README.md

In dtdibaba/StatTools: Biostatistical tools to produce clean data analysis results with StatTools

regby {StatTools}

The StatTools is an R package that includes the regby, brs (for univariate regression), and freq functions. The regby function extracts summary results of stratified regression analyses. The freq function creates a frequency and percentage for a given categorical variable.

Description

The regby function helps to extract the important regression summaries such as coefficients, confidence intervals, and P-value in stratified analysis. Currently, the regby function is implemented for the most commonly used regression analysis such as linear regression, logistic regression, Poisson regression, proportional odds logistic regression (using polr in MASS package), multinomial regression (using multinom nnet package), Cox proportional hazard regression (using coxph in survival package), and linear mixed models (lmer in lme4 package). For poisson regression, use the glm model with fam = poisson and EXP = TRUE to get incidence rate ratio.

Usage

regby( datain, byVar, frmlYX, fam = NULL, Pred, Factor = FALSE, Intercept = FALSE, EXP = TRUE, Model, col.names = TRUE, colname, … )

Arguments

datain Is the input dataset

byVar Is the stratifying categorical variable.

frmlYX The model formula.

fam Distribution family for the output variable. Examples are binomial, poisson, etc.

Pred Is a list containing the predictor variables names in the order they appear in the model formula.For example, if Z is a factor predictor variable and has a, b, c, and d levels, unless otherwise the reference is re-leveled, the coefficients will be output in alphabetical order with the first level being the reference level. Thus, include in the “Pred” list the levels for which the coefficients are output as Pred = c(“Other-predictors”, “Zb”, “Zc”, “Zd”) in the order they appear in the model formula.

Factor

Whether there are categorical predictors in the model. It defaults to FALSE.

Intercept

Whether you want the intercept output or not. It defaults to FALSE. If you want the intercept, include Intercept as the first list Pred name lists.

EXP Whether you want the exponentiation of the estimate and CIs. EXP defaults to TRUE.

Model The regression function name such as lm, glm, coxph.

col.names Whether the user wants to rename column names. Default = TRUE.

colname lists of the column header names.

… Expandable.

EXAMPLES

devtools::install_github("dtdibaba/StatTools") 
library(StatTools)

Logistic regression analysis with a continuous and categorical predictors, and intercept not requested

library(StatTools)
set.seed(123896) 
requireNamespace("htmlTable",quietly = TRUE)
x = rnorm(100)
z = sample(letters[1:4], 100, TRUE) 
R <- c('B', 'W') 
Cat <- sample(R, 100, TRUE) 
y = rbinom(100, 1, 0.5)
data1 <- data.frame(x = x,z = z, Cat = Cat, y = y ) 


# If Factor == TRUE include the level labels of the predictor as separate names.<br>
 regby(datain = data1, byVar = 'Cat',
 frmlYX = formula(y ~  x+z), 
 fam = binomial, 
 Model = "glm", 
 Pred = c("Intercept",
  "X","Zb",
 "Zc", "Zd"),  colname = c("Strata", "Variable", "OR(95%CIs)", "P-value" ),
 Factor = TRUE, Intercept = FALSE, EXP = TRUE)

Multiple linear regression analysis with a continuous and categorical predictors, and intercept included

regby(datain = data1, byVar = 'Cat', frmlYX = formula(y ~  x+z), fam = guassian,
Model = "lm",Pred = c("Intercept", "X","Zb", "Zc", "Zd"), 
colname = c("Strata", "Variable", "Beta (95%CIs)", "P-value" ), 
Factor = TRUE, Intercept = TRUE, EXP = FALSE)

regby(datain = mtcars, byVar = 'cyl', frmlYX = formula(disp ~  factor(gear)+factor(am)+vs), fam = guassian,
      Model = "lm",Pred = NULL, 
      colname = c("Strata", "Variable", "Beta (95%CIs)", "P-value" ), 
      Factor = TRUE, Intercept = TRUE, EXP = FALSE)

Cox proportional hazard regression analysis with a continuous predictor

set.seed(1243567)
t <- rnorm(100, 15, 3)
y <- rbinom(100, 1, 0.5)
Cat <- sample(c("M", "F"), 100, TRUE)
x <- rnorm(100, 5, 2)
z <- rpois(100,1)
z <- factor(z)
data2 <- data.frame(t = t, x = x, Cat = Cat, y = y,z)
require('survival')

regby(datain = data2, byVar = 'Cat', frmlYX = formula(Surv(t,y) ~  x),
Model = "coxph", Pred = c( "X"),  colname = c("Strata", "Variable",
"HR (95%CIs)", "P-value" ), Factor = TRUE, Intercept = FALSE)

Proportional odds ordered logistic regression ```r x <- rnorm(50) z <- sample(c(letters[1:5]), 50, TRUE) Cat <- sample(R, 50, TRUE) y <- rbinom(50, 1, 0.5) data3 <- data.frame(x = x, z = z, Cat = Cat, y = y)

regby(datain = data3, byVar = 'Cat', frmlYX = formula(z ~ x), Model = "polr", colname = c("Strata", "Variable", "Beta (95%CIs)", "P-value", "Cum_Prob", "OR" ), Factor = TRUE, Intercept = FALSE, col.names = TRUE)

<br>

Multinomial logistic regression
```r
 regby(datain = data3, byVar = 'Cat',  frmlYX = (z ~  x), Model  =  "multinom", 
 colname = c("Strata", "Variable", "OR (95%CIs)", "P-value" ), Factor = TRUE,
 Intercept = FALSE)

Linear mixed effect models r regby(datain = data3, byVar = 'Cat', frmlYX = (x ~ y+(1|z)), Model = "lmer", col.names = FALSE)

Results

dtdibaba/StatTools documentation built on Jan. 27, 2025, 3:59 p.m.