Package 'DirectStandardisation'

Adjusted Means and Proportions by Direct Standardisation

Description

Adjusted Means and Proportions by Direct Standardisation

Details

Calculates adjusted means and proportions of a variable by groups defined by another variable, standardised to the structure of the adjustment variables in the whole dataset using direct standardisation.

Index of help topics:

DirectStandardisation-package
                        Adjusted Means and Proportions by Direct
                        Standardisation
adjmeans                Calculate adjusted means using direct
                        standardisation
adjprop                 Calculate adjusted proportions using direct
                        standardisation

Author(s)

Christiana Kartsonaki

Maintainer: Christiana Kartsonaki <[email protected]>

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Calculate adjusted means using direct standardisation

Description

Calculates adjusted means of a variable by groups defined by another variable using direct standardisation to the structure of the dataset, as defined by one or more variables.

Usage

adjmeans(dataset, outcome_vars, categorical_vars, outcome_var_labels = NULL, 
categorical_var_labels = NULL, adjustment_vars = c("age", "sex"), 
adjustment_var_labels = NULL, format_table = FALSE, 
transpose_table = FALSE, ndigits = 2, title = "")

Arguments

dataset	A dataframe containing all variables to be used.
outcome_vars	The names of the outcome variables in dataset (character vector). These are the variables of which adjusted means will be calculated.
categorical_vars	A character vector containing the names of categorical variables which define the groups by which adjusted means will be calculated. They must exist in dataset.
outcome_var_labels	Labels for the outcome variable to be printed in the table produced. This must be a list of length equal to the number of outcome variables, with each element a list of length two, each element of which is a character with a label. Note that if there is only one variable it should be list(list("Label 1", "Label 2")). Defaults to outcome_vars.
categorical_var_labels	Labels for the categorical variables by which means will be calculated, to be printed in the table produced. This must be a list of length equal to the number of variables by which adjusted means will be calculated, with each element a list of length two, the first element of which is a character with a label for the variable and the second element a character vector with labels for the levels of the variable. For example for two variables, the first of which has 3 levels and the second 2, list(list("Variable 1", c("Group 1", "Group 2", "Group 3")), list("Variable 2", c("Group 1", "Group 2"))). Note that if there is only one variable it should be list(list("Variable 1", c("Group 1", "Group 2", "Group 3"))). If null, the levels of the variable are used.
adjustment_vars	A character vector containing the variable names of categorical variables to be adjusted for. The default is age and sex, which standardises means of the subgroups to the age and sex structure of the overall dataset.
adjustment_var_labels	A character vector with labels for the variables adjusted for, to be printed in the table produced. If null, the variable names are used.
format_table	Whether the output table should be formatted. Defaults to FALSE.
transpose_table	Whether the output table should be transposed. Defaults to FALSE.
ndigits	Number of digits to be printed (defaults to 2).
title	A title for the table (defaults to blank).

Details

The function produces a table of means of some outcome variable by one or more categorical variables using direct standardisation with target population a population with proportions within each group specified by some variables (default is age and sex) identical for all categories of the categorical variable and equal to the overall proportion in the data.

Value

A data frame of adjusted means with categorical variables defining the groupings as rows and outcome categories as columns.

Author(s)

Christiana Kartsonaki <[email protected]>

See Also

adjprop

Examples

# Example 1

# generate a dataframe with sleep duration, sex and age group
data <- data.frame("sleep" = rnorm(50, mean = 8, sd = 1.5), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

adjmeans(dataset = data, outcome_vars = "sleep", 
categorical_vars = "sex", outcome_var_label = "Sleep duration", 
categorical_var_labels = list(list("Sex", c("Female", "Male"))), 
adjustment_vars = "age_group", adjustment_var_labels = "age", 
title = "Means of sleep duration by sex.")

# Example 2

# generate a dataframe with sleep duration, sex and age group
data <- data.frame("sleep" = rnorm(50, mean = 8, sd = 1.5), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

# no labels, more digits
adjmeans(dataset = data, outcome_vars = "sleep", 
categorical_vars = "sex", adjustment_vars = "age_group", 
ndigits = 4, title = "Means of sleep duration by sex.")

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Calculate adjusted proportions using direct standardisation

Description

Calculates adjusted proportions of a variable by groups defined by another variable using direct standardisation to the structure of the dataset, as defined by one or more variables.

