Fitted-model summary and ANOVA tables

stat_fit_tb() fits a model and returns a "tidy" version of the model's summary or ANOVA table, using 'tidy() methods from packages 'broom', 'broom.mixed', or other 'broom' extensions. The annotation is added to the plots in tabular form.

Usage

stat_fit_tb(
  mapping = NULL,
  data = NULL,
  geom = "table_npc",
  position = "identity",
  ...,
  method = "lm",
  method.args = list(formula = y ~ x),
  n.min = 2L,
  fit.seed = NA,
  tidy.args = list(),
  tb.type = "fit.summary",
  tb.vars = NULL,
  tb.params = NULL,
  digits = 3,
  p.digits = digits,
  label.x = "center",
  label.y = "top",
  table.theme = NULL,
  table.rownames = FALSE,
  table.colnames = TRUE,
  table.hjust = 1,
  parse = FALSE,
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE
)

Arguments

mapping: The aesthetic mapping, usually constructed with aes(). Only needs to be set at the layer level if you are overriding the plot defaults.
data: A layer specific dataset, only needed if you want to override the plot defaults.
geom: The geometric object to use display the data
position: The position adjustment to use for overlapping points on this layer.
...: other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.
method: function or character If character, "lm", "rlm", "lmrob", "lts", "gls", "ma", "sma", "segreg", "rq" or the name of a model fit function are accepted, possibly followed by the fit function's method argument separated by a colon (e.g. "rlm:M"). If a function is different to lm(), rlm(), ltsReg(), gls(), ma, sma, it must have formal parameters named formula, data, and weights. See Details.
method.args, tidy.args: lists of arguments to pass to method and to tidy().
n.min: integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.
fit.seed: RNG seed argument passed to set.seed(). Defaults to NA, indicating that set.seed() should not be called.
tb.type: character One of "fit.summary", "fit.anova" or "fit.coefs".
tb.vars, tb.params: character or numeric vectors, optionally named, used to select and/or rename the columns or the parameters in the table returned.
digits: integer indicating the number of significant digits to be used for all numeric values in the table.
p.digits: integer indicating the number of decimal places to round p-values to, with those rounded to zero displayed as the next larger possible value preceded by "<". If p.digits is outside the range 1..22 no rounding takes place.
label.x, label.y: numeric with range 0..1 "normalized parent coordinates" (npc units) or character if using geom_text_npc() or geom_label_npc(). If using geom_text() or geom_label() numeric in native data units. If too short they will be recycled.
table.theme: NULL, list or function A 'gridExtra' ttheme definition, or a constructor for a ttheme or NULL for default.
table.rownames, table.colnames: logical flag to enable or disabling printing of row names and column names.
table.hjust: numeric Horizontal justification for the core and column headings of the table.
parse: logical Passed to the geom. If TRUE, the labels will be parsed into expressions and displayed as described in plotmath. Default is TRUE if output.type = "expression" and FALSE otherwise.
na.rm: a logical indicating whether NA values should be stripped before the computation proceeds.
show.legend: logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
inherit.aes: If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.

Value

A tibble with columns named fm.tb (a tibble returned by tidy() with possibly renamed and subset columns and rows, within a list), fm.tb.type (copy of argument passed to tb.type), fm.class (the class of the fitted model object), fm.method (the fit function's name), fm.call (the call if available), x and y.

To explore the values returned by this statistic, which vary depending on the model fitting function and model formula we suggest the use of geom_debug.

Details

stat_fit_tb() Applies a model fitting function per panel, using the grouping factors from aesthetic mappings in the fitted model. This is suitable, for example for analysis of variance used to test for differences among groups.

The argument to method can be any fit method for which a suitable tidy() method is available, including non-linear regression. Fit methods retain their default arguments unless overridden.

Computed variables

The output of tidy() is returned as a single "cell" in a tibble (i.e., a tibble nested within a tibble). The returned data object contains a single tibble, containing the result from a single model fit to all data in a panel. If grouping is present, it is ignored in the sense of returning a single table, but the grouping aesthetic can be a term in the fitted model.

