gamlj_gmixed: Generalized Mixed Model

In gamlj/gamlj: GAMLj Suite for linear models

Generalized Mixed Model

Description

Generalized Mixed Model

Usage

gamlj_gmixed(
  formula = NULL,
  data = NULL,
  model_type = "logistic",
  dep = NULL,
  factors = NULL,
  covs = NULL,
  model_terms = NULL,
  fixed_intercept = TRUE,
  cluster = cluster,
  re = NULL,
  re_corr = "all",
  re_lrt = FALSE,
  re_ci = FALSE,
  nested_terms = NULL,
  nested_intercept = NULL,
  nested_re = NULL,
  es = list("expb"),
  expb_ci = TRUE,
  estimates_ci = FALSE,
  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",
  plot_scale = "response",
  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,
  scale_missing = "complete",
  norm_test = FALSE
)

Arguments

formula	(optional) the formula to use, see the examples
data	the data as a data frame
model_type	Select the generalized linear model: logistic,probit,nb for negative binomial poisson,multinomial, ordinal for cumulative logit models.
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.
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 and "1" is passed explicitly to the formula.#'
cluster	a vector of strings naming the clustering variables from data, not needed if formula is defined.
re	a list of lists specifying the models random effects.
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.
re_lrt	TRUE or FALSE (default), LRT for the random effects
re_ci	TRUE or FALSE (default), confidence intervals for the random effects
nested_terms	a list of character vectors describing effects terms for the nested model. It can be passed as right-hand formula.
nested_intercept	TRUE (default) or FALSE, estimates fixed intercept. Not needed if formula is used.
nested_re	a list of lists specifying the models random effects.
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.
expb_ci	TRUE (default) or FALSE , exp(B) CI in table
estimates_ci	TRUE or FALSE (default), parameters CI in table
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
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 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.
plot_re	TRUE or FALSE (default), add predicted values based on random effect in plot
plot_re_method	.
plot_scale	Plot ordinal model predicted values in as probabilities (response) or predicted class (mean.class)
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
scale_missing	.
norm_test	TRUE or FALSE (default), provide a test for normality of residuals

Value

A results object containing:

results$model					a property
results$info					a table
results$main$r2					a table of R
results$main$fit					a table
results$main$anova					a table of omnibus
results$main$coefficients					a table
results$main$contrastCodeTables					an array of contrast coefficients tables
results$main$marginals					a table
results$main$relativerisk					a table
results$main$random					a table
results$main$randomcov					a table
results$main$multirandom					an array
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$plotnotes					a html
results$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)
clustermanymodels$ybin<-factor(clustermanymodels$ybin)
GAMLj3::gamlj_gmixed(formula = ybin ~ 1 + x+( 1|cluster ),
                     data = clustermanymodels,
                     model_type="logistic"
                     )
       

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