Package 'APCI' reference manual

Title:	A New Age-Period-Cohort Model for Describing and Investigating Inter-Cohort Differences and Life Course Dynamics
Description:	It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
Authors:	Jiahui Xu [aut, cre], Liying Luo [aut]
Maintainer:	Jiahui Xu <[email protected]>
License:	GPL-2
Version:	1.0.8
Built:	2025-01-22 05:57:36 UTC
Source:	https://github.com/jiahui1902/apci

Construct a cohort index matrix for any number of age and period groups

Description

This function returns a cohort index matrix for any number of age and period groups. The cohort index matrix will then be used to extract age-period interaction effects contained in each cohort.

Usage

ageperiod_group(age_range, period_range,
age_interval, period_interval,
age_group = NULL, period_group = NULL)
ageperiod_group(age_range, period_range,
age_interval, period_interval,
age_group = NULL, period_group = NULL)

Arguments

age_range, period_range

Numeric vector indicating the actual age and period range (e.g., 10 to 59 years old from 2000 to 2019).

age_interval, period_interval, age_group, period_group

Numeric values or character vectors indicating how age and period are grouped. age_interval and period_interval are numbers indicating the width of age and period groups respectively. age_group and period_group are character vectors explicitly listing all potential age and period groups. Either age_interval(period_interval) or age_group (period_group) have to be defined when unequal_interval is TRUE.

Value

It returns a matrix respresenting the relationship among age, period, and cohort groups under the current setting.

Examples

## age and period groups have equal width
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_interval = 5, period_interval = 5)
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_group = c("10-14","15-19","20-24","25-29",
                              "30-34","35-39","40-44","45-49",
                              "50-54","55-59"),
                period_group = c("2000-2004","2005-2009","2010-2014","2015-2019"))

## age and period groups have unequal width
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_interval = 10, period_interval = 5)
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_group = c("10-19","20-29","30-39","40-49","50-59"),
                period_group = c("2000-2004","2005-2009","2010-2014","2015-2019"))
## age and period groups have equal width
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_interval = 5, period_interval = 5)
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_group = c("10-14","15-19","20-24","25-29",
                              "30-34","35-39","40-44","45-49",
                              "50-54","55-59"),
                period_group = c("2000-2004","2005-2009","2010-2014","2015-2019"))

## age and period groups have unequal width
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_interval = 10, period_interval = 5)
ageperiod_group(age_range = 10:59, period_range = 2000:2019,
                age_group = c("10-19","20-29","30-39","40-49","50-59"),
                period_group = c("2000-2004","2005-2009","2010-2014","2015-2019"))

Run apci model

Description

run APC-I model

Arguments

`outcome`	An object of class character containing the name of the outcome variable. The outcome variable can be continuous, categorical, or count.
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`cohort`	An optional object of class character representing cohort membership index in the data. Usually, the cohort index can be generated from the age group index and time period index in the data because of the intrinsic relationship among these three time-related indices.
`weight`	An optional vector of sample weights to be used in the model fitting process. If non-NULL, the weights will be used in the first step to estimate the model. Observations with negative weights will be automatically dropped in modeling.
`covariate`	An optional vector of characters, representing the name(s) of the user-specified covariate(s) to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the data again.
`data`	A data frame containing the outcome variable, age group indicator, period group indicator, and covariates to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the input data again.
`family`	Used to specify the statistical distribution of the error term and link function to be used in the model. Usually, it is a character string naming a family function. For example, family can be "binomial", "multinomial"", or "gaussian". Users could also check R package glm for more details of family functions.
`dev.test`	Logical, specifying if the global F test (step 1) should be implemented before running the APC-I model. If TRUE, apci will first run the global F test and report the test results; otherwise, apci will skip this step and return NULL. The default setting is TRUE. But users should be careful that the algorithm will not automatically stop even if there is no significant cohort average deviation.
`print`	Logical, specifying if the intermediate results should be displayed on the screen in running the model. The default setting is TRUE in order to show the results explicitly although it can be too clumpy when the intermediate results are shown on the screen.
`gee`	logical, indicating if the data is cross-sectional data or longitudinal/panel data. If `TRUE`, the generalized estimating equation will be used to correct the standard error estimates. The default is `FALSE`, indicating that the data are cross-sectional.
`id`	A vector of character, specifying the cluster index in longitudinal data. It is required when `gee` is `TRUE`. The length of the vector should be the same as the number of observations.
`corstr`	a character string, specifying a possible correlation structure in the error terms when `gee` is `TRUE`. The following are allowed: `independence`, `fixed`, `stat\_M\_dep`, `non\_stat\_M\_dep`, `exchangeable`, `AR-M` and `unstructured`. The default value is `exchangeable`.
`unequal_interval`	Logical, indicating if age and period groups are of the same width. The default is set as `TRUE`.
`age_range`, `period_range`	Numeric vector indicating the actual age and period range (e.g., 10 to 59 years old from 2000 to 2019).
`age_interval`, `period_interval`, `age_group`, `period_group`	Numeric values or character vectors indicating how age and period are grouped. `age_interval` and `period_interval` are numbers indicating the width of age and period groups respectively. `age_group` and `period_group` are character vectors explicitly listing all potential age and period groups. Either `age_interval`(`period_interval`) or `age_group` (`period_group`) have to be defined when `unequal_interval` is `TRUE`.
`...`

