easy.binomial.twostage: Fits two-stage binomial for describing depdendence in...

easy.binomial.twostageR Documentation

Fits two-stage binomial for describing depdendence in binomial data using marginals that are on logistic form using the binomial.twostage funcion, but call is different and easier and the data manipulation is build into the function. Useful in particular for family design data.

Description

If clusters contain more than two times, the algoritm uses a compososite likelihood based on the pairwise bivariate models.

Usage

easy.binomial.twostage(
  margbin = NULL,
  data = parent.frame(),
  method = "nr",
  response = "response",
  id = "id",
  Nit = 60,
  detail = 0,
  silent = 1,
  weights = NULL,
  control = list(),
  theta = NULL,
  theta.formula = NULL,
  desnames = NULL,
  deshelp = 0,
  var.link = 1,
  iid = 1,
  step = 1,
  model = "plackett",
  marginal.p = NULL,
  strata = NULL,
  max.clust = NULL,
  se.clusters = NULL
)

Arguments

margbin

Marginal binomial model

data

data frame

method

Scoring method

response

name of response variable in data frame

id

name of cluster variable in data frame

Nit

Number of iterations

detail

Detail for more output for iterations

silent

Debug information

weights

Weights for log-likelihood, can be used for each type of outcome in 2x2 tables.

control

Optimization arguments

theta

Starting values for variance components

theta.formula

design for depedence, either formula or design function

desnames

names for dependence parameters

deshelp

if 1 then prints out some data sets that are used, on on which the design function operates

var.link

Link function for variance

iid

Calculate i.i.d. decomposition

step

Step size

model

model

marginal.p

vector of marginal probabilities

strata

strata for fitting

max.clust

max clusters used for i.i.d. decompostion

se.clusters

clusters for iid decomposition for roubst standard errors

Details

The reported standard errors are based on the estimated information from the likelihood assuming that the marginals are known. This gives correct standard errors in the case of the plackett distribution (OR model for dependence), but incorrect for the clayton-oakes types model. The OR model is often known as the ALR model. Our fitting procedures gives correct standard errors due to the ortogonality and is fast.

Examples

data(twinstut)
twinstut0 <- subset(twinstut, tvparnr<10000)
twinstut <- twinstut0
twinstut$binstut <- (twinstut$stutter=="yes")*1
theta.des <- model.matrix( ~-1+factor(zyg),data=twinstut)
margbin <- glm(binstut~factor(sex)+age,data=twinstut,family=binomial())
bin <- binomial.twostage(margbin,data=twinstut,var.link=1,
		         clusters=twinstut$tvparnr,theta.des=theta.des,detail=0,
	                 method="nr")
summary(bin)
lava::estimate(coef=bin$theta,vcov=bin$var.theta,f=function(p) exp(p))

twinstut$cage <- scale(twinstut$age)
theta.des <- model.matrix( ~-1+factor(zyg)+cage,data=twinstut)
bina <- binomial.twostage(margbin,data=twinstut,var.link=1,
		         clusters=twinstut$tvparnr,theta.des=theta.des,detail=0)
summary(bina)

theta.des <- model.matrix( ~-1+factor(zyg)+factor(zyg)*cage,data=twinstut)
bina <- binomial.twostage(margbin,data=twinstut,var.link=1,
		         clusters=twinstut$tvparnr,theta.des=theta.des)
summary(bina)

out <- easy.binomial.twostage(stutter~factor(sex)+age,data=twinstut,
                              response="binstut",id="tvparnr",var.link=1,
			          theta.formula=~-1+factor(zyg1))
summary(out)

## refers to zygosity of first subject in eash pair : zyg1
## could also use zyg2 (since zyg2=zyg1 within twinpair's))
desfs <- function(x,num1="zyg1",namesdes=c("mz","dz","os"))
    c(x[num1]=="mz",x[num1]=="dz",x[num1]=="os")*1

out3 <- easy.binomial.twostage(binstut~factor(sex)+age,
                               data=twinstut, response="binstut",id="tvparnr",
                               var.link=1,theta.formula=desfs,
                               desnames=c("mz","dz","os"))
summary(out3)

