Package 'MultiSpline'

Built-in model comparison workflow

Description

Compares a nonlinear spline model against simpler alternatives—a linear model and optional polynomial terms—to help researchers justify the spline approach over simpler specifications.

For each model, the function reports:

• AIC and BIC

• Log-likelihood (and likelihood-ratio test versus the linear model where available)

• Number of parameters (df)

• Residual variance / deviance

Mixed models are compared on their maximum-likelihood fits: the spline model is refitted by ML if it was estimated by REML, and the comparison models are fitted by ML. REML likelihoods cannot be compared across models with different fixed effects. The comparison models use the same random-effects structure as the spline model (random intercepts only if the spline model has random spline slopes).

Usage

nl_compare(
  object,
  polynomial_degrees = c(2L, 3L),
  digits = 3L,
  return_models = FALSE
)

Arguments

object	An nl_fit object (the spline model to compare against).
polynomial_degrees	Integer vector of polynomial degrees to include as additional comparators. Default c(2L, 3L). Set to integer(0) to skip.
digits	Integer; decimal places for the output table. Default 3.
return_models	Logical; if TRUE, also returns the fitted comparison model objects. Default FALSE.

Value

A list (invisibly) of class "nl_compare" with:

table

A data frame with the comparison statistics.

models

Named list of fitted models (when return_models = TRUE).

best

Name of the model with the lowest AIC.

The table is pretty-printed automatically.

See Also

nl_fit, nl_knots

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
nl_compare(fit, polynomial_degrees = 2)

---

First and second derivatives of the fitted curve

Description

Computes the slope (first derivative) and curvature (second derivative) of the fitted curve with respect to x, with delta-method standard errors and pointwise confidence intervals.

Usage

nl_derivatives(
  object,
  x_seq = NULL,
  time_levels = NULL,
  controls_fixed = NULL,
  level = 0.95,
  type = c("conditional", "marginal"),
  scale = c("response", "link"),
  h = NULL,
  ...
)

Arguments

object	An nl_fit object. (Passing a data frame from nl_predict(), as in version 0.2.0, still returns derivative estimates but no intervals, with a warning.)
x_seq, time_levels, controls_fixed	Grid specification, as in nl_predict.
level	Confidence level. Default 0.95.
type, scale	As in nl_predict: derivatives of the conditional or marginal curve, on the response or link scale.
h	Step for the central differences. Default NULL uses $10^{-4}$ times the range of x. Because the basis is evaluated exactly, the result is insensitive to h.
...	Ignored; absorbs arguments of the 0.2.0 interface.

Details

Derivatives are taken of the model itself, not of a table of predictions. For each grid value the spline basis is evaluated at $x \pm h$ with the knots of the fitted model, giving the derivative as a linear combination $d^\top\beta$ (identity link) or, for other links, via the chain rule. Its variance is $g^\top V g$, where $g$ is the gradient with respect to the fixed effects and $V$ their covariance matrix. This accounts for the strong correlation between neighbouring points on the curve, which a calculation from a prediction table cannot do.

Intervals are pointwise, not simultaneous: they describe uncertainty at each x separately.

Value

A data frame with the grid columns, fit, d1, d1_se, d1_lwr, d1_upr, d2, d2_se, d2_lwr, and d2_upr.

See Also

nl_turning_points, nl_plot

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
deriv <- nl_derivatives(fit, x_seq = 0:9)
deriv[, c("Days", "d1", "d1_lwr", "d1_upr")]

---

Fit a nonlinear (spline or GAM) single-level or multilevel model

Description

Fits a nonlinear regression model for an outcome y with a focal predictor x, modeled using natural cubic splines ("ns"), B-splines ("bs"), or GAM smooths ("gam").

Mixed models are fitted with lme4. The level structure can be given simply through cluster (one grouping variable, or two that are nested or crossed) or in full through random, which accepts any lme4 random-effects specification.