Value

`model`	A summary of the fitted generalized linear regression. It displays the coefficients, standard errors, etc.
`dev_global`	The results of the global F test. It shows that if the interaction terms are significant as a component of the generalized linear regression model.
`intercept`	The overall intercept.
`age_effect`	A vector, representing the estimated age effect for each age group.
`period_effect`	A vector, representing the estimated period effect for each time period.
`cohort_average`	A vector, representing the cohort average effects for comparing inter-cohort differences.
`cohort_slope`	A vector, representing intra-cohort life-course changes.

Examples

library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)
## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## check model results
summary(APC_I)

APC_I$model
APC_I$dev_global
APC_I$dev_local
APC_I$intercept
APC_I$age_effect
APC_I$period_effect
APC_I$cohort_average
APC_I$cohort_slope
library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)
## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## check model results
summary(APC_I)

APC_I$model
APC_I$dev_global
APC_I$dev_local
APC_I$intercept
APC_I$age_effect
APC_I$period_effect
APC_I$cohort_average
APC_I$cohort_slope

Visualization for the APC-I model results

Description

Visualize the APC-I model results in a simple bar plot.

Usage

apci.bar(model, age, period, outcome_var,
cohort_label = NULL, ...)
apci.bar(model, age, period, outcome_var,
cohort_label = NULL, ...)

Arguments

`model`	A list, inheriting the corresponding results generated by function apci.
`age`	A vector, representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`outcome_var`	An object of class character representing the name of the outcome variable used in APC-I model. The outcome variable itself can be numerical and categorical.
`cohort_label`	A vector, representing the labels of cohort groups in the x asix.
`...`

Examples

library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)

## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## plot the bar plot
apci.bar(model = APC_I, age = "acc",period = "pcc")
library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)

## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## plot the bar plot
apci.bar(model = APC_I, age = "acc",period = "pcc")

Visualization for data exploration or model results

Description

Visualize the outcome or APC-I model results in a simple plot.

Usage

apci.plot(model, age, period, outcome_var,
type = "model", quantile = NULL, ...)
apci.plot(model, age, period, outcome_var,
type = "model", quantile = NULL, ...)

Arguments

`model`	A list, inheriting the corresponding results generated by function apci.
`outcome_var`	An object of class character representing the name of the outcome variable used in APC-I model. The outcome variable itself can be numerical and categorical.
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`type`	Character, "explore" or "model". If type is "explore", plots for age and period raw scores will be generated. If type is "model", model results will be plotted. The default setting is "model".
`quantile`	A number between 0 and 1, representing the percentiles to be used in visualizing the data or model. If NULL, the original magnitude will be used.
`...`

Visualize APC-I model results

Description

Visualize cohort effects from the APC-I model results using a heatmap

Usage

apci.plot.heatmap(model, age, period, color_map = NULL,
color_scale = NULL, quantile = NULL, ...)
apci.plot.heatmap(model, age, period, color_map = NULL,
color_scale = NULL, quantile = NULL, ...)

