缺少置信区间使 model 预测和绘图变得复杂
[英]Missing confidence intervals complicate model predictions and plots
问题描述
I am running a model where the response variable is proportion, inflated with a majority of 'zero' and quite a few '1' responses, the terms of the model interact.
我正在运行 model ，其中响应变量是比例，大多数“零”和相当多的“1”响应膨胀，model 的条款相互作用。 I have therefore opted for a GALMSS model with family=BEINF.因此，我选择了带有 family=BEINF 的 GALMSS model。
The model coefficients seem reasonable, yet when I try to calculate or plot the marginal effects for the model I run into trouble. model 系数似乎是合理的，但是当我尝试计算 plot 时，model 的边际效应遇到了麻烦。 Based on the errors and warnings I receive I think this may be because there is no data for some levels of factors when used in certain interactions.根据我收到的错误和警告，我认为这可能是因为在某些交互中使用某些级别的因素时没有数据。 Therefore some confidence intervals are NA.因此，一些置信区间是 NA。
I am looking for a way to work around this as I'd like to plot the marginal effects with use of confidence intervals, but am unsure how.我正在寻找一种方法来解决这个问题，因为我想使用置信区间来 plot 边际效应，但我不确定如何。
Below are my coding attempts, at the bottom of the post is dput for a reproducible example.以下是我的编码尝试，在帖子的底部是一个可重复的示例。
pr1 <- gamlss(Winproportion~Opponent:Age + Number_opps:Age, family=BEINF,data=pr1data,method=mixed())
summary(pr1, level=0.95) #Model summary output
Could not compute variance-covariance matrix of predictions.无法计算预测的方差-协方差矩阵。 No confidence intervals are returned.不返回置信区间。
confint(pr1, level=0.95) #model confidence intervals
#Attempt to plot anyways
plot_model(pr1, type = "pred", terms = c("Number_opps","Age","Opponent"),dodge=0.5)
Could not compute variance-covariance matrix of predictions.无法计算预测的方差-协方差矩阵。 No confidence intervals are returned.不返回置信区间。
I attempted to circumvent this using ggpredict, which did not work either我试图使用 ggpredict 来规避这个问题，但这也不起作用
ggpredict(Winproportion,terms=c("Number_opps","Age","Opponent"),ci.lvl=0.95,what="mu")
But it returns the same 'Could not compute vcov...' Libraries但它返回相同的“无法计算 vcov...”
library(dplyr)
library(ggplot2)
library(sjPlot)
library(gamlss.dist)
library(gamlss)
library(ggeffects)
CODE:代码：
structure(list(Age = structure(c(2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L), .Label = c("Adult", "Child"), class = "factor"),
Number_opps = c(5, 4, 5, 5, 7, 7, 7, 5, 4, 5, 7, 6, 7, 7,
5, 6, 6, 5, 5, 7, 4, 5, 4, 7, 7, 6, 7, 7, 6, 6, 6, 5, 4,
6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 4, 6, 7, 7, 7, 6, 4, 5,
5, 5, 5, 7, 7, 7, 3, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 4, 5,
3, 3, 3, 4, 5, 6, 5, 5, 4, 3, 3, 3, 4, 4, 3, 6, 4, 6, 5,
6, 5, 5, 4, 5, 4, 7, 6, 7, 5, 5, 7, 3, 5, 4, 3, 2, 4, 4,
4, 5, 4, 3, 6, 3, 4, 4, 3, 6, 6, 4, 3, 6, 6, 6, 6, 6, 6,
4, 4, 3, 3, 5, 5, 6, 7, 4, 4, 4, 5, 4, 4, 4, 7, 7, 6, 7,
3, 7, 6, 4, 3, 5, 3, 4, 4, 2, 2, 1, 6, 5, 5, 5, 3, 3, 6,
6, 4, 5, 6, 3, 4, 5, 5, 4, 6, 5, 7, 5, 5, 7, 7, 4, 7, 7,
