logpoisson {glmmAK} | R Documentation |
Fits the poisson log-linear regression model using the maximum-likelihood. The log-likelihood is maximized using the Newton-Raphson algorithm (the same as Fisher scoring in this case). The function returns the inverse of the observed and expected information matrix.
logpoisson(y, x, offset=0, epsilon=1e-08, maxit=25, trace=FALSE) ## S3 method for class 'logpoisson': print(x, ...) ## S3 method for class 'logpoisson': summary(object, ...)
y |
response vector taking integer values or zero. |
x |
matrix or data.frame with covarites.
Intercept is included by default in the model and should not be included in x .
|
offset |
possible offset term. It is assumed to be equal to zero if not specified. |
epsilon |
positive convergence tolerance
epsilon. The iterations converge when
abs((l[new] - l[old])/l[new]) <= epsilon, where l denotes the value of the log-likelihood. |
maxit |
integer giving the maximal number of iterations. |
trace |
logical indicating if output should be produced for each iteration. |
object |
an object of class "logpoisson". |
... |
other arguments passed to print or summary . |
An object of class "logpoisson". This has components
coefficients |
the coefficients of the linear predictor. |
loglik |
the value of the log-likelihood. |
score |
the score vector. |
vcov |
the inverse of the information matrix. |
linear.predictors |
the values of the linear predictor for each observation. |
fitted.values |
the values of fitted counts for each observation. |
converged |
logical indicating whether the optimization routine converged. |
iter |
number of iterations performed |
y |
|
x |
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
Agresti, A. (2002). Categorical Data Analysis. Second edition. Hoboken: John Wiley & Sons. Section 7.2.
glm
.
set.seed(1977) n <- 100 x1 <- rbinom(n, 1, 0.4) x2 <- runif(n, 0, 1) eta <- 5 + 0.1*x1 -0.2*x2 mu <- exp(eta) y <- rpois(n, mu) ### Fit the model using poisson Xmat <- data.frame(x1=x1, x2=x2) fit <- logpoisson(y=y, x=Xmat) summary(fit) ### Fit the model using standard glm fit0 <- glm(y~x1+x2, family=poisson(link="log")) summary(fit0)