R/gamljmixed.h.R
In mcfanda/gamlj: GAMLj Suite for linear models
# This file is automatically generated, you probably don't want to edit this
gamljmixedOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
.caller = "lmer",
.interface = "jamovi",
dep = NULL,
factors = NULL,
covs = NULL,
model_terms = NULL,
fixed_intercept = TRUE,
nested_terms = NULL,
comparison = FALSE,
nested_intercept = TRUE,
estimates_ci = TRUE,
re_ci = FALSE,
donotrun = FALSE,
ci_method = "wald",
boot_r = 1000,
ci_width = 95,
contrasts = NULL,
show_contrastnames = FALSE,
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_extremes = FALSE,
emmeans = NULL,
posthoc = NULL,
posthoc_ci = FALSE,
simple_x = NULL,
simple_mods = NULL,
simple_interactions = FALSE,
covs_conditioning = "mean_sd",
ccra_steps = 1,
ccm_value = 1,
ccp_value = 25,
covs_scale_labels = "labels",
adjust = list(
"bonf"),
model_type = "lmer",
covs_scale = NULL,
dep_scale = "none",
scale_missing = "colwise",
norm_test = FALSE,
cluster = NULL,
re = list(
list()),
nested_re = list(
list()),
re_corr = "all",
re_modelterms = TRUE,
re_crossedclusters = FALSE,
re_nestedclusters = FALSE,
re_listing = "none",
reml = TRUE,
re_lrt = FALSE,
res_struct = "id",
df_method = "Satterthwaite",
norm_plot = FALSE,
qq_plot = FALSE,
resid_plot = FALSE,
cluster_boxplot = FALSE,
cluster_respred = FALSE,
cluster_respred_grid = FALSE,
rand_hist = FALSE,
mute = FALSE,
more_fit_indices = FALSE, ...) {
super$initialize(
package="GAMLj3",
name="gamljmixed",
requiresData=TRUE,
...)
private$...caller <- jmvcore::OptionString$new(
".caller",
.caller,
default="lmer",
hidden=TRUE)
private$...interface <- jmvcore::OptionString$new(
".interface",
.interface,
default="jamovi",
hidden=TRUE)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
default=NULL,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..model_terms <- jmvcore::OptionTerms$new(
"model_terms",
model_terms,
default=NULL)
private$..fixed_intercept <- jmvcore::OptionBool$new(
"fixed_intercept",
fixed_intercept,
default=TRUE)
private$..nested_terms <- jmvcore::OptionTerms$new(
"nested_terms",
nested_terms,
default=NULL)
private$..comparison <- jmvcore::OptionBool$new(
"comparison",
comparison,
default=FALSE)
private$..nested_intercept <- jmvcore::OptionBool$new(
"nested_intercept",
nested_intercept,
default=TRUE)
private$..estimates_ci <- jmvcore::OptionBool$new(
"estimates_ci",
estimates_ci,
default=TRUE)
private$..re_ci <- jmvcore::OptionBool$new(
"re_ci",
re_ci,
default=FALSE)
private$..donotrun <- jmvcore::OptionBool$new(
"donotrun",
donotrun,
default=FALSE)
private$..ci_method <- jmvcore::OptionList$new(
"ci_method",
ci_method,
default="wald",
options=list(
"wald",
"quantile"))
private$..boot_r <- jmvcore::OptionNumber$new(
"boot_r",
boot_r,
min=1,
default=1000)
private$..ci_width <- jmvcore::OptionNumber$new(
"ci_width",
ci_width,
min=50,
max=99.9,
default=95)
private$..contrasts <- jmvcore::OptionArray$new(
"contrasts",
contrasts,
items="(factors)",
default=NULL,
template=jmvcore::OptionGroup$new(
"contrasts",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"simple",
"deviation",
"dummy",
"difference",
"helmert",
"repeated",
"polynomial"),
default="simple"))))
private$..show_contrastnames <- jmvcore::OptionBool$new(
"show_contrastnames",
show_contrastnames,
default=FALSE)
private$..show_contrastcodes <- jmvcore::OptionBool$new(
"show_contrastcodes",
show_contrastcodes,
default=FALSE)
private$..plot_x <- jmvcore::OptionVariable$new(
"plot_x",
plot_x,
default=NULL)
private$..plot_z <- jmvcore::OptionVariable$new(
"plot_z",
plot_z,
default=NULL)
private$..plot_by <- jmvcore::OptionVariables$new(
"plot_by",
plot_by,
default=NULL)
private$..plot_raw <- jmvcore::OptionBool$new(
"plot_raw",
plot_raw,
default=FALSE)
private$..plot_yscale <- jmvcore::OptionBool$new(
"plot_yscale",
plot_yscale,
default=FALSE)
private$..plot_xoriginal <- jmvcore::OptionBool$new(
"plot_xoriginal",
plot_xoriginal,
default=FALSE)
private$..plot_black <- jmvcore::OptionBool$new(
"plot_black",
plot_black,
default=FALSE)
private$..plot_around <- jmvcore::OptionList$new(
"plot_around",
plot_around,
options=list(
"none",
"ci",
"se"),
default="none")
private$..plot_re <- jmvcore::OptionBool$new(
"plot_re",
plot_re,
default=FALSE)
private$..plot_re_method <- jmvcore::OptionList$new(
"plot_re_method",
plot_re_method,
default="average",
options=list(
"average",
"full"))
private$..plot_extremes <- jmvcore::OptionBool$new(
"plot_extremes",
plot_extremes,
default=FALSE)
private$..emmeans <- jmvcore::OptionTerms$new(
"emmeans",
emmeans,
default=NULL)
private$..posthoc <- jmvcore::OptionTerms$new(
"posthoc",
posthoc,
default=NULL)
private$..posthoc_ci <- jmvcore::OptionBool$new(
"posthoc_ci",
posthoc_ci,
default=FALSE)
private$..simple_x <- jmvcore::OptionVariable$new(
"simple_x",
simple_x,
default=NULL)
private$..simple_mods <- jmvcore::OptionVariables$new(
"simple_mods",
simple_mods,
default=NULL)
private$..simple_interactions <- jmvcore::OptionBool$new(
"simple_interactions",
simple_interactions,
default=FALSE)
private$..covs_conditioning <- jmvcore::OptionList$new(
"covs_conditioning",
covs_conditioning,
options=list(
"mean_sd",
"percent",
"range"),
default="mean_sd")
private$..ccra_steps <- jmvcore::OptionNumber$new(
"ccra_steps",
ccra_steps,
default=1,
min=1,
max=50)
private$..ccm_value <- jmvcore::OptionNumber$new(
"ccm_value",
ccm_value,
default=1)
private$..ccp_value <- jmvcore::OptionNumber$new(
"ccp_value",
ccp_value,
default=25,
min=5,
max=50)
private$..covs_scale_labels <- jmvcore::OptionList$new(
"covs_scale_labels",
covs_scale_labels,
options=list(
"labels",
"values",
"values_labels",
"uvalues",
"uvalues_labels"),
default="labels")
private$..adjust <- jmvcore::OptionNMXList$new(
"adjust",
adjust,
options=list(
"none",
"bonf",
"tukey",
"holm",
"scheffe",
"sidak"),
default=list(
"bonf"))
private$..predicted <- jmvcore::OptionOutput$new(
"predicted")
private$..residuals <- jmvcore::OptionOutput$new(
"residuals")
private$..model_type <- jmvcore::OptionList$new(
"model_type",
model_type,
hidden=TRUE,
options=list(
"lmer"),
default="lmer")
private$..covs_scale <- jmvcore::OptionArray$new(
"covs_scale",
covs_scale,
items="(covs)",
default=NULL,
template=jmvcore::OptionGroup$new(
"covs_scale",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"centered",
"standardized",
"clusterbasedcentered",
"clustermeans",
"clusterbasedstandardized",
"none"),
default="centered"))))
private$..dep_scale <- jmvcore::OptionList$new(
"dep_scale",
dep_scale,
options=list(
"none",
"centered",
"standardized",
"clusterbasedcentered",
"clustermeans",
"clusterbasedstandardized"),
default="none")
private$..scale_missing <- jmvcore::OptionList$new(
"scale_missing",
scale_missing,
options=list(
"colwise",
"complete"),
default="colwise")
private$..norm_test <- jmvcore::OptionBool$new(
"norm_test",
norm_test,
default=FALSE)
private$..cluster <- jmvcore::OptionVariables$new(
"cluster",
cluster,
default=NULL,
suggested=list(
"nominal"))
private$..re <- jmvcore::OptionArray$new(
"re",
re,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"re",
NULL))
private$..nested_re <- jmvcore::OptionArray$new(
"nested_re",
nested_re,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"nested_re",
NULL))
private$..re_corr <- jmvcore::OptionList$new(
"re_corr",
re_corr,
options=list(
"all",
