glm                   package:base                   R Documentation

_F_i_t_t_i_n_g _G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'glm' is used to fit generalized linear models, specified by
     giving a symbolic description of the linear predictor and a
     description of the error distribution.

_U_s_a_g_e:

     glm(formula, family = gaussian, data, weights = NULL, subset = NULL,
         na.action, start = NULL, etastart = NULL, mustart = NULL,
         offset = NULL, control = glm.control(...), model = TRUE,
         method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, ...)

     glm.fit(x, y, weights = rep(1, nobs),
             start = NULL, etastart = NULL, mustart = NULL,
             offset = rep(0, nobs), family = gaussian(),
             control = glm.control(), intercept = TRUE)

     ## S3 method for class 'glm':
     weights(object, type = c("prior", "working"), ...)

_A_r_g_u_m_e_n_t_s:

 formula: a symbolic description of the model to be fit. The details of
          model specification are given below.

  family: a description of the error distribution and link function to
          be used in the model. This can be a character string naming a
          family function, a family function or the result of a call to
          a family function.  (See 'family' for details of family
          functions.)

    data: an optional data frame containing the variables in the model.
           By default the variables are taken from
          'environment(formula)', typically the environment from which
          'glm' is called.

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

  subset: an optional vector specifying a subset of observations to be
          used in the fitting process.

na.action: a function which indicates what should happen when the data
          contain 'NA's.  The default is set by the 'na.action' setting
          of 'options', and is 'na.fail' if that is unset.  The
          "factory-fresh" default is 'na.omit'.

   start: starting values for the parameters in the linear predictor.

etastart: starting values for the linear predictor.

 mustart: starting values for the vector of means.

  offset: this can be used to specify an _a priori_ known component to
          be included in the linear predictor during fitting.

 control: a list of parameters for controlling the fitting process. 
          See the documentation for 'glm.control' for details.

   model: a logical value indicating whether _model frame_ should be
          included as a component of the returned value.

  method: the method to be used in fitting the model. The default
          method '"glm.fit"' uses iteratively reweighted least squares
          (IWLS).  The only current alternative is '"model.frame"'
          which returns the model frame and does no fitting.

    x, y: For 'glm': logical values indicating whether the response
          vector and model matrix used in the fitting process should be
          returned as components of the returned value.

          For 'glm.fit': 'x' is a design matrix of dimension 'n * p',
          and 'y' is a vector of observations of length 'n'. 

contrasts: an optional list. See the 'contrasts.arg' of
          'model.matrix.default'.

  object: an object inheriting from class '"glm"'.

    type: character, partial matching allowed.  Type of weights to
          extract from the fitted model object.

intercept: logical. Should an intercept be included?

     ...: further arguments passed to or from other methods.

_D_e_t_a_i_l_s:

     A typical predictor has the form 'response ~ terms' where
     'response' is the (numeric) response vector and 'terms' is a
     series of terms which specifies a linear predictor for 'response'.
     For 'binomial' models the response can also be specified as a
     'factor' (when the first level denotes failure and all others
     success) or as a two-column matrix with the columns giving the
     numbers of successes and failures.  A terms specification of the
     form 'first + second' indicates all the terms in 'first' together
     with all the terms in 'second' with duplicates removed.

     A specification of the form 'first:second' indicates the the set
     of terms obtained by taking the interactions of all terms in
     'first' with all terms in 'second'. The specification
     'first*second' indicates the _cross_ of 'first' and 'second'. This
     is the same as 'first + second + first:second'.

     'glm.fit' and 'glm.fit.null' are the workhorse functions: the
     former calls the latter for a null model (with no intercept).

     If more than one of 'etastart', 'start' and 'mustart' is
     specified, the first in the list will be used.

_V_a_l_u_e:

     'glm' returns an object of class inheriting from '"glm"' which
     inherits from the class '"lm"'. See later in this section.

     The function 'summary' (i.e., 'summary.glm') can be used to obtain
     or print a summary of the results and the function 'anova' (i.e.,
     'anova.glm') to produce an analysis of variance table.

     The generic accessor functions 'coefficients', 'effects',
     'fitted.values' and 'residuals' can be used to extract various
     useful features of the value returned by 'glm'.

     'weights' extracts a vector of weights, one for each case in the
     fit (after subsetting and 'na.action').

