Summary method for the mnl function

# S3 method for mnl
summary(object, sum_check = "", conf_lev = 0.95, test_var = "", dec = 3, ...)

Arguments

object

Return value from mnl

sum_check

Optional output. "confint" to show coefficient confidence interval estimates. "rrr" to show relative risk ratios (RRRs) and confidence interval estimates.

conf_lev

Confidence level to use for coefficient and RRRs confidence intervals (.95 is the default)

test_var

Variables to evaluate in model comparison (i.e., a competing models Chi-squared test)

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/mnl.html for an example in Radiant

See also

mnl to generate the results

plot.mnl to plot the results

predict.mnl to generate predictions

plot.model.predict to plot prediction output

Examples

result <- mnl( ketchup, rvar = "choice", evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"), lev = "heinz28" )
#> Warning: NaNs produced
summary(result)
#> Multinomial logistic regression (MNL) #> Data : ketchup #> Response variable : choice #> Base level : heinz28 in choice #> Explanatory variables: price.heinz28, price.heinz32, price.heinz41, price.hunts32 #> Null hyp.: there is no effect of x on choice #> Alt. hyp.: there is an effect of x on choice #> #> RRR coefficient std.error z.value p.value #> heinz32 (Intercept) 1.564 0.740 6.452 < .001 *** #> heinz32 price.heinz28 1.099 0.094 0.076 14.439 < .001 *** #> heinz32 price.heinz32 NaN NaN 0.135 -17.033 < .001 *** #> heinz32 price.heinz41 NaN NaN 0.118 -3.182 0.001 ** #> heinz32 price.hunts32 0.053 -2.934 0.103 0.516 0.606 #> heinz41 (Intercept) 1.560 1.092 4.358 < .001 *** #> heinz41 price.heinz28 0.745 -0.295 0.136 5.483 < .001 *** #> heinz41 price.heinz32 NaN NaN 0.232 -1.542 0.123 #> heinz41 price.heinz41 NaN NaN 0.175 -8.857 < .001 *** #> heinz41 price.hunts32 NaN NaN 0.209 -1.688 0.091 . #> hunts32 (Intercept) -0.643 1.309 0.402 0.688 #> hunts32 price.heinz28 1.282 0.248 0.126 10.200 < .001 *** #> hunts32 price.heinz32 0.536 -0.623 0.246 2.185 0.029 * #> hunts32 price.heinz41 NaN NaN 0.188 -0.495 0.620 #> hunts32 price.hunts32 NaN NaN 0.208 -12.789 < .001 *** #> #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Pseudo R-squared: 0.2 #> Log-likelihood: -2511.212, AIC: 5052.423, BIC: 5141.473 #> Chi-squared: 1255.653 df(14), p.value < .001 #> Nr obs: 2,798 #>