Package 'sgboost'

Create a sparse-group boosting formula

Description

Creates a mboost formula that allows to fit a sparse-group boosting model based on boosted Ridge Regression with mixing parameter alpha. The formula consists of a group baselearner part with degrees of freedom 1-alpha and individual baselearners with degrees of freedom alpha. Groups should be defined through group_df. The corresponding modeling data should not contain categorical variables with more than two categories, as they are then treated as a group only.

Usage

create_formula(
  alpha = 0.3,
  group_df = NULL,
  blearner = "bols",
  outcome_name = "y",
  group_name = "group_name",
  var_name = "var_name",
  intercept = FALSE
)

Arguments

alpha	Numeric mixing parameter. For alpha = 0 only group baselearners and for alpha = 1 only individual baselearners are defined.
group_df	input data.frame containing variable names with group structure.
blearner	Type of baselearner. Default is 'bols'.
outcome_name	String indicating the name of dependent variable. Default is "y"
group_name	Name of column in group_df indicating the group structure of the variables. Default is ⁠"group_name⁠.
var_name	Name of column in group_df containing the variable names to be used as predictors. Default is "var_name". should not contain categorical variables with more than two categories, as they are then treated as a group only.
intercept	Logical, should intercept be used?

Value

Character containing the formula to be passed to mboost::mboost() yielding the sparse-group boosting for a given value mixing parameter alpha.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- create_formula(alpha = 0.3, group_df = group_df)
sgb_model <- mboost(formula = sgb_formula, data = df)
summary(sgb_model)

---

Aggregated and raw coefficients in a sparse group boosting model

Description

Computes the aggregated coefficients from group and individual baselearners. Also returns the raw coefficients associated with each baselearner.

Usage

get_coef(sgb_model)

Arguments

sgb_model

Model of type mboost to compute the coefficients for.

Details

in a sparse group boosting models a variable in a dataset can be selected as an individual variable or as a group. Therefore there can be two associated effect sizes for the same variable. This function aggregates both and returns it in a data.frame.

Value

List of data.frames containing the a data.frame '$raw' with the variable and the raw (Regression) coefficients and the data.frame '$aggregated' with the aggregated (Regression) coefficients.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- create_formula(alpha = 0.3, group_df = group_df)
sgb_model <- mboost(formula = sgb_formula, data = df)
sgb_coef <- get_coef(sgb_model)

---

Path of aggregated and raw coefficients in a sparse-group boosting model

Description

Computes the aggregated coefficients from group and individual baselearners for each boosting iteration.

Usage

get_coef_path(sgb_model)

Arguments

sgb_model

Model of type mboost to compute the coefficient path for .

Details

in a sparse-group boosting models a variable in a dataset can be selected as an individual variable or as a group. Therefore there can be two associated effect sizes for the same variable. This function aggregates both and returns it in a data.frame for each boosting iteration

Value

List of data.frames containing the a data.frame ⁠$raw⁠ with the variable and the raw (Regression) coefficients and the data.frame ⁠$aggregated⁠ with the aggregated (Regression) coefficients.

See Also

get_coef()

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- create_formula(alpha = 0.3, group_df = group_df)
sgb_model <- mboost(formula = sgb_formula, data = df)
sgb_coef_path <- get_coef_path(sgb_model)

---

Variable importance of a sparse-group boosting model

Description

Variable importance is computed as relative reduction of loss-function attributed to each predictor (groups and individual variables). Returns a list of two data.frames. The first contains the variable importance of a sparse-group model in a data.frame for each predictor. The second one contains the aggregated relative importance of all groups vs. individual variables.

Usage

get_varimp(sgb_model)

Arguments

sgb_model

Model of type mboost to compute the variable importance for.

Value

List of two data.frames. ⁠$raw⁠ contains the name of the variables, group structure and variable importance on both group and individual variable basis. ⁠$group_importance⁠ contains the the aggregated relative importance of all group baselearners and of all individual variables.

See Also

mboost::varimp() which this function uses.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- as.formula(create_formula(alpha = 0.3, group_df = group_df))
sgb_model <- mboost(formula = sgb_formula, data = df)
sgb_varimp <- get_varimp(sgb_model)

---

Visualizing a sparse-group boosting model

Description

Radar or scatter/lineplot visualizing the effects sizes relative to the variable importance in a sparse-group boosting model. Works also for a regular mboost model.

