predict.gam               package:mgcv               R Documentation

_P_r_e_d_i_c_t_i_o_n _f_r_o_m _f_i_t_t_e_d _G_A_M _m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Takes a fitted 'gam' object produced by 'gam()'  and produces
     predictions given a new set of values for the model covariates  or
     the original values used for the model fit.

_U_s_a_g_e:

     predict.gam(object,newdata,type="link",se.fit=FALSE,...)

_A_r_g_u_m_e_n_t_s:

  object: a fitted 'gam' object as produced by 'gam()'. 

 newdata: A data frame containing the values of the model covariates at
          which predictions are required. If this is not provided then
          predictions corresponding to the original data are returned.
          If 'newdata' is provided then it should contain all the
          variables needed for prediction: a warning is generated if
          not.

    type: When this has the value '"link"' (default) the linear
          predictor (possibly with associated standard errors) is
          returned. When 'type="terms"' each component of the  linear
          predictor is returned seperately (possibly with standard
          errors): this includes  parametric model components, followed
          by each smooth component, but excludes any offset. When
          'type="response"' predictions  on the scale of the response
          are returned (possibly with approximate standard errors).
          When 'type="lpmatrix"' then a matrix is returned which yields
          the values of the linear predictor (minus any offset) when
          applied to the parameter vector (in this case 'se.fit' is
          ignored). The latter option is most useful for getting
          variance estimates for integrated quantities. 

  se.fit: when this is TRUE (not default) standard error estimates are
          returned for each prediction.

     ...: other arguments.

_D_e_t_a_i_l_s:

     The standard errors produced by 'predict.gam' are based on the
     Bayesian posterior covariance matrix of the parameters 'Vp' in the
     fitted gam object.

_V_a_l_u_e:

     If 'type=="lpmatrix"' then a matrix is returned which will give a
     vector of linear predictor values (minus any offest) at the
     supplied covariate values, when applied to the model coefficient
     vector.  Otherwise, if 'se.fit' is 'TRUE' then a 2 item list is
     returned with items (both arrays) 'fit' and 'se.fit' containing
     predictions and associated standard error estimates, otherwise an 
     array of predictions is returned. The dimensions of the returned
     arrays depends on whether  'type' is '"terms"' or not: if it is
     then the array is 2 dimensional with each  term in the linear
     predictor separate, otherwise the array is 1 dimensional and
     contains the  linear predictor/predicted values (or corresponding
     s.e.s). The linear predictor returned termwise will  not include
     the offset.

     'newdata' can be a data frame, list or model.frame: if it's a
     model frame then all variables must be supplied.

_W_A_R_N_I_N_G:

     Note that the behaviour of this function is not identical to 
     'predict.gam()' in Splus.

     Note that the

_A_u_t_h_o_r(_s):

     Simon N. Wood simon@stats.gla.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Gu and Wahba (1991) Minimizing GCV/GML scores with multiple
     smoothing parameters via the Newton method. SIAM J. Sci. Statist.
     Comput. 12:383-398

     Wood, S.N. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

     <URL: http://www.stats.gla.ac.uk/~simon/>

_S_e_e _A_l_s_o:

     'gam', 'gamm', 'plot.gam'

_E_x_a_m_p_l_e_s:

     library(mgcv)
     n<-200
     sig2<-4
     x0 <- runif(n, 0, 1)
     x1 <- runif(n, 0, 1)
     x2 <- runif(n, 0, 1)
     x3 <- runif(n, 0, 1)
     pi <- asin(1) * 2
     y <- 2 * sin(pi * x0)
     y <- y + exp(2 * x1) - 3.75887
     y <- y + 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10 - 1.396
     e <- rnorm(n, 0, sqrt(abs(sig2)))
     y <- y + e
     b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3))
     newd<-data.frame(x0=(0:30)/30,x1=(0:30)/30,x2=(0:30)/30,x3=(0:30)/30)
     pred<-predict.gam(b,newd)
      

