toy_data: Synthetic Age-Period-Cohort Dataset
In MAPCtools: Multivariate Age-Period-Cohort (MAPC) Modeling for Health Data
toy_data R Documentation
Synthetic Age-Period-Cohort Dataset
Description
A toy dataset generated to illustrate modeling of age, period, and cohort effects, including interactions with education and sex.
This data simulates count outcomes (e.g., disease incidence or event counts) as a function of demographic variables using a Poisson process.
Usage
data(toy_data)
Format
A data frame with 10000 rows and 7 variables:
- age
Age of individuals, sampled uniformly from 20 to 59.
- period
Calendar year of observation, sampled uniformly from 1990 to 2019.
- education
Factor for education level, with levels 1, 2 and 3.
- sex
Factor indicating biological sex, with levels: "male", "female".
- count
Simulated event count, generated from a Poisson distribution.
- known_rate
The true Poisson rate used to generate count, computed from the log-linear model.
- cohort
Derived variable indicating year of birth (period - age).
Details
The underlying event rate is modeled on the log scale as a linear combination of age, period, sex, education, and an age-education interaction.
The count outcome is drawn from a Poisson distribution with this rate. This dataset is handy for testing APC models.
The true log-rate is computed (for observation n) as:
\log(\lambda_n)
= \beta_0
+ \beta_{\text{period}}\,\bigl(2020 - \text{period}_n\bigr)
+ \beta_{\text{sex}}\,I(\text{sex}_n = \text{female}) \\[6pt]
\quad
+ \beta_{\text{edu}}\,(\text{edu level}_n)
+ \beta_{\text{edu-age}}\,(\text{age}_n - 20)\,(\text{edu level}_n - 1)\,I(\text{age}_n \le 40) \\[6pt]
\quad
+ \beta_{\text{edu-age}}\,(60 - \text{age}_n)\,(\text{edu level}_n - 1)\,I(\text{age}_n > 40)
where the rate decreases over time (periods), increases with age up to age 40, and decreases after. The coefficients used are:
-
intercept = 1.0
-
b_period = 0.02
-
b_sex = 0.5 (female effect)
-
b_education_base = 0.5
-
b_education_age_interaction = 0.015
Source
Simulated data, created using base R and tibble.
MAPCtools documentation built on June 25, 2025, 5:09 p.m.
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| toy_data | R Documentation |
Synthetic Age-Period-Cohort Dataset
Description
A toy dataset generated to illustrate modeling of age, period, and cohort effects, including interactions with education and sex. This data simulates count outcomes (e.g., disease incidence or event counts) as a function of demographic variables using a Poisson process.
Usage
data(toy_data)
Format
A data frame with 10000 rows and 7 variables:
- age
Age of individuals, sampled uniformly from 20 to 59.
- period
Calendar year of observation, sampled uniformly from 1990 to 2019.
- education
Factor for education level, with levels 1, 2 and 3.
- sex
Factor indicating biological sex, with levels: "male", "female".
- count
Simulated event count, generated from a Poisson distribution.
- known_rate
The true Poisson rate used to generate
count, computed from the log-linear model.- cohort
Derived variable indicating year of birth (period - age).
Details
The underlying event rate is modeled on the log scale as a linear combination of age, period, sex, education, and an age-education interaction. The count outcome is drawn from a Poisson distribution with this rate. This dataset is handy for testing APC models.
The true log-rate is computed (for observation n) as:
\log(\lambda_n)
= \beta_0
+ \beta_{\text{period}}\,\bigl(2020 - \text{period}_n\bigr)
+ \beta_{\text{sex}}\,I(\text{sex}_n = \text{female}) \\[6pt]
\quad
+ \beta_{\text{edu}}\,(\text{edu level}_n)
+ \beta_{\text{edu-age}}\,(\text{age}_n - 20)\,(\text{edu level}_n - 1)\,I(\text{age}_n \le 40) \\[6pt]
\quad
+ \beta_{\text{edu-age}}\,(60 - \text{age}_n)\,(\text{edu level}_n - 1)\,I(\text{age}_n > 40)
where the rate decreases over time (periods), increases with age up to age 40, and decreases after. The coefficients used are:
-
intercept = 1.0 -
b_period = 0.02 -
b_sex = 0.5(female effect) -
b_education_base = 0.5 -
b_education_age_interaction = 0.015
Source
Simulated data, created using base R and tibble.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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