Package 'modelit'

Description

The {modelit} package provides an extension to base R functions for model fitting like lm(), glm() or nls() with enhanced plots and utilitarian functions.

Important functions

• fit_model() creates a model_fit object that has many methods.

• tabularise() methods for lm, glm, nls, model_fit, anova and aov objects.

• chart() methods for lm, glm, nls and model_fit objects.

• as.function() transforms an lm or nls model into a function that can be plotted using stat_function().

Author(s)

Maintainer: Philippe Grosjean [email protected] (ORCID)

Authors:

• Guyliann Engels [email protected] (ORCID)

See Also

Useful links:

• https://github.com/SciViews/modelit

• https://www.sciviews.org/modelit/

• https://sciviews.r-universe.dev/modelit

• Report bugs at https://github.com/SciViews/modelit/issues

Description

This function attempts to compute anova or deviance tables using the standard stats::anova() function. The original object is attached as an attribute to the result for reference.

Usage

anova_(object, ...)

Arguments

Value

An anova object with an additional "object" attribute containing the original input.

Examples

is_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
anova(is_lm)

anova_(is_lm)
attr(anova_(is_lm), "object")

Description

anova.lm_() is a method for objects of class lm_, extending the standard stats::anova() functionality. It returns an ANOVA table similar to the base version but includes additional metadata such as variable labels (if available), making the output more informative for interpretation and reporting.

This method is part of the experimental lm_() modeling framework.

Usage

## S3 method for class 'lm_'
anova(object, ...)

Arguments

Value

An object of class anova_, which inherits from anova and may include a labels component if available in the original model.

Examples

data(iris)

# Add labels to variables
attr(iris$Sepal.Length, "label") <- "Sepal Length (cm)"
attr(iris$Petal.Length, "label") <- "Petal Length (cm)"

# Fit model using lm_()
model <- lm_(iris, Petal.Length ~ Sepal.Length + Species)

# Get ANOVA table with labels
anova_model <- anova(model)
anova_model

# Access labels
anova_model$labels

Description

Transform an lm or glm model that has only two variables into a function (useful for plotting, see examples).

Usage

## S3 method for class 'lm'
as.function(x, ...)

Arguments

Value

A function with argument x that returns the values predicted by the model for these values of x.

Examples

data("trees", package = "datasets")
trees_lm1 <- lm(Volume ~ Girth, data = trees)
trees_lm2 <- lm(Volume ~ Girth + I(Girth^2), data = trees)

# Compare these two models on a chart
library(chart)
chart(trees, Volume ~ Girth) +
  geom_point() +
  stat_function(fun = as.function(trees_lm1), col = "red") +
  stat_function(fun = as.function(trees_lm2), col = "blue")

# The created function can also be used for easy predictions
trees_fn1 <- as.function(trees_lm1)
trees_fn1(10:20) # Volume for Girth 10:20

Description

Transforming an nls model into a function could be useful to plot or otherwise manipulate it, see examples.

Usage

## S3 method for class 'nls'
as.function(x, ...)

Arguments

Value

A function with argument x that returns the values predicted by the model for these values of x.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(weight ~ SSlogis(Time, Asym, xmid, scal), data = chick1)

# Show this on a ggplot
library(ggplot2)
p <- ggplot(chick1, aes(x = Time, y = weight)) +
  geom_point() +
  stat_function(fun = as.function(chick1_logis), col = "red")
p

# Visually compare with another model (four-parameter logistic):
chick1_fpl <- nls(weight ~ SSfpl(Time, A, B, xmid, scal), data = chick1)

p + stat_function(fun = as.function(chick1_fpl), col = "blue")

Description

The methods autoplot() or chart() for lm or glm objects. If type = model (by default for chart()), a scatterplot with the model superimposed is produced, providing the model has only two numeric variables (or a combination of these). The other types allow to analyze the residuals of the model.

Usage

## S3 method for class 'lm'
chart(
  data,
  type = "model",
  ...,
  origdata = NULL,
  title,
  labels = "AUTO",
  name = deparse(substitute(data)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

autoplot.lm(
  object,
  origdata = NULL,
  type = c("model", "resfitted", "qqplot", "scalelocation", "cooksd", "resleverage",
    "cookleverage", "reshist", "resautocor"),
  title,
  xlab,
  ylab,
  ...,
  name = deparse(substitute(object)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

Arguments

Value

The ggplot object produced.

