[英]Strange big number of variables in multinom() function in R
When I run the multinom()
function in R, the number of variables in the result is very big while I only have a few predictor variables in the formula. 当我在R中运行multinom()
函数时,结果中的变量数量非常大,而我在公式中只有一些预测变量。 Can anyone explain to me why this is happening and how can I resolve it? 任何人都可以向我解释为什么会发生这种情况,我该如何解决? ( mv_daily
only takes 0 and 1, icu_loc
takes 0,1,2 in the data.) ( mv_daily
只取0和1, icu_loc
在数据中取0,1,2。)
I tried 3 predictor variables and the number of variables in the result increased to 1230! 我尝试了3个预测变量,结果中的变量数增加到1230! The program takes each distinct value of a predictor variable as a different variable in the results and gives it a different coefficient. 程序将预测变量的每个不同值作为结果中的不同变量,并给出不同的系数。
newdata2 <- read.csv("~/Desktop/input_multinom_reg_March9_csv.csv")
library(nnet)
test <- multinom(state_tomorrow ~ mv_daily + icu_loc, newdata2,maxit=400,MaxNWts=2000)
Results: 结果:
Call:
multinom(formula = state_tomorrow ~ mv_day2 + icu_loc, data = newdata2,
maxit = 400, MaxNWts = 2000)
Coefficients:
(Intercept) mv_daily icu_loc
F 3.6303751 -1.1223394 -0.3681095
H 1.2178084 -1.3153864 0.3721295
IND 0.4628305 -2.1366738 -1.2530020
PR 2.2952981 -1.3085620 -0.4032178
RRT 0.1000952 -0.6432881 0.7659957
# weights: 24 (15 variable)
initial value 18682.675986
iter 10 value 12929.391832
iter 20 value 12341.441938
final value 12284.346914
Data look like this: 数据看起来像这样:
id state_tomorrow day mv_daily icu_loc
1 F 1 0 1
1 RRT 2 1 1
2 PR 4 1 0
2 PR 5 1 2
在估计多项式模型时,应该期望对每个因子级别进行单独的参数估计。
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