publish.glm: Tabulize regression coefficients with confidence intervals...

In Publish: Format Output of Various Routines in a Suitable Way for Reports and Publication

Tabulize regression coefficients with confidence intervals and p-values.

Description

Tabulate the results of a generalized linear regression analysis.

Usage

## S3 method for class 'glm'
publish(
  object,
  confint.method,
  pvalue.method,
  digits = c(2, 4),
  print = TRUE,
  factor.reference = "extraline",
  intercept = ifelse((is.null(object$family) || object$family$family == "gaussian"), 1L,
    0L),
  units = NULL,
  ...
)

Arguments

object	A glm object.
confint.method	See regressionTable.
pvalue.method	See regressionTable.
digits	A vector of two integer values. These determine how to round numbers (first value) and p-values (second value). E.g., c(1,3) would mean 1 digit for all numbers and 3 digits for p-values. The actual rounding is done by summary.regressionTable.
print	If FALSE do not print results.
factor.reference	Style for showing results for categorical. See regressionTable.
intercept	See regressionTable.
units	See regressionTable.
...	passed to summary.regressionTable and also to labelUnits.
reference	Style for showing results for categorical variables. If "extraline" show an additional line for the reference category.

Details

The table shows changes in mean for linear regression and odds ratios for logistic regression (family = binomial).

Value

Table with regression coefficients, confidence intervals and p-values.

Author(s)

Thomas Alexander Gerds <[e-mail]>

Examples

data(Diabetes)
## Linear regression
f = glm(bp.2s~frame+gender+age,data=Diabetes)
publish(f)
publish(f,factor.reference="inline")
publish(f,pvalue.stars=TRUE)
publish(f,ci.format="(l,u)")

### interaction
fit = glm(bp.2s~frame+gender*age,data=Diabetes)
summary(fit)
publish(fit)

Fit = glm(bp.2s~frame*gender+age,data=Diabetes)
publish(Fit)

## Logistic regression
Diabetes$hyper1 <- factor(1*(Diabetes$bp.1s>140))
lrfit <- glm(hyper1~frame+gender+age,data=Diabetes,family=binomial)
publish(lrfit)

### interaction
lrfit1 <- glm(hyper1~frame+gender*age,data=Diabetes,family=binomial)
publish(lrfit1)

lrfit2 <- glm(hyper1~frame*gender+age,data=Diabetes,family=binomial)
publish(lrfit2)

## Poisson regression
data(trace)
trace <- Units(trace,list("age"="years"))
fit <- glm(dead ~ smoking+sex+age+Time+offset(log(ObsTime)), family="poisson",data=trace)
rtf <- regressionTable(fit,factor.reference = "inline")
summary(rtf)
publish(fit)

## gls regression
if (requireNamespace("nlme",quietly=TRUE)){
    requireNamespace("lava",quietly=TRUE)
library(lava)
library(nlme)
m <- lvm(Y ~ X1 + gender + group + Interaction)
distribution(m, ~gender) <- binomial.lvm()
distribution(m, ~group) <- binomial.lvm(size = 2)
constrain(m, Interaction ~ gender + group) <- function(x){x[,1]*x[,2]}
d <- sim(m, 1e2)
d$gender <- factor(d$gender, labels = letters[1:2])
d$group <- factor(d$group)

e.gls <- gls(Y ~ X1 + gender*group, data = d,
             weights = varIdent(form = ~1|group))
publish(e.gls)

## lme
fm1 <- lme(distance ~ age*Sex, 
            random = ~1|Subject,
            data = Orthodont) 
res <- publish(fm1)
}

Publish documentation built on Jan. 18, 2023, 1:08 a.m.