Package 'TimeSpaceAnalysis' reference manual

Title:	Statistical tools for time-space analysis
Description:	Use Geometric Data Analysis approaches (e.g. MCA or MFA), time pattern analysis (see "time sequence clustering") and places chronologies (see "time geography") analysis.
Authors:	Fabian Mundt
Maintainer:	Fabian Mundt <[email protected]>
License:	AGPL (>= 3) \| file LICENSE
Version:	2.0.6
Built:	2025-03-09 15:18:18 UTC
Source:	https://github.com/inventionate/TimeSpaceAnalysis

Concat the categories of a variable

Description

Concat the categories of a variable

Usage

add_path(
  res_gda_quali,
  var,
  var_levels = NULL,
  exclude = NULL,
  axes = 1:2,
  linetype = "dashed",
  colour = "black",
  size = 1
)
add_path(
  res_gda_quali,
  var,
  var_levels = NULL,
  exclude = NULL,
  axes = 1:2,
  linetype = "dashed",
  colour = "black",
  size = 1
)

Arguments

`res_gda_quali`	GDA result.
`var`	variable name.
`var_levels`	categories to concat.
`exclude`	categories to exclude.
`axes`	axes to plot.
`linetype`	linetype of concat path.
`colour`	colour of concat path.
`size`	size of concat path.

Value

ggplo2 path geom.

Optimise ggplot2 plot.

Description

Optimise ggplot2 plot.

Usage

add_theme(plot, font_family = "Fira Sans Condensed Medium")
add_theme(plot, font_family = "Fira Sans Condensed Medium")

Arguments

`plot`	ggplot2 object.
`font_family`	plot overall font family.

Value

ggplot2 geoms.

Extract list of MFA group indices to exclude (for sMFA).

Description

Extract list of MFA group indices to exclude (for sMFA).

Usage

excl_mfa_group(df_mfa, group_mfa, pattern)
excl_mfa_group(df_mfa, group_mfa, pattern)

Arguments

`df_mfa`	MFA optmised data frame.
`group_mfa`	MFA group definition.
`pattern`	search pattern (regular expression).

Value

list with MFA group specific exclude indices.

Extract legends form ggplot2 objects.

Description

Extract legends form ggplot2 objects.

Usage

extract_legend(p)
extract_legend(p)

Arguments

`p`	ggplot2 object (plot) containing legends (guides).

Value

plottable legend grob.

Add supplementary individuals.

Description

Add supplementary individuals.

Usage

fviz_add_sup_ind(
  res_gda,
  sup_ind = NULL,
  colour = "red",
  ind_visible = FALSE,
  label = NULL,
  size = 10,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  axes = 1:2
)
fviz_add_sup_ind(
  res_gda,
  sup_ind = NULL,
  colour = "red",
  ind_visible = FALSE,
  label = NULL,
  size = 10,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  axes = 1:2
)

Arguments

`res_gda`	MCA results.
`sup_ind`	supplementary individual profiles (data.frame).
`colour`	colour of point and labels.
`ind_visible`	show individual points.
`label`	label names.
`size`	label size.
`group`	vector containing group definition.
`group_names`	names of the groups.
`group_style`	style to plot (vector containing "shape", "colour" or "both).
`axes`	the GDA dimensions to plot.

Value

ggplot2 visalization of supplementary individuals.

Visualize HCPC hclus trees.

Description

Visualize HCPC hclus trees.

Usage

fviz_dendrogram(
  res_hcpc,
  palette = NULL,
  cluster = 1,
  labels = FALSE,
  circle = FALSE,
  hline = 0.8,
  pointsize = 2,
  linetype = "dashed",
  cut_height = NULL,
  title = NULL,
  cut_upper = NULL,
  colour_upper = "#555555",
  hlabel = NULL,
  hlabel_pos = 0.001
)
fviz_dendrogram(
  res_hcpc,
  palette = NULL,
  cluster = 1,
  labels = FALSE,
  circle = FALSE,
  hline = 0.8,
  pointsize = 2,
  linetype = "dashed",
  cut_height = NULL,
  title = NULL,
  cut_upper = NULL,
  colour_upper = "#555555",
  hlabel = NULL,
  hlabel_pos = 0.001
)

Arguments

`res_hcpc`	(s)HCPC results.
`palette`	colour definition per cluster.
`cluster`	amount of clusters.
`labels`	plot labels (boolean).
`circle`	plot circle (boolean).
`hline`	hline height.
`pointsize`	leaves pointsize.
`linetype`	hline linetype.
`cut_height`	cut dendrogram at specific hight.
`title`	the plot title.
`cut_upper`	style upper dendrogram.
`colour_upper`	colour of the upper dendrogram.
`hlabel`	label of hline.
`hlabel_pos`	position of hlabel.

Value

ggplot2 dendrogram visualization.

Title

Description

Title

Usage

fviz_gda_conc_ellipse(
  res_gda,
  level = 0.8647,
  alpha = 0.1,
  colour = "black",
  linetype = "dotted",
  density = FALSE,
  fill = NA,
  axes = 1:2,
  scale_size = 1,
  title = "GDA individuals plot",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  blank = FALSE
)
fviz_gda_conc_ellipse(
  res_gda,
  level = 0.8647,
  alpha = 0.1,
  colour = "black",
  linetype = "dotted",
  density = FALSE,
  fill = NA,
  axes = 1:2,
  scale_size = 1,
  title = "GDA individuals plot",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  blank = FALSE
)

Arguments

`res_gda`	GDA result.
`level`	ellipse level (default 86.47%).
`alpha`	opacity level (default 0.1).
`colour`	ellipse border colour.
`linetype`	ellipse edge linetype.
`density`	show density contours (boolean).
`fill`	ellipse fill colour.
`axes`	the GDA dimensions to plot.
`scale_size`	scale minimal point size.
`title`	the plot title.
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).
`blank`	visualisation without labels and tick values.

Value

ggplot2 GDA visualisation with concentration ellipse.

Visualize interaction cloud.

Description

Visualize interaction cloud.

