如何计算每个自变量在新数据帧上的预测能力
How to calculate the prediction power of each independent variable on a new data frame
我想计算每个独立 variable.I 的预测能力，有一个名为 df 的训练数据框和一个名为 df1 的测试数据框。我写了一个代码，应该附加基于每个 cloumn 的预测结果作为测试数据的一部分 frame.My 代码给出了一个奇怪的结果：它只呈现一个变量的预测结果并且没有它 name.I would like to see所有变量预测及其名称 too.I 都是函数编写的新手，因此欢迎任何帮助。
df <- read.table(text = " target birds wolfs
32 9 7
56 8 4
11 2 8
22 2 3
33 8 3
54 1 2
34 7 16
66 1 5
74 17 7
52 8 7
45 2 7
65 20 3
99 6 3
88 1 1
77 3 11
55 30 1 ",header = TRUE)
df1 <- read.table(text = " target birds wolfs
34 9 7
23 8 4
43 2 8
45 2 3
65 8 3
23 1 2
22 7 16
99 1 5
56 17 7
32 8 7
19 2 7
91 20 3
78 6 3
62 1 1
78 3 11
69 30 1 ",header = TRUE)
这是我使用的代码
for(i in names(df))
{
if(is.numeric(df[3,i])) ##if row 3 is numeric, the entire column is
{
fit_pred <- predict(lm(df[,i] ~ target, data=df), newdata=df1)
res <- fit_pred
g<-as.data.frame(cbind(df1,res))
g
}
}
我得到的输出是：
userid target birds wolfs res
10 321 45 8 7 0.0515967
8 608 33 1 5 0.1696638
3 234 23 2 8 0.1696638
7 294 44 7 1 0.0515967
2 444 46 8 4 0.0515967
11 226 90 2 7 0.1696638
9 123 89 9 7 0.0515967
1 222 67 9 7 0.0515967
5 678 43 8 3 0.0515967
15 999 12 3 9 0.1696638
6 987 33 1 2 0.1696638
14 225 18 1 1 0.1696638
16 987 83 1 1 0.1696638
12 556 77 2 3 0.1696638
您不应在此处使用 for 循环。您应该是 xxapply 系列函数之一。这是执行此操作的 R 方式：
fit_pred <- function(x)predict(lm(x ~ target, data=df), newdata=df1)
do.call(cbind,lapply(df,fit_pre))
- 我将你的代码封装在一个函数中
- 我使用
lapply 遍历所有列
do.call 和 cbind 汇总结果
这是一个使用包 dplyr 和 tidyr 的过程，以便基于 y~x 组合（您指定的因变量 ~ 您指定的自变量）创建模型，然后使用这些模型来预测新数据。
其背后的想法是 y 和 x 变量都可能改变（即使这里你只有 "target" 作为 y）。我正在使用您在开头指定的数据帧 df 和 df1（我不知道为什么 "target" 在您的输出中变成二进制）。
运行 逐步了解其工作原理并对其进行修改以更好地适应您的过程 objective。
library(dplyr)
library(tidyr)
# input what you want as independent variables y and dependent x
ynames = c("target")
xnames = c("birds","wolfs")
###### build models
# create and reshape train y dataframes
dty = df[ynames]
dty = dty %>% gather(yvariable, yvalue)
# create and reshape train x dataframes
dtx = df[xnames]
dtx = dtx %>% gather(xvariable, xvalue)
# build model for each y~x combination
dt_model =
dty %>% do(data.frame(.,dtx)) %>% # create combinations of y and x variables
group_by(yvariable, xvariable) %>% # for each pair y and x
do(model = lm(yvalue~xvalue, data=.)) # build the lm y~x
# you've managed to create a model for each combination and it's stored in a dataframe
dt_model
# yvariable xvariable model
# 1 target birds <S3:lm>
# 2 target wolfs <S3:lm>
####### predict
# create and reshape test y dataframes
dty = df1[ynames]
dty = dty %>% gather(yvariable, yvalue)
# create and reshape test x dataframes
dtx = df1[xnames]
dtx = dtx %>% gather(xvariable, xvalue)