Model formula and model fitting

A ggplot statistic receives as data a data frame that is not the one passed as argument by the user, but instead a data frame with the variables mapped to aesthetics. In stat_poly_eq() the compute function is applied by group, each call "seeing" the subset of data for an individual group. As supported models are for regression lines, variables mapped to x and y should both be continuous, i.e., numeric or date time and model formulas defined using x and y as variable names.

The interpretation of the argument passed to formula is enhanced compared to stat_smooth(). Formulas with x as explanatory variable work as in stat_smooth() but formulas with y as explanatory variable are also accepted. orientation is set automatically based on which explanatory variable appears in the formula. Spline-based smoothers are only partially supported.

Model fit methods supported

Several model fit functions are supported explicitly (see tables), and some of their differences smoothed out. Compatibility is checked late, based on the class of the returned fitted model object. This makes it possible to use wrapper functions that do model selection or other adjustments to the fit procedure on a per panel or per group basis. Moreover, if the value returned as model fit object is NULL or NA, plotting is skipped on a per group within panel basis.

In the case of fitted model objects of classes not explicitly supported, an attempt is made to find the usual accessors and/or fitted object members, and if found, either complete or partial support is frequently achieved. In this case a message is issued encouraging users to check the validity of the values extracted as the structure of fitted model objects belonging to different classes and the values returned by their accessors can vary, potentially resulting in decoding errors leading to the return of wrong values for estimates.

The argument to parameter method can be either the name of a function object, possibly using double colon notation in case its package is not attached, or a character string matching the function name for functions in the search path. This approach makes it possible to support model fit functions that are not dependencies of 'ggpmisc'. Either by attaching the package where the function is defined and passing it by name or as string, or using double colon notation when passing the name of the function.

User-defined functions can be passed as argument to parameter method as long as they have parameters formula, data subset and possibly weights. Additional arguments can be passed to any method as a named list through parameter method.args. As in stat_smooth() prior weights are passed to the model fit functions' weights (plural!) parameter by mapping a numeric variable to plot aesthetic weight (singular!).

Tables 1 lists natively supported model fit functions, with the caveat that only some 'broom' methods' specializations have been actually tested with statistics from 'ggpmisc'. In addition, the statistics based on 'broom' methods require the user to tailor their behaviour by passing additional arguments in the call and occasionally some detective work to find out the names of variables in the returned data frame as these names are set by methods from 'broom'.

Table 1. Model fit methods supported by the different statistics available in package 'ggpmisc'. Column \(f\) indicates whether computations are done by group (G) or by plot panel (P).

Statistic	\(f\)	Supported model fit methods
`stat_poly_line()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", others with methods `predict()` or `fitted()`
`stat_poly_eq()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", others with needed accesors
`stat_quant_line()`	G	"rq", "rqss"
`stat_quant_band()`	G	"rq", "rqss"
`stat_quant_eq()`	G	"rq", "rqss"
`stat_ma_line()`	G	"SMA", "MA", "RMA", "OLS"
`stat_ma_eq()`	G	"SMA", "MA", "RMA", "OLS"
`stat_fit_residuals()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", "rq", "rqss" others with method `residuals()`
`stat_fit_fitted()`	G	"lm", "rlm", "lts", "gls", "rq", "rqss" others with method `fitted()`
`stat_fit_deviations()`	G	"lm", "rlm", "lts", "gls", "rq", "rqss" others with methods `fitted()` and `weights()`
`stat_fit_augment()`	G	any with 'broom' method `augment()`
`stat_fit_glance()`	G	any with 'broom' method `glance()`
`stat_fit_tidy()`	G	any with 'broom' method `tidy()`
`stat_fit_tb()`	P	any with 'broom' method `tidy()`

The single colon notation is based on parsing the name and is available when passing the name of the fit method as a character string. In a string such as "head:tail" the "head" gives the name of the model fit function and the "tail" gives the argument to pass it's method parameter. This is only a convenience, as method.args can be also used. In some methods, i.e., splines, the default formula = y ~ x needs to be overridden by the user.

Table 2 lists the correspondence of pre-defined method names to model fit method functions. As mentioned above, these are only a subset of the model fit methods that are expected to work. When using these names there is no need for users to attach additional packages but the packages must be available (installed).