Usage

nl_fit(
  data,
  y,
  x,
  time = NULL,
  cluster = NULL,
  nested = FALSE,
  controls = NULL,
  random = NULL,
  method = c("ns", "bs", "gam"),
  df = 4,
  df_range = 2:8,
  df_criterion = c("AIC", "BIC"),
  k = 5,
  bs_degree = 3,
  random_slope = FALSE,
  family = stats::gaussian(),
  REML = TRUE,
  ...
)

Arguments

data	A data frame (often long format for longitudinal data).
y	Outcome variable name (string).
x	Focal nonlinear predictor name (string). Must be numeric.
time	Optional time variable name (string).
cluster	Optional character vector of grouping variable name(s) for random effects, e.g. NULL, "id", or c("id", "school") for two-way clustering.
nested	Logical; only used when length(cluster) == 2. If TRUE, the first grouping variable is nested within the second: cluster = c("student", "school") gives (1 | school/student). (Version 0.2.0 built the reverse, (1 | student/school), which omitted the school effect.) If FALSE (default), uses cross-classified (1 | g1) + (1 | g2). For anything more complex use random.
controls	Optional character vector of additional fixed-effect terms. Plain variable names and expressions are both accepted, e.g. c("age", "factor(race)", "I(age^2)"). Factor and character variables are expanded to indicator columns automatically.
random	Optional character string giving the full random-effects part of an lme4 formula, e.g. "(1 | school/student)" or "(1 | idcode) + (1 | indcode)". When supplied it overrides cluster, nested, and random_slope, so any level structure that lme4 accepts can be used. Grouping variables are read from the string.
method	Spline basis to use: "ns" (natural cubic spline, default), "bs" (B-spline), or "gam" (GAM smooth via mgcv::gam()). Multilevel fits require "ns" or "bs".
df	Degrees of freedom for the spline basis. Supply a single integer greater than or equal to 1, or the string "auto" to trigger automatic selection by information criterion. Default 4.
df_range	Integer vector of candidate df values evaluated when df = "auto". Default 2:8.
df_criterion	Information criterion used for automatic df selection: "AIC" (default) or "BIC".
k	Basis dimension for mgcv::s() when method = "gam". Default 5.
bs_degree	Polynomial degree for splines::bs() when method = "bs". Default 3 (cubic).
random_slope	Logical; if TRUE and cluster is supplied, fits random spline slopes in addition to random intercepts. Currently implemented only for single-level clustering. Default FALSE.
family	A family object such as stats::gaussian() (default) or stats::binomial(). For multilevel fits, gaussian() uses lme4::lmer() and all other families use lme4::glmer().
REML	Logical; for Gaussian mixed models, fit the final model by REML (default TRUE). Automatic df selection and all model comparisons always use maximum likelihood, because REML likelihoods are not comparable across different fixed effects.
...	Additional arguments passed to the underlying fitting function (stats::lm(), mgcv::gam(), lme4::lmer(), or lme4::glmer()).

Value

An object of class "nl_fit" (a named list). It contains the fitted model object, the method, variable names (y, x, time, cluster, controls), spline settings (df, df_selected, df_search, k, bs_degree), flags (nested, random_slope), the family, the model formula, the call, and metadata used for prediction (x_info, levels_info, control_defaults).

Spline basis

method = "ns" uses splines::ns(): a natural cubic spline (cubic between knots, linear beyond the boundary knots). Interior knots are placed at equally spaced quantiles of x; the boundary knots are the range of x. df is the number of basis columns (interior knots = df - 1). With the same knots it spans the same functions as Stata's restricted cubic splines (mkspline, cubic, makespline rcs); the defaults differ only in knot placement (Stata puts the outer knots at percentiles, ns() at the range). The basis computed on the estimation data is stored in the model and reused for every prediction and derivative.

See Also

nl_predict, nl_derivatives, nl_compare, nl_r2, nl_knots

Examples

# Single-level natural spline
fit1 <- nl_fit(mtcars, y = "mpg", x = "hp", df = 3)
fit1

# Repeated measures: days nested in subjects
fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)

# Any lme4 random-effects structure via `random`
fit2 <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
               random = "(1 | Subject)", df = 3)

---

Cluster heterogeneity in nonlinear effects

Description

Quantifies and visualises how much the nonlinear relationship between x and y varies across clusters, using a model with random spline slopes.