"none",
"block"),
default="all")
private$..re_modelterms <- jmvcore::OptionBool$new(
"re_modelterms",
re_modelterms,
default=TRUE)
private$..re_crossedclusters <- jmvcore::OptionBool$new(
"re_crossedclusters",
re_crossedclusters,
default=FALSE)
private$..re_nestedclusters <- jmvcore::OptionBool$new(
"re_nestedclusters",
re_nestedclusters,
default=FALSE)
private$..re_listing <- jmvcore::OptionList$new(
"re_listing",
re_listing,
options=list(
"none",
"main",
"way2",
"way3",
"all"),
default="none")
private$..reml <- jmvcore::OptionBool$new(
"reml",
reml,
default=TRUE)
private$..re_lrt <- jmvcore::OptionBool$new(
"re_lrt",
re_lrt,
default=FALSE)
private$..res_struct <- jmvcore::OptionList$new(
"res_struct",
res_struct,
default="id",
options=list(
"id",
"cs",
"un",
"ar1"))
private$..df_method <- jmvcore::OptionList$new(
"df_method",
df_method,
default="Satterthwaite",
options=list(
"Satterthwaite",
"Kenward-Roger"))
private$..norm_plot <- jmvcore::OptionBool$new(
"norm_plot",
norm_plot,
default=FALSE)
private$..qq_plot <- jmvcore::OptionBool$new(
"qq_plot",
qq_plot,
default=FALSE)
private$..resid_plot <- jmvcore::OptionBool$new(
"resid_plot",
resid_plot,
default=FALSE)
private$..cluster_boxplot <- jmvcore::OptionBool$new(
"cluster_boxplot",
cluster_boxplot,
default=FALSE)
private$..cluster_respred <- jmvcore::OptionBool$new(
"cluster_respred",
cluster_respred,
default=FALSE)
private$..cluster_respred_grid <- jmvcore::OptionBool$new(
"cluster_respred_grid",
cluster_respred_grid,
default=FALSE)
private$..rand_hist <- jmvcore::OptionBool$new(
"rand_hist",
rand_hist,
default=FALSE)
private$..mute <- jmvcore::OptionBool$new(
"mute",
mute,
default=FALSE)
private$..more_fit_indices <- jmvcore::OptionBool$new(
"more_fit_indices",
more_fit_indices,
default=FALSE)
self$.addOption(private$...caller)
self$.addOption(private$...interface)
self$.addOption(private$..dep)
self$.addOption(private$..factors)
self$.addOption(private$..covs)
self$.addOption(private$..model_terms)
self$.addOption(private$..fixed_intercept)
self$.addOption(private$..nested_terms)
self$.addOption(private$..comparison)
self$.addOption(private$..nested_intercept)
self$.addOption(private$..estimates_ci)
self$.addOption(private$..re_ci)
self$.addOption(private$..donotrun)
self$.addOption(private$..ci_method)
self$.addOption(private$..boot_r)
self$.addOption(private$..ci_width)
self$.addOption(private$..contrasts)
self$.addOption(private$..show_contrastnames)
self$.addOption(private$..show_contrastcodes)
self$.addOption(private$..plot_x)
self$.addOption(private$..plot_z)
self$.addOption(private$..plot_by)
self$.addOption(private$..plot_raw)
self$.addOption(private$..plot_yscale)
self$.addOption(private$..plot_xoriginal)
self$.addOption(private$..plot_black)
self$.addOption(private$..plot_around)
self$.addOption(private$..plot_re)
self$.addOption(private$..plot_re_method)
self$.addOption(private$..plot_extremes)
self$.addOption(private$..emmeans)
self$.addOption(private$..posthoc)
self$.addOption(private$..posthoc_ci)
self$.addOption(private$..simple_x)
self$.addOption(private$..simple_mods)
self$.addOption(private$..simple_interactions)
self$.addOption(private$..covs_conditioning)
self$.addOption(private$..ccra_steps)
self$.addOption(private$..ccm_value)
self$.addOption(private$..ccp_value)
self$.addOption(private$..covs_scale_labels)
self$.addOption(private$..adjust)
self$.addOption(private$..predicted)
self$.addOption(private$..residuals)
self$.addOption(private$..model_type)
self$.addOption(private$..covs_scale)
self$.addOption(private$..dep_scale)
self$.addOption(private$..scale_missing)
self$.addOption(private$..norm_test)
self$.addOption(private$..cluster)
self$.addOption(private$..re)
self$.addOption(private$..nested_re)
self$.addOption(private$..re_corr)
self$.addOption(private$..re_modelterms)
self$.addOption(private$..re_crossedclusters)
self$.addOption(private$..re_nestedclusters)
self$.addOption(private$..re_listing)
self$.addOption(private$..reml)
self$.addOption(private$..re_lrt)
self$.addOption(private$..res_struct)
self$.addOption(private$..df_method)
self$.addOption(private$..norm_plot)
self$.addOption(private$..qq_plot)
self$.addOption(private$..resid_plot)
self$.addOption(private$..cluster_boxplot)
self$.addOption(private$..cluster_respred)
self$.addOption(private$..cluster_respred_grid)
self$.addOption(private$..rand_hist)
self$.addOption(private$..mute)
self$.addOption(private$..more_fit_indices)
}),
active = list(
.caller = function() private$...caller$value,
.interface = function() private$...interface$value,
dep = function() private$..dep$value,
factors = function() private$..factors$value,
covs = function() private$..covs$value,
model_terms = function() private$..model_terms$value,
fixed_intercept = function() private$..fixed_intercept$value,
nested_terms = function() private$..nested_terms$value,
comparison = function() private$..comparison$value,
nested_intercept = function() private$..nested_intercept$value,
estimates_ci = function() private$..estimates_ci$value,
re_ci = function() private$..re_ci$value,
donotrun = function() private$..donotrun$value,
ci_method = function() private$..ci_method$value,
boot_r = function() private$..boot_r$value,
ci_width = function() private$..ci_width$value,
contrasts = function() private$..contrasts$value,
show_contrastnames = function() private$..show_contrastnames$value,
show_contrastcodes = function() private$..show_contrastcodes$value,
plot_x = function() private$..plot_x$value,
plot_z = function() private$..plot_z$value,
plot_by = function() private$..plot_by$value,
plot_raw = function() private$..plot_raw$value,
plot_yscale = function() private$..plot_yscale$value,
plot_xoriginal = function() private$..plot_xoriginal$value,
plot_black = function() private$..plot_black$value,
plot_around = function() private$..plot_around$value,
plot_re = function() private$..plot_re$value,
plot_re_method = function() private$..plot_re_method$value,
plot_extremes = function() private$..plot_extremes$value,
emmeans = function() private$..emmeans$value,
posthoc = function() private$..posthoc$value,
posthoc_ci = function() private$..posthoc_ci$value,
simple_x = function() private$..simple_x$value,
simple_mods = function() private$..simple_mods$value,
simple_interactions = function() private$..simple_interactions$value,
covs_conditioning = function() private$..covs_conditioning$value,
ccra_steps = function() private$..ccra_steps$value,
ccm_value = function() private$..ccm_value$value,
ccp_value = function() private$..ccp_value$value,
covs_scale_labels = function() private$..covs_scale_labels$value,
adjust = function() private$..adjust$value,
predicted = function() private$..predicted$value,
residuals = function() private$..residuals$value,
model_type = function() private$..model_type$value,
covs_scale = function() private$..covs_scale$value,
dep_scale = function() private$..dep_scale$value,
scale_missing = function() private$..scale_missing$value,
norm_test = function() private$..norm_test$value,
cluster = function() private$..cluster$value,
re = function() private$..re$value,
nested_re = function() private$..nested_re$value,
re_corr = function() private$..re_corr$value,
re_modelterms = function() private$..re_modelterms$value,
re_crossedclusters = function() private$..re_crossedclusters$value,
