使用 broom 和 tidyverse 总结 r 平方 gams
Using broom and tidyverse to summarise r squared gams
我发布了一个问题 and was able to reproduce Claus' answer 以在虹膜数据上使用 tidyverse 计算加法模型中每个物种的多个 r 平方值。但是,包发生更新,现在不计算 R-sq 值。不知道为什么...
这是子句响应和输出
library(tidyverse)
library(broom)
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
# Species R.square df logLik AIC BIC deviance df.residual
# 1 setosa 0.5363514 2.546009 -1.922197 10.93641 17.71646 3.161460 47.45399
# 2 versicolor 0.2680611 2.563623 -3.879391 14.88603 21.69976 3.418909 47.43638
# 3 virginica 0.1910916 2.278569 -7.895997 22.34913 28.61783 4.014793 47.72143
然而我的代码和输出产生了 R.square <dbl [1]>
值
library(tidyverse)
library(broom)
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
Species R.square df logLik AIC BIC deviance
<fctr> <list> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa <dbl [1]> 2.396547 -1.973593 10.74028 17.23456 3.167966
2 versicolor <dbl [1]> 2.317501 -4.021222 14.67745 21.02058 3.438361
3 virginica <dbl [1]> 2.278569 -7.895997 22.34913 28.61783 4.014793
任何人都可以提供有关原因的见解吗?
我和 OP 一样 sessionInfo
(见上面的评论)。我可以通过使用 map_dbl
强制 R 平方为双精度数来解决这个问题。我不完全确定为什么它对 Akrun 有效...?
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map_dbl(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
# A tibble: 3 x 8
Species R.square df logLik AIC BIC deviance df.residual
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa 0.536 2.55 -1.92 10.9 17.7 3.16 47.5
2 versicolor 0.268 2.56 -3.88 14.9 21.7 3.42 47.4
3 virginica 0.191 2.28 -7.90 22.3 28.6 4.01 47.7
我发布了一个问题
这是子句响应和输出
library(tidyverse)
library(broom)
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
# Species R.square df logLik AIC BIC deviance df.residual
# 1 setosa 0.5363514 2.546009 -1.922197 10.93641 17.71646 3.161460 47.45399
# 2 versicolor 0.2680611 2.563623 -3.879391 14.88603 21.69976 3.418909 47.43638
# 3 virginica 0.1910916 2.278569 -7.895997 22.34913 28.61783 4.014793 47.72143
然而我的代码和输出产生了 R.square <dbl [1]>
值
library(tidyverse)
library(broom)
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
Species R.square df logLik AIC BIC deviance
<fctr> <list> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa <dbl [1]> 2.396547 -1.973593 10.74028 17.23456 3.167966
2 versicolor <dbl [1]> 2.317501 -4.021222 14.67745 21.02058 3.438361
3 virginica <dbl [1]> 2.278569 -7.895997 22.34913 28.61783 4.014793
任何人都可以提供有关原因的见解吗?
我和 OP 一样 sessionInfo
(见上面的评论)。我可以通过使用 map_dbl
强制 R 平方为双精度数来解决这个问题。我不完全确定为什么它对 Akrun 有效...?
iris %>% nest(-Species) %>%
mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
results = map(fit, glance),
R.square = map_dbl(fit, ~ summary(.)$r.sq)) %>%
unnest(results) %>%
select(-data, -fit)
# A tibble: 3 x 8
Species R.square df logLik AIC BIC deviance df.residual
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa 0.536 2.55 -1.92 10.9 17.7 3.16 47.5
2 versicolor 0.268 2.56 -3.88 14.9 21.7 3.42 47.4
3 virginica 0.191 2.28 -7.90 22.3 28.6 4.01 47.7