dc.glm: predicted values and discrete change

In glm.predict: Predicted Values and Discrete Changes for GLM

predicted values and discrete change

Description

The function calculates the predicted values and the difference of two cases with the confidence interval. It can be used for any glm model.

Usage

## S3 method for class 'glm'
dc(model, values = NULL, sim.count = 1000, conf.int = 0.95, 
  sigma = NULL, set.seed = NULL, values1 = NULL, values2 = NULL,
  type = c("any", "simulation", "bootstrap"), summary = TRUE)

Arguments

model	the model-Object generated with glm() or glm.nb()
values	the values of case 1 and 2 as vector in the order how they appear in the summary(model) Estimate. Values is if values1 and values2 are specified after each other in the same vector. Either values or values1 and values2 have to be specified.
sim.count	OPTIONAL numbers of simulations to be done by the function. default: 1000
conf.int	OPTIONAL the confidence interval used by the function. default: 0.95
sigma	OPTIONAL the variance-covariance matrix, can be changed when having for example robust or clustered vcov. default: vcov(model)
set.seed	OPTIONAL set a seed for the random number generator
values1	the values of case 1 as vector in the order how they appear in the summary(model) Estimate. Has to be defined if values is not defined.
values2	the values of case 2 as vector in the order how they appear in the summary(model) Estimate. Has to be defined if values is not defined.
type	OPTIONAL choose between simulation and bootstrap, "any" chooses between those two according to the number of cases (bootstrap if n < 1000)
summary	OPTIONAL if mean/quantiles should be return or all simulated values (default: TRUE)

Details

The function makes a simulation for the two cases and compares them to each other.

Value

The output is a matrix have in the first column the predicted values, in the second column the lower value of the confidence interval and in the third column the upper value of the confidence interval.

Author(s)

Benjamin Schlegel, [e-mail]

Examples

model1 = glm(Sex ~ Height + Smoke + Pulse, data=MASS::survey, family=binomial(link=logit))
summary(model1)
# comparing a person with the height 150cm to 151cm
dc(model1, values1 = c(1,150,1,0,0,mean(MASS::survey$Pulse,na.rm=TRUE)),
  values2 = c(1,151,1,0,0,mean(MASS::survey$Pulse,na.rm=TRUE)))
# the higher person has a greater probability to be a man
# the difference is significant, because the confidence interval
# does not include the 0

glm.predict documentation built on Dec. 2, 2022, 5:12 p.m.