7, 7, 3, 4, 5, 4, 4, 3, 7, 7, 2, 5, 4, 5, 5, 4, 6, 5, 5,
3, 7, 4, 5, 5, 3, 5, 7, 5, 3, 3, 7, 2, 6, 4, 3, 3, 5, 7,
7, 7, 5, 5, 4, 4, 5, 5, 5, 5, 4, 5, 5, 4, 4, 4, 4, 6, 5,
5, 4, 4, 6, 6, 5, 6, 5, 5, 5, 4, 3, 7, 6, 7, 5, 4, 7, 5,
6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 4, 6, 7, 4, 4, 4, 4, 7, 7,
6, 7, 3, 7, 4, 4, 5, 4, 6, 5, 5, 5, 6, 6, 4, 6, 5, 5, 4,
4, 6, 4, 4, 4, 4, 5, 4, 4, 7, 5, 5, 7, 7, 7, 7, 7, 4, 7,
4, 4, 5, 4, 4, 4, 4, 7, 7, 4, 5, 4, 6, 5, 5, 7, 5, 3, 4,
7, 5, 4, 4, 7, 6, 5, 7, 7, 7, 5, 5, 4, 5, 5, 5, 5, 4, 5,
5, 2, 3, 3, 4, 3, 3, 2, 2, 4, 4, 4, 4, 5, 6, 5, 5, 2, 3,
3, 3, 4, 4, 3, 6, 4, 6, 5, 6, 5, 5, 3, 4, 5, 4, 3, 3, 3,
4, 7, 1, 7, 5, 4, 5, 7, 5, 4, 3, 3, 4, 4, 4, 2, 3, 3, 3,
4, 3, 3, 3, 3, 6, 3, 2, 3, 2, 4, 3, 6, 6, 6, 6, 6, 6, 4,
4, 2, 4, 6, 7, 3, 3, 4, 4, 4, 4, 4, 2, 4, 4, 4, 7, 7, 6,
7, 3, 7, 6, 4, 4, 4, 3, 5, 3, 4, 4, 3, 5, 5, 5, 2, 3, 3,
6, 6, 3, 3, 2, 3, 4, 5, 5, 3, 4, 4, 3, 3, 3, 4, 4, 4, 4,
5, 4, 4, 7, 5, 2, 3, 3, 5, 7, 7, 4, 7, 7, 7, 3, 4, 7, 3,
4, 5, 4, 4, 4, 4, 3, 2, 3, 7, 7, 4, 5, 4, 3, 3, 3, 5, 5,
4, 6, 3, 3, 3, 5, 5, 3, 3, 7, 3, 4, 5, 5, 3, 5, 3, 4, 7,
5, 3, 2, 3, 3, 3, 4, 4, 3, 7, 4, 4, 3, 3, 3, 3, 5, 4, 5,
5, 7, 7, 7, 3, 5, 4, 5, 3, 3, 5, 6, 3, 4, 6, 4, 6, 6, 4,
7, 6, 7, 5, 7, 3, 4, 2, 5, 6, 4, 4, 6, 6, 6, 6, 6, 6, 6,
6, 4, 5, 5, 6, 7, 4, 4, 4, 5, 4, 7, 7, 6, 7, 7, 6, 4, 6,
3, 6, 6, 4, 5, 6, 3, 4, 4, 6, 7, 7, 7, 7, 7, 7, 7, 3, 4,
4, 7, 7, 4, 5, 5, 7, 5, 5, 7, 7, 2, 6, 5, 4, 2, 5, 5, 5,
7, 7, 7, 5, 5, 4, 4, 2, 5, 5, 5, 5, 5, 4, 5, 5, 5, 2, 3,
3, 4, 3, 3, 2, 2, 3, 4, 4, 4, 4, 5, 6, 5, 5, 2, 4, 3, 3,
3, 3, 4, 4, 4, 3, 6, 4, 6, 5, 6, 5, 5, 3, 4, 5, 4, 3, 3,
3, 4, 7, 6, 2, 2, 7, 2, 2, 5, 4, 5, 7, 5, 4, 3, 3, 4, 4,
4, 2, 3, 3, 2, 5, 3, 4, 3, 3, 3, 3, 6, 3, 2, 4, 3, 6, 6,
2, 2, 4, 6, 6, 6, 6, 6, 6, 4, 4, 3, 3, 2, 4, 5, 5, 6, 7,
3, 3, 4, 4, 5, 4, 4, 4, 2, 4, 4, 4, 7, 7, 6, 7, 7, 6, 4,
4, 4, 3, 3, 5, 3, 4, 4, 2, 2, 6, 3, 5, 5, 5, 2, 3, 3, 3,
6, 6, 5, 6, 3, 3, 2, 3, 3, 4, 5, 5, 3, 4, 4, 4, 6, 3, 3,
3, 4, 4, 4, 4, 5, 4, 2, 2, 4, 7, 5, 2, 3, 3, 5, 7, 7, 4,
7, 7, 7, 3, 4, 7, 3, 3, 4, 4, 5, 4, 4, 4, 4, 3, 2, 3, 7,
7, 4, 5, 4, 3, 3, 3, 5, 5, 4, 6, 3, 3, 3, 5, 5, 3, 3, 7,
3, 4, 5, 5, 3, 5, 3, 4, 7, 5, 3, 2, 3, 3, 3, 2, 4, 4, 3,
7, 6, 4, 4, 3, 3, 3, 3, 5, 4, 2, 5, 5, 5, 7, 7, 7, 3, 5,
5, 4, 4, 2, 5, 5, 5, 5, 5, 4, 5, 5, 4, 5, 3, 4, 3, 3, 3,
2, 2, 2, 2, 4, 4, 4, 4, 5, 6, 5, 5, 4, 3, 4, 4, 4, 6, 6,
5, 6, 5, 5, 3, 4, 5, 4, 3, 1, 3, 7, 6, 2, 2, 7, 2, 2, 5,
4, 5, 7, 3, 5, 3, 4, 4, 4, 3, 3, 2, 5, 3, 4, 3, 3, 4, 3,
3), Opponent = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L), .Label = c("Opp1", "Opp2", "Opp3", "Opp4",
"Opp5", "Opp6", "Opp7"), class = "factor"), Winproportion = c(0.365384615384615,
0, 0.0588235294117647, 0.133333333333333, 0.317307692307692,
0, 0, 0.285714285714286, 0, 0, 0, 0, 0, 0, 0, 0.163934426229508,
0.109090909090909, 0.303370786516854, 0, 0, 0.0714285714285714,