Examples

library(chart)
data(trees, package = "datasets")
trees_lm <- lm(Volume ~ Girth, data = trees)
chart(trees_lm) # origdata not needed because untransformed variables
# Residuals analysis
chart$resfitted(trees_lm)
chart$qqplot(trees_lm)
chart$scalelocation(trees_lm)
chart$cooksd(trees_lm)
chart$resleverage(trees_lm)
chart$cookleverage(trees_lm)
chart$reshist(trees_lm, bins = 15)
chart$resautocor(trees_lm)

# The four most important residual analysis plots in one figure
chart$residuals(trees_lm)

trees_lm2 <-  lm(Volume ~ log(Girth), data = trees)
chart(trees_lm2, origdata = trees) # origdata needed, cf. transformed Girth
trees_lm3 <- lm(Volume ~ Girth + Height, data = trees)
# chart(trees_lm3) # Error because more than 2 variables!
# Polynomial regressions work too
trees_lm4 <- lm(Volume ~ I(Girth^2) + Girth, data = trees)
chart(trees_lm4)
# or using poly()
trees_lm5 <- lm(Volume ~ poly(Girth, 3), data = trees)
chart(trees_lm5, origdata = trees) # origdata required here!

Description

The methods autoplot() or chart() for nls objects. If type = model (by default for chart()), a scatterplot with the model superimposed is produced,. The other types allow to analyze the residuals of the model.

Usage

## S3 method for class 'nls'
chart(
  data,
  type = "model",
  ...,
  title,
  labels = "AUTO",
  name = deparse(substitute(data)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

autoplot.nls(
  object,
  type = c("model", "resfitted", "qqplot", "scalelocation", "reshist", "resautocor"),
  title,
  xlab,
  ylab,
  ...,
  name = deparse(substitute(object)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

Arguments

Value

The ggplot object produced.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(weight ~ SSlogis(Time, Asym, xmid, scal), data = chick1)
library(chart)
chart(chick1_logis)
# Residuals analysis
chart$resfitted(chick1_logis)
chart$qqplot(chick1_logis)
chart$scalelocation(chick1_logis)
chart$reshist(chick1_logis, bins = 15)
chart$resautocor(chick1_logis)

# The four most important residual analysis plots in one figure
chart$residuals(chick1_logis)

Description

Create the model equation of several self-starting nonlinear models available in the stats package.

Usage

## S3 method for class 'nls'
equation(
  object,
  ital_vars = FALSE,
  use_coefs = FALSE,
  coef_digits = 2L,
  fix_signs = TRUE,
  swap_var_names = NULL,
  var_names = swap_var_names,
  op_latex = c("\\cdot", "\\times"),
  ...
)

## S3 method for class 'summary.nls'
equation(
  object,
  ital_vars = FALSE,
  use_coefs = FALSE,
  coef_digits = 2L,
  fix_signs = TRUE,
  swap_var_names = NULL,
  op_latex = c("\\cdot", "\\times"),
  ...
)

Arguments

Value

A character string with a LaTeX equation.

Examples

equation <- equatiomatic::equation
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]
chick1_nls <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))
summary(chick1_nls)

equation(chick1_nls)
equation(summary(chick1_nls))

chick1_nls2 <- nls(data = chick1,
  weight ~ SSlogis(Time, Asym = A, xmid = x, scal = scale))
summary(chick1_nls2)

equation(chick1_nls2)
equation(summary(chick1_nls2))

equation(summary(chick1_nls2), swap_var_names = c(
  weight = "Body weight [gm]",
  Time = "Number of days"))

Description

fit_model() takes a model_spec object from {parsnip} and it fits is. Then, usual methods like summary(), or coef() can be applied directly on it, while it can still be used as the {tidymodels} recommends it.

Usage

fit_model(data, formula, ..., type = NULL, env = parent.frame())

