In IndrajeetPatil/ggstatplot: 'ggplot2' Based Plots with Statistical Details

#| label = "setup",
#| include = FALSE
options(
  tibble.width = Inf,
  pillar.bold = TRUE,
  pillar.neg = TRUE,
  pillar.subtle_num = TRUE,
  pillar.min_chars = Inf
)

# use max # CPUs
options(mc.cores = parallel::detectCores())

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  error = TRUE,
  package.startup.message = FALSE
)

# for faster post-MCMC computations
future::plan("multicore")

You can cite this package/vignette as:

#| label = "citation",
#| echo = FALSE,
#| comment = ""
citation("ggstatsplot")

Lifecycle:

The function ggcoefstats generates dot-and-whisker plots for regression models saved in a tidy data frame. The tidy data frames are prepared using parameters::model_parameters. Additionally, if available, the model summary indices are also extracted from performance::model_performance.

In this vignette, we will see examples of how to use this function. We will try to cover as many classes of objects as possible. Unfortunately, there is no single dataset that will be helpful for carrying out all types of regression analyses and, therefore, we will use various datasets to explore data-specific hypotheses using regression models.

General structure of the plots

Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:

The dot-whisker plot contains a dot representing the estimate and their confidence intervals (95% is the default). The estimate can either be effect sizes (for tests that depend on the F-statistic) or regression coefficients (for tests with t-, $\chi^{2}$-, and z-statistic), etc. The confidence intervals can sometimes be asymmetric if bootstrapping was used.
The label attached to dot will provide more details from the statistical test carried out and it will typically contain estimate, statistic, and p-value.
The caption will contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike's Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the "better" the model is.
The output of this function will be a {ggplot2} object and, thus, it can be further modified (e.g., change themes, etc.) with {ggplot2} functions.

Supported models

Most of the regression models that are supported in the underlying packages are also supported by ggcoefstats.

#| label = "supported"
insight::supported_models()

Summary of graphics

graphical element | geom_ used | argument for further modification --------- | ------- | -------------------------- regression estimate | ggplot2::geom_point| point.args error bars | ggplot2::geom_errorbarh | errorbar.args vertical line | ggplot2::geom_vline | vline.args label with statistical details | ggrepel::geom_label_repel | stats.label.args

Summary of meta-analysis tests

Hypothesis testing and Effect size estimation

Type | Test | Effect size | CI? | Function used ----------- | -------------------- | -------- | --- | ----- Parametric | Meta-analysis via random-effects models | $\beta$ | ✅ | metafor::metafor Robust | Meta-analysis via robust random-effects models | $\beta$ | ✅ | metaplus::metaplus Bayes | Meta-analysis via Bayesian random-effects models | $\beta$ | ✅ | metaBMA::meta_random

Examples of supported models

First let's load the needed library.

#| label = "log_ggstats"

The following demos are in no particular order.

linear mixed-effects model (`lmer`/`lmerMod`)

#| label = "lmer1",
#| fig.height = 8,
#| fig.width = 6
# set up
library(lme4)



# lm model
mod1 <- stats::lm(formula = scale(rating) ~ scale(budget), data = movies_long)

# merMod model
mod2 <- lme4::lmer(
  formula = scale(rating) ~ scale(budget) + (budget | genre),
  data = movies_long,
  control = lme4::lmerControl(calc.derivs = FALSE)
)

# combining the two different plots
combine_plots(
  plotlist = list(
    # model 1: simple linear model
    ggcoefstats(
      x = mod1,
      title = "linear model",
      exclude.intercept = TRUE # hide the intercept
    ) +
      ggplot2::labs(x = parse(text = "'standardized regression coefficient' ~italic(beta)")),
    # model 2: linear mixed-effects model
    ggcoefstats(
      x = mod2,
      title = "linear mixed-effects model",
      exclude.intercept = TRUE # hide the intercept
    ) +
      ggplot2::labs(
        x = parse(text = "'standardized regression coefficient' ~italic(beta)"),
        y = "fixed effects"
      )
  ),
  plotgrid.args = list(nrow = 2),
  annotation.args = list(title = "Relationship between movie budget and its IMDB rating")
)

Note that for mixed-effects models, only the fixed effects are shown because there are no confidence intervals for random effects terms. In case, you would like to see these terms, you can enter the same object you entered as x argument to parameters::model_parameters:

#| label = "lmer2"


library(lme4)
library(parameters)

# tidy output
parameters::model_parameters(
  lme4::lmer(
    formula = scale(rating) ~ scale(budget) + (budget | genre),
    data = movies_long,
    control = lme4::lmerControl(calc.derivs = FALSE)
  )
)

summary grid from `emmeans` package (`emmGrid`)

#| label = "emmGrid",
#| fig.height = 5,
#| fig.width = 5


library(emmeans)

# linear model for sales of oranges per day
oranges_lm1 <-
  stats::lm(
    formula = sales1 ~ price1 + price2 + day + store,
    data = oranges
  )

# reference grid; see vignette("basics", package="emmeans")
oranges_rg1 <- emmeans::ref_grid(oranges_lm1)
marginal <- emmeans::emmeans(oranges_rg1, "day")


ggcoefstats(
  x = marginal,
  point.args = list(color = "darkgreen", shape = 9),
  title = "summary grid from `emmeans` package"
)

Another example with output containing effect sizes

#| label = "emmGrid2",
#| fig.height = 5,
#| fig.width = 5


library(emmeans)

# model and effect sizes
fiber.lm <- lm(strength ~ diameter + machine, data = fiber)
emm <- emmeans::emmeans(fiber.lm, "machine")
es <- emmeans::eff_size(emm, sigma = sigma(fiber.lm), edf = df.residual(fiber.lm))


ggcoefstats(
  x = es,
  title = "summary grid with effect sizes\nfrom `emmeans` package"
)

Bayesian additive models for location scale and shape (`bamlss`)

#| label = "bamlss",
#| fig.height = 5,
#| fig.width = 6


library(bamlss)
data("SwissLabor", package = "AER")

# formula
f <- participation ~ income + age + education + youngkids + oldkids + foreign + I(age^2)

# model
b_bamlss <-
  bamlss::bamlss(
    formula = f,
    family = "binomial",
    data = SwissLabor
  )


ggcoefstats(
  x = b_bamlss,
  title = "Bayesian additive models \nfor location scale and shape"
)

Bayes Factor (`BFBayesFactor`)

#| label = "BFBayesFactor",
#| fig.width = 10,
#| fig.height = 10


library(BayesFactor)


# one sample t-test
mod1 <- ttestBF(mtcars$wt, mu = 3)

# independent t-test
mod2 <- ttestBF(formula = wt ~ am, data = mtcars)

# paired t-test
mod3 <- ttestBF(x = sleep$extra[1:10], y = sleep$extra[11:20], paired = TRUE)

# correlation
mod4 <- correlationBF(y = iris$Sepal.Length, x = iris$Sepal.Width)

# contingency tabs (not supported)
data("raceDolls")
mod5 <- contingencyTableBF(
  raceDolls,
  sampleType = "indepMulti",
  fixedMargin = "cols"
)

# anova
data("puzzles")
mod6 <- anovaBF(
  formula = RT ~ shape * color + ID,
  data = puzzles,
  whichRandom = "ID",
  whichModels = "top",
  progress = FALSE
)

# regression-1
mod7 <- regressionBF(rating ~ ., data = attitude, progress = FALSE)

# meta-analysis
t <- c(-.15, 2.39, 2.42, 2.43, -.15, 2.39, 2.42, 2.43)
N <- c(100, 150, 97, 99, 99, 97, 100, 150)
mod8 <- meta.ttestBF(t, N, rscale = 1, nullInterval = c(0, Inf))

# proportion test
mod9 <- proportionBF(y = 15, N = 25, p = .5)

# list of plots
combine_plots(
  plotlist = list(
    ggcoefstats(mod1, title = "one sample t-test"),
    ggcoefstats(mod2, title = "independent t-test"),
    ggcoefstats(mod3, title = "paired t-test"),
    ggcoefstats(mod4, title = "correlation"),
    ggcoefstats(mod5, title = "contingency table"),
    ggcoefstats(mod6, title = "anova"),
    ggcoefstats(mod7, title = "regression-1"),
    ggcoefstats(mod8, title = "meta-analysis"),
    ggcoefstats(mod9, title = "proportion test")
  ),
  annotation.args = list(title = "Example from `BayesFactor` package")
)

Fixed Effects Individual Slope Estimator (`feis`)

#| label = "feis",
#| fig.height = 8,
#| fig.width = 6


library(feisr)
data("mwp", package = "feisr")

# model
feis.mod <-
  feisr::feis(
    formula = lnw ~ marry + enrol + as.factor(yeargr) | exp + I(exp^2),
    data = mwp,
    id = "id",
    robust = TRUE
  )


ggcoefstats(
  x = feis.mod,
  title = "Fixed Effects Individual Slope Estimator"
)

omnibus ANOVA (`aov`)

#| label = "aov1",
#| fig.height = 6,
#| fig.width = 8



library(ggplot2)

# model
mod_aov <- stats::aov(formula = rating ~ mpaa * genre, data = movies_long)


ggcoefstats(
  x = mod_aov,
  point.args = list(color = "red", size = 4, shape = 15), # changing the point geom
  package = "dutchmasters", # package from which color palette is to be taken
  palette = "milkmaid", # color palette for labels
  title = "omnibus ANOVA", # title for the plot
  exclude.intercept = TRUE
) +
  # further modification with the ggplot2 commands
  # note the order in which the labels are entered
  ggplot2::scale_y_discrete(labels = c("MPAA", "Genre", "Interaction term")) +
  ggplot2::labs(x = "effect size estimate (eta-squared)", y = NULL)

Note that we can also use this function for model selection. You can try out different models with the code below and see how the AIC and BIC values change.

#| label = "aov2",
#| fig.height = 10,
#| fig.width = 10





combine_plots(
  plotlist = list(
    # model 1
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa, data = movies_long),
      title = "1. Only MPAA ratings"
    ),
    # model 2
    ggcoefstats(
      x = stats::aov(formula = rating ~ genre, data = movies_long),
      title = "2. Only genre"
    ),
    # model 3
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa + genre, data = movies_long),
      title = "3. Additive effect of MPAA and genre"
    ),
    # model 4
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa * genre, data = movies_long),
      title = "4. Multiplicative effect of MPAA and genre"
    )
  ),
  annotation.args = list(title = "Model selection using ggcoefstats")
)

conditional logit models (`mclogit`)

#| label = "mclogit",
#| fig.height = 4,
#| fig.width = 4


library(mclogit)
data(Transport)

# model
mod_mclogit <-
  mclogit::mclogit(
    formula = cbind(resp, suburb) ~ distance + cost,
    data = Transport,
    control = mclogit::mclogit.control(trace = FALSE)
  )


ggcoefstats(
  x = mod_mclogit,
  title = "conditional logit models"
)

mixed conditional logit models (`mmclogit`)

#| label = "mmclogit",
#| fig.height = 8,
#| fig.width = 7


library(mclogit)
data(electors)

# model
mod_mclogit <-
  mclogit::mclogit(
    formula = cbind(Freq, interaction(time, class)) ~
      econ.left / class + welfare / class + auth / class,
    random = ~ 1 | party.time,
    data = within(electors, party.time <- interaction(party, time))
  )


ggcoefstats(
  x = mod_mclogit,
  title = "Mixed Conditional Logit Models"
)

anova with `car` package (`Anova`)

#| label = "Anova",
#| fig.height = 6,
#| fig.width = 6


library(car)


# model
mod_Anova <-
  car::Anova(stats::lm(
    formula = conformity ~ fcategory * partner.status,
    data = Moore,
    contrasts = list(fcategory = contr.sum, partner.status = contr.sum)
  ))


ggcoefstats(
  x = mod_Anova,
  title = "Anova with `car`"
)

ANOVA with `car` package on multiple linear models (`Anova.mlm`)

#| label = "Anova.mlm",
#| fig.height = 6,
#| fig.width = 6,
#| eval = FALSE


library(car)

# data
dv <- c(1, 3, 4, 2, 2, 3, 2, 5, 6, 3, 4, 4, 3, 5, 6)
subject <- factor(c(
  "s1", "s1", "s1", "s2", "s2", "s2", "s3", "s3", "s3",
  "s4", "s4", "s4", "s5", "s5", "s5"
))
myfactor <- factor(c(
  "f1", "f2", "f3", "f1", "f2", "f3", "f1", "f2", "f3",
  "f1", "f2", "f3", "f1", "f2", "f3"
))
mydata <- data.frame(dv, subject, myfactor)

dvm <- with(mydata, cbind(
  dv[myfactor == "f1"],
  dv[myfactor == "f2"], dv[myfactor == "f3"]
))

# model
mlm1 <- lm(dvm ~ 1)

rfactor <- factor(c("f1", "f2", "f3"))

# model
mlm1.aov <-
  car::Anova(mlm1,
    idata = data.frame(rfactor),
    idesign = ~rfactor,
    type = "III"
  )


ggcoefstats(
  x = mlm1.aov,
  title = "ANOVA with `car` on multiple linear models"
)

Anova with `ez` package

#| label = "anova_ez",
#| fig.height = 4,
#| fig.width = 4

library(ez)
data(ANT)

# run an ANOVA on the mean correct RT data.
rt_anova <-
  suppressWarnings(ez::ezANOVA(
    data = ANT[ANT$error == 0, ],
    dv = rt,
    wid = subnum,
    within = cue,
    detailed = TRUE,
    return_aov = TRUE
  ))


ggcoefstats(
  x = rt_anova$aov,
  title = "Anova with `ez` package"
)

linear model (`lm`)

#| label = "lm",
#| fig.height = 8,
#| fig.width = 8



# data
df <- dplyr::filter(
  movies_long,
  genre %in% c(
    "Action",
    "Action Comedy",
    "Action Drama",
    "Comedy",
    "Drama",
    "Comedy Drama"
  )
)


ggcoefstats(
  x = stats::lm(formula = rating ~ genre, data = df),
  sort = "ascending", # sorting the terms of the model based on estimate values
  ggtheme = ggplot2::theme_gray(), # changing the default theme
  stats.label.color = c("#CC79A7", "darkgreen", "#0072B2", "darkred", "black", "red"),
  title = "Movie ratings by their genre",
  subtitle = "Source: www.imdb.com"
)

The same output will also be returned for objects of type summary.lm.

standardized regression coefficients with `lm` (`lm.beta`)

#| label = "lm.beta",
#| fig.width = 10,
#| fig.height = 6


library(MASS)
library(lm.beta)

# model
mod.lm <- stats::lm(formula = mpg ~ wt, data = mtcars)
mod.robust <- MASS::rlm(formula = mpg ~ wt, data = mtcars)
mod.beta <- lm.beta::lm.beta(mod.lm)
mod.robust.beta <- lm.beta::lm.beta(mod.robust)


combine_plots(
  plotlist = list(
    ggcoefstats(mod.beta),
    ggcoefstats(mod.robust.beta, stats.labels = FALSE)
  ),
  annotation.args = list(title = "standardized regression coefficients with `lm`")
)

bounded memory linear regression (`biglm`)

#| label = "biglm",
#| fig.height = 4,
#| fig.width = 6


library(biglm)

# model
bfit1 <-
  biglm(
    formula = scale(mpg) ~ scale(wt) + scale(disp),
    data = mtcars
  )


ggcoefstats(
  x = bfit1,
  title = "bounded memory simple linear regression"
)

bounded memory general linear regression (`bigglm`)

