Risk.display {epicalc}R Documentation

Tables for multivariate odds ratio, incidence density etc

Description

Display of various epidemiological modelling results in a medically understandable format

Usage

logistic.display(logistic.model, alpha = 0.05, crude = TRUE, crude.p.value = FALSE, 
    decimal = 2) 
regress.display(regress.model, alpha = 0.05, crude = FALSE, crude.p.value = FALSE, 
    decimal = 2) 
idr.display(count.model, decimal = 3, alpha = 0.05) 
mlogit.display(multinom.model, decimal = 2, alpha = 0.05) 
ordinal.or.display(ordinal.model, decimal = 3, alpha = 0.05)  
tableGlm (model, modified.coeff.array, decimal) 

Arguments

logistic.model a model from a logistic regression
regress.model a model from linear regression
alpha significance level
crude whether crude odds ratios should also be displayed
crude.p.value whether crude P value should also be displayed
decimal number of decimal places displayed
count.model a model from a Poisson or negative binomial regression
multinom.model a model from multinomial or polytomous regression
ordinal.model a model from an ordinal logistic regression
model model passed from logistic.display or regress.display to tableGlm
modified.coeff.array array modified by from coefficient array and sent to the function 'tableGlm' to produce the output

Details

R provides several epidemiological modelling techniques. The functions above display these results in a format easier for medical people to understand.

The function 'tableGlm' is not for general use. It is called by 'logistic.display' and 'regress.display' to receive the 'modified.coeff.array' and produce the output table.

The output from 'logistic.display' and 'regress.display' are ready to write (using 'write.csv') to a .csv file which can then be copied to Word document for a manuscript. This approach can substantially reduce time and errors due conventional manual copying.

Value

'logistic.display' and 'regress.display' each produces an output table. See 'details'.

Author(s)

Virasakdi Chongsuvivatwong <cvirasak@medicine.psu.ac.th>

See Also

'glm', 'confint'

Examples

model0 <- glm(case ~ induced + spontaneous, family=binomial, data=infert)
summary(model0)
logistic.display(model0)

data(ANCdata)
glm1 <- glm(death ~ anc + clinic, family=binomial, data=ANCdata)
logistic.display(glm1)
 
library(MASS)
model1 <- glm(Origin ~ Weight + AirBags + DriveTrain, family=binomial, data=Cars93)
logistic.display(model1, decimal=3, crude.p.value=TRUE)

reg1 <- lm(Price ~ Weight + AirBags + DriveTrain, data=Cars93)
regress.display(reg1)

reg2 <- glm(Price ~ Weight + AirBags + DriveTrain, data=Cars93)
regress.display(reg2)

library(nnet)

# Ordinal logistic regression
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
house.plr
ordinal.or.display(house.plr)

# Polytomous or multinomial logistic regression
house.multinom <- multinom(Sat ~ Infl + Type + Cont, weights = Freq, 
        data = housing)
summary(house.multinom)
mlogit.display(house.multinom, alpha=.01) # with 99 percent confidence limits.

[Package epicalc version 2.6.0.5 Index]