lava (version 1.8.0)

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 default
estimate(
  x = NULL,
  f = NULL,
  ...,
  data,
  id,
  iddata,
  stack = TRUE,
  average = FALSE,
  subset,
  score.deriv,
  level = 0.95,
  IC = robust,
  type = c("robust", "df", "mbn"),
  keep,
  use,
  regex = FALSE,
  ignore.case = FALSE,
  contrast,
  null,
  vcov,
  coef,
  robust = TRUE,
  df = NULL,
  print = NULL,
  labels,
  label.width,
  only.coef = FALSE,
  back.transform = NULL,
  folds = 0,
  cluster,
  R = 0,
  null.sim
)

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 ic 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

IC

if TRUE (default) the influence function decompositions are also returned (extract with IC method)

type

type of small-sample correction

keep

(optional) index of parameters to keep from final result

use

(optional) index of parameters to use in calculations

regex

If TRUE use regular expression (perl compatible) for keep,use arguments

ignore.case

Ignore case-sensitiveness in regular expression

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

back.transform

(optional) transform of parameters and confidence intervals

folds

(optional) aggregate influence functions (divide and conquer)

cluster

(obsolete) alias for 'id'.

R

Number of simulations (simulated p-values)

null.sim

Mean under the null for simulations

Details

influence function decomposition of estimator \(\widehat{\theta}\) based on data \(Z_1,\ldots,Z_n\): $$\sqrt{n}(\widehat{\theta}-\theta) = \frac{1}{\sqrt{n}}\sum_{i=1}^n IC(Z_i; P) + o_p(1)$$ can be extracted with the IC method.

See Also

estimate.array

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 cbind(0,1,-1)
estimate(g,as.list(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,z,z-x,2*z-3*x)
estimate(g,"?")  ## Wildcards
estimate(g,"*Int*","z")
estimate(g,"1","2"-"3",null=c(0,1))
estimate(g,2,3)

## 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,seed=1)
e <- estimate(list(m,m),list(d1,d2))
estimate(e) ## Wrong
ee <- estimate(e, id=rep(seq(nrow(d1)), 2)) ## Clustered
ee
estimate(lm(y~x,d1))

## Marginalize
f <- function(p,data)
  list(p0=lava:::expit(p["(Intercept)"] + p["z"]*data[,"z"]),
       p1=lava:::expit(p["(Intercept)"] + p["x"] + p["z"]*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
IC(estimate(l1,id=id1,stack=FALSE))
IC(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))

## If all models were estimated on the same data we could use the
## syntax:
## Reduce(merge,estimate(list(l1,l2,l3)))

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

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


## Monte Carlo approach, simple trend test example

m <- categorical(lvm(),~x,K=5)
regression(m,additive=TRUE) <- y~x
d <- simulate(m,100,seed=1,'y~x'=0.1)
l <- lm(y~-1+factor(x),data=d)

f <- function(x) coef(lm(x~seq_along(x)))[2]
null <- rep(mean(coef(l)),length(coef(l))) ## just need to make sure we simulate under H0: slope=0
estimate(l,f,R=1e2,null.sim=null)

estimate(l,f)