lm_table {forestmangr}

Fit linear regressions by group, and get different output options.

Description

With this function it's possible to fit linear regressions by a grouping variable, 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.

Usage

lm_table(
  df,
  model,
  .groups = NA,
  output = "table",
  est.name = "est",
  keep_model = FALSE,
  rmoutliers = FALSE,
  fct_to_filter = NA,
  rmlevels = NA,
  boolean_filter = NA,
  onlyfiteddata = FALSE,
  del_boolean = FALSE
)

Arguments

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 "~".

.groups

Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default: NA.

output

Selects different output options. Can be either "table", "merge", "merge_est" and "nest". See details for explanations for each option. Default: "table".

est.name

Name of the estimated y value. Used only if est.name = TRUE. Default: "est".

keep_model

If TRUE, a column containing lm object(s) is kept in the output. Useful if the user desires to get more information on the regression. Default: FALSE.

rmoutliers

If TRUE, outliers are filtered out using the IQR method. Default: FALSE.

fct_to_filter

Name of a factor or character column to be used as a filter to remove levels. Default: NA.

rmlevels

Levels of the fct_to_filter variable to be removed from the fit Default: NA.

boolean_filter

Name of a Boolean column to be used as a filter to remove data. Default: NA.

onlyfiteddata

If TRUE, the output data will be the same as the fitted (and possibly filtered) data. Default: FALSE.

del_boolean

If TRUE, the Boolean column supplied will be deleted after use. Default: FALSE.

Details

With this function there's no more need to use the do function when fitting a linear regression in a pipe line. It's also possible to easily make fit multiple regressions, specifying a grouping variable. In addition to that, the default output sets each coefficient as a column, making it easy to call coefficients by name or position when estimating values.

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.

Value

A data frame. Different data frame options are available using the output argument.

Author(s)

Sollano Rabelo Braga [e-mail]

Examples

library(forestmangr)
library(dplyr)

data("exfm19")
head(exfm19)

# Fit Schumacher and Hall model for volume estimation, and get
# coefficient, R2 and error values:

lm_table(exfm19, log(VWB) ~  log(DBH) + log(TH))   

# Fit SH model by group:
lm_table(exfm19, log(VWB) ~  log(DBH) + log(TH), "STRATA")

# This can also be done using dplyr::group_by:
exfm19 %>% 
  group_by(STRATA) %>% 
  lm_table(log(VWB) ~  log(DBH) + log(TH) )
  
# It's possible to merge the original data with the table containg the coefficients
# using the output parameter:
fit <- lm_table(exfm19, log(VWB) ~  log(DBH) + log(TH), "STRATA", output = "merge")
head(fit)

# It's possible to merge the original data with the table,
# and get the estimated values for this model:
fit <- lm_table(exfm19, log(VWB) ~  log(DBH) + log(TH),"STRATA",
 output = "merge_est", est.name = "VWB_EST") 
head(fit)
       
# It's possible to further customize the output,
# unnesting the nested variables provided when output is defined as "nest":
lm_table(exfm19, log(VWB) ~  log(DBH) + log(TH),"STRATA", output = "nest")