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-
- _R_e_g_r_e_s_s_i_o_n _D_i_a_g_n_o_s_t_i_c_s
-
- lm.influence(z)
-
- rstudent(z)
- dfbetas(z)
- dffits(z)
- covratio(z)
-
- _A_r_g_u_m_e_n_t_s:
-
- z : the results returned by lm.
-
- _D_e_s_c_r_i_p_t_i_o_n:
-
- This suite of functions can be used to compute some of
- the regression diagnostics discussed in Belsley, Kuh
- and Welsch (1980).
-
- The primary function is lm.influence which is used by
- the other functions to compute the basic statistics of
- interest. The other functions produce particular diag-
- nostic quantities from the fundamental quantities pro-
- duced by lm.influence.
-
- _V_a_l_u_e_s:
-
- A list containing the following components:
-
- hat : a vector containing the diagonal of the ``hat''
- matrix.
-
- coef : the change in the estimated coefficients which
- results when the i-th case is dropped from the
- regression is contained in the i-th row of this
- matrix.
-
- sigma : a vector whose i-th element contains the estimate
- of the residual standard deviation obtained when
- the i-th case is dropped from the regression.
-
- _R_e_f_e_r_e_n_c_e_s:
-
- Belsley, D. A., E. Kuh and R. E. Welsch (1980).
- Regression Diagnostics. New York: Wiley.
-
- _S_e_e _A_l_s_o:
-
- lm.
-
- _E_x_a_m_p_l_e_s:
-
- # Analysis of the life-cycle savings data
- # given in Belsley, Kuh and Welsch.
- data(savings)
- z <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data=savings)
- rstudent(z)
- dfbetas(z)
- dffits(z)
- covratio(z)
-
-