问题描述
当通过回归控制其他变量时，如何以均分-逐变量的方式绘制均值的barplot?
How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion?
我进行了一项研究，以找出哪种水果更宜人:芒果，香蕉或苹果.为此，我继续进行随机抽样100人.我要求他们以1-5的等级来评价每个水果的喜欢程度.我还收集了有关它们的一些人口统计信息:性别，年龄，受教育程度以及它们是否有色盲，因为我认为色觉可能会改变结果.但是我的问题是，在收集数据之后，我意识到我的样本可能不能很好地代表一般人群.我有80％的男性，而在人口中，性别的分配更为平均.在我的样本中，教育水平相当统一，尽管在人群中，仅持有高中文凭比拥有博士学位更为普遍.年龄也不是代表.
I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or not because I think color vision might alter the results. But my problem is that after data collection, I realize that my sample might not represent the general population well. I have 80% males while in the population sex is more evenly split. Education level in my sample is pretty uniform, even though in the population it's more common to hold only highschool diploma than have a PhD. Age is not representative as well.
因此，仅根据我的样本计算出喜欢水果的方法，就可能将结论归纳到总体水平上而受到限制.解决此问题的一种方法是运行多元回归以控制有偏差的人口统计数据.
Therefore, just calculating means for fruit liking based on my sample is likely to be limited in terms of generalizing conclusions to the population level. One way to deal with this problem is by running a multiple regression to control for the biased demographics data.
我想在条形图中绘制回归结果，在该条中，我根据色觉水平(是否有色盲)(并排)拆分条形图.
I want to plot the results of the regression(s) in a barplot, where I split bars (side-by-side) according to color vision levels (colorblind or not).
library(tidyverse)
set.seed(123)
fruit_liking_df <-
data.frame(
id = 1:100,
i_love_apple = sample(c(1:5), 100, replace = TRUE),
i_love_banana = sample(c(1:5), 100, replace = TRUE),
i_love_mango = sample(c(1:5), 100, replace = TRUE),
age = sample(c(20:70), 100, replace = TRUE),
is_male = sample(c(0, 1), 100, prob = c(0.2, 0.8), replace = TRUE),
education_level = sample(c(1:4), 100, replace = TRUE),
is_colorblinded = sample(c(0, 1), 100, replace = TRUE)
)
> as_tibble(fruit_liking_df)
## # A tibble: 100 x 8
## id i_love_apple i_love_banana i_love_mango age is_male education_level is_colorblinded
## <int> <int> <int> <int> <int> <dbl> <int> <dbl>
## 1 1 3 5 2 50 1 2 0
## 2 2 3 3 1 49 1 1 0
## 3 3 2 1 5 70 1 1 1
## 4 4 2 2 5 41 1 3 1
## 5 5 3 1 1 49 1 4 0
## 6 6 5 2 1 29 0 1 0
## 7 7 4 5 5 35 1 3 0
## 8 8 1 3 5 24 0 3 0
## 9 9 2 4 2 55 1 2 0
## 10 10 3 4 2 69 1 4 0
## # ... with 90 more rows
如果我只想获取每个水果喜欢水平的平均值
fruit_liking_df_for_barplot <-
fruit_liking_df %>%
pivot_longer(.,
cols = c(i_love_apple, i_love_banana, i_love_mango),
names_to = "fruit",
values_to = "rating") %>%
select(id, fruit, rating, everything())
ggplot(fruit_liking_df_for_barplot, aes(fruit, rating, fill = as_factor(is_colorblinded))) +
stat_summary(fun = mean,
geom = "bar",
position = "dodge") +
## errorbars
stat_summary(fun.data = mean_se,
geom = "errorbar",
position = "dodge") +
## bar labels
stat_summary(
aes(label = round(..y.., 2)),
fun = mean,
geom = "text",
position = position_dodge(width = 1),
vjust = 2,
color = "white") +
scale_fill_discrete(name = "is colorblind?",
labels = c("not colorblind", "colorblind")) +
ggtitle("liking fruits, without correcting for demographics")
-
我将校正人口中的平均年龄45
I will correct for the average age in the population which is 45
我将按性别正确纠正50-50的比例
I will correct for the correct 50-50 split for sex
我将更正普通教育水平，即高中(数据中编码为 2 )
I will correct for the common education level that is highschool (coded 2 in my data)
我也有理由相信年龄会以非线性方式影响水果的喜好，因此我也会对此加以说明.
I also have a reason to believe that age affects the liking of fruits in a non-linear way, so I will account for that as well.
lm(水果〜I(年龄-45)+ I((年龄-45)^ 2)+ I(是男性-0.5)+ I(教育程度-2)
我将通过相同的模型运行三个水果数据(苹果，香蕉，芒果)，提取截距，并在控制了人口统计数据后将其视为校正后的均值.