#| label = "bigglm",
#| fig.height = 4,
#| fig.width = 6


library(biglm)
data(trees)

# model
mod_bigglm <-
  biglm::bigglm(
    formula = log(Volume) ~ log(Girth) + log(Height),
    data = trees,
    chunksize = 10,
    sandwich = TRUE
  )


ggcoefstats(
  x = mod_bigglm,
  title = "bounded memory general linear regression"
)

efficient (general) linear model (`elm`/`eglm`)

#| label = "eflm",
#| fig.height = 5,
#| fig.width = 12


library(eflm)

# models
mod_elm <- eflm::elm(mpg ~ wt, data = mtcars)
mod_eglm <- eflm::eglm(mpg ~ wt, family = gaussian(), data = mtcars)


combine_plots(
  plotlist = list(
    ggcoefstats(
      x = mod_elm,
      title = "efficient linear model"
    ),
    ggcoefstats(
      x = mod_eglm,
      title = "efficient general linear model"
    )
  ),
  plotgrid.args = list(nrow = 1),
  annotation.args = list(title = "efficient (general) linear model using `eflm`")
)

nonlinear model using generalized least squares (`gnls`)

#| label = "gnls",
#| fig.height = 6,
#| fig.width = 8

library(nlme)

# variance increases with a power of the absolute fitted values
mod_gnls <- gnls(
  model = weight ~ SSlogis(Time, Asym, xmid, scal),
  data = Soybean,
  weights = varPower()
)

ggcoefstats(
  x = mod_gnls,
  title = "nonlinear model using generalized least squares"
)

analysis of factorial experiments (`mixed`)

#| label = "mixed",
#| fig.height = 4,
#| fig.width = 8


library(afex)
library(MEMSS)
data("Machines", package = "MEMSS")

# simple model with random-slopes for repeated-measures factor
m1_afex <-
  afex::mixed(
    formula = score ~ Machine + (Machine | Worker),
    data = Machines
  )

# suppress correlations among random effect parameters with || and expand_re=TRUE
m2_afex <-
  afex::mixed(
    formula = score ~ Machine + (Machine || Worker),
    data = Machines,
    expand_re = TRUE
  )


combine_plots(
  plotlist = list(
    ggcoefstats(m1_afex, title = "example-1"),
    ggcoefstats(m2_afex, title = "example-2")
  ),
  annotation.args = list(title = "analysis of factorial experiments (using `afex`)")
)

anova using `afex` (`afex_aov`)

#| label = "afex_aov",
#| fig.height = 6,
#| fig.width = 6


library(afex)
data(obk.long)

# model
fit_all <-
  afex::aov_ez(
    "id",
    "value",
    obk.long,
    between = c("treatment"),
    within = c("phase")
  )


ggcoefstats(
  x = fit_all,
  title = "anova using `afex`"
)

joint model for survival and longitudinal data measured with error (`joint`)

#| label = "joint",
#| eval = FALSE


library(joineR)

# data
data(heart.valve)
heart.surv <- UniqueVariables(heart.valve,
  var.col = c("fuyrs", "status"),
  id.col = "num"
)
heart.long <- heart.valve[, c("num", "time", "log.lvmi")]
heart.cov <- UniqueVariables(heart.valve,
  c("age", "hs", "sex"),
  id.col = "num"
)
heart.valve.jd <- jointdata(
  longitudinal = heart.long,
  baseline = heart.cov,
  survival = heart.surv,
  id.col = "num",
  time.col = "time"
)

# model
mod_joint <- joint(
  data = heart.valve.jd,
  long.formula = log.lvmi ~ 1 + time + hs,
  surv.formula = Surv(fuyrs, status) ~ hs,
  model = "intslope"
)


ggcoefstats(
  x = mod_joint,
  title = "joint model for survival and longitudinal data measured with error"
)

risk regression (`riskRegression`)

#| label = "riskRegression",
#| fig.height = 6,
#| fig.width = 6


library(riskRegression)
library(prodlim)
data(Melanoma, package = "riskRegression")

# tumor thickness on the log-scale
Melanoma$logthick <- log(Melanoma$thick)

# absolute risk model
multi.arr <-
  riskRegression::ARR(
    formula = Hist(time, status) ~ logthick + sex + age + ulcer,
    data = Melanoma,
    cause = 1
  )


ggcoefstats(
  x = multi.arr,
  title = "risk regression"
)

Aalen's additive regression model for censored data (`aareg`)

#| label = "aareg",
#| fig.height = 5,
#| fig.width = 5

library(survival)


# model
afit <-
  survival::aareg(
    formula = Surv(time, status) ~ age + sex + ph.ecog,
    data = lung,
    dfbeta = TRUE
  )


ggcoefstats(
  x = afit,
  title = "Aalen's additive regression model",
  subtitle = "(for censored data)",
  k = 3
)

multivariate generalized linear mixed models (`MCMCglmm`)

#| label = "MCMCglmm",
#| fig.height = 4,
#| fig.width = 6


library(lme4)
library(MCMCglmm)
data(sleepstudy)

# model
mm0 <-
  MCMCglmm::MCMCglmm(
    fixed = scale(Reaction) ~ scale(Days),
    random = ~Subject,
    data = lme4::sleepstudy,
    nitt = 4000,
    pr = TRUE,
    verbose = FALSE
  )


ggcoefstats(
  x = mm0,
  title = "multivariate generalized linear mixed model",
  conf.method = "HPDinterval",
  robust = TRUE # additional arguments passed to `parameters::model_parameters`
)

STAR Models with BayesX (`bayesx`)

#| label = "bayesx",
#| fig.height = 4,
#| fig.width = 5

set.seed(111)
library(R2BayesX)

## generate some data
n <- 200

## regressor
dat <- data.frame(x = runif(n, -3, 3))

## response
dat$y <- with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6))

## estimate models with bayesx REML and MCMC
b1 <- R2BayesX::bayesx(y ~ sx(x), method = "REML", data = dat)


ggcoefstats(
  x = b1,
  title = "STAR Models with BayesX"
)

Generalised Joint Regression Models with Binary/Continuous/Discrete/Survival Margins (`gjrm`)

#| label = "gjrm",
#| width = 7,
#| height = 7
library(GJRM)

# data

dat <- data.frame(
  x1 = rnorm(40, 1:10),
  x2 = rnorm(40, 1:30),
  bid1 = sample(1:5, 40, replace = TRUE),
  bid2 = sample(1:5, 40, replace = TRUE),
  y1 = sample(0:1, 40, replace = TRUE),
  y2 = sample(0:1, 40, replace = TRUE)
)

f.list <- list(
  y1 ~ bid1 + x1 + x2,
  y2 ~ bid2 + x1 + x2
)

# model
mod_gjrm <- gjrm(f.list, dat, Model = "B", margins = c("probit", "probit"))


ggcoefstats(
  x = mod_gjrm,
  title = "Generalised Joint Regression Models\n with Binary/Continuous/Discrete/Survival Margins"
)

Heckman-style selection and treatment effect models (`selection`)

#| label = "selection",
#| width = 6,
#| height = 6
library(sampleSelection)
library(wooldridge) # for data
data(mroz)

# model
mod_selection <- selection(
  inlf ~ educ + kidslt6 + kidsge6,
  wage ~ educ + exper + expersq,
  method = "2step",
  data = mroz
)


ggcoefstats(
  x = mod_selection,
  title = "Heckman-style selection and treatment effect models"
)

inference in spatial GLMMs (`HLfit`)

#| label = "HLfit",
#| width = 4,
#| height = 4


library(spaMM)
data("wafers")
data("scotlip")

# model
mod_HLfit <-
  fitme(
    formula = y ~ 1 + (1 | batch),
    family = Gamma(log),
    data = wafers
  )


ggcoefstats(
  x = mod_HLfit,
  title = "Inference in spatial GLMMs"
)

robust linear mixed-effects models (`rlmer`)

#| label = "rlmer",
#| width = 5,
#| height = 5
s

library(robustlmm)

# model
roblmm.mod <-
  robustlmm::rlmer(
    formula = scale(Reaction) ~ scale(Days) + (Days | Subject),
    data = sleepstudy,
    rho.sigma.e = psi2propII(smoothPsi, k = 2.28),
    rho.sigma.b = chgDefaults(smoothPsi, k = 5.11, s = 10)
  )


ggcoefstats(
  x = roblmm.mod,
  title = "robust estimation of linear mixed-effects model",
  conf.level = 0.90
)

linear mixed-effects models with `lmerTest` (`lmerModLmerTest`)

#| label = "lmerModLmerTest",
#| width = 8,
#| height = 18


library(lmerTest)

# fit linear mixed model to the ham data:
fm <-
  lmerTest::lmer(
    formula = Informed.liking ~ Gender + Information * Product + (1 | Consumer) +
      (1 | Consumer:Product),
    data = ham
  )


ggcoefstats(
  x = fm,
  title = "linear mixed-effects models with `lmerTest`"
)

non-linear mixed-effects model (`nlmer`/`nlmerMod`)

#| label = "nlmer",
#| fig.height = 5,
#| fig.width = 6
# data
library(lme4)

startvec <- c(Asym = 200, xmid = 725, scal = 350)

# model
nm1 <-
  lme4::nlmer(
    formula = circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym | Tree,
    data = Orange,
    start = startvec
  )


ggcoefstats(
  x = nm1,
  title = "non-linear mixed-effects model"
)

non-linear least-squares model (`nls`)

#| label = "nls",
#| fig.height = 5,
#| fig.width = 5




# model
mod_nls <-
  stats::nls(
    formula = rating ~ k / budget + c,
    data = movies_long,
    start = list(k = 1, c = 0)
  )


ggcoefstats(
  x = mod_nls,
  title = "non-linear least squares regression",
  subtitle = "Non-linear relationship between budget and rating"
)

conditional generalized linear models for clustered data (`cglm`)

#| label = "cglm",
#| fig.height = 6,
#| fig.width = 6


library(cglm)
data(teenpov)

# model
fit.ide <-
  cglm::cglm(
    method = "ts",
    formula = hours ~ nonpov + inschool + spouse + age + mother,
    data = teenpov,
    id = "ID",
    link = "identity"
  )


ggcoefstats(
  x = fit.ide,
  title = "conditional generalized linear models for clustered data"
)

joint model to time-to-event data and multivariate longitudinal data (`mjoint`)

#| label = "mjoint",
#| fig.height = 8,
#| fig.width = 8


library(joineRML)
data(heart.valve)

# data
hvd <- heart.valve[!is.na(heart.valve$log.grad) &
  !is.na(heart.valve$log.lvmi) &
  heart.valve$num <= 50, ]

# model
fit_mjoint <- joineRML::mjoint(
  formLongFixed = list(
    "grad" = log.grad ~ time + sex + hs,
    "lvmi" = log.lvmi ~ time + sex
  ),
  formLongRandom = list(
    "grad" = ~ 1 | num,
    "lvmi" = ~ time | num
  ),
  formSurv = Surv(fuyrs, status) ~ age,
  data = hvd,
  inits = list("gamma" = c(0.11, 1.51, 0.80)),
  timeVar = "time"
)

# extract the survival fixed effects and plot them
ggcoefstats(
  x = fit_mjoint,
  component = "conditional",
  package = "yarrr",
  palette = "basel",
  title = "joint model to time-to-event data and multivariate longitudinal data"
)

stationary linear model (`slm`)

#| label = "slm",
#| fig.height = 6,
#| fig.width = 6


library(slm)
data("shan")

# model
mod_slm <-
  slm::slm(
    myformula = shan$PM_Xuhui ~ .,
    data = shan,
    method_cov_st = "fitAR",
    model_selec = -1
  )


ggcoefstats(
  x = mod_slm,
  conf.level = 0.90,
  title = "stationary linear models",
  package = "rcartocolor",
  palette = "Vivid"
)

generalized linear model (`glm`)

#| label = "glm1",
#| fig.height = 6,
#| fig.width = 6




# having a look at the Titanic dataset
df <- as.data.frame(Titanic)

# model
mod_glm <-
  stats::glm(
    formula = Survived ~ Sex + Age,
    data = df,
    weights = df$Freq,
    family = stats::binomial(link = "logit")
  )


ggcoefstats(
  x = mod_glm,
  ggtheme = ggthemes::theme_economist_white(),
  title = "generalized linear model (glm)",
  vline.args = list(color = "red", linetype = "solid")
)

Note: The exact statistic will depend on the family used for glm models: Some families will have a t statistic associated with them, while others a z statistic. The function will figure this out for you.

#| label = "glm_full",
#| fig.height = 13,
#| fig.width = 10
# creating data frames to use for regression analyses



# data frame #1
df.counts <-
  data.frame(
    treatment = gl(n = 3, k = 3, length = 9),
    outcome = gl(n = 3, k = 1, length = 9),
    counts = c(18, 17, 15, 20, 10, 20, 25, 13, 12)
  ) %>%
  tibble::as_tibble()

# data frame #2
df.clotting <-
  data.frame(
    u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
    lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
    lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
  ) %>%
  tibble::as_tibble()

# data frame #3
x1 <- stats::rnorm(50)
y1 <- stats::rpois(n = 50, lambda = exp(1 + x1))
df.3 <- data.frame(x = x1, y = y1) %>%
  tibble::as_tibble()

# data frame #4
x2 <- stats::rnorm(50)
y2 <- rbinom(
  n = 50,
  size = 1,
  prob = stats::plogis(x2)
)

df.4 <- data.frame(x = x2, y = y2) %>%
  tibble::as_tibble()

# combining all plots in a single plot
combine_plots(
  plotlist = list(
    # Family: Poisson
    ggcoefstats(
      x = stats::glm(
        formula = counts ~ outcome + treatment,
        data = df.counts,
        family = stats::poisson(link = "log")
      ),
      title = "Family: Poisson",
      stats.label.color = "black"
    ),
    # Family: Gamma
    ggcoefstats(
      x = stats::glm(
        formula = lot1 ~ log(u),
        data = df.clotting,
        family = stats::Gamma(link = "inverse")
      ),
      title = "Family: Gamma",
      stats.label.color = "black"
    ),
    # Family: Quasi
    ggcoefstats(
      x = stats::glm(
        formula = y ~ x,
        family = quasi(variance = "mu", link = "log"),
        data = df.3
      ),
      title = "Family: Quasi",
      stats.label.color = "black"
    ),
    # Family: Quasibinomial
    ggcoefstats(
      x = stats::glm(
        formula = y ~ x,
        family = stats::quasibinomial(link = "logit"),
        data = df.4
      ),
      title = "Family: Quasibinomial",
      stats.label.color = "black"
    ),
    # Family: Quasipoisson
    ggcoefstats(
      x = stats::glm(
        formula = y ~ x,
        family = stats::quasipoisson(link = "log"),
        data = df.4
      ),
      title = "Family: Quasipoisson",
      stats.label.color = "black"
    ),
    # Family: Gaussian
    ggcoefstats(
      x = stats::glm(
        formula = Sepal.Length ~ Species,
        family = stats::gaussian(link = "identity"),
        data = iris
      ),
      title = "Family: Gaussian",
      stats.label.color = "black"
    )
  ),
  plotgrid.args = list(ncol = 2),
  annotation.args = list(title = "Exploring models with different `glm` families")
)

The version of glm implemented in rms is also supported.