## S3 method for class 'model_fit'
summary(object, ...)

## S3 method for class 'model_fit'
anova(object, ...)

## S3 method for class 'model_fit'
plot(x, y, ...)

## S3 method for class 'model_fit'
chart(data, ..., type = "model", env = parent.frame())

## S3 method for class 'model_fit'
as.function(x, ...)

## S3 method for class 'model_fit'
coef(object, ...)

## S3 method for class 'model_fit'
vcov(object, ...)

## S3 method for class 'model_fit'
confint(object, parm, level = 0.95, ...)

## S3 method for class 'model_fit'
fitted(object, ...)

## S3 method for class 'model_fit'
residuals(object, ...)

## S3 method for class 'model_fit'
rstandard(model, ...)

## S3 method for class 'model_fit'
cooks.distance(model, ...)

## S3 method for class 'model_fit'
hatvalues(model, ...)

## S3 method for class 'model_fit'
deviance(object, ...)

## S3 method for class 'model_fit'
AIC(object, ..., k = 2)

## S3 method for class 'model_fit'
BIC(object, ...)

## S3 method for class 'model_fit'
family(object, ...)

## S3 method for class 'model_fit'
nobs(object, ...)

## S3 method for class 'model_fit'
formula(x, ...)

## S3 method for class 'model_fit'
variable.names(object, ...)

## S3 method for class 'model_fit'
labels(object, ...)

Arguments

Value

A model_fit object.

Examples

library(parsnip)
data(trees, package = "datasets")

# Take the habit to prefix your regression model specs by `reg_`
reg_lm <- linear_reg(mod = "regression", engine = "lm")
trees_fit <- fit_model$reg_lm(data = trees, Volume ~ Girth)

# You can use summary(), AIC(), anova(), tidy(), glance(), etc. directly
summary(trees_fit)
anova(trees_fit)
AIC(trees_fit)
coef(trees_fit)
library(chart)
chart(trees_fit)
# etc.

Description

glm_() is an experimental wrapper around the base stats::glm() function. It behaves similarly to glm(), but enriches the returned object with additional metadata. The order of the arguments differs from glm(), and the function uses evaluation through svBase::prepare_data_dot and svBase::recall_with_data_dot to support the data-dot mechanism.

Usage

glm_(data = (.), formula, ..., .data = data)

Arguments

Value

An object of class glm_, which inherits from glm, and includes additional components such as labels. If no additional attributes are added, a standard glm object is returned.

Examples

data(iris)

# Add labels to variables
attr(iris$Sepal.Length, "label") <- "Sepal Length (cm)"
attr(iris$Petal.Length, "label") <- "Petal Length (cm)"

# Fit the model using lm_()
res <- glm_(iris, formula = Petal.Length ~ Sepal.Length + Species)

res
class(res)
summary(res)

# Access labels
res$labels

Description

lm_() is an experimental wrapper around the base stats::lm() function. It behaves similarly to lm(), but enriches the returned object with additional metadata. The order of the arguments differs from lm(), and the function uses evaluation through svBase::prepare_data_dot and svBase::recall_with_data_dot to support the data-dot mechanism.

Usage

lm_(data = (.), formula, ..., .data = data)

Arguments

Value

An object of class lm_, which inherits from lm, and includes additional components such as labels. If no additional attributes are added, a standard lm object is returned.

Examples

data(iris)

# Add labels to variables
attr(iris$Sepal.Length, "label") <- "Sepal Length (cm)"
attr(iris$Petal.Length, "label") <- "Petal Length (cm)"

# Fit the model using lm_()
res <- lm_(iris, formula = Petal.Length ~ Sepal.Length + Species)

. <- iris
res1 <- lm_(Petal.Length ~ Sepal.Length + Species)


res
class(res)
summary(res)

# Access labels
res$labels

Description

add_predictions() and add_residuals() are pipe-friendly functions to add predictions or residuals to a data frame. geom_ref_line() adds a vertical of horizontal reference line. rmse() (the root-mean-squared-error), [mae[]] (the mean absolute error), qae() (the quantiles of absolute error) and rsquare() (the variance of the predictions divided by the variance of the response) are useful model metrics.

Usage

add_predictions(data, model, var = "pred", type = NULL)

add_residuals(data, model, var = "resid")

geom_ref_line(h, v, color = "red", colour = color, size = 1)

rmse(model, data)

mae(model, data)

qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))

rsquare(model, data)

Arguments

Value

A function with argument x that returns the values predicted by the model for these values of x.

Examples

data(trees, package = "datasets")
trees_lm <- lm(Volume ~ Girth + I(Girth^2), data = trees)
rmse(trees_lm, trees)
rsquare(trees_lm, trees)
mae(trees_lm, trees)
qae(trees_lm, trees, probs = c(0, 0.25, 0.5, 0.75, 1)) # Resids five numbers

add_predictions(trees, trees_lm)
add_residuals(trees, trees_lm)

library(chart)
chart(trees_lm) +
  geom_ref_line(h = 0) # Not particularly useful here, just an example

Description

nls_() is an experimental wrapper around the base stats::nls() function. It behaves similarly to glm(), but enriches the returned object with additional metadata. The order of the arguments differs from glm(), and the function uses evaluation through svBase::recall_with_data_dot() to support the data-dot mechanism.