The function:

1. Refits the model with random spline slopes (if not already fitted with random_slope = TRUE).

2. Extracts cluster-specific predicted curves (BLUPs).

3. Returns the standard deviation of the cluster-specific curves at each x value, and a plot of the cluster-specific trajectories.

4. Performs a likelihood-ratio test comparing the random-slope model against a random-intercept-only model to assess whether heterogeneity is statistically significant. Because the null hypothesis puts variances on the boundary of the parameter space, the reported p-value is conservative.

Usage

nl_het(
  object,
  n_clusters_plot = 30L,
  x_seq = NULL,
  level = 0.95,
  plot = TRUE,
  seed = 42L
)

Arguments

object	An nl_fit object fitted with a single cluster variable (and either random_slope = TRUE or FALSE; in the latter case the model is refit internally).
n_clusters_plot	Maximum number of cluster curves to display in the trajectory plot. Default 30L. Use Inf for all clusters.
x_seq	Optional numeric vector of x values for prediction.
level	Confidence level for the population-mean ribbon. Default 0.95.
plot	Logical; print the trajectory plot. Default TRUE.
seed	Integer seed for reproducibility when sub-sampling clusters. Default 42L.

Value

A list (invisibly) with:

trajectory_df

Long data frame of cluster-specific predicted values.

between_cluster_sd

Data frame: the standard deviation of the cluster-specific predicted values (response scale) across the displayed clusters at each x value. Replaces the mislabelled slope_variance of version 0.2.0.

lrt

LRT comparing random-slope vs random-intercept model (NULL if random_slope = TRUE was supplied).

plot

A ggplot object.

See Also

nl_fit, nl_derivatives

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
het <- nl_het(fit, plot = FALSE)
het$lrt

---

Intraclass correlation coefficients for a multilevel nl_fit model

Description

Extracts variance components from a multilevel nl_fit object and computes intraclass correlation coefficients (ICCs) for each grouping level plus the residual.

The ICC for grouping factor $g$ is defined as:

$ICC_g = \frac{\sigma^2_g}{\sum_j \sigma^2_j + \sigma^2_\epsilon}$

For Gaussian models $\sigma^2_\epsilon$ is the residual variance. For binary models it is the latent-scale variance ($\pi^2/3$ for logit, 1 for probit, $\pi^2/6$ for complementary log-log), which matches Stata's estat icc. With nested factors each value is the share of that level alone; the correlation between two units that share a lower-level group is the sum of the shares of that group and every group above it. With random slopes the ICC depends on x; the function then averages each component over the data and warns.

Usage

nl_icc(object, include_residual = TRUE)

Arguments

object	An nl_fit object returned by nl_fit that was fitted with one or more cluster variables (i.e., a multilevel model fitted via lme4::lmer() or lme4::glmer()).
include_residual	Logical; if TRUE (default), includes the residual variance component ICC_resid in the output so that all values sum to 1.

Value

A named numeric vector of ICCs, one per grouping factor (named ICC_<groupname>) plus Residual for the residual variance (when include_residual = TRUE). All values sum to 1.

See Also

nl_fit

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
nl_icc(fit)

---

Automatic knot / degrees-of-freedom selection for spline models

Description

Performs a grid search over candidate degrees of freedom (df) for a natural cubic spline ("ns") or B-spline ("bs") model and selects the best value by an information criterion (AIC or BIC). A diagnostic plot of the criterion against df is returned. Mixed models are fitted by maximum likelihood for this search, because REML criteria are not comparable across different fixed effects.

This function is called internally by nl_fit when df = "auto", but it can also be called directly for exploration before fitting the final model.

Usage

nl_knots(
  data,
  y,
  x,
  time = NULL,
  cluster = NULL,
  nested = FALSE,
  controls = NULL,
  random = NULL,
  method = c("ns", "bs"),
  df_range = 2:10,
  criterion = c("AIC", "BIC"),
  family = stats::gaussian(),
  plot = TRUE,
  ...
)

Arguments

data	A data frame.
y	Outcome variable name (string).
x	Focal predictor name (string).
time	Optional time variable name.
cluster	Optional character vector of cluster variable names.
nested	Logical; nested clustering. Default FALSE.
controls	Optional character vector of control variable names.
random	Optional full random-effects string, as in nl_fit.
method	Either "ns" (default) or "bs".
df_range	Integer vector of candidate df values. Default 2:10.
criterion	Either "AIC" (default) or "BIC".
family	A family object. Default stats::gaussian().
plot	Logical; if TRUE, prints a diagnostic plot of criterion vs df. Default TRUE.
...	Additional arguments passed to the underlying fitter.