0.142857142857143, 0, 0.111111111111111, 0.263157894736842,
0, 0, 0.5, 0.428571428571429, 0, 0.230769230769231, 0, 0,
0, 0.0909090909090909, 0, 0, 0, 0, 0, 0, 0.00892857142857143,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.201923076923077,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.448275862068966, 0, 0, 0, 0,
0, 0, 0, 0, 0.0454545454545455, 0.375, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0666666666666667, 0, 0, 0, 0.0714285714285714,
0, 0, 0, 0.0666666666666667, 0, 0, 0, 0, 0, 0, 0.166666666666667,
0.5, 0.0666666666666667, 0, 0, 0, 0, 0, 0, 0.0714285714285714,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.196428571428571, 0, 0, 0, 0, 0.0444444444444444,
0, 0, 0.102564102564103, 0.444444444444444, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.25, 0, 0, 0, 0, 0, 0.0689655172413793, 0, 0, 0, 0,
0, 0, 0, 0.233333333333333, 0, 0, 0.161290322580645, 0, 0,
0, 0, 0, 0.111111111111111, 0, 0.125, 0.010989010989011,
0, 0, 0.438461538461538, 0.542857142857143, 0, 0, 0, 0.025,
0.243243243243243, 0, 0, 0, 0, 0.0232558139534884, 0, 0,
0.32258064516129, 0, 0, 0, 0.571428571428571, 0, 0.0288461538461538,
0.214285714285714, 0.0476190476190476, 0, 0, 0.258620689655172,
0, 0, 0.32, 0.0689655172413793, 0.103448275862069, 0.277777777777778,
0.673076923076923, 0, 0.125, 0.727272727272727, 0.375, 0.384615384615385,
0.26530612244898, 0, 0, 0, 0, 0, 0.2, 0.444444444444444,
0.571428571428571, 0.2, 0, 0, 0, 0.153846153846154, 0, 0,
0.037037037037037, 0.8, 1, 0, 0.703703703703704, 0.0294117647058824,
0, 0.0983606557377049, 0.0545454545454545, 0.177570093457944,
0.512820512820513, 0.296296296296296, 0.21875, 0.321428571428571,
0.385714285714286, 0, 0.516129032258065, 0.4, 0.5625, 0.0326086956521739,
0.166666666666667, 0.428571428571429, 0.444444444444444,
0, 0.358974358974359, 0, 0, 0, 0.26027397260274, 0.615384615384615,
1, 0, 0, 0, 0, 0, 0.115384615384615, 0, 0.375, 0, 0, 0.125,
0, 0.666666666666667, 0, 0.75, 0.105263157894737, 0.454545454545455,
0, 0, 0, 0, 0, 0, 0.275862068965517, 0, 0, 0, 0.545454545454545,
0.647058823529412, 0.214285714285714, 0, 0.0303030303030303,
0, 0.142857142857143, 0, 0, 0.181818181818182, 0.866666666666667,
0.0357142857142857, 0.0731707317073171, 0.363636363636364,
0.592592592592593, 0.444444444444444, 0, 0.153846153846154,
0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.0858895705521472, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1875, 0.0909090909090909,
0, 0, 0, 0, 0, 0, 0.0151515151515152, 0.375, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.0232558139534884, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.0476190476190476, 0, 0.232142857142857, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.133333333333333, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0.05, 0.107142857142857,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.0714285714285714, 0, 0, 0, 0, 0, 0.152173913043478,
