lm.summaries              package:stats              R Documentation

_A_c_c_e_s_s_i_n_g _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:

     All these functions are 'methods' for class '"lm"'  objects.

_U_s_a_g_e:

     ## S3 method for class 'lm':
     family(object, ...)

     ## S3 method for class 'lm':
     formula(x, ...)

     ## S3 method for class 'lm':
     residuals(object,
               type = c("working", "response", "deviance","pearson", "partial"),
               ...)

     weights(object, ...)

_A_r_g_u_m_e_n_t_s:

object, x: an object inheriting from class 'lm', usually the result of
          a call to 'lm' or 'aov'.

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

    type: the type of residuals which should be returned.

_D_e_t_a_i_l_s:

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

     The working and response residuals are "observed - fitted".  The
     deviance and pearson residuals are weighted residuals, scaled by
     the square root of the weights used in fitting.  The partial
     residuals are a matrix with each column formed by omitting a term
     from the model.  In all these, zero weight cases are never omitted
     (as opposed to the standardized 'rstudent' residuals).

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M. (1992) _Linear models._ Chapter 4 of _Statistical
     Models in S_ eds J. M. Chambers and T. J. Hastie, Wadsworth &
     Brooks/Cole.

_S_e_e _A_l_s_o:

     The model fitting function 'lm', 'anova.lm'.

     'coef', 'deviance', 'df.residual', 'effects', 'fitted', 'glm' for
     *generalized* linear models, 'influence' (etc on that page) for
     regression diagnostics, 'weighted.residuals', 'residuals',
     'residuals.glm', 'summary.lm'.

_E_x_a_m_p_l_e_s:

     ##-- Continuing the  lm(.) example:
     coef(lm.D90)# the bare coefficients

     ## The 2 basic regression diagnostic plots [plot.lm(.) is preferred]
     plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
     abline(h=0, lty=2, col = 'gray')

     qqnorm(residuals(lm.D90))