Value

A list with:

best_df

The df value with the lowest criterion value.

search_table

Data frame with columns df and criterion.

criterion

The criterion used ("AIC" or "BIC").

plot

A ggplot object (if plot = TRUE).

See Also

nl_fit, nl_compare

Examples

nl_knots(mtcars, y = "mpg", x = "hp", df_range = 2:5, plot = FALSE)

---

Plot predictions and derivatives from nl_fit models

Description

Visualises results from nl_predict or nl_derivatives. The type argument selects the plot:

• "trajectory": predicted outcome vs x with optional confidence ribbon (v1 behaviour, now default).

• "slope": first derivative (marginal effect) vs x.

• "curvature": second derivative vs x.

• "combo": trajectory + slope panels side by side.

Usage

nl_plot(
  pred_df = NULL,
  deriv_df = NULL,
  x = NULL,
  time = NULL,
  type = c("trajectory", "slope", "curvature", "combo"),
  show_ci = TRUE,
  ci_level = 0.95,
  show_turning_points = FALSE,
  turning_points = NULL,
  zero_line = TRUE,
  y_lab = NULL,
  x_lab = NULL,
  title = NULL,
  legend_title = NULL
)

Arguments

pred_df	A data frame from nl_predict (for type = "trajectory" or "combo").
deriv_df	A data frame from nl_derivatives (for type = "slope", "curvature", or "combo").
x	Character; name of the focal predictor column. Taken from the nl_predict() / nl_derivatives() output by default.
time	Optional character; name of the time column. Auto-detected if NULL.
type	Plot type: "trajectory" (default), "slope", "curvature", or "combo".
show_ci	Logical; add confidence ribbons. Default TRUE.
ci_level	Numeric; confidence level when deriving CI from se.fit. Default 0.95.
show_turning_points	Logical; overlay turning-point markers on the trajectory plot. Default FALSE.
turning_points	Data frame from nl_turning_points (only used when show_turning_points = TRUE).
zero_line	Logical; for slope / curvature plots, draw a dashed horizontal line at zero. Default TRUE.
y_lab	Y-axis label.
x_lab	X-axis label. Defaults to x.
title	Optional plot title.
legend_title	Optional legend title.

Value

A ggplot object. For "combo", a single combined figure if patchwork is installed, otherwise a list of two plots.

See Also

nl_predict, nl_derivatives, nl_turning_points

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
pred  <- nl_predict(fit)
deriv <- nl_derivatives(fit)
nl_plot(pred)
nl_plot(deriv_df = deriv, type = "slope")

---

Generate predictions from an nl_fit model

Description

Evaluates the fitted curve over a grid of the focal predictor x (and over time levels, if the model has a time interaction), holding control variables at fixed values.

Usage

nl_predict(
  object,
  x_seq = NULL,
  time_levels = NULL,
  controls_fixed = NULL,
  se = TRUE,
  level = 0.95,
  type = c("conditional", "marginal"),
  scale = c("response", "link"),
  re_form = NA,
  glmer_ci = c("delta", "boot"),
  n_boot = 500L,
  ...
)

Arguments

object	An nl_fit object.
x_seq	Optional numeric vector of x values.
time_levels	Optional vector of time levels.
controls_fixed	Optional named list of fixed control values.
se	Logical; include standard errors and intervals. Default TRUE.
level	Confidence level. Default 0.95.
type	"conditional" (default) or "marginal"; see What is predicted.
scale	"response" (default) or "link".
re_form	Retained for compatibility. Only NA (random effects set to zero) is supported; use nl_het for cluster-specific curves.
glmer_ci	For non-Gaussian mixed models: "delta" (default) or "boot" (parametric bootstrap via lme4::bootMer()).
n_boot	Number of bootstrap replicates when glmer_ci = "boot".
...	Reserved for future use.