0.314285714285714, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.234042553191489, 0, 0.0526315789473684, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.0344827586206897, 0, 0.310344827586207,
0, 0, 0, 0.0588235294117647, 0, 0, 0, 0, 0.424242424242424,
0, 0, 0.0769230769230769, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.0526315789473684, 0, 0.25, 0.0659340659340659, 0, 0, 0,
0, 0, 0, 0.0571428571428571, 0, 0.5, 0.15, 0, 0, 0.025, 0.216216216216216,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0666666666666667, 0,
0, 0, 0.0454545454545455, 0.0434782608695652, 0, 0, 0, 0.355769230769231,
0, 0.529411764705882, 0, 0.144230769230769, 0.0714285714285714,
0.142857142857143, 0, 0.571428571428571, 0, 0.37037037037037,
1, 0, 0.0416666666666667, 0, 0, 0.181818181818182, 0, 0.523255813953488,
0.4, 0, 0.0714285714285714, 0, 0.5, 0.37037037037037, 0.104651162790698,
0, 0.666666666666667, 0, 0, 0.277777777777778, 0.176470588235294,
0.5, 0.411764705882353, 0.344262295081967, 0.254545454545455,
0.0654205607476635, 0.0769230769230769, 0.277777777777778,
0.53125, 0.285714285714286, 0.257142857142857, 0.666666666666667,
0.561797752808989, 0.142857142857143, 0, 0, 0.275, 0.0714285714285714,
0.285714285714286, 0.571428571428571, 0.688888888888889,
0.111111111111111, 0.263157894736842, 0.256410256410256,
0.555555555555556, 0.25, 0.285714285714286, 0.0625, 0.0526315789473684,
0.5, 0.153846153846154, 0, 0.3125, 0, 0, 0, 0.526315789473684,
0, 0, 0.0909090909090909, 0, 0, 0, 0, 0, 0, 0.633333333333333,
0.645161290322581, 0, 0.5625, 0, 0.625, 0.0625, 0.153846153846154,
0, 0, 0, 0, 0.122699386503067, 0.387096774193548, 0.176470588235294,
0, 0, 0.792452830188679, 0, 0, 0, 0.0961538461538462, 0.357142857142857,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.396551724137931, 0, 0.0555555555555556,
0, 0, 0, 0.6, 0, 0, 0.375, 0, 0.285714285714286, 1, 0, 0,
0, 0, 0.307692307692308, 0.212121212121212, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.222222222222222, 0.285714285714286,
0.2, 0, 0.0212765957446809, 0, 0, 1, 0.230769230769231, 0.0952380952380952,
0, 0.464285714285714, 0, 0.0833333333333333, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.764705882352941, 0, 0, 0, 0, 0,
0.434782608695652, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.166666666666667,
0.375, 0.1875, 0, 0.2, 0, 0, 0, 0, 0, 0, 0.3, 0.196428571428571,
0, 0, 0, 0.4375, 0, 0, 0, 0, 0, 0, 0.0512820512820513, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.304347826086957,
0.485714285714286, 0, 0, 0, 0, 0, 0, 0.458333333333333, 0,
0, 0, 0, 0, 0, 0.0625, 0, 0, 0, 0, 0, 0.714285714285714,
0.67741935483871, 0.51063829787234, 0, 0.105263157894737,
0, 0, 0, 0, 0.857142857142857, 0.5, 0, 0, 0, 0, 0, 0, 0.0344827586206897,
0, 0.344827586206897, 0, 0, 0, 0, 0, 0, 0, 0, 0.230769230769231,