re_nestedclusters = function() private$..re_nestedclusters$value,
re_listing = function() private$..re_listing$value,
reml = function() private$..reml$value,
re_lrt = function() private$..re_lrt$value,
res_struct = function() private$..res_struct$value,
df_method = function() private$..df_method$value,
norm_plot = function() private$..norm_plot$value,
qq_plot = function() private$..qq_plot$value,
resid_plot = function() private$..resid_plot$value,
cluster_boxplot = function() private$..cluster_boxplot$value,
cluster_respred = function() private$..cluster_respred$value,
cluster_respred_grid = function() private$..cluster_respred_grid$value,
rand_hist = function() private$..rand_hist$value,
mute = function() private$..mute$value,
more_fit_indices = function() private$..more_fit_indices$value),
private = list(
...caller = NA,
...interface = NA,
..dep = NA,
..factors = NA,
..covs = NA,
..model_terms = NA,
..fixed_intercept = NA,
..nested_terms = NA,
..comparison = NA,
..nested_intercept = NA,
..estimates_ci = NA,
..re_ci = NA,
..donotrun = NA,
..ci_method = NA,
..boot_r = NA,
..ci_width = NA,
..contrasts = NA,
..show_contrastnames = NA,
..show_contrastcodes = NA,
..plot_x = NA,
..plot_z = NA,
..plot_by = NA,
..plot_raw = NA,
..plot_yscale = NA,
..plot_xoriginal = NA,
..plot_black = NA,
..plot_around = NA,
..plot_re = NA,
..plot_re_method = NA,
..plot_extremes = NA,
..emmeans = NA,
..posthoc = NA,
..posthoc_ci = NA,
..simple_x = NA,
..simple_mods = NA,
..simple_interactions = NA,
..covs_conditioning = NA,
..ccra_steps = NA,
..ccm_value = NA,
..ccp_value = NA,
..covs_scale_labels = NA,
..adjust = NA,
..predicted = NA,
..residuals = NA,
..model_type = NA,
..covs_scale = NA,
..dep_scale = NA,
..scale_missing = NA,
..norm_test = NA,
..cluster = NA,
..re = NA,
..nested_re = NA,
..re_corr = NA,
..re_modelterms = NA,
..re_crossedclusters = NA,
..re_nestedclusters = NA,
..re_listing = NA,
..reml = NA,
..re_lrt = NA,
..res_struct = NA,
..df_method = NA,
..norm_plot = NA,
..qq_plot = NA,
..resid_plot = NA,
..cluster_boxplot = NA,
..cluster_respred = NA,
..cluster_respred_grid = NA,
..rand_hist = NA,
..mute = NA,
..more_fit_indices = NA)
)
gamljmixedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedResults",
inherit = jmvcore::Group,
active = list(
model = function() private$..model,
info = function() private$.items[["info"]],
main = function() private$.items[["main"]],
posthoc = function() private$.items[["posthoc"]],
simpleEffects = function() private$.items[["simpleEffects"]],
simpleInteractions = function() private$.items[["simpleInteractions"]],
emmeans = function() private$.items[["emmeans"]],
mainPlots = function() private$.items[["mainPlots"]],
plotnotes = function() private$.items[["plotnotes"]],
assumptions = function() private$.items[["assumptions"]],
predicted = function() private$.items[["predicted"]],
residuals = function() private$.items[["residuals"]]),
private = list(
..model = NA),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Mixed Model")
private$..model <- NULL
self$add(jmvcore::Table$new(
options=options,
name="info",
title="Model Info",
columns=list(
list(
`name`="info",
`type`="text",
`title`="Info"),
list(
`name`="value",
`type`="text",
`title`=""),
list(
`name`="specs",
`type`="text",
`title`="")),
refs="gamlj"))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
r2 = function() private$.items[["r2"]],
fit = function() private$.items[["fit"]],
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]],
contrastCodeTables = function() private$.items[["contrastCodeTables"]],
random = function() private$.items[["random"]],
randomcov = function() private$.items[["randomcov"]],
ranova = function() private$.items[["ranova"]],
res_corr = function() private$.items[["res_corr"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="main",
title="Model Results")
self$add(jmvcore::Table$new(
options=options,
name="r2",
title="Model Fit",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"nested_terms",
"nested_intercept",
"comparison",
"nested_re"),
columns=list(
list(
`name`="model",
`title`="Model",
`visible`="(comparison)"),
list(
`name`="type",
`type`="text",
`title`="Type"),
list(
`name`="r2",
`title`="R\u00B2",
`type`="number",
`format`="zto"),
list(
`name`="df1",
`title`="df",
`type`="integer"),
list(
`name`="test",
`title`="LRT X\u00B2",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
refs="goodness"))
self$add(jmvcore::Table$new(
options=options,
name="fit",
title="Additional Indices",
visible="(more_fit_indices)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"nested_terms",
"nested_intercept",
"comparison",
"nested_re"),
columns=list(
list(
`name`="info",
`type`="text",
`title`="Info"),
list(
`name`="value",
`type`="text",
`title`="Model Value"),
list(
`name`="nested",
`type`="text",
`title`="Nested Model",
`visible`="(comparison)"),
list(
`name`="diff",
`type`="text",
`title`="\u0394",
`visible`="(comparison)",
`format`="zto"),
list(
`name`="specs",
`type`="text",
`title`="Comment"))))
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="Fixed Effects Omnibus Tests",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="f",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="df",
`type`="number"),
list(
`name`="df2",
`title`="df (res)",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates (Fixed coefficients)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="source",
`title`="Names",
`type`="text"),
list(
`name`="label",
`title`="Effect",
`type`="text",
`visible`="(show_contrastnames)"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
refs="parameters"))
self$add(jmvcore::Array$new(
options=options,
name="contrastCodeTables",
title="Contrast Coefficients",
visible="(show_contrastcodes)",
items="(factors)",
clearWith=list(
"contrasts"),
template=jmvcore::Table$new(
options=options,
title="Factor: ___key___",
columns=list(
list(
`name`="cname",
`title`="Name",
`type`="text",
`visible`="(show_contrastnames)"),
list(
`name`="clab",
`title`="Contrast",
`type`="text"),
list(
`name`="bogus",
`title`="bogus",
`type`="text",
`visible`=FALSE)))))
self$add(jmvcore::Table$new(
options=options,
name="random",
title="Random Components",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="groups",
`title`="Groups",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="var1",
`title`="Name",
`type`="text"),
list(
`name`="vcov",
`title`="Variance",
`type`="number"),
list(
`name`="sdcor",
`title`="SD",
`type`="number"),
list(
`name`="sd.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(re_ci)"),
list(
`name`="sd.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(re_ci)"),
list(
`name`="icc",
`title`="ICC",
`type`="number"),
list(
`name`="phi",
`title`="Phi",
`type`="number",
`visible`="(res_struct:ar1)"),
list(
`name`="rho",
`title`="rho",
`type`="number",
`visible`="(res_struct:cs)"))))
self$add(jmvcore::Table$new(
options=options,
name="randomcov",
title="Random Parameters correlations",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="groups",
`title`="Groups",
`combineBelow`=TRUE,
`type`="text"),
list(
`name`="var1",
`title`="param1",
`type`="text"),
list(
`name`="var2",
`title`="param2",
`type`="text"),
list(
`name`="vcov",
`title`="Cov.",
`type`="number"),
list(
`name`="sdcor",
`title`="Corr.",
`type`="number"),
list(
