boxcox package:MASS R Documentation _B_o_x-_C_o_x _T_r_a_n_s_f_o_r_m_a_t_i_o_n_s _f_o_r _L_i_n_e_a_r _M_o_d_e_l_s _D_e_s_c_r_i_p_t_i_o_n: Computes and optionally plots profile log-likelihoods for the parameter of the Box-Cox power transformation. _U_s_a_g_e: boxcox(object, ...) ## Default S3 method: boxcox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, interp, eps = 1/50, xlab = expression(lambda), ylab = "log-Likelihood", ...) ## S3 method for class 'formula': boxcox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, interp, eps = 1/50, xlab = expression(lambda), ylab = "log-Likelihood", ...) ## S3 method for class 'lm': boxcox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, interp, eps = 1/50, xlab = expression(lambda), ylab = "log-Likelihood", ...) _A_r_g_u_m_e_n_t_s: object: a formula or fitted model object. Currently only 'lm' and 'aov' objects are handled. lambda: vector of values of 'lambda' - default (-2, 2) in steps of 0.1. plotit: logical which controls whether the result should be plotted. interp: logical which controls whether spline interpolation is used. Default to 'TRUE' if plotting with 'lambda' of length less than 100. eps: Tolerance for 'lambda = 0'; defaults to 0.02. xlab: defaults to '"lambda"'. ylab: defaults to '"log-Likelihood"'. ...: additional parameters to be used in the model fitting. _V_a_l_u_e: A list of the 'lambda' vector and the computed profile log-likelihood vector, invisibly if the result is plotted. _S_i_d_e _E_f_f_e_c_t_s: If 'plotit = TRUE' plots loglik _vs_ 'lambda' and indicates a 95% confidence interval about the maximum observed value of 'lambda'. If 'interp = TRUE', spline interpolation is used to give a smoother plot. _R_e_f_e_r_e_n_c_e_s: Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations (with discussion). _Journal of the Royal Statistical Society B_, *26*, 211-252. Venables, W. N. and Ripley, B. D. (2002) _Modern Applied Statistics with S._ Fourth edition. Springer. _E_x_a_m_p_l_e_s: boxcox(Volume ~ log(Height) + log(Girth), data = trees, lambda = seq(-0.25, 0.25, length = 10)) boxcox(Days+1 ~ Eth*Sex*Age*Lrn, data = quine, lambda = seq(-0.05, 0.45, len = 20))