我需要将列表转换为数据框。我的列表包含按天运行的lm函数的输出，并且是一个包含521个元素的大列表。
我尝试了以下代码：
som=MiniSom(x=10,y=10,input_len=15,sigma=1.0,learning_rate=0.5)
som.random_weights_init(x)
som.train_random(data=x,num_iteration=100)
from pylab import bone, pcolor, colorbar, plot, show
bone()
pcolor(som.distance_map().T)
colorbar()
markers = ['o', 's']
colors = ['r', 'g']
for i, x1 in enumerate(x):
w = som.winner(x)
plot(w[0] + 0.5,
w[1] + 0.5,
markers[y[i]],
markeredgecolor = colors[y[i]],
markerfacecolor = 'None',
markersize = 10,
markeredgewidth = 2)
show()
但我收到此错误：
df <- bind_rows(modelsT, .id = "column_label")
是否可以解决该错误？还是另一个选择，如何将列表转换为数据框？
答案 0 :(得分：1)
这取决于您的需求：
model <- lm(mpg ~ disp, data = mtcars)
coef(model)
# (Intercept) disp
# 29.59985476 -0.04121512
library(dplyr)
bind_rows(coef(model), coef(model))
# # A tibble: 2 x 2
# `(Intercept)` disp
# <dbl> <dbl>
# 1 29.6 -0.0412
# 2 29.6 -0.0412
正如@ jay.sf刚刚建议的那样
coef(summary(model))
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 29.59985476 1.229719515 24.070411 3.576586e-21
# disp -0.04121512 0.004711833 -8.747152 9.380327e-10
bind_rows(as.data.frame(coef(summary(model))), as.data.frame(coef(summary(model))))
# Estimate Std. Error t value Pr(>|t|)
# 1 29.59985476 1.229719515 24.070411 3.576586e-21
# 2 -0.04121512 0.004711833 -8.747152 9.380327e-10
# 3 29.59985476 1.229719515 24.070411 3.576586e-21
# 4 -0.04121512 0.004711833 -8.747152 9.380327e-10
通常，如果要从结构中获取类似data.frame的内容，请查看其str的结构。可能会造成混淆，但是
str(model)
# List of 12
# $ coefficients : Named num [1:2] 29.5999 -0.0412
# ..- attr(*, "names")= chr [1:2] "(Intercept)" "disp"
# $ residuals : Named num [1:32] -2.01 -2.01 -2.35 2.43 3.94 ...
# ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
# $ effects : Named num [1:32] -113.65 -28.44 -1.79 2.65 3.92 ...
# ..- attr(*, "names")= chr [1:32] "(Intercept)" "disp" "" "" ...
# $ rank : int 2
# $ fitted.values: Named num [1:32] 23 23 25.1 19 14.8 ...
# ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
# $ assign : int [1:2] 0 1
# $ qr :List of 5
# ..$ qr : num [1:32, 1:2] -5.657 0.177 0.177 0.177 0.177 ...
# .. ..- attr(*, "dimnames")=List of 2
# .. .. ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
# .. .. ..$ : chr [1:2] "(Intercept)" "disp"
# .. ..- attr(*, "assign")= int [1:2] 0 1
# ..$ qraux: num [1:2] 1.18 1.09
# ..$ pivot: int [1:2] 1 2
# ..$ tol : num 1e-07
# ..$ rank : int 2
# ..- attr(*, "class")= chr "qr"
# $ df.residual : int 30
# $ xlevels : Named list()
# $ call : language lm(formula = mpg ~ disp, data = mtcars)
# $ terms :Classes 'terms', 'formula' language mpg ~ disp
# .. ..- attr(*, "variables")= language list(mpg, disp)
# .. ..- attr(*, "factors")= int [1:2, 1] 0 1
# .. .. ..- attr(*, "dimnames")=List of 2
# .. .. .. ..$ : chr [1:2] "mpg" "disp"
# .. .. .. ..$ : chr "disp"
# .. ..- attr(*, "term.labels")= chr "disp"
# .. ..- attr(*, "order")= int 1
# .. ..- attr(*, "intercept")= int 1
# .. ..- attr(*, "response")= int 1
# .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
# .. ..- attr(*, "predvars")= language list(mpg, disp)
# .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
# .. .. ..- attr(*, "names")= chr [1:2] "mpg" "disp"
# $ model :'data.frame': 32 obs. of 2 variables:
# ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
# ..$ disp: num [1:32] 160 160 108 258 360 ...
# ..- attr(*, "terms")=Classes 'terms', 'formula' language mpg ~ disp
# .. .. ..- attr(*, "variables")= language list(mpg, disp)
# .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
# .. .. .. ..- attr(*, "dimnames")=List of 2
# .. .. .. .. ..$ : chr [1:2] "mpg" "disp"
# .. .. .. .. ..$ : chr "disp"
# .. .. ..- attr(*, "term.labels")= chr "disp"
# .. .. ..- attr(*, "order")= int 1
# .. .. ..- attr(*, "intercept")= int 1
# .. .. ..- attr(*, "response")= int 1
# .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
# .. .. ..- attr(*, "predvars")= language list(mpg, disp)
# .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
# .. .. .. ..- attr(*, "names")= chr [1:2] "mpg" "disp"
# - attr(*, "class")= chr "lm"
和
str(summary(model))
# List of 11
# $ call : language lm(formula = mpg ~ disp, data = mtcars)
# $ terms :Classes 'terms', 'formula' language mpg ~ disp
# .. ..- attr(*, "variables")= language list(mpg, disp)
# .. ..- attr(*, "factors")= int [1:2, 1] 0 1
# .. .. ..- attr(*, "dimnames")=List of 2
# .. .. .. ..$ : chr [1:2] "mpg" "disp"
# .. .. .. ..$ : chr "disp"
# .. ..- attr(*, "term.labels")= chr "disp"
# .. ..- attr(*, "order")= int 1
# .. ..- attr(*, "intercept")= int 1
# .. ..- attr(*, "response")= int 1
# .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
# .. ..- attr(*, "predvars")= language list(mpg, disp)
# .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
# .. .. ..- attr(*, "names")= chr [1:2] "mpg" "disp"
# $ residuals : Named num [1:32] -2.01 -2.01 -2.35 2.43 3.94 ...
# ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
# $ coefficients : num [1:2, 1:4] 29.59985 -0.04122 1.22972 0.00471 24.07041 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:2] "(Intercept)" "disp"
# .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
# $ aliased : Named logi [1:2] FALSE FALSE
# ..- attr(*, "names")= chr [1:2] "(Intercept)" "disp"
# $ sigma : num 3.25
# $ df : int [1:3] 2 30 2
# $ r.squared : num 0.718
# $ adj.r.squared: num 0.709
# $ fstatistic : Named num [1:3] 76.5 1 30
# ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
# $ cov.unscaled : num [1:2, 1:2] 1.43e-01 -4.85e-04 -4.85e-04 2.10e-06
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:2] "(Intercept)" "disp"
# .. ..$ : chr [1:2] "(Intercept)" "disp"
# - attr(*, "class")= chr "summary.lm"
向您显示各个事物的外观，matrix形式的任何事物都可以使用as.data.frame()（可选地，stringsAsFactors=FALSE来轻松转换）。
答案 1 :(得分：0)
我通过使用以下代码解决了该问题：
coef <- ldply(modelsT, coef)