#| label = "rms_Glm",
#| fig.height = 5,
#| fig.width = 5



library(rms)

# Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
f_Glm <- rms::Glm(counts ~ outcome + treatment, family = poisson())


ggcoefstats(
  x = f_Glm,
  title = "rms' implementation of glm"
)

modified fitting for generalized linear models (`glm2`)

#| label = "glm2",
#| fig.height = 5,
#| fig.width = 5


library(glm2)
y <- c(1, 1, 1, 0)

# model
fit_glm2 <-
  glm2::glm2(
    formula = y ~ 1,
    family = binomial(link = "logit"),
    control = glm.control(trace = FALSE)
  )


ggcoefstats(
  x = fit_glm2,
  title = "greater stability for fitting generalized linear models"
)

Fit Generalized Estimating Equations with `geepack` (`geeglm`)

#| label = "geeglm",
#| fig.height = 7,
#| fig.width = 7


library(geepack)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
mf <- formula(Weight ~ Cu * (Time + I(Time^2) + I(Time^3)))

# model
gee1 <-
  geeglm(
    mf,
    data = dietox,
    id = Pig,
    family = poisson("identity"),
    corstr = "ar1"
  )


ggcoefstats(
  x = gee1,
  title = "Fit Generalized Estimating Equations",
  package = "ggsci",
  palette = "category20c_d3"
)

ordinal regression model (`orm`)

#| label = "orm",
#| fig.height = 5,
#| fig.width = 5

library(rms)


# data
n <- 100
y <- round(runif(n), 2)
x1 <- sample(c(-1, 0, 1), n, TRUE)
x2 <- sample(c(-1, 0, 1), n, TRUE)

# model
g <- rms::orm(y ~ x1 + x2, eps = 1e-5)


ggcoefstats(
  x = g,
  title = "Ordinal Regression Model"
)

logistic regression model (`lrm`)

#| label = "lrm",
#| fig.height = 12,
#| fig.width = 7

library(rms)


# data
n <- 500
x1 <- runif(n, -1, 1)
x2 <- runif(n, -1, 1)
x3 <- sample(0:1, n, TRUE)
y <- x1 + 0.5 * x2 + x3 + rnorm(n)
y <- as.integer(cut2(y, g = 10))
dd <- datadist(x1, x2, x3)
options(datadist = "dd")

# model
f_lrm <- rms::lrm(y ~ x1 + pol(x2, 2) + x3, eps = 1e-7) # eps to check against rstan


ggcoefstats(
  x = f_lrm,
  title = "Logistic Regression Model",
  package = "ggsci",
  palette = "category20c_d3"
)

Two-Stage Least Squares Instrumental Variables Regression (`iv_robust`)

#| label = "iv_robust",
#| fig.height = 6,
#| fig.width = 8


library(fabricatr)
library(estimatr)

# data
dat <-
  fabricate(
    N = 40,
    Y = rpois(N, lambda = 4),
    Z = rbinom(N, 1, prob = 0.4),
    D = Z * rbinom(N, 1, prob = 0.8),
    X = rnorm(N),
    G = sample(letters[1:4], N, replace = TRUE)
  )

# instrument for treatment `D` with encouragement `Z`
mod_ivrobust <- estimatr::iv_robust(formula = Y ~ D + X | Z + X, data = dat)


ggcoefstats(
  x = mod_ivrobust,
  title = "Two-Stage Least Squares Instrumental Variables Regression"
)

ordinary least squares with robust standard errors (`lm_robust`)

#| label = "lm_robust",
#| fig.height = 5,
#| fig.width = 5


library(estimatr)

# model
mod_lmrobust <-
  estimatr::lm_robust(
    formula = mpg ~ gear + wt + cyl,
    data = mtcars
  )


ggcoefstats(
  x = mod_lmrobust,
  title = "ordinary least squares with robust standard errors"
)

fitting negative binomial GLM (`negbin`)

Just to demonstrate that this can be done, let's also flip the axes:

#| label = "negbin",
#| fig.height = 6,
#| fig.width = 8

library(MASS)
library(lme4)
set.seed(101)

# data
dd <-
  expand.grid(
    f1 = factor(1:3),
    f2 = LETTERS[1:2],
    g = 1:9,
    rep = 1:15,
    KEEP.OUT.ATTRS = FALSE
  )
mu <- 5 * (-4 + with(dd, as.integer(f1) + 4 * as.numeric(f2)))
dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5)

# model
m.glm <- MASS::glm.nb(formula = y ~ f1 * f2, data = dd)


ggcoefstats(
  x = m.glm,
  title = "generalized linear model (GLM) for the negative binomial family",
  only.significant = TRUE,
  stats.label.args = list(size = 2.5, direction = "both")
) +
  ggplot2::coord_flip()

generalized linear mixed-effects model (`glmer`/`glmerMod`)

#| label = "glmer",
#| fig.height = 5,
#| fig.width = 5


library(lme4)

# model
mod_glmer <-
  lme4::glmer(
    formula = Survived ~ Sex + Age + (Sex + Age | Class),
    data = Titanic_full,
    family = stats::binomial(link = "logit"),
    control = lme4::glmerControl(
      optimizer = "Nelder_Mead",
      calc.derivs = FALSE,
      boundary.tol = 1e-7
    )
  )


ggcoefstats(
  x = mod_glmer,
  title = "generalized linear mixed-effects model"
)

Fitting Generalized Linear Mixed Models using MCML (`glmm`)

#| label = "glmm",
#| fig.height = 5,
#| fig.width = 5

library(glmm)
data(BoothHobert)
set.seed(1234)

# model
mod.mcml1 <-
  glmm::glmm(
    fixed = y ~ 0 + x1,
    random = list(y ~ 0 + z1),
    varcomps.names = c("z1"),
    data = BoothHobert,
    family.glmm = bernoulli.glmm,
    m = 100,
    doPQL = TRUE
  )


ggcoefstats(
  x = mod.mcml1,
  title = "Fitting Generalized Linear Mixed Models using MCML"
)

fitting negative binomial GLMM (`glmer.nb`)

#| label = "glmer.nb",
#| fig.height = 7,
#| fig.width = 6

library(MASS)
library(lme4)
set.seed(101)

# data
dd <-
  expand.grid(
    f1 = factor(1:3),
    f2 = LETTERS[1:2],
    g = 1:9,
    rep = 1:15,
    KEEP.OUT.ATTRS = FALSE
  )
mu <- 5 * (-4 + with(dd, as.integer(f1) + 4 * as.numeric(f2)))
dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5)

# model
m.nb <- lme4::glmer.nb(formula = y ~ f1 * f2 + (1 | g), data = dd)


ggcoefstats(
  x = m.nb,
  title = "generalized linear mixed-effects model (GLMM) for the negative binomial family"
)

Zero-Inflated Count Data Regression (`zeroinfl`)

#| label = "zeroinfl",
#| fig.height = 6,
#| fig.width = 6


library(pscl)

# data
data("bioChemists", package = "pscl")

# model
mod_zeroinfl <-
  pscl::zeroinfl(
    formula = art ~ . | 1,
    data = bioChemists,
    dist = "negbin"
  )


ggcoefstats(
  x = mod_zeroinfl,
  title = "Zero-Inflated Count Data Regression"
)

Vector Generalized Additive Models (`vgam`)

#| label = "vgam",
#| fig.height = 4,
#| fig.width = 4


library(VGAM)
pneumo <- transform(pneumo, let = log(exposure.time))

# model
mod_vgam <-
  VGAM::vgam(
    cbind(normal, mild, severe) ~ s(let),
    cumulative(parallel = TRUE),
    data = pneumo,
    trace = FALSE
  )


ggcoefstats(
  x = mod_vgam,
  title = "Vector Generalized Additive Models"
)

Vector Generalized Linear Models (`vglm`)

#| label = "vglm",
#| fig.height = 4,
#| fig.width = 6


library(VGAM)
pneumo <- transform(pneumo, let = log(exposure.time))

# model
mod_vglm <-
  VGAM::vglm(
    formula = cbind(normal, mild, severe) ~ let,
    family = multinomial,
    data = pneumo
  )


ggcoefstats(
  x = mod_vglm,
  title = "Vector Generalized Linear Models"
)

Reduced-Rank Vector Generalized Linear Models (`rrvglm`)

#| label = "rrvglm",
#| fig.height = 6,
#| fig.width = 6


library(VGAM)

# data
nn <- 1000 # Number of observations
delta1 <- 3.0 # Specify this
delta2 <- 1.5 # Specify this; should be greater than unity
a21 <- 2 - delta2
mydata <- data.frame(x2 = runif(nn), x3 = runif(nn))
mydata <- transform(mydata, mu = exp(2 + 3 * x2 + 0 * x3))
mydata <- transform(mydata,
  y2 = rnbinom(nn, mu = mu, size = (1 / delta1) * mu^a21)
)

# model
rrnb2 <-
  VGAM::rrvglm(
    formula = y2 ~ x2 + x3,
    family = negbinomial(zero = NULL),
    data = mydata,
    trace = FALSE
  )


ggcoefstats(
  x = rrnb2,
  title = "Reduced-Rank Vector Generalized Linear Models"
)

Vector autoregression models (`varest`)

#| label = "varest",
#| fig.height = 12,
#| fig.width = 7


library(vars)
data(Canada)

# model
mod_varest <- vars::VAR(Canada, p = 2, type = "none")


ggcoefstats(
  x = mod_varest,
  title = "Vector autoregression models"
)

Constrained Generalized Additive Model Fitting (`cgam`)

#| label = "cgam",
#| fig.height = 4,
#| fig.width = 6


library(cgam)
data(cubic)

# model
m_cgam <- cgam::cgam(formula = y ~ incr.conv(x), data = cubic)


ggcoefstats(
  x = m_cgam,
  title = "Constrained Generalized Additive Model Fitting"
)

Constrained Generalized Additive Mixed-Effects Model Fitting (`cgamm`)

#| label = "cgamm",
#| fig.height = 4,
#| fig.width = 7


library(cgam)

# simulate a balanced data set with 30 clusters
# each cluster has 30 data points
n <- 30
m <- 30

# the standard deviation of between cluster error terms is 1
# the standard deviation of within cluster error terms is 2
sige <- 1
siga <- 2

# generate a continuous predictor
x <- 1:(m * n)
for (i in 1:m) {
  x[(n * (i - 1) + 1):(n * i)] <- round(runif(n), 3)
}
# generate a group factor
group <- trunc(0:((m * n) - 1) / n) + 1

# generate the fixed-effect term
mu <- 10 * exp(10 * x - 5) / (1 + exp(10 * x - 5))

# generate the random-intercept term asscosiated with each group
avals <- rnorm(m, 0, siga)

# generate the response
y <- 1:(m * n)
for (i in 1:m) {
  y[group == i] <- mu[group == i] + avals[i] + rnorm(n, 0, sige)
}

# use REML method to fit the model
ans <- cgam::cgamm(formula = y ~ s.incr(x) + (1 | group), reml = TRUE)


ggcoefstats(
  x = ans,
  title = "Constrained Generalized Additive Mixed-Effects Model Fitting"
)

shape constrained additive models (`scam`)

#| label = "scam"


library(scam)

# data
n <- 200
x1 <- runif(n) * 6 - 3
f1 <- 3 * exp(-x1^2) # unconstrained term
f1 <- (f1 - min(f1)) / (max(f1) - min(f1)) # function scaled to have range [0,1]
x2 <- runif(n) * 4 - 1
f2 <- exp(4 * x2) / (1 + exp(4 * x2)) # monotone increasing smooth
f2 <- (f2 - min(f2)) / (max(f2) - min(f2)) # function scaled to have range [0,1]
f <- f1 + f2
y <- f + rnorm(n) * 0.1
dat <- data.frame(x1 = x1, x2 = x2, y = y)

# model
b_scam <-
  scam::scam(
    y ~ s(x1, k = 15, bs = "cr", m = 2) + s(x2, k = 25, bs = "mpi", m = 2),
    family = gaussian(link = "identity"),
    data = dat,
    not.exp = FALSE
  )


ggcoefstats(
  x = b_scam,
  title = "Shape constrained additive models"
)

Hurdle Models for Count Data Regression (`hurdle`)

#| label = "hurdle",
#| fig.height = 10,
#| fig.width = 8


library(pscl)
data("bioChemists", package = "pscl")

# geometric-poisson
fm_hp2 <-
  pscl::hurdle(
    formula = art ~ .,
    data = bioChemists,
    zero = "geometric"
  )


ggcoefstats(
  x = fm_hp2,
  only.significant = TRUE,
  conf.level = 0.99,
  title = "Hurdle Models for Count Data Regression"
)

beta-binomial mixed-effects model (`BBmm`)

#| label = "BBmm",
#| fig.height = 5,
#| fig.width = 6

if (isFALSE("PROreg" %in% installed.packages())) {
  install.packages("https://cran.r-project.org/src/contrib/Archive/PROreg/PROreg_1.0.tar.gz",
    repos = NULL,
    type = "source"
  )
}
library(PROreg)


# defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5, -1.1)
sigma <- 0.5

# simulating the covariate and random effects
x <- runif(k, 0, 10)
X <- model.matrix(~x)
z <- as.factor(rBI(k, 4, 0.5, 2))
Z <- model.matrix(~ z - 1)
u <- rnorm(5, 0, sigma)

# the linear predictor and simulated response variable
eta <- beta[1] + beta[2] * x + crossprod(t(Z), u)
p <- 1 / (1 + exp(-eta))
y <- rBB(k, m, p, phi)
dat <- data.frame(cbind(y, x, z))
dat$z <- as.factor(dat$z)

# apply the model
mod_BBmm <-
  PROreg::BBmm(
    fixed.formula = y ~ x,
    random.formula = ~z,
    m = m,
    data = dat
  )


ggcoefstats(
  x = mod_BBmm,
  title = "beta-binomial mixed-effects model"
)

beta-binomial logistic regression model (`BBreg`)

#| label = "BBreg",
#| fig.height = 5,
#| fig.width = 5

set.seed(18)
library(PROreg)

# we simulate a covariate, fix the paramters of the beta-binomial
# distribution and simulate a response variable.
# then we apply the model, and try to get the same values.
k <- 1000
m <- 10
x <- rnorm(k, 5, 3)
beta <- c(-10, 2)
p <- 1 / (1 + exp(-1 * (beta[1] + beta[2] * x)))
phi <- 1.2
y <- PROreg::rBB(k, m, p, phi)

# model
mod_BBreg <- PROreg::BBreg(y ~ x, m)


ggcoefstats(
  x = mod_BBreg,
  title = "beta-binomial logistic regression model"
)

binary choice models with fixed effects (`bife`)

#| label = "bife",
#| fig.height = 10,
#| fig.width = 8


library(bife)

# binary choice models with fixed effects
mod_bife <-
  bife::bife(
    formula = LFP ~ I(AGE^2) + log(INCH) + KID1 + KID2 + KID3 + factor(TIME) | ID,
    data = psid
  )


ggcoefstats(
  x = mod_bife,
  title = "binary choice models with fixed effects"
)

Dirichlet regression model (`DirichReg`)

#| label = "DirichReg",
#| fig.height = 8,
#| fig.width = 6


library(DirichletReg)

# data
ALake <- ArcticLake
ALake$Y <- DR_data(ALake[, 1:3])

# fit a quadratic Dirichlet regression models ("common")
mod_DirichReg <- DirichletReg::DirichReg(Y ~ depth + I(depth^2), ALake)


ggcoefstats(
  x = mod_DirichReg,
  title = "Dirichlet Regression"
)

robust generalized linear models (`robmixglm`)

#| label = "robmixglm",
#| fig.height = 4,
#| fig.width = 4


library(robmixglm)
library(MASS)
data(forbes)

