Package 'lvmisc' reference manual

Title:	Veras Miscellaneous
Description:	Contains a collection of useful functions for basic data computation and manipulation, wrapper functions for generating 'ggplot2' graphics, including statistical model diagnostic plots, methods for computing statistical models quality measures (such as AIC, BIC, r squared, root mean squared error) and general utilities.
Authors:	Lucas Veras [aut, cre]
Maintainer:	Lucas Veras <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.2
Built:	2025-02-18 04:58:11 UTC
Source:	https://github.com/verasls/lvmisc

Abort based on issues with function argument

Description

Create a custom error condition created with rlang::abort() with a - hopefully - more useful error message and metadata.

Usage

abort_argument_type(arg, must, not)

abort_argument_class(arg, must, not)

abort_argument_length(arg, must, not)

abort_argument_diff_length(arg1, arg2)

abort_argument_value(arg, valid_values)
abort_argument_type(arg, must, not)

abort_argument_class(arg, must, not)

abort_argument_length(arg, must, not)

abort_argument_diff_length(arg1, arg2)

abort_argument_value(arg, valid_values)

Arguments

`arg`	A character string with the argument name.
`must`	A character string specifying a condition the argument must fulfill.
`not`	Either a character string specifying a condition the argument must not fulfill or the bare (unquoted) argument name. In the last case, the function evaluates the argument type (`abort_argument_type()`) or length (`abort_argument_length()`) and displays the result in the error message.
`arg1`, `arg2`	A character string with the argument name.
`valid_values`	A character vector with the valid values.

Value

Each function returns a classed error condition. abort_argument_type() returns a error_argument_type class, abort_argument_length() returns a error_argument_length class, abort_argument_diff_length() returns a error_argument_diff_length class and abort_argument_value() returns a error_argument_value class.

Abort based on column not being found in a data frame

Description

Creates a custom error condition created with rlang::abort() with a - hopefully - more useful error message and metadata.

Usage

abort_column_not_found(data, col_name)
abort_column_not_found(data, col_name)

Arguments

`data`	A data frame.
`col_name`	A character vector with the column name.

Value

Returns an error condition of classerror_column_not_found.

Abort method if class is not implemented

Description

Creates a custom error condition created with rlang::abort() with a - hopefully - more useful error message and metadata.

Usage

abort_no_method_for_class(fun, class, ...)
abort_no_method_for_class(fun, class, ...)

Arguments

`fun`	A character vector with the function name.
`class`	A character vector with the class name.
`...`	Extra message to be added to the error message. Must be character string.

Value

Returns an error condition of classerror_no_method_for_class.

Abort if required package is not installed

Description

Creates a custom error condition created with rlang::abort() with a - hopefully - more useful error message and metadata.

Usage

abort_package_not_installed(package)
abort_package_not_installed(package)

Arguments

package

A character string with the required package name.

Value

Returns an error condition of classerror_package_not_installed.

Model accuracy

Description

Computes some common model accuracy indices, such as the R squared, mean absolute error, mean absolute percent error and root mean square error.

Usage

accuracy(model, na.rm = FALSE)

## Default S3 method:
accuracy(model, na.rm = FALSE)

## S3 method for class 'lvmisc_cv'
accuracy(model, na.rm = FALSE)

## S3 method for class 'lm'
accuracy(model, na.rm = FALSE)

## S3 method for class 'lmerMod'
accuracy(model, na.rm = FALSE)
accuracy(model, na.rm = FALSE)

## Default S3 method:
accuracy(model, na.rm = FALSE)

## S3 method for class 'lvmisc_cv'
accuracy(model, na.rm = FALSE)

## S3 method for class 'lm'
accuracy(model, na.rm = FALSE)

## S3 method for class 'lmerMod'
accuracy(model, na.rm = FALSE)

Arguments

`model`	An object of class `lvmisc_cv` or an object containing a model.
`na.rm`	A logical value indicating whether or not to strip `NA` values to compute the indices. Defaults to `FALSE`.

