| Title: | Convert Irregular Longitudinal Data to Regular Intervals and Perform Clustering |
|---|---|
| Description: | Convert irregularly spaced longitudinal data into regular intervals for further analysis, and perform clustering using advanced machine learning techniques. The package is designed for handling complex longitudinal datasets, optimizing them for research in healthcare, demography, and other fields requiring temporal data modeling. |
| Authors: | Atanu Bhattacharjee [aut, cre, ctb], Tanmoy Majumdar [aut, ctb] |
| Maintainer: | Atanu Bhattacharjee <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-01 05:13:27 UTC |
| Source: | https://github.com/cran/ILRCM |
This function takes irregular longitudinal data and converts it into regularly spaced intervals using linear interpolation. It then computes the relative change in the response variable between consecutive time points, clusters the data based on these changes, and provides various visualizations of the process.
irregclst(data, subject_id_col, time_col, response_col, rel, interval_length)irregclst(data, subject_id_col, time_col, response_col, rel, interval_length)
data |
A data frame containing the irregular longitudinal data. |
subject_id_col |
A character string representing the name of the column with the subject IDs. |
time_col |
A character string representing the name of the column with time values. |
response_col |
A character string representing the name of the column with the response values. |
rel |
Relative change method such as SRC, CARC and SWRC. |
interval_length |
A numeric value indicating the length of the regular intervals to which the time values should be converted. |
The irregclst function handles irregular longitudinal data by:
Interpolating response values at regular time intervals.
Calculating the relative change in the response values across time points.
Clustering subjects based on these relative changes using alphabet labels ("a", "b", ..., "h") corresponding to different levels of deviation from the mean.
Resolving cluster ties using a sum of squares criterion.
Visualizations of the data include plots for both the original irregular data and the regularized data, as well as histograms of time distributions and relative change trends.
A list containing:
regular_data: A data frame of the regularized longitudinal data.
regular_data_wide: A wide-format version of the regularized data.
relative_change: A data frame containing the relative changes in response values.
cluster_data: A data frame with cluster assignments for each subject at each time step.
cluster_data_reduced: A reduced version of cluster_data with only subject IDs and their final cluster assignments.
merged_data: The wide-format data merged with the final cluster assignments.
plot_irregular: A ggplot object showing the original irregular data.
plot_regular: A ggplot object showing the regularized data.
plot_change: A ggplot object showing the relative changes over time.
histogram_irregular: A ggplot object showing the histogram of irregular time distribution.
histogram_regular: A ggplot object showing the histogram of regular time distribution.
author name
Reference
seealso
## data(sdata) #Using relative change method: Simple relative change (SRC) fit1 <- irregclst(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) #for showing the regularized data in long format fit1$regular_data fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2<-irregclst(sdata,"subject_id","time","response",rel="CARC",interval_length=3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data## data(sdata) #Using relative change method: Simple relative change (SRC) fit1 <- irregclst(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) #for showing the regularized data in long format fit1$regular_data fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2<-irregclst(sdata,"subject_id","time","response",rel="CARC",interval_length=3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data
This function takes irregular longitudinal data and converts it into regularly spaced intervals using linear interpolation. It then computes the relative change in the response variable between consecutive time points, clusters the data based on these changes, and provides various visualizations of the process.
irregclst1(data, subject_id_col, time_col, response_col, rel, interval_length)irregclst1(data, subject_id_col, time_col, response_col, rel, interval_length)
data |
A data frame containing the irregular longitudinal data. |
subject_id_col |
A character string representing the name of the column with the subject IDs. |
time_col |
A character string representing the name of the column with time values. |
response_col |
A character string representing the name of the column with the response values. |
rel |
Relative change method such as SRC, CARC and SWRC. |
interval_length |
A numeric value indicating the length of the regular intervals to which the time values should be converted. |
The irregclst1 function handles irregular longitudinal data by:
Interpolating response values at regular time intervals without replacing the last responses.
Calculating the relative change in the response values across time points.
Clustering subjects based on these relative changes using alphabet labels ("a", "b", ..., "h") corresponding to different levels of deviation from the mean.
Resolving cluster ties using a sum of squares criterion.
Visualizations of the data include plots for both the original irregular data and the regularized data, as well as histograms of time distributions and relative change trends.
A list containing:
regular_data: A data frame of the regularized longitudinal data.
regular_data_wide: A wide-format version of the regularized data.
relative_change: A data frame containing the relative changes in response values.
cluster_data: A data frame with cluster assignments for each subject at each time step.
cluster_data_reduced: A reduced version of cluster_data with only subject IDs and their final cluster assignments.
merged_data: The wide-format data merged with the final cluster assignments.
plot_irregular: A ggplot object showing the original irregular data.
plot_regular: A ggplot object showing the regularized data.
plot_change: A ggplot object showing the relative changes over time.
histogram_irregular: A ggplot object showing the histogram of irregular time distribution.
histogram_regular: A ggplot object showing the histogram of regular time distribution.
author name
Reference
seealso
## data(sdata) #' #Using relative change method: Simple relative change (SRC) fit1 <- irregclst1(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) fit1$regular_data #for showing the regularized data in long format fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2 <- irregclst1(sdata, "subject_id", "time", "response", rel="CARC", interval_length = 3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst1(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data #### data(sdata) #' #Using relative change method: Simple relative change (SRC) fit1 <- irregclst1(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) fit1$regular_data #for showing the regularized data in long format fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2 <- irregclst1(sdata, "subject_id", "time", "response", rel="CARC", interval_length = 3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst1(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data ##
This function takes irregular longitudinal data and converts it into regularly spaced intervals using linear interpolation. It then computes the relative change in the response variable between consecutive time points, clusters the data based on these changes, and provides various visualizations of the process.