0.454545454545455, 0, 0, 0, 0.461538461538462, 0, 0, 0, 0.195121951219512,
0, 0, 0, 0, 0.037037037037037, 0, 0, 0.105263157894737, 0.0289855072463768,
0.125, 0.21978021978022, 0, 0, 0, 0.181818181818182, 0.3,
0.238461538461538, 0.342857142857143, 0, 0.125, 0.7, 0, 0,
0.225, 0.135135135135135, 0, 0, 0, 0, 0, 0, 0.302325581395349,
0, 1, 0.931034482758621, 0.666666666666667, 0, 0.666666666666667,
0.333333333333333, 0, 0, 0, 0.0263157894736842, 0.0909090909090909,
0.130434782608696, 0, 0, 0.60655737704918, 0.0865384615384615,
0, 0, 0, 0, 0, 0.00961538461538462, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.111111111111111, 0, 0, 0, 0,
0, 0, 0.213114754098361)), row.names = c(1L, 3L, 7L, 13L,
14L, 16L, 21L, 37L, 43L, 45L, 103L, 104L, 109L, 121L, 143L, 165L,
166L, 197L, 198L, 203L, 209L, 210L, 215L, 225L, 226L, 227L, 228L,
231L, 232L, 252L, 262L, 266L, 284L, 285L, 305L, 312L, 314L, 317L,
320L, 321L, 329L, 352L, 353L, 355L, 361L, 370L, 383L, 396L, 410L,
412L, 413L, 422L, 428L, 433L, 434L, 435L, 437L, 442L, 443L, 444L,
445L, 449L, 451L, 452L, 453L, 457L, 458L, 461L, 462L, 464L, 466L,
469L, 474L, 480L, 484L, 487L, 488L, 492L, 493L, 495L, 498L, 499L,
500L, 501L, 502L, 504L, 505L, 506L, 507L, 508L, 509L, 510L, 511L,
513L, 514L, 523L, 524L, 525L, 530L, 534L, 541L, 542L, 543L, 545L,
547L, 549L, 554L, 555L, 556L, 557L, 564L, 566L, 572L, 573L, 575L,
579L, 580L, 584L, 586L, 587L, 590L, 591L, 592L, 593L, 594L, 595L,
596L, 597L, 601L, 603L, 604L, 605L, 618L, 619L, 621L, 624L, 628L,
629L, 630L, 631L, 633L, 634L, 636L, 646L, 647L, 648L, 649L, 650L,
652L, 653L, 659L, 660L, 662L, 664L, 666L, 667L, 668L, 669L, 670L,
673L, 675L, 676L, 677L, 680L, 681L, 683L, 684L, 685L, 687L, 688L,
693L, 698L, 699L, 700L, 705L, 706L, 717L, 726L, 727L, 732L, 733L,
735L, 737L, 738L, 741L, 742L, 750L, 751L, 755L, 760L, 761L, 764L,
771L, 773L, 774L, 776L, 778L, 782L, 786L, 787L, 790L, 791L, 795L,
796L, 798L, 804L, 808L, 809L, 810L, 812L, 814L, 817L, 818L, 820L,
830L, 831L, 832L, 833L, 835L, 837L, 838L, 854L, 856L, 858L, 863L,
865L, 866L, 867L, 870L, 872L, 873L, 874L, 878L, 881L, 882L, 883L,
892L, 902L, 903L, 904L, 909L, 913L, 914L, 916L, 924L, 926L, 928L,
929L, 930L, 931L, 932L, 935L, 936L, 937L, 945L, 946L, 951L, 955L,
956L, 963L, 966L, 994L, 1000L, 1007L, 1008L, 1013L, 1014L, 1015L,
1016L, 1017L, 1018L, 1035L, 1042L, 1045L, 1056L, 1064L, 1065L,
1066L, 1067L, 1068L, 1069L, 1070L, 1071L, 1073L, 1076L, 1077L,
1083L, 1087L, 1094L, 1096L, 1097L, 1098L, 1104L, 1105L, 1106L,
1109L, 1120L, 1121L, 1124L, 1125L, 1127L, 1133L, 1134L, 1135L,
1137L, 1138L, 1139L, 1145L, 1147L, 1148L, 1153L, 1154L, 1156L,
1159L, 1162L, 1163L, 1170L, 1171L, 1174L, 1176L, 1181L, 1185L,
1186L, 1187L, 1188L, 1194L, 1195L, 1198L, 1199L, 1201L, 1212L,
1216L, 1217L, 1225L, 1235L, 1236L, 1237L, 1238L, 1239L, 1249L,
1250L, 1252L, 1254L, 1275L, 1277L, 1279L, 1284L, 1286L, 1287L,