Details

The method for the lm class (or for the lvmisc_cv class of a lm) returns a data frame with the columns AIC (Akaike information criterion), BIC (Bayesian information criterion), R2 (R squared), R2_adj (adjusted R squared), MAE (mean absolute error), MAPE (mean absolute percent error) and RMSE (root mean square error).

The method for the lmerMod (or for the lvmisc_cv class of a lmerMod) returns a data frame with the columns R2_marg and R2_cond instead of the columns R2 and R2_adj. All the other columns are the same as the method for lm. R2_marg is the marginal R squared, which considers only the variance by the fixed effects of a mixed model, and R2_cond is the conditional R squared, which considers both fixed and random effects variance.

Value

An object of class lvmisc_accuracy. See "Details" for more information.

Examples

mtcars <- tibble::as_tibble(mtcars, rownames = "car")
m <- stats::lm(disp ~ mpg, mtcars)
cv <- loo_cv(m, mtcars, car, keep = "used")

accuracy(m)
accuracy(cv)
mtcars <- tibble::as_tibble(mtcars, rownames = "car")
m <- stats::lm(disp ~ mpg, mtcars)
cv <- loo_cv(m, mtcars, car, keep = "used")

accuracy(m)
accuracy(cv)

Bias

Description

Computes the bias (mean error) between the input vectors.

Usage

bias(actual, predicted, na.rm = FALSE)
bias(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

A double scalar with the bias value.

Examples

actual <- runif(10)
predicted <- runif(10)

bias(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

bias(actual, predicted)

Compute body mass index (BMI)

Description

bmi calculates the BMI in kilograms per meter squared.

Usage

bmi(mass, height)
bmi(mass, height)

Arguments

mass, height

A numerical vector with body mass and height data. mass unit must be kilograms and height unit must be meters. If the height unit is centimeters, it is converted to meters before BMI computation and a warning is shown.

Value

Returns a double vector with the element-wise body mass index (BMI).

Examples

mass <- sample(50:100, 20)
height <- rnorm(20, mean = 1.7, sd = 0.2)

bmi(mass, height)
mass <- sample(50:100, 20)
height <- rnorm(20, mean = 1.7, sd = 0.2)

bmi(mass, height)

Classify body mass index (BMI) category

Description

bmi_cat returns the element-wise BMI category as factor with 6 levels:

Underweight (18.5 < BMI)
Normal weight (18.5 $\le$ BMI < 25)
Overweight (25 $\le$ BMI < 30)
Obesity class I (30 $\le$ BMI < 35)
Obesity class II (35 $\le$ BMI < 40)
Obesity class III (BMI $\ge$ 40)

Usage

bmi_cat(bmi)
bmi_cat(bmi)

Arguments

bmi

A numeric vector with BMI data. BMI unit must be meters per square meter.

Value

A vector of class factor with 6 levels: "Underweight", "Normal weight", "Overweight", "Obesity class I", "Obesity class II" and "Obesity class III".

Examples

mass <- sample(50:100, 20)
height <- rnorm(20, mean = 1.7, sd = 0.2)
bmi <- bmi(mass, height)

bmi_cat(bmi)
mass <- sample(50:100, 20)
height <- rnorm(20, mean = 1.7, sd = 0.2)
bmi <- bmi(mass, height)

bmi_cat(bmi)

Center variable

Description

Center a variable by subtracting the mean from each element. Centering can be performed by the grand mean when by = NULL (the default), or by group means when by is a factor variable.

Usage

center_variable(variable, scale = FALSE, by = NULL)
center_variable(variable, scale = FALSE, by = NULL)

Arguments

`variable`	A numeric vector.
`scale`	A logical vector. If `scale = TRUE`, the centered values of `variable` are divided by their standard deviation.
`by`	A vector with the `factor` class.

Value

A numeric vector.

Examples

df <- data.frame(
  id = 1:20,
  group = as.factor(sample(c("A", "B"), 20, replace = TRUE)),
  body_mass = rnorm(20, mean = 65, sd = 12)
)

df$body_mass_centered <- center_variable(df$body_mass, by = df$group)
df
df <- data.frame(
  id = 1:20,
  group = as.factor(sample(c("A", "B"), 20, replace = TRUE)),
  body_mass = rnorm(20, mean = 65, sd = 12)
)

df$body_mass_centered <- center_variable(df$body_mass, by = df$group)
df

Clear the console

Description

Clear the console by printing 50 times the new line character ("\n").