dty %>% do(data.frame(.,dtx)) %>% # create combinations of y and x variables
group_by(yvariable, xvariable) %>% # for each pair y and x
do(data.frame(., pred = predict(dt_model$model[dt_model$yvariable==.$yvariable &
dt_model$xvariable==.$xvariable][[1]]))) %>% # get the corresponding model and predict new data
ungroup()
# yvariable yvalue xvariable xvalue pred
# 1 target 34 birds 9 54.30627
# 2 target 23 birds 8 53.99573
# 3 target 43 birds 2 52.13249
# 4 target 45 birds 2 52.13249
# 5 target 65 birds 8 53.99573
# 6 target 23 birds 1 51.82195
# 7 target 22 birds 7 53.68519
# 8 target 99 birds 1 51.82195
# 9 target 56 birds 17 56.79059
# 10 target 32 birds 8 53.99573
# 11 target 19 birds 2 52.13249
# 12 target 91 birds 20 57.72220
# 13 target 78 birds 6 53.37465
# 14 target 62 birds 1 51.82195
# 15 target 78 birds 3 52.44303
# 16 target 69 birds 30 60.82760
# 17 target 34 wolfs 7 51.49364
# 18 target 23 wolfs 4 56.38136
# 19 target 43 wolfs 8 49.86441
# 20 target 45 wolfs 3 58.01059
# 21 target 65 wolfs 3 58.01059
# 22 target 23 wolfs 2 59.63983
# 23 target 22 wolfs 16 36.83051
# 24 target 99 wolfs 5 54.75212
# 25 target 56 wolfs 7 51.49364
# 26 target 32 wolfs 7 51.49364
# 27 target 19 wolfs 7 51.49364
# 28 target 91 wolfs 3 58.01059
# 29 target 78 wolfs 3 58.01059
# 30 target 62 wolfs 1 61.26907
# 31 target 78 wolfs 11 44.97669
# 32 target 69 wolfs 1 61.26907
我想计算每个独立 variable.I 的预测能力，有一个名为 df 的训练数据框和一个名为 df1 的测试数据框。我写了一个代码，应该附加基于每个 cloumn 的预测结果作为测试数据的一部分 frame.My 代码给出了一个奇怪的结果：它只呈现一个变量的预测结果并且没有它 name.I would like to see所有变量预测及其名称 too.I 都是函数编写的新手，因此欢迎任何帮助。
df <- read.table(text = " target birds wolfs
32 9 7
56 8 4
11 2 8
22 2 3
33 8 3
54 1 2
34 7 16
66 1 5
74 17 7
52 8 7
45 2 7
65 20 3
99 6 3
88 1 1
77 3 11
55 30 1 ",header = TRUE)
df1 <- read.table(text = " target birds wolfs
34 9 7
23 8 4
43 2 8
45 2 3
65 8 3
23 1 2
22 7 16
99 1 5
56 17 7
32 8 7
19 2 7
91 20 3
78 6 3
62 1 1
78 3 11
69 30 1 ",header = TRUE)
这是我使用的代码
for(i in names(df))
{
if(is.numeric(df[3,i])) ##if row 3 is numeric, the entire column is
{
fit_pred <- predict(lm(df[,i] ~ target, data=df), newdata=df1)
res <- fit_pred
g<-as.data.frame(cbind(df1,res))
g
}
}
我得到的输出是：
userid target birds wolfs res
10 321 45 8 7 0.0515967
8 608 33 1 5 0.1696638
3 234 23 2 8 0.1696638
7 294 44 7 1 0.0515967
2 444 46 8 4 0.0515967
11 226 90 2 7 0.1696638
9 123 89 9 7 0.0515967
1 222 67 9 7 0.0515967
5 678 43 8 3 0.0515967
15 999 12 3 9 0.1696638
6 987 33 1 2 0.1696638
14 225 18 1 1 0.1696638
16 987 83 1 1 0.1696638
12 556 77 2 3 0.1696638
您不应在此处使用 for 循环。您应该是 xxapply 系列函数之一。这是执行此操作的 R 方式：
fit_pred <- function(x)predict(lm(x ~ target, data=df), newdata=df1)
