| Title: | Build and Compare Statistical Models |
| Version: | 0.1.3 |
| Description: | Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) https://github.com/p-mq/Percentile_based_averaging. |
| Depends: | R (≥ 4.0.0) |
| Imports: | basecamb, survival, survAUC, DescTools, Hmisc, stats, utils |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| URL: | https://github.com/p-mq/BlanketStatsments |
| BugReports: | https://github.com/p-mq/BlanketStatsments/issues |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2026-04-04 16:05:54 UTC; Research |
| Author: | Dr J. Peter Amin Marquardt
 [aut, cre] |
| Maintainer: | Dr J. Peter Amin Marquardt <peter@kmarquardt.de> |
| Repository: | CRAN |
| Date/Publication: | 2026-04-10 09:30:14 UTC |
Build formula for statistical models
Description
Build formula used in statistical models from vectors of strings. Copied from basecamb package to avoid dependency
Usage
.build_model_formula(outcome, predictors, censor_event = NULL)
Arguments
outcome |
character denoting the column with the outcome. |
predictors |
vector of characters denoting the columns with the predictors. |
censor_event |
character denoting the column with the censoring event, for use in Survival-type models. |
Value
formula for use in statistical models
Author(s)
J. Peter Marquardt
Source
Generic wrapper method to calculate C-statistics
Description
Calculate concordance statistics for a list of statistical models on the same data set
Usage
blanket_c_statistic(df, model_list, modality = "logistic", verbose = FALSE)
Arguments
df |
data.frame containing the data set. If evaluating independently, use the test set. |
model_list |
list of statistical models of type lm, glm or coxph to be evaluated. |
modality |
character specifying model type. Currently accepts 'linear', 'logistic', and 'cox' |
verbose |
logical. TRUE activates printout messages. |
Value
list of doubles with the AUC values for the evaluated models on the specified data set.
Author(s)
J. Peter Marquardt
Blanket redundancy analysis
Description
Perform a blanket redundancy analysis on a list of existing models
Usage
blanket_redundancy_analysis(
model_list,
data,
r2_threshold = 0.9,
nk = 0,
verbose = FALSE
)
Arguments
model_list |
a list of statistical regression model of class linear, logistic or coxph |
data |
data.frame used to create the models |
r2_threshold |
float threshold value to consider a parameter redundant |
nk |
number of knots in splicing |
verbose |
ctivate printouts of key findings |
Value
an list of objects of class "redun"
Author(s)
J. Peter Marquardt
See Also
[blanket_stats()]
Examples
data <- survival::lung
models_to_run <- list(
'OS' = list('outcome' = 'time', 'modality' = 'cox', 'event_censor' = 'status'),
'weight_loss' = list('outcome' = 'wt.loss', 'modality' = 'linear', 'event_censor' = NA))
predictor_sets <- list('age' = c('age'), 'age_ecog' = c('age', 'ph.ecog'))
covariates = c('sex')
bl_stats <- blanket_statsments(data, models_to_run, predictor_sets, covariates)
blanket_redundancy_analysis(bl_stats, data)
Run multiple slightly different models of same type
Description
Run the same model (type, outcome, and covariates) with different sets of predictors
Usage
blanket_stats(
df,
outcome,
predictor_sets,
covariates = c(),
modality = "linear",
event_censor = NA,
verbose = FALSE
)
Arguments
df |
data.frame containing the data set. |
outcome |
character designating the column with the outcome of interest |
predictor_sets |
named list or character vectors containing columns with predictors |
covariates |
vector of characters denoting columns with covariables |
modality |
character denoting model type. Currently limited to 'linear', 'logistic', and 'cox' |
event_censor |
character denoting column with censor event. For coxph models only |
verbose |
logical. TRUE activates printout messages. |
Value
named list of models
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
outcome <- 'time'
predictor_sets <- list('age' = c('age'),'age_ecog' = c('age', 'ph.ecog'))
covariates = c('sex')
modality <- 'cox'
event_censor <- 'status'
bl_stats <- blanket_stats(data, outcome, predictor_sets, covariates, modality, event_censor)
Run multiple different models with different sets of predictors
Description
Wraps blanket_stats. Run a list of models with different modalities/outcomes for a list of different predictor sets with the same covariables.
Usage
blanket_statsments(
df,
models_to_run,
predictor_sets,
covariates = c(),
verbose = FALSE
)
Arguments
df |
data.frame containing the data set. |
models_to_run |
either a named list or data.frame type, with every entry/row having the keys/columns outcome, modality, and event_censor |
predictor_sets |
named list of lists containing the set of predictors. See blanket_stats for details |
covariates |
vector of characters denoting columns with covariables |
verbose |
logical. TRUE activates printout messages. |
Value
named list of named lists of models
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
models_to_run <- list('OS' = list(
'outcome' = 'time', 'modality' = 'cox', 'event_censor' = 'status'),
'weight_loss' = list('outcome' = 'wt.loss', 'modality' = 'linear', 'event_censor' = NA))
predictor_sets <- list('age' = c('age'),'age_ecog' = c('age', 'ph.ecog'))
covariates = c('sex')
bl_stats <- blanket_statsments(data, models_to_run, predictor_sets, covariates)
Build a cox model
Description
Build a Cox proportional hazards model from data and meta-parameters
Usage
build_cox_model(
df,
event_time,
event_censor,
predictors,
covariates = c(),
verbose = FALSE
)
Arguments
df |
data.frame containing the data set |
event_time |
character denoting column with event time |
event_censor |
character denoting column specifying events/censoring |
predictors |
character vector denoting columns with independent variables of interest |
covariates |
character vector denoting columns with independent variables not of interest. Covariates are mathematically identical to predictors but will be ignored in reporting |
verbose |
logical. TRUE activates printout messages |
Value
A Cox proportional hazards model
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
mod <- build_cox_model(data, 'time', 'status', c('age', 'sex'))
Build a generic regression model model
Description
Build a generic regression model from data and meta-parameters. Currently only available for linear and logistic types.