Usage

cl()
cl()

Value

Prints to console. Called by its side-effects.

Clean observations

Description

Replace valid observations by NAs when a given subject has more then max_na missing values.

Usage

clean_observations(data, id, var, max_na)
clean_observations(data, id, var, max_na)

Arguments

`data`	A data frame, or data frame extension (e.g. a tibble).
`id`	The bare (unquoted) name of the column that identifies each subject.
`var`	The bare (unquoted) name of the column to be cleaned.
`max_na`	An integer indicating the maximum number of `NA`s per subject.

Value

The original data with the var observations matching the max_na criterion replaced by NA.

Examples

set.seed(10)

data <- data.frame(
  id = rep(1:5, each = 4),
  time = rep(1:4, 5),
  score = sample(c(1:5, rep(NA, 2)), 20, replace = TRUE)
)

clean_observations(data, id, score, 1)
set.seed(10)

data <- data.frame(
  id = rep(1:5, each = 4),
  time = rep(1:4, 5),
  score = sample(c(1:5, rep(NA, 2)), 20, replace = TRUE)
)

clean_observations(data, id, score, 1)

Compare models accuracy

Description

Computes some common model accuracy indices of several different models at once, allowing model comparison.

Usage

compare_accuracy(..., rank_by = NULL, quiet = FALSE)
compare_accuracy(..., rank_by = NULL, quiet = FALSE)

Arguments

`...`	A list of models. The models can be of the same or of different classes, including `lvmisc_cv` class.
`rank_by`	A character string with the name of an accuracy index to rank the models by.
`quiet`	A logical indicating whether or not to show any warnings. If `FALSE` (the default) no warnings are shown.

Value

A data.frame with a model per row and an index per column.

Examples

m1 <- lm(Sepal.Length ~ Species, data = iris)
m2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
m3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
compare_accuracy(m1, m2, m3)

if (require(lme4, quietly = TRUE)) {
  mtcars <- tibble::as_tibble(mtcars, rownames = "cars")
  m1 <- lm(Sepal.Length ~ Species, data = iris)
  m2 <- lmer(
    Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris
  )
  m3 <- lm(disp ~ mpg * hp, mtcars)
  cv3 <- loo_cv(m3, mtcars, cars)
  compare_accuracy(m1, m2, cv3, rank_by = "AIC")
}
m1 <- lm(Sepal.Length ~ Species, data = iris)
m2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
m3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
compare_accuracy(m1, m2, m3)

if (require(lme4, quietly = TRUE)) {
  mtcars <- tibble::as_tibble(mtcars, rownames = "cars")
  m1 <- lm(Sepal.Length ~ Species, data = iris)
  m2 <- lmer(
    Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris
  )
  m3 <- lm(disp ~ mpg * hp, mtcars)
  cv3 <- loo_cv(m3, mtcars, cars)
  compare_accuracy(m1, m2, cv3, rank_by = "AIC")
}

Create a project

Description

Creates a project structure, including sub-directories, and initialization of a git repository.

Usage

create_proj(
  path,
  sub_dirs = "default",
  use_git = TRUE,
  use_gitignore = "default",
  use_readme = TRUE
)
create_proj(
  path,
  sub_dirs = "default",
  use_git = TRUE,
  use_gitignore = "default",
  use_readme = TRUE
)

Arguments

`path`	A path to a directory that does not exist.
`sub_dirs`	A character vector. If `sub_dirs = "default"`, it creates 'code/', 'data/', 'docs/', 'figures/' and 'tables/' sub-directories. Otherwise, it creates the sub-directories specified in the character vector.
`use_git`	A logical value indicating whether or not to initialize a git repository. Defaults to `TRUE`.
`use_gitignore`	A character vector. If `use_gitignore = "default"`, it adds a .gitignore file with the files generated by your operating system and by R, as well as some common file extensions. The default .gitignore is as generated by gitignore.io. To create a custom .gitignore, add the files to be ignored in a character vector. If you do not want to create a .gitignore file, set `use_gitignore = NULL`.
`use_readme`	A logical value. If `TRUE` (default), adds an empty 'README.md' file.

