multilogit1 — multivariate logit regression
res = multilogit1(y,x,bet0,maxit,tol)
y = response variable vector (nobs x 1)
- the response variable should be coded sequentially from 0 to ncat, i.e., y in (0,1,2, ,ncat) entries
x = matrix of covariates (nobs x nvar)
bet0 = optional starting values for bet (nvar x ncat+1) (default=0)
maxit = optional maximum number of iterations (default=100)
tol = optional convergence tolerance (default=1e-6)
res = a results tlist with
- res('meth') = 'multilogit'
- res('beta') = (nvar x ncat) matrix of bet coefficients: [bet_1 bet_2 bet_ncat] under the normalization bet_0 = 0
- res('coeff') = (nvar*ncat x 1) vector of beta coefficients: [beta_1 ; beta_2 ; ; beta_ncat] under normalization beta_0 = 0
- res('covb') = (nvar*ncat x nvar*ncat) covariance matrix of coefficients
- res('tstat') = (nvar*ncat x 1) vector of t-statistics
- res('pvalue') = (nvar*ncat x 1) vector of corresponding p-values
- res('y') = (nobs x ncat+1) matrix of data
- res('yhat') = (nobs x ncat+1) matrix of fitted values probabilities: [P_0 P_1 P_ncat] where P_j = [P_1j ; P_2j ; ; P_nobsj]
- res('llike') = unrestricted log likelihood
- res('lratio') = LR test statistic against intercept-only model (all bets=0), distributed chi-squared with (nvar-1)*ncat degrees of freedom
- res('nobs') = number of observations
- res('nvar') = number of variables
- res('ncat') = number of categories of dependent variable (including the reference category j = 0)
- res('count') = vector of counts of each value taken by y, i.e., couunt = [#y=0 #y=1 #y=ncat]
- res('r2mf') = McFadden pseudo-R^2
- res('rsqr') = Estrella pseudo-R^2