Usage

fviz_gda_interaction(
  res_gda,
  df_var_quali,
  var_quali,
  title = "MCA quali interaction effects",
  mean_alpha = 0.75,
  path_linetype = "solid",
  path_size = 1,
  path_colour = "black",
  scale_mean_points = TRUE,
  axes = 1:2,
  palette = "Set1",
  path_alpha = 1,
  impute = TRUE,
  variable = "both",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL
)
fviz_gda_interaction(
  res_gda,
  df_var_quali,
  var_quali,
  title = "MCA quali interaction effects",
  mean_alpha = 0.75,
  path_linetype = "solid",
  path_size = 1,
  path_colour = "black",
  scale_mean_points = TRUE,
  axes = 1:2,
  palette = "Set1",
  path_alpha = 1,
  impute = TRUE,
  variable = "both",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL
)

Arguments

`res_gda`	MCA result.
`df_var_quali`	crossed variable data frame.
`var_quali`	name of crossed supplementary variable.
`title`	plot title.
`mean_alpha`	alpha of the mean point.
`path_linetype`	linetype of concat path.
`path_size`	size of concat path.
`path_colour`	colour of concat path.
`scale_mean_points`	scale mean points (boolean).
`axes`	axes to plot.
`palette`	used colour brewer palette.
`path_alpha`	opacity of the path.
`impute`	use imputation to handle missing data.
`variable`	which diagram to plot (vector containing 1, 2 or "both").
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`labels`	label axes (vector of length 4; left, right, top, bottom).

Value

ggplot2 interaction cloud visualizsation.

Title

Description

Title

Usage

fviz_gda_quali_ellipses(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  facet = TRUE,
  alpha_point = 0.75,
  conc_linetype = "solid",
  conf_linetype = "solid",
  scale_mean_points = TRUE,
  axes = 1:2,
  colour = "Set1",
  impute = TRUE,
  concentration_ellipses = TRUE,
  confidence_ellipses = FALSE,
  conf_colour = FALSE,
  plot_modif_rates = TRUE,
  ncol = 3,
  individuals = TRUE,
  impute_ncp = 2,
  reorder = NULL,
  alpha_ellipses = 0.15,
  print_eta2 = TRUE,
  axis_lab_name = "Achse",
  label_mean_points = TRUE,
  highlight = FALSE,
  profiles = NULL,
  labels = NULL,
  axes_annotate_alpha = 0.3,
  density = FALSE,
  global_conc_ellipses = TRUE,
  in_freq = FALSE,
  facet_title_size = 14
)
fviz_gda_quali_ellipses(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  facet = TRUE,
  alpha_point = 0.75,
  conc_linetype = "solid",
  conf_linetype = "solid",
  scale_mean_points = TRUE,
  axes = 1:2,
  colour = "Set1",
  impute = TRUE,
  concentration_ellipses = TRUE,
  confidence_ellipses = FALSE,
  conf_colour = FALSE,
  plot_modif_rates = TRUE,
  ncol = 3,
  individuals = TRUE,
  impute_ncp = 2,
  reorder = NULL,
  alpha_ellipses = 0.15,
  print_eta2 = TRUE,
  axis_lab_name = "Achse",
  label_mean_points = TRUE,
  highlight = FALSE,
  profiles = NULL,
  labels = NULL,
  axes_annotate_alpha = 0.3,
  density = FALSE,
  global_conc_ellipses = TRUE,
  in_freq = FALSE,
  facet_title_size = 14
)

Arguments

`res_gda`	GDA (MCA, MFA) result (rownames have to be individual questionnaire IDs).
`df_var_quali`	data frame of one quali variable.
`var_quali`	name if quali variable.
`title`	plot title.
`facet`	whether facet ellipses or not (boolean).
`alpha_point`	opacity of individual points.
`conc_linetype`	linetype of concentration ellipses.
`conf_linetype`	linetype of confidence ellipses.
`scale_mean_points`	scale mean point size in respect of the group size (boolean).
`axes`	the GDA dimensions to plot.
`colour`	Colour brewer scale or FALSE.
`impute`	impute missing data (boolean).
`concentration_ellipses`	plot concentration ellipse (boolean).
`confidence_ellipses`	plot confidence ellipses (boolean).
`conf_colour`	colour confidence ellipses (boolean).
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`ncol`	Number of facet columns.
`individuals`	show individual points (boolean).
`impute_ncp`	number of dimensions to predict missing values.
`reorder`	numeric vector containing new level order (index).
`alpha_ellipses`	concentration ellipses fill alpha.
`print_eta2`	print eta2 value per axis (boolean).
`axis_lab_name`	name of axis label.
`label_mean_points`	show labels (boolean).
`highlight`	show facets with highlighted group (boolean).
`profiles`	optional add specific profiles (tibble).
`labels`	label axes (vector of length 4; left, right, top, bottom).
`axes_annotate_alpha`	alpha value of axes annotations.
`density`	show density contours (boolean).
`global_conc_ellipses`	should the global concentration ellipse be shown (boolean).
`in_freq`	order by number of observations with each level (largest first) (boolean).
`facet_title_size`	size of the facet stripe title (numeric).

Value

ggplot2 visualization with concentration and quali var ellipses.

Visualize supplementary variables.

Description

Visualize supplementary variables.

Usage

fviz_gda_quali_supvar(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  path = FALSE,
  linetype = "solid",
  axes = 1:2,
  scale_point = TRUE,
  size_point = 3,
  scale_text = FALSE,
  size_text = 3,
  palette = "Set1",
  impute = TRUE,
  plot_modif_rates = TRUE,
  impute_ncp = 2,
  relevel = NULL,
  print_eta2 = TRUE,
  axis_lab_name = "Achse",
  axes_annotate_alpha = 0.3,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  accuracy = 0.1,
  pos_adjust = 0.001,
  colour_point = FALSE
)
fviz_gda_quali_supvar(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  path = FALSE,
  linetype = "solid",
  axes = 1:2,
  scale_point = TRUE,
  size_point = 3,
  scale_text = FALSE,
  size_text = 3,
  palette = "Set1",
  impute = TRUE,
  plot_modif_rates = TRUE,
  impute_ncp = 2,
  relevel = NULL,
  print_eta2 = TRUE,
  axis_lab_name = "Achse",
  axes_annotate_alpha = 0.3,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  accuracy = 0.1,
  pos_adjust = 0.001,
  colour_point = FALSE
)

