README.md

In nkappelmann/PubHelper: Facilitating Result-Processing for Scientific Publication

PubHelper

This R package includes functions that help me in running GLM analyses and in preparing manuscripts for scientific publications.

Installation

To install the PubHelper package in R, first install the devtools package, which allows package installation from Github

{r} install.packages("devtools")

Next, load devtools, install the PubHelper package, and start using the package:

library("devtools")
install_github("nkappelmann/PubHelper")
library("PubHelper")

getGLMTable, formatGLMTable & mapGLMTables

getGLMTable, formatGLMTable and mapGLMTables make it easier to extract results from regression analyses.

Below is one example using the airquality toy data included with R.

data(airquality)

getGLMTable allows retrieving the estimates from a regression model:

model = lm(Ozone ~ Wind + Solar.R, data = airquality)
getGLMTable(model)

It's also possible to retrieve a reduced output without the intercept and potential covariates:

getGLMTable(model, intercept = FALSE, exclude.covariates = "Solar.R")

The formatGLMTable function works in the same manner, but formats results similar to scientific publications:

formatGLMTable(model)
formatGLMTable(model, intercept = FALSE, exclude.covariates = "Solar.R")

Lastly, mapGLMTables runs a multitude of different models and maps getGLMTable to retrieve these models in a summary data.frame. Here, covariates and the intercept are included automatically:

mapGLMTables(data = airquality, y = "Ozone", x = c("Solar.R", "Wind"), z = "Temp")

Other functions

Several other functions are currently included in the package such as to compute pooled mean and standard deviation (SD), SD inference from 95% confidence intervals, and conversion between Odds Ratios (OR) and Standardised Mean Difference (SMD) effect sizes.

nkappelmann/PubHelper documentation built on Aug. 7, 2021, 5:09 a.m.