I have a dataset with five categorical variables. And I ran a multinomial logistic regression with the function multinom in package nnet, and then derived the p values from the coefficients. But I do not know how to interpret the results.
The p values were derived according to UCLA's tutorial: https://stats.idre.ucla.edu/r/dae/multinomial-logistic-regression/ .
Just like this:
z <- summary(test)$coefficients/summary(test)$standard.errors
p <- (1 - pnorm(abs(z), 0, 1)) * 2
p
And I got this:
(Intercept) Age1 Age2 Age3 Age4 Unit1 Unit2 Unit3 Unit4 Unit5 Level1 Level2 Area1 Area2
Not severe 0.7388029 9.094373e-01 0 0.000000e+00 0.000000e+00 0 0.75159758 0 0 0.0000000 0.8977727 0.9333862 0.6285447 0.4457171
Very severe 0.0000000 1.218272e-09 0 6.599380e-06 7.811761e-04 0 0.00000000 0 0 0.0000000 0.7658748 0.6209889 0.0000000 0.0000000
Severe 0.0000000 8.744405e-08 0 1.052835e-06 3.299770e-04 0 0.00000000 0 0 0.0000000 0.8843606 0.4862364 0.0000000 0.0000000
Just so so 0.0000000 1.685045e-07 0 5.507560e-03 2.973261e-06 0 0.08427447 0 NaN 0.3010429 0.5552963 0.7291180 0.0000000 0.0000000
Not severe at all 0.0000000 0.000000e+00 0 0.000000e+00 0.000000e+00 0 NaN NaN 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
But how should I interpret these p values? Age3 is significantly related to Very severe? I am green to statistics and have no idea. Help me understand the results please. Thank you in advance.