# lqm.counts

##### Quantile Regression for Counts

This function is used to fit a quantile regression model when the response is a count variable.

- Keywords
- quantiles for counts

##### Usage

```
lqm.counts(formula, data, weights = NULL, offset = NULL, contrasts = NULL,
tau = 0.5, M = 50, zeta = 1e-05, B = 0.999, cn = NULL, alpha = 0.05,
control = list())
```

##### Arguments

- formula
an object of class

`formula`

: a symbolic description of the model to be fitted.- data
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lqm is called.

- weights
an optional vector of weights to be used in the fitting process.

- offset
an optional offset to be included in the model frame.

- contrasts
an optional list. See the

`contrasts.arg`

of`model.matrix.default`

.- tau
quantile to be estimated.

- M
number of dithered samples.

- zeta
small constant (see References).

- B
right boundary for uniform random noise U[0,B] to be added to the response variable (see References).

- cn
small constant to be passed to

`F.lqm`

(see References).- alpha
significance level.

- control
list of control parameters of the fitting process. See

`lqmControl`

.

##### Details

A linear quantile regression model if fitted to the log--transformed response. Additional tranformation functions will be implemented. The notation used here follows closely that of Machado and Santos Silva (2005).

##### Value

an object of class "lqm.counts" containing the following components

the estimated quantile.

regression quantile (on the log--scale).

predicted quantile (on the response scale).

coefficients, standard errors, etc.

the model matrix.

the model response.

offset.

the number of observations.

specified number of dithered samples for standard error estimation.

actual number of dithered samples used for standard error estimation that gave an invertible D matrix (Machado and Santos Silva, 2005).

names for theta.

the terms object used.

the number of residual degrees of freedom.

starting values for theta.

list of control parameters used for optimization (see `lqmControl`

).

##### References

Machado JAF and Santos Silva JMC (2005). Quantiles for counts. Journal of the American Statistical Association, 100(472), 1226--1237.

##### Examples

```
# NOT RUN {
n <- 100
x <- runif(n)
test <- data.frame(x = x, y = rpois(n, 2*x))
lqm.counts(y ~ x, data = test, M = 50)
# }
```

*Documentation reproduced from package lqmm, version 1.5.3, License: GPL (>= 2)*