I will run the three fruits data (apple, banana, mango) through the same model, extract the intercept, and consider that as the corrected mean after controlling for the demographics data.
library(broom)
dep_vars <- c("i_love_apple",
"i_love_banana",
"i_love_mango")
regresults_only_colorblind <-
lapply(dep_vars, function(dv) {
tmplm <-
lm(
get(dv) ~ I(age - 45) + I((age - 45)^2) + I(is_male - 0.5) + I(education_level - 2),
data = filter(fruit_liking_df, is_colorblinded == 1)
)
broom::tidy(tmplm) %>%
slice(1) %>%
select(estimate, std.error)
})
data_for_corrected_barplot_only_colorblind <-
regresults_only_colorblind %>%
bind_rows %>%
rename(intercept = estimate) %>%
add_column(dep_vars, .before = c("intercept", "std.error"))
## # A tibble: 3 x 3
## dep_vars intercept std.error
## <chr> <dbl> <dbl>
## 1 i_love_apple 3.07 0.411
## 2 i_love_banana 2.97 0.533
## 3 i_love_mango 3.30 0.423
然后仅针对色盲绘制校正后的条形图
ggplot(data_for_corrected_barplot_only_colorblind,
aes(x = dep_vars, y = intercept)) +
geom_bar(stat = "identity", width = 0.7, fill = "firebrick3") +
geom_errorbar(aes(ymin = intercept - std.error, ymax = intercept + std.error),
width = 0.2) +
geom_text(aes(label=round(intercept, 2)), vjust=1.6, color="white", size=3.5) +
ggtitle("liking fruits after correction for demogrpahics \n colorblind subset only")
dep_vars <- c("i_love_apple",
"i_love_banana",
"i_love_mango")
regresults_only_colorvision <-
lapply(dep_vars, function(dv) {
tmplm <-
lm(
get(dv) ~ I(age - 45) + I((age - 45)^2) + I(is_male - 0.5) + I(education_level - 2),
data = filter(fruit_liking_df, is_colorblinded == 0) ## <- this is the important change here
)
broom::tidy(tmplm) %>%
slice(1) %>%
select(estimate, std.error)
})
data_for_corrected_barplot_only_colorvision <-
regresults_only_colorvision %>%
bind_rows %>%
rename(intercept = estimate) %>%
add_column(dep_vars, .before = c("intercept", "std.error"))
ggplot(data_for_corrected_barplot_only_colorvision,
aes(x = dep_vars, y = intercept)) +
geom_bar(stat = "identity", width = 0.7, fill = "orchid3") +
geom_errorbar(aes(ymin = intercept - std.error, ymax = intercept + std.error),
width = 0.2) +
geom_text(aes(label=round(intercept, 2)), vjust=1.6, color="white", size=3.5) +
ggtitle("liking fruits after correction for demogrpahics \n colorvision subset only")
这主要是关于 ggplot 和图形的问题.但是，可以看出，我的方法很长(即不简洁)并且是重复的.尤其是相对于仅以未经校正的方式获取barplot的简便性而言，如开头所述.如果有人对如何使代码更短，更简单有想法，我将感到非常高兴.
This is primarily a question about ggplot and graphics. However, as can be seen, my method is long (i.e., not concise) and repetitive. Especially relative to the simplicity of just getting barplot for uncorrected means, as demonstrated in the beginning. I will be very happy if someone has also ideas on how to make the code shorter and simpler.
推荐答案
我不认为您在将模型拟合到数据子集时会得出想要的统计量.提出您要问的问题的更好方法是使用更完整的模型(包括模型中的盲目性)，然后计算模型对比，以了解各组之间的平均得分差异.
I'm not convinced that you're getting out the statistical quantities that you want when fit the model on the data subsets. A better way to ask the questions you want to ask would be with a more complete model (include blindness in the model) and then compute model contrasts for differences in the mean score between each group.
话虽这么说，这里有一些代码可以满足您的需求.
That being said, here is some code that does what you want.
- 首先我们
pivot_longer水果列，以便您的数据采用长格式. - 然后我们
group_by水果类型和失明变量，并调用nest，这为我们提供了每种水果类型和失明类别的单独数据集. - 然后，我们使用
purrr :: map将模型拟合到每个数据集. -
broom :: tidy和broom :: confint_tidy为我们提供了模型所需的统计信息. - 然后，我们需要取消模型摘要的嵌套，并专门过滤掉与截距相对应的行.
- 我们现在拥有创建图形所需的数据，其余的留给您.