Arguments

`model`	A list, inheriting the corresponding results generated by function apci.
`age`	A vector, representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`color_map`	A vector, representing the color palettes to be used in the figure. The default setting is greys if color_map is NULL. Alternations, for example, can be c("blue", "yellow"), blues, etc.
`color_scale`	A vector including two numbers indicating the limit of the values to be plotted. The first number is the minimum value to be visualized and the second is the maximum value to be visualized. If NULL, the algorithm will automatically select the limits from the data (estimation results) to set up the scale.
`quantile`	A number between 0 and 1, representing the percentiles to be used in visualizing the data or model. If NULL, the original magnitude will be used.
`...`

Examples

library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)

## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## plot heatmap
apci.plot.heatmap(model = APC_I, age = "acc",period = 'pcc',
                  color_map = c('blue','yellow'))

library("APCI")
## load data
test_data <- APCI::women9017
test_data$acc <- as.factor(test_data$acc)
test_data$pcc <- as.factor(test_data$pcc)

## run APCI model
APC_I <- apci(outcome = "inlfc",
              age = "acc",
              period = "pcc",
              cohort = "ccc",
              weight = "wt",
              data = test_data,dev.test=FALSE,
              family = "gaussian")

## plot heatmap
apci.plot.heatmap(model = APC_I, age = "acc",period = 'pcc',
                  color_map = c('blue','yellow'))

Visualize APC-I model results

Description

Visualize cohort effects from the APC-I model results using a hexagram

Usage

apci.plot.hexagram(model, age, period, first_age,
first_period, interval, first_age_isoline = NULL,
first_period_isoline = NULL, isoline_interval = NULL,
color_scale = NULL, color_map = NULL, line_width = 0.5,
line_color = "grey", label_size = 0.5,
label_color = "black", scale_units = "Quintile",
wrap_cohort_labels = TRUE, quantile = NULL)
apci.plot.hexagram(model, age, period, first_age,
first_period, interval, first_age_isoline = NULL,
first_period_isoline = NULL, isoline_interval = NULL,
color_scale = NULL, color_map = NULL, line_width = 0.5,
line_color = "grey", label_size = 0.5,
label_color = "black", scale_units = "Quintile",
wrap_cohort_labels = TRUE, quantile = NULL)

Arguments

`model`	A list, inheriting the corresponding results generated by function apci.
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`color_scale`	A vector including two numbers indicating the limit of the values to be plotted. The first number is the minimum value to be visualized and the second is the maximum value to be visualized. If NULL, the algorithm will automatically select the limits from the data (estimation results) to set up the scale.
`color_map`	A vector, representing the color palettes to be used in the figure. The default setting is greys if color_map is NULL. Alternations, for example, can be c("blue", "yellow"), blues, etc.
`first_age`
`first_period`
`interval`
`first_age_isoline`
`first_period_isoline`
`isoline_interval`
`line_width`
`line_color`
`label_size`
`label_color`
`scale_units`
`wrap_cohort_labels`
`quantile`

data exploration: visualize age, period, and cohort patterns in the outcome before modeling.

Description

visualize age, period, and cohort patterns before modeling.

Usage

apci.plot.raw(data, outcome_var, age, period, ...)
apci.plot.raw(data, outcome_var, age, period, ...)

Arguments

`data`	A data frame containing the outcome variable, age group indicator, period group indicator, and covariates to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the input data again.
`outcome_var`	An object of class character containing the name of the outcome variable. The outcome variable can be continuous, categorical, or count.
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`...`

Examples

# plot the raw scores
apci.plot.raw(data = simulation, outcome_var = "y",
              age = "age", period = "period")
# plot the raw scores
apci.plot.raw(data = simulation, outcome_var = "y",
              age = "age", period = "period")

compute inter-cohort avarage deviations and intra-cohort life-course dynamics

Description

Compute inter- and intra-cohort deviations.

Usage

cohortdeviation(A,
  P,
  C,
  model = temp6,
  weight = "wt",
  covariate,
  gee=FALSE,
  unequal_interval = FALSE,
  age_range = NULL,
  period_range = NULL,
  age_interval = NULL,
  period_interval = NULL,
  age_group = NULL,
  period_group = NULL,
  ...)
cohortdeviation(A,
  P,
  C,
  model = temp6,
  weight = "wt",
  covariate,
  gee=FALSE,
  unequal_interval = FALSE,
  age_range = NULL,
  period_range = NULL,
  age_interval = NULL,
  period_interval = NULL,
  age_group = NULL,
  period_group = NULL,
  ...)