     An object of class '"glm"' is a list containing at least the
     following components:

coefficients: a named vector of coefficients

residuals: the _working_ residuals, that is the residuals in the final
          iteration of the IWLS fit.

fitted.values: the fitted mean values, obtained by transforming the
          linear predictors by the inverse of the link function.

    rank: the numeric rank of the fitted linear model.

  family: the 'family' object used.

linear.predictors: the linear fit on link scale.

deviance: up to a constant, minus twice the maximized log-likelihood. 
          Where sensible, the constant is chosen so that a saturated
          model has deviance zero.

     aic: Akaike's _An Information Criterion_, minus twice the
          maximized log-likelihood plus twice the number of
          coefficients (so assuming that the dispersion is known.

null.deviance: The deviance for the null model, comparable with
          'deviance'. The null model will include the offset, and an
          intercept if there is one in the model

    iter: the number of iterations of IWLS used.

 weights: the _working_ weights, that is the weights in the final
          iteration of the IWLS fit.

prior.weights: the case weights initially supplied.

df.residual: the residual degrees of freedom.

 df.null: the residual degrees of freedom for the null model.

       y: the 'y' vector used. (It is a vector even for a binomial
          model.)

converged: logical. Was the IWLS algorithm judged to have converged?

boundary: logical. Is the fitted value on the boundary of the
          attainable values?

    call: the matched call.

 formula: the formula supplied.

   terms: the 'terms' object used.

    data: the 'data argument'.

  offset: the offset vector used.

 control: the value of the 'control' argument used.

  method: the name of the fitter function used, in R always
          '"glm.fit"'.

contrasts: (where relevant) the contrasts used.

 xlevels: (where relevant) a record of the levels of the factors used
          in fitting.


     In addition, non-empty fits will have components 'qr', 'R' and
     'effects' relating to the final weighted linear fit.

     Objects of class '"glm"' are normally of class 'c("glm", "lm")',
     that is inherit from class '"lm"', and well-designed methods for
     class '"lm"' will be applied to the weighted linear model at the
     final iteration of IWLS.  However, care is needed, as extractor
     functions for class '"glm"' such as 'residuals' and 'weights' do
     *not* just pick out the component of the fit with the same name.

     If a 'binomial' 'glm' model is specified by giving a two-column
     response, the weights returned by 'prior.weights' are the total
     numbers of cases (factored by the supplied case weights) and the
     component 'y' of the result is the proportion of successes.

_A_u_t_h_o_r(_s):

     The original R implementation of 'glm' was written by Simon Davies
     working for Ross Ihaka at the University of Auckland, but has
     since been extensively re-written by members of the R Core team.

     The design was inspired by the S function of the same name
     described in Hastie & Pregibon (1992).

_R_e_f_e_r_e_n_c_e_s:

     Dobson, A. J. (1990) _An Introduction to Generalized Linear
     Models._ London: Chapman and Hall.

     Hastie, T. J. and Pregibon, D. (1992) _Generalized linear models._
     Chapter 6 of _Statistical Models in S_ eds J. M. Chambers and T.
     J. Hastie, Wadsworth & Brooks/Cole.

     McCullagh P. and Nelder, J. A. (1989) _Generalized Linear Models._
     London: Chapman and Hall.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ New York: Springer.

_S_e_e _A_l_s_o:

     'anova.glm', 'summary.glm', etc. for 'glm' methods, and the
     generic functions 'anova', 'summary', 'effects', 'fitted.values',
     and 'residuals'. Further, 'lm' for non-generalized _linear_
     models.

     'esoph', 'infert' and 'predict.glm' have examples of fitting
     binomial glms.

_E_x_a_m_p_l_e_s:

     ## Dobson (1990) Page 93: Randomized Controlled Trial :
     counts <- c(18,17,15,20,10,20,25,13,12)
     outcome <- gl(3,1,9)
     treatment <- gl(3,3)
     print(d.AD <- data.frame(treatment, outcome, counts))
     glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
     anova(glm.D93)
     summary(glm.D93)

     ## an example with offsets from Venables & Ripley (2002, p.189)

     ## Don't run: 
     ## Need the anorexia data from a recent version of the package 'MASS':
     library(MASS)
     data(anorexia)
     ## End Don't run
     anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                 family = gaussian, data = anorexia)
     summary(anorex.1)

     # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
     clotting <- data.frame(
         u = c(5,10,15,20,30,40,60,80,100),
         lot1 = c(118,58,42,35,27,25,21,19,18),
         lot2 = c(69,35,26,21,18,16,13,12,12))
     summary(glm(lot1 ~ log(u), data=clotting, family=Gamma))
     summary(glm(lot2 ~ log(u), data=clotting, family=Gamma))

