Package 'tornado'

Generic Importance Plot

Description

Generic Importance Plot

Usage

importance(model_final, ...)

Arguments

model_final	a model object
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance.glm importance.lm importance.cv.glmnet importance.survreg

---

Plot Variable Importance for a GLMNET model

Description

Plot Variable Importance for a GLMNET model

Usage

## S3 method for class 'cv.glmnet'
importance(model_final, model_data, form, dict = NA, nperm = 500, ...)

Arguments

model_final	a model object
model_data	the data used to fit the model
form	the model formula
dict	a variable dictionary for plotting
nperm	the number of permutations used to calculate the importance
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance

Examples

if (requireNamespace("glmnet", quietly = TRUE))
{
  form <- formula(mpg ~ cyl*wt*hp)
  mf <- model.frame(form, data = mtcars)
  mm <- model.matrix(mf, mf)
  gtest <- glmnet::cv.glmnet(x = mm, y = mtcars$mpg, family = "gaussian")
  imp <- importance(gtest, mtcars, form, nperm = 50)
  plot(imp)
}

---

GLM variable importance plot

Description

GLM variable importance plot

Usage

## S3 method for class 'glm'
importance(model_final, model_null, dict = NA, ...)

Arguments

model_final	a model object
model_null	a glm object for the null model
dict	a dictionary to translate the model variables to plotting variables
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance

Examples

gtest <- glm(mpg ~ cyl*wt*hp + gear + carb, data=mtcars, family=gaussian)
gtestreduced <- glm(mpg ~ 1, data=mtcars, family=gaussian)
imp <- importance(gtest, gtestreduced)
plot(imp)

gtest <- glm(mpg ~ cyl + wt + hp + gear + carb, data=mtcars, family=gaussian)
gtestreduced <- glm(mpg ~ 1, data=mtcars, family=gaussian)
imp <- importance(gtest, gtestreduced)
plot(imp)

gtest <- glm(vs ~ wt + disp + gear, data=mtcars, family=binomial(link="logit"))
gtestreduced <- glm(vs ~ 1, data=mtcars, family=binomial(link="logit"))
imp <- importance(gtest, gtestreduced)
plot(imp)

---

Linear Model variable importance plot

Description

Linear Model variable importance plot

Usage

## S3 method for class 'lm'
importance(model_final, model_null, dict = NA, ...)

Arguments

model_final	a model object
model_null	a lm object for the null model
dict	a dictionary to translate the model variables to plotting variables
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance

Examples

gtest <- lm(mpg ~ cyl*wt*hp + gear + carb, data=mtcars)
gtestreduced <- lm(mpg ~ 1, data=mtcars)
imp <- importance(gtest, gtestreduced)
plot(imp)

gtest <- lm(mpg ~ cyl + wt + hp + gear + carb, data=mtcars)
gtestreduced <- lm(mpg ~ 1, data=mtcars)
imp <- importance(gtest, gtestreduced)
plot(imp)

---

Create a variable importance plot for a survreg model

Description

Create a variable importance plot for a survreg model

Usage

## S3 method for class 'survreg'
importance(model_final, model_data, dict = NA, nperm = 500, ...)

Arguments

model_final	a model object
model_data	the data used to fit the model
dict	a plotting dictionary for models terms
nperm	the number of permutations used to calculate the importance
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance

Examples

model_final <- survival::survreg(survival::Surv(futime, fustat) ~ ecog.ps*rx + age,
                       data = survival::ovarian,
                       dist = "weibull")
imp <- importance(model_final, survival::ovarian, nperm = 50)
plot(imp)

---

Importance Plot for the caret::train objects

Description

Importance Plot for the caret::train objects

Usage

## S3 method for class 'train'
importance(model_final, ...)

Arguments

model_final	a model object
...	arguments passed to other methods

Value

an object of type importance_plot

type	the type of importance plot
data	the importance data required for the plot

See Also

importance

Examples

if (requireNamespace("caret", quietly = TRUE) &
    requireNamespace("randomForest", quietly = TRUE))
{
  model_final <- caret::train(x = subset(mtcars, select = -mpg), y = mtcars$mpg, method = "rf")
  imp <- importance(model_final)
  plot(imp)
}

