psycho (version 0.4.91)

get_predicted.merMod: Compute predicted values of lm models.

Description

Compute predicted from a lm model.

Usage

# S3 method for merMod
get_predicted(fit, newdata = "model", prob = NULL,
  odds_to_probs = TRUE, iter = 100, seed = NULL,
  re.form = "default", use.u = FALSE, ...)

Arguments

fit

An lm model.

newdata

A data frame in which to look for variables with which to predict. If omitted, the model matrix is used. If "model", the model's data is used.

prob

Probability of confidence intervals (0.95 will compute 2.5-97.5% CI). Can also be a list of probs (e.g., c(0.90, 0.95)). Default to NULL as it takes a very long time to compute (see bootMer).

odds_to_probs

Transform log odds ratios in logistic models to probabilies.

iter

An integer indicating the number of iterations for bootstrapping (when prob is not null).

seed

An optional seed to use.

re.form

Formula for random effects to condition on. If NULL, include all random effects; if NA or ~0, include no random effects (see predict.merMod). If "default", then will ne NULL if the random are present in the data, and NA if not.

use.u

logical, indicating whether the spherical random effects should be simulated / bootstrapped as well. If TRUE, they are not changed, and all inference is conditional on these values. If FALSE, new normal deviates are drawn (seebootMer).

...

Arguments passed to or from other methods.

Value

dataframe with predicted values.

Examples

# NOT RUN {
library(psycho)
library(ggplot2)

fit <- lmerTest::lmer(Tolerating ~ Adjusting + (1 | Salary), data = affective)

refgrid <- psycho::refdata(affective, "Adjusting")
predicted <- get_predicted(fit, newdata = refgrid)

ggplot(predicted, aes(x = Adjusting, y = Tolerating_Predicted)) +
  geom_line()

predicted <- get_predicted(fit, newdata = refgrid, prob = 0.95, iter = 100) # Takes a long time

ggplot(predicted, aes(x = Adjusting, y = Tolerating_Predicted)) +
  geom_line() +
  geom_ribbon(aes(
    ymin = Tolerating_CI_2.5,
    ymax = Tolerating_CI_97.5
  ),
  alpha = 0.1
  )



fit <- lme4::glmer(Sex ~ Adjusting + (1 | Salary), data = affective, family = "binomial")

refgrid <- psycho::refdata(affective, "Adjusting")
predicted <- get_predicted(fit, newdata = refgrid)

ggplot(predicted, aes(x = Adjusting, y = Sex_Predicted)) +
  geom_line()

predicted <- get_predicted(fit, newdata = refgrid, prob = 0.95, iter = 100) # Takes a long time

ggplot(predicted, aes(x = Adjusting, y = Sex_Predicted)) +
  geom_line() +
  geom_ribbon(aes(
    ymin = Sex_CI_2.5,
    ymax = Sex_CI_97.5
  ),
  alpha = 0.1
  )
# }