Value

Path to the newly created project, invisibly.

Divide variable based on quantiles

Description

Creates a factor based on equally spaced quantiles of a variable.

Usage

divide_by_quantile(data, n, na.rm = TRUE)
divide_by_quantile(data, n, na.rm = TRUE)

Arguments

`data`	A numeric vector.
`n`	An integer specifying the number of levels in the factor to be created.
`na.rm`	A logical vector indicating whether the `NA` values should be removed before the quantiles are computed.

Value

A vector of class factor indicating in which quantile the element in data belongs.

Examples

x <- c(sample(1:20, 9), NA)
divide_by_quantile(x, 3)
x <- c(sample(1:20, 9), NA)
divide_by_quantile(x, 3)

Error

Description

Computes the element-wise error between the input vectors.

Usage

error(actual, predicted)
error(actual, predicted)

Arguments

`actual`	A numeric vector with the actual values
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.

Value

Returns a double vector with the element-wise error values.

Examples

actual <- runif(10)
predicted <- runif(10)

error(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

error(actual, predicted)

Absolute error

Description

Computes the element-wise absolute errors between the input vectors.

Usage

error_abs(actual, predicted)
error_abs(actual, predicted)

Arguments

`actual`	A numeric vector with the actual values
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.

Value

Returns a double vector with the element-wise absolute error values.

Examples

actual <- runif(10)
predicted <- runif(10)

error_abs(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

error_abs(actual, predicted)

Absolute percent error

Description

Computes the element-wise absolute percent errors between the input vectors.

Usage

error_abs_pct(actual, predicted)
error_abs_pct(actual, predicted)

Arguments

`actual`	A numeric vector with the actual values
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.

Value

Returns a double vector with the element-wise absolute percent error values.

A vector of the class lvmisc_percent with the element-wise absolute percent error values.

Examples

actual <- runif(10)
predicted <- runif(10)

error_abs_pct(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

error_abs_pct(actual, predicted)

Percent error

Description

Computes the element-wise percent error between the input vectors.

Usage

error_pct(actual, predicted)
error_pct(actual, predicted)

Arguments

`actual`	A numeric vector with the actual values
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.

Value

Returns a double vector with the element-wise percent error values.

A vector of the class lvmisc_percent with the element-wise percent error values.

Examples

actual <- runif(10)
predicted <- runif(10)

error_pct(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

error_pct(actual, predicted)

Squared error

Description

Computes the element-wise squared errors between the input vectors.

Usage

error_sqr(actual, predicted)
error_sqr(actual, predicted)

Arguments

`actual`	A numeric vector with the actual values
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.

Value

Returns a double vector with the element-wise squared error values.

Examples

actual <- runif(10)
predicted <- runif(10)

error_sqr(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

error_sqr(actual, predicted)

Extract information from the trained models from a cross-validation

Description

Extract information from the trained models from a cross-validation

Usage

get_cv_fixed_eff(cv)

get_cv_r2(cv)
get_cv_fixed_eff(cv)

get_cv_r2(cv)

Arguments

`cv`	An object of class `lvmisc_cv`.

Value

get_cv_fixed_eff() returns a tibble with the estimated value for each coefficient of each trained model and its associated standard error. get_cv_r2() returns a tibble with the R squared for each of the trained models.

Check whether value is outlier

Description

is_outlier returns a logical vector indicating whether a value is an outlier based on the rule of 1.5 times the interquartile range above the third quartile or below the first quartile.

Usage

is_outlier(x, na.rm = FALSE)
is_outlier(x, na.rm = FALSE)

Arguments

`x`	A numerical vector
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

A logical vector.

Examples

x <- c(1:8, NA, 15)
is_outlier(x, na.rm = TRUE)
x <- c(1:8, NA, 15)
is_outlier(x, na.rm = TRUE)

Limits of agreement

Description

Computes the Bland-Altman limits of agreement between the input vectors.