 ## Reduce Ex.Timings
n <- 5000
set.seed(100)
dd <- simBinFam(n,beta=0.3)
binfam <- fast.reshape(dd,varying=c("age","x","y"))
## mother, father, children  (ordered)
head(binfam)

########### ########### ########### ########### ########### ###########
####  simple analyses of binomial family data
########### ########### ########### ########### ########### ###########
desfs <- function(x,num1="num1",num2="num2")
{
     pp <- 1*(((x[num1]=="m")*(x[num2]=="f"))|(x[num1]=="f")*(x[num2]=="m"))
     pc <- (x[num1]=="m" | x[num1]=="f")*(x[num2]=="b1" | x[num2]=="b2")*1
     cc <- (x[num1]=="b1")*(x[num2]=="b1" | x[num2]=="b2")*1
     c(pp,pc,cc)
}

ud <- easy.binomial.twostage(y~+1,data=binfam,
     response="y",id="id",
     theta.formula=desfs,desnames=c("pp","pc","cc"))
summary(ud)

udx <- easy.binomial.twostage(y~+x,data=binfam,
     response="y",id="id",
     theta.formula=desfs,desnames=c("pp","pc","cc"))
summary(udx)

########### ########### ########### ########### ########### ###########
####  now allowing parent child POR to be different for mother and father
########### ########### ########### ########### ########### ###########

desfsi <- function(x,num1="num1",num2="num2")
{
    pp <- (x[num1]=="m")*(x[num2]=="f")*1
    mc <- (x[num1]=="m")*(x[num2]=="b1" | x[num2]=="b2")*1
    fc <- (x[num1]=="f")*(x[num2]=="b1" | x[num2]=="b2")*1
    cc <- (x[num1]=="b1")*(x[num2]=="b1" | x[num2]=="b2")*1
    c(pp,mc,fc,cc)
}

udi <- easy.binomial.twostage(y~+1,data=binfam,
     response="y",id="id",
     theta.formula=desfsi,desnames=c("pp","mother-child","father-child","cc"))
summary(udi)

##now looking to see if interactions with age or age influences marginal models
##converting factors to numeric to make all involved covariates numeric
##to use desfai2 rather then desfai that works on binfam

nbinfam <- binfam
nbinfam$num <- as.numeric(binfam$num)
head(nbinfam)

desfsai <- function(x,num1="num1",num2="num2")
{
    pp <- (x[num1]=="m")*(x[num2]=="f")*1
### av age for pp=1 i.e parent pairs
    agepp <- ((as.numeric(x["age1"])+as.numeric(x["age2"]))/2-30)*pp
    mc <- (x[num1]=="m")*(x[num2]=="b1" | x[num2]=="b2")*1
    fc <- (x[num1]=="f")*(x[num2]=="b1" | x[num2]=="b2")*1
    cc <- (x[num1]=="b1")*(x[num2]=="b1" | x[num2]=="b2")*1
    agecc <- ((as.numeric(x["age1"])+as.numeric(x["age2"]))/2-12)*cc
    c(pp,agepp,mc,fc,cc,agecc)
}

desfsai2 <- function(x,num1="num1",num2="num2")
{
    pp <- (x[num1]==1)*(x[num2]==2)*1
    agepp <- (((x["age1"]+x["age2"]))/2-30)*pp ### av age for pp=1 i.e parent pairs
    mc <- (x[num1]==1)*(x[num2]==3 | x[num2]==4)*1
    fc <- (x[num1]==2)*(x[num2]==3 | x[num2]==4)*1
    cc <- (x[num1]==3)*(x[num2]==3 | x[num2]==4)*1
    agecc <- ((x["age1"]+x["age2"])/2-12)*cc ### av age for children
    c(pp,agepp,mc,fc,cc,agecc)
}

udxai2 <- easy.binomial.twostage(y~+x+age,data=binfam,
     response="y",id="id",
     theta.formula=desfsai,
     desnames=c("pp","pp-age","mother-child","father-child","cc","cc-age"))
summary(udxai2)


mets documentation built on Oct. 2, 2022, 5:06 p.m.