View source: R/gamljgmixed.f.R
gamlj_gmixed | R Documentation |
Generalized Mixed Model
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
)
formula |
(optional) the formula to use, see the examples |
data |
the data as a data frame |
model_type |
Select the generalized linear model:
|
dep |
a string naming the dependent variable from |
factors |
a vector of strings naming the fixed factors from
|
covs |
a vector of strings naming the covariates from |
model_terms |
a list of character vectors describing fixed effects
terms. Not needed if |
fixed_intercept |
|
cluster |
a vector of strings naming the clustering variables from
|
re |
a list of lists specifying the models random effects. |
re_corr |
|
re_lrt |
|
re_ci |
|
nested_terms |
a list of character vectors describing effects terms for the nested model. It can be passed as right-hand formula. |
nested_intercept |
|
nested_re |
a list of lists specifying the models random effects. |
es |
Effect size indices. |
expb_ci |
|
estimates_ci |
|
ci_method |
The method used to compute the confidence intervals. |
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 |
show_contrastnames |
|
show_contrastcodes |
|
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 |
|
plot_yscale |
|
plot_xoriginal |
|
plot_black |
|
plot_around |
|
plot_re |
|
plot_re_method |
. |
plot_scale |
Plot ordinal model predicted values in as probabilities
( |
emmeans |
a rhs formula with the terms specifying the marginal means
to estimate (of the form |
posthoc |
a rhs formula with the terms specifying the table to apply
the comparisons (of the form |
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 |
|
ccm_value |
Covariates conditioning mean offset value: how many
st.deviations around the means used to condition simple effects and plots.
Used if |
ccp_value |
Covariates conditioning percentile offset value: number of
percentiles around the median used to condition simple effects and plots.
Used if |
covs_scale_labels |
how the levels of a continuous moderator should
appear in tables and plots: |
adjust |
one or more of |
covs_scale |
a list of lists specifying the covariates scaling, one of
|
scale_missing |
. |
norm_test |
|
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)
data(clustermanymodels)
clustermanymodels$ybin<-factor(clustermanymodels$ybin)
GAMLj3::gamlj_gmixed(formula = ybin ~ 1 + x+( 1|cluster ),
data = clustermanymodels,
model_type="logistic"
)
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