`name`="sd.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(re_ci)",
`superTitle`="Confidence Intervals"),
list(
`name`="sd.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(re_ci)",
`superTitle`="Confidence Intervals"))))
self$add(jmvcore::Table$new(
options=options,
name="ranova",
title="Random Effect LRT",
visible="(re_lrt)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct"),
columns=list(
list(
`name`="test",
`title`="Test",
`combineBelow`=TRUE,
`type`="text"),
list(
`name`="npar",
`title`="N. par",
`type`="number"),
list(
`name`="AIC",
`title`="AIC",
`type`="number"),
list(
`name`="LRT",
`title`="LRT",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="res_corr",
title="Residual correlations",
visible="(res_struct:un)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"res_struct"),
columns=list(
list(
`name`="index",
`title`="Index",
`type`="text"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="posthoc",
title="Post Hoc Tests",
items="(posthoc)",
template=jmvcore::Table$new(
options=options,
title="Post Hoc comparison: ___key___",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"adjust"),
columns=list(
list(
`name`="estimate",
`title`="Difference",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(posthoc_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(posthoc_ci)"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:none)"),
list(
`name`="bonf",
`title`="p<sub>bonferroni</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:bonf)"),
list(
`name`="tukey",
`title`="p<sub>tukey</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:tukey)"),
list(
`name`="holm",
`title`="p<sub>holm</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:holm)"),
list(
`name`="scheffe",
`title`="p<sub>scheffe</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:scheffe)"),
list(
`name`="sidak",
`title`="p<sub>sidak</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:sidak)")))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="simpleEffects",
title="Simple Effects")
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="ANOVA for Simple Effects of ___key___",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="test",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="Num df",
`type`="number"),
list(
`name`="df2",
`title`="Den df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates for simple effects of ___key___",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="contrast",
`title`="Effect",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="simpleInteractions",
title="Simple Interactions",
visible="(simple_interactions)",
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="Interaction: ___key___",
clearWith=list())
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="ANOVA",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="effect",
`title`="Effect",
`type`="text"),
list(
`name`="test",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="df1",
`type`="number"),
list(
`name`="df2",
`title`="df2",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="effect",
`title`="Effect",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="est.ci.lower",
`title`="Lower",
`type`="number"),
list(
`name`="est.ci.upper",
`title`="Upper",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options)))
self$add(jmvcore::Array$new(
options=options,
name="emmeans",
title="Estimated Marginal Means",
visible=FALSE,
items="(emmeans)",
template=jmvcore::Table$new(
options=options,
title="Estimate Marginal Means - ___key___",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="estimate",
`title`="Mean",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="est.ci.lower",
`title`="Lower",
`type`="number"),
list(
`name`="est.ci.upper",
`title`="Upper",
`type`="number")))))
self$add(jmvcore::Array$new(
options=options,
name="mainPlots",
title="Results Plots",
template=jmvcore::Image$new(
options=options,
title="",
renderFun=".mainPlot",
width=700,
height=500)))
self$add(jmvcore::Html$new(
options=options,
name="plotnotes",
visible=FALSE))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
normtest = function() private$.items[["normtest"]],
qqplot = function() private$.items[["qqplot"]],
normPlot = function() private$.items[["normPlot"]],
residPlot = function() private$.items[["residPlot"]],
clusterBoxplot = function() private$.items[["clusterBoxplot"]],
clusterResPred = function() private$.items[["clusterResPred"]],
clusterResPredGrid = function() private$.items[["clusterResPredGrid"]],
randHist = function() private$.items[["randHist"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assumptions",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="normtest",
title="Test for Normality of residuals",
visible="(norm_test)",
clearWith=list(
"model_terms",
"dep",
"reml",
"re"),
columns=list(
list(
`name`="name",
`title`="Test",
`type`="number"),
list(
`name`="test",
`title`="Statistics",
`type`="number"),
list(
`name`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qqplot",
title="Q-Q Plot",
visible="(qq_plot)",
width=700,
height=500,
renderFun=".qqPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"model_terms",
"dep_scale")))
self$add(jmvcore::Image$new(
options=options,
name="normPlot",
title="Residual histogram",
visible="(norm_plot)",
width=700,
height=500,
renderFun=".normPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes")))
self$add(jmvcore::Image$new(
options=options,
name="residPlot",
title="Residual-Predicted Scatterplot",
visible="(resid_plot)",
width=700,
height=700,
renderFun=".residPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"model_terms",
"dep_scale",
"plot_extremes")))
self$add(jmvcore::Array$new(
options=options,
name="clusterBoxplot",
title="Residuals by cluster boxplot",
visible="(cluster_boxplot)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterBoxplot",
width=700,
height=900)))
self$add(jmvcore::Array$new(
options=options,
name="clusterResPred",
title="Residuals-Predicted by cluster",
visible="(cluster_respred)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterResPred",
width=700,
height=700)))
self$add(jmvcore::Array$new(
options=options,
name="clusterResPredGrid",
title="Residuals-Predicted Grid",
visible="(cluster_respred_grid)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterResPredGrid",
width=700,
height=900)))
self$add(jmvcore::Array$new(
options=options,
name="randHist",
title="Random coefficients histogram",
visible="(rand_hist)",
clearWith=list(
"dep",
"dep_scale",
"model_terms"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".randHist",
width=700,
height=500)))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="predicted",
title="Predicted Vales",
varTitle="`MIXED_PRED_${ dep }`",
varDescription="Predicted values",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct")))
self$add(jmvcore::Output$new(
options=options,
name="residuals",
title="Residuals Vales",
varTitle="`MIXED_RES_${ dep }`",
varDescription="Residuals values",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct")))},
.setModel=function(x) private$..model <- x))
gamljmixedBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "GAMLj3",
name = "gamljmixed",
version = c(3,0,0),
options = options,
results = gamljmixedResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
mcfanda/gamlj documentation built on April 15, 2024, 11:16 a.m.