# model
forbes.robustmix <- robmixglm(100 * log10(pres) ~ bp, data = forbes)


ggcoefstats(
  x = forbes.robustmix,
  title = "robust generalized linear models"
)

generalized linear models with extra parameters (`glmx`)

#| label = "glmx",
#| fig.height = 6,
#| fig.width = 8

library(glmx)
library(MASS)
set.seed(1)
d <- data.frame(x = runif(200, -1, 1))
d$y <- rnbinom(200, mu = exp(0 + 3 * d$x), size = 1)

# model
m_nb1 <-
  glmx::glmx(
    formula = y ~ x,
    data = d,
    family = negative.binomial,
    xlink = "log",
    xstart = 0
  )

ggcoefstats(
  x = m_nb1,
  title = "Generalized Linear Models with Extra Parameters"
)

generalized linear mixed model trees (`glmertree`)

#| label = "glmertree",
#| fig.height = 6,
#| fig.width = 12


library(glmertree)
data("DepressionDemo", package = "glmertree")

# fit normal linear regression LMM tree for continuous outcome
lt <- glmertree::lmertree(
  formula = depression ~ treatment | cluster | age + anxiety + duration,
  data = DepressionDemo
)

# fit logistic regression GLMM tree for binary outcome
gt <- glmertree::glmertree(
  formula = depression_bin ~ treatment | cluster | age + anxiety + duration,
  data = DepressionDemo
)


combine_plots(
  plotlist = list(
    ggcoefstats(
      x = lt$lmer,
      title = "normal linear regression LMM tree for continuous outcome"
    ),
    ggcoefstats(
      x = lt$lmer,
      title = "logistic regression GLMM tree for binary outcome"
    )
  )
)

generalized linear mixed models using Penalized Quasi-Likelihood (`glmmPQL`)

#| label = "glmmPQL",
#| fig.height = 5,
#| fig.width = 6


library(MASS)
library(nlme)

# model
mod_glmmPQL <-
  MASS::glmmPQL(
    fixed = y ~ trt + I(week > 2),
    random = ~ 1 | ID,
    family = binomial,
    data = bacteria,
    verbose = FALSE
  )


ggcoefstats(
  x = mod_glmmPQL,
  title = "generalized linear mixed models \nusing Penalized Quasi-Likelihood"
)

generalized linear mixed models using Template Model Builder (`glmmTMB`)

glmmTMB package allows for flexibly fitting generalized linear mixed models (GLMMs) and extensions. Model objects from this package are also supported.

#| label = "glmmTMB",
#| fig.height = 5,
#| fig.width = 6
# set up
library(glmmTMB)
library(lme4)


# model
mod_glmmTMB <-
  glmmTMB::glmmTMB(
    formula = Reaction ~ Days + (Days | Subject),
    data = sleepstudy,
    family = glmmTMB::truncated_poisson()
  )


ggcoefstats(
  x = mod_glmmTMB,
  title = "generalized linear mixed models using Template Model Builder"
)

Another example (given the number of terms, let's only display labels for significant effects):

#| label = "glmmTMB2",
#| fig.height = 14,
#| fig.width = 8


library(glmmTMB)
data(Salamanders)

# model
zipm3 <-
  glmmTMB(count ~ spp * mined + (1 | site),
    zi = ~ spp * mined,
    data = Salamanders,
    family = "poisson"
  )


ggcoefstats(
  x = zipm3,
  package = "palettesForR",
  palette = "Inkscape",
  only.significant = TRUE
)

generalized linear mixed models using AD Model Builder (`glmmadmb`)

#| label = "glmmadmb",
#| fig.height = 5,
#| fig.width = 7

if (isFALSE("glmmADMB" %in% installed.packages())) {
  install.packages("glmmADMB",
    repos = c(
      "http://glmmadmb.r-forge.r-project.org/repos",
      getOption("repos")
    ),
    type = "source"
  )
}
library(glmmADMB)

# simulate values
set.seed(101)
d <- data.frame(f = factor(rep(LETTERS[1:10], each = 10)), x = runif(100))
u <- rnorm(10, sd = 2)
d$eta <- with(d, u[f] + 1 + 4 * x)
pz <- 0.3
zi <- rbinom(100, size = 1, prob = pz)
d$y <- ifelse(zi, 0, rpois(100, lambda = exp(d$eta)))

# fit
zipmodel <-
  glmmADMB::glmmadmb(
    formula = y ~ x + (1 | f),
    data = d,
    family = "poisson",
    zeroInflation = TRUE
  )


ggcoefstats(
  x = zipmodel,
  title = "generalized linear mixed models using AD Model Builder"
)

multilevel model to a list of data frames (`merModList`)

#| label = "merModList",
#| fig.height = 5,
#| fig.width = 6


library(lme4)
library(merTools)

# data
sim_list <-
  replicate(
    n = 10,
    expr = sleepstudy[sample(row.names(sleepstudy), 180), ],
    simplify = FALSE
  )
fml <- "Reaction ~ Days + (Days | Subject)"

# model
mod_lmerModList <- lmerModList(fml, data = sim_list)


ggcoefstats(
  x = mod_lmerModList,
  title = "a multilevel model to a list of data frames"
)

cumulative link models (`clm`)

#| label = "clm",
#| fig.height = 6,
#| fig.width = 8


library(ordinal)

# model
mod_clm <- ordinal::clm(formula = rating ~ temp * contact, data = wine)


ggcoefstats(
  x = mod_clm,
  stats.label.color = "black",
  title = "cumulative link model (clm)",
  subtitle = "(using `ordinal` package)"
) +
  ggplot2::labs(x = "logit regression coefficient", y = NULL)

cumulative link models - older version (`clm2`)

#| label = "clm2",
#| fig.height = 6,
#| fig.width = 6


library(ordinal)
library(MASS)
data(housing, package = "MASS")

# data
tab26 <- with(soup, table("Product" = PROD, "Response" = SURENESS))
dimnames(tab26)[[2]] <- c("Sure", "Not Sure", "Guess", "Guess", "Not Sure", "Sure")
dat26 <- expand.grid(sureness = as.factor(1:6), prod = c("Ref", "Test"))
dat26$wghts <- c(t(tab26))

# model
mod_clm2 <-
  ordinal::clm2(
    location = sureness ~ prod,
    scale = ~prod,
    data = dat26,
    weights = wghts,
    link = "logistic"
  )


ggcoefstats(
  x = mod_clm2,
  title = "older version of `clm`"
)

cumulative link mixed models (`clmm`)

#| label = "clmm1",
#| fig.height = 6,
#| fig.width = 6


library(ordinal)

# model
mod_clmm <- ordinal::clmm(
  formula = rating ~ temp + contact + (1 | judge),
  data = wine
)

# to speed up calculations, we will use just 10% of the dataset
ggcoefstats(
  x = mod_clmm,
  title = "cumulative link mixed model (clmm)",
  subtitle = "(using `ordinal` package)"
) +
  ggplot2::labs(
    x = "coefficient from ordinal mixed-effects regression",
    y = "fixed effects"
  )

cumulative link mixed models - older version (`clmm2`)

#| label = "clmm2",
#| fig.height = 6,
#| fig.width = 6


library(ordinal)

# data
dat <- subset(soup, as.numeric(as.character(RESP)) <= 24)
dat$RESP <- dat$RESP[drop = TRUE]

# model
mod_clmm2 <-
  ordinal::clmm2(
    SURENESS ~ PROD,
    random = RESP,
    data = dat,
    link = "probit",
    Hess = TRUE,
    method = "ucminf",
    threshold = "symmetric"
  )


ggcoefstats(
  x = mod_clmm2,
  title = "older version of cumulative link mixed models"
)

marginal effects estimation (`margins`)

#| label = "margins",
#| fig.height = 6,
#| fig.width = 6


library(margins)

# logit model
mod_log <- glm(
  formula = am ~ cyl + hp + wt,
  data = mtcars,
  family = binomial
)

# convert to marginal effects with margins::margins()
marg_log <- margins(mod_log)


ggcoefstats(
  x = marg_log,
  title = "marginal effects estimation"
)

Linear Regression with Interval-Censored Dependent Variable (`semLm`)

#| label = "semLm",
#| fig.height = 5,
#| fig.width = 6


library(smicd)

# Load and prepare data
data <- Exam
classes <- c(1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.7, 8.5, Inf)
data$examsc.class <- cut(data$examsc, classes)

# run model with random intercept and default settings
mod_semLm <-
  smicd::semLm(
    formula = examsc.class ~ standLRT + schavg,
    data = data,
    classes = classes
  )


ggcoefstats(
  x = mod_semLm,
  title = "Linear Regression with \nInterval-Censored Dependent Variable"
)

Linear Mixed Regression with Interval-Censored Dependent Variable (`semLme`)

#| label = "semLme",
#| fig.height = 5,
#| fig.width = 6


library(smicd)

# Load and prepare data
data <- Exam
classes <- c(1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.7, 8.5, Inf)
data$examsc.class <- cut(data$examsc, classes)

# run model with random intercept and default settings
model1 <-
  smicd::semLme(
    formula = examsc.class ~ standLRT + schavg + (1 | school),
    data = data,
    classes = classes
  )


ggcoefstats(
  x = model1,
  title = "Linear Mixed Regression with \nInterval-Censored Dependent Variable"
)

bias reduction in Binomial-response GLMs (`brglm`)

#| label = "brglm",
#| fig.height = 6,
#| fig.width = 5


library(brglm)
data("lizards")

# fit the model using maximum likelihood mean bias-reduced fit
lizards.brglm <-
  brglm::brglm(
    cbind(grahami, opalinus) ~ height + diameter + light + time,
    family = binomial(logit),
    data = lizards,
    method = "brglm.fit"
  )


ggcoefstats(
  x = lizards.brglm,
  only.significant = TRUE,
  title = "bias reduction in Binomial-response GLMs"
)

bias reduction in generalized linear models (`brglm2`)

#| label = "brglm2",
#| fig.height = 6,
#| fig.width = 5


library(brglm2)
data("lizards")

# fit the model using maximum likelihood mean bias-reduced fit:
lizardsBR_mean <-
  stats::glm(
    formula = cbind(grahami, opalinus) ~ height + diameter + light + time,
    family = binomial(logit),
    data = lizards,
    method = "brglmFit"
  )


ggcoefstats(
  x = lizardsBR_mean,
  only.significant = TRUE,
  title = "bias reduction in generalized linear models"
)

Bias Reduction For Multinomial Response Models Using The Poisson Trick (`brmultinom`)

#| label = "brmultinom",
#| fig.height = 12,
#| fig.width = 7


library(MASS)
library(brglm2)
data("housing", package = "MASS")

# Maximum likelihood using brmultinom with baseline category 'Low'
houseML1 <-
  brglm2::brmultinom(
    formula = Sat ~ Infl + Type + Cont,
    weights = Freq,
    data = housing,
    type = "ML",
    ref = 1
  )


ggcoefstats(
  x = houseML1,
  title = "Bias Reduction For Multinomial Response Models Using The Poisson Trick"
)

bias reduction for adjacent category logit models (`bracl`)

#| label = "bracl",
#| fig.height = 6,
#| fig.width = 7


library(brglm2)
data("stemcell")

# bias reduction for adjacent category logit models
# for ordinal responses using the Poisson trick
fit_bracl <-
  brglm2::bracl(
    formula = research ~ as.numeric(religion) + gender,
    weights = frequency,
    data = stemcell,
    type = "ML"
  )


ggcoefstats(
  x = fit_bracl,
  title = "bias reduction for adjacent category logit models"
)

generalized linear models subject to population constraints

#| label = "glmc",
#| fig.height = 5,
#| fig.width = 6


library(glmc)

# data
n <- rbind(c(5903, 230), c(5157, 350))
mat <- matrix(0, nrow = sum(n), ncol = 2)
mat <-
  rbind(
    matrix(1, nrow = n[1, 1], ncol = 1) %*% c(0, 0),
    matrix(1, nrow = n[1, 2], ncol = 1) %*% c(0, 1),
    matrix(1, nrow = n[2, 1], ncol = 1) %*% c(1, 0),
    matrix(1, nrow = n[2, 2], ncol = 1) %*% c(1, 1)
  )

# specifying the population constraints
gfr <- .06179 * matrix(1, nrow = nrow(mat), ncol = 1)
g <- matrix(1, nrow = nrow(mat), ncol = 1)
amat <- matrix(mat[, 2] * g - gfr, ncol = 1)

# defining constraints in the data frame.
hrh <- data.frame(birth = mat[, 2], child = mat[, 1], constraints = amat)

# model
gfit <-
  glmc::glmc(
    formula = birth ~ child,
    data = hrh,
    family = "binomial",
    emplik.method = "Owen",
    control = glmc::glmc.control(
      trace.optim = 0,
      trace.glm = FALSE,
      maxit.glm = 10,
      maxit.weights = 200,
      itertrace.weights = FALSE
    )
  )


ggcoefstats(
  x = gfit,
  title = "generalized linear models subject to population constraints"
)

Bayesian linear mixed-effects models (`blmerMod`)

#| label = "blmerMod",
#| fig.height = 5,
#| fig.width = 5


library(blme)

# data
data(sleepstudy)
sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
sleepstudy$mysubgrp <- NA
for (i in 1:5) {
  filter_group <- sleepstudy$mygrp == i
  sleepstudy$mysubgrp[filter_group] <-
    sample(1:30, size = sum(filter_group), replace = TRUE)
}

# model
mod_blmer <-
  blme::blmer(
    formula = scale(Reaction) ~ scale(Days) + (1 + Days | Subject),
    data = sleepstudy,
    cov.prior = NULL,
    REML = FALSE
  )


ggcoefstats(
  x = mod_blmer,
  title = "Bayesian linear mixed-effects models"
)

Bayesian generalized linear mixed-effects models (`bglmerMod`)

#| label = "bglmerMod",
#| fig.height = 6,
#| fig.width = 6


library(blme)

# model
mod_bglmer <-
  blme::bglmer(
    formula = Reaction ~ Days + (1 + Days | Subject),
    data = sleepstudy,
    cov.prior = NULL,
    fixef.prior = normal
  )


ggcoefstats(
  x = mod_bglmer,
  title = "Bayesian generalized linear mixed-effects models"
)

ordered logistic or probit regression (`polr`)

#| label = "polr",
#| fig.height = 6,
#| fig.width = 8
# polr model

library(MASS)
polr.mod <-
  MASS::polr(
    formula = Sat ~ Infl + Type + Cont,
    weights = Freq,
    data = housing
  )


ggcoefstats(
  x = polr.mod,
  coefficient.type = "both",
  title = "ordered logistic or probit regression",
  subtitle = "using `MASS` package"
)

multiple linear regression models (`mlm`)

#| label = "mlm",
#| fig.height = 5,
#| fig.width = 6


library(effectsize)

# model (converting all numeric columns in data to z-scores)
mod_mlm <-
  stats::lm(
    formula = cbind(mpg, disp) ~ wt,
    data = effectsize::standardize(mtcars)
  )


ggcoefstats(
  x = mod_mlm,
  title = "multiple linear regression models"
)

anova on multiple linear regression models (`maov`)

#| label = "maov",
#| fig.height = 8,
#| fig.width = 6



# model
fit <- lm(cbind(mpg, disp, hp) ~ factor(cyl), data = mtcars)
m_maov <- aov(fit)


ggcoefstats(
  x = m_maov,
  title = "anova on multiple linear regression models",
  package = "ggsci",
  palette = "springfield_simpsons",
  conf.level = 0.90
)

multinomial logistic regression models (`multinom`)

