BAGofT {BAGofT} | R Documentation |

`BAGofT`

is used to test the goodness-of-fit of binary classifiers.
The test statistic is constructed based on the results from multiple splittings.
In each split, the test first
splits the data into a training set and a validation set. Then,
it adaptively obtains a partition based on the training set and performs a goodness-of-fit test on the validation set. Details can be found in Zhang, Ding and Yang (2021).

```
BAGofT(testModel, parFun = parRF(), data, nsplits = 100,
ne = floor(5*nrow(data)^(1/2)), nsim = 100)
```

`testModel` |
a function that generates predicted results from the classifier to test. Details can be found in |

`parFun` |
a function that generates the adaptive partition. The default is ‘parRF()’ that generates a partition by random forest. More information can be found in |

`data` |
a data frame containing the response and covariates used in the model together with the other covariates not in the model but considered used to generate the partition. |

`nsplits` |
number of splits. The default is 100. |

`ne` |
the size of the validation set. The default is floor(5*nrow(data)^(1/2)). |

`nsim` |
the number of simulated datasets to calculate the bootstrap |

`p.value` |
the bootstrap |

`p.value2` |
the bootstrap |

`p.value3` |
the bootstrap |

`pmean` |
the BAGofT test statistic (which combines the results from multiple splitting by taking the average). |

`pmedian` |
an alternative BAGofT test statistic (which combines the results from multiple splitting by taking the sample median). |

`pmin` |
an alternative BAGofT test statistic (which combines the results from multiple splitting by taking the minimum). |

`simRes` |
a list that contains the simulated test statitics used to generate the bootstrap |

`singleSplit.results` |
a list that contains the results from each splitting. Its elements are as follows. ‘singleSplit.results[[k]]$chisq’: The chi-squared statistic of the BAGofT test from the ‘singleSplit.results[[k]]$p.value’: The ‘singleSplit.results[[k]]$ngp’: The number of groups chosen by the adaptive partition. ‘singleSplit.results[[k]]$contri’: The weighted sum of squares from each group. ‘singleSplit.results[[k]]$parRes’: Variable importance (or other results from custom partition functions) from the adaptive partition. |

Zhang, Ding and Yang (2021) "Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning" arXiv preprint arXiv:1911.03063v2 (2021).

```
## Not run:
###################################################
# Generate a sample dataset.
###################################################
# set the random seed
set.seed(20)
# set the number of observations
n <- 200
# generate covariates data
x1dat <- runif(n, -3, 3)
x2dat <- rnorm(n, 0, 1)
x3dat <- rchisq(n, 4)
# set coefficients
beta1 <- 1
beta2 <- 1
beta3 <- 1
# calculate the linear predictor data
lindat <- x1dat * beta1 + x2dat * beta2 + x3dat * beta3
# calculate the probabilities by inverse logit link
pdat <- 1/(1 + exp(-lindat))
# generate the response data
ydat <- sapply(pdat, function(x) stats :: rbinom(1, 1, x))
# generate the dataset
dat <- data.frame(y = ydat, x1 = x1dat, x2 = x2dat,
x3 = x3dat)
###################################################
# Obtain the testing result
###################################################
# Test a logistic regression that misses 'x3'. The partition
# variables are 'x1', 'x2', and 'x3'.
testRes <- BAGofT(testModel =testGlmBi(formula = y ~ x1 + x2 , link = "logit"),
parFun = parRF(parVar = c("x1", "x2", "x3")),
data = dat)
# the bootstrap p-value is 0. Therefore, the test is rejected
print(testRes$p.value)
# the variable importance from the adaptive partition shows that x3 is likely
# to be the reason for the overfitting (,which is correct since the formula
# fm misses the x3).
print(VarImp(testRes))
## End(Not run)
```

[Package *BAGofT* version 1.0.0 Index]