A print facility for feNmlm objects. It can compute different types of standard errors.

Share:

Description

This function is very similar to usual summary functions as it provides the table of coefficients along with other information on the fit of the estimation.

Usage

1
2
## S3 method for class 'feNmlm'
print(x, sd = c("standard", "white","cluster","twoway"),cluster, ...)

Arguments

x

A feNmlm object.

sd

Character scalar. Which kind of standard error should be prompted: “standard” (default), “White”, or “cluster”?

cluster

A list of vectors. Used only if sd = "cluster" or sd="twoway". The vectors should give the cluster of each observation. Note that if the estimation was run using dummy, the standard error is automatically clustered along the cluster given in feNmlm.

...

Currently unused.

Author(s)

Laurent Berge

See Also

See also feNmlm.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
#The data
n = 100
x = rnorm(n,1,5)**2
y = rnorm(n,-1,5)**2
z = rpois(n,x*y)
base = data.frame(x,y,z)

#Comparing the results of a 'linear' function
est0L = feNmlm(z~0,base,~log(x)+log(y),family="poi")
est0NL = feNmlm(z~a*log(x)+b*log(y),base,start = list(a=0,b=0),
					family="poisson", linear.fml=~1)

print(est0L)
print(est0NL)

#Generating a non-linear relation
z2 = rpois(n,x + y)
base$z2 = z2

#Using a non-linear form
est1L = feNmlm(z2~0,base,~log(x)+log(y),family="poi")
est1NL = feNmlm(z2~log(a*x + b*y),base,start = list(a=1,b=2),family="poisson")