# mlreg

From statmod v0.6
by Gordon Smyth

##### Fit a Linear Model by Maximum Likelihood

Fits a linear model by maximum likelihood assuming a variety of response distributions.

- Keywords
- regression

##### Usage

```
mlreg.fit(X, y, weights=NULL, dist="logistic", init=NULL, scale=NULL)
mlreg.fit.zero(y, weights=NULL, dist="logistic", init=NULL, scale=NULL)
```

##### Arguments

- X
- the design matrix. Rows containing missing values (in X or y) will be removed.
- y
- numeric response vector. Missing values will be removed.
- weights
- vector of non-negative weights.
- dist
- character string giving the name of the response distribution. The possibilities are "weibull", "exponential", "gaussian", "logistic", "lognormal" or "loglogistic".
- init
- numeric vector of initial values for the parameters.
- scale
- if specified then the scale parameter is fixed at the given value.

##### Details

This function is merely a convenient wrapper for calling the
`survreg.fit`

function, which is part of the `survival`

library by Terry Therneau.
It fits the model y = X*b + scale*e where b is the vector of regression coefficients and e is a vector of mean-zero errors, by maximum likelihood.
The function `mlreg.fit.zero`

assumes that the mean is zero and fits y = scale*e, estimating only the scale parameter.

##### Value

- See the documentation for
`survreg.object`

##### See Also

##### Examples

```
x <- 1:50
y <- x + 2*rnorm(50)
X <- cbind(1,x)
out <- mlreg.fit(X,y,dist="logistic")
```

*Documentation reproduced from package statmod, version 0.6, License: GPL version 2 or newer*

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