Usage

loa(actual, predicted, na.rm = FALSE)
loa(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

A named list with the lower and upper limits of agreement values, respectively.

Examples

actual <- runif(10)
predicted <- runif(10)

loa(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

loa(actual, predicted)

Leave-one-out cross-validation

Description

Cross-validates the model using the leave-one-out approach. In this method each subject's data is separated into a testing data set, and all other subject's are kept in the training data set, with as many resamples as the number of subjects in the original data set. It computes the model's predicted value in the testing data set for each subject.

Usage

loo_cv(model, data, id, keep = "all")

## Default S3 method:
loo_cv(model, data, id, keep = "all")

## S3 method for class 'lm'
loo_cv(model, data, id, keep = "all")

## S3 method for class 'lmerMod'
loo_cv(model, data, id, keep = "all")
loo_cv(model, data, id, keep = "all")

## Default S3 method:
loo_cv(model, data, id, keep = "all")

## S3 method for class 'lm'
loo_cv(model, data, id, keep = "all")

## S3 method for class 'lmerMod'
loo_cv(model, data, id, keep = "all")

Arguments

`model`	An object containing a model.
`data`	A data frame.
`id`	The bare (unquoted) name of the column which identifies subjects.
`keep`	A character string which controls which columns are present in the output. Can be one of three options: `"all"`: The default. Retain all variables in the original data frame plus the `".actual"` and `".predicted"` columns. `"used"`: Keeps only the `"id"` column of the original data frame, plus the `".actual"` and `".predicted"` columns. `"none"`: Returns just the `".actual"` and '".predicted" columns.

Value

Returns an object of class lvmisc_cv. A tibble containing the ".actual" and ".predicted" columns.

Examples

mtcars$car <- row.names(mtcars)
m <- stats::lm(disp ~ mpg, mtcars)
loo_cv(m, mtcars, car, keep = "used")
mtcars$car <- row.names(mtcars)
m <- stats::lm(disp ~ mpg, mtcars)
loo_cv(m, mtcars, car, keep = "used")

Last error

Description

lt() prints the last error and the full backtrace and le() returns the last error with a simplified backtrace. These functions are just wrappers to rlang::last_trace() and rlang::last_error() respectively.

Usage

lt()

le()
lt()

le()

Value

An object of class rlang_trace.

An object of class rlang_error.

Number of elements in a vector.

Description

lunique returns the number of non-NA unique elements and lna returns the number of NAs.

Usage

lunique(x)

lna(x)
lunique(x)

lna(x)

Arguments

x

A vector.

Value

A non-negative integer.

Examples

x <- sample(c(1:3, NA), 10, replace = TRUE)
lunique(x)
lna(x)
x <- sample(c(1:3, NA), 10, replace = TRUE)
lunique(x)
lna(x)

Mean error

Description

Computes the average error between the input vectors.

Usage

mean_error(actual, predicted, na.rm = FALSE)
mean_error(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the mean error value.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error(actual, predicted)

Mean absolute error

Description

Computes the average absolute error between the input vectors.

Usage

mean_error_abs(actual, predicted, na.rm = FALSE)
mean_error_abs(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the mean absolute error value.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error_abs(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error_abs(actual, predicted)

Mean absolute percent error

Description

Computes the average absolute percent error between the input vectors.

Usage

mean_error_abs_pct(actual, predicted, na.rm = FALSE)
mean_error_abs_pct(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the mean absolute percent error value.

A vector of the class lvmisc_percent.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error_abs_pct(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error_abs_pct(actual, predicted)

Mean percent error

Description

Computes the average percent error between the input vectors.

Usage

mean_error_pct(actual, predicted, na.rm = FALSE)
mean_error_pct(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the mean percent error value.

A vector of the class lvmisc_percent.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error_pct(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error_pct(actual, predicted)

Mean square error

Description

Computes the average square error between the input vectors.

Usage

mean_error_sqr(actual, predicted, na.rm = FALSE)
mean_error_sqr(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the mean square error value.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error_sqr(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error_sqr(actual, predicted)

Root mean square error

Description

Computes the root mean square error between the input vectors.

