| estimate.default {lava} | R Documentation |
Estimation of functional of parameters.
Wald tests, robust standard errors, cluster robust standard errors,
LRT (when f is not a function)...
## Default S3 method:
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, use, contrast, null,
vcov, coef, robust = TRUE, df = NULL, print = NULL, labels, label.width,
only.coef = FALSE, transform.ci = NULL, folds = 0, cluster)
x |
model object ( |
f |
transformation of model parameters and (optionally) data, or contrast matrix (or vector) |
... |
additional arguments to lower level functions |
data |
|
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 |
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 |
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.ci |
(optional) transform of parameters and confidence intervals |
folds |
(optional) aggregate influence functions (divide and conquer) |
cluster |
(obsolete) alias for 'id'. |
## 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["(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
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