irregclst2(data, subject_id_col, time_col, response_col, rel, interval_length)irregclst2(data, subject_id_col, time_col, response_col, rel, interval_length)
data |
A data frame containing the irregular longitudinal data. |
subject_id_col |
A character string representing the name of the column with the subject IDs. |
time_col |
A character string representing the name of the column with time values. |
response_col |
A character string representing the name of the column with the response values. |
rel |
Relative change method such as SRC, CARC and SWRC. |
interval_length |
A numeric value indicating the length of the regular intervals to which the time values should be converted. |
The irregclst2 function handles irregular longitudinal data by:
Interpolating response values at regular time intervals and replaing the last responses using linear regression model.
Calculating the relative change in the response values across time points.
Clustering subjects based on these relative changes using alphabet labels ("a", "b", ..., "h") corresponding to different levels of deviation from the mean.
Resolving cluster ties using a sum of squares criterion.
Visualizations of the data include plots for both the original irregular data and the regularized data, as well as histograms of time distributions and relative change trends.
A list containing:
regular_data: A data frame of the regularized longitudinal data.
regular_data_wide: A wide-format version of the regularized data.
relative_change: A data frame containing the relative changes in response values.
cluster_data: A data frame with cluster assignments for each subject at each time step.
cluster_data_reduced: A reduced version of cluster_data with only subject IDs and their final cluster assignments.
merged_data: The wide-format data merged with the final cluster assignments.
plot_irregular: A ggplot object showing the original irregular data.
plot_regular: A ggplot object showing the regularized data.
plot_change: A ggplot object showing the relative changes over time.
histogram_irregular: A ggplot object showing the histogram of irregular time distribution.
histogram_regular: A ggplot object showing the histogram of regular time distribution.
author name
Reference
seealso
## data(sdata) #Using relative change method: Simple relative change (SRC) fit1 <- irregclst2(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) fit1$regular_data #for showing the regularized data in long format fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2 <- irregclst2(sdata, "subject_id", "time", "response", rel="CARC", interval_length = 3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst2(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data #### data(sdata) #Using relative change method: Simple relative change (SRC) fit1 <- irregclst2(sdata, "subject_id", "time", "response", rel="SRC", interval_length = 3) fit1$regular_data #for showing the regularized data in long format fit1$regular_data_wide #for showing the regularized data in wide format fit1$cluster_data #dataset consisting clusters for different time points fit1$merged_data #for showing the regularized data in wide format with final cluster fit1$plot_regular #For plotting regularized longitudinal data fit1$plot_irregular #For plotting irregular longitudinal data fit1$plot_change #For plotting relative change fit1$histogram_irregular #histogram for time of irregular data fit1$histogram_regular #histogram for time of regular data #Using relative change method: Cumulative average relative change (CARC) fit2 <- irregclst2(sdata, "subject_id", "time", "response", rel="CARC", interval_length = 3) fit2$regular_data #for showing the regularized data in long format fit2$regular_data_wide #for showing the regularized data in wide format fit2$cluster_data #dataset consisting clusters for different time points fit2$merged_data #for showing the regularized data in wide format with final cluster fit2$plot_regular #For plotting regularized longitudinal data fit2$plot_irregular #For plotting irregular longitudinal data fit2$plot_change #For plotting relative change fit2$histogram_irregular #histogram for time of irregular data fit2$histogram_regular #histogram for time of regular data #Using relative change method: Weighted sum relative change (WSRC) fit3 <- irregclst2(sdata, "subject_id", "time", "response", rel="WSRC", interval_length = 3) fit3$regular_data #for showing the regularized data in long format fit3$regular_data_wide #for showing the regularized data in wide format fit3$cluster_data #dataset consisting clusters for different time points fit3$merged_data #for showing the regularized data in wide format with final cluster fit3$plot_regular #For plotting regularized longitudinal data fit3$plot_irregular #For plotting irregular longitudinal data fit3$plot_change #For plotting relative change fit3$histogram_irregular #histogram for time of irregular data fit3$histogram_regular #histogram for time of regular data ##
Simulated irregular longitudinal data for 1000 patients. This dataset contains irregularly spaced time points and responses for analysis.
data(sdata)data(sdata)
A data frame with 8631 rows and 3 variables:
ID of subjects
Irregular time points.
Response values at different time points.
data(sdata) head(sdata)data(sdata) head(sdata)
Longitudinal height and weight measurements during ages 0-2 years for a representative sample of 1933 Dutch children born in 1988-1989. The dataset smocc is the full dataset.
data(smocc)data(smocc)
A data frame with 1942 rows and 7 variables:
ID, unique id of each child (numeric)
Decimal age, 0-2.68 years (numeric)
Sex, "male" or "female" (character)
Gestational age, completed weeks (numeric)
Birth weight in grammes (numeric)
Height measurement in cm (numeric)
Height in SDS relative Fourth Dutch Growth Study 1997 (numeric)
data(smocc) head(smocc)data(smocc) head(smocc)