1288L, 1293L, 1294L, 1295L, 1299L, 1302L, 1303L, 1304L, 1310L,
1311L, 1312L, 1313L, 1314L, 1315L, 1317L, 1318L, 1323L, 1324L,
1325L, 1326L, 1329L, 1330L, 1334L, 1335L, 1336L, 1338L, 1341L,
1342L, 1344L, 1345L, 1346L, 1347L, 1348L, 1349L, 1350L, 1351L,
1352L, 1353L, 1354L, 1355L, 1356L, 1357L, 1358L, 1361L, 1363L,
1365L, 1366L, 1369L, 1372L, 1376L, 1377L, 1383L, 1384L, 1387L,
1389L, 1391L, 1393L, 1397L, 1398L, 1399L, 1400L, 1403L, 1404L,
1407L, 1408L, 1411L, 1412L, 1413L, 1414L, 1415L, 1417L, 1420L,
1426L, 1430L, 1432L, 1433L, 1434L, 1435L, 1436L, 1437L, 1438L,
1439L, 1443L, 1445L, 1454L, 1456L, 1463L, 1466L, 1467L, 1468L,
1470L, 1471L, 1475L, 1476L, 1477L, 1483L, 1485L, 1486L, 1487L,
1488L, 1489L, 1490L, 1491L, 1492L, 1494L, 1495L, 1497L, 1498L,
1501L, 1503L, 1504L, 1506L, 1508L, 1509L, 1516L, 1517L, 1518L,
1519L, 1520L, 1522L, 1524L, 1526L, 1530L, 1532L, 1533L, 1534L,
1539L, 1540L, 1541L, 1542L, 1543L, 1545L, 1546L, 1551L, 1552L,
1553L, 1554L, 1555L, 1556L, 1558L, 1559L, 1560L, 1566L, 1568L,
1569L, 1570L, 1572L, 1573L, 1574L, 1575L, 1577L, 1579L, 1580L,
1583L, 1584L, 1585L, 1591L, 1592L, 1594L, 1595L, 1602L, 1603L,
1607L, 1608L, 1609L, 1610L, 1612L, 1613L, 1615L, 1616L, 1619L,
1620L, 1622L, 1625L, 1626L, 1627L, 1628L, 1629L, 1632L, 1633L,
1634L, 1635L, 1636L, 1637L, 1638L, 1640L, 1641L, 1646L, 1649L,
1650L, 1651L, 1652L, 1654L, 1656L, 1657L, 1658L, 1659L, 1660L,
1662L, 1663L, 1664L, 1665L, 1666L, 1670L, 1671L, 1672L, 1673L,
1676L, 1677L, 1678L, 1679L, 1680L, 1684L, 1685L, 1687L, 1691L,
1697L, 1698L, 1700L, 1705L, 1706L, 1721L, 1727L, 1729L, 1737L,
1743L, 1750L, 1751L, 1761L, 1764L, 1768L, 1769L, 1770L, 1772L,
1786L, 1787L, 1788L, 1793L, 1804L, 1805L, 1806L, 1810L, 1817L,
1827L, 1836L, 1842L, 1843L, 1849L, 1850L, 1855L, 1856L, 1857L,
1858L, 1859L, 1860L, 1864L, 1881L, 1882L, 1884L, 1887L, 1891L,
1892L, 1893L, 1894L, 1899L, 1909L, 1910L, 1911L, 1912L, 1915L,
1916L, 1922L, 1936L, 1944L, 1946L, 1947L, 1948L, 1950L, 1951L,
1956L, 1961L, 1968L, 1969L, 1989L, 1996L, 1998L, 2001L, 2004L,
2005L, 2013L, 2014L, 2024L, 2027L, 2036L, 2037L, 2045L, 2049L,
2050L, 2067L, 2072L, 2073L, 2080L, 2094L, 2095L, 2096L, 2106L,
2108L, 2109L, 2112L, 2117L, 2118L, 2119L, 2121L, 2126L, 2128L,
2129L, 2130L, 2133L, 2134L, 2135L, 2136L, 2137L, 2141L, 2142L,
2144L, 2145L, 2146L, 2150L, 2152L, 2153L, 2154L, 2155L, 2156L,
2157L, 2161L, 2163L, 2164L, 2165L, 2166L, 2167L, 2168L, 2171L,
2172L, 2176L, 2177L, 2178L, 2179L, 2180L, 2182L, 2183L, 2184L,
2185L, 2186L, 2187L, 2188L, 2189L, 2190L, 2191L, 2192L, 2193L,
2194L, 2195L, 2196L, 2197L, 2198L, 2199L, 2200L, 2203L, 2205L,
2207L, 2208L, 2209L, 2212L, 2213L, 2214L, 2215L, 2216L, 2218L,
2219L, 2225L, 2226L, 2229L, 2231L, 2233L, 2235L, 2239L, 2240L,
2241L, 2242L, 2245L, 2246L, 2247L, 2248L, 2249L, 2250L, 2253L,
2254L, 2255L, 2256L, 2257L, 2259L, 2262L, 2264L, 2268L, 2270L,
2271L, 2272L, 2273L, 2274L, 2276L, 2277L, 2278L, 2279L, 2280L,