Arguments

`res_gda`	GDA result.
`df_var_quali`	crossed variable data frame.
`var_quali`	crossed variable name.
`title`	plot title.
`path`	plot path (boolean).
`linetype`	specify path linetype.
`axes`	which axes should be plotted.
`scale_point`	scale points by weight (boolean).
`size_point`	define point size.
`scale_text`	scale text by weight (boolean).
`size_text`	define text size.
`palette`	RColorBrewer palette.
`impute`	impute missing data (boolean).
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`impute_ncp`	number of dimensions to predict missing values.
`relevel`	character vector containing new level order.
`print_eta2`	print eta2 value per axis (boolean).
`axis_lab_name`	name of axis label.
`axes_annotate_alpha`	alpha value of axes annotations.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).
`accuracy`	numeric vector (defaults to 0.1).
`pos_adjust`	numeric vector for axis labels adjustment (defaults to 0.001)
`colour_point`	should the points be coloured (boolean)?

Value

ggplot2 visualization of supplementary variables.

Visualize additive cloud.

Description

Visualize additive cloud.

Usage

fviz_gda_structure(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  scale_mean_points = TRUE,
  axes = 1:2,
  palette = "Set1",
  impute = TRUE,
  cloud = "both",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  axes_annotate_alpha = 0.3
)
fviz_gda_structure(
  res_gda,
  df_var_quali,
  var_quali,
  title = NULL,
  scale_mean_points = TRUE,
  axes = 1:2,
  palette = "Set1",
  impute = TRUE,
  cloud = "both",
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  axes_annotate_alpha = 0.3
)

Arguments

`res_gda`	MCA result.
`df_var_quali`	crossed variable data frame.
`var_quali`	name of quali variable.
`title`	plot title.
`scale_mean_points`	scale points (boolean).
`axes`	which axis to plot.
`palette`	colour palette (boolean).
`impute`	impute missing data (boolean).
`cloud`	which cloud should be plotted (string: real, fitted, both, deviation)
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`axes_annotate_alpha`	alpha value of axes annotations.

Value

ggplot2 visualization of additive cloud.

Visualization of trajectories (connected active and passive individual points).

Description

Visualization of trajectories (connected active and passive individual points).

Usage

fviz_gda_trajectory(
  res_gda,
  select = list(name = NULL, within_inertia = NULL, case = NULL),
  title = NULL,
  axes = 1:2,
  ind_labels = FALSE,
  time_point_names = NULL,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  legend_x = 0.12,
  legend_y = 0.9,
  axes_annotate_alpha = 0.3,
  xlim = NULL,
  ylim = NULL
)
fviz_gda_trajectory(
  res_gda,
  select = list(name = NULL, within_inertia = NULL, case = NULL),
  title = NULL,
  axes = 1:2,
  ind_labels = FALSE,
  time_point_names = NULL,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  labels = NULL,
  legend_x = 0.12,
  legend_y = 0.9,
  axes_annotate_alpha = 0.3,
  xlim = NULL,
  ylim = NULL
)

Arguments

`res_gda`	MCA result (rownames have to be questionnaire IDs including time number, e.g. 87654_1).
`select`	vector of names, within_inertia of individuals selection (within_inertia: vector containing the number of high variation and low variationindividuals) or case (vector containing NULL, complete, or incomplete).
`title`	the plot title
`axes`	axes to plot.
`ind_labels`	plot labels (boolean).
`time_point_names`	vector containing the name of the time points.
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`legend_x`	x position of legend.
`legend_y`	y position of legend.
`axes_annotate_alpha`	alpha value of axes annotations.
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).

Value

trajectory ggplot2 visualization.

Visualization of trajectory structuring factor ellipses.

Description

Visualization of trajectory structuring factor ellipses.

Usage

fviz_gda_trajectory_ellipses(
  res_gda,
  df_var_quali,
  var_quali,
  axes = 1:2,
  impute = TRUE,
  time_point_names = NULL,
  ind_points = TRUE,
  concentration_ellipse = TRUE,
  title = NULL,
  plot_modif_rates = TRUE,
  alpha = 0.15,
  select = NULL,
  select_facet = TRUE,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  axes_annotate_alpha = 0.3,
  complete_obs = FALSE,
  facet_title_size = 14,
  density = FALSE,
  ellipses = TRUE
)
fviz_gda_trajectory_ellipses(
  res_gda,
  df_var_quali,
  var_quali,
  axes = 1:2,
  impute = TRUE,
  time_point_names = NULL,
  ind_points = TRUE,
  concentration_ellipse = TRUE,
  title = NULL,
  plot_modif_rates = TRUE,
  alpha = 0.15,
  select = NULL,
  select_facet = TRUE,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  axes_annotate_alpha = 0.3,
  complete_obs = FALSE,
  facet_title_size = 14,
  density = FALSE,
  ellipses = TRUE
)

Arguments

`res_gda`	MCA result (rownames have to be questionnaire IDs including time number, e.g. 87654_1).
`df_var_quali`	data frame containing one qualitative variable (with IDs as rownames).
`var_quali`	name of the structuring variable.
`axes`	the axes to plot.
`impute`	use imputation for missing data.
`time_point_names`	vector containing the name of the time points.
`ind_points`	show individuals (boolean).
`concentration_ellipse`	plot concentration ellipses (boolean).
`title`	title of the plot.
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`alpha`	ellipse fill alpha.
`select`	choose cluster/category.
`select_facet`	facet clusters/categories (boolean.)
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	x limits.
`ylim`	y limits.
`axes_annotate_alpha`	alpha value of axes annotations.
`complete_obs`	plot only complete observations (boolean).
`facet_title_size`	size of the facet stripe title (numeric).
`density`	should 2D density lines be drawn (boolean).
`ellipses`	should ellipses be drawn (boolean).

Value

ggplot2 visualization.

Visualize trajectories and structuring factors.

Description

Visualize trajectories and structuring factors.

