regression.model.functions {kyotil}

Regression Model Functions

Description

getFormattedSummary prints a table of regression coefficient estimates and standard errors.

Usage

getFormattedSummary(fits, type = 12, est.digits = 2, se.digits = 2,
 robust, random = FALSE, VE = FALSE, to.trim = FALSE,
 rows = NULL, coef.direct = FALSE, trunc.large.est =
 TRUE, scale.factor = 1, p.digits = 3, remove.leading0
 = FALSE, p.adj.method = "fdr", ...)

getVarComponent(object, ...)

getFixedEf(object, ...)

risk.cal(risk, binary.outcome, weights = NULL, ngroups = NULL, 
    cuts = NULL, main = "", add = FALSE, show.emp.risk = TRUE, 
    lcol = 2, ylim = NULL, scale = c("logit", "risk"))
interaction.table(fit, v1, v2, v1.type = "continuous", v2.type = "continuous",
logistic.regression = TRUE)


## S3 method for class 'coxph'
getFixedEf(object, exp=FALSE,robust=FALSE, ...)

## S3 method for class 'gam'
getFixedEf(object, ...)

## S3 method for class 'gee'
getFixedEf(object, exp = FALSE, ...)

## S3 method for class 'geese'
getFixedEf(object, robust = TRUE, ...)
## S3 method for class 'tps'
getFixedEf(object, exp=FALSE, robust=TRUE, ...)

## S3 method for class 'glm'
 getFixedEf(object, exp = FALSE, robust = TRUE, ret.robcov = FALSE, 
    ...)  

## S3 method for class 'svyglm'
 getFixedEf(object, exp = FALSE, robust = TRUE, ...)  
## S3 method for class 'svy_vglm'
 getFixedEf(object, exp = FALSE, robust = TRUE, ...)  

## S3 method for class 'svycoxph'
 getFixedEf(object, exp = FALSE, robust = TRUE, ...)  

## S3 method for class 'inla'
getFixedEf(object, ...)

## S3 method for class 'lm'
getFixedEf(object, ...)

## S3 method for class 'lme'
getFixedEf(object, ...)

## S3 method for class 'logistf'
getFixedEf(object, exp = FALSE, ...)

## S3 method for class 'matrix'
getFixedEf(object, ...)

## S3 method for class 'MIresult'
getFixedEf(object, ...)

## S3 method for class 'hyperpar.inla'
getVarComponent(object, transformation = NULL, ...)

## S3 method for class 'matrix'
getVarComponent(object, ...)

## S3 method for class 'geese'
coef(object, ...)
## S3 method for class 'tps'
coef(object, ...)

## S3 method for class 'geese'
predict(object, x, ...)
## S3 method for class 'tps'
predict(object, newdata = NULL, type = c("link", "response"), ...)

## S3 method for class 'geese'
residuals(object, y, x,...)

## S3 method for class 'geese'
vcov(object, ...)
## S3 method for class 'tps'
vcov(object, robust, ...)

## S3 method for class 'logistf'
vcov(object, ...)

Arguments

...

tbd...

object

tbdobject

fit

tbdfit

coef.direct

tbdfit

robust

Boolean, whether to return robust variance estimate

exp

tbdexp

remove.leading0

tbdexp

p.adj.method

tbdexp

cuts

tbdfits

ret.robcov

tbdfits

fits

tbdfits

type

tbdtype

est.digits

tbdest.digits

se.digits

tbdse.digits

p.digits

tbdse.digits

random

tbdrandom

VE

tbdrandom

transformation

tbdtransformation

weights

tbdv1

v1

tbdv1

v2

tbdv2

v1.type

tbdv1.type

v2.type

tbdv2.type

logistic.regression

tbdlogistic.regression

newdata

tbdx

x

tbdx

y

tbdy

to.trim

tbdy

rows

tbdy

risk

tbdfit

binary.outcome

tbdfit

ngroups

tbdfit

main

tbdfit

add

tbdfit

show.emp.risk

tbdfit

lcol

tbdfit

ylim

tbdfit

scale

tbdfit

trunc.large.est

tbdfit

scale.factor

tbdfit

Details

getFormattedSummary: from a list of fits, say lmer, inla fits, return formatted summary controlled by "type". For a matrix, return Monte Carlo variance random=TRUE returns variance components type=1: est type=2: est (se) type=3: est (2.5 percent, 97.5 percent) type=4: est se

getFixedEf returns a matrix, first column coef, second column se,

getFixedEf.matrix used to get mean and sd from a jags or winbugs sample, getVarComponent.matrix and getFixedEf.matrix do the same thing. Each column of samples is a variable

interaction.table expects coef and vcov to work with fit.

Examples

## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
glm.D9 <- glm(weight ~ group)
getFormattedSummary (list(lm.D9, glm.D9), robust=FALSE)