R Package Documentation
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# This file is automatically generated, you probably don't want to edit this
gamljmixedOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
.caller = "lmer",
.interface = "jamovi",
dep = NULL,
factors = NULL,
covs = NULL,
model_terms = NULL,
fixed_intercept = TRUE,
nested_terms = NULL,
comparison = FALSE,
nested_intercept = TRUE,
estimates_ci = TRUE,
re_ci = FALSE,
donotrun = FALSE,
ci_method = "wald",
boot_r = 1000,
ci_width = 95,
contrasts = NULL,
show_contrastnames = FALSE,
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_extremes = FALSE,
emmeans = NULL,
posthoc = NULL,
posthoc_ci = FALSE,
simple_x = NULL,
simple_mods = NULL,
simple_interactions = FALSE,
covs_conditioning = "mean_sd",
ccra_steps = 1,
ccm_value = 1,
ccp_value = 25,
covs_scale_labels = "labels",
adjust = list(
"bonf"),
model_type = "lmer",
covs_scale = NULL,
dep_scale = "none",
scale_missing = "colwise",
norm_test = FALSE,
cluster = NULL,
re = list(
list()),
nested_re = list(
list()),
re_corr = "all",
re_modelterms = TRUE,
re_crossedclusters = FALSE,
re_nestedclusters = FALSE,
re_listing = "none",
reml = TRUE,
re_lrt = FALSE,
res_struct = "id",
df_method = "Satterthwaite",
norm_plot = FALSE,
qq_plot = FALSE,
resid_plot = FALSE,
cluster_boxplot = FALSE,
cluster_respred = FALSE,
cluster_respred_grid = FALSE,
rand_hist = FALSE,
mute = FALSE,
more_fit_indices = FALSE, ...) {
super$initialize(
package="GAMLj3",
name="gamljmixed",
requiresData=TRUE,
...)
private$...caller <- jmvcore::OptionString$new(
".caller",
.caller,
default="lmer",
hidden=TRUE)
private$...interface <- jmvcore::OptionString$new(
".interface",
.interface,
default="jamovi",
hidden=TRUE)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
default=NULL,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..model_terms <- jmvcore::OptionTerms$new(
"model_terms",
model_terms,
default=NULL)
private$..fixed_intercept <- jmvcore::OptionBool$new(
"fixed_intercept",
fixed_intercept,
default=TRUE)
private$..nested_terms <- jmvcore::OptionTerms$new(
"nested_terms",
nested_terms,
default=NULL)
private$..comparison <- jmvcore::OptionBool$new(
"comparison",
comparison,
default=FALSE)
private$..nested_intercept <- jmvcore::OptionBool$new(
"nested_intercept",
nested_intercept,
default=TRUE)
private$..estimates_ci <- jmvcore::OptionBool$new(
"estimates_ci",
estimates_ci,
default=TRUE)
private$..re_ci <- jmvcore::OptionBool$new(
"re_ci",
re_ci,
default=FALSE)
private$..donotrun <- jmvcore::OptionBool$new(
"donotrun",
donotrun,
default=FALSE)
private$..ci_method <- jmvcore::OptionList$new(
"ci_method",
ci_method,
default="wald",
options=list(
"wald",
"quantile"))
private$..boot_r <- jmvcore::OptionNumber$new(
"boot_r",
boot_r,
min=1,
default=1000)
private$..ci_width <- jmvcore::OptionNumber$new(
"ci_width",
ci_width,
min=50,
max=99.9,
default=95)
private$..contrasts <- jmvcore::OptionArray$new(
"contrasts",
contrasts,
items="(factors)",
default=NULL,
template=jmvcore::OptionGroup$new(
"contrasts",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"simple",
"deviation",
"dummy",
"difference",
"helmert",
"repeated",
"polynomial"),
default="simple"))))
private$..show_contrastnames <- jmvcore::OptionBool$new(
"show_contrastnames",
show_contrastnames,
default=FALSE)
private$..show_contrastcodes <- jmvcore::OptionBool$new(
"show_contrastcodes",
show_contrastcodes,
default=FALSE)
private$..plot_x <- jmvcore::OptionVariable$new(
"plot_x",
plot_x,
default=NULL)
private$..plot_z <- jmvcore::OptionVariable$new(
"plot_z",
plot_z,
default=NULL)
private$..plot_by <- jmvcore::OptionVariables$new(
"plot_by",
plot_by,
default=NULL)
private$..plot_raw <- jmvcore::OptionBool$new(
"plot_raw",
plot_raw,
default=FALSE)
private$..plot_yscale <- jmvcore::OptionBool$new(
"plot_yscale",
plot_yscale,
default=FALSE)
private$..plot_xoriginal <- jmvcore::OptionBool$new(
"plot_xoriginal",
plot_xoriginal,
default=FALSE)
private$..plot_black <- jmvcore::OptionBool$new(
"plot_black",
plot_black,
default=FALSE)
private$..plot_around <- jmvcore::OptionList$new(
"plot_around",
plot_around,
options=list(
"none",
"ci",
"se"),
default="none")
private$..plot_re <- jmvcore::OptionBool$new(
"plot_re",
plot_re,
default=FALSE)
private$..plot_re_method <- jmvcore::OptionList$new(
"plot_re_method",
plot_re_method,
default="average",
options=list(
"average",
"full"))
private$..plot_extremes <- jmvcore::OptionBool$new(
"plot_extremes",
plot_extremes,
default=FALSE)
private$..emmeans <- jmvcore::OptionTerms$new(
"emmeans",
emmeans,
default=NULL)
private$..posthoc <- jmvcore::OptionTerms$new(
"posthoc",
posthoc,
default=NULL)
private$..posthoc_ci <- jmvcore::OptionBool$new(
"posthoc_ci",
posthoc_ci,
default=FALSE)
private$..simple_x <- jmvcore::OptionVariable$new(
"simple_x",
simple_x,
default=NULL)
private$..simple_mods <- jmvcore::OptionVariables$new(
"simple_mods",
simple_mods,
default=NULL)
private$..simple_interactions <- jmvcore::OptionBool$new(
"simple_interactions",
simple_interactions,
default=FALSE)
private$..covs_conditioning <- jmvcore::OptionList$new(
"covs_conditioning",
covs_conditioning,
options=list(
"mean_sd",
"percent",
"range"),
default="mean_sd")
private$..ccra_steps <- jmvcore::OptionNumber$new(
"ccra_steps",
ccra_steps,
default=1,
min=1,
max=50)
private$..ccm_value <- jmvcore::OptionNumber$new(
"ccm_value",
ccm_value,
default=1)
private$..ccp_value <- jmvcore::OptionNumber$new(
"ccp_value",
ccp_value,
default=25,
min=5,
max=50)
private$..covs_scale_labels <- jmvcore::OptionList$new(
"covs_scale_labels",
covs_scale_labels,
options=list(
"labels",
"values",
"values_labels",
"uvalues",
"uvalues_labels"),
default="labels")
private$..adjust <- jmvcore::OptionNMXList$new(
"adjust",
adjust,
options=list(
"none",
"bonf",
"tukey",
"holm",
"scheffe",
"sidak"),
default=list(
"bonf"))
private$..predicted <- jmvcore::OptionOutput$new(
"predicted")
private$..residuals <- jmvcore::OptionOutput$new(
"residuals")
private$..model_type <- jmvcore::OptionList$new(
"model_type",
model_type,
hidden=TRUE,
options=list(
"lmer"),
default="lmer")
private$..covs_scale <- jmvcore::OptionArray$new(
"covs_scale",
covs_scale,
items="(covs)",
default=NULL,
template=jmvcore::OptionGroup$new(
"covs_scale",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"centered",
"standardized",
"clusterbasedcentered",
"clustermeans",
"clusterbasedstandardized",
"none"),
default="centered"))))
private$..dep_scale <- jmvcore::OptionList$new(
"dep_scale",
dep_scale,
options=list(
"none",
"centered",
"standardized",
"clusterbasedcentered",
"clustermeans",
"clusterbasedstandardized"),
default="none")
private$..scale_missing <- jmvcore::OptionList$new(
"scale_missing",
scale_missing,
options=list(
"colwise",
"complete"),
default="colwise")
private$..norm_test <- jmvcore::OptionBool$new(
"norm_test",
norm_test,
default=FALSE)
private$..cluster <- jmvcore::OptionVariables$new(
"cluster",
cluster,
default=NULL,
suggested=list(
"nominal"))
private$..re <- jmvcore::OptionArray$new(
"re",
re,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"re",
NULL))
private$..nested_re <- jmvcore::OptionArray$new(
"nested_re",
nested_re,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"nested_re",
NULL))
private$..re_corr <- jmvcore::OptionList$new(
"re_corr",
re_corr,
options=list(
"all",
"none",
"block"),
default="all")
private$..re_modelterms <- jmvcore::OptionBool$new(
"re_modelterms",
re_modelterms,
default=TRUE)
private$..re_crossedclusters <- jmvcore::OptionBool$new(