Usage

mean_error_sqr_root(actual, predicted, na.rm = FALSE)
mean_error_sqr_root(actual, predicted, na.rm = FALSE)

Arguments

`actual`	A numeric vector with the actual values.
`predicted`	A numeric vector with the predicted values. Each element in this vector must be a prediction for the corresponding element in `actual`.
`na.rm`	A logical value indicating whether `NA` values should be stripped before the computation proceeds. Defaults to `FALSE`.

Value

Returns a double scalar with the root mean square error value.

Examples

actual <- runif(10)
predicted <- runif(10)

mean_error_sqr_root(actual, predicted)
actual <- runif(10)
predicted <- runif(10)

mean_error_sqr_root(actual, predicted)

Value matching

Description

Value matching

Usage

x %!in% table
x %!in% table

Arguments

`x`	Vector with the values to be matched.
`table`	Vector with the values to be matched against.

Value

A logical vector indicating which values are not in table.

Examples

x <- 8:12
x %!in% 1:10
x <- 8:12
x %!in% 1:10

Print all rows of a data frame or tibble

Description

Shortcut to print all rows of a data frame or tibble. Useful to inspect the whole tibble, as it prints by default only the first 20 rows.

Usage

pa(data)
pa(data)

Arguments

data

A data frame or tibble.

Value

Prints data and returns it invisibly.

Examples

df <- dplyr::starwars
pa(df)
df <- dplyr::starwars
pa(df)

`percent` vector

Description

Creates a double vector that represents percentages. When printed, it is multiplied by 100 and suffixed with ⁠%⁠.

Usage

percent(x = double())

is_percent(x)

as_percent(x)
percent(x = double())

is_percent(x)

as_percent(x)

Arguments

`x`	For `percent()`: A numeric vector For `is_percent()`: An object to test. For `as_percent()`: An object to cast.

Value

An S3 vector of class lvmisc_percent.

Examples

percent(c(0.25, 0.5, 0.75))
percent(c(0.25, 0.5, 0.75))

Computes the percent change

Description

percent_change returns the element-wise percent change between two numeric vectors.

Usage

percent_change(baseline, followup)
percent_change(baseline, followup)

Arguments

baseline, followup

A numeric vector with data to compute the percent change.

Value

A vector of class lvmisc_percent.

Examples

baseline <- sample(20:40, 10)
followup <- baseline * runif(10, min = 0.5, max = 1.5)

percent_change(baseline, followup)
baseline <- sample(20:40, 10)
followup <- baseline * runif(10, min = 0.5, max = 1.5)

percent_change(baseline, followup)

Create a Bland-Altman plot

Description

Create a Bland-Altman plot as described by Bland & Altman (1986).

Usage

plot_bland_altman(x, ...)
plot_bland_altman(x, ...)

Arguments

`x`	An object of class `lvmisc_cv` or an object containing a model.
`...`	Additional arguments to be passed to `ggplot2::aes()`.

Value

A ggplot object.

References

Bland, J.M. & Altman, D.G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 8(1), 307-10. doi:10.1016/S0140-6736(86)90837-8

Examples

mtcars <- tibble::as_tibble(mtcars, rownames = "car")
m <- stats::lm(disp ~ mpg, mtcars)
cv <- loo_cv(m, mtcars, car)
plot_bland_altman(cv, colour = as.factor(am))
mtcars <- tibble::as_tibble(mtcars, rownames = "car")
m <- stats::lm(disp ~ mpg, mtcars)
cv <- loo_cv(m, mtcars, car)
plot_bland_altman(cv, colour = as.factor(am))

Plot model diagnostics

Description

Plotting functions for some common model diagnostics.

Usage

plot_model(model)

plot_model_residual_fitted(model)

plot_model_scale_location(model)

plot_model_qq(model)

plot_model_cooks_distance(model)

plot_model_multicollinearity(model)
plot_model(model)

plot_model_residual_fitted(model)

plot_model_scale_location(model)

plot_model_qq(model)

plot_model_cooks_distance(model)

plot_model_multicollinearity(model)

Arguments

model

An object containing a model.