Arguments

`A`, `P`, `C`	The numbers of age groups, period groups, and cohort groups are defined separately.
`model`	A generalized linear model generated from the internal function temp_model
`weight`	An optional vector of sample weights to be used in the model fitting process. If non-NULL, the weights will be used in the first step to estimate the model. Observations with negative weights will be automatically dropped in modeling.
`covariate`	An optional vector of characters, representing the name(s) of the user-specified covariate(s) to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the data again.
`gee`	logical, indicating if the data is cross-sectional data or longitudinal/panel data. If `TRUE`, the generalized estimating equation will be used to correct the standard error estimates. The default is `FALSE`, indicating that the data are cross-sectional.
`unequal_interval`	Logical, indicating if age and period groups are of the same width. The default is set as `TRUE`.
`age_range`, `period_range`	Numeric vector indicating the actual age and period range (e.g., 10 to 59 years old from 2000 to 2019).
`age_interval`, `period_interval`, `age_group`, `period_group`	Numeric values or character vectors indicating how age and period are grouped. `age_interval` and `period_interval` are numbers indicating the width of age and period groups respectively. `age_group` and `period_group` are character vectors explicitly listing all potential age and period groups. Either `age_interval`(`period_interval`) or `age_group` (`period_group`) have to be defined when `unequal_interval` is `TRUE`.
`...`

calculate x coordinate values

Description

Calculate x coordinate values for the hexagram. This is an intermediate function.

Usage

compute_xcoordinate(p)
compute_xcoordinate(p)

Arguments

p

calculate y coordinate values

Description

Calculate y coordinate values for the hexagram. This is an intermediate function.

Usage

compute_ycoordinate(p, a)
compute_ycoordinate(p, a)

Arguments

`p`
`a`

Labor force participation data for men from 1990 to 1979 in CPS

Description

the dataset for men

Usage

data("cpsmen")data("cpsmen")

Format

A data frame with 10000 observations on the following 7 variables.

asecwt: weight
year: a factor indicating period groups with levels 1 2 3 4 5 6
age: a factor indicating age groups with levels 1 2 3 4 5 6 7 8 9
labforce: labor Force participation rate
educ: education level
educr: education level
educc: education level

Women's labor force participation data from the 1990 to 2019 Current Population Survey (CPS)

Description

the dataset for women's labor force participation from the 1990 through 2019 CPS.

Usage

data("cpswomen")data("cpswomen")

Format

A data frame with 1,0000 observations and the following 7 variables.

asecwt: weight
year: a factor indicating period groups with levels 1 2 3 4 5 6
age: a factor indicating age groups with levels 1 2 3 4 5 6 7 8 9
labforce: labor Force participation rate
educ: education level
educr: education level
educc: education level

estimate age main effects and period main effects

Description

estimate age and period main effects from the APCI model

Usage

maineffect(A, P, C, model = temp6, data, gee=FALSE,
...)
maineffect(A, P, C, model = temp6, data, gee=FALSE,
...)

Arguments

`A`, `P`, `C`	The numbers of age groups, period groups, and cohort groups separately.
`model`	A generalized linear regression model generated from the internal function temp_model
`data`	A data frame containing the outcome variable, age group indicator, period group indicator, and covariates to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the input data again.
`gee`	logical, indicating if the data is cross-sectional data or longitudinal/panel data. If `TRUE`, the generalized estimating equation will be used to correct the standard error estimates. The default is `FALSE`, indicating that the data are cross-sectional.
`...`

Simulated Dataset

Description

A simulated dataset for APC-I analysis.

Usage

data("simulation")data("simulation")

Format

A data frame with 1,0000 observations and the following 3 variables.

y: a numeric
age: a numeric
period: a numeric

Estimate APC-I model

Description

Step 1 of the APCI model: estimate a generalized linear model.

Usage

temp_model(data,
           outcome = "inlfc",
           age = "acc",
           period = "pcc",
           cohort = NULL,
           weight = NULL,
           covariate = NULL,
           family = "quasibinomial",
           gee = FALSE,
           id = NULL,
           corstr = "exchangeable",
           ...)
temp_model(data,
           outcome = "inlfc",
           age = "acc",
           period = "pcc",
           cohort = NULL,
           weight = NULL,
           covariate = NULL,
           family = "quasibinomial",
           gee = FALSE,
           id = NULL,
           corstr = "exchangeable",
           ...)

