R: Accuracy of predictions from model fit

pred_accuracy {sjstats}

R Documentation

Accuracy of predictions from model fit

Description

This function calculates the predictive accuracy of linear or logistic regression models.

Usage

pred_accuracy(data, fit, method = c("cv", "boot"), k = 5, n = 1000)

Arguments

`data`	A data frame.
`fit`	Fitted model object of class `lm` or `glm`, the latter being a logistic regression model (binary response).
`method`	Character string, indicating whether crossvalidation (`method = "cv"`) or bootstrapping (`method = "boot"`) is used to compute the accuracy values.
`k`	The number of folds for the kfold-crossvalidation.
`n`	Number of bootstraps to be generated.

Details

For linar models, the accuracy is the correlation coefficient between the actual and the predicted value of the outcome. For logistic regression models, the accuracy corresponds to the AUC-value, calculated with the auc-function.

The accuracy is the mean value of multiple correlation resp. AUC-values, which are either computed with crossvalidation or nonparametric bootstrapping (see argument method). The standard error is the standard deviation of the computed correlation resp. AUC-values.

Value

A list with two values: The accuracy of the model predictions, i.e. the proportion of accurately predicted values from the model and its standard error, std.error.

Examples

data(efc)
fit <- lm(neg_c_7 ~ barthtot + c161sex, data = efc)

# accuracy for linear model, with crossvalidation
pred_accuracy(efc, fit)

# accuracy for linear model, with bootstrapping
pred_accuracy(efc, fit, method = "boot", n = 100)

# accuracy for logistic regression, with crossvalidation
efc$services <- sjmisc::dicho(efc$tot_sc_e, dich.by = 0, as.num = TRUE)
fit <- glm(services ~ neg_c_7 + c161sex + e42dep,
           data = efc, family = binomial(link = "logit"))
pred_accuracy(efc, fit)

[Package sjstats version 0.14.2-3 Index]