Usage

nls_(data = (.), formula, model = TRUE, ..., .data = data)

Arguments

Value

An object of class nls_, which inherits from nls, and includes additional components such as labels. If no additional attributes are added, a standard nls object is returned.

Examples

chick1 <- ChickWeight[ChickWeight$Chick == 1, ]
# Add labels to variables
attr(chick1$weight, "label") <- "Body weight [gm]"
attr(chick1$Time, "label") <- "Number of days"

chick1_nls <- nls_(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))


chick1_nls
class(chick1_nls)
summary(chick1_nls)

# Access labels
chick1_nls

Description

This function attempts to summarize an object using the standard base::summary() function. The original object is attached as an attribute to the result for reference.

Usage

summary_(object, ...)

Arguments

Value

A summary object with an additional "object" attribute containing the original input.

Examples

is_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
summary(is_lm)

summary_(is_lm)
attr(summary_(is_lm), "object")

Description

summary.lm_() is a method for objects of class lm_, extending the standard summary.lm() functionality. It returns a summary object similar to summary.lm, but includes additional metadata such as variable labels (if available), making the output more informative for reporting and interpretation.

This method is part of the experimental lm_() modeling framework.

Usage

## S3 method for class 'lm_'
summary(object, ...)

Arguments

Value

An object of class summary.lm_, which inherits from summary.lm and includes an optional labels component if available in the original model.

Examples

data(iris)

# Add labels to variables
attr(iris$Sepal.Length, "label") <- "Sepal Length (cm)"
attr(iris$Petal.Length, "label") <- "Petal Length (cm)"
attr(iris$Species, "label") <- "Iris Species"

# Fit model using lm_()
model <- lm_(iris, formula = Petal.Length ~ Sepal.Length + Species)

# Get summary with labels
summary_model <- summary(model)
summary_model

# Access labels
summary_model$labels

Description

Extract and format the table of coefficients from a glm object, similar to stats::coef(), but in a rich-formatted flextable object.

Usage

## S3 method for class 'glm'
tabularise_coef(
  data,
  header = FALSE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  footer = FALSE,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object is returned. You can print it in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange it with the {flextable} functions.

Examples

iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$coef(iris_glm)

# If the 'iris' dataset has labels and units, they can be used to enhance
# the output table
iris <- svBase::labelise(iris, self = FALSE, label = list(
    Sepal.Length = "Length of the sepals",
    Petal.Length = "Length of the petals",
    Species = "Species"), units = c(rep("cm", 4), NA))

iris_glm1 <- glm(data = iris, Petal.Length ~ Sepal.Length + Species)
tabularise::tabularise$coef(iris_glm1)

Description

This function extracts and formats the table of coefficients from an lm object, similar to stats::coef(), but in a rich-formatted table using {flextable}.

Usage

## S3 method for class 'lm'
tabularise_coef(
  data,
  header = FALSE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

data(iris)
# Fit a simple linear model: Petal.Length as a function of Sepal.Length
iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$coef(iris_lm)

# If the 'iris' dataset has labels and units, they can be used to enhance
# the output table
iris <- svBase::labelise(iris, self = FALSE, label = list(
    Sepal.Length = "Length of the sepals",
    Petal.Length = "Length of the petals",
    Species = "Species"), units = c(rep("cm", 4), NA))

iris_lm1 <- lm(data = iris, Petal.Length ~ Sepal.Length + Species)
tabularise::tabularise$coef(iris_lm1)

# The same table but without showing the model equation
tabularise::tabularise$coef(iris_lm, equation = FALSE)

iris_lm2 <- lm(data = iris, Petal.Length ~ Sepal.Length * Species)
tabularise::tabularise$coef(iris_lm2)

Description

This method extracts and formats the coefficients from an nls object, similar to stats::coef(), but in flextable object.

Usage

## S3 method for class 'nls'
tabularise_coef(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  footer = TRUE,
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]
# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))

tabularise::tabularise$coef(chick1_logis)

Description

Create a rich-formatted {flextable} object with the table of coefficients from the summary() of a glm object.

