mean_differences_lm: A combination of mean comparison/separation and visualization...

In DeependraD/expdean: Collection of Design and Analysis Functions for Agriculture and Related Fields Data

Description

This package tidies up regression output to give mean summary table, using various other packages in cohert linear model and mean separation using DMRT/LSD or alike tests, from 'agricolae' package this function works best in conjuction with 'lm_list'

Usage

mean_differences_lm(model_list, treatment_factor_list)

Arguments

model_list	A list of linear model (lm) objects. This list must be named with respective response variable name in order that dataframe contains tidy names for columns
treatment_factor_list	A list of single treatment factor or multiple treatment factors or interaction between two treatment factors. In any way the terms fitted to the all response must all be same.

Value

A tbl

Examples

## Not run: 
# model marginal mean
mt_trial <- mtcars 
  rownames_to_column("vehicle_name") 
  as_tibble() 
  mutate_at(c("vehicle_name", "vs", "am"), as.factor)

mt_lm1 <- lm(`mpg`~`am`*`vs` + `hp`, data = mt_trial)
mt_lm2 <- lm(`drat`~`am`*`vs` + `hp` + `disp`, data = mt_trial)
mt_lm3 <- lm(`qsec`~`am`*`vs` + `hp`, data = mt_trial)

mt_named_list <- list(mt_lm1, mt_lm2, mt_lm3) 
  set_names(c("response_1st", "response_2nd", "response_3rd"))

mt_treatment_factor_list <- list("am", "vs", c("am", "vs"))

# main effects and interaction effects means for two factors using lm tidier
mean_differences_lm(model_list = mt_named_list, treatment_factor_list = mt_treatment_factor_list)

## End(Not run)

DeependraD/expdean documentation built on Nov. 25, 2019, 12:33 a.m.