Usage

adjprop(dataset, outcome_vars, categorical_vars, outcome_var_labels = NULL, 
categorical_var_labels = NULL, adjustment_vars = c("age", "sex"), 
adjustment_var_labels = NULL, format_table = FALSE,
transpose_table = FALSE, percentage = FALSE, ndigits = 2, title = "")

Arguments

dataset	A data frame containing all variables to be used.
outcome_vars	The names of the outcome variables in dataset (character vector). These are the variables of which adjusted proportions will be calculated.
categorical_vars	A character vector containing the names of categorical variables which define the groups by which adjusted proportions will be calculated. They must exist in dataset.
outcome_var_labels	Labels for the outcome variable to be printed in the table produced. This must be a list of length equal to the number of outcome variables, with each element a list of length two, the first element of which is a character with a label for the variable and the second element a character vector with labels for the levels of the variable. For example for two variables, the first of which has 3 levels and the second 2, list(list("Variable 1", c("Group 1", "Group 2", "Group 3")), list("Variable 2", c("Group 1", "Group 2"))). Note that if there is only one variable it should be list(list("Variable 1", c("Group 1", "Group 2", "Group 3"))). If null, the names and levels of the variables are used.
categorical_var_labels	Labels for the categorical variables by which proportions will be calculated, to be printed in the table produced. This must be a list of length equal to the number of variables by which adjusted proportions will be calculated, with each element a list of length two, the first element of which is a character with a label for the variable and the second element a character vector with labels for the levels of the variable. For example for two variables, the first of which has 3 levels and the second 2, list(list("Variable 1", c("Group 1", "Group 2", "Group 3")), list("Variable 2", c("Group 1", "Group 2"))). Note that if there is only one variable it should be list(list("Variable 1", c("Group 1", "Group 2", "Group 3"))). If null, the levels of the variable are used.
adjustment_vars	A character vector containing the names of categorical variables to be adjusted for. The default is age and sex, which standardises proportions of the subgroups to the age and sex structure of the overall dataset.
adjustment_var_labels	A character vector with labels for the variables adjusted for, to be printed in the table produced. If null, the variable names are used.
format_table	Whether the output table should be formatted. Defaults to FALSE.
transpose_table	Whether the output table should be transposed. Defaults to FALSE.
ndigits	Number of digits to be printed (defaults to 2).
percentage	If TRUE, percentages instead of proportions are calculated. The default is FALSE.
title	A title for the table (defaults to blank).

Details

The function produces a table of proportions of some outcome variable by one or more categorical variables using direct standardisation with target population a population with proportions within each group specified by some variables (default is age and sex) identical for all categories of the categorical variable and equal to the overall proportion in the data.

Value

A data frame of adjusted proportions with categorical variables defining the groupings as rows and outcome categories as columns.

Author(s)

Christiana Kartsonaki <[email protected]>

See Also

adjmeans

Examples

# Example 1

# generate a dataframe with sleep deprivation (binary), sex and age group
data <- data.frame("sleep_deprivation" = rbinom(50, size = 1, prob = 0.5), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

adjprop(dataset = data, outcome_vars = "sleep_deprivation", 
categorical_vars = "sex", 
outcome_var_labels = list(list("Sleep deprivation", levels(as.factor(data$sleep_deprivation)))), 
categorical_var_labels = list(list("Sex", c("Female", "Male"))), 
adjustment_vars = "age_group", adjustment_var_labels = "age", 
title = "Proportions of sleep deprivation by sex.")

# Example 2

# generate a dataframe with sleep duration group (3 categories), sex and age group
data <- data.frame("sleep_group" = rbinom(500, size = 2, prob = 0.3), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

adjprop(dataset = data, outcome_vars = "sleep_group", 
categorical_vars = "sex", outcome_var_labels = list(list("Sleep duration group", 
c("Group 1", "Group 2", "Group 3"))),
categorical_var_labels = list(list("Sex", c("Female", "Male"))), 
adjustment_vars = "age_group", adjustment_var_labels = "age", 
title = "Proportions of sleep duration groups by sex.")

# Example 3

# generate a dataframe with sleep duration group (3 categories), sex and age group
data <- data.frame("sleep_group" = rbinom(500, size = 2, prob = 0.3), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

# no labels, more digits
adjprop(dataset = data, outcome_vars = "sleep_group", 
categorical_vars = "sex", adjustment_vars = "age_group", 
adjustment_var_labels = "age", ndigits = 4, 
title = "Proportions of sleep duration groups by sex.")

# Example 4

# generate a dataframe with sleep duration group (4 categories), sex and age group
data <- data.frame("sleep_group" = rbinom(500, size = 3, prob = 0.25), 
"sex" = c(rep("m", 25), rep("f", 25)), 
"age_group" = rep(c("20-29", "30-39", "40-49", "50-59", "60-69"), 5))

# no labels, proportions
adjprop(dataset = data, outcome_vars = "sleep_group", 
categorical_vars = "sex", adjustment_vars = "age_group", 
adjustment_var_labels = "age", title = "Proportions of sleep duration groups by sex.")

# no labels, percentages
adjprop(dataset = data, outcome_vars = "sleep_group", 
categorical_vars = "sex", adjustment_vars = "age_group", 
adjustment_var_labels = "age", percentage = TRUE, 
title = "Proportions of sleep duration groups by sex.")

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