Usage

## S3 method for class 'summary.glm'
tabularise_coef(
  data,
  header = TRUE,
  title = NULL,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object that you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)
iris_glm_sum <- summary(iris_glm)
tabularise::tabularise_coef(iris_glm_sum)

Description

Create a rich-formatted table using the table of coefficients of the summary.lm object

Usage

## S3 method for class 'summary.lm'
tabularise_coef(
  data,
  header = TRUE,
  title = header,
  equation = header,
  footer = FALSE,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  lang = getOption("SciViews_lang", "en"),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
iris_lm_sum <- summary(iris_lm)
tabularise::tabularise$coef(iris_lm_sum)

Description

This function extracts and formats the table of coefficients from a summary.nls object, similar to stats::coef(), but in flextable object.

Usage

## S3 method for class 'summary.nls'
tabularise_coef(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  footer = FALSE,
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]
# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))
chick1_logis_sum <- summary(chick1_logis)

tabularise::tabularise$coef(chick1_logis_sum)
tabularise::tabularise$coef(chick1_logis_sum, header = FALSE, equation = TRUE)

Description

Create a rich-formatted table from an anova object

Usage

## S3 method for class 'anova'
tabularise_default(
  data,
  header = TRUE,
  title = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object you can print in different form or rearrange with the {flextable} functions.

Examples

is <- data.io::read("iris", package = "datasets")

is_lm1 <- lm(data = is, petal_length ~ species)

library(tabularise)

anova(is_lm1) |> tabularise_default()
# identical
anova(is_lm1) |> tabularise()
# Use labels
anova(is_lm1) |> tabularise(origdata = is)

# alternative with anova_() in {modelit} package
anova_(is_lm1) |> tabularise()

is_lm2 <- lm(data = is, petal_length ~ sepal_length + species)

anova(is_lm1, is_lm2) |> tabularise(origdata = is)
anova_(is_lm1, is_lm2) |> tabularise()

Description

Create a rich-formatted table from an aov object

Usage

## S3 method for class 'aov'
tabularise_default(data, ...)

Arguments

Value

flextable object you can print in different form or rearrange with the {flextable} functions.

Examples

iris_aov <- aov(data = iris, Petal.Length ~ Species)
tabularise::tabularise$tidy(iris_aov)

Description

Create a rich-formatted table from a glm object

Usage

## S3 method for class 'glm'
tabularise_default(
  data,
  header = FALSE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  footer = FALSE,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object is returned. You can print it in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange it with the {flextable} functions.

Examples

iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise(iris_glm)
tabularise::tabularise(iris_glm, header = TRUE, footer = TRUE)
tabularise::tabularise(iris_glm, header = TRUE, footer = FALSE)
tabularise::tabularise(iris_glm, header = TRUE, equation = NA,footer = TRUE)

Description

The default tabularise() method for lm objects create a minimalist table with result of the analysis in a rich-formatted tabular presentation.

Usage

## S3 method for class 'lm'
tabularise_default(data, ..., kind = "ft")

Arguments

Value

A flextable object that you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise(iris_lm)

Description

This method extracts and formats an nls object, similar to print(), but in flextable object.

Usage

## S3 method for class 'nls'
tabularise_default(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  footer = TRUE,
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]
# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))

tabularise::tabularise(chick1_logis)

Description

Create a rich-formatted table version of the summary() of a glm object.

Usage

## S3 method for class 'summary.glm'
tabularise_default(
  data,
  header = TRUE,
  title = NULL,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  footer = TRUE,
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object that you can print in different form or rearrange with the {flextable} functions.

Examples

iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)
iris_glm_sum <- summary(iris_glm)
tabularise::tabularise(iris_glm_sum)

Description

Create a rich-formatted table from an summary.lm object

Usage

## S3 method for class 'summary.lm'
tabularise_default(data, ..., footer = TRUE)

Arguments

Value

A flextable object you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
iris_lm_sum <- summary(iris_lm)
tabularise::tabularise(iris_lm_sum)

Description

Create a table of a summary.nls object. This table looks like the output of print.summary.nls() but richly formatted. The tabularise_coef() function offers more customization options for this object.

