predict.lm               package:base               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:

     predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, 
             interval = c("none", "confidence", "prediction"),
             level = 0.95, type = c("response", "terms"),
             terms = NULL, ...)

_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)

_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'.

_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'.

_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")