Usage
build_reg_model(
df,
outcome,
predictors,
covariates = c(),
modality = "linear",
verbose = FALSE
)
Arguments
df |
data.frame containing the data set |
outcome |
character denoting column with the outcome of interest |
predictors |
character vector denoting columns with independent variables of interest |
covariates |
character vector denoting columns with independent variables not of interest. Covariates are mathematically identical to predictors but will be ignored in reporting |
modality |
character designating type. Currently limited to 'linear' and 'logistic'. |
verbose |
logical. TRUE activates printout messages |
Value
A regression model of linear or logistic type
Author(s)
J. Peter Marquardt
Examples
mod <- build_reg_model(data.frame('outcome' = c(1,2), 'pred' = c(3,4)), 'outcome', c('pred'))
Calculate Uno's C for a given model.
Description
Calculate Uno's concordance statistic for any model. CAVE: If you want to evaluate a model trained on a different dataset, df should be limited to the test set.
Usage
calculate_Uno_c(df, model, verbose = FALSE)
Arguments
df |
data.frame containing the data set. If evaluating independently, use the test set. |
model |
statistical model of type coxph to be evaluated. |
verbose |
logical. TRUE activates printout messages. |
Value
double AUC value for the evaluated model on the specified data set.
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
cancer_mod <- survival::coxph(survival::Surv(time, status)~age, data = data)
calculate_Uno_c(data, cancer_mod)
Redundancy analysis
Description
Perform a redundancy analysis on an existing model
Usage
redundancy_analysis(model, data, r2_threshold = 0.9, nk = 0)
Arguments
model |
a statistical regression model of class linear, logistic or coxph |
data |
data.frame used to create the model |
r2_threshold |
float threshold value to consider a parameter redundant |
nk |
number of knots in splicing |
Value
an object of class "redun"
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
mod <- build_reg_model(data, 'meal.cal', c('sex', 'age'))
redundancy_analysis(mod, data)
Table results of blanket redundancy analysis
Description
Table results of a blanket redundancy analysis on a list of existing models
Usage
table_blanket_redundancies(blanket_redundancies, digits = 2)
Arguments
blanket_redundancies |
list of lists of redun objects generated by blanket_redundancy_analysis() |
digits |
integer number of decimals to include |
Value
a data.frame tabling the key results
Author(s)
J. Peter Marquardt
See Also
[table_predictors()], [blanket_redundancy_analysis()]
Examples
data <- survival::lung
models_to_run <- list(
'OS' = list('outcome' = 'time', 'modality' = 'cox', 'event_censor' = 'status'),
'weight_loss' = list('outcome' = 'wt.loss', 'modality' = 'linear', 'event_censor' = NA))
predictor_sets <- list('age' = c('age'), 'age_ecog' = c('age', 'ph.ecog'))
covariates = c('sex')
bl_stats <- blanket_statsments(data, models_to_run, predictor_sets, covariates)
bl_redun <- blanket_redundancy_analysis(bl_stats, data)
table_blanket_redundancies(bl_redun)
Table results of multiple different models with different sets of predictors
Description
Wraps blanket_stats. Run a list of models with different modalities/outcomes for a list of different predictor sets with the same covariables.
Usage
table_blanket_statsments(df, blanket_statsment_models)
Arguments
df |
data.frame containing the data set. |
blanket_statsment_models |
list of models produced by blanket_statsments() |
Value
data.frame with tabled results
Author(s)
J. Peter Marquardt
See Also
[blanket_statsments()] for models and [table_predictors()] for tabling results
Examples
data <- survival::lung
models_to_run <- list('OS' = list(
'outcome' = 'time', 'modality' = 'cox', 'event_censor' = 'status'),
'weight_loss' = list('outcome' = 'wt.loss', 'modality' = 'linear', 'event_censor' = NA))
predictor_sets <- list('age' = c('age'),'age_ecog' = c('age', 'ph.ecog'))
covariates = c('sex')
bl_stats <- blanket_statsments(data, models_to_run, predictor_sets, covariates)
tbl <- table_blanket_statsments(data, bl_stats)
Table model predictor performance
Description
Extract coefficients and p-values only for regression models and table them
Usage
table_predictors(df, model, predictors)
Arguments
df |
data.frame containing the data set. If evaluating independently, use the test set. |
model |
statistical model to be evaluated. |
predictors |
vector of characters designating columns of interest. Non-specified independent variables will not be included. |
Value
data.frame with coefficients and p-values for predictor variables
Author(s)
J. Peter Marquardt
Examples
data <- survival::lung
mod <- build_reg_model(data, 'age', 'sex')
tbl <- table_predictors(data, mod, 'sex')