ggstatsplot (version 0.1.1)

ggcoefstats_label_maker: Create labels with statistical details for ggcoefstats.

Description

Create labels with statistical details for ggcoefstats.

Usage

ggcoefstats_label_maker(x, tidy_df = NULL, glance_df = NULL,
  statistic = NULL, k = 2, effsize = "eta", partial = TRUE, ...)

Arguments

x

A model object to be tidied with broom::tidy, or a tidy data frame containing results. If a data frame is to be plotted, it must contain columns named term (names of predictors), or estimate (corresponding estimates of coefficients or other quantities of interest). Other optional columns are conf.low and conf.high (for confidence intervals); p.value. It is important that all term names should be unique.

tidy_df

Tidy dataframe from broomExtra::tidy.

glance_df

Glance model summary dataframe from broom::glance (default: NULL). This is optional argument. If provide, the glance summary will be used to write caption for the final plot.

statistic

Which statistic is to be displayed (either "t" or "f"or "z") in the label. This is especially important if the x argument in ggcoefstats is a dataframe in which case the function wouldn't know what kind of model it is dealing with.

k

Number of decimal places expected for results displayed in labels (Default : k = 2).

effsize

Character describing the effect size to be displayed: "eta" (default) or "omega". This argument is relevant only for models objects of class aov, anova, and aovlist.

partial

Logical that decides if partial eta-squared or omega-squared are returned (Default: TRUE). If FALSE, eta-squared or omega-squared will be returned. Valid only for objects of class aov, anova, or aovlist.

...

Currently ignored.

...

Currently ignored.

Examples

# NOT RUN {
# show all columns in output tibble
options(tibble.width = Inf)

# for reproducibility
set.seed(123)

#------------------------- models with *t*-statistic ------------------
# model with t-statistic
ggstatsplot:::ggcoefstats_label_maker(x = broomExtra::tidy(stats::lm(
  data = mtcars, formula = wt ~ cyl * mpg
)), statistic = "t")

# (in case `x` is not a dataframe, no need to specify `statistic` argument;
# this will be figured out by the function itself)

#------------------------- models with *t*-statistic ------------------

# dataframe
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)
)

# model
mod <-
  stats::glm(
    formula = lot1 ~ log(u),
    data = clotting,
    family = Gamma
  )

# model with t-statistic
ggstatsplot:::ggcoefstats_label_maker(
  x = mod,
  tidy_df = broomExtra::tidy(
    x = mod,
    conf.int = TRUE,
    conf.level = 0.95
  )
)

#------------------------- models with *z*-statistic --------------------

# preparing dataframe
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
d.AD <- data.frame(treatment, outcome, counts)

# model
mod <- stats::glm(
  formula = counts ~ outcome + treatment,
  family = poisson(),
  data = d.AD
)

# creating tidy dataframe with label column
ggstatsplot:::ggcoefstats_label_maker(x = mod, tidy_df = broomExtra::tidy(mod))

#------------------------- models with *f*-statistic --------------------
# creating a model object
op <- options(contrasts = c("contr.helmert", "contr.poly"))
npk.aov <- stats::aov(formula = yield ~ block + N * P * K, data = npk)

# extracting a tidy dataframe with effect size estimate and their CIs
tidy_df <-
  ggstatsplot::lm_effsize_ci(
    object = npk.aov,
    effsize = "omega",
    partial = FALSE,
    nboot = 50
  ) %>%
  dplyr::rename(.data = ., estimate = omegasq, statistic = F.value)

# including a new column with a label
ggstatsplot:::ggcoefstats_label_maker(
  x = npk.aov,
  tidy_df = tidy_df,
  effsize = "omega",
  partial = FALSE
)
# }

Run the code above in your browser using DataCamp Workspace