#| label = "multinom",
#| fig.height = 8,
#| fig.width = 6


library(nnet)
library(MASS)
utils::example(topic = birthwt, echo = FALSE)

# model
bwt.mu <-
  nnet::multinom(
    formula = low ~ .,
    data = bwt,
    trace = FALSE
  )


ggcoefstats(
  x = bwt.mu,
  title = "multinomial logistic regression models",
  package = "ggsci",
  palette = "default_ucscgb"
)

multilevel mediation model (`bmlm`)

#| label = "bmlm",
#| fig.height = 7,
#| fig.width = 5


library(bmlm)

# model
fit_bmlm <- bmlm::mlm(BLch9, verbose = FALSE)

# exctrating summary
df_summary <-
  bmlm::mlm_summary(fit_bmlm) %>%
  dplyr::rename(
    estimate = Mean,
    term = Parameter,
    std.error = SE,
    conf.low = `2.5%`,
    conf.high = `97.5%`
  )


ggcoefstats(
  x = df_summary,
  title = "Bayesian multilevel mediation models with Stan"
)

proportional odds and related models (`svyolr`)

#| label = "svyolr",
#| fig.height = 8,
#| fig.width = 6


library(survey)
data(api)

dclus1 <-
  survey::svydesign(
    id = ~dnum,
    weights = ~pw,
    data = apiclus1,
    fpc = ~fpc
  )

# update
dclus1 <- update(dclus1, mealcat = cut(meals, c(0, 25, 50, 75, 100)))

# model
m_svyolr <-
  survey::svyolr(
    formula = mealcat ~ avg.ed + mobility + stype,
    design = dclus1
  )


ggcoefstats(
  x = m_svyolr,
  title = "proportional odds and related models",
  coefficient.type = "both"
)

survey-weighted generalized linear models (`svyglm`)

#| label = "svyglm",
#| fig.height = 5,
#| fig.width = 5
# data
library(survey)

data(api)
dstrat <-
  survey::svydesign(
    id = ~1,
    strata = ~stype,
    weights = ~pw,
    data = apistrat,
    fpc = ~fpc
  )

# model
mod_svyglm <-
  survey::svyglm(
    formula = sch.wide ~ ell + meals + mobility,
    design = dstrat,
    family = quasibinomial()
  )


ggcoefstats(
  x = mod_svyglm,
  title = "survey-weighted generalized linear model"
)

design-based inference for vector generalised linear models (`svy_vglm`)

#| fig.height = 7,
#| fig.width = 5


library(svyVGAM)
data(api)

# data
dclus2 <- svydesign(
  id = ~ dnum + snum,
  fpc = ~ fpc1 + fpc2,
  data = apiclus2
)

# model
mod_svy_vglm <- svy_vglm(api00 ~ api99 + mobility + ell,
  design = dclus2,
  family = uninormal()
)


ggcoefstats(
  x = mod_svy_vglm,
  title = "design-based inference for\nvector generalised linear models"
)

estimation of limited dependent variable models (`mhurdle`)

#| label = "mhurdle",
#| fig.height = 6,
#| fig.width = 8

data("Interview", package = "mhurdle")
library(mhurdle)

# independent double hurdle model
idhm <- mhurdle::mhurdle(
  formula = vacations ~ car + size | linc + linc2 | 0,
  data = Interview,
  dist = "ln",
  h2 = TRUE,
  method = "bfgs"
)


ggcoefstats(
  x = idhm,
  title = "estimation of limited dependent variable models"
)

repeated measures ANOVA (`aovlist`)

#| label = "aovlist1",
#| fig.height = 6,
#| fig.width = 8




# specifying the model (note the error structure)
mod_aovlist <-
  stats::aov(
    formula = value ~ attribute * measure + Error(id / (attribute * measure)),
    data = iris_long
  )


ggcoefstats(
  x = mod_aovlist,
  effsize = "eta",
  ggtheme = ggthemes::theme_fivethirtyeight(),
  title = "Variation in measurements for Iris species",
  subtitle = "Source: Iris data set (by Fisher or Anderson)",
  caption = "Results from 2 by 2 RM ANOVA"
) +
  ggplot2::theme(plot.subtitle = ggplot2::element_text(size = 11, face = "plain"))

robust regression with `robust` package (`lmRob`, `glmRob`)

#| label = "robust",
#| fig.height = 10,
#| fig.width = 6
combine_plots(
  plotlist = list(
    ggcoefstats(
      x = robust::glmRob(
        formula = Survived ~ Sex,
        data = Titanic_full,
        family = stats::binomial(link = "logit")
      ),
      title = "generalized robust linear model",
      ggtheme = ggthemes::theme_fivethirtyeight()
    ),
    ggcoefstats(
      x = robust::lmRob(
        formula = Sepal.Length ~ Sepal.Width * Species,
        data = iris
      ),
      title = "robust linear model",
      package = "awtools",
      palette = "a_palette",
      ggtheme = ggthemes::theme_tufte()
    )
  ),
  # arguments relevant for `combine_plots` function
  annotation.args = list(title = "Robust variants of `lmRob` and `glmRob` \n(from`robust` package)"),
  plotgrid.args = list(nrow = 2)
)

robust regression with `robustbase` package (`lmrob`, `glmrob`)

Another alternative is to use robust models, as implemented in the robustbase package.

#| label = "robustbase",
#| fig.height = 10,
#| fig.width = 6
library(robustbase)

# data frame
data(coleman)
clotting <-
  data.frame(
    u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
    lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
    lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
  )

# combined plot for both generalized and simple robust models
combine_plots(
  plotlist = list(
    ggcoefstats(
      x = glmrob(
        formula = lot1 ~ log(u),
        data = clotting,
        family = Gamma
      ),
      title = "generalized robust linear model"
    ),
    ggcoefstats(
      x = lmrob(formula = Y ~ ., data = coleman),
      title = "robust linear model"
    )
  ),
  # arguments relevant for `combine_plots`
  annotation.args = list(title = "Robust variants of `lmRob` and `glmRob` \n(from`robustbase` package)"),
  plotgrid.args = list(nrow = 2)
)

MM-type estimators for linear regression on compositional data (`complmrob`)

#| label = "complmrob",
#| fig.height = 7,
#| fig.width = 6


library(complmrob)

# data
crimes <- data.frame(
  lifeExp = state.x77[, "Life Exp"],
  USArrests[, c("Murder", "Assault", "Rape")]
)

# model
mUSArr <- complmrob::complmrob(formula = lifeExp ~ ., data = crimes)


ggcoefstats(
  x = mUSArr,
  title = "MM-type estimators for linear regression on compositional data"
)

fit a nonlinear heteroscedastic model via maximum likelihood (`nlreg`)

#| label = "nlreg",
#| fig.height = 5,
#| fig.width = 7

library(nlreg)
library(boot)
data(calcium)

# homoscedastic model fit
calcium.nl <-
  nlreg::nlreg(
    formula = cal ~ b0 * (1 - exp(-b1 * time)),
    start = c(b0 = 4, b1 = 0.1),
    data = calcium
  )


ggcoefstats(
  x = calcium.nl,
  conf.int = FALSE,
  title = "fit a nonlinear heteroscedastic model via maximum likelihood"
)

fit a linear model with multiple group fixed effects (`felm`)

#| label = "felm",
#| fig.height = 5,
#| fig.width = 5


library(lfe)

# create covariates
x <- rnorm(1000)
x2 <- rnorm(length(x))

# individual and firm
id <- factor(sample(20, length(x), replace = TRUE))
firm <- factor(sample(13, length(x), replace = TRUE))

# effects for them
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))

# left hand side
u <- rnorm(length(x))
y <- x + 0.5 * x2 + id.eff[id] + firm.eff[firm] + u

# estimate and print result
est <- lfe::felm(formula = y ~ x + x2 | id + firm)


ggcoefstats(
  x = est,
  title = "linear model with multiple group fixed effects"
)

linear models for panel data (`plm`)

#| label = "plm",
#| fig.height = 5,
#| fig.width = 5
# data

library(plm)
data("Produc", package = "plm")

# model
plm.mod <-
  plm::plm(
    formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
    data = Produc,
    index = c("state", "year")
  )


ggcoefstats(
  x = plm.mod,
  title = "linear models for panel data"
)

multinomial logit model (`mlogit`)

#| label = "mlogit",
#| fig.height = 8,
#| fig.width = 7


library(mlogit)

# data
data("Fishing", package = "mlogit")
Fish <-
  mlogit::mlogit.data(Fishing,
    varying = c(2:9),
    shape = "wide",
    choice = "mode"
  )

# a "mixed" model
m_mlogit <- mlogit::mlogit(mode ~ price + catch | income, data = Fish)


ggcoefstats(
  x = m_mlogit,
  title = "multinomial logit model"
)

Cox proportional hazards regression model (`coxph`)

#| label = "coxph",
#| fig.height = 4,
#| fig.width = 4


library(survival)

# create the simplest-test data set
test1 <- list(
  time = c(4, 3, 1, 1, 2, 2, 3),
  status = c(1, 1, 1, 0, 1, 1, 0),
  x = c(0, 2, 1, 1, 1, 0, 0),
  sex = c(0, 0, 0, 0, 1, 1, 1)
)

# fit a stratified model
mod_coxph <-
  survival::coxph(
    formula = Surv(time, status) ~ x + strata(sex),
    data = test1
  )


ggcoefstats(
  x = mod_coxph,
  title = "Cox proportional hazards regression model"
)

Another example with frailty term.

#| label = "coxph.penal",
#| fig.height = 4,
#| fig.width = 4


library(survival)

# model
mod_coxph <- survival::coxph(
  formula = Surv(time, status) ~ age + sex + frailty(inst),
  data = lung
)


ggcoefstats(
  x = mod_coxph,
  title = "Proportional Hazards Regression Model\nwith Frailty penalty function"
)

mixed effects Cox model (`coxme`)

#| label = "coxme",
#| fig.height = 8,
#| fig.width = 6


library(survival)
library(coxme)

# model
fit <- coxme::coxme(
  formula = Surv(y, uncens) ~ trt + (1 | center),
  data = eortc
)


ggcoefstats(
  x = fit,
  title = "mixed effects Cox model"
)

robust Cox proportional hazards regression model (`coxr`)

#| label = "coxr",
#| fig.height = 6,
#| fig.width = 6


library(coxrobust)

# create a simple test data set using the attached function `gen_data`
a <- coxrobust::gen_data(200, c(1, 0.1, 2), cont = 0.05, p.censor = 0.30)

# model
mod_coxr <- coxrobust::coxr(Surv(time, status) ~ X1 + X2 + X3, data = a, trunc = 0.9)


ggcoefstats(
  x = mod_coxr,
  title = "robust Cox proportional hazards regression model"
)

truncated Gaussian Regression Models (`truncreg`)

#| label = "truncreg",
#| fig.height = 5,
#| fig.width = 5


library(truncreg)
library(survival)

# data
data("tobin", package = "survival")

# model
cragg_trunc <-
  truncreg::truncreg(
    formula = durable ~ age + quant,
    data = tobin,
    subset = durable > 0
  )


ggcoefstats(
  x = cragg_trunc,
  title = "Truncated Gaussian Regression Models"
)

Fitting Linear Quantile Models

#| label = "lqm",
#| fig.height = 5,
#| fig.width = 5


library(lqmm)

# data
n <- 500
p <- 1:3 / 4
test <- data.frame(x = runif(n, 0, 1))
test$y <- 30 + test$x + rnorm(n)

# model
fit.lqm <-
  lqmm::lqm(
    y ~ x,
    data = test,
    tau = p,
    control = list(verbose = FALSE, loop_tol_ll = 1e-9),
    fit = TRUE
  )


ggcoefstats(
  x = fit.lqm,
  title = "Fitting Linear Quantile Models"
)

Fitting Linear Quantile Mixed Models

#| label = "lqmm",
#| fig.height = 4,
#| fig.width = 5


library(lqmm)

# data
M <- 50
n <- 10
test <- data.frame(x = runif(n * M, 0, 1), group = rep(1:M, each = n))
test$y <- 10 * test$x + rep(rnorm(M, 0, 2), each = n) + rchisq(n * M, 3)

# model
fit.lqmm <-
  lqmm::lqmm(
    fixed = y ~ x,
    random = ~1,
    group = group,
    data = test,
    tau = 0.5,
    nK = 11,
    type = "normal"
  )


ggcoefstats(
  x = fit.lqmm,
  title = "Fitting Linear Quantile Mixed Models"
)

autoregressive integrated moving average (`Arima`)

#| label = "arima",
#| fig.height = 5,
#| fig.width = 5



# model
fit <- stats::arima(x = lh, order = c(1, 0, 0))


ggcoefstats(
  x = fit,
  title = "autoregressive integrated moving average"
)

high performance linear model (`speedlm`/`speedglm`)

Example of high performance linear model-

#| label = "speedlm",
#| fig.height = 5,
#| fig.width = 5

library(speedglm)


# model
mod_speedlm <-
  speedglm::speedlm(
    formula = mpg ~ wt + qsec,
    data = mtcars,
    fitted = TRUE
  )


ggcoefstats(
  x = mod_speedlm,
  title = "high performance linear model"
)

Example of high performance generalized linear model-

#| label = "speedglm",
#| fig.height = 5,
#| fig.width = 5


library(speedglm)

# data
n <- 50000
k <- 5
y <- rgamma(n, 1.5, 1)
x <- round(matrix(rnorm(n * k), n, k), digits = 3)
colnames(x) <- paste("s", 1:k, sep = "")
da <- data.frame(y, x)
fo <- as.formula(paste("y~", paste(paste("s", 1:k, sep = ""), collapse = "+")))

# model
mod_speedglm <-
  speedglm::speedglm(
    formula = fo,
    data = da,
    family = stats::Gamma(log)
  )


ggcoefstats(
  x = mod_speedglm,
  title = "high performance generalized linear model"
)

parametric survival regression model (`survreg`)

#| label = "survreg",
#| fig.height = 5,
#| fig.width = 5


library(survival)

# model
mod_survreg <-
  survival::survreg(
    formula = Surv(futime, fustat) ~ ecog.ps + rx,
    data = ovarian,
    dist = "logistic"
  )


ggcoefstats(
  x = mod_survreg,
  ggtheme = hrbrthemes::theme_ipsum_rc(),
  package = "ggsci",
  palette = "legacy_tron",
  title = "parametric survival regression model"
)

Competing Risks Regression (`crr`)

#| label = "crr",
#| fig.height = 6,
#| fig.width = 6

set.seed(10)
library(cmprsk)

# simulated data to test
ftime <- rexp(200)
fstatus <- sample(0:2, 200, replace = TRUE)
cov <- matrix(runif(600), nrow = 200)
dimnames(cov)[[2]] <- c("x1", "x2", "x3")

# model
mod_crr <- cmprsk::crr(ftime, fstatus, cov)


ggcoefstats(
  x = mod_crr,
  title = "Competing Risks Regression"
)

tobit regression (`tobit`)

#| label = "tobit",
#| fig.height = 5,
#| fig.width = 5


library(AER)
data("Affairs", package = "AER")

# model
m_tobit <-
  AER::tobit(
    formula = affairs ~ age + yearsmarried + religiousness + occupation + rating,
    data = Affairs
  )


ggcoefstats(
  x = m_tobit,
  title = "tobit regression"
)

censored regression (tobit) regression (`censReg`)

#| label = "censReg",
#| fig.height = 5,
#| fig.width = 5


library(censReg)
data("Affairs", package = "AER")

# model
estResult <-
  censReg::censReg(
    formula = affairs ~ age + yearsmarried + religiousness + occupation + rating,
    data = Affairs
  )


ggcoefstats(
  x = estResult,
  title = "censored regression (tobit) regression"
)

relative risk regression model for case-cohort studies (`cch`)

#| label = "cch",
#| fig.height = 6,
#| fig.width = 6


library(survival)

# examples come from cch documentation
subcoh <- nwtco$in.subcohort
selccoh <- with(nwtco, rel == 1 | subcoh == 1)
ccoh.data <- nwtco[selccoh, ]
ccoh.data$subcohort <- subcoh[selccoh]

## central-lab histology
ccoh.data$histol <- factor(ccoh.data$histol, labels = c("FH", "UH"))

## tumour stage
ccoh.data$stage <- factor(ccoh.data$stage, labels = c("I", "II", "III", "IV"))
ccoh.data$age <- ccoh.data$age / 12 # Age in years

# model
fit.ccP <-
  survival::cch(
    formula = Surv(edrel, rel) ~ stage + histol + age,
    data = ccoh.data,
    subcoh = ~subcohort,
    id = ~seqno,
    cohort.size = 4028
  )


ggcoefstats(
  x = fit.ccP,
  title = "relative risk regression model",
  subtitle = "(for case-cohort studies)",
  conf.level = 0.99
)

ridge regression (`ridgelm`)

For ridge regression, neither statistic values nor confidence intervals for estimates are available, so only estimates will be displayed.

