| Type: | Package |
| Title: | Linear, Logistic and Generalized Linear Models Regressions for the EnvWAS/EWAS Approach |
| Version: | 1.0.0 |
| Maintainer: | Marwan El Homsi <marwan.el-homsi@inserm.fr> |
| Description: | Tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. It includes three functions. One function for linear regression, a second for logistic regression and a last one for generalized linear models. |
| Depends: | R (≥ 4.3) |
| Imports: | stats, devtools, dplyr, ggplot2, MASS |
| Suggests: | knitr, rmarkdown, mlbench |
| License: | GPL (≥ 3) |
| URL: | https://github.com/EHMarwan/Elja |
| BugReports: | https://github.com/EHMarwan/Elja/issues |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-06-30 19:54:06 UTC; Marwan |
| Author: | Marwan El Homsi 
[aut, cre, cph] (affiliation: Desbrest Institute of Epidemiology
and Public Health, Univ Montpellier, INSERM, Montpellier, France.),
Isabella Annesi-Maesano
[ctb, ths,
fnd] (affiliation1: Desbrest Institute of Epidemiology and Public
Health, Univ Montpellier, INSERM, Montpellier, France.,
affiliation2: Department of Allergic and Respiratory Diseases,
Montpellier University Hospital, Montpellier.) |
| Repository: | CRAN |
| Date/Publication: | 2023-07-03 16:20:05 UTC |
Generalized Linear Models regression for EnvWAS/EWAS analysis
Description
A tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. This function is espacially for generalized linear models 'glm' and allows the addition of adjustment variables.
Usage
ELJAglm(
var,
var_adjust = NULL,
family = binomial(link = "logit"),
data,
manplot = TRUE,
nbvalmanplot = 100,
Bonferroni = FALSE,
FDR = FALSE,
manplotsign = FALSE
)
Arguments
var |
A categorical and binary variable. It is generally your outcome. |
var_adjust |
A vector containing the names of the fixed adjustment variables for all the models. |
family |
The family and the link use for the glm function. |
data |
A dataframe containing all the variables needed for the analysis. |
manplot |
Generate a Manhattan plot of the results of the analysis. |
nbvalmanplot |
The number of variables to include in each Manhattan plot. |
Bonferroni |
Add a dashed bar to the Manhattan plot showing the Bonferroni significance threshold. |
FDR |
Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values. |
manplotsign |
Generates a Manhattan plot with only significant results (p<0.05). |
Value
A Dataframe with results for each variable of the model.
References
Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.
Examples
### Loading the PIMA dataset contained in the mlbench package
library(mlbench)
data(PimaIndiansDiabetes)
### Using ELJAlinear to perform EWAS analysis
ELJAglm(var = 'diabetes',data = PimaIndiansDiabetes,
family = binomial(link = "logit"), manplot = TRUE, Bonferroni = TRUE,
FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results
Linear regression for EnvWAS/EWAS analysis
Description
A tool for Environment-Wide Association Studies (EnvWAS / EWAS) namely repeated analyses allowing to estimate the relationships between several environmental factors and a health events. This function is especially for linear regressions and allows the addition of adjustment variables.
Usage
ELJAlinear(
var,
var_adjust = NULL,
data,
manplot = TRUE,
nbvalmanplot = 100,
Bonferroni = FALSE,
FDR = FALSE,
manplotsign = FALSE
)
Arguments
var |
A categorical and binary variable. It is generally your outcome. |
var_adjust |
A vector containing the names of the fixed adjustment variables for all the models. |
data |
A dataframe containing all the variables needed for the analysis. |
manplot |
Generate a Manhattan plot of the results of the analysis. |
nbvalmanplot |
The number of variables to include in each Manhattan plot. |
Bonferroni |
Add a dashed bar to the Manhattan plot showing the Bonferroni significance level. |
FDR |
Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values. |
manplotsign |
Generates a Manhattan plot with only significant results (p<0.05). |
Value
A Dataframe with results for each variable of the model.
References
Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.
Examples
### Loading the PIMA dataset contained in the mlbench package
library(mlbench)
data(PimaIndiansDiabetes)
### Using ELJAlinear to perform EWAS analysis
ELJAlinear(var = 'pregnant',data = PimaIndiansDiabetes,manplot = TRUE,
Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results
Logistic regression tool for EnvWAS/EWAS analysis
Description
A tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. This function is espacially for logistic regression based on the glm function with a binomial family with a logit link and allows the addition of adjustment variables.
Usage
ELJAlogistic(
var,
var_adjust = NULL,
data,
manplot = TRUE,
nbvalmanplot = 100,
Bonferroni = FALSE,
FDR = FALSE,
manplotsign = FALSE
)
Arguments
var |
A categorical and binary variable. It is generally your outcome. |
var_adjust |
A vector containing the names of the fixed adjustment variables for all the models. |
data |
A dataframe containing all the variables needed for the analysis. |
manplot |
Generate a Manhattan plot of the results of the analysis. |
nbvalmanplot |
The number of variables to include in each Manhattan plot. |
Bonferroni |
Add a dashed bar to the Manhattan plot showing the Bonferroni significance level. |
FDR |
Add a dashed bar to the Manhattan plot showing the False Discovery Rate (Benjamini-Hochberg method) significance threshold. NA if all p-values > FDR corrected p-values. |
manplotsign |
Generates a Manhattan plot with only significant results (p<0.05). |
Value
A Dataframe with results for each variable of the model.
References
Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300. MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/ Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.
Examples
### Loading the PIMA dataset contained in the mlbench package
library(mlbench)
data(PimaIndiansDiabetes)
### Using ELJAlinear to perform EWAS analysis
ELJAlogistic(var = 'diabetes',data = PimaIndiansDiabetes,manplot = TRUE,
Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results