forestmangr: lm_table – R documentation

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lm_table

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
)

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`.

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 sollanorb@gmail.com

Examples

library(forestmangr)
library(dplyr)

data("exfm19")
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")

forestmangr

Forest Mensuration and Management

v0.9.3

MIT + file LICENSE

Authors

Sollano Rabelo Braga [aut, cre, cph], Marcio Leles Romarco de Oliveira [aut], Eric Bastos Gorgens [aut]

Initial release

2021-01-24