How to make a forest plot for a mixed model-CodePudding

How to make a forest plots for mixed models co-effiecents and their corresponding confidence interval. I tried this code

Model = lme (fixed = score~ Age Sex yearsofeducation walkspeed,
random = ~1|ID, 
data=DB,
na.action = na.omit, method = "ML", 
)
plot_summs (model)

However, I want the OR in the forest plots to be ordered in a descending fashion. Thanks for the help.

CodePudding user response：

I would call this a "coefficient plot", not a "forest plot". (A forest plot is used in meta-analyses, when you are comparing the magnitude of estimates of the same effect from many different studies.)

example setup

This is a slightly silly example, but should be close enough to yours (not clear to me why you're mentioning OR (= odds ratios?), these are typically from a logistic regression ... ?)

library(nlme)
mtcars <- transform(mtcars, cylgear = interaction(cyl, gear))
m1 <- lme(mpg ~ disp   hp   drat   wt   qsec,
    random = ~1|cylgear,
    data = mtcars)

coefficient plots: dotwhisker

You could get approximately what you want directly from the dotwhisker package, but it won't sort effects (or not easily, as far as I know):

library(dotwhisker)
library(broom.mixed)  ## required to 'tidy' (process) lme fits
dwplot(m1, effects = "fixed")

coefficient plots: tidyverse

I usually do the processing myself, as I prefer increased flexibility.

library(tidyverse)
tt <- (m1
    ## extract estimates and CIs
    |> tidy(effects = "fixed", conf.int = TRUE)
    ## usually *don't* want to compare intercept (dwplot does this automatically)
    |> filter(term != "(Intercept)")
    ## scale parameters by 2SD - usually necessary for comparison
    |> dotwhisker::by_2sd(data = mtcars)
    ## take only the bits we need, rename some (cosmetic)
    |> select(term, estimate, lwr = conf.low, upr = conf.high)
    ## order terms by estimate value
    |> mutate(across(term, ~reorder(factor(.), estimate)))
)

gg0 <- (ggplot(tt,
               aes(estimate, term))
      geom_pointrange(aes(xmin = lwr, xmax = upr))
      geom_vline(xintercept = 0, lty = 2)
)
print(gg0)

The only remaining/possibility tricky question here is what to do if you have positive and negative coefficients of similar magnitude. If you want to sort by absolute value then

|> mutate(across(term, ~reorder(factor(.), estimate, 
 FUN = function(x) mean(abs(x)))

although this gets a bit ugly.

If you like the tidyverse you can substitute forcats::fct_reorder for reorder.

CodePudding user response：

I’m just adding one more option to Ben Bolker’s excellent answer: using the modelsummary package. (Disclaimer: I am the author.)

With that package, you can use the modelplot() function to create a forest plot, and the coef_map argument to rename and reorder coefficients. If you are estimating a logit model and want the odds ratios, you can use the exponentiate argument.

The order in which you insert coefficients in the coef_map vector sorts them in the plot, from bottom to top. For example:

library(lme4)
library(modelsummary)

mod <- lmer(mpg ~ wt   drat   (1 | gear), data = mtcars)

modelplot(
    mod,
    coef_map = c("(Intercept)" = "Constant",
                 "drat" = "Rear Axle Ratio",
                 "wt" = "Weight"))