Usage

plot_effects(
  sgb_model,
  plot_type = "radar",
  prop = 0,
  n_predictors = 30,
  max_char_length = 5,
  base_size = 8
)

Arguments

sgb_model	Model of type mboost to be used.
plot_type	String indicating the type of visualization to use. 'radar' refers to a radar plot using polar coordinates. Here the angle is relative to the cumulative relative importance of predictors and the radius is proportional to the effect size. "clock" does the same as "radar" but uses clock coordinates instead of polar coordinates. "scatter" uses the effect size as y-coordinate and the cumulative relative importance as x-axis in a classical Scatter plot.
prop	Numeric value indicating the minimal importance a predictor/baselearner has to have to be plotted. Default value is zero, meaning all predictors are plotted. By increasing prop the number of plotted variables can be reduced. One can also use n_predictors for limiting the number of variables to be plotted directly.
n_predictors	The maximum number of predictors to be plotted. Default is 30. Alternative to prop.
max_char_length	The maximum character length of a predictor to be printed. Default is 5. For long variable names one may adjust this number.
base_size	The base_size argument to be passed to the ggplot2 theme ggplot2::theme_classic to be used to control the overall size of the figure. Default value is 8.

Value

ggplot2 object mapping the effect sizes and variable importance.

See Also

get_coef(), get_varimp() which this function uses.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- as.formula(create_formula(alpha = 0.3, group_df = group_df))
sgb_model <- mboost(formula = sgb_formula, data = df)
plot_effects(sgb_model)

---

Coefficient path of a sparse-group boosting model

Description

Shows how the effect sizes change throughout the boosting iterations in a sparse-group boosting model. Works also for a regular mboost models. Color indicates the selection of group or individual variables within a boosting iteration.

Usage

plot_path(sgb_model, max_char_length = 5, base_size = 8)

Arguments

sgb_model	Model of type mboost to be used.
max_char_length	The maximum character length of a predictor to be printed. Default is 5. For long variable names one may adjust this number.
base_size	The base_size argument to be passed to the ggplot2 theme ggplot2::theme_bw to be used to control the overall size of the figure. Default value is 8.

Value

ggplot2 object mapping the effect sizes and variable importance.

See Also

get_coef_path() which this function uses.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- as.formula(create_formula(alpha = 0.4, group_df = group_df))
sgb_model <- mboost(formula = sgb_formula, data = df)
plot_path(sgb_model)

---

Variable importance bar plot of a sparse group boosting model

Description

Visualizes the variable importance of a sparse-group boosting model. Color indicates if a predictor is an individual variable or a group.

Usage

plot_varimp(
  sgb_model,
  prop = 0,
  n_predictors = 30,
  max_char_length = 15,
  base_size = 8
)

Arguments

sgb_model	Model of type mboost to plot the variable importance.
prop	Numeric value indicating the minimal importance a predictor/baselearner has to have. Default value is zero, meaning all predictors are plotted. By increasing prop the number of plotted variables can be reduced. One can also use 'n_predictors' for limiting the number of variables to be plotted directly.
n_predictors	The maximum number of predictors to be plotted. Default is 30. Alternative to 'prop'.
max_char_length	The maximum character length of a predictor to be printed. Default is 15. For larger groups or long variable names one may adjust this number to differentiate variables from groups.
base_size	The base_size argument to be passed to the ggplot2 theme ggplot2::theme_bw to be used to control the overall size of the figure. Default value is 8.

Details

Note that aggregated group and individual variable importance printed in the legend is based only on the plotted variables and not on all variables that were selected in the sparse-group boosting model.

Value

object of type ggplot2.

See Also

get_varimp which this function uses.

Examples

library(mboost)
library(dplyr)
set.seed(1)
df <- data.frame(
  x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100),
  x4 = rnorm(100), x5 = runif(100)
)
df <- df %>%
  mutate_all(function(x) {
    as.numeric(scale(x))
  })
df$y <- df$x1 + df$x4 + df$x5
group_df <- data.frame(
  group_name = c(1, 1, 1, 2, 2),
  var_name = c("x1", "x2", "x3", "x4", "x5")
)

sgb_formula <- as.formula(create_formula(alpha = 0.3, group_df = group_df))
sgb_model <- mboost(formula = sgb_formula, data = df)
sgb_varimp <- plot_varimp(sgb_model)

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