Table 2. Available predefined method names, the model fit functions they call, the packages where the functions reside, the class of the returned fitted model object and the arguments that can be passed to their method parameter using single colon notation.

Predefined method names	Model fit methods	R package	Object class
"lm", "lm:qr"	`lm()`	'stats'	"lm"
"rlm", "rlm:M", "rlm:MM"	`rlm()`	'MASS'	"rlm" ("lm")
"lts", "ltsReg"	`ltsReg()`	'robustbase'	"lts"
"ma", "sma", "sma:SMA", "sma:MA", "sma:OLS"	`sma()`	'smatr'	"ma" or "sma"
"gls", "gls:REML", "gls:ML"	`gls()`	'nlme'	"gls"
"rq", "rq:sfn", "rq:sfnc", "rq:lasso"	`rq()`	'quantreg'	"rq"
"rqss", "rqss:sfn", "rqss:sfnc", "rqss:lasso"	`rqss()`	'quantreg'	"rqss"
"SMA", "MA", "RMA", "OLS"	`lmodel2()`	'lmodel2'	("list")

Aesthetics

stat_fit_tb() understands the following aesthetics. Required aesthetics are displayed in bold and defaults are displayed for optional aesthetics:

•	`x`
•	`y`
•	`group`	→ inferred
•	`hjust`	→ `"inward"`
•	`label`	→ `after_stat(fm.tb)`
•	`vjust`	→ `"inward"`

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Examples

# Package 'broom' needs to be installed to run these examples.
# We check availability before running them to avoid errors.
broom.installed <- requireNamespace("broom", quietly = TRUE)

if (broom.installed)
  library(broom)

# data for examples
  x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
  covariate <- sqrt(x) + rnorm(9)
  group <- factor(c(rep("A", 4), rep("B", 5)))
  my.df <- data.frame(x, group, covariate)

gginnards.installed  <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

## covariate is a numeric or continuous variable
# Linear regression fit summary, all defaults
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb() +
    expand_limits(y = 70)


# we can use geom_debug_panel() and str() to inspect the returned value
# and discover the variables that can be mapped to aesthetics with
# after_stat()
if (broom.installed && gginnards.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(geom = "debug_panel", dbgfun.data = str) +
    expand_limits(y = 70)
#> Warning: Ignoring unknown parameters: `table.theme`, `table.rownames`, `table.colnames`,
#> `table.hjust`, and `parse`

#> 'data.frame':	1 obs. of  13 variables:
#>  $ PANEL         : Factor w/ 1 level "1": 1
#>  $ x             : num 7.47
#>  $ y             : num 70
#>  $ group         : int -1
#>  $ fm.tb         :List of 1
#>   ..$ : tibble [2 × 5] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ term     : chr  "(Intercept)" "x"
#>   .. ..$ estimate : num  28.9 2.58
#>   .. ..$ std.error: num  12.6 1.71
#>   .. ..$ statistic: num  2.29 1.51
#>   .. ..$ p.value  : chr  "0.056" "0.175"
#>  $ fm.tb.type    : chr "fit.summary"
#>  $ fm.class      : chr "lm"
#>  $ fm.method     : chr "lm"
#>  $ fm.formula    :List of 1
#>   ..$ formula:Class 'formula'  language y ~ x
#>   .. .. ..- attr(*, ".Environment")=<environment: 0x000002188250eac0> 
#>  $ fm.formula.chr: Named chr "y ~ x"
#>   ..- attr(*, "names")= chr "formula"
#>  $ npcx          : num NA
#>  $ npcy          : num NA
#>  $ label         :List of 1
#>   ..$ : tibble [2 × 5] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ term     : chr  "(Intercept)" "x"
#>   .. ..$ estimate : num  28.9 2.58
#>   .. ..$ std.error: num  12.6 1.71
#>   .. ..$ statistic: num  2.29 1.51
#>   .. ..$ p.value  : chr  "0.056" "0.175"
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (anonymous function):"
#> NULL

# Linear regression fit summary, with default formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.summary") +
    expand_limits(y = 70)


# Linear regression fit summary, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(digits = 2,
                p.digits = 4,
                tb.params = c("intercept" = 1, "covariate" = 2),
                tb.vars = c(Term = 1, Estimate = 2,
                            "italic(s)" = 3, "italic(t)" = 4,
                            "italic(P)" = 5),
                parse = TRUE) +
    expand_limits(y = 70)