Value

A data frame (tibble) with the grid columns, fit, se.fit, lwr, and upr. Attributes type and scale record what was predicted.

What is predicted

• The grid has 200 evenly spaced values of x between its 1st and 99th sample percentiles unless x_seq is supplied. Each row is an exact evaluation of the fitted model; nothing is interpolated. Plots join these points with straight line segments.

• Controls are held at their sample mean (numeric) or first level (factor / character) unless set in controls_fixed.

• type = "conditional" (default) sets all random effects to zero: the curve for a cluster whose random effects are at their mean. For Gaussian models this equals the population-averaged curve.

• type = "marginal" averages the response-scale mean over the estimated random-effect distribution (Gauss-Hermite quadrature). For non-identity links (e.g. logistic models) this population-averaged curve is flatter than the conditional curve. Available for random intercepts and for the random spline slopes created by nl_fit(random_slope = TRUE).

• scale = "response" (default) returns means on the outcome scale (probabilities for binary outcomes); scale = "link" returns the linear predictor.

• Standard errors use the delta method with the full covariance matrix of the fixed effects, treating variance components as known. Intervals are formed on the link scale and transformed, so they stay inside the range of the outcome.

See Also

nl_fit, nl_plot, nl_derivatives

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
pred <- nl_predict(fit)
head(pred)
attr(pred, "type")

---

Multilevel R-squared decomposition for nl_fit models

Description

Computes a suite of R-squared statistics for models fitted by nl_fit. For single-level models the standard R-squared and adjusted R-squared are returned. For multilevel models (lmerMod / glmerMod) two quantities are reported: the Nakagawa-Schielzeth marginal R-squared (variance explained by fixed effects only) and the conditional R-squared (fixed plus all random effects), together with a level-specific variance partition table analogous to the r2_mlm / Raudenbush-Bryk approach.

Usage

nl_r2(object, digits = 4L)

Arguments

object	An nl_fit object returned by nl_fit.
digits	Integer; decimal places for display. Default 4.

Details

Marginal and conditional R-squared for LMMs follow the Nakagawa and Schielzeth (2013) formulae extended to multiple random effects by Nakagawa, Johnson and Schielzeth (2017). The fixed-effects variance $\sigma^2_f$ is computed as the variance of the linear predictor from fixed effects only ($\hat{\mu} = X\hat{\beta}$).

With random slopes, each random-effects term contributes the variance of its contribution to the linear predictor averaged over the estimation sample, $\overline{z_i^\top \Sigma z_i}$ (Johnson 2014). For GLMMs the distribution-specific variance is $\pi^2/3$ (logit), 1 (probit), $\pi^2/6$ (complementary log-log), or the lognormal approximation for Poisson models (Nakagawa et al. 2017).

The level-specific variance partition (r2_mlm-style) decomposes the total modelled variance ($\sigma^2_f + \sum \sigma^2_j + \sigma^2_\epsilon$) to show how much each source contributes, printed as a breakdown table.

Value

A list of class "nl_r2" returned invisibly and pretty-printed automatically. It contains type (one of "OLS", "GAM", "LMM", or "GLMM"), r2 (a named numeric vector: R2 and R2_adj for OLS; R2_dev for GAM; R2m and R2c for LMM/GLMM), and variance_partition (a data frame with columns component, variance, and proportion for multilevel models, or NULL for single-level models).

References

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R-squared from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142.

Nakagawa, S., Johnson, P. C. D., & Schielzeth, H. (2017). The coefficient of determination R-squared and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.

Johnson, P. C. D. (2014). Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models. Methods in Ecology and Evolution, 5(9), 944–946.

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309–338.

See Also

nl_fit, nl_icc

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
nl_r2(fit)

---

Tidy coefficient table for an nl_fit model

Description

Produces a tidy coefficient table for a model fitted by nl_fit. For linear mixed models (lmerMod), optional p-values and denominator degrees of freedom are obtained via lmerTest (Satterthwaite method). For GLMMs (glmerMod), z-tests are reported from summary().

