# print.feNmlm: A print facility for 'feNmlm' objects. It can compute... In FENmlm: Fixed Effects Nonlinear Maximum Likelihood Models

## 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 `feNmlm`.
 ``` 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") ```