gamlj_glm: Generalized Linear Model

In gamlj/gamlj: GAMLj Suite for linear models

Generalized Linear Model

Description

Generalized Linear Model with options to estimate logistic, probit, ordinal, multinomial and custum link function models. Provides options to estimate posthoc, simple effects and slopes, plots of main effects and interaction. Model comparison is also an option.

Usage

gamlj_glm(
  formula = NULL,
  data,
  model_type = "linear",
  dep = NULL,
  factors = NULL,
  covs = NULL,
  offset = NULL,
  model_terms = NULL,
  fixed_intercept = TRUE,
  nested_intercept = NULL,
  custom_family = "gaussian",
  custom_link = "identity",
  nested_terms = NULL,
  omnibus = "LRT",
  estimates_ci = FALSE,
  ci_method = "wald",
  boot_r = 1000,
  ci_width = 95,
  contrasts = NULL,
  show_contrastnames = TRUE,
  show_contrastcodes = FALSE,
  vcov = FALSE,
  plot_x = NULL,
  plot_z = NULL,
  plot_by = NULL,
  plot_raw = FALSE,
  plot_yscale = FALSE,
  plot_xoriginal = FALSE,
  plot_black = FALSE,
  plot_around = "ci",
  emmeans = NULL,
  posthoc = NULL,
  simple_x = NULL,
  simple_mods = NULL,
  simple_interactions = FALSE,
  covs_conditioning = "mean_sd",
  ccm_value = 1,
  ccp_value = 25,
  covs_scale_labels = "labels",
  adjust = list("bonf"),
  covs_scale = NULL,
  expb_ci = TRUE,
  es = list("expb"),
  propodds_test = FALSE,
  plot_scale = "response"
)

Arguments

formula	(optional) the formula of the model compatible with glm. If not passed, model terms should be defined as a list in model_terms option.
data	the data as a data.frame
model_type	define the type of model (link function and distribution combination) required. It can be linear, poisson, poiover, nb, logistic, probit, custom, ordinal, multinomial, and beta.
dep	a string naming the dependent variable from data; the variable must be numeric. Not needed if formula is used.
factors	a vector of strings naming the fixed factors from data. Not needed if formula is used.
covs	a vector of strings naming the covariates from data. Not needed if formula is used.
offset	a vector of strings naming the offset variables.
model_terms	a list of character vectors describing fixed effects terms. Not needed if formula is used.
fixed_intercept	TRUE (default) or FALSE, estimates fixed intercept. Not needed if formula is used.
nested_intercept	TRUE (default) or FALSE, estimates fixed intercept. Not needed if formula is used.
custom_family	Distribution family for the custom model, accepts gaussian, binomial, gamma and inverse_gaussian.
custom_link	Distribution family for the model_type="custom", accepts identity, log and inverse. Use onemu2 for 1/mu^2).
nested_terms	a list of character vectors describing effects terms for nested model. It can be passed as right-hand formula.
omnibus	Whether the omnibus test for the model should be wald or LRT.
estimates_ci	TRUE (default) or FALSE , coefficients CI in tables
ci_method	The method used to compute the confidence intervals. 'wald' uses the Wald method to compute standard errors and confidence intervals. 'profile' computes Profile Likelihood Based Confidence Interval, in which the bounds are chosen based on the percentiles of the chi-square distribution around the maximum likelihood estimate. 'quantile' performs a non-parametric boostrap, with 'boot_r' repetitions, and compute the CI based on the percentiles of the boostrap distribution. 'bcai' implements the bias-corrected bootstrap method.
boot_r	a number bootstrap repetitions.
ci_width	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the plots.
contrasts	a named vector of the form c(var1="type", var2="type2") specifying the type of contrast to use, one of deviation, simple, dummy, difference, helmert, repeated or 'polynomial'. If NULL, simple is used. Can also be passed as a list of list of the form list(list(var="var1",type="type1")).
show_contrastnames	TRUE or FALSE (default), shows raw names of the contrasts variables in tables
show_contrastcodes	TRUE or FALSE (default), shows contrast coefficients tables
vcov	TRUE or FALSE (default), shows coefficients covariances
plot_x	a string naming the variable placed on the horizontal axis of the plot
plot_z	a string naming the variable represented as separate lines in the plot
plot_by	a list of variables defining the levels at which a separate plot should be produced.
plot_raw	TRUE or FALSE (default), plot raw data along the predicted values
plot_yscale	TRUE or FALSE (default), set the Y-axis range equal to the range of the observed values.
plot_xoriginal	TRUE or FALSE (default), use original scale for covariates.
plot_black	TRUE or FALSE (default), use different linetypes per levels.
plot_around	'none' (default), 'ci', or 'se'. Use no error bars, use confidence intervals, or use standard errors on the plots, respectively.
emmeans	a rhs formula with the terms specifying the marginal means to estimate (of the form '~x+x:z')
posthoc	a rhs formula with the terms specifying the table to apply the comparisons (of the form '~x+x:z'). The formula is not expanded, so x*z becomes x+z and not x+z+x:z. It can be passed also as a list of the form 'list("x","z",c("x","z")'
simple_x	The variable for which the simple effects (slopes) are computed
simple_mods	the variable that provides the levels at which the simple effects are computed
simple_interactions	should simple Interactions be computed
covs_conditioning	'mean_sd' (default), 'custom' , or 'percent'. Use to condition the covariates (if any)
ccm_value	how many st.deviations around the means used to condition simple effects and plots. Used if simpleScale='mean_sd'
ccp_value	offsett (number of percentiles) around the median used to condition simple effects and plots. Used if simpleScale='percent'
covs_scale_labels	how the levels of a continuous moderator should appear in tables and plots: labels, values and values_labels, ovalues, ovalues_labels. The latter two refer to the variable original levels, before scaling.
adjust	one or more of 'none', 'bonf','tukey' 'holm'; provide no, Bonferroni, Tukey and Holm Post Hoc corrections respectively.
covs_scale	a named vector of the form c(var1='type', var2='type2') specifying the transformation to apply to covariates, one of 'centered' to the mean, 'standardized','log' or 'none'. 'none' leaves the variable as it is.
expb_ci	TRUE (default) or FALSE , exp(B) CI in table
es	Effect size indices. expb (default) exponentiates the coefficients. For dichotomous dependent variables relative risk indices (RR) can be obtained. marginals computes the marginal effects.
propodds_test	Test parallel lines assumptions in cumulative link model (ordinal regression)
plot_scale	Chi-squared computation method. 'lrt' (default) gives LogLikelihood ration test, 'wald' gives the Wald Chi-squared.

Value

A results object containing:

results$model					The underlying estimated model
results$info					a table
results$main$r2					a table of R
results$main$fit					a table
results$main$anova					a table of ANOVA results
results$main$coefficients					a table
results$main$vcov					a table
results$main$contrastCodeTables					an array of contrast coefficients tables
results$main$marginals					a table
results$main$relativerisk					a table
results$main$paralleltest					a table
results$posthoc					an array of post-hoc tables
results$simpleEffects$anova					a table of ANOVA for simple effects
results$simpleEffects$coefficients					a table
results$simpleInteractions					an array of simple interactions tables
results$emmeans					an array of predicted means tables
results$mainPlots					an array of results plots
results$plotnotes					a html
results$predicted					an output
results$residuals					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$info$asDF

as.data.frame(results$info)

Examples

   data <- emmeans::neuralgia
   GAMLj3::gamlj_glm(formula = Pain ~ Duration,
                    data = data,
                    model_type = "logistic"
                    )
     

gamlj/gamlj documentation built on April 17, 2024, 7:51 p.m.