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
1 2 3 
Arguments
dataset 
A dataframe containing all variables to be used. 
outcome_var_name 
The name of the outcome variable in 
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 
outcome_label 
A label for the outcome variable to be printed in the table produced. 
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 for 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,

adjustment_vars 
A character vector containing the variable names of categorical variables to be adjusted for. The default is 
adjustment_var_labels 
A character vector with labels for the variables adjusted for, to be printed in the table produced. 
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 matrix of adjusted means with categorical variables defining the groupings as rows and outcome categories as columns.
Author(s)
Christiana Kartsonaki <christiana.kartsonaki@gmail.com>
See Also
adjprop
Examples
1 2 3 4 5 6 7 8 9 10  # 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("2029", "3039", "4049", "5059", "6069"), 5))
adjmeans(dataset = data, outcome_var_name = "sleep",
categorical_vars = "sex", outcome_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.")