Details

plot_model_residual_fitted() plots the model residuals versus the fitted values. plot_model_scale_location() plots the square root of absolute value of the model residuals versus the fitted values. plot_model_qq() plots a QQ plot of the model standardized residuals. plot_model_cooks_distance() plots a bat chart of each observation Cook's distance value. plot_model_multicollinearity() plots a bar chart of the variance inflation factor (VIF) for each of the model terms. plot_model() returns a plot grid with all the applicable plot diagnostics to a given model.

Value

A ggplot object.

Examples

m <- lm(disp ~ mpg + hp + cyl + mpg:cyl, mtcars)
plot_model(m)
plot_model_residual_fitted(m)
plot_model_scale_location(m)
plot_model_qq(m)
plot_model_cooks_distance(m)
plot_model_multicollinearity(m)
m <- lm(disp ~ mpg + hp + cyl + mpg:cyl, mtcars)
plot_model(m)
plot_model_residual_fitted(m)
plot_model_scale_location(m)
plot_model_qq(m)
plot_model_cooks_distance(m)
plot_model_multicollinearity(m)

Quick plotting

Description

These functions are intended to be used to quickly generate simple exploratory plots using the package ggplot2.

Usage

plot_scatter(data, x, y, ...)

plot_line(data, x, y, ...)

plot_hist(data, x, bin_width = NULL, ...)

plot_qq(data, x, ...)
plot_scatter(data, x, y, ...)

plot_line(data, x, y, ...)

plot_hist(data, x, bin_width = NULL, ...)

plot_qq(data, x, ...)

Arguments

`data`	A data frame.
`x`, `y`	x and y aesthetics as the bare (unquoted) name of a column in `data`.
`...`	Additional arguments to be passed to the `ggplot2::aes()` function.
`bin_width`	The width of the bins in a histogram. When `NULL` (default), it uses the number of bins in `bins` (defaults to 30). You can also use one of the character strings `"Sturges"`, `"scott"` or `"FD"` to use one of the methods to determine the bin width as in `grDevices::nclass.*()`

Value

A ggplot object.

Examples

plot_scatter(mtcars, disp, mpg, color = factor(cyl))
plot_line(Orange, age, circumference, colour = Tree)
plot_hist(iris, Petal.Width, bin_width = "FD")
plot_qq(mtcars, mpg)
plot_scatter(mtcars, disp, mpg, color = factor(cyl))
plot_line(Orange, age, circumference, colour = Tree)
plot_hist(iris, Petal.Width, bin_width = "FD")
plot_qq(mtcars, mpg)

Compute R squared

Description

Returns the R squared values according to the model class.

Usage

r2(model)

## Default S3 method:
r2(model)

## S3 method for class 'lm'
r2(model)

## S3 method for class 'lmerMod'
r2(model)
r2(model)

## Default S3 method:
r2(model)

## S3 method for class 'lm'
r2(model)

## S3 method for class 'lmerMod'
r2(model)

Arguments

model

An object containing a model.

Details

R squared computations.

Value

If the model is a linear model, it returns a data.frame with the R squared and adjusted R squared values. If the model is a linear mixed model it return a data.frame with the marginal and conditional R squared values as described by Nakagawa and Schielzeth (2013). See the formulas for the computations in "Details".

R squared

$R^2 = \frac{var(\hat{y})}{var(\epsilon)}$

Where $var(\hat{y})$ is the variance explained by the model and $var(\epsilon)$ is the residual variance.

Adjusted R squared

$R_{adj}^{2} = 1 - (1 - R^2)\frac{n - 1}{n - p - 1}$

Where $n$ is the number of data points and $p$ is the number of predictors in the model.

Marginal R squared

$R_{marg}^{2} = \frac{var(f)}{var(f) + var(r) + var(\epsilon)}$

Where $var(f)$ is the variance of the fixed effects, $var(r)$ is the variance of the random effects and $var(\epsilon)$ is the residual variance.

Conditional R squared

$R_{cond}^{2} = \frac{var(f) + var(r)}{var(f) + var(r) + var(\epsilon)}$

References

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. doi:10.1111/j.2041-210x.2012.00261.x.