do.call(cbind,lapply(df,fit_pre))
- 我将你的代码封装在一个函数中
- 我使用
lapply遍历所有列 do.call和cbind汇总结果
这是一个使用包 dplyr 和 tidyr 的过程，以便基于 y~x 组合（您指定的因变量 ~ 您指定的自变量）创建模型，然后使用这些模型来预测新数据。
其背后的想法是 y 和 x 变量都可能改变（即使这里你只有 "target" 作为 y）。我正在使用您在开头指定的数据帧 df 和 df1（我不知道为什么 "target" 在您的输出中变成二进制）。
运行 逐步了解其工作原理并对其进行修改以更好地适应您的过程 objective。
library(dplyr)
library(tidyr)
# input what you want as independent variables y and dependent x
ynames = c("target")
xnames = c("birds","wolfs")
###### build models
# create and reshape train y dataframes
dty = df[ynames]
dty = dty %>% gather(yvariable, yvalue)
# create and reshape train x dataframes
dtx = df[xnames]
dtx = dtx %>% gather(xvariable, xvalue)
# build model for each y~x combination
dt_model =
dty %>% do(data.frame(.,dtx)) %>% # create combinations of y and x variables
group_by(yvariable, xvariable) %>% # for each pair y and x
do(model = lm(yvalue~xvalue, data=.)) # build the lm y~x
# you've managed to create a model for each combination and it's stored in a dataframe
dt_model
# yvariable xvariable model
# 1 target birds <S3:lm>
# 2 target wolfs <S3:lm>
####### predict
# create and reshape test y dataframes
dty = df1[ynames]
dty = dty %>% gather(yvariable, yvalue)
# create and reshape test x dataframes
dtx = df1[xnames]
dtx = dtx %>% gather(xvariable, xvalue)
dty %>% do(data.frame(.,dtx)) %>% # create combinations of y and x variables
group_by(yvariable, xvariable) %>% # for each pair y and x
do(data.frame(., pred = predict(dt_model$model[dt_model$yvariable==.$yvariable &
dt_model$xvariable==.$xvariable][[1]]))) %>% # get the corresponding model and predict new data
ungroup()
# yvariable yvalue xvariable xvalue pred
# 1 target 34 birds 9 54.30627
# 2 target 23 birds 8 53.99573
# 3 target 43 birds 2 52.13249
# 4 target 45 birds 2 52.13249
# 5 target 65 birds 8 53.99573
# 6 target 23 birds 1 51.82195
# 7 target 22 birds 7 53.68519
# 8 target 99 birds 1 51.82195
# 9 target 56 birds 17 56.79059
# 10 target 32 birds 8 53.99573
# 11 target 19 birds 2 52.13249
# 12 target 91 birds 20 57.72220
# 13 target 78 birds 6 53.37465
# 14 target 62 birds 1 51.82195
# 15 target 78 birds 3 52.44303
# 16 target 69 birds 30 60.82760
# 17 target 34 wolfs 7 51.49364
# 18 target 23 wolfs 4 56.38136
# 19 target 43 wolfs 8 49.86441
# 20 target 45 wolfs 3 58.01059
# 21 target 65 wolfs 3 58.01059
# 22 target 23 wolfs 2 59.63983
# 23 target 22 wolfs 16 36.83051
# 24 target 99 wolfs 5 54.75212
# 25 target 56 wolfs 7 51.49364
# 26 target 32 wolfs 7 51.49364
# 27 target 19 wolfs 7 51.49364
# 28 target 91 wolfs 3 58.01059
# 29 target 78 wolfs 3 58.01059
# 30 target 62 wolfs 1 61.26907
# 31 target 78 wolfs 11 44.97669
# 32 target 69 wolfs 1 61.26907