#| label = "ridgelm",
#| fig.height = 5,
#| fig.width = 5


library(MASS)

# model
names(longley)[1] <- "y"
mod_ridgelm <- MASS::lm.ridge(formula = y ~ ., data = longley)


ggcoefstats(
  x = mod_ridgelm,
  title = "ridge regression"
)

generalized additive models with integrated smoothness estimation (`gam`)

Important: These model outputs contains both parametric and smooth terms. ggcoefstats only displays the parametric terms.

#| label = "gam_mgcv",
#| fig.height = 5,
#| fig.width = 5


library(mgcv)

# model
g_gam <-
  mgcv::gam(
    formula = mpg ~ s(hp) + am + qsec,
    family = stats::quasi(),
    data = mtcars
  )


ggcoefstats(
  x = g_gam,
  title = "generalized additive models with \nintegrated smoothness estimation",
  subtitle = "using `mgcv` package"
)

#| label = "bam_mgcv",
#| fig.height = 5,
#| fig.width = 5


library(mgcv)

# data
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12

# model
b_bam <-
  mgcv::bam(
    formula = y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
      s(x3, bs = bs),
    data = dat
  )


ggcoefstats(
  x = b_bam,
  title = "generalized additive models for \nvery large datasets"
)

generalized additive model (`Gam`)

#| label = "Gam",
#| fig.height = 5,
#| fig.width = 5


library(gam)

# model
mod_gam <- gam::gam(
  formula = mpg ~ s(hp, 4) + am + qsec,
  data = mtcars
)


ggcoefstats(
  x = mod_gam,
  title = "generalized additive model",
  subtite = "(using `gam` package)"
)

linear mixed-effects models (`lme`)

#| label = "lme",
#| fig.height = 5,
#| fig.width = 5


library(lme4)
library(nlme)
data("sleepstudy")

# model
mod_lme <-
  nlme::lme(
    fixed = Reaction ~ Days,
    random = ~ 1 + Days | Subject,
    data = sleepstudy
  )


ggcoefstats(
  x = mod_lme,
  title = "linear mixed-effects models (`lme`)"
)

linear model using generalized least squares (`gls`)

The nlme package provides a function to fit a linear model using generalized least squares. The errors are allowed to be correlated and/or have unequal variances.

#| label = "gls",
#| fig.height = 5,
#| fig.width = 6


library(nlme)

# model
mod_gls <-
  nlme::gls(
    model = follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time),
    data = Ovary,
    correlation = corAR1(form = ~ 1 | Mare)
  )


ggcoefstats(
  x = mod_gls,
  stats.label.color = "black",
  ggtheme = hrbrthemes::theme_ipsum_ps(),
  title = "generalized least squares model"
)

inference for estimated coefficients (`coeftest`)

#| label = "coeftest",
#| fig.height = 4,
#| fig.width = 4


library(lmtest)

# load data and fit model
data("Mandible", package = "lmtest")
fm <- stats::lm(formula = length ~ age, data = Mandible, subset = (age <= 28))

# the following commands lead to the same tests
ct <- lmtest::coeftest(fm)


ggcoefstats(
  x = ct,
  plot = "inference for estimated coefficients",
  conf.level = 0.99
)

robust regression using an M estimator (`rlm`)

#| label = "rlm",
#| fig.height = 5,
#| fig.width = 5



# model
mod_rlm <- MASS::rlm(formula = mpg ~ am * cyl, data = mtcars)


ggcoefstats(
  x = mod_rlm,
  point.args = list(color = "red", shape = 15),
  vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
  title = "robust regression using an M estimator",
  ggtheme = ggthemes::theme_stata()
)

quantile regression (`rq`)

#| label = "rq",
#| fig.height = 5,
#| fig.width = 6


library(quantreg)

# data
data(stackloss)

# model
mod_rq <-
  quantreg::rq(
    formula = stack.loss ~ .,
    data = stackloss,
    method = "br"
  )


ggcoefstats(
  x = mod_rq,
  se.type = "iid",
  title = "quantile regression"
)

multiple response quantile regression (`rqs`)

#| label = "rqs",
#| fig.height = 7,
#| fig.width = 6


library(quantreg)
data("engel")

# model
fm <-
  quantreg::rq(
    formula = foodexp ~ income,
    data = engel,
    tau = 1:4 / 10
  )


ggcoefstats(
  x = fm,
  title = "multiple response quantile regression (`rqs`)"
)

nonlinear quantile regression estimates (`nlrq`)

#| label = "nlrq",
#| fig.height = 5,
#| fig.width = 5


library(quantreg)

Dat <- NULL
Dat$x <- rep(1:25, 20)
Dat$y <- stats::SSlogis(Dat$x, 10, 12, 2) * rnorm(500, 1, 0.1)

# then fit the median using nlrq
Dat.nlrq <-
  quantreg::nlrq(
    formula = y ~ SSlogis(x, Asym, mid, scal),
    data = Dat,
    tau = 0.5,
    trace = FALSE
  )


ggcoefstats(
  x = Dat.nlrq,
  title = "non-linear quantile regression",
  se.type = "nid"
)

additive quantile regression (`rqss`)

#| label = "rqss",
#| fig.height = 4,
#| fig.width = 4


library(quantreg)
data(CobarOre)

# model
fCO <- quantreg::rqss(
  formula = z ~ qss(cbind(x, y), lambda = .08),
  data = CobarOre
)

# model info
ggcoefstats(
  x = fCO,
  title = "Additive Quantile Regression"
)

censored quantile regression (`crq`)

#| label = "crq",
#| fig.height = 5,
#| fig.width = 5
# crq example with left censoring
set.seed(1968)
library(quantreg)

# data
n <- 200
x <- rnorm(n)
y <- 5 + x + rnorm(n)
c <- 4 + x + rnorm(n)
d <- (y > c)

# model
f_crq <- quantreg::crq(survival::Surv(pmax(y, c), d, type = "left") ~ x,
  method = "Portnoy"
)


ggcoefstats(
  x = f_crq,
  title = "censored quantile regression models"
)

instrumental-variable regression (`ivreg`)

#| label = "ivreg",
#| fig.height = 7,
#| fig.width = 5

suppressPackageStartupMessages(library(AER))

data("CigarettesSW", package = "AER")

# model
ivr <-
  AER::ivreg(
    formula = log(packs) ~ income | population,
    data = CigarettesSW,
    subset = year == "1995"
  )


ggcoefstats(
  x = ivr,
  title = "instrumental-variable regression"
)

instrumental variables probit regression (`ivprobit`)

#| label = "ivprobit",
#| fig.height = 6,
#| fig.width = 6


library(ivprobit)
data(eco)

# model
pro <-
  ivprobit::ivprobit(
    formula = d2 ~ ltass + roe + div | eqrat + bonus | ltass + roe + div + gap + cfa,
    data = eco
  )


ggcoefstats(
  x = pro,
  title = "instrumental variables probit regression"
)

instrumental fixed effect panel regression (`ivFixed`)

#| label = "ivFixed",
#| fig.height = 6,
#| fig.width = 6


library(ivfixed)

#  data
pib <- as.matrix(c(12, 3, 4, 0.4, 0.7, 5, 0.7, 0.3, 0.6, 89, 7, 8, 45, 7, 4, 5, 0.5, 5),
  nrows = 18,
  ncols = 1
)
tir <- as.matrix(c(12, 0.3, 4, 0.4, 7, 12, 3.0, 6.0, 45, 7.0, 0.8, 44, 65, 23, 4, 6, 76, 9),
  nrows = 18,
  ncols = 1
)
inf <- as.matrix(c(1.2, 3.6, 44, 1.4, 0.78, 54, 0.34, 0.66, 12, 0.7, 8.0, 12, 65, 43, 5, 76, 65, 8),
  nrows = 18,
  ncols = 1
)
npl <- as.matrix(c(0.2, 3.8, 14, 2.4, 1.7, 43, 0.2, 0.5, 23, 7.8, 88, 36, 65, 3, 44, 65, 7, 34),
  nrows = 18,
  ncols = 1
)
mdata <- data.frame(p = pib, t = tir, int = inf, np = npl)

# model
ivf <- ivfixed::ivFixed(t ~ p + int | p + np, mdata, n = 6, t = 3)


ggcoefstats(
  x = ivf,
  title = "instrumental fixed effect panel regression"
)

causal mediation analysis (`mediate`)

#| label = "mediate",
#| fig.height = 5,
#| fig.width = 5


library(mediation)
data(jobs)

# base models
b <-
  stats::lm(
    formula = job_seek ~ treat + econ_hard + sex + age,
    data = jobs
  )
c <-
  stats::lm(
    formula = depress2 ~ treat + job_seek + econ_hard + sex + age,
    data = jobs
  )

# mediation model
mod_mediate <-
  mediation::mediate(
    model.m = b,
    model.y = c,
    sims = 50,
    treat = "treat",
    mediator = "job_seek"
  )


ggcoefstats(
  x = mod_mediate,
  title = "causal mediation analysis"
)

Model II regression (`lmodel2`)

#| label = "lmodel2",
#| fig.height = 6,
#| fig.width = 6


library(lmodel2)
data(mod2ex2)

# model
Ex2.res <-
  lmodel2::lmodel2(
    formula = Prey ~ Predators,
    data = mod2ex2,
    range.y = "relative",
    range.x = "relative",
    nperm = 99
  )


ggcoefstats(
  x = Ex2.res,
  title = "Model II regression"
)

Multivariate Ordinal Regression Models (`mvord`)

#| label = "mvord",
#| fig.height = 6,
#| fig.width = 5

library(mvord)

# toy example
data(data_mvord_toy)

# wide data format with MMO2
res_mvord <- mvord(
  formula = MMO2(Y1, Y2) ~ 0 + X1 + X2,
  data = data_mvord_toy
)


ggcoefstats(
  x = res_mvord,
  title = "Multivariate Ordinal Regression Models"
)

generalized additive models for location scale and shape (`gamlss`)

#| label = "gamlss",
#| fig.height = 5,
#| fig.width = 6


library(gamlss)

# model
g_gamlss <-
  gamlss::gamlss(
    formula = y ~ pb(x),
    sigma.fo = ~ pb(x),
    family = BCT,
    data = abdom,
    method = mixed(1, 20)
  )


ggcoefstats(
  x = g_gamlss,
  title = "generalized additive models \nfor location scale and shape"
)

Bayesian Gaussian Graphical Model (`BGGM`)

#| label = "BGGM",
#| fig.height = 10,
#| fig.width = 6

library(BGGM)

# data
Y <- dplyr::select(ptsd, B1:B5)

# fit model (note + 1, due to zeros)
fit_ggm <- estimate(Y + 1, type = "ordinal", iter = 250)


ggcoefstats(
  x = fit_ggm,
  title = "Bayesian Gaussian Graphical Model"
)

Linear Equation System Estimation (`systemfit`)

#| label = "systemfit",
#| fig.height = 7,
#| fig.width = 8
library(systemfit)

# data
data("Kmenta")
eqDemand <- consump ~ price + income
eqSupply <- consump ~ price + farmPrice + trend
system <- list(demand = eqDemand, supply = eqSupply)
inst1 <- ~ income + farmPrice
inst2 <- ~ income + farmPrice + trend
instlist <- list(inst1, inst2)

# model
m_systemfit <- systemfit(
  system,
  "2SLS",
  inst = instlist,
  data = Kmenta
)


ggcoefstats(
  x = m_systemfit,
  title = "Linear Equation System Estimation"
)

generalized method of moment estimation (`gmm`)

#| label = "gmm",
#| fig.height = 8,
#| fig.width = 5


library(gmm)

# examples come from the "gmm" package
## CAPM test with GMM
data(Finance)
r <- Finance[1:300, 1:10]
rm <- Finance[1:300, "rm"]
rf <- Finance[1:300, "rf"]
z <- as.matrix(r - rf)
t <- nrow(z)
zm <- rm - rf
h <- matrix(zm, t, 1)
res_gmm <- gmm::gmm(z ~ zm, x = h)


ggcoefstats(
  x = res_gmm,
  package = "palettetown",
  palette = "victreebel",
  title = "generalized method of moment estimation"
)

Spatial simultaneous autoregressive model estimation\n by maximum likelihood (`Sarlm`)

#| label = "Sarlm"
library(spatialreg)

# data
data(oldcol, package = "spdep")
listw <- spdep::nb2listw(COL.nb, style = "W")
ev <- eigenw(listw)
W <- as(listw, "CsparseMatrix")
trMatc <- trW(W, type = "mult")

# model
COL.lag.eig <- lagsarlm(
  CRIME ~ INC + HOVAL,
  data = COL.OLD,
  listw = listw,
  method = "eigen",
  quiet = FALSE,
  control = list(pre_eig = ev, OrdVsign = 1)
)


ggcoefstats(
  x = COL.lag.eig,
  title = "Spatial simultaneous autoregressive model estimation\n by maximum likelihood"
)

fit a GLM with lasso or elasticnet regularization (`glmnet`)

Although these models are not directly supported in ggcoefstats because of the sheer number of terms that are typically present. But this function can still be used to selectively show few of the terms of interest:

#| label = "glmnet",
#| fig.height = 4,
#| fig.width = 4

library(glmnet)
set.seed(2014)


x <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
fit1 <- glmnet::glmnet(x, y)
(df <- broom::tidy(fit1))

# displaying only a certain step
ggcoefstats(x = dplyr::filter(df, step == 4))

exponential-family random graph models (`ergm`)

#| label = "ergm",
#| fig.height = 5,
#| fig.width = 5
# load the Florentine marriage network data

suppressPackageStartupMessages(library(ergm))
data(florentine)

# fit a model where the propensity to form ties between
# families depends on the absolute difference in wealth
gest <- ergm::ergm(flomarriage ~ edges + absdiff("wealth"))


ggcoefstats(
  x = gest,
  conf.level = 0.99,
  title = "exponential-family random graph models"
)