Usage

fviz_gda_trajectory_quali(
  res_gda,
  df_var_quali,
  var_quali,
  var_quali_select = NULL,
  axes = 1:2,
  ind_labels = FALSE,
  title = NULL,
  time_point_names = NULL,
  select = list(name = NULL, within_inertia = NULL, case = NULL),
  impute = TRUE,
  plot_modif_rates = TRUE,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  axes_annotate_alpha = 0.3,
  case_names = NULL,
  label_x_limits = NA,
  label_y_limits = NA
)
fviz_gda_trajectory_quali(
  res_gda,
  df_var_quali,
  var_quali,
  var_quali_select = NULL,
  axes = 1:2,
  ind_labels = FALSE,
  title = NULL,
  time_point_names = NULL,
  select = list(name = NULL, within_inertia = NULL, case = NULL),
  impute = TRUE,
  plot_modif_rates = TRUE,
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  axes_annotate_alpha = 0.3,
  case_names = NULL,
  label_x_limits = NA,
  label_y_limits = NA
)

Arguments

`res_gda`	MCA result (rownames have to be questionnaire IDs including time number, e.g. 87654_1).
`df_var_quali`	data frame containing one qualitative variable (with IDs as rownames).
`var_quali`	name of the structuring variable.
`var_quali_select`	the name of the selected categories/clusters.
`axes`	the axes to plot.
`ind_labels`	plot labels (boolean).
`title`	the plot title.
`time_point_names`	vector containing the name of the time points.
`select`	select vector of names, within_inertia of individuals selection (within_inertia: vector containing the number of high variation and low variationindividuals) or case (vector containing NULL, complete, or incomplete).
`impute`	use imputation for missing data.
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	numeric vector of 2.
`ylim`	numeric vector of 2.
`axes_annotate_alpha`	alpha value of axes annotations.
`case_names`	named character vector containing names of cases.
`label_x_limits`	constrain the labels to a specific area. Limits are specified in data coordinates.
`label_y_limits`	constrain the labels to a specific area. Limits are specified in data coordinates.

Value

ggplot2 visualization.

Visualization of the separated concentration ellipses of the sample.

Description

Visualization of the separated concentration ellipses of the sample.

Usage

fviz_gda_trajectory_sample(
  res_gda,
  time_point_names = NULL,
  axes = 1:2,
  ind_points = TRUE,
  concentration_ellipse = TRUE,
  complete = TRUE,
  title = NULL,
  plot_modif_rates = TRUE,
  alpha = 0.15,
  axis_lab_name = "Achse",
  axes_annotate_alpha = 0.3,
  labels = NULL,
  legend_x = 0.12,
  legend_y = 0.9,
  xlim = NULL,
  ylim = NULL
)
fviz_gda_trajectory_sample(
  res_gda,
  time_point_names = NULL,
  axes = 1:2,
  ind_points = TRUE,
  concentration_ellipse = TRUE,
  complete = TRUE,
  title = NULL,
  plot_modif_rates = TRUE,
  alpha = 0.15,
  axis_lab_name = "Achse",
  axes_annotate_alpha = 0.3,
  labels = NULL,
  legend_x = 0.12,
  legend_y = 0.9,
  xlim = NULL,
  ylim = NULL
)

Arguments

`res_gda`	MCA result (rownames have to be questionnaire IDs including time number, e.g. 87654_1).
`time_point_names`	vector containing the name of the time points.
`axes`	the axes to plot.
`ind_points`	show individuals (boolean).
`concentration_ellipse`	show or hide overall concentration ellipse (boolean).
`complete`	plot only complete cases (boolean).
`title`	title of the plot
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`alpha`	ellipse fill alpha.
`axis_lab_name`	name of axis label.
`axes_annotate_alpha`	alpha value of axes annotations.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`legend_x`	x position of legend.
`legend_y`	y position of legend.
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).

Value

ggplot2 visualization.

Visualize specific contributing modalities in a plane.

Description

Visualize specific contributing modalities in a plane.

Usage

fviz_gda_var(
  res_gda,
  contrib = "auto",
  title = NULL,
  axes = 1:2,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  textsize = 4,
  colour_palette = "Set1",
  individuals = FALSE,
  individuals_size = "auto",
  individuals_alpha = 0.5,
  individuals_names = FALSE,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  group_lab_name = "Themengruppen",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  alpha = 1
)
fviz_gda_var(
  res_gda,
  contrib = "auto",
  title = NULL,
  axes = 1:2,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  textsize = 4,
  colour_palette = "Set1",
  individuals = FALSE,
  individuals_size = "auto",
  individuals_alpha = 0.5,
  individuals_names = FALSE,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  group_lab_name = "Themengruppen",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  alpha = 1
)

Arguments

`res_gda`	GDA result data frame.
`contrib`	"auto" calculates the optimal modalities to show (based on the basic criterion). Otherwise define an amount of modalities to plot.
`title`	plot title.
`axes`	the GDA dimensions to plot.
`group`	vector containing group definition.
`group_names`	names of the groups.
`group_style`	style to plot (vector containing "shape", "colour" or "both).
`textsize`	size of the text.
`colour_palette`	name of the used colour palette.
`individuals`	show individual points/ biplot (boolean).
`individuals_size`	set individual point size manual or "auto".
`individuals_alpha`	set alpha value.
`individuals_names`	plot individual names (boolean).
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`group_lab_name`	name of variable groups.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).
`alpha`	numeric value between 0 and 1.

Value

ggplot2 visualization containing selected modalities.

Visualize specific contributing modalities.

Description

Visualize specific contributing modalities.

