cora_fixed_effects.R

library(lme4)

# broom tidies up model summaries
library(broom) # install.packages("broom")

# fit a mixed model
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

# print the model output
fm1
# Linear mixed model fit by REML ['lmerMod']
# Formula: Reaction ~ Days + (Days | Subject)
# Data: sleepstudy
# REML criterion at convergence: 1743.628
# Random effects:
#   Groups   Name        Std.Dev. Corr
# Subject  (Intercept) 24.740       
# Days         5.922   0.07
# Residual             25.592       
# Number of obs: 180, groups:  Subject, 18
# Fixed Effects:
#   (Intercept)         Days  
# 251.41        10.47  

# show the tidy model summary
tidy(fm1)
#                        term     estimate
# 1                  (Intercept) 251.40510485
# 2                         Days  10.46728596
# 3       sd_(Intercept).Subject  24.74044768
# 4              sd_Days.Subject   5.92213326
# 5 cor_(Intercept).Days.Subject   0.06555134
# 6      sd_Observation.Residual  25.59181589
#   std.error statistic    group
# 1  6.824556 36.838310    fixed
# 2  1.545789  6.771485    fixed
# 3        NA        NA  Subject
# 4        NA        NA  Subject
# 5        NA        NA  Subject
# 6        NA        NA Residual

# show the fixed effects
fixef(fm1)
# (Intercept)        Days 
#   251.40510    10.46729 

# pull the fixed effects function directly from lme4.
lme4::fixef(fm1)
# (Intercept)        Days 
# 251.40510    10.46729 

# if this doesn't work, try this:

fixed_effects <- function(x){ # x is a model
  
  # get the names of the coefficients
  Term <- x %>% fixef %>% names
  
  # get the values of the coefficients
  Estimate <- x %>% fixef %>% as.numeric
  
  # put it into a dataframe
  fixed_effects_df <- data_frame(Term, Estimate)
  
  fixed_effects_df
  
}

library(dplyr) # install.packages("dplyr")
fixed_effects(fm1)