TERGM by bootstrapped pseudolikelihood or MCMC MLE (`btergm`)

#| label = "btergm",
#| fig.height = 5,
#| fig.width = 6

library(network)
library(btergm)


# create 10 random networks with 10 actors
networks <- list()
for (i in 1:10) {
  mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
  diag(mat) <- 0 # loops are excluded
  nw <- network(mat) # create network object
  networks[[i]] <- nw # add network to the list
}

# create 10 matrices as covariate
covariates <- list()
for (i in 1:10) {
  mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
  covariates[[i]] <- mat # add matrix to the list
}

# model
fit_btergm <-
  btergm::btergm(
    formula = networks ~ edges + istar(2) + edgecov(covariates),
    parallel = "multicore",
    ncpus = 4,
    R = 100,
    verbose = FALSE
  )


ggcoefstats(
  x = fit_btergm,
  title = "Terms used in Exponential Family Random Graph Models",
  subtitle = "by bootstrapped pseudolikelihood or MCMC MLE"
)

generalized autoregressive conditional heteroscedastic regression (`garch`)

#| label = "garch",
#| fig.height = 5,
#| fig.width = 6


library(tseries)
data(EuStockMarkets)

# model
dax <- diff(log(EuStockMarkets))[, "DAX"]
dax.garch <- tseries::garch(x = dax, trace = FALSE)


ggcoefstats(
  x = dax.garch,
  title = "generalized autoregressive conditional heteroscedastic"
)

Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations (`bayesGARCH`)

#| label = "bayesGARCH",
#| fig.height = 4,
#| fig.width = 5


library(bayesGARCH)

# data
data(dem2gbp)
y <- dem2gbp[1:750]

# run the sampler (2 chains)
mod_bayesGARCH <- bayesGARCH::bayesGARCH(y,
  control = list(n.chain = 4, l.chain = 200, refresh = 1000)
)


ggcoefstats(
  x = mod_bayesGARCH,
  title = "Bayesian Estimation of the GARCH(1,1) Model \nwith Student-t Innovations"
)

maximum-likelihood fitting of univariate distributions (`fitdistr`)

#| label = "fitdistr",
#| fig.height = 4,
#| fig.width = 4


library(MASS)
x <- rnorm(100, 5, 2)

# model
fit_fitdistr <- MASS::fitdistr(x, dnorm, list(mean = 3, sd = 1))


ggcoefstats(
  x = fit_fitdistr,
  title = "maximum-likelihood fitting of univariate distributions",
  ggtheme = ggthemes::theme_pander()
)

maximum likelihood estimation (`mle2`)

#| label = "mle2",
#| fig.height = 4,
#| fig.width = 4


library(bbmle)

# data
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x, y)

# custom function
LL <- function(ymax = 15, xhalf = 6) {
  -sum(stats::dpois(y, lambda = ymax / (1 + x / xhalf), log = TRUE))
}

# use default parameters of LL
fit_mle2 <- bbmle::mle2(LL, fixed = list(xhalf = 6))


ggcoefstats(
  x = fit_mle2,
  title = "maximum likelihood estimation",
  ggtheme = ggthemes::theme_excel_new()
)

General Linear Hypotheses (`glht`)

#| label = "glht",
#| fig.height = 5,
#| fig.width = 6


library(multcomp)

# multiple linear model, swiss data
lmod <- lm(Fertility ~ ., data = swiss)

# model
mod_glht <-
  multcomp::glht(
    model = lmod,
    linfct = c(
      "Agriculture=0",
      "Examination=0",
      "Education=0",
      "Catholic=0",
      "Infant.Mortality=0"
    )
  )


ggcoefstats(
  x = mod_glht,
  title = "General Linear Hypotheses (using `multcomp`)"
)

Cochrane-Orcutt estimation (`orcutt`)

#| label = "orcutt",
#| fig.height = 5,
#| fig.width = 5

library(orcutt)


# model
reg <- stats::lm(formula = mpg ~ wt + qsec + disp, data = mtcars)
co <- orcutt::cochrane.orcutt(reg)


ggcoefstats(
  x = co,
  title = "Cochrane-Orcutt estimation"
)

confusion matrix (`confusionMatrix`)

#| label = "confusionMatrix",
#| fig.height = 4,
#| fig.width = 4

library(caret)


# setting up confusion matrix
two_class_sample1 <- as.factor(sample(letters[1:2], 100, TRUE))
two_class_sample2 <- as.factor(sample(letters[1:2], 100, TRUE))

two_class_cm <- caret::confusionMatrix(two_class_sample1, two_class_sample2)


ggcoefstats(
  x = two_class_cm,
  by.class = TRUE,
  title = "confusion matrix"
)

dose-response models/curves (`drc`)

#| label = "drc",
#| fig.height = 7,
#| fig.width = 5


library(drc)

# model
mod_drc <-
  drc::drm(
    formula = dead / total ~ conc,
    curveid = type,
    weights = total,
    data = selenium,
    fct = LL.2(),
    type = "binomial"
  )


ggcoefstats(
  x = mod_drc,
  conf.level = 0.99,
  title = "Dose-Response Curves"
)

Firth's bias-reduced logistic regression (`logitsf`)

#| label = "logitsf",
#| fig.height = 4,
#| fig.width = 4


library(logistf)

# data frame
data <- data.frame(time = 1:12, event = c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1))

# model
mod_logistf <- logistf::logistf(event ~ time, data)


ggcoefstats(
  x = mod_logistf,
  title = "Firth's bias-reduced logistic regression"
)

Flexible parametric regression for time-to-event data (`flexsurvreg`)

#| label = "flexsurvreg",
#| fig.height = 6,
#| fig.width = 7


library(flexsurv)
data(ovarian)

# compare generalized gamma fit with Weibull
fitg <-
  flexsurv::flexsurvreg(
    formula = Surv(futime, fustat) ~ 1,
    data = ovarian,
    dist = "gengamma"
  )

ggcoefstats(
  x = fitg,
  title = "Flexible parametric regression for time-to-event data"
)

panel regression models fit via multilevel modeling (`wblm`)

#| label = "wblm",
#| fig.height = 6,
#| fig.width = 7


library(panelr)

# data
data("WageData")
wages <- panelr::panel_data(data = WageData, id = id, wave = t)

# model
mod_wblm <-
  panelr::wbm(
    formula = lwage ~ lag(union) + wks | blk + fem | blk * lag(union),
    data = wages
  )


ggcoefstats(
  x = mod_wblm,
  title = "panel regression models fit via multilevel modeling"
)

panel regression models fit via generalized estimating equations (`wbgee`)

#| label = "wbgee",
#| fig.height = 8,
#| fig.width = 6


library(panelr)
data("WageData")

# data
wages <- panelr::panel_data(data = WageData, id = id, wave = t)

# model
mod_wbgee <-
  panelr::wbgee(
    formula = lwage ~ lag(union) + wks | blk + fem | blk * lag(union),
    data = wages
  )


ggcoefstats(
  x = mod_wbgee,
  conf.level = 0.99,
  title = "panel regression models fit via generalized estimating equations"
)

Censored Regression with Conditional Heteroscedasticy (`crch`)

#| label = "crch",
#| fig.height = 4,
#| fig.width = 8


library(crch)
data("RainIbk")

# mean and standard deviation of square root transformed ensemble forecasts
RainIbk$sqrtensmean <- apply(sqrt(RainIbk[, grep("^rainfc", names(RainIbk))]), 1, mean)
RainIbk$sqrtenssd <- apply(sqrt(RainIbk[, grep("^rainfc", names(RainIbk))]), 1, sd)

# fit linear regression model with Gaussian distribution
CRCH <-
  crch::crch(
    formula = sqrt(rain) ~ sqrtensmean,
    data = RainIbk,
    dist = "gaussian"
  )


ggcoefstats(
  x = CRCH,
  title = "Censored Regression with Conditional Heteroscedasticy"
)

Marginal Models For Correlated Ordinal Multinomial Responses (`LORgee`)

#| label = "LORgee",
#| fig.height = 10,
#| fig.width = 8


library(multgee)
data("arthritis")

# model
fit_LORgee <-
  multgee::ordLORgee(
    formula = y ~ factor(time) + factor(trt) + factor(baseline),
    link = "logit",
    id = id,
    repeated = time,
    data = arthritis,
    LORstr = "uniform"
  )


ggcoefstats(
  x = fit_LORgee,
  title = "Marginal Models For Correlated Ordinal Multinomial Responses"
)

summary measures for count data (`epi.2by2`)

#| label = "epi.2by2",
#| fig.height = 4,
#| fig.width = 6


library(epiR)

# data
dat <- matrix(c(13, 2163, 5, 3349), nrow = 2, byrow = TRUE)
rownames(dat) <- c("DF+", "DF-")
colnames(dat) <- c("FUS+", "FUS-")

# model
fit_epiR <-
  epiR::epi.2by2(
    dat = as.table(dat),
    method = "cross.sectional",
    conf.level = 0.95,
    units = 100,
    outcome = "as.columns"
  )

# tidy data frame
ggcoefstats(
  x = fit_epiR,
  title = "Summary measures for count data presented in a 2 by 2 table"
)

marginal effects for a probit regression (`probitmfx`)

#| label = "probitmfx",
#| fig.height = 4,
#| fig.width = 5


library(mfx)

# simulate some data
set.seed(12345)
n <- 1000
x <- rnorm(n)

# binary outcome
y <- ifelse(pnorm(1 + 0.5 * x + rnorm(n)) > 0.5, 1, 0)
data <- data.frame(y, x)

# model
mod_probitmfx <- mfx::probitmfx(formula = y ~ x, data = data)


ggcoefstats(
  x = mod_probitmfx,
  title = "marginal effects for a probit regression"
)

marginal effects for a beta regression (`betamfx`)

#| label = "betamfx",
#| fig.height = 4,
#| fig.width = 5


library(mfx)

# simulate some data
n <- 1000
x <- rnorm(n)

# beta outcome
y <- rbeta(n, shape1 = plogis(1 + 0.5 * x), shape2 = (abs(0.2 * x)))

# use Smithson and Verkuilen correction
y <- (y * (n - 1) + 0.5) / n

# data
d <- data.frame(y, x)

# model
mod_betamfx <- mfx::betamfx(y ~ x | x, data = d)


ggcoefstats(
  x = mod_betamfx,
  title = "marginal effects for a beta regression"
)

marginal effects for a logit regression (`logitmfx`)

Same example can also be used for logitor object.

#| label = "logitmfx",
#| fig.height = 4,
#| fig.width = 5


library(mfx)

# simulate some data
n <- 1000
x <- rnorm(n)

# binary outcome
y <- ifelse(pnorm(1 + 0.5 * x + rnorm(n)) > 0.5, 1, 0)

# data
data <- data.frame(y, x)

# model
mod_logitmfx <- logitmfx(formula = y ~ x, data = data)


ggcoefstats(
  x = mod_logitmfx,
  title = "marginal effects for a logit regression"
)

marginal effects for a negative binomial regression (`negbinmfx`)

Same example can also be used for negbinirr object.

#| label = "negbinmfx",
#| fig.height = 4,
#| fig.width = 5
# simulate some data

n <- 1000

# data
x <- rnorm(n)
y <- rnegbin(n, mu = exp(1 + 0.5 * x), theta = 0.5)
data <- data.frame(y, x)

# model
mod_negbinmfx <- negbinmfx(formula = y ~ x, data = data)


ggcoefstats(
  x = mod_negbinmfx,
  title = "marginal effects for a negative binomial regression"
)

marginal effects for a Poisson regression (`poissonmfx`)

Same example can also be used for poissonirr object.

#| label = "poissonmfx",
#| fig.height = 4,
#| fig.width = 5
# simulate some data

n <- 1000
x <- rnorm(n)
y <- rnegbin(n, mu = exp(1 + 0.5 * x), theta = 0.5)
data <- data.frame(y, x)

# model
mod_poissonmfx <- poissonmfx(formula = y ~ x, data = data)


ggcoefstats(
  x = mod_poissonmfx,
  title = "marginal effects for a Poisson regression"
)

Bayesian generalized (non-)linear multivariate multilevel models (`brmsfit`)

#| label = "brmsfit",
#| fig.height = 5,
#| fig.width = 7


library(brms)

# prior
bprior1 <- prior(student_t(5, 0, 10), class = b) + prior(cauchy(0, 2), class = sd)

# model
fit_brm <-
  brms::brm(
    formula = count ~ Age + Base * Trt + (1 | patient),
    data = epilepsy,
    family = poisson(),
    prior = bprior1,
    silent = TRUE,
    refresh = 0
  )


ggcoefstats(
  x = fit_brm,
  standardize = TRUE,
  title = "Bayesian generalized (non-)linear multivariate multilevel models",
  subtitle = "using `brms` package"
)

Let's see another example where we use brms to run the same model on multiple datasets-

#| label = "brmsfit_multiple",
#| fig.height = 5,
#| fig.width = 7


library(brms)
library(mice)

# data
imp <- mice::mice(data = nhanes2, print = FALSE)

# fit the model using brms
fit_brm_m <-
  brms::brm_multiple(
    formula = bmi ~ age + hyp + chl,
    data = imp,
    chains = 1,
    refresh = 0
  )


ggcoefstats(
  x = fit_brm_m,
  title = "Same `brms` model with multiple datasets",
  conf.level = 0.99
)

Bayesian nonlinear models via Stan (`nlstanreg`)

#| label = "nlstanreg",
#| fig.height = 5,
#| fig.width = 5,
#| error = TRUE


library(rstanarm)
data("Orange", package = "datasets")
Orange$circumference <- Orange$circumference / 100
Orange$age <- Orange$age / 100

# model
fit_nlstanreg <-
  rstanarm::stan_nlmer(
    formula = circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym | Tree,
    data = Orange,
    # for speed only
    chains = 1,
    iter = 1000,
    refresh = 0
  )


ggcoefstats(
  x = fit_nlstanreg,
  title = "Bayesian nonlinear models via Stan"
)

Markov Chain Monte Carlo objects (`mcmc`)

#| label = "mcmc",
#| fig.height = 5,
#| fig.width = 5
# loading the data

library(coda)
data(line)

# select first chain
x1 <- line[[1]]


ggcoefstats(
  x = x1,
  title = "Markov Chain Monte Carlo objects",
  robust = TRUE,
  ess = TRUE
)

MCMC with Just Another Gibbs Sampler (`rjags`)

#| label = "R2jags",
#| fig.height = 8,
#| fig.width = 6


library(R2jags)

# An example model file is given in:
model.file <- system.file(package = "R2jags", "model", "schools.txt")

# data
J <- 8.0
y <- c(28.4, 7.9, -2.8, 6.8, -0.6, 0.6, 18.0, 12.2)
sd <- c(14.9, 10.2, 16.3, 11.0, 9.4, 11.4, 10.4, 17.6)

# setting up model
jags.data <- list("y", "sd", "J")
jags.params <- c("mu", "sigma", "theta")
jags.inits <- function() {
  list("mu" = rnorm(1), "sigma" = runif(1), "theta" = rnorm(J))
}

# model fitting
jagsfit <-
  R2jags::jags(
    data = list("y", "sd", "J"),
    inits = jags.inits,
    jags.params,
    n.iter = 10,
    model.file = model.file
  )


ggcoefstats(
  x = jagsfit,
  title = "Markov Chain Monte Carlo with Just Another Gibbs Sampler (JAGS)"
)

latent variable model (`lavaan`)

#| label = "lavaan",
#| fig.height = 10,
#| fig.width = 6


library(lavaan)

# The Holzinger and Swineford (1939) example
HS.model <- " visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 "

# model
fit_lavaan <-
  lavaan::lavaan(
    HS.model,
    data = HolzingerSwineford1939,
    auto.var = TRUE,
    auto.fix.first = TRUE,
    auto.cov.lv.x = TRUE
  )

# tidy
ggcoefstats(
  x = fit_lavaan,
  title = "latent variable model",
  conf.level = 0.99
)

Bayesian latent variable model (`blavaan`)

Too time-consuming to be run here, so currently skipped.