Arguments

`data`	A data frame containing the outcome variable, age group indicator, period group indicator, and covariates to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the input data again.
`outcome`	An object of class character containing the name of the outcome variable. The outcome variable can be continuous, categorical, or count.
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`cohort`	An optional object of class character representing cohort membership index in the data. Usually, the cohort index can be generated from the age group index and time period index in the data because of the intrinsic relationship among these three time-related indices.
`weight`	An optional vector of sample weights to be used in the model fitting process. If non-NULL, the weights will be used in the first step to estimate the model. Observations with negative weights will be automatically dropped in modeling.
`covariate`	An optional vector of characters, representing the name(s) of the user-specified covariate(s) to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the data again.
`family`	Used to specify the statistical distribution of the error term and link function to be used in the model. Usually, it is a character string naming a family function. For example, family can be "binomial", "multinomial"", or "gaussian". Users could also check R package glm for more details of family functions.
`gee`	logical, indicating if the data is cross-sectional data or longitudinal/panel data. If `TRUE`, the generalized estimating equation will be used to correct the standard error estimates. The default is `FALSE`, indicating that the data are cross-sectional.
`id`	A vector of character, specifying the cluster index in longitudinal data. It is required when `gee` is `TRUE`. The length of the vector should be the same as the number of observations.
`corstr`	a character string, specifying a possible correlation structure in the error terms when `gee` is `TRUE`. The following are allowed: `independence`, `fixed`, `stat\_M\_dep`, `non\_stat\_M\_dep`, `exchangeable`, `AR-M` and `unstructured`. The default value is `exchangeable`.
`...`

local and global F test

Description

implement local and global F test for APCI model

Usage

tests(model, age = "acc", period = "pcc",
cohort = "ccc", A, P, C, data, weight = "wt",
family, outcome, ...)
tests(model, age = "acc", period = "pcc",
cohort = "ccc", A, P, C, data, weight = "wt",
family, outcome, ...)

Arguments

`model`	A generalized linear regression model generated from the internal function temp_model
`age`	An object of class character representing the age group index taking on a small number of distinct values in the data. Usually, the vector should be converted to a factor (or the terms of "category" and "enumerated type").
`period`	An object of class character, similar to the argument of age, representing the time period index in the data.
`cohort`	An optional object of class character representing cohort membership index in the data. Usually, the cohort index can be generated from the age group index and time period index in the data because of the intrinsic relationship among these three time-related indices.
`A`, `P`, `C`	The numbers of age groups, period groups, and cohort groups separately.
`data`	A data frame containing the outcome variable, age group indicator, period group indicator, and covariates to be used in the model. If the variable(s) are not found in data, there will be an error message reminding the users to check the input data again.
`weight`	An optional vector of sample weights to be used in the model fitting process. If non-NULL, the weights will be used in the first step to estimate the model. Observations with negative weights will be automatically dropped in modeling.
`family`	Used to specify the statistical distribution of the error term and link function to be used in the model. Usually, it is a character string naming a family function. For example, family can be "binomial", "multinomial"", or "gaussian". Users could also check R package glm for more details of family functions.
`outcome`	An object of class character containing the name of the outcome variable. The outcome variable can be continuous, categorical, or count.
`...`

women9017

Description

A sample dataset

Usage

women9017
women9017

Format

A data frame with 1,000 observations and 23 variables.

ac: a numeric vector
acc: a numeric vector
age: a numeric vector
cc: a numeric vector
ccc: a numeric vector
cohort: a numeric vector
educ: a numeric vector
educc: a numeric vector
educr: a numeric vector
inlfc: a numeric vector
labforce: a numeric vector
lfc: a numeric vector
marst: a numeric vector
marstc: a numeric vector
marstr: a numeric vector
nc: a numeric vector
ncc: a numeric vector
nchild: a numeric vector
pc: a numeric vector
pcc: a numeric vector
wt: a numeric vector
wtsupp: a numeric vector
year: a numeric vector

Details

test

Source

CPS

References

Luo and Hodges (2019)

Package 'APCI'

Help Index

Construct a cohort index matrix for any number of age and period groups

Description

Usage

Arguments

Value

Examples

Run apci model

Description

Arguments

Value

Examples

Visualization for the APC-I model results

Description

Usage

Arguments

Examples

Visualization for data exploration or model results

Description

Usage

Arguments

Visualize APC-I model results

Description

Usage

Arguments

Examples

Visualize APC-I model results

Description

Usage

Arguments

data exploration: visualize age, period, and cohort patterns in the outcome before modeling.

Description

Usage

Arguments

Examples

compute inter-cohort avarage deviations and intra-cohort life-course dynamics

Description

Usage

Arguments

calculate x coordinate values

Description

Usage

Arguments

calculate y coordinate values

Description

Usage

Arguments

Labor force participation data for men from 1990 to 1979 in CPS

Description

Usage

Format

Women's labor force participation data from the 1990 to 2019 Current Population Survey (CPS)

Description

Usage

Format

estimate age main effects and period main effects

Description

Usage

Arguments

Simulated Dataset

Description

Usage

Format

Estimate APC-I model

Description

Usage

Arguments

local and global F test

Description

Usage

Arguments

women9017

Description

Usage

Format

Details

Source

References