---

Plot an Importance Plot object

Description

Plot an Importance Plot object

Usage

## S3 method for class 'importance_plot'
plot(
  x,
  plot = TRUE,
  nvar = NA,
  col_imp_alone = "#69BE28",
  col_imp_cumulative = "#427730",
  geom_bar_control = list(fill = "#69BE28"),
  ...
)

Arguments

x	a importance_plot object
plot	boolean to determine if the plot is displayed, or just returned
nvar	the number of variables to plot in order of importance
col_imp_alone	the color used for the variance explained by each variable alone
col_imp_cumulative	the color used for the cumulative variance explained
geom_bar_control	list of arguments to control the plotting of ggplot2::geom_bar
...	future arguments

Value

the plot

Examples

gtest <- lm(mpg ~ cyl + wt + hp + gear + carb, data = mtcars)
gtestreduced <- lm(mpg ~ 1, data = mtcars)
imp <- importance(gtest, gtestreduced)
plot(imp)

gtest <- survival::survreg(survival::Surv(futime, fustat) ~ ecog.ps*rx + age,
                           data = survival::ovarian,
                           dist = "weibull")
imp <- importance(gtest, survival::ovarian, nperm = 50)
plot(imp)

---

Plot a Tornado Plot object

Description

Plot a Tornado Plot object

Usage

## S3 method for class 'tornado_plot'
plot(
  x,
  plot = TRUE,
  nvar = NA,
  xlabel = "Model Response",
  sensitivity_colors = c("grey", "#69BE28"),
  geom_bar_control = list(width = NULL),
  geom_point_control = list(fill = "black", col = "black"),
  ...
)

Arguments

x	a tornado_plot object
plot	boolean to determine if the plot is displayed, or just returned
nvar	the number of variables to plot
xlabel	a label for the x-axis
sensitivity_colors	a two element character vector of the bar colors for a lower value and upper value
geom_bar_control	a list of ggplot2::geom_bar options
geom_point_control	a list of ggplot2::geom_point
...	future arguments

Value

the plot

Examples

gtest <- lm(mpg ~ cyl*wt*hp, data = mtcars)
tp <- tornado(gtest, type = "PercentChange", alpha = 0.10, xlabel = "MPG")
plot(tp)

---

print data in an importance_plot

Description

print data in an importance_plot

Usage

## S3 method for class 'importance_plot'
print(x, ...)

Arguments

x	the object to be printed
...	further arguments passed to print.data.frame

Examples

gtest <- glm(vs ~ wt + disp + gear, data=mtcars, family=binomial(link="logit"))
gtestreduced <- glm(vs ~ 1, data=mtcars, family=binomial(link="logit"))
g <- importance(gtest, gtestreduced)
print(g)

---

print data in a tornado_plot

Description

print data in a tornado_plot

Usage

## S3 method for class 'tornado_plot'
print(x, ...)

Arguments

x	the object to be printed
...	further arguments passed to print.data.frame

Examples

gtest <- lm(mpg ~ cyl*wt*hp, data = mtcars)
tp <- tornado(gtest, type = "PercentChange", alpha = 0.10, xlabel = "MPG")
print(tp)

---

Quantile for Ordered Factors

Description

Quantile for Ordered Factors

Usage

## S3 method for class 'ordered'
quantile(x, probs = seq(0, 1, 0.25), ...)

Arguments

x	an ordered factor
probs	the desired quatiles
...	arugments passed on

Value

ordered factor levels at the desired quantiles

Examples

quantile(ordered(rep(c("C","B","A"), each=30), levels=c("C","B","A")),
         probs <- seq(0, 1, 0.25))

---

Generic tornado plotting method

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

tornado(model, type, alpha, dict, ...)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado.lm, tornado.glm, tornado.cv.glmnet, tornado.survreg, tornado.coxph, tornado.train

---

Cox Proportional Hazards Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'coxph'
tornado(model, type = "PercentChange", alpha = 0.1, dict = NA, modeldata, ...)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
modeldata	the data used to fit the model
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

