Plot for detecting violation of assumptions about residuals such as
non-linearity, constant variances and outliers. It can also be used to
examine model fit.

ols_plot_resid_stud_fit(model, threshold = NULL, print_plot = TRUE)

## Arguments

model |
An object of class `lm` . |

threshold |
Threshold for detecting outliers. Default is 2. |

print_plot |
logical; if `TRUE` , prints the plot else returns a plot object. |

## Value

`ols_plot_resid_stud_fit`

returns a list containing the
following components:

outliersa `data.frame`

with observation number, fitted values and deleted studentized
residuals that exceed the `threshold`

for classifying observations as
outliers/influential observations

threshold`threshold`

for classifying an observation as an outlier/influential observation

## Details

Studentized deleted residuals (or externally studentized residuals) is the
deleted residual divided by its estimated standard deviation. Studentized
residuals are going to be more effective for detecting outlying Y
observations than standardized residuals. If an observation has an externally
studentized residual that is larger than 2 (in absolute value) we can call
it an outlier.

## Deprecated Function

`ols_dsrvsp_plot()`

has been deprecated. Instead use `ols_plot_resid_stud_fit()`

.

## See also

[ols_plot_resid_lev()], [ols_plot_resid_stand()],
[ols_plot_resid_stud()]

## Examples

ols_plot_resid_stud_fit(model, threshold = 3)