"re_crossedclusters",
re_crossedclusters,
default=FALSE)
private$..re_nestedclusters <- jmvcore::OptionBool$new(
"re_nestedclusters",
re_nestedclusters,
default=FALSE)
private$..re_listing <- jmvcore::OptionList$new(
"re_listing",
re_listing,
options=list(
"none",
"main",
"way2",
"way3",
"all"),
default="none")
private$..reml <- jmvcore::OptionBool$new(
"reml",
reml,
default=TRUE)
private$..re_lrt <- jmvcore::OptionBool$new(
"re_lrt",
re_lrt,
default=FALSE)
private$..res_struct <- jmvcore::OptionList$new(
"res_struct",
res_struct,
default="id",
options=list(
"id",
"cs",
"un",
"ar1"))
private$..df_method <- jmvcore::OptionList$new(
"df_method",
df_method,
default="Satterthwaite",
options=list(
"Satterthwaite",
"Kenward-Roger"))
private$..norm_plot <- jmvcore::OptionBool$new(
"norm_plot",
norm_plot,
default=FALSE)
private$..qq_plot <- jmvcore::OptionBool$new(
"qq_plot",
qq_plot,
default=FALSE)
private$..resid_plot <- jmvcore::OptionBool$new(
"resid_plot",
resid_plot,
default=FALSE)
private$..cluster_boxplot <- jmvcore::OptionBool$new(
"cluster_boxplot",
cluster_boxplot,
default=FALSE)
private$..cluster_respred <- jmvcore::OptionBool$new(
"cluster_respred",
cluster_respred,
default=FALSE)
private$..cluster_respred_grid <- jmvcore::OptionBool$new(
"cluster_respred_grid",
cluster_respred_grid,
default=FALSE)
private$..rand_hist <- jmvcore::OptionBool$new(
"rand_hist",
rand_hist,
default=FALSE)
private$..mute <- jmvcore::OptionBool$new(
"mute",
mute,
default=FALSE)
private$..more_fit_indices <- jmvcore::OptionBool$new(
"more_fit_indices",
more_fit_indices,
default=FALSE)
self$.addOption(private$...caller)
self$.addOption(private$...interface)
self$.addOption(private$..dep)
self$.addOption(private$..factors)
self$.addOption(private$..covs)
self$.addOption(private$..model_terms)
self$.addOption(private$..fixed_intercept)
self$.addOption(private$..nested_terms)
self$.addOption(private$..comparison)
self$.addOption(private$..nested_intercept)
self$.addOption(private$..estimates_ci)
self$.addOption(private$..re_ci)
self$.addOption(private$..donotrun)
self$.addOption(private$..ci_method)
self$.addOption(private$..boot_r)
self$.addOption(private$..ci_width)
self$.addOption(private$..contrasts)
self$.addOption(private$..show_contrastnames)
self$.addOption(private$..show_contrastcodes)
self$.addOption(private$..plot_x)
self$.addOption(private$..plot_z)
self$.addOption(private$..plot_by)
self$.addOption(private$..plot_raw)
self$.addOption(private$..plot_yscale)
self$.addOption(private$..plot_xoriginal)
self$.addOption(private$..plot_black)
self$.addOption(private$..plot_around)
self$.addOption(private$..plot_re)
self$.addOption(private$..plot_re_method)
self$.addOption(private$..plot_extremes)
self$.addOption(private$..emmeans)
self$.addOption(private$..posthoc)
self$.addOption(private$..posthoc_ci)
self$.addOption(private$..simple_x)
self$.addOption(private$..simple_mods)
self$.addOption(private$..simple_interactions)
self$.addOption(private$..covs_conditioning)
self$.addOption(private$..ccra_steps)
self$.addOption(private$..ccm_value)
self$.addOption(private$..ccp_value)
self$.addOption(private$..covs_scale_labels)
self$.addOption(private$..adjust)
self$.addOption(private$..predicted)
self$.addOption(private$..residuals)
self$.addOption(private$..model_type)
self$.addOption(private$..covs_scale)
self$.addOption(private$..dep_scale)
self$.addOption(private$..scale_missing)
self$.addOption(private$..norm_test)
self$.addOption(private$..cluster)
self$.addOption(private$..re)
self$.addOption(private$..nested_re)
self$.addOption(private$..re_corr)
self$.addOption(private$..re_modelterms)
self$.addOption(private$..re_crossedclusters)
self$.addOption(private$..re_nestedclusters)
self$.addOption(private$..re_listing)
self$.addOption(private$..reml)
self$.addOption(private$..re_lrt)
self$.addOption(private$..res_struct)
self$.addOption(private$..df_method)
self$.addOption(private$..norm_plot)
self$.addOption(private$..qq_plot)
self$.addOption(private$..resid_plot)
self$.addOption(private$..cluster_boxplot)
self$.addOption(private$..cluster_respred)
self$.addOption(private$..cluster_respred_grid)
self$.addOption(private$..rand_hist)
self$.addOption(private$..mute)
self$.addOption(private$..more_fit_indices)
}),
active = list(
.caller = function() private$...caller$value,
.interface = function() private$...interface$value,
dep = function() private$..dep$value,
factors = function() private$..factors$value,
covs = function() private$..covs$value,
model_terms = function() private$..model_terms$value,
fixed_intercept = function() private$..fixed_intercept$value,
nested_terms = function() private$..nested_terms$value,
comparison = function() private$..comparison$value,
nested_intercept = function() private$..nested_intercept$value,
estimates_ci = function() private$..estimates_ci$value,
re_ci = function() private$..re_ci$value,
donotrun = function() private$..donotrun$value,
ci_method = function() private$..ci_method$value,
boot_r = function() private$..boot_r$value,
ci_width = function() private$..ci_width$value,
contrasts = function() private$..contrasts$value,
show_contrastnames = function() private$..show_contrastnames$value,
show_contrastcodes = function() private$..show_contrastcodes$value,
plot_x = function() private$..plot_x$value,
plot_z = function() private$..plot_z$value,
plot_by = function() private$..plot_by$value,
plot_raw = function() private$..plot_raw$value,
plot_yscale = function() private$..plot_yscale$value,
plot_xoriginal = function() private$..plot_xoriginal$value,
plot_black = function() private$..plot_black$value,
plot_around = function() private$..plot_around$value,
plot_re = function() private$..plot_re$value,
plot_re_method = function() private$..plot_re_method$value,
plot_extremes = function() private$..plot_extremes$value,
emmeans = function() private$..emmeans$value,
posthoc = function() private$..posthoc$value,
posthoc_ci = function() private$..posthoc_ci$value,
simple_x = function() private$..simple_x$value,
simple_mods = function() private$..simple_mods$value,
simple_interactions = function() private$..simple_interactions$value,
covs_conditioning = function() private$..covs_conditioning$value,
ccra_steps = function() private$..ccra_steps$value,
ccm_value = function() private$..ccm_value$value,
ccp_value = function() private$..ccp_value$value,
covs_scale_labels = function() private$..covs_scale_labels$value,
adjust = function() private$..adjust$value,
predicted = function() private$..predicted$value,
residuals = function() private$..residuals$value,
model_type = function() private$..model_type$value,
covs_scale = function() private$..covs_scale$value,
dep_scale = function() private$..dep_scale$value,
scale_missing = function() private$..scale_missing$value,
norm_test = function() private$..norm_test$value,
cluster = function() private$..cluster$value,
re = function() private$..re$value,
nested_re = function() private$..nested_re$value,
re_corr = function() private$..re_corr$value,
re_modelterms = function() private$..re_modelterms$value,
re_crossedclusters = function() private$..re_crossedclusters$value,
re_nestedclusters = function() private$..re_nestedclusters$value,
re_listing = function() private$..re_listing$value,
reml = function() private$..reml$value,
re_lrt = function() private$..re_lrt$value,
res_struct = function() private$..res_struct$value,
df_method = function() private$..df_method$value,
norm_plot = function() private$..norm_plot$value,