Usage

fviz_gda_var_axis(
  res_gda,
  axis = 1,
  contrib = "auto",
  title = NULL,
  axes = 1:2,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  textsize = 4,
  colour_palette = "Set1",
  individuals = FALSE,
  individuals_size = "auto",
  individuals_alpha = 0.5,
  individuals_names = FALSE,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  group_lab_name = "Themengruppen",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  alpha = 1
)
fviz_gda_var_axis(
  res_gda,
  axis = 1,
  contrib = "auto",
  title = NULL,
  axes = 1:2,
  group = NULL,
  group_names = NULL,
  group_style = "both",
  textsize = 4,
  colour_palette = "Set1",
  individuals = FALSE,
  individuals_size = "auto",
  individuals_alpha = 0.5,
  individuals_names = FALSE,
  plot_modif_rates = TRUE,
  axis_lab_name = "Achse",
  group_lab_name = "Themengruppen",
  labels = NULL,
  xlim = NULL,
  ylim = NULL,
  alpha = 1
)

Arguments

`res_gda`	GDA result data frame.
`axis`	dimension to be filtered.
`contrib`	"auto" calculates the optimal modalities to show (based on the basic criterion). Otherwise define an amount of modalities to plot.
`title`	plot title.
`axes`	the GDA dimensions to plot.
`group`	vector containing group definition.
`group_names`	names of the groups.
`group_style`	style to plot (vector containing "shape", "colour" or "both).
`textsize`	size of the text.
`colour_palette`	name of the used colour palette.
`individuals`	show individual points/ biplot (boolean).
`individuals_size`	set individual point size manual or "auto".
`individuals_alpha`	set alpha value.
`individuals_names`	plot individual names (boolean).
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`axis_lab_name`	name of axis label.
`group_lab_name`	name of variable groups.
`labels`	label axes (vector of length 4; left, right, top, bottom).
`xlim`	x Axis limits (vector of length 2).
`ylim`	y Axis limits (vector of length 2).
`alpha`	numeric value between 0 and 1.

Value

ggplot2 visualization containing selected modalities.

Visualize MCA variable representation square.

Description

Visualize MCA variable representation square.

Usage

fviz_mca_var_corr(
  res_gda,
  axes = c(1, 2),
  geom = c("point", "text"),
  labelsize = 4,
  pointsize = 2,
  invisible = NULL,
  labels = TRUE,
  repel = TRUE,
  select = list(name = NULL, eta2 = NULL),
  plot_modif_rates = TRUE,
  title = "MCA - Variable Representation"
)
fviz_mca_var_corr(
  res_gda,
  axes = c(1, 2),
  geom = c("point", "text"),
  labelsize = 4,
  pointsize = 2,
  invisible = NULL,
  labels = TRUE,
  repel = TRUE,
  select = list(name = NULL, eta2 = NULL),
  plot_modif_rates = TRUE,
  title = "MCA - Variable Representation"
)

Arguments

`res_gda`	MCA result.
`axes`	axes to plot.
`geom`	whether points or labels to plot.
`labelsize`	size of labels.
`pointsize`	size of points.
`invisible`	hide "passive" or "active" variables.
`labels`	label points or not (boolean).
`repel`	repel labels (boolean).
`select`	selection of variables (names) or eta2 values (all above value).
`plot_modif_rates`	plot modified rates instead of eigenvalue percentage (boolean).
`title`	plot title.

Value

ggplot2 visualization of variable correlation square (variables representation).

Calculate axis contributions.

Description

Calculate axis contributions.

Usage

gda_describe_axis(res_gda, axis = 1, contrib = "auto")
gda_describe_axis(res_gda, axis = 1, contrib = "auto")

Arguments

`res_gda`	MCA result.
`axis`	which axis to calculate.
`contrib`	"auto" calculates the optimal modalities to show (based on the basic criterion). Otherwise define an amount of modalities to plot.

Value

list containing axis contribution results.

Calculate group contributions.

Description

Calculate group contributions.

Usage

gda_describe_group(res_gda, group = NULL, group_names = NULL)
gda_describe_group(res_gda, group = NULL, group_names = NULL)

Arguments

`res_gda`	MCA result.
`group`	vector containing group definition.
`group_names`	names of the groups.

Value

list containing group results.

Optimise data frame for Geometric Data Analysis

Description

Optimise data frame for Geometric Data Analysis

Usage

gda_optimise_df(df_name, mod_excl = NA, prop_na_excl = 0.2, rename_na = FALSE)
gda_optimise_df(df_name, mod_excl = NA, prop_na_excl = 0.2, rename_na = FALSE)

Arguments

`df_name`	name of the data frame to optimise.
`mod_excl`	specify, which modalities should excluded.
`prop_na_excl`	overall level to exclude specified modalities.
`rename_na`	rename NA with label

Value

optimised data frame.

Extract trajectory data.

Description

Extract trajectory data.

Usage

get_gda_trajectory(res_gda, time_point_names = NULL, complete_obs = FALSE)
get_gda_trajectory(res_gda, time_point_names = NULL, complete_obs = FALSE)

Arguments

`res_gda`	GDA result.
`time_point_names`	name of the separated time points.
`complete_obs`	plot only complete observations (boolean).

Value

list containing time point separated ind coord and time point names.

Exctract index of specific modalities.

Description

Exctract index of specific modalities.

Usage

get_index_mod(df_gda, pattern = "Fehlender Wert")
get_index_mod(df_gda, pattern = "Fehlender Wert")

Arguments

`df_gda`	GDA optimised data frame.
`pattern`	search pattern (regular expression).

Value

indices of modalities.

Reshapes MCA results.

Description

Reshapes MCA results.

Usage

get_mca_var_corr(res_mca, axes = 1:2)
get_mca_var_corr(res_mca, axes = 1:2)

Arguments

`res_mca`	MCA result.
`axes`	axes selection.

Value

data frame with MCA variable names, types, coords and eta2 values.

Extract the corresponding group id of MFA variable categories.

Description

Extract the corresponding group id of MFA variable categories.

Usage

get_mfa_mod_group_id(res_mfa)
get_mfa_mod_group_id(res_mfa)

Arguments

res_mfa

MFA result.

Value

vector containing group ids in MFA result order.

Extract coords of categories to concat.

Description

Extract coords of categories to concat.

Usage

get_path_coord(res_gda_quali, var, var_levels = NULL, exclude = NULL)
get_path_coord(res_gda_quali, var, var_levels = NULL, exclude = NULL)

Arguments

`res_gda_quali`	GDA result.
`var`	variable names.
`var_levels`	variable categories to concat.
`exclude`	categories to exclude.

Value

data frame with path coords.

Reshape place chronology data.

