publish.glm {Publish}

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)
}