Fit linear regressions by group, with the option of removing outliers using a interactive plot of residuals.
With this function it's possible to fit linear regressions by a grouping variable, and evaluate each equation via a interactive plot of residuals, and get a data frame. with each column as a coefficient and quality of fit variables, and other output options. Works with dplyr grouping functions.
lm_resid(df, model, output_mode = "table", est.name = "est", group_print = NA)
df |
A data frame. |
model |
A linear regression model, with or without quotes. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
output_mode |
Selects different output options. Can be either |
est.name |
Name of the estimated y value. Used only if |
group_print |
This argument is only used internally by another function. Please ignore. |
this function uses lm_table as a basis, but calls a plot of residuals for each fitted model, for the user to evaluate. If one decides to remove any of the points, one can click and drag, and then click on the 'remove points' button. After that, one must simply click 'done' and the coefficients will be printed out.
It's possible to use the output argument to get a merged table if output="merge", that binds
the original data frame and the fitted coefficients.
If output="merge_est" we get a merged table as well, but with y estimated using the coefficients. If the fit is made using groups, this is taken into account, i.e. the estimation is made by group.
If output="nest", a data frame with nested columns is provided. This can be used if the user desires to get a customized output.
A data frame. Different data frame options are available using the output argument.
Sollano Rabelo Braga sollanorb@gmail.com
if (interactive() ){
library(forestmangr)
library(dplyr)
data("exfm19")
# Fit SH model:
lm_resid_group(exfm19, log(VWB) ~ log(DBH) + log(TH))
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.