tabularise()
for lm and glm object. Managing a conflict between auto.labs= and equation=.Argument kind =
added to all tabularise()
methods and dependency update to v0.6.0 of {tabularise}.
Enhanced vignette and README file.
License switched to MIT.
...
argument in equation.nl()
and equation.summary.nls()
was causing a warning in R CMD check
. Corrected now.autoplot.nls()
: X and Y axes were inverted. Also the deprecated aes_string()
function is replaced with aes()
with proper arguments.tabularise()
.Use of the generic equation()
. equation_nls()
is transformed into an equation()
method for nls and summary.nls objects. Implementation of ital_vars=
, use_coefs=
, coef_digits=
and fix_signs=
arguments in order to get parameterized as well as non parameterized equation, with or without italic for the variables.
Dependency to {equatiomatic} is dropped. It is now included in {tabularise}.
The default for title=
argument is now automatically set to FALSE
if the table is generated inside a chunk that has tbl-cap
YAML entry defined (thus replaced by that caption).
The tabularise_default()
methods for summary.lm and summary.glm were lacking important information (they were identical to tabularise_coef)
for the same objects. Corrected now.
tabularise_default()
and tabularise_tidy()
added for anova objects.
tabularise_tidy()
added for aov objects.
tabularise_default()
, tabularise_coef()
, tabularise_glance()
, tabularise_tidy()
for lm objects
tabularise_default()
, tabularise_coef()
for summary.lm objects
tabularise_coef()
, tabularise_glance()
, tabularise_tidy()
for glm objects
tabularise_coef()
for summary.glm objects
tabularise_default()
, tabularise_coef()
, tabularise_glance()
, tabularise_tidy()
for nls objects
tabularise_default()
, tabularise_coef()
for summary.nls objects
autoplot()
and chart()
for objects lm, glm and nls: both the plot of the model and several residual analyses, plus a composed 2x2 plot for residuals analysis.
as.function()
for lm and nls objects.
Reexportation of several functions from {modelr}: add_predictions()
, add_residuals()
, geom_ref_line()
, rmse()
, rsquare()
, mae()
and qae()
.
fit_model()
to easily fit a {parsnip} model, and a series of methods like summary()
, anova()
or chart()
directly implemented for these objects.
SciViews::R
p <- chart(data = mtcars, wt ~ mpg) + geom_point()
p <- p + geom_smooth(method = "lm", formula = y ~ I(x^2) + x)
p <- p + theme_void() + hexSticker::theme_transparent()
dir.create ("inst/figures", showWarnings = FALSE)
hexSticker::sticker(p, package = "modelit", p_size = 9, s_x = 1, s_y = .75,
s_width = 1.3, s_height = 0.9, h_fill="lightblue3", h_color="blue",
filename = "inst/figures/modelit.png")
NEWS.md
file to track changes to the package.