get_sims() is a function to simulate quantities of interest from a multivariate normal distribution for "new data" from a regression model.

get_sims(model, newdata, nsim, seed)

Arguments

model

a model object

newdata

A data frame on some quantities of interest to be simulated

nsim

Number of simulations to be run

seed

An optional seed to set

Value

get_sims() returns a data frame (as a tibble) with the quantities of interest and identifying information about the particular simulation number.

Details

This (should) be a flexible function that takes a merMod object (estimated from lme4, blme, etc.) or a lm or glm object and generates some quantities of interest when paired with new data of observations of interest. Of note: I've really only tested this function with linear models, generalized linear models, and their mixed model equivalents. For mixed models, this approach does not offer support for the incorporation of the random effects or the random slopes. It's just for the fixed effects, which is typically what most people want anyway. Users who want to better incorporate the random intercepts or slope could find that support in the merTools package.

Author

Steven V. Miller

Examples

# Note: these models are dumb, but they illustrate how it works. M1 <- lm(mpg ~ hp, mtcars) # Note: this function requires the DV to appear somewhere, anywhere in the "new data" newdat <- data.frame(mpg = 0, hp = c(mean(mtcars$hp) - sd(mtcars$hp), mean(mtcars$hp), mean(mtcars$hp) + sd(mtcars$hp))) get_sims(M1, newdat, 100, 8675309)
#> # A tibble: 300 x 2 #> y sim #> <dbl> <dbl> #> 1 23.8 1 #> 2 19.5 1 #> 3 15.2 1 #> 4 24.8 2 #> 5 20.0 2 #> 6 15.2 2 #> 7 23.9 3 #> 8 19.3 3 #> 9 14.7 3 #> 10 25.1 4 #> # … with 290 more rows
# Note: this is likely a dumb model, but illustrates how it works. mtcars$mpgd <- ifelse(mtcars$mpg > 25, 1, 0) M2 <- glm(mpgd ~ hp, mtcars, family=binomial(link="logit")) # Again: this function requires the DV to be somewhere, anywhere in the "new data" newdat$mpgd <- 0 # Note: the simulations are returned on their original "link". Here, that's a "logit" # You can adjust that accordingly. `plogis(y)` will convert those to probabilities. get_sims(M2, newdat, 100, 8675309)
#> # A tibble: 300 x 2 #> y sim #> <dbl> <dbl> #> 1 0.312 1 #> 2 -6.85 1 #> 3 -14.0 1 #> 4 0.623 2 #> 5 -2.07 2 #> 6 -4.76 2 #> 7 0.355 3 #> 8 -5.84 3 #> 9 -12.0 3 #> 10 -0.735 4 #> # … with 290 more rows
#> Loading required package: Matrix
M3 <- lmer(mpg ~ hp + (1 | cyl), mtcars) # Random effects are not required here since we're passing over them. get_sims(M3, newdat, 100, 8675309)
#> # A tibble: 300 x 2 #> y sim #> <dbl> <dbl> #> 1 21.7 1 #> 2 19.9 1 #> 3 18.0 1 #> 4 24.9 2 #> 5 23.0 2 #> 6 21.1 2 #> 7 22.4 3 #> 8 19.9 3 #> 9 17.4 3 #> 10 19.3 4 #> # … with 290 more rows