qq_plot = function() private$..qq_plot$value,
resid_plot = function() private$..resid_plot$value,
cluster_boxplot = function() private$..cluster_boxplot$value,
cluster_respred = function() private$..cluster_respred$value,
cluster_respred_grid = function() private$..cluster_respred_grid$value,
rand_hist = function() private$..rand_hist$value,
mute = function() private$..mute$value,
more_fit_indices = function() private$..more_fit_indices$value),
private = list(
...caller = NA,
...interface = NA,
..dep = NA,
..factors = NA,
..covs = NA,
..model_terms = NA,
..fixed_intercept = NA,
..nested_terms = NA,
..comparison = NA,
..nested_intercept = NA,
..estimates_ci = NA,
..re_ci = NA,
..donotrun = NA,
..ci_method = NA,
..boot_r = NA,
..ci_width = NA,
..contrasts = NA,
..show_contrastnames = NA,
..show_contrastcodes = NA,
..plot_x = NA,
..plot_z = NA,
..plot_by = NA,
..plot_raw = NA,
..plot_yscale = NA,
..plot_xoriginal = NA,
..plot_black = NA,
..plot_around = NA,
..plot_re = NA,
..plot_re_method = NA,
..plot_extremes = NA,
..emmeans = NA,
..posthoc = NA,
..posthoc_ci = NA,
..simple_x = NA,
..simple_mods = NA,
..simple_interactions = NA,
..covs_conditioning = NA,
..ccra_steps = NA,
..ccm_value = NA,
..ccp_value = NA,
..covs_scale_labels = NA,
..adjust = NA,
..predicted = NA,
..residuals = NA,
..model_type = NA,
..covs_scale = NA,
..dep_scale = NA,
..scale_missing = NA,
..norm_test = NA,
..cluster = NA,
..re = NA,
..nested_re = NA,
..re_corr = NA,
..re_modelterms = NA,
..re_crossedclusters = NA,
..re_nestedclusters = NA,
..re_listing = NA,
..reml = NA,
..re_lrt = NA,
..res_struct = NA,
..df_method = NA,
..norm_plot = NA,
..qq_plot = NA,
..resid_plot = NA,
..cluster_boxplot = NA,
..cluster_respred = NA,
..cluster_respred_grid = NA,
..rand_hist = NA,
..mute = NA,
..more_fit_indices = NA)
)
gamljmixedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedResults",
inherit = jmvcore::Group,
active = list(
model = function() private$..model,
info = function() private$.items[["info"]],
main = function() private$.items[["main"]],
posthoc = function() private$.items[["posthoc"]],
simpleEffects = function() private$.items[["simpleEffects"]],
simpleInteractions = function() private$.items[["simpleInteractions"]],
emmeans = function() private$.items[["emmeans"]],
mainPlots = function() private$.items[["mainPlots"]],
plotnotes = function() private$.items[["plotnotes"]],
assumptions = function() private$.items[["assumptions"]],
predicted = function() private$.items[["predicted"]],
residuals = function() private$.items[["residuals"]]),
private = list(
..model = NA),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Mixed Model")
private$..model <- NULL
self$add(jmvcore::Table$new(
options=options,
name="info",
title="Model Info",
columns=list(
list(
`name`="info",
`type`="text",
`title`="Info"),
list(
`name`="value",
`type`="text",
`title`=""),
list(
`name`="specs",
`type`="text",
`title`="")),
refs="gamlj"))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
r2 = function() private$.items[["r2"]],
fit = function() private$.items[["fit"]],
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]],
contrastCodeTables = function() private$.items[["contrastCodeTables"]],
random = function() private$.items[["random"]],
randomcov = function() private$.items[["randomcov"]],
ranova = function() private$.items[["ranova"]],
res_corr = function() private$.items[["res_corr"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="main",
title="Model Results")
self$add(jmvcore::Table$new(
options=options,
name="r2",
title="Model Fit",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"nested_terms",
"nested_intercept",
"comparison",
"nested_re"),
columns=list(
list(
`name`="model",
`title`="Model",
`visible`="(comparison)"),
list(
`name`="type",
`type`="text",
`title`="Type"),
list(
`name`="r2",
`title`="R\u00B2",
`type`="number",
`format`="zto"),
list(
`name`="df1",
`title`="df",
`type`="integer"),
list(
`name`="test",
`title`="LRT X\u00B2",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
refs="goodness"))
self$add(jmvcore::Table$new(
options=options,
name="fit",
title="Additional Indices",
visible="(more_fit_indices)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"nested_terms",
"nested_intercept",
"comparison",
"nested_re"),
columns=list(
list(
`name`="info",
`type`="text",
`title`="Info"),
list(
`name`="value",
`type`="text",
`title`="Model Value"),
list(
`name`="nested",
`type`="text",
`title`="Nested Model",
`visible`="(comparison)"),
list(
`name`="diff",
`type`="text",
`title`="\u0394",
`visible`="(comparison)",
`format`="zto"),
list(
`name`="specs",
`type`="text",
`title`="Comment"))))
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="Fixed Effects Omnibus Tests",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="f",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="df",
`type`="number"),
list(
`name`="df2",
`title`="df (res)",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates (Fixed coefficients)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="source",
`title`="Names",
`type`="text"),
list(
`name`="label",
`title`="Effect",
`type`="text",
`visible`="(show_contrastnames)"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
refs="parameters"))
self$add(jmvcore::Array$new(
options=options,
name="contrastCodeTables",
title="Contrast Coefficients",
visible="(show_contrastcodes)",
items="(factors)",
clearWith=list(
"contrasts"),
template=jmvcore::Table$new(
options=options,
title="Factor: ___key___",
columns=list(
list(
`name`="cname",
`title`="Name",
`type`="text",
`visible`="(show_contrastnames)"),
list(
`name`="clab",
`title`="Contrast",
`type`="text"),
list(
`name`="bogus",
`title`="bogus",
`type`="text",
`visible`=FALSE)))))
self$add(jmvcore::Table$new(
options=options,
name="random",
title="Random Components",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="groups",
`title`="Groups",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="var1",
`title`="Name",
`type`="text"),
list(
`name`="vcov",
`title`="Variance",
`type`="number"),
list(
`name`="sdcor",
`title`="SD",
`type`="number"),
list(
`name`="sd.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(re_ci)"),
list(
`name`="sd.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(re_ci)"),
list(
`name`="icc",
`title`="ICC",
`type`="number"),
list(
`name`="phi",
`title`="Phi",
`type`="number",
`visible`="(res_struct:ar1)"),
list(
`name`="rho",
`title`="rho",
`type`="number",
`visible`="(res_struct:cs)"))))
self$add(jmvcore::Table$new(
options=options,
name="randomcov",
title="Random Parameters correlations",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci"),
columns=list(
list(
`name`="groups",
`title`="Groups",
`combineBelow`=TRUE,
`type`="text"),
list(
`name`="var1",
`title`="param1",
`type`="text"),
list(
`name`="var2",
`title`="param2",
`type`="text"),
list(
`name`="vcov",
`title`="Cov.",
`type`="number"),
list(
`name`="sdcor",
`title`="Corr.",
`type`="number"),
list(
`name`="sd.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(re_ci)",
`superTitle`="Confidence Intervals"),
list(
`name`="sd.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(re_ci)",
`superTitle`="Confidence Intervals"))))
self$add(jmvcore::Table$new(
options=options,
name="ranova",
title="Random Effect LRT",
visible="(re_lrt)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct"),
columns=list(
list(
`name`="test",
`title`="Test",
`combineBelow`=TRUE,
`type`="text"),
list(
`name`="npar",