Usage

## S3 method for class 'summary.nls'
tabularise_default(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  footer = TRUE,
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

See Also

tabularise::tabularise(), tabularise::tabularise_tidy(), tabularise_coef.summary.nls()

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))
chick1_logis_sum <- summary(chick1_logis)

tabularise::tabularise(chick1_logis_sum)
tabularise::tabularise(chick1_logis_sum, footer = FALSE)

growth <- data.io::read("urchin_growth", package = "data.io")
growth_logis <- nls(data = growth, diameter ~ SSlogis(age, Asym, xmid, scal))
chart::chart(growth_logis)
tabularise::tabularise(summary(growth_logis)) # No labels
tabularise::tabularise(summary(growth_logis), origdata = growth) # with labels
tabularise::tabularise(summary(growth_logis), origdata = growth,
  equation = FALSE, show.signif.stars = FALSE)

Description

Turn the glance of glm object into a rich-formatted table with {flextable}. The table can be printed in different formats (HTML, LaTeX, Word, PowerPoint), or rearranged later on.

Usage

## S3 method for class 'glm'
tabularise_glance(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object is produced that you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$glance(iris_glm)
tabularise::tabularise$glance(iris_glm, equation = FALSE)
tabularise::tabularise$glance(iris_glm, equation = "my personal equation")

Description

Create a rich-formatted table with the 'glance' information from an lm object.

Usage

## S3 method for class 'lm'
tabularise_glance(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different form or rearrange with the {flextable} functions.

Examples

iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$glance(iris_lm)

Description

Extract the information contained in an nls object in a table as it could be obtained by broom::glance(). Here, the table is nicely formatted as an (almost) publication-ready form (good for informal reports, notebooks, etc).

Usage

## S3 method for class 'nls'
tabularise_glance(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

See Also

tabularise::tabularise_glance(), tabularise_coef.summary.nls()

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))

tabularise::tabularise$glance(chick1_logis)
tabularise::tabularise$glance(chick1_logis, lang = "fr")

Description

Tidy version of the anova object into a flextable object

Usage

## S3 method for class 'anova'
tabularise_tidy(data, ...)

Arguments

Value

A flextable object you can print in different form or rearrange with the {flextable} functions.

Examples

is <- data.io::read("iris", package = "datasets")

is_lm1 <- lm(data = is, petal_length ~ species)

library(tabularise)

anova(is_lm1) |> tabularise_tidy()
# identical
anova(is_lm1) |> tabularise$tidy()
# Use labels
anova(is_lm1) |> tabularise$tidy(origdata = is)

# alternative with anova_() in {modelit} package
anova_(is_lm1) |> tabularise$tidy()

Description

Tidy version of the aov object into a flextable object

Usage

## S3 method for class 'aov'
tabularise_tidy(data, ...)

Arguments

Value

flextable object you can print in different form or rearrange with the {flextable} functions.

Examples

iris_aov <- aov(data = iris, Petal.Length ~ Species)
tabularise::tabularise$tidy(iris_aov)

Description

Turn the tidy of glm object into a rich-formatted table with {flextable}. The table can be printed in different formats (HTML, LaTeX, Word, PowerPoint), or rearranged later on.

Usage

## S3 method for class 'glm'
tabularise_tidy(
  data,
  header = TRUE,
  title = NULL,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

Value

A flextable object is returned. You can print it in different formats (HTML, LaTeX, Word, PowerPoint), or rearrange it with the {flextable} functions.

Examples

#' # If the 'iris' dataset has labels and units, they can be used to enhance
# the output table
iris <- svBase::labelise(iris, self = FALSE, label = list(
    Sepal.Length = "Length of the sepals",
    Petal.Length = "Length of the petals",
    Species = "Species"), units = c(rep("cm", 4), NA))
iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)

tabularise::tabularise$tidy(iris_glm)
tabularise::tabularise$tidy(iris_glm, conf.int = TRUE)
tabularise::tabularise$tidy(iris_glm, conf.int = TRUE, equation = NA)

Description

Create a rich-formatted table with the 'tidy' information from an lm object.

Usage

## S3 method for class 'lm'
tabularise_tidy(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  lang = getOption("SciViews_lang", "en"),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different formats (HTML, LaTeX, Word, PowerPoint) or rearrange with the {flextable} functions.

Examples

iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$tidy(iris_lm)

Description

Extract the information contained in a nls object into a rectangular table as it could be obtained by broom::tidy(). Here, the table is nicely formatted as an (almost) publication-ready form (good for informal reports, notebooks, etc).

Usage

## S3 method for class 'nls'
tabularise_tidy(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

See Also

tabularise::tabularise(), tabularise::tabularise_tidy(), tabularise_coef.summary.nls()

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))

tabularise::tabularise$tidy(chick1_logis)
tabularise::tabularise$tidy(chick1_logis, lang = "fr")