gamlj_lm: General Linear Model

In gamlj/gamlj: GAMLj Suite for linear models

General Linear Model

Description

General Linear Model. Estimates models using lm() function and provides options to facilitate estimation of interactions, simple slopes, simple effects, post-hoc tests, contrast analysis, effect size indexes and visualization of the results.

Usage

gamlj_lm(
  formula = NULL,
  data,
  dep = NULL,
  fixed_intercept = TRUE,
  factors = NULL,
  covs = NULL,
  model_terms = NULL,
  nested_terms = NULL,
  nested_intercept = NULL,
  omnibus = "F",
  estimates_ci = TRUE,
  betas_ci = FALSE,
  ci_width = 95,
  ci_method = "wald",
  boot_r = 1000,
  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_scale = NULL,
  covs_conditioning = "mean_sd",
  ccm_value = 1,
  ccp_value = 25,
  covs_scale_labels = "labels",
  adjust = list("bonf"),
  posthoc_es = list("dm"),
  d_ci = FALSE,
  es = list("beta", "etap"),
  homo_test = FALSE,
  qq_plot = FALSE,
  norm_test = FALSE,
  norm_plot = FALSE,
  resid_plot = FALSE,
  intercept_info = FALSE,
  es_info = FALSE,
  dep_scale = "none",
  se_method = "standard"
)

Arguments

formula	(optional) the formula of the model, see the examples. If not passed model terms should be defined as a list in model_terms option.
data	the data as a data frame
dep	a string naming the dependent variable from data; the variable must be numeric. Not needed if formula is used.
fixed_intercept	TRUE (default) or FALSE, estimates fixed intercept. Overridden if formula is used and contains ~1 or ~0.
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.
model_terms	a list of character vectors describing fixed effects terms. Not needed if formula is used.
nested_terms	A right-hand formula for the nested model. It can be passed as a list of character vectors describing effects terms for the nested model.
nested_intercept	TRUE (default) or FALSE, estimates fixed intercept. Overridden if model_ters is a formula and contains ~1 or ~0..
omnibus	Omnibus tests are based on F-test F (default) or loglikelihood ration test LRT.
estimates_ci	TRUE (default) or FALSE , parameters CI in table
betas_ci	TRUE (default) or FALSE , parameters CI in table
ci_width	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the plots.
ci_method	the method to compute the confidence intervals. It can be 'wald' (default) for large samples confidence intervals, 'quantile' for percentile bootstrap method, or 'bcai' for bias corrected accelarated method.
boot_r	a number bootstrap repetitions.
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	a character vector with the variable(s) providing the levels at which the simple effects are computed
simple_interactions	should simple Interactions be computed
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' or 'none'. 'none' leaves the variable as it is.
covs_conditioning	'mean_sd' (default), or 'percent'. How to condition covariates in simple effects and plots. 'mean_sd' for mean +/- 'ccp_value' * sd. 'percent' for median +/-'ccp_value' for percentiles.
ccm_value	how many st.deviations around the means used to condition simple effects and plots. Used if covs_conditioning='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 orginal levels, before scaling.
adjust	adjustment method for postho tests. One or more of 'none', 'bonf','tukey' 'holm'; provide no, Bonferroni, Tukey and Holm Post Hoc corrections respectively.
posthoc_es	effect size indices for mean comparisons. One or more of 'dm', 'ds','g' for Cohen's d (dm=model SD,ds=sample SD ) or Hedge's g
d_ci	TRUE or FALSE (default), d confidence intervals
es	a list of effect sizes to print out. They can be: "eta" for eta-squared, 'etap' for partial eta-squared, 'omega' for omega-squared, 'omegap' for partial omega-squared, 'epsilon' for epsilon-squared, 'epsilonp' for partial epsilon-squared and 'beta' for standardized coefficients (betas). Default is "beta" and "parEta".
homo_test	TRUE or FALSE (default), perform homogeneity tests
qq_plot	TRUE or FALSE (default), provide a Q-Q plot of residuals
norm_test	TRUE or FALSE (default), provide a test for normality of residuals
norm_plot	TRUE or FALSE (default), provide a histogram of residuals superimposed by a normal distribution
resid_plot	TRUE or FALSE (default), provide a scatterplot of the residuals against predicted
intercept_info	TRUE or FALSE (default), provide ìnformation about the intercept (F test, effect size indexes)
es_info	TRUE or FALSE (default), provide ìnformation about the effect size indexes
dep_scale	Re-scale the dependent variable.
se_method	Method to compute the standard error. Classical standard errors is the default standard. Four methods for heteroschedasticy-consistent standard errors are available: HC0, HC1,HC2,HC3, from package sandwich . See vcovHC for details.

Value

A results object containing:

results$model					a property
results$info					a table
results$main$r2					a table of R
results$main$intercept					a table of information for the model intercept
results$main$anova					a table of ANOVA results
results$main$effectsizes					a table of effect size indeces
results$main$coefficients					a table
results$main$vcov					a table
results$main$contrastCodeTables					an array of contrast coefficients tables
results$posthoc					an array of post-hoc tables
results$posthocEffectSize					an array of post-hoc effect size
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$assumptions$homotest					a table of homogeneity tests
results$assumptions$normtest					a table of normality tests
results$assumptions$qqplot					a q-q plot
results$assumptions$normPlot					Residual histogram
results$assumptions$residPlot					Residual Predicted plot
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('ToothGrowth')
GAMLj3::gamlj_lm(formula = len ~ supp,  data = ToothGrowth)

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