Usage

nl_summary(
  object,
  digits = 3,
  pvals = TRUE,
  df_method = c("satterthwaite", "none")
)

Arguments

object	An nl_fit object returned by nl_fit.
digits	Integer; number of decimal places for rounding. If NULL, no rounding is applied. Default 3.
pvals	Logical; if TRUE, attempts to include p-values. Default TRUE.
df_method	For lmerMod with pvals = TRUE: "satterthwaite" (requires lmerTest) or "none". Default "satterthwaite".

Value

A data frame (or tibble) with columns: Term, Estimate, Std.Error, df (if available), statistic, and p.value (if available).

See Also

nl_fit, summary.nl_fit

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
nl_summary(fit)

---

Identify turning points and inflection regions

Description

From the derivative table returned by nl_derivatives, finds turning points (values of x where the first derivative crosses zero), inflection regions (intervals where the second derivative changes sign), and slope regions (contiguous stretches where the curve is increasing, decreasing, or flat).

Usage

nl_turning_points(
  deriv_df,
  x = attr(deriv_df, "x"),
  time = attr(deriv_df, "time"),
  tol = 1e-06
)

Arguments

deriv_df	A data frame returned by nl_derivatives.
x	Character; name of the focal predictor column. Taken from the nl_derivatives() output by default.
time	Optional character; name of the time column. Turning points are identified separately within each time level when supplied.
tol	Numeric tolerance for near-zero detection of d1. Default 1e-6.

Value

A named list with three elements. turning_points is a data frame with columns x, time (if applicable), fit, and type (one of "maximum", "minimum", or "saddle"). inflection_regions is a data frame with columns x_start, x_end, time, and direction ("concave_to_convex" or "convex_to_concave"). slope_regions is a data frame with columns x_start, x_end, direction, and time.

See Also

nl_derivatives, nl_plot

Examples

fit <- nl_fit(mtcars, y = "mpg", x = "hp", df = 3)
tp <- nl_turning_points(nl_derivatives(fit))
tp$slope_regions

---

Joint Wald test of the focal predictor

Description

Tests whether x is associated with the outcome at all, i.e. whether every fixed-effect coefficient of a term involving x (all spline basis columns, and their interactions with time) is zero. The individual spline coefficients reported by nl_summary have no separate interpretation; this joint test is the meaningful one. To test nonlinearity (spline versus a straight line) use nl_compare.

Usage

nl_wald(object)

Arguments

object

An nl_fit object fitted with method = "ns" or "bs".

Value

A one-row data frame with the Wald chi-square statistic, its degrees of freedom, and the p-value. Variance components are treated as known, so for small numbers of clusters the test is approximate.

See Also

nl_compare, nl_summary

Examples

fit <- nl_fit(lme4::sleepstudy, y = "Reaction", x = "Days",
              cluster = "Subject", df = 3)
nl_wald(fit)

---

Print method for nl_fit objects

Description

Compact console display for objects returned by nl_fit. Shows key metadata (method, outcome, predictor, time, clustering, family, and controls) and the fitted model formula.

Usage

## S3 method for class 'nl_fit'
print(x, ...)

Arguments

x	An object of class nl_fit.
...	Further arguments (currently ignored).

Value

x invisibly.

---

Print method for summary_nl_fit objects

Description

Prints a summary_nl_fit object returned by summary.nl_fit.

Usage

## S3 method for class 'summary_nl_fit'
print(x, ...)

Arguments

x	A summary_nl_fit object.
...	Further arguments passed to print.

Value

x invisibly.

---

Summary method for nl_fit objects

Description

Produces a tidy coefficient table via nl_summary and wraps it in a summary_nl_fit object for pretty printing.

Usage

## S3 method for class 'nl_fit'
summary(
  object,
  digits = 3,
  pvals = TRUE,
  df_method = c("satterthwaite", "none"),
  ...
)

Arguments

object	An nl_fit object returned by nl_fit.
digits	Number of decimal places for rounding. Default 3.
pvals	Logical; if TRUE, attempts to include p-values. Default TRUE.
df_method	For lmerMod: "satterthwaite" (requires lmerTest) or "none". Default "satterthwaite".
...	Further arguments passed to nl_summary.

Value

An object of class summary_nl_fit containing call, formula, method, and table.

---