Examples

m1 <- lm(Sepal.Length ~ Species, data = iris)
r2(m1)
if (require(lme4, quietly = TRUE)) {
  m2 <- lmer(
    Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris
  )
  r2(m2)
}
m1 <- lm(Sepal.Length ~ Species, data = iris)
r2(m1)
if (require(lme4, quietly = TRUE)) {
  m2 <- lmer(
    Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris
  )
  r2(m2)
}

Repeat baseline levels

Description

Returns a vector with the length equal to the number of rows in the data with the baseline value of the var repeated for every time value of each id.

Usage

repeat_baseline_values(data, var, id, time, baseline_level, repeat_NA = TRUE)
repeat_baseline_values(data, var, id, time, baseline_level, repeat_NA = TRUE)

Arguments

`data`	A data frame.
`var`	The bare (unquoted) name of the column with the values to be repeated.
`id`	The bare (unquoted) name of the column that identifies each subject.
`time`	The bare (unquoted) name of the column with the time values.
`baseline_level`	The value of `time` corresponding the baseline.
`repeat_NA`	A logical vector indicating whether or not `NA` values in the `var` will correspond to `NA` values in return vector. Defaults to `TRUE`.

Value

A vector of the same lenght and class of var.

Examples

df <- data.frame(
 id = rep(1:5, each = 4),
 time = rep(1:4, 5),
 score = rnorm(20, mean = 10, sd = 2)
)

df$baseline_score <- repeat_baseline_values(df, score, id, time, 1)
df
df <- data.frame(
 id = rep(1:5, each = 4),
 time = rep(1:4, 5),
 score = rnorm(20, mean = 10, sd = 2)
)

df$baseline_score <- repeat_baseline_values(df, score, id, time, 1)
df

Capture a backtrace

Description

Captures the sequence of calls that lead to the current function. It is just a wrapper to rlang::trace_back().

Usage

tb(...)
tb(...)

Arguments

...

Passed to rlang::trace_back().

Value

An object of class rlang_trace.

Variance inflation factor

Description

Computes the variance inflation factor (VIF). The VIF is a measure of how much the variance of a regression coefficient is increased due to collinearity.

Usage

vif(model)

## Default S3 method:
vif(model)

## S3 method for class 'lm'
vif(model)

## S3 method for class 'lmerMod'
vif(model)
vif(model)

## Default S3 method:
vif(model)

## S3 method for class 'lm'
vif(model)

## S3 method for class 'lmerMod'
vif(model)

Arguments

model

An object containing a model.

Details

VIF interpretation

As a rule of thumb for the interpretation of the VIF value, a VIF less than 5 indicates a low correlation of a given model term with the others, a VIF between 5 and 10 indicates a moderate correlation and a VIF greater than 10 indicates a high correlation.

Value

It returns a data.frame with three columns: the name of the model term, the VIF value and its classification (see "Details").

References

James, G., Witten, D., Hastie, T., & Tibshirani, R. (eds.). (2013). An introduction to statistical learning: with applications in R. New York: Springer.

Examples

m <- lm(disp ~ mpg + cyl + mpg:cyl, mtcars)
vif(m)
m <- lm(disp ~ mpg + cyl + mpg:cyl, mtcars)
vif(m)

Package 'lvmisc'

Help Index

Abort based on issues with function argument

Description

Usage

Arguments

Value

See Also

Abort based on column not being found in a data frame

Description

Usage

Arguments

Value

See Also

Abort method if class is not implemented

Description

Usage

Arguments

Value

See Also

Abort if required package is not installed

Description

Usage

Arguments

Value

See Also

Model accuracy

Description

Usage

Arguments

Details

Value

Examples

Bias

Description

Usage

Arguments

Value

See Also

Examples

Compute body mass index (BMI)

Description

Usage

Arguments

Value

See Also

Examples

Classify body mass index (BMI) category

Description

Usage

Arguments

Value

See Also

Examples

Center variable

Description

Usage

Arguments

Value

Examples

Clear the console

Description

Usage

Value

Clean observations

Description

Usage

Arguments

Value

Examples

Compare models accuracy

Description

Usage

Arguments

Value

Examples

Create a project

Description

Usage

Arguments