Description

Reshape place chronology data.

Usage

get_places_chronology(
  data,
  id = "all",
  weekday = "all",
  title,
  exclude_sleep = TRUE
)
get_places_chronology(
  data,
  id = "all",
  weekday = "all",
  title,
  exclude_sleep = TRUE
)

Arguments

`data`	a data frame, which contains place chronology data.
`id`	vector, which contains questionnaiere ids.
`weekday`	vector, which contains a day selection.
`title`	specify plot title.
`exclude_sleep`	exclude sleep duration (boolean).

Value

reshaped data frame for further visualization.

Reshpape place chronology time pattern data.

Description

Reshpape place chronology time pattern data.

Usage

get_places_chronology_time_pattern(oc_data, id = "all", weekday = "all")
get_places_chronology_time_pattern(oc_data, id = "all", weekday = "all")

Arguments

`id`	vector, which contains questionnaire ids. Use "all" if you want to plot all ids.
`weekday`	vector, which contains days to plot.
`data`	data frame, which contains place chronology time pattern data.

Value

reshaped data frame for further visualization.

Reshape time pattern data.

Description

Reshape time pattern data.

Usage

get_time_pattern(data, id = "all", reshape_data = TRUE)
get_time_pattern(data, id = "all", reshape_data = TRUE)

Arguments

`data`	data frame which contains time pattern data.
`id`	vector which contains questionnaire ids.
`reshape_data`	whether reshape data or not. Use this option if your data is column wise concentration (e. g. "mo_seminar")

Value

reshaped data frame for further visualization.

Reshape time pattern profile data frame.

Description

Reshape time pattern profile data frame.

Usage

get_time_pattern_profile(data_tp, id = "all")
get_time_pattern_profile(data_tp, id = "all")

Arguments

`data_tp`	data frame containing questionnaire_id, kml3d results and time pattern data.
`id`	time pattern number.

Value

Reshaped data frame.

Reshape time pattern series data.

Description

Reshape time pattern series data.

Usage

get_time_pattern_series(data_tp)
get_time_pattern_series(data_tp)

Arguments

data_tp

data frame including questionnaire_id, kml3d results and time pattern data.

Value

Reshaped data frame.

Calculate modified rates

Description

Calculate modified rates

Usage

modified_rates(mca_res)
modified_rates(mca_res)

Arguments

mca_res

FactoMineR MCA object.

Value

Modified rates as tibble.

Visualize a barplot.

Description

Visualize a barplot.

Usage

plot_barplot(
  df_origin,
  df_var,
  sort = FALSE,
  bar_abs_size = 3.5,
  bar_rel_size = 3,
  axes_rel_small = 0.6,
  show_missing = TRUE,
  digits = 1,
  flip = FALSE
)
plot_barplot(
  df_origin,
  df_var,
  sort = FALSE,
  bar_abs_size = 3.5,
  bar_rel_size = 3,
  axes_rel_small = 0.6,
  show_missing = TRUE,
  digits = 1,
  flip = FALSE
)

Arguments

`df_origin`	source data farme (tibble).
`df_var`	categorical variable name.
`sort`	sort bars (boolean).
`bar_abs_size`	size of absolute values in plot.
`bar_rel_size`	size of relative values in plot.
`axes_rel_small`	relative value for small axes text (labels, titles …).
`show_missing`	include missing values in plot or not (boolean).
`digits`	amount of label value digits.
`flip`	flip axes (boolean).

Value

ggplot2 barplot.

Plot single or multiple place chronologies in different scales.

Description

Plot single or multiple place chronologies in different scales.

Usage

plot_places_chronology(
  data,
  id,
  weekday = "all",
  size_range = NULL,
  colour_path = "black",
  size_path = 2,
  alpha_path = 0.25,
  alpha_points = 0.85,
  linetype_path = "solid",
  force_repel = 3,
  title = NULL,
  axis_label = FALSE,
  xlim = NULL,
  ylim = NULL,
  xextra = 3,
  print_place_duration = TRUE,
  point_padding = unit(1, "lines"),
  exclude_sleep = TRUE,
  facets = FALSE
)
plot_places_chronology(
  data,
  id,
  weekday = "all",
  size_range = NULL,
  colour_path = "black",
  size_path = 2,
  alpha_path = 0.25,
  alpha_points = 0.85,
  linetype_path = "solid",
  force_repel = 3,
  title = NULL,
  axis_label = FALSE,
  xlim = NULL,
  ylim = NULL,
  xextra = 3,
  print_place_duration = TRUE,
  point_padding = unit(1, "lines"),
  exclude_sleep = TRUE,
  facets = FALSE
)

Arguments

`data`	a data frame (columns: ID, day, duration, place, address, lon, lat, prop_duration).
`id`	vector, which contains questionnaire ids. Choosa "all" to compute all ids.
`weekday`	vector, which contains the weekday to plot.
`size_range`	specify the size for visualizatipn of duration.
`colour_path`	sepcify the path line colour.
`size_path`	specify the path line size.
`alpha_path`	specify the path line alpha value [0:1].
`alpha_points`	specify the point alpha value [0:1].
`linetype_path`	specify the linetype of the path line.
`force_repel`	specify how heavy the repel algorithmn should be.
`title`	title of the plot.
`axis_label`	show or hide axis labels (boolean).
`xlim`	specify plot x limits.
`ylim`	specify plot y limits.
`xextra`	extra space for time plot (units in cm).
`print_place_duration`	print place overall duration (hours).
`point_padding`	Amount of padding around labeled point. Defaults to unit(0, "lines").
`exclude_sleep`	exclude sleep duration (boolean).
`facets`	plot facets (boolean).

Value

ggplot2 visualization of place chronology data.

Plot place chronologies map structure and zoom in.

Description

Plot place chronologies map structure and zoom in.