2281L, 2285L, 2287L, 2288L, 2289L, 2296L, 2298L, 2302L, 2303L,
2305L, 2308L, 2309L, 2310L, 2312L, 2313L, 2315L, 2317L, 2318L,
2319L, 2325L, 2327L, 2328L, 2329L, 2330L, 2331L, 2332L, 2333L,
2336L, 2337L, 2339L, 2340L, 2343L, 2344L, 2345L, 2346L, 2348L,
2350L, 2351L, 2352L, 2353L, 2357L, 2358L, 2359L, 2360L, 2361L,
2362L, 2364L, 2365L, 2366L, 2367L, 2368L, 2371L, 2372L, 2374L,
2375L, 2376L, 2377L, 2381L, 2382L, 2383L, 2384L, 2385L, 2387L,
2388L, 2389L, 2390L, 2393L, 2394L, 2395L, 2396L, 2397L, 2398L,
2400L, 2401L, 2402L, 2406L, 2407L, 2408L, 2410L, 2411L, 2412L,
2414L, 2415L, 2416L, 2417L, 2419L, 2421L, 2422L, 2425L, 2426L,
2427L, 2433L, 2434L, 2435L, 2436L, 2437L, 2439L, 2444L, 2445L,
2449L, 2450L, 2451L, 2452L, 2454L, 2455L, 2457L, 2458L, 2461L,
2462L, 2464L, 2467L, 2468L, 2469L, 2470L, 2471L, 2474L, 2475L,
2476L, 2477L, 2478L, 2479L, 2480L, 2482L, 2483L, 2488L, 2491L,
2492L, 2493L, 2494L, 2496L, 2498L, 2499L, 2500L, 2501L, 2502L,
2504L, 2505L, 2506L, 2507L, 2508L, 2509L, 2512L, 2513L, 2514L,
2515L, 2517L, 2518L, 2519L, 2520L, 2521L, 2522L, 2526L, 2527L,
2529L, 2530L, 2533L, 2538L, 2539L, 2540L, 2542L, 2547L, 2548L,
2549L, 2550L, 2551L, 2554L, 2555L, 2556L, 2557L, 2558L, 2562L,
2563L, 2565L, 2566L, 2567L, 2569L, 2571L, 2575L, 2576L, 2577L,
2578L, 2579L, 2580L, 2581L, 2582L, 2584L, 2586L, 2587L, 2588L,
2589L, 2592L, 2593L, 2597L, 2598L, 2600L, 2601L, 2606L, 2607L,
2608L, 2610L, 2612L, 2613L, 2614L, 2615L, 2616L, 2617L, 2618L,
2619L, 2620L, 2624L, 2625L, 2626L, 2629L, 2630L, 2633L, 2634L,
2635L, 2636L, 2637L, 2639L, 2640L, 2646L, 2647L, 2648L, 2650L,
2656L, 2660L, 2661L, 2662L, 2666L, 2667L, 2668L, 2669L, 2670L,
2671L, 2674L, 2675L, 2940L, 2941L, 2947L), class = "data.frame")
1 个解决方案
解决方案1
0 2022-08-31 23:09:57
You can try the development version of the marginaleffects package , which can compute standard errors for predicted values using the delta method.您可以尝试marginaleffects效应 package 的开发版本，它可以使用 delta 方法计算预测值的标准误差。 (Disclaimer: I am the author.) Install it from Github: （免责声明：我是作者。）从 Github 安装它：
library(remotes)
install_github("vincentarelbundock/marginaleffects")
Estimate the model:估计 model：
library(marginaleffects)
library(gamlss)
pr1data <- na.omit(pr1data)
pr1data <- subset(pr1data, !is.na(Age))
pr1 <- gamlss(
Winproportion ~ Opponent:Age + Number_opps:Age,
family = BEINF,
data = pr1data,
method = mixed())
Call the predictions() function and display the first 6 rows of the results:调用predictions() function 并显示结果的前 6 行：
predictions(pr1, what = "mu") |> head()
#> rowid type predicted std.error Winproportion Opponent Age Number_opps
#> 1 1 response 0.2852127 0.06649437 0.07142857 Opp5 Adult 4
#> 2 2 response 0.2458822 0.05741226 0.14285714 Opp5 Adult 5
#> 3 3 response 0.2852127 0.06649437 0.00000000 Opp5 Adult 4
#> 4 4 response 0.1787867 0.04544875 0.11111111 Opp5 Adult 7