# Linear regression ANOVA table, with default formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova") +
    expand_limits(y = 70)


# Linear regression ANOVA table, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova",
                tb.params = c("Covariate" = 1, 2),
                tb.vars = c(Effect = 1, d.f. = 2,
                            "M.S." = 4, "italic(F)" = 5,
                            "italic(P)" = 6),
                parse = TRUE) +
    expand_limits(y = 67)


# Linear regression fit coeficients, with default formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.coefs") +
    expand_limits(y = 67)


# Linear regression fit coeficients, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.coefs",
                tb.params = c(a = 1, b = 2),
                tb.vars = c(Term = 1, Estimate = 2)) +
    expand_limits(y = 67)


## x is also a numeric or continuous variable
# Polynomial regression, with default formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(method.args = list(formula = y ~ poly(x, 2))) +
    expand_limits(y = 70)


# Polynomial regression, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(method.args = list(formula = y ~ poly(x, 2)),
                tb.params = c("x^0" = 1, "x^1" = 2, "x^2" = 3),
                tb.vars = c("Term" = 1, "Estimate" = 2, "S.E." = 3,
                            "italic(t)" = 4, "italic(P)" = 5),
                parse = TRUE) +
    expand_limits(y = 70)


## group is a factor or discrete variable
# ANOVA summary, with default formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb() +
    expand_limits(y = 70)


# ANOVA table, with default formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova") +
    expand_limits(y = 70)


# ANOVA table, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova",
                tb.vars = c(Effect = "term", "df", "italic(F)" = "statistic",
                            "italic(P)" = "p.value"),
                tb.params = c(Group = 1, Error = 2),
                parse = TRUE)


# ANOVA table, with manual table formatting
# using column names with partial matching
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova",
                tb.vars = c(Effect = "term", "df", "italic(F)" = "stat",
                            "italic(P)" = "p"),
                tb.params = c(Group = "x", Error = "Resid"),
                parse = TRUE)


# ANOVA summary, with default formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb() +
    expand_limits(y = 70)


## covariate is a numeric variable and group is a factor
# ANCOVA (covariate not plotted) ANOVA table, with default formatting
if (broom.installed)
  ggplot(my.df, aes(group, x, z = covariate)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova",
                method.args = list(formula = y ~ x + z))


# ANCOVA (covariate not plotted) ANOVA table, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(group, x, z = covariate)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.anova",
                method.args = list(formula = y ~ x + z),
                tb.vars = c(Effect = 1, d.f. = 2,
                            "M.S." = 4, "italic(F)" = 5,
                            "italic(P)" = 6),
                tb.params = c(Group = 1,
                              Covariate = 2,
                              Error = 3),
                parse = TRUE)


## group is a factor or discrete variable
# t-test, minimal output, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(method = "t.test",
              tb.vars = c("italic(t)" = "statistic",
                          "italic(P)" = "p.value"),
              parse = TRUE)
#> Dropping param names from table!
#> 'formula' extracted from arguments


# t-test, more detailed output, with manual table formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(method = "t.test",
              tb.vars = c("\"Delta \"*italic(x)" = "estimate",
                          "CI low" = "conf.low", "CI high" = "conf.high",
                          "italic(t)" = "statistic",
                          "italic(P)" = "p.value"),
              parse = TRUE) +
    expand_limits(y = 67)
#> 'formula' extracted from arguments


# t-test (equal variances assumed), minimal output, with manual
# table formatting
if (broom.installed)
  ggplot(my.df, aes(group, x)) +
    geom_point() +
    stat_fit_tb(method = "t.test",
                method.args = list(formula = y ~ x, var.equal = TRUE),
                tb.vars = c("italic(t)" = "statistic",
                            "italic(P)" = "p.value"),
                parse = TRUE)
#> Dropping param names from table!
#> 'formula' extracted from arguments


## covariate is a numeric or continuous variable
# Linear regression using a table theme and non-default position
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(table.theme = ttheme_gtlight,
                npcx = "left", npcy = "bottom") +
    expand_limits(y = 35)