Usage

plot_places_chronology_meaning(
  data,
  id,
  weekday = "all",
  size_range = NULL,
  colour_path = "black",
  size_path = 2,
  alpha_path = 0.25,
  alpha_points = 1,
  linetype_path = "solid",
  title = NULL,
  axis_label = FALSE,
  print_place_duration = TRUE,
  exclude_sleep = TRUE,
  facets = FALSE,
  facets_include_place = NULL,
  facets_include_all = FALSE,
  exclude_na = FALSE,
  exclude = NULL,
  exclude_meaning = NULL,
  meanings = NULL,
  map = FALSE,
  map_zoom = 10,
  map_add_x = 0.2,
  map_add_y = 0.1,
  graph = TRUE,
  area_fill = "white",
  area_colour = "black",
  area_alpha = 0,
  area_size = 1.5,
  con_size = 5,
  area_linetype = "solid",
  area_expand = 0.5,
  area_label_fontsize = c(12, 10),
  area_buffer = 10,
  map_scalebar = TRUE,
  map_scalebar_location = "topright",
  map_scalebar_text_size = 4.5,
  map_scalebar_box_size = 0.015,
  map_scalebar_border_size = 0.85,
  map_scalebar_dist = 1,
  map_scalebar_text_dist = 0.02,
  map_scalebar_unit_pos_dist = 0.5
)
plot_places_chronology_meaning(
  data,
  id,
  weekday = "all",
  size_range = NULL,
  colour_path = "black",
  size_path = 2,
  alpha_path = 0.25,
  alpha_points = 1,
  linetype_path = "solid",
  title = NULL,
  axis_label = FALSE,
  print_place_duration = TRUE,
  exclude_sleep = TRUE,
  facets = FALSE,
  facets_include_place = NULL,
  facets_include_all = FALSE,
  exclude_na = FALSE,
  exclude = NULL,
  exclude_meaning = NULL,
  meanings = NULL,
  map = FALSE,
  map_zoom = 10,
  map_add_x = 0.2,
  map_add_y = 0.1,
  graph = TRUE,
  area_fill = "white",
  area_colour = "black",
  area_alpha = 0,
  area_size = 1.5,
  con_size = 5,
  area_linetype = "solid",
  area_expand = 0.5,
  area_label_fontsize = c(12, 10),
  area_buffer = 10,
  map_scalebar = TRUE,
  map_scalebar_location = "topright",
  map_scalebar_text_size = 4.5,
  map_scalebar_box_size = 0.015,
  map_scalebar_border_size = 0.85,
  map_scalebar_dist = 1,
  map_scalebar_text_dist = 0.02,
  map_scalebar_unit_pos_dist = 0.5
)

Arguments

`data`	a data frame (columns: ID, day, duration, place, address, lon, lat, prop_duration).
`id`	vector, which contains questionnaire ids. Choose "all" to compute all ids.
`weekday`	vector, which contains the weekday to plot.
`size_range`	specify the size for visualizatipn of duration.
`colour_path`	sepcify the path line colour.
`size_path`	specify the path line size.
`alpha_path`	specify the path line alpha value [0:1].
`alpha_points`	specify the point alpha value [0:1].
`linetype_path`	specify the linetype of the path line.
`title`	title of the plot.
`axis_label`	show or hide axis labels (boolean).
`print_place_duration`	print place overall duration (hours).
`exclude_sleep`	exclude sleep duration (boolean).
`facets`	plot facets (boolean).
`facets_include_place`	explicit include places in facets (vector).
`facets_include_all`	include all place names in facet plot (boolean).
`exclude_na`	drop NA places (boolean).
`exclude`	exclude specific places from the plot (vector).
`exclude_meaning`	meanings to be excluded (vector).
`meanings`	give places a meaning for grouping (vector).
`map`	use map background (boolean).
`map_zoom`	map zoom level.
`map_add_x`	adjust map x area.
`map_add_y`	adjust map y area.
`graph`	plot graph (boolean).
`area_fill`	fill colour of meaning area.
`area_colour`	line colour of meaning area.
`area_alpha`	alpha of meaning area.
`area_size`	size of meaning area.
`con_size`	size of the label connector (numeric).
`area_linetype`	linetype of meaning area.
`area_expand`	size of the area expand (numeric).
`area_label_fontsize`	area label fontsize (vector).
`area_buffer`	The size of the region around the mark where labels cannot be placed (in mm).
`map_scalebar`	show a scale bar (boolean).
`map_scalebar_location`	location of the scalebar.
`map_scalebar_text_size`	size of the scale text.
`map_scalebar_box_size`	size of the box.
`map_scalebar_border_size`	size of the border.
`map_scalebar_dist`	displayed disctance.
`map_scalebar_text_dist`	distance between box and text.
`map_scalebar_unit_pos_dist`	add space between scalebar values and unit.

Value

ggplot2 visualization of place chronology data.

Plot place chronologies activity paths.

Description

Plot place chronologies activity paths.

Usage

plot_places_chronology_path(
  data,
  id,
  recodeded_places = NULL,
  recode_week = c(`Woche 4` = "5", `Woche 3` = "4", `Woche 2` = "3", `Woche 1` = "2")
)
plot_places_chronology_path(
  data,
  id,
  recodeded_places = NULL,
  recode_week = c(`Woche 4` = "5", `Woche 3` = "4", `Woche 2` = "3", `Woche 1` = "2")
)

Arguments

`data`	a data frame (columns: ID, day, duration, place, address, lon, lat, prop_duration).
`id`	vector, which contains questionnaire ids. Choose "all" to compute all ids.
`recodeded_places`	recode leves of place labels by named vector.
`week`	recode leves of week labels by named vector.

Value

ggplot2 visualization of place chronology path.

Plot place chronology time pattern data.

Description

Plot place chronology time pattern data.