#| label = "blavaan",
#| fig.height = 10,
#| fig.width = 6,
#| eval = FALSE


library(blavaan)

# The Holzinger and Swineford (1939) example
HS.model <- " visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 "

# model
fit_blavaan <-
  blavaan::blavaan(
    HS.model,
    data = HolzingerSwineford1939,
    auto.var = TRUE,
    auto.fix.first = TRUE,
    auto.cov.lv.x = TRUE
  )

# tidy
ggcoefstats(
  x = fit_blavaan,
  title = "Bayesian latent variable model"
)

Bayesian regression models via Stan (`stanreg`)

#| label = "stanreg",
#| fig.height = 5,
#| fig.width = 5
# set up

library(rstanarm)

# model
fit_stanreg <-
  rstanarm::stan_glm(
    formula = mpg ~ wt + am,
    data = mtcars,
    chains = 1,
    refresh = 0
  )


ggcoefstats(
  x = fit_stanreg,
  title = "Bayesian generalized linear models via Stan"
)

Bayesian multivariate generalized linear models via Stan (`stanmvreg`)

#| label = "stanmvreg",
#| fig.height = 5,
#| fig.width = 5,
#| error = TRUE


library(rstanarm)
pbcLong$ybern <- as.integer(pbcLong$logBili >= mean(pbcLong$logBili))

# model
mod_stanmvreg <-
  rstanarm::stan_mvmer(
    formula = list(
      ybern ~ year + (1 | id),
      albumin ~ sex + year + (year | id)
    ),
    data = pbcLong,
    cores = 4,
    seed = 12345,
    iter = 1000,
    refresh = 0
  )


ggcoefstats(
  x = mod_stanmvreg,
  title = "Bayesian multivariate generalized linear models \nvia Stan"
)

Bayesian quantile regression (`bayesQR`)

#| label = "bayesQR",
#| fig.height = 6,
#| fig.width = 6
# needed library
library(bayesQR)

# Simulate data from heteroskedastic regression
set.seed(66)
n <- 200
X <- runif(n = n, min = 0, max = 10)
X <- X
y <- 1 + 2 * X + rnorm(n = n, mean = 0, sd = .6 * X)

# model
mod_bayesqr <-
  bayesQR(y ~ X,
    quantile = c(.05, .25, .5, .75, .95),
    alasso = TRUE,
    ndraw = 500
  )


ggcoefstats(
  x = mod_bayesqr,
  title = "Bayesian quantile regression"
)

Bayesian binary and ordinal logistic regression (`blrm`)

#| label = "blrm",
#| fig.height = 8,
#| fig.width = 10


library(rmsb)
library(rms)
library(Hmisc)
getHdata(titanic3)
dd <- datadist(titanic3)
options(datadist = "dd")

# model
mod_blrm <- rmsb::blrm(survived ~ (rcs(age, 5) + sex + pclass)^2, data = titanic3)


ggcoefstats(
  x = mod_blrm,
  title = "Bayesian binary and ordinal logistic regression"
)

Compound Poisson Generalized Linear Models (`cpglm`)

#| label = "cpglm",
#| fig.height = 6,
#| fig.width = 10
# set up

library(cplm)

# model
mod_cpglm <-
  cpglm(
    formula = RLD ~ factor(Zone) * factor(Stock),
    data = FineRoot
  )


ggcoefstats(
  x = mod_cpglm,
  title = "Compound Poisson Generalized Linear Models"
)

Compound Poisson Generalized Linear Mixed Models (`cpglmm`)

#| label = "cpglmm",
#| fig.height = 6,
#| fig.width = 10
# set up

library(cplm)

# model
mod_cpglmm <-
  cpglmm(
    formula = RLD ~ Stock + Spacing + (1 | Plant),
    data = FineRoot
  )


ggcoefstats(
  x = mod_cpglmm,
  title = "Compound Poisson Generalized Linear Mixed Models"
)

Bayesian Compound Poisson Linear Models (`bcplm`)

#| label = "bcplm",
#| fig.height = 6,
#| fig.width = 10
# set up

library(cplm)

# model
mod_bcplm <-
  bcplm(
    formula = RLD ~ factor(Zone) * factor(Stock),
    data = FineRoot,
    tune.iter = 2000,
    n.iter = 6000,
    n.burnin = 1000,
    n.thin = 5,
    n.report = 0
  )


ggcoefstats(
  x = mod_bcplm,
  title = "Bayesian Compound Poisson Linear Models"
)

Multivariate Imputation by Chained Equations (`mice`)

#| label = "mice",
#| fig.height = 6,
#| fig.width = 6
library(mice)
data(nhanes2)
imp <- mice(nhanes2)


ggcoefstats(
  x = with(data = imp, exp = lm(bmi ~ age + hyp + chl)),
  title = "Multivariate Imputation by Chained Equations"
)

estimations with multiple fixed-effects (`fixest`)

#| label = "fixest",
#| fig.height = 10,
#| fig.width = 10

library(fixest)

# data
data(trade)

# generating data for a simple example
set.seed(1)
n <- 100
x <- rnorm(n, 1, 5)**2
y <- rnorm(n, -1, 5)**2
z1 <- rpois(n, x * y) + rpois(n, 2)
base <- data.frame(x, y, z1)

# creating different `fixest` regression model objects
mod_fx1 <-
  fixest::femlm(
    fml = Euros ~ log(dist_km) | Origin + Destination + Product,
    data = trade
  )

mod_fx2 <-
  fixest::fepois(
    fml = Sepal.Length ~ Sepal.Width + Petal.Length | Species,
    data = iris
  )

mod_fx3 <-
  fixest::feols(
    fml = Sepal.Length ~ Sepal.Width + Petal.Length | Species,
    data = iris
  )

mod_fx4 <-
  fixest::feNmlm(
    z1 ~ 1, base,
    NL.fml = ~ a * log(x) + b * log(y),
    NL.start = list(a = 0, b = 0)
  )

# combining plots
combine_plots(
  plotlist = list(
    ggcoefstats(mod_fx1, title = "femlm"),
    ggcoefstats(mod_fx2, title = "fepois"),
    ggcoefstats(mod_fx3, title = "feols"),
    ggcoefstats(mod_fx4, title = "feNmlm")
  ),
  labels = "auto",
  annotation.args = list(title = "fast and user-friendly fixed-effects estimations")
)

multivariate analysis of variance (`manova`)

#| label = "manova",
#| fig.height = 8,
#| fig.width = 8



# fake a 2nd response variable
npk2 <- within(npk, foo <- rnorm(24))

# model
m_manova <- manova(cbind(yield, foo) ~ block + N * P * K, npk2)


ggcoefstats(
  x = m_manova,
  title = "multivariate analysis of variance"
)

Regression models with `list` outputs

Note that a number of regression models will return an object of class list, in which case this function will fail. But often you can extract the object of interest from this list and use it to plot the regression coefficients.

#| label = "list_gamm4",
#| fig.height = 6,
#| fig.width = 10

library(gamm4)


# data
dat <- gamSim(1, n = 400, scale = 2)

# now add 20 level random effect `fac'...
dat$fac <- fac <- as.factor(sample(1:20, 400, replace = TRUE))
dat$y <- dat$y + model.matrix(~ fac - 1) %*% rnorm(20) * .5

# model object
br <-
  gamm4::gamm4(
    formula = y ~ s(x0) + x1 + s(x2),
    data = dat,
    random = ~ (1 | fac)
  )

# looking at the classes of the objects contained in the list
purrr::map(br, class)


combine_plots(
  plotlist = list(
    # first object plot (only parametric terms are shown)
    ggcoefstats(
      x = br$gam,
      title = "generalized additive model (parametric terms)",
      k = 3
    ),
    # second object plot
    ggcoefstats(
      x = br$mer,
      title = "linear mixed-effects model",
      k = 3
    )
  ),
  plotgrid.args = list(nrow = 1)
)

Meta-analysis

In case the estimates you are displaying come from multiple studies, you can also use this function to carry out random-effects meta-analysis. The data frame you enter must contain at the minimum the following three columns-

term: a column with names/identifiers to annotate each study/effect
estimate: a column with the observed effect sizes or outcomes
std.error: a column the corresponding standard errors

parametric

#| label = "meta1",
#| fig.height = 7,
#| fig.width = 8


library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)


ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  bf.message = TRUE,
  meta.type = "parametric",
  title = "parametric random-effects meta-analysis"
)

robust

#| label = "meta2",
#| fig.height = 7,
#| fig.width = 8


library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)


ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  meta.type = "robust",
  title = "robust random-effects meta-analysis"
)

Bayesian

#| label = "meta3",
#| fig.height = 7,
#| fig.width = 8


library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)


ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  meta.type = "bayes",
  title = "Bayesian random-effects meta-analysis"
)

Data frames

Sometimes you don't have a model object but a custom data frame that you want display using this function. If a data frame is to be plotted, it must contain columns named term (names of predictors), and estimate (corresponding estimates of coefficients or other quantities of interest). Other optional columns are conf.low and conf.high (for confidence intervals), and p.value. You will also have to specify the type of statistic relevant for regression models ("t", "z", "f", "chi") in case you want to display statistical labels.

You can also provide a data frame containing all the other relevant information for additionally displaying labels with statistical information.

#| label = "dataframe",
#| fig.height = 7,
#| fig.width = 7
# let's make up a data frame (with all available details)
df_full <-
  tibble::tribble(
    ~term, ~statistic, ~estimate, ~std.error, ~p.value, ~df.error,
    "study1", 0.158, 0.0665, 0.778, 0.875, 5L,
    "study2", 1.33, 0.542, 0.280, 0.191, 10L,
    "study3", 1.24, 0.045, 0.030, 0.001, 12L,
    "study4", 0.156, 0.500, 0.708, 0.885, 8L,
    "study5", 0.33, 0.032, 0.280, 0.101, 2L,
    "study6", 1.04, 0.085, 0.030, 0.001, 3L
  )


ggcoefstats(
  x = df_full,
  meta.analytic.effect = TRUE,
  statistic = "t",
  package = "LaCroixColoR",
  palette = "paired"
)

Other types of outputs

This function can also be used to extract outputs other than a plot, although it is much more preferable to use the underlying functions instead (parameters::model_parameters).

#| label = "other_output",
#| error = TRUE




# data
DNase1 <- subset(DNase, Run == 1)

# using a selfStart model
nlmod <- stats::nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)

# tidy data frame
ggcoefstats(nlmod, output = "tidy")

# glance summary
ggcoefstats(nlmod, output = "glance")

Not supported

This vignette was supposed to give a comprehensive account of regression models supported by ggcoefstats. The list of supported models will keep expanding as additional tidiers are added to the parameters and performance packages.

Note that not all models supported in these packages will be supported by ggcoefstats. In particular, classes of objects for which there is no column for estimate (e.g., kmeans, optim, muhaz, survdiff, zoo, etc.) are not supported.

Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues

IndrajeetPatil/ggstatplot documentation built on April 26, 2024, 10:27 a.m.

rdrr.io home R language documentation Run R code online

CRAN packages Bioconductor packages R-Forge packages GitHub packages

Note that we can't provide technical support on individual packages. You should contact the package authors for that.

IndrajeetPatil/ggstatplot 'ggplot2' Based Plots with Statistical Details

In IndrajeetPatil/ggstatplot: 'ggplot2' Based Plots with Statistical Details

General structure of the plots

Supported models

Summary of graphics

Summary of meta-analysis tests

Examples of supported models

linear mixed-effects model (lmer/lmerMod)

summary grid from emmeans package (emmGrid)

Bayesian additive models for location scale and shape (bamlss)

Bayes Factor (BFBayesFactor)

Fixed Effects Individual Slope Estimator (feis)

omnibus ANOVA (aov)

conditional logit models (mclogit)

mixed conditional logit models (mmclogit)

anova with car package (Anova)

ANOVA with car package on multiple linear models (Anova.mlm)

Anova with ez package

linear model (lm)

standardized regression coefficients with lm (lm.beta)

bounded memory linear regression (biglm)

bounded memory general linear regression (bigglm)

efficient (general) linear model (elm/eglm)

nonlinear model using generalized least squares (gnls)

analysis of factorial experiments (mixed)

anova using afex (afex_aov)

joint model for survival and longitudinal data measured with error (joint)

risk regression (riskRegression)

Aalen's additive regression model for censored data (aareg)

multivariate generalized linear mixed models (MCMCglmm)

STAR Models with BayesX (bayesx)

Generalised Joint Regression Models with Binary/Continuous/Discrete/Survival Margins (gjrm)

Heckman-style selection and treatment effect models (selection)

inference in spatial GLMMs (HLfit)

robust linear mixed-effects models (rlmer)

linear mixed-effects models with lmerTest (lmerModLmerTest)

non-linear mixed-effects model (nlmer/nlmerMod)

non-linear least-squares model (nls)

conditional generalized linear models for clustered data (cglm)

joint model to time-to-event data and multivariate longitudinal data (mjoint)

stationary linear model (slm)

generalized linear model (glm)

modified fitting for generalized linear models (glm2)

Fit Generalized Estimating Equations with geepack (geeglm)

ordinal regression model (orm)

logistic regression model (lrm)

Two-Stage Least Squares Instrumental Variables Regression (iv_robust)

ordinary least squares with robust standard errors (lm_robust)

fitting negative binomial GLM (negbin)

generalized linear mixed-effects model (glmer/glmerMod)

Fitting Generalized Linear Mixed Models using MCML (glmm)

fitting negative binomial GLMM (glmer.nb)

Zero-Inflated Count Data Regression (zeroinfl)

Vector Generalized Additive Models (vgam)

Vector Generalized Linear Models (vglm)

Reduced-Rank Vector Generalized Linear Models (rrvglm)

Vector autoregression models (varest)

Constrained Generalized Additive Model Fitting (cgam)

Constrained Generalized Additive Mixed-Effects Model Fitting (cgamm)

shape constrained additive models (scam)

Hurdle Models for Count Data Regression (hurdle)

beta-binomial mixed-effects model (BBmm)

beta-binomial logistic regression model (BBreg)

binary choice models with fixed effects (bife)

Dirichlet regression model (DirichReg)

robust generalized linear models (robmixglm)

generalized linear models with extra parameters (glmx)

generalized linear mixed model trees (glmertree)

generalized linear mixed models using Penalized Quasi-Likelihood (glmmPQL)

generalized linear mixed models using Template Model Builder (glmmTMB)

generalized linear mixed models using AD Model Builder (glmmadmb)

multilevel model to a list of data frames (merModList)

cumulative link models (clm)

cumulative link models - older version (clm2)

cumulative link mixed models (clmm)

cumulative link mixed models - older version (clmm2)

marginal effects estimation (margins)

Linear Regression with Interval-Censored Dependent Variable (semLm)

Linear Mixed Regression with Interval-Censored Dependent Variable (semLme)

bias reduction in Binomial-response GLMs (brglm)

IndrajeetPatil/ggstatplot
'ggplot2' Based Plots with Statistical Details