我正在尝试组织使用mlogit获得的结果,以便导出到xtable
的LaTeX。但是,我发现很难在相邻的专栏中准备结果,这在学术出版物中经常出现。
特别是,我在最后一步遇到问题,其中方程式需要彼此相邻移动。
我现在举一个小数据框的例子,到目前为止我已经走了多远。如果有更简单的方法,如果你让我知道,我会很高兴。
#--------------------------- Create test data and run model --------------------#
id <- 1:12
color <- factor(rep(c("blue","red","yellow"), each=4))
value1 <- round(rnorm(12)*5,1)
value2 <- round(runif(12),1)
factor1 <- factor(rep(c("A", "B"), 6))
data_sample <- data.frame(id, color, value1, value2, factor1)
# Reshape data
data_sample2 <- mlogit.data(data_sample, choice="color", shape="wide" )
# Run model
mlogit.ds <- mlogit(color ~ 1 | value2 + value1 + factor1, data=data_sample2)
#summary(mlogit.ds)
# Save model summary
mlogit.ds <- summary(mlogit.ds)
#-------------------------- Prepare table -------------------------------#
mlogit_table <- data.frame(mlogit.ds$CoefTable)
mlogit_table <- mlogit_table[c(1,4)] # to keep only estimates and p-values
mlogit_table <- mlogit_table[order(rownames(mlogit_table)),] # to group all equations together
mlogit_table
Estimate Pr...t..
red:(intercept) 2.33034676 0.4653448
red:factor1B 0.13591855 0.9506175
red:value1 0.26639321 0.2072482
red:value2 -5.64821495 0.1956896
yellow:(intercept) 5.32776498 0.1372126
yellow:factor1B -3.30689681 0.2688475
yellow:value1 -0.09929715 0.6394161
yellow:value2 -7.28057244 0.1335184
#------------------------ Desired result ------------------------------#
red p yellow p
intercept -0.5522404 0.7597343 0.50745137 0.7349326
factor1B -0.6573629 0.7289306 -0.08885928 0.9528689
value1 -0.4058873 0.1495544 0.05956548 0.7833022
value2 0.6370185 0.8398007 -1.30156671 0.6051921
我需要帮助创建一个解决方案,该解决方案可以适应不同数量的方程(取决于结果变量有多少级别)和每个方程的不同长度(取决于预测变量的数量)。
答案 0 :(得分:3)
这是一种方法:
# extract data
tab <- summary(mlogit.ds)$CoefTable[, c(1, 4)]
# find values of outcome variable
ind <- sub("^(\\w+):.*", "\\1", rownames(tab))
# create table
mlogit_table <- do.call(cbind, split(as.data.frame(tab), ind))
# change row names
rownames(mlogit_table) <- sub("^(\\w+:)", "", rownames(mlogit_table))
结果:
red.Estimate red.Pr(>|t|) yellow.Estimate yellow.Pr(>|t|)
(intercept) -1.9697934 0.3301242 -4.4497945 0.19866621
value2 5.7087164 0.1550275 8.7793979 0.09833026
value1 -0.0838299 0.8377691 -0.4767750 0.29019742
factor1B -0.3583036 0.8447884 0.1671317 0.94356618