_R_e_g_r_e_s_s_i_o_n _f_o_r _a _p_a_r_a_m_e_t_r_i_c _s_u_r_v_i_v_a_l _m_o_d_e_l
	
	     survreg(formula, data=sys.parent(), subset, na.action,
	     link=c("log", "identity"),
	     dist=c("extreme", "logistic", "gaussian", "exponential"),
	     fixed, eps=0.0001, init, iter.max=10, model=F, x=F, y=F, ...)
	
	_A_r_g_u_m_e_n_t_s:
	
	      formula:
	     a formula expression as for other regression models.
	     See the documentation for lm and formula for details.
	
	         data:
	     optional data frame in which to interpret the variables
	     occuring in the formula.
	
	       subset:
	     subset of the observations to be used in the fit.
	
	    na.action:
	     function to be used to handle any NAs in the data.
	
	         link:
	     transformation to be used on the y variable.
	
	         dist:
	     assumed distribution for the transformed y variable.
	
	        fixed:
	     a list of fixed parameters, most often just the scale.
	     (When I implement the t-dist, it will include the
	     degrees of freedom).
	
	          eps:
	     convergence criteria for the computation.  Iteration
	     continues until the relative change in log likelihood
	     is less than eps.
	
	         init:
	     optional vector of initial values for the paramters.
	
	     iter.max:
	     maximum number of iterations to be performed.
	
	        model:
	     if TRUE, the model frame is returned.
	
	            x:
	     if TRUE, then the X matrix is returned.
	
	            y:
	     if TRUE, then the y vector (or survival times) is
	     returned.
	
	     all the optional arguments to lm, including
	     singular.ok.
	
	     Value:
	
	     an object of class survreg is returned, which inherits
	     from class glm.
	
	       This routine is not as robust against nearly singular
	     X matrices as lm(); the problem occurs when we expli-
	     citly invert the covariance matrix with solve().  This
	     can sometimes be solved by subtracting the mean from
	     all continuous covariates.
	
	_E_x_a_m_p_l_e_s:
	
	     survreg(Surv(futime, fustat) ~ ecog.ps + rx, fleming, dist='extreme',
	             link='log', fixed=list(scale=1))   #Fit an exponential