Examples

gtest <- survival::coxph(survival::Surv(stop, event) ~ rx + size + number,
                           survival::bladder)
torn <- tornado(gtest, modeldata = survival::bladder, type = "PercentChange",
             alpha = 0.10)
plot(torn, xlabel = "Risk")

---

GLMNET Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'cv.glmnet'
tornado(
  model,
  type = "PercentChange",
  alpha = 0.1,
  dict = NA,
  modeldata,
  form,
  s = "lambda.1se",
  ...
)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
modeldata	the raw data used to fit the glmnet model
form	the model formula
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used.
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado

Examples

if (requireNamespace("glmnet", quietly = TRUE))
{
  form <- formula(mpg ~ cyl*wt*hp)
  mf <- model.frame(form, data = mtcars)
  mm <- model.matrix(form, data = mf)
  gtest <- glmnet::cv.glmnet(x = mm, y= mtcars$mpg, family = "gaussian")
  torn <- tornado(gtest, modeldata = mtcars, form = formula(mpg ~ cyl*wt*hp), s = "lambda.1se",
                  type = "PercentChange", alpha = 0.10)
  plot(torn, xlabel = "MPG")
}

---

GLM Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'glm'
tornado(model, type = "PercentChange", alpha = 0.1, dict = NA, ...)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado

Examples

gtest <- glm(mpg ~ cyl*wt*hp, data = mtcars, family = gaussian)
torn <- tornado(gtest, type = "PercentChange", alpha = 0.10)
plot(torn, xlabel = "MPG")

---

Linear Model Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'lm'
tornado(model, type = "PercentChange", alpha = 0.1, dict = NA, ...)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado

Examples

gtest <- lm(mpg ~ cyl*wt*hp, data = mtcars)
torn <- tornado(gtest, type = "PercentChange", alpha = 0.10)
plot(torn, xlabel = "MPG")

---

Survreg Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'survreg'
tornado(model, type = "PercentChange", alpha = 0.1, dict = NA, modeldata, ...)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
modeldata	the data used to fit the model
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado

Examples

gtest <- survival::survreg(survival::Surv(futime, fustat) ~ ecog.ps + rx,
                           survival::ovarian,
                           dist='weibull', scale=1)
torn <- tornado(gtest, modeldata = survival::ovarian, type = "PercentChange",
             alpha = 0.10, xlabel = "futime")
plot(torn, xlabel = "Survival Time")

---

Caret Tornado Diagram

Description

A tornado plot is a visualization of the range of outputs expected from a variety of inputs, or alternatively, the sensitivity of the output to the range of inputs. The center of the tornado is plotted at the response expected from the mean of each input variable. For a given variable, the width of the tornado is determined by the range of the variable, a multiplicative factor of the variable, or a quantile of the variable. Variables are ordered vertically with the widest bar at the top and narrowest at the bottom. Only one variable is moved from its mean value at a time. Factors or categorical variables have also been added to these plots by plotting dots at the resulting output as each factor is varied through all of its levels. The base factor level is chosen as the input variable for the center of the tornado.

Usage

## S3 method for class 'train'
tornado(
  model,
  type = "PercentChange",
  alpha = 0.1,
  dict = NA,
  class_number = NA,
  ...
)

Arguments

model	a model object
type	PercentChange, percentiles, ranges, or StdDev
alpha	the level of change, the percentile level, or the number of standard deviations
dict	a dictionary to translate variables for the plot. The dictionary must be a list or data.frame with elements old and new. The old element must contain each variable in the model.
class_number	for classification models, which number of the class that will be plotted
...	further arguments, not used

Value

a tornado_plot object

type	the type of tornado plot
data	the data required for the plot
family	the model family if available

See Also

tornado

Examples

if (requireNamespace("caret", quietly = TRUE) &
    requireNamespace("randomForest", quietly = TRUE))
{
  gtest <- caret::train(x = subset(mtcars, select = -mpg), y = mtcars$mpg, method = "rf")
  torn <- tornado(gtest, type = "PercentChange", alpha = 0.10)
  plot(torn, xlabel = "MPG")
}

---