R: Breaking Down of Model Predictions for glm models

broken.glm {breakDown} | R Documentation

## Breaking Down of Model Predictions for glm models

### Description

Breaking Down of Model Predictions for glm models

### Usage

    ## S3 method for class 'glm'
    broken(model, new_observation, ..., baseline = 0,
      predict.function = stats::predict.glm)

### Arguments

`model` | a glm model
`new_observation` | a new observation with columns that corresponds to variables used in the model
`...` | other parameters
`baseline` | the origin/baseline for the breakDown plots, where the rectangles start. It may be a number or a character "Intercept". In the latter case the orgin will be set to model intercept.
`predict.function` | function that will calculate predictions out of model (typically `predict` or `betas`)

### Value

an object of the broken class

### Examples

    # example for wine data
    wine$qualityb <- factor(wine$quality > 5.5, labels = c("bad", "good"))
    modelg <- glm(qualityb~fixed.acidity + volatile.acidity + citric.acid +
                  residual.sugar + chlorides + free.sulfur.dioxide +
                  total.sulfur.dioxide + density + pH + sulphates + alcohol,
        data=wine, family = "binomial")
    new_observation <- wine[1,]
    br <- broken(modelg, new_observation)
    logit <- function(x) exp(x)/(1+exp(x))
    plot(br, logit)

    # example for HR_data
    model <- glm(left~., data = HR_data, family = "binomial")
    explain_1 <- broken(model, HR_data[1,])
    explain_1
    plot(explain_1)
    plot(explain_1, trans = function(x) exp(x)/(1+exp(x)))

    explain_2 <- broken(model, HR_data[1,], predict.function = betas)
    explain_2
    plot(explain_2, trans = function(x) exp(x)/(1+exp(x)))

[Package breakDown version 0.1.6 Index]