gamlj_mixed: Mixed Model

In gamlj/gamlj: GAMLj Suite for linear models

Mixed Model

Description

Mixed Linear Model. Estimates models using lmer and lmer functions 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_mixed(
  formula = NULL,
  data,
  dep = NULL,
  factors = NULL,
  covs = NULL,
  cluster = NULL,
  model_terms = NULL,
  re = NULL,
  reml = TRUE,
  re_lrt = FALSE,
  re_ci = FALSE,
  fixed_intercept = TRUE,
  nested_terms = NULL,
  nested_intercept = NULL,
  nested_re = NULL,
  omnibus = "LRT",
  estimates_ci = TRUE,
  ci_method = "wald",
  boot_r = 1000,
  ci_width = 95,
  contrasts = NULL,
  show_contrastnames = TRUE,
  show_contrastcodes = FALSE,
  plot_x = NULL,
  plot_z = NULL,
  plot_by = NULL,
  plot_raw = FALSE,
  plot_yscale = FALSE,
  plot_xoriginal = FALSE,
  plot_black = FALSE,
  plot_around = "none",
  plot_re = FALSE,
  plot_re_method = "average",
  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,
  dep_scale = "none",
  scale_missing = "complete",
  norm_test = FALSE,
  df_method = "Satterthwaite",
  norm_plot = FALSE,
  qq_plot = FALSE,
  resid_plot = FALSE,
  cluster_boxplot = FALSE,
  cluster_respred = FALSE,
  rand_hist = FALSE
)

Arguments

formula	(optional) the formula of the linear mixed model as defined in lmer.
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.
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.
cluster	a vector of strings naming the clustering variables from data. Not necessary if formula is defined.
model_terms	a list of character vectors describing fixed effects terms. Not needed if formula is used.
re	a list of lists specifying the models random effects.
reml	TRUE (default) or FALSE, should the Restricted ML be re_corrused rather than ML
re_lrt	TRUE or FALSE (default), LRT for the random effects
re_ci	TRUE or FALSE (default), confidence intervals for the random effects
fixed_intercept	TRUE (default) or FALSE, estimates fixed intercept. Overridden if formula is used and contains ~1 or ~0.
nested_terms	a list of character vectors describing effects terms for nestet. It can be passed as right-hand formula.
nested_intercept	TRUE (default) or FALSE, estimates fixed intercept. Not needed if nested_terms is used.
nested_re	a list of lists specifying the models random effects.
omnibus	TRUE (default) or FALSE, estimates fixed intercept. Not needed if formula is used.
estimates_ci	TRUE (default) or FALSE , parameters CI in table
ci_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
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 string naming the variables defining the levels for multiple plots
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 line type per levels.
plot_around	'none' (default), 'ci', or 'se'. Use no error bars, use confidence intervals, or use standard errors on the plots, respectively.
plot_re	TRUE or FALSE (default), add predicted values based on random effect in plot
plot_re_method	Method to plot the random effects. average plots the random effect of the selected variable averaging across all other predictors in the model. full plots the predicted values based on the whole model.
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	Covariates conditioning mean offset value: how many st.deviations around the means used to condition simple effects and plots. Used if covs_conditioning='mean_sd'
ccp_value	Covariates conditioning percentile offset value: number of percentiles around the median used to condition simple effects and plots. Used if covs_conditioning='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	one or more of 'none', 'bonf','tukey' 'holm','scheffe', 'tukey'; provide no, Bonferroni, Tukey and Holm Post Hoc corrections respectively.
covs_scale	a list of lists specifying the covariates scaling, one of 'centered to the mean', 'standardized', or 'none'. 'none' leaves the variable as it is
dep_scale	Re-scale the dependent variable.
scale_missing	.
norm_test	TRUE or FALSE (default), provide a test for normality of residuals
df_method	The method for computing the denominator degrees of freedom and F-statistics. "Satterthwaite" (default) uses Satterthwaite’s method; "Kenward-Roger" uses Kenward-Roger’s method, "lme4" returns the lme4-anova table, i.e., using the anova method for lmerMod objects as defined in the lme4-package
norm_plot	TRUE or FALSE (default), provide a histogram of residuals superimposed by a normal distribution
qq_plot	TRUE or FALSE (default), provide a Q-Q plot of residuals
resid_plot	TRUE or FALSE (default), provide a scatterplot of the residuals against predicted
cluster_boxplot	TRUE or FALSE (default), provide a boxplot of random effects by the first defined clustering variable
cluster_respred	TRUE or FALSE (default), residuals vs predicted by the first defined clustering variable
rand_hist	TRUE or FALSE (default), provide histogram of random Coefficients
re_corr	'all', 'none' (default), or 'block'. When random effects are passed as list of length 1, it decides whether the effects should be correlated, non correlated. If 're' is a list of lists of length > 1, the option is automatially set to 'block'. The option is ignored if the model is passed using formula.

Value

A results object containing:

results$model					a property
results$info					a table
results$main$r2					a table of R
results$main$anova					a table of ANOVA results
results$main$coefficients					a table
results$main$contrastCodeTables					an array of contrast coefficients tables
results$main$random					a table
results$main$randomcov					a table
results$main$ranova					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$plotnotes					a html
results$assumptions$normtest					a table of normality tests
results$assumptions$qqplot					a q-q plot
results$assumptions$normplot					Residual histogram
results$assumptions$residPlot					Residual Predicted plot
results$assumptions$clusterBoxplot					Residuals boxplot by cluster
results$assumptions$clusterResPred					an array of random coefficients histograms
results$assumptions$randHist					an array of random coefficients histograms
results$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(clustermanymodels)
GAMLj3::gamlj_mixed(formula = ycont ~ 1 + x+( 1|cluster ),
                     data = clustermanymodels
                     )

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