lm.summaries              package:base              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' or `summary.lm'
     and `anova.lm' objects.

_U_s_a_g_e:

     anova(object, ...)
     anovalist.lm(object, ..., test = NULL)
     summary(object, correlation = FALSE)
     coefficients(object) ; coef(object)
     df.residual(object)
     family(object)
     formula(object)
     fitted.values(object)
     residuals(object,
               type=c("working","response", "deviance","pearson", "partial"), ...)
     weights(object)

     print(summary.lm.obj, digits = max(3, getOption("digits") - 3),
           symbolic.cor = p > 4,
           signif.stars= getOption("show.signif.stars"), ...)

_A_r_g_u_m_e_n_t_s:

object, x: an object of class `lm', usually, a result of a call to
          `lm'.

_D_e_t_a_i_l_s:

     `print.summary.lm' tries to be smart about formatting the
     coefficients, standard errors, etc. and additionally gives
     ``significance stars'' if `signif.stars' is `TRUE'.

     `anova.lm' produces an analysis of variance (`anova') table.

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

_V_a_l_u_e:

     The function `summary.lm' computes and returns a list of summary
     statistics of the fitted linear model given in `lm.obj', using the
     components (list elements) `"call"' and `"terms"' from its
     argument, plus 

residuals: the weighted residuals, the usual residuals rescaled by the
          square root of the weights specified in the call to `lm'.

coefficients: a p x 4 matrix with columns for the estimated
          coefficient, its standard error, t-statistic and
          corresponding (two-sided) p-value.

   sigma: the square root of the estimated variance of the random error

                    sigma^2 = 1/(n-p) Sum(R[i]^2),

          where R[i] is the i-th residual, `residuals[i]'.

      df: degrees of freedom, a 3-vector (p, n-p, p*).

fstatistic: a 3-vector with the value of the F-statistic with its
          numerator and denominator degrees of freedom.

r.squared: R^2, the ``fraction of variance explained by the model'',

              R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2),

          where y* is the mean of y[i] if there is an intercept and
          zero otherwise.

adj.r.squared: the above R^2 statistic ``adjusted'', penalizing for
          higher p.

cov.unscaled: a p x p matrix of (unscaled) covariances of the coef[j],
          j=1, ..., p.

correlation: the correlation matrix corresponding to the above
          `cov.unscaled', if `correlation = TRUE' is specified.

_W_a_r_n_i_n_g:

     The comparison between two or more models by `anova' or
     `anovalist.lm' will only be valid if they are fitted to the same
     dataset. This may be a problem if there are missing values and R's
     default of `na.action = na.omit' is used.

_S_e_e _A_l_s_o:

     The model fitting function `lm'.

     `anova' for the ANOVA table, `coefficients', `deviance',
     `effects', `fitted.values', `glm' for generalized linear models,
     `lm.influence' for regression diagnostics, `weighted.residuals',
     `residuals', `residuals.glm', `summary'.

_E_x_a_m_p_l_e_s:

     ##-- Continuing the  lm(.) example:
     coef(lm.D90)# the bare coefficients
     sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
     sld90
     coef(sld90)# much more

     ## 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))