`title`="N. par",
`type`="number"),
list(
`name`="AIC",
`title`="AIC",
`type`="number"),
list(
`name`="LRT",
`title`="LRT",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="res_corr",
title="Residual correlations",
visible="(res_struct:un)",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"res_struct"),
columns=list(
list(
`name`="index",
`title`="Index",
`type`="text"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="posthoc",
title="Post Hoc Tests",
items="(posthoc)",
template=jmvcore::Table$new(
options=options,
title="Post Hoc comparison: ___key___",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"adjust"),
columns=list(
list(
`name`="estimate",
`title`="Difference",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(posthoc_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(posthoc_ci)"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:none)"),
list(
`name`="bonf",
`title`="p<sub>bonferroni</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:bonf)"),
list(
`name`="tukey",
`title`="p<sub>tukey</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:tukey)"),
list(
`name`="holm",
`title`="p<sub>holm</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:holm)"),
list(
`name`="scheffe",
`title`="p<sub>scheffe</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:scheffe)"),
list(
`name`="sidak",
`title`="p<sub>sidak</sub>",
`type`="number",
`format`="zto,pvalue",
`visible`="(adjust:sidak)")))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="simpleEffects",
title="Simple Effects")
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="ANOVA for Simple Effects of ___key___",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="test",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="Num df",
`type`="number"),
list(
`name`="df2",
`title`="Den df",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates for simple effects of ___key___",
visible=FALSE,
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="contrast",
`title`="Effect",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="simpleInteractions",
title="Simple Interactions",
visible="(simple_interactions)",
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="Interaction: ___key___",
clearWith=list())
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="ANOVA",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="effect",
`title`="Effect",
`type`="text"),
list(
`name`="test",
`title`="F",
`type`="number"),
list(
`name`="df1",
`title`="df1",
`type`="number"),
list(
`name`="df2",
`title`="df2",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="effect",
`title`="Effect",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="est.ci.lower",
`title`="Lower",
`type`="number"),
list(
`name`="est.ci.upper",
`title`="Upper",
`type`="number"),
list(
`name`="test",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options)))
self$add(jmvcore::Array$new(
options=options,
name="emmeans",
title="Estimated Marginal Means",
visible=FALSE,
items="(emmeans)",
template=jmvcore::Table$new(
options=options,
title="Estimate Marginal Means - ___key___",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct",
"ci_width",
"ci_method",
"boot_r",
"re_ci",
"simple_x",
"simple_mods",
"simple_scale",
"ccm_value",
"ccp_value",
"ccra_steps",
"covs_scale_labels",
"covs_conditioning"),
columns=list(
list(
`name`="estimate",
`title`="Mean",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="number"),
list(
`name`="est.ci.lower",
`title`="Lower",
`type`="number"),
list(
`name`="est.ci.upper",
`title`="Upper",
`type`="number")))))
self$add(jmvcore::Array$new(
options=options,
name="mainPlots",
title="Results Plots",
template=jmvcore::Image$new(
options=options,
title="",
renderFun=".mainPlot",
width=700,
height=500)))
self$add(jmvcore::Html$new(
options=options,
name="plotnotes",
visible=FALSE))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
normtest = function() private$.items[["normtest"]],
qqplot = function() private$.items[["qqplot"]],
normPlot = function() private$.items[["normPlot"]],
residPlot = function() private$.items[["residPlot"]],
clusterBoxplot = function() private$.items[["clusterBoxplot"]],
clusterResPred = function() private$.items[["clusterResPred"]],
clusterResPredGrid = function() private$.items[["clusterResPredGrid"]],
randHist = function() private$.items[["randHist"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assumptions",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="normtest",
title="Test for Normality of residuals",
visible="(norm_test)",
clearWith=list(
"model_terms",
"dep",
"reml",
"re"),
columns=list(
list(
`name`="name",
`title`="Test",
`type`="number"),
list(
`name`="test",
`title`="Statistics",
`type`="number"),
list(
`name`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qqplot",
title="Q-Q Plot",
visible="(qq_plot)",
width=700,
height=500,
renderFun=".qqPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"model_terms",
"dep_scale")))
self$add(jmvcore::Image$new(
options=options,
name="normPlot",
title="Residual histogram",
visible="(norm_plot)",
width=700,
height=500,
renderFun=".normPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes")))
self$add(jmvcore::Image$new(
options=options,
name="residPlot",
title="Residual-Predicted Scatterplot",
visible="(resid_plot)",
width=700,
height=700,
renderFun=".residPlot",
requiresData=TRUE,
clearWith=list(
"dep",
"model_terms",
"dep_scale",
"plot_extremes")))
self$add(jmvcore::Array$new(
options=options,
name="clusterBoxplot",
title="Residuals by cluster boxplot",
visible="(cluster_boxplot)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterBoxplot",
width=700,
height=900)))
self$add(jmvcore::Array$new(
options=options,
name="clusterResPred",
title="Residuals-Predicted by cluster",
visible="(cluster_respred)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterResPred",
width=700,
height=700)))
self$add(jmvcore::Array$new(
options=options,
name="clusterResPredGrid",
title="Residuals-Predicted Grid",
visible="(cluster_respred_grid)",
clearWith=list(
"dep",
"dep_scale",
"model_terms",
"plot_extremes"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".clusterResPredGrid",
width=700,
height=900)))
self$add(jmvcore::Array$new(
options=options,
name="randHist",
title="Random coefficients histogram",
visible="(rand_hist)",
clearWith=list(
"dep",
"dep_scale",
"model_terms"),
template=jmvcore::Image$new(
options=options,
title="$key",
renderFun=".randHist",
width=700,
height=500)))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="predicted",
title="Predicted Vales",
varTitle="`MIXED_PRED_${ dep }`",
varDescription="Predicted values",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct")))
self$add(jmvcore::Output$new(
options=options,
name="residuals",
title="Residuals Vales",
varTitle="`MIXED_RES_${ dep }`",
varDescription="Residuals values",
clearWith=list(
"dep",
"reml",
"factors",
"covs",
"cluster",
"covs_scale",
"dep_scale",
"scale_missing",
"model_terms",
"fixed_intercept",
"se_method",
"mute",
"re",
"re_corr",
"df_method",
"relm",
"contrasts",
"covs_scale",
"mute",
"donotrun",
"res_struct")))},
.setModel=function(x) private$..model <- x))
gamljmixedBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"gamljmixedBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "GAMLj3",
name = "gamljmixed",
version = c(3,0,0),
options = options,
results = gamljmixedResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
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