lava (version 1.4.1)

estimate.default: Estimation of functional of parameters

Description

Estimation of functional of parameters. Wald tests, robust standard errors, cluster robust standard errors, LRT (when f is not a function)...

Usage

## S3 method for class 'default':
estimate(x = NULL, f = NULL, ..., data, id, iddata,
  stack = TRUE, average = FALSE, subset, score.deriv, level = 0.95,
  iid = TRUE, type = c("robust", "df", "mbn"), keep, contrast, null, vcov,
  coef, robust = TRUE, df = NULL, print = NULL, labels, label.width,
  only.coef = FALSE, transform, folds = 0)

Arguments

x

model object (glm, lvmfit, ...)

f

transformation of model parameters and (optionally) data, or contrast matrix (or vector)

...

additional arguments to lower level functions

data

data.frame

id

(optional) id-variable corresponding to iid decomposition of model parameters.

iddata

(optional) id-variable for 'data'

stack

If TRUE (default) the i.i.d. decomposition is automatically stacked according to 'id'

average

If TRUE averages are calculated

subset

(optional) subset of data.frame on which to condition (logical expression or variable name)

score.deriv

(optional) derivative of mean score function

level

Level of confidence limits

iid

If TRUE (default) the iid decompositions are also returned (extract with iid method)

type

Type of small-sample correction

keep

(optional) Index of parameters to keep

contrast

(optional) Contrast matrix for final Wald test

null

(optional) Null hypothesis to test

vcov

(optional) covariance matrix of parameter estimates (e.g. Wald-test)

coef

(optional) parameter coefficient

robust

If TRUE robust standard errors are calculated. If FALSE p-values for linear models are calculated from t-distribution

df

Degrees of freedom (default obtained from 'df.residual')

print

(optional) print function

labels

(optional) names of coefficients

label.width

(optional) max width of labels

only.coef

if TRUE only the coefficient matrix is return

transform

(optional) transform of parameters and confidence intervals

folds

(optional) Aggregate influence functions (divide and conquer)

Examples

## Simulation from logistic regression model
m <- lvm(y~x+z);
distribution(m,y~x) <- binomial.lvm("logit")
d <- sim(m,1000)
g <- glm(y~z+x,data=d,family=binomial())
g0 <- glm(y~1,data=d,family=binomial())

## LRT
estimate(g,g0)

## Plain estimates (robust standard errors)
estimate(g)

## Testing contrasts
estimate(g,null=0)
estimate(g,rbind(c(1,1,0),c(1,0,2)))
estimate(g,rbind(c(1,1,0),c(1,0,2)),null=c(1,2))
estimate(g,2:3) ## same as rbind(c(0,1,0),c(0,0,1))
## Alternative syntax
estimate(g,"z","z"-"x",2*"z"-3*"x")
estimate(g,"1","2"-"3",null=c(0,1))
## Usual (non-robust) confidence intervals
estimate(g,robust=FALSE)

## Transformations
estimate(g,function(p) p[1]+p[2])

## Multiple parameters
e <- estimate(g,function(p) c(p[1]+p[2],p[1]*p[2]))
e
vcov(e)

## Label new parameters
estimate(g,function(p) list("a1"=p[1]+p[2],"b1"=p[1]*p[2]))
##'
## Multiple group
m <- lvm(y~x)
m <- baptize(m)
d2 <- d1 <- sim(m,50)
e <- estimate(list(m,m),list(d1,d2))
estimate(e) ## Wrong
estimate(e,id=rep(seq(nrow(d1)),2))
estimate(lm(y~x,d1))

## Marginalize
f <- function(p,data)
  list(p0=lava:::expit(p[1] + p[3]*data[,"z"]),
       p1=lava:::expit(p[1] + p[2] + p[3]*data[,"z"]))
e <- estimate(g, f, average=TRUE)
e
estimate(e,diff)
estimate(e,cbind(1,1))

## Clusters and subset (conditional marginal effects)
d$id <- rep(seq(nrow(d)/4),each=4)
estimate(g,function(p,data)
         list(p0=lava:::expit(p[1] + p["z"]*data[,"z"])),
         subset=d$z>0, id=d$id, average=TRUE)

## More examples with clusters:
m <- lvm(c(y1,y2,y3)~u+x)
d <- sim(m,10)
l1 <- glm(y1~x,data=d)
l2 <- glm(y2~x,data=d)
l3 <- glm(y3~x,data=d)

## Some random id-numbers
id1 <- c(1,1,4,1,3,1,2,3,4,5)
id2 <- c(1,2,3,4,5,6,7,8,1,1)
id3 <- seq(10)

## Un-stacked and stacked i.i.d. decomposition
iid(estimate(l1,id=id1,stack=FALSE))
iid(estimate(l1,id=id1))

## Combined i.i.d. decomposition
e1 <- estimate(l1,id=id1)
e2 <- estimate(l2,id=id2)
e3 <- estimate(l3,id=id3)
(a2 <- merge(e1,e2,e3))

## Same:
iid(a1 <- merge(l1,l2,l3,id=list(id1,id2,id3)))

iid(merge(l1,l2,l3,id=TRUE)) # one-to-one (same clusters)
iid(merge(l1,l2,l3,id=FALSE)) # independence