#> 5 5 response 0.1787867 0.04544875 0.26315789 Opp5 Adult 7
#> 6 6 response 0.2103789 0.05048144 0.00000000 Opp5 Adult 6
Obviously, you could then construct symmetric intervals as usual:显然，您可以像往常一样构造对称间隔：
predictions(pr1, what = "mu") |>
transform(
conf.low = predicted - 1.96 * std.error,
conf.high = predicted + 1.96 * std.error) |>
head()
#> rowid type predicted std.error Winproportion Opponent Age Number_opps
#> 1 1 response 0.2852127 0.06649437 0.07142857 Opp5 Adult 4
#> 2 2 response 0.2458822 0.05741226 0.14285714 Opp5 Adult 5
#> 3 3 response 0.2852127 0.06649437 0.00000000 Opp5 Adult 4
#> 4 4 response 0.1787867 0.04544875 0.11111111 Opp5 Adult 7
#> 5 5 response 0.1787867 0.04544875 0.26315789 Opp5 Adult 7
#> 6 6 response 0.2103789 0.05048144 0.00000000 Opp5 Adult 6
#> conf.low conf.high
#> 1 0.15488379 0.4155417
#> 2 0.13335415 0.3584102
#> 3 0.15488379 0.4155417
#> 4 0.08970716 0.2678663
#> 5 0.08970716 0.2678663
#> 6 0.11143532 0.3093226
And you can make predictions for user-specified values of the regressors using the datagrid() function and the newdata argument.您可以使用datagrid() function 和newdata参数对用户指定的回归量值进行预测。 There are many examples in the vignette.小插曲中有很多例子。
Finally, note that the plot_cap() function will only return confidence intervals when the model is linear, or when it is a model for which we can make predictions on the link function and then backtransform them. Finally, note that the plot_cap() function will only return confidence intervals when the model is linear, or when it is a model for which we can make predictions on the link function and then backtransform them. This is not the case here, but you can still do this:这不是这里的情况，但你仍然可以这样做：
plot_cap(pr1, condition = "Number_opps", what = "mu")
声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.
相关问题
贝叶斯线性回归模型预测的置信区间 - Confidence intervals on predictions for a Bayesian linear regression model
(R) 为绘图添加置信区间 - (R) Adding Confidence Intervals To Plots
使用pcse软件包预测预测的值和置信区间 - Predicting values and confidence intervals of predictions with the pcse package
逻辑回归预测的置信区间 - Confidence intervals for predictions from logistic regression
如何为我的模型预测获得偏差校正的引导程序置信区间 - How can I get bias corrected bootstrap confidence intervals for my model predictions
在R — ggplot2中混合模型中的模型预测中添加置信区间？ - Adding confidence intervals from model predictions in mixed models in R — ggplot2?
在 R 中以 95% 的置信区间绘制密度图 - plot density plots with confidence intervals of 95% in R
我在 R 循环上有问题，我想生成一个特定的 output 并在不同样本上应用线性 model 的预测和置信区间 - I have problem on R loop, I want to generate a specific output with predictions and confidence intervals applying linear model on different samples
R - 对每组数据使用不同的模型进行预测和置信区间 - R - Making predictions and confidence intervals with different models for each group of data
R：使用预测功能将标准误差和置信区间添加到预测中 - R: Using the predict function to add standard error and confidence intervals to predictions
相关标签