predict.lm               package:stats               R Documentation

_P_r_e_d_i_c_t _m_e_t_h_o_d _f_o_r _L_i_n_e_a_r _M_o_d_e_l _F_i_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Predicted values based on linear model object

_U_s_a_g_e:

     ## S3 method for class 'lm':
     predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, 
             interval = c("none", "confidence", "prediction"),
             level = 0.95, type = c("response", "terms"),
             terms = NULL, na.action = na.pass, ...)

_A_r_g_u_m_e_n_t_s:

  object: Object of class inheriting from '"lm"'

 newdata: Data frame in which to predict

  se.fit: A switch indicating if standard errors are required.

   scale: Scale parameter for std.err. calculation

      df: Degrees of freedom for scale

interval: Type of interval calculation

   level: Tolerance/confidence level

    type: Type of prediction (response or model term)

   terms: If 'type="terms"', which terms (default is all terms)

na.action: function determining what should be done with missing values
          in 'newdata'.  The default is to predict 'NA'.

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

_D_e_t_a_i_l_s:

     'predict.lm' produces predicted values, obtained by evaluating the
     regression function in the frame 'newdata' (which defaults to
     'model.frame(object)'.  If the logical 'se.fit' is 'TRUE',
     standard errors of the predictions are calculated.  If the numeric
     argument 'scale' is set (with optional 'df'), it is used as the
     residual standard deviation in the computation of the standard
     errors, otherwise this is extracted from the model fit. Setting
     'intervals' specifies computation of confidence or prediction
     (tolerance) intervals at the specified 'level'.

     If the fit is rank-deficient, some of the columns of the design
     matrix will have been dropped.  Prediction from such a fit only
     makes sense if 'newdata' is contained in the same subspace as the
     original data. That cannot be checked accurately, so a warning is
     issued.

_V_a_l_u_e:

     'predict.lm' produces a vector of predictions or a matrix of
     predictions and bounds with column names 'fit', 'lwr', and 'upr'
     if 'interval' is set.  If 'se.fit' is 'TRUE', a list with the
     following components is returned:  

     fit: vector or matrix as above

  se.fit: standard error of predictions

residual.scale: residual standard deviations

      df: degrees of freedom for residual

_N_o_t_e:

     Offsets specified by 'offset' in the fit by 'lm' will not be
     included in predictions, whereas those specified by an offset term
     in the formula will be.

_S_e_e _A_l_s_o:

     The model fitting function 'lm', 'predict', 'SafePrediction'

_E_x_a_m_p_l_e_s:

     ## Predictions
     x <- rnorm(15)
     y <- x + rnorm(15)
     predict(lm(y ~ x))
     new <- data.frame(x = seq(-3, 3, 0.5))
     predict(lm(y ~ x), new, se.fit = TRUE)
     pred.w.plim <- predict(lm(y ~ x), new, interval="prediction")
     pred.w.clim <- predict(lm(y ~ x), new, interval="confidence")
     matplot(new$x,cbind(pred.w.clim, pred.w.plim[,-1]),
             lty=c(1,2,2,3,3), type="l", ylab="predicted y")

