #> .. ..- attr(*, "description")= chr "## Titanic\n\nThis dataset describes the survival status of individual passengers on the Titanic. The titanic d"| __truncated__ #> $ entropy : logi TRUE #> $ linout : logi FALSE #> $ rv : Factor w/ 2 levels "Yes","No": 1 1 2 2 2 1 1 2 1 2 ... #> $ not_vary : chr(0) #> $ df_name : chr "titanic" #> $ vars : chr [1:2] "pclass" "sex" #> $ rvar : chr "survived" #> $ evar : chr [1:2] "pclass" "sex" #> $ type : chr "classification" #> $ lev : chr "Yes" #> $ size : num 1 #> $ decay : num 0.5 #> $ wts : NULL #> $ seed : chr NA #> $ check : chr "standardize" #> $ form :Class 'formula' language survived ~ . #> .. ..- attr(*, ".Environment")=<environment: 0x12141108> #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:3] "nn" "model" "list"#> Neural Network #> Activation function : Linear (regression) #> Data : diamonds #> Response variable : price #> Explanatory variables: carat, clarity #> Network size : 1 #> Parameter decay : 0.5 #> Network : 8-1-1 with 11 weights #> Nr obs : 3,000 #> Weights : #> b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 #> -1.71 2.01 0.31 0.50 0.69 0.76 0.96 0.94 1.01 #> b->o h1->o #> -0.72 2.56