Save Models to Local Disk
Shixiang Wang
Central South Universitywangshx@csu.edu.cn
2025-09-22
Source:vignettes/save-model-file.Rmd
save-model-file.Rmd
library(bregr)
#> Welcome to 'bregr' package!
#> =======================================================================
#> You are using bregr version 1.3.0
#>
#> Project home : https://github.com/WangLabCSU/bregr
#> Documentation: https://wanglabcsu.github.io/bregr/
#> Cite as : https://doi.org/10.1002/mdr2.70028
#> Wang, S., Peng, Y., Shu, C., Wang, C., Yang, Y., Zhao, Y., Cui, Y., Hu, D. and Zhou, J.-G. (2025),
#> bregr: An R Package for Streamlined Batch Processing and Visualization of Biomedical Regression Models. Med Research.
#> =======================================================================
#> Two global options have been introduced to control whether models are
saved as local files (bregr.save_model, default is
FALSE) and where they should be saved
(bregr.path, default uses a temporary path).
options(bregr.save_model = TRUE)
# Set model save path if necessary
# options(bregr.path = "/model/to/path")
m <- breg(mtcars) |>
br_set_y("mpg") |>
br_set_x(colnames(mtcars)[2:4]) |>
br_set_x2("vs") |>
br_set_model("gaussian") |>
br_run()
#> model save is enabled with result path /tmp/RtmpYrukkH
#> This message is displayed once per session.
options(bregr.save_model = FALSE)In summary, the models have been saved to a unique path. We can verify this by examining the model objects:
m@models
#> $cyl
#> /tmp/RtmpYrukkH/2f680b9a-9798-11f0-9d4c-7c1e5286d3f5.qs
#>
#> $disp
#> /tmp/RtmpYrukkH/2f9b8222-9798-11f0-9d4c-7c1e5286d3f5.qs
#>
#> $hp
#> /tmp/RtmpYrukkH/2fc8b1e8-9798-11f0-9d4c-7c1e5286d3f5.qsWe can retrieve the saved models using the following commands:
br_get_models(m, 1)
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
br_get_models(m, c(1, 3))
#> $cyl
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
#>
#> $hp
#>
#> Call: stats::glm(formula = mpg ~ hp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) hp vs
#> 26.96300 -0.05453 2.57622
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 422.7 AIC: 181.4
br_get_models(m, "cyl")
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
br_get_models(m, c("cyl", "hp"))
#> $cyl
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
#>
#> $hp
#>
#> Call: stats::glm(formula = mpg ~ hp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) hp vs
#> 26.96300 -0.05453 2.57622
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 422.7 AIC: 181.4
br_get_models(m)
#> $cyl
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
#>
#> $disp
#>
#> Call: stats::glm(formula = mpg ~ disp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) disp vs
#> 27.9493 -0.0369 1.4950
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 308.4 AIC: 171.3
#>
#> $hp
#>
#> Call: stats::glm(formula = mpg ~ hp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) hp vs
#> 26.96300 -0.05453 2.57622
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 422.7 AIC: 181.4The modeling results should remain consistent, regardless of whether they are saved or not.
Run without saving models.
m2 <- breg(mtcars) |>
br_set_y("mpg") |>
br_set_x(colnames(mtcars)[2:4]) |>
br_set_x2("vs") |>
br_set_model("gaussian") |>
br_run()
m2@models
#> $cyl
#>
#> Call: stats::glm(formula = mpg ~ cyl + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) cyl vs
#> 39.6250 -3.0907 -0.9391
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 306 AIC: 171.1
#>
#> $disp
#>
#> Call: stats::glm(formula = mpg ~ disp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) disp vs
#> 27.9493 -0.0369 1.4950
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 308.4 AIC: 171.3
#>
#> $hp
#>
#> Call: stats::glm(formula = mpg ~ hp + vs, family = stats::gaussian,
#> data = data)
#>
#> Coefficients:
#> (Intercept) hp vs
#> 26.96300 -0.05453 2.57622
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
#> Null Deviance: 1126
#> Residual Deviance: 422.7 AIC: 181.4
all.equal(m, m2)
#> [1] TRUE