The model_parameters() function (also accessible via the shortcut parameters()) allows you to extract the parameters and their characteristics from various models in a consistent way. It can be considered as a lightweight alternative to broom::tidy(), with some notable differences:
The names of the returned data frame are specific to their content. For instance, the column containing the statistic is named following the statistic name, i.e., t, z, etc., instead of a generic name such as statistic (however, you can get standardized (generic) column names using standardize_names()).
It is able to compute or extract indices not available by default, such as **p*-values, CIs**, etc.
It includes feature engineering capabilities, including parameters bootstrapping.
cor.test(iris$Sepal.Length, iris$Sepal.Width) %>%
parameters()
t.test(mpg ~ vs, data = mtcars) %>%
parameters()
library(BayesFactor)
BayesFactor::correlationBF(iris$Sepal.Length, iris$Sepal.Width) %>%
parameters()
BayesFactor::ttestBF(formula = mpg ~ vs, data = mtcars) %>%
parameters()Indices of effect size for ANOVAs, such as partial and non-partial versions of eta_squared(), epsilon_sqared() or omega_squared() are powered by the effectsize-package. However, parameters uses these function to compute such indices for parameters summaries, including confidence intervals
aov(Sepal.Length ~ Species, data = iris) %>%
parameters(
omega_squared = "partial",
eta_squared = "partial",
epsilon_squared = "partial"
)Let’s complicate things further with an interaction term:
aov(Sepal.Length ~ Species * Sepal.Width, data = iris) %>%
parameters(
omega_squared = "partial",
eta_squared = "partial",
ci = .8
)parameters() (resp. its alias model_parameters()) also works on repeated measures ANOVAs, whether computed from aov() or from a mixed model.
aov(mpg ~ am + Error(gear), data = mtcars) %>%
parameters()parameters() (resp. its alias model_parameters()) was mainly built with regression models in mind. It works for many types of models and packages, including mixed models and Bayesian models.
glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
parameters()
# show Odds Ratios and Wald-method for degrees of freedom
glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
parameters(exponentiate = TRUE, df_method = "wald")
# show Odds Ratios and include model summary
glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
parameters(exponentiate = TRUE, summary = TRUE)
library(lme4)
lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris) %>%
parameters()
lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris) %>%
parameters(effects = "fixed")
library(GLMMadaptive)
library(glmmTMB)
data("Salamanders")
model <- mixed_model(
count ~ spp + mined,
random = ~ 1 | site,
zi_fixed = ~ spp + mined,
family = zi.negative.binomial(),
data = Salamanders
)
parameters(model)
library(glmmTMB)
sim1 <- function(nfac = 40, nt = 100, facsd = 0.1, tsd = 0.15, mu = 0, residsd = 1) {
dat <- expand.grid(fac = factor(letters[1:nfac]), t = 1:nt)
n <- nrow(dat)
dat$REfac <- rnorm(nfac, sd = facsd)[dat$fac]
dat$REt <- rnorm(nt, sd = tsd)[dat$t]
dat$x <- rnorm(n, mean = mu, sd = residsd) + dat$REfac + dat$REt
dat
}
set.seed(101)
d1 <- sim1(mu = 100, residsd = 10)
d2 <- sim1(mu = 200, residsd = 5)
d1$sd <- "ten"
d2$sd <- "five"
dat <- rbind(d1, d2)
model <- glmmTMB(x ~ sd + (1 | t), dispformula = ~sd, data = dat)
parameters(model)model_parameters() also works with Bayesian models from the rstanarm package:
library(rstanarm)
# if you are unfamiliar with the `refresh` argument here, it just avoids
# printing few messages to the console
stan_glm(mpg ~ wt * cyl, data = mtcars, refresh = 0) %>%
parameters()Additionally, it also works for models from the brms package.
For more complex models, specific model components can be printed using the arguments effects and component arguments.
library(brms)
data(fish)
set.seed(123)
# fitting a model using `brms`
model <- brm(
bf(
count ~ persons + child + camper + (1 | persons),
zi ~ child + camper + (1 | persons)
),
data = fish,
family = zero_inflated_poisson(),
refresh = 0
)
parameters(model, component = "conditional")
parameters(model, effects = "all", component = "all")To include information about the random effect parameters (group levels), set group_level = TRUE:
parameters(model, effects = "all", component = "conditional", group_level = TRUE)The parameters package extends the support to structural models.
library(psych)
psych::pca(mtcars, nfactors = 3) %>%
parameters()We will avoid displaying a graph while carrying out factor analysis:
library(FactoMineR)
FactoMineR::FAMD(iris, ncp = 3, graph = FALSE) %>%
parameters()
library(lavaan)
model <- lavaan::cfa(" visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 ",
data = HolzingerSwineford1939
)
model_parameters(model)blavaan to be done.
parameters() also works for rma-objects from the metafor package.
library(metafor)
mydat <- data.frame(
effectsize = c(-0.393, 0.675, 0.282, -1.398),
standarderror = c(0.317, 0.317, 0.13, 0.36)
)
rma(yi = effectsize, sei = standarderror, method = "REML", data = mydat) %>%
model_parameters()There is a plot()-method implemented in the see-package. Several examples are shown in this vignette.