Usage

plot_places_chronology_time_pattern(
  data,
  id = "all",
  weekday = "all",
  graph = TRUE,
  print_prop_duration = TRUE,
  legend = TRUE,
  bar_width = 1,
  ncol = 3,
  labels = NULL,
  facet_label = TRUE,
  legend_bottom = TRUE,
  legend_cols = 2
)
plot_places_chronology_time_pattern(
  data,
  id = "all",
  weekday = "all",
  graph = TRUE,
  print_prop_duration = TRUE,
  legend = TRUE,
  bar_width = 1,
  ncol = 3,
  labels = NULL,
  facet_label = TRUE,
  legend_bottom = TRUE,
  legend_cols = 2
)

Arguments

`data`	data frame, which contains place chronology time pattern data.
`id`	vector, which contains questionnaire ids. Use "all" if you want to plot all ids.
`weekday`	weekday vector, which contains days to plot.
`graph`	whether to plot or not to plot the praph (boolean)
`print_prop_duration`	whether to print or not to print prop duration data.
`legend`	show or hide legends (boolean).
`bar_width`	specify the width of the bars.
`ncol`	number of cols, if there are multiple plots (facets).
`labels`	facet labels.
`facet_label`	show facets (boolean).
`legend_bottom`	show legend on bottom (boolean).
`legend_cols`	number of legend cols (numeric).

Value

ggplot2 visualization of place chronology time pattern data.

Plot single or multiple time pattern.

Description

Plot single or multiple time pattern.

Usage

plot_time_pattern(
  data,
  id = "all",
  ncol = 4,
  reshape_data = TRUE,
  print_prop_duration = TRUE,
  fluid = FALSE,
  labels = NULL,
  legend = TRUE,
  facet = TRUE
)
plot_time_pattern(
  data,
  id = "all",
  ncol = 4,
  reshape_data = TRUE,
  print_prop_duration = TRUE,
  fluid = FALSE,
  labels = NULL,
  legend = TRUE,
  facet = TRUE
)

Arguments

`data`	data frame which contains time pattern data.
`id`	vector which contains questionnaire ids.
`ncol`	number of cols, if there are multiple plots (facets).
`reshape_data`	whether reshape data or not.
`print_prop_duration`	whether to print or not to print prop duration data (boolean).
`fluid`	should be static bars or fluid lines visualized (boolean).
`labels`	facet labels.
`legend`	show legend (boolean).
`facet`	plot facets (boolean).

Value

ggplot2 visualization of time pattern data.

Plot average time pattern profiles.

Description

Plot average time pattern profiles.

Usage

plot_time_pattern_profile(data_tp, id = "all", ncol = 4, fluid = FALSE)
plot_time_pattern_profile(data_tp, id = "all", ncol = 4, fluid = FALSE)

Arguments

`data_tp`	data frame containing questionnaire_id, kml3d results and time pattern data.
`id`	time pattern to plot.
`ncol`	facet columns.
`fluid`	should be static bars or fluid lines visualized (boolean).

Value

ggplot2 avgerage time pattern profile plot.

Plot time pattern series data.

Description

Plot time pattern series data.

Usage

plot_time_pattern_series(
  data_tp,
  alpha = 0.3,
  individual_lines = FALSE,
  title = "Time pattern profiles (kml3d results)",
  hour_limits = c(0, 24),
  hour_scale = c(0, 4, 8, 12)
)
plot_time_pattern_series(
  data_tp,
  alpha = 0.3,
  individual_lines = FALSE,
  title = "Time pattern profiles (kml3d results)",
  hour_limits = c(0, 24),
  hour_scale = c(0, 4, 8, 12)
)

Arguments

`data_tp`	data frame including questionnaire_id, kml3d results and time pattern data.
`alpha`	opacity of the time pattern lines.
`individual_lines`	show individual time pattern lines (boolean).
`title`	plot title.
`hour_limits`	y axis limits (hours).
`hour_scale`	y axis breaks (hours).

Value

ggplot2 time pattern series plot.

Calculate crossed variables double breakdown of variance.

Description

Calculate crossed variables double breakdown of variance.

Usage

supvar_crossing_stats(
  res_gda,
  var_quali_df,
  var_quali,
  impute = TRUE,
  axes = 1:2
)
supvar_crossing_stats(
  res_gda,
  var_quali_df,
  var_quali,
  impute = TRUE,
  axes = 1:2
)

Arguments

`res_gda`	MCA result.
`var_quali_df`	the supplementary data frame.
`var_quali`	crossed supplementary variable (vector separated by "_").
`impute`	impute missing data (boolean).
`axes`	the GDA dimensions to calculate double breakdown of variance.

Value

Returns a list:

`var`	double breakdown of variance.
`reg`	result of linear regressions.

Calculate results for supplementary variables.

Description

Calculate results for supplementary variables.

Usage

supvar_stats(res_gda, var_quali_df, var_quali, impute = TRUE, impute_ncp = 2)
supvar_stats(res_gda, var_quali_df, var_quali, impute = TRUE, impute_ncp = 2)

Arguments

`res_gda`	GDA result.
`var_quali_df`	the supplementary data frame.
`var_quali`	supplementary variable name (string).
`impute`	impute missing data (boolean).
`impute_ncp`	number of dimensions to predict missing values.

Value

Returns a list:

`weight`	numeric vector of categories weights
`cord`	data frame of categories coordinates
`cos2`	data frame of categories square cosine
`var`	data frame of categories within variances, variance between and within categories and variable square correlation ratio (eta2)
`v.test`	data frame of categories test-values
`supvar`	vector of the supplementary variable categories

Package 'TimeSpaceAnalysis'

Help Index

Concat the categories of a variable

Description

Usage

Arguments

Value

Optimise ggplot2 plot.

Description

Usage

Arguments

Value

Extract list of MFA group indices to exclude (for sMFA).

Description

Usage

Arguments

Value

Extract legends form ggplot2 objects.

Description

Usage

Arguments

Value

Add supplementary individuals.

Description

Usage

Arguments

Value

Visualize HCPC hclus trees.

Description

Usage

Arguments

Value

Title

Description

Usage

Arguments

Value

Visualize interaction cloud.

Description

Usage

Arguments

Value

Title

Description

Usage

Arguments

Value

Visualize supplementary variables.

Description

Usage

Arguments

Value

Visualize additive cloud.

Description

Usage

Arguments

Value

Visualization of trajectories (connected active and passive individual points).

Description

Usage

Arguments

Value

Visualization of trajectory structuring factor ellipses.

Description

Usage

Arguments

Value

Visualize trajectories and structuring factors.

Description

Usage

Arguments

Value

Visualization of the separated concentration ellipses of the sample.

Description

Usage

Arguments

Value

Visualize specific contributing modalities in a plane.

Description

Usage