- First we
pivot_longerthe fruit columns so that your data is in long format. - Then we
group_bythe fruit type and the blindness variables and callnestwhich gives us separate datasets for each fruit type and blindness categories. - We then use
purrr::mapto fit a model to each of those datasets. broom::tidyandbroom::confint_tidygive us the statistics we want for the models.- Then we need to unnest the model summaries and filter down specifically to the rows which correspond to the intercept.
- We now have the data we need to create the figure, I'll leave the rest to you.
library(tidyverse)
set.seed(123)
fruit_liking_df <-
data.frame(
id = 1:100,
i_love_apple = sample(c(1:5), 100, replace = TRUE),
i_love_banana = sample(c(1:5), 100, replace = TRUE),
i_love_mango = sample(c(1:5), 100, replace = TRUE),
age = sample(c(20:70), 100, replace = TRUE),
is_male = sample(c(0, 1), 100, prob = c(0.2, 0.8), replace = TRUE),
education_level = sample(c(1:4), 100, replace = TRUE),
is_colorblinded = sample(c(0, 1), 100, replace = TRUE)
)
model_fits <- fruit_liking_df %>%
pivot_longer(starts_with("i_love"), values_to = "fruit") %>%
group_by(name, is_colorblinded) %>%
nest() %>%
mutate(model_fit = map(data, ~ lm(data = .x, fruit ~ I(age - 45) +
I((age - 45)^2) +
I(is_male - 0.5) +
I(education_level - 2))),
model_summary = map(model_fit, ~ bind_cols(broom::tidy(.x), broom::confint_tidy(.x))))
model_fits %>%
unnest(model_summary) %>%
filter(term == "(Intercept)") %>%
ggplot(aes(x = name, y = estimate, group = is_colorblinded,
fill = as_factor(is_colorblinded), colour = as_factor(is_colorblinded))) +
geom_bar(stat = "identity", position = position_dodge(width = .95)) +
geom_errorbar(stat = "identity", aes(ymin = conf.low, ymax = conf.high),
colour = "black", width = .15, position = position_dodge(width = .95))
编辑
如果您希望拟合一个模型(因此，增加样本量并减少估计值的se).您可以将is_colorblind作为 factor 的因素拉入模型.
In the case where you'd rather fit a single model (thus increasing sample size and reducing se of your estimates). You could pull is_colorblind into the model as a factor.
lm(data = .x, fruit ~ I(age - 45) +
I((age - 45)^2) + I(is_male - 0.5) +
I(education_level - 2) +
as.factor(is_colorblind))
然后，您将希望获得两个观测值的预测，即色盲的平均人".和不是色盲的普通人":
You would then want to get predictions for two observations, the "average person who is colorblind" and the "average person who is not colorblind":
new_data <- expand_grid(age = 45, is_male = .5,
education_level = 2.5, is_colorblinded = c(0,1))
然后，您可以像以前一样进行操作，使新模型适合某些功能编程，但使用 group_by(name)代替 name 和 is_colorblind
You could then do as before, fitting the new model with some functional programming, but group_by(name) instead of name and is_colorblind.
model_fits_ungrouped <- fruit_liking_df %>%
pivot_longer(starts_with("i_love"), values_to = "fruit") %>%
group_by(name) %>%
tidyr::nest() %>%
mutate(model_fit = map(data, ~ lm(data = .x, fruit ~ I(age - 45) +
I((age - 45)^2) +
I(is_male - .5) +
I(education_level - 2) +
as.factor(is_colorblinded))),
predicted_values = map(model_fit, ~ bind_cols(new_data,
as.data.frame(predict(newdata = new_data, .x,
type = "response", se.fit = T))) %>%
rowwise() %>%
mutate(estimate = fit,
conf.low = fit - qt(.975, df) * se.fit,
conf.high = fit + qt(.975, df) * se.fit)))
这样，您将对旧的绘图代码进行较小的更改:
With this you would make a minor change to the old plotting code:
model_fits_ungrouped %>%
unnest(predicted_values) %>%
ggplot(aes(x = name, y = estimate, group = is_colorblinded,
fill = as_factor(is_colorblinded), colour = as_factor(is_colorblinded))) +
geom_bar(stat = "identity", position = position_dodge(width = .95)) +
geom_errorbar(stat = "identity", aes(ymin = conf.low, ymax = conf.high),
colour = "black", width = .15, position = position_dodge(width = .95))
在比较两个图(分组图和子图)时，您会发现置信区间缩小并且均值的估计值大多接近3.这被视为我们做得比分组模型，因为我们知道关于采样分布的基本事实.
When you compare the two plots, grouped and subgrouped, you'll notice that the confidence intervals shrink and the estimates for the means mostly get closer to 3. This would be seen as a sign that we are doing a bit better than the subgrouped model, since we know the ground truth with regards to the sampled distributions.
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