我有一个数据集，如下所示：
set.seed(1)
TDT <- data.table(Group = c(rep("A",40),rep("B",60)),
Id = c(rep(1,20),rep(2,20),rep(3,20),rep(4,20),rep(5,20)),
Time = rep(seq(as.Date("2010-01-03"), length=20, by="1 month") - 1,5),
norm = round(runif(100)/10,2),
y = sample(100,100),
x2 = round(rnorm(100,0.75,0.3),2),
x3 = round(rnorm(100,0.75,0.3),2),
x4 = round(rnorm(100,0.75,0.3),2),
x5 = round(rnorm(100,0.75,0.3),2))
我想用我选择的TDT的相关性来创建一个新的数据集。
为了获得因变量和选定的自变量（xcor）与所有其他自变量的相关性，我尝试过：
# getting the numerical variables
numcols <- names(Filter(is.numeric,TDT))
# getting the correlation of y with all x
TDT.y.cor<- TDT[, cor(.SD, y),.SDcols = numcols, by = Time]
# getting the correlation of selected x with all other x
xcor <- c("x2","x3")
TDT.x.cor <- TDT[, cor(.SD, xcor),.SDcols = numcols, by = Time]
但是最后一个给出了错误：
Error in cor(.SD, xcor) :
'y' must be numeric
所以现在我想做一个for循环。像这样：
xcor <- list ("x2","x3")
for (i in xcor) {
TDT.xcor[[i]].cor <- TDT[, cor(.SD, xcor[[i]]),.SDcols = numcols, by = Time]
}
这不是正确的语法。有什么建议吗？
答案 0 :(得分：2)
免责声明。此答案使用了我编写的manymodelr的开发人员版本。
要获取所有xs和y之间的相关性，我们可以使用：
manymodelr::get_var_corr(Filter(is.numeric,TDT),
comparison_var = "y",get_all = TRUE)
这将产生：
Comparison_Var Other_Var p_value Correlation lower_ci upper_ci
1 y Id 0.7660224000 0.03013023 -0.16727786 0.2252155
2 y norm 0.5323952847 0.06316715 -0.13492501 0.2564040
3 y x2 0.7032924284 -0.03855859 -0.23321046 0.1590642
4 y x3 0.4898005278 0.06985644 -0.12832239 0.2626704
5 y x4 0.2702013263 0.11131634 -0.08700408 0.3011500
6 y x5 0.0009658689 0.32510267 0.13746236 0.4902174
仅对选择变量（即x和几个xs）进行操作：
manymodelr::get_var_corr(Filter(is.numeric,TDT),
comparison_var = "x2",get_all = FALSE,other_vars = c("x4","x5"))
Comparison_Var Other_Var p.value Correlation lower_ci upper_ci
1 x2 x4 0.5593246 -0.0590762 -0.25256366 0.1389543
2 x2 x5 0.1787881 0.1355323 -0.06255102 0.3233427
您可以保存结果并获取其他统计信息，例如摘要输出。
编辑：
要使用特定变量，可以使用变体get_var_corr_，它支持以下组合（第一列可以舍弃）：
get_var_corr_(filter_df) %>%
filter(Comparison_Var %in% c("x2","y") & Other_Var %in% c("x4","x3"))
.id Comparison_Var Other_Var p.value Correlation lower_ci upper_ci
1 Var2 y x3 0.4898005 0.06985644 -0.12832239 0.2626704
2 Var2 x2 x3 0.6239815 -0.04961512 -0.24365870 0.1482477
3 Var2 y x4 0.2702013 0.11131634 -0.08700408 0.3011500
4 Var2 x2 x4 0.5593246 -0.05907620 -0.25256366 0.1389543
答案 1 :(得分：1)
另一个选择：
numcols <- names(Filter(is.numeric,TDT))
xcor <- c("x2", "x3")
TDT[, data.table(var=xcor, t(cor(.SD[, mget(numcols)], .SD[, mget(xcor)]))), by=Time]
输出：
Time var Id norm y x2 x3 x4 x5
1: 2010-01-02 x2 -0.04843595 0.26582680 -0.66584960 1.000000000 -0.061024243 -0.69292534 0.194408505
2: 2010-01-02 x3 0.39631671 -0.26906428 -0.58788152 -0.061024243 1.000000000 -0.54623949 -0.787149320
3: 2010-02-02 x2 0.08165416 0.26828706 -0.10444724 1.000000000 -0.120104310 0.08966978 -0.687626977
4: 2010-02-02 x3 -0.77420649 0.06331042 -0.62424401 -0.120104310 1.000000000 -0.92782037 0.520999829
5: 2010-03-02 x2 0.53328988 -0.76471756 0.09583857 1.000000000 -0.255684070 -0.57216005 0.583055924
6: 2010-03-02 x3 -0.03208419 -0.40639968 -0.93857812 -0.255684070 1.000000000 0.21778224 -0.690868245
7: 2010-04-02 x2 -0.40027209 0.12681443 -0.04596013 1.000000000 0.817854430 -0.31396988 0.188876433
8: 2010-04-02 x3 0.10876596 -0.23779053 -0.06729634 0.817854430 1.000000000 -0.12087380 0.162171044
9: 2010-05-02 x2 0.47014025 0.39570025 -0.10324192 1.000000000 -0.353894786 0.62548822 0.861633507
10: 2010-05-02 x3 0.60481815 0.64806521 0.64669485 -0.353894786 1.000000000 -0.76302787 -0.032024563
11: 2010-06-02 x2 -0.29866753 -0.03715892 0.02678853 1.000000000 0.722765758 0.85305747 0.215180135
12: 2010-06-02 x3 -0.71939635 0.18631833 0.20915545 0.722765758 1.000000000 0.90883928 0.338190647
13: 2010-07-02 x2 0.19959128 -0.06868888 -0.38153376 1.000000000 -0.205761177 -0.13574954 0.106505491
14: 2010-07-02 x3 -0.82150037 0.53153382 -0.56734304 -0.205761177 1.000000000 0.23196740 0.254086025
15: 2010-08-02 x2 -0.84788586 0.06205274 0.04195958 1.000000000 0.290333143 -0.17452641 -0.387409233
16: 2010-08-02 x3 0.17476552 -0.39239246 0.07572605 0.290333143 1.000000000 0.12847199 -0.417170776
17: 2010-09-02 x2 -0.28424279 -0.35470966 0.36559401 1.000000000 -0.004094751 -0.60601077 0.429553721
18: 2010-09-02 x3 0.17492786 0.12234080 -0.68318074 -0.004094751 1.000000000 -0.71591012 -0.867131063
19: 2010-10-02 x2 -0.59009254 -0.30596460 -0.20279720 1.000000000 -0.418992781 -0.33474959 0.331402645
20: 2010-10-02 x3 -0.03950447 -0.57650894 0.47707048 -0.418992781 1.000000000 0.61174441 -0.635031910
21: 2010-11-02 x2 0.44280504 0.15356142 -0.41594497 1.000000000 0.130022288 -0.57901168 0.056711167
22: 2010-11-02 x3 0.10088665 -0.19195643 0.47057457 0.130022288 1.000000000 0.64359334 0.947634532
23: 2010-12-02 x2 0.93528272 -0.08754048 0.47678028 1.000000000 0.640307246 0.76212510 0.129150650
24: 2010-12-02 x3 0.33922514 -0.12389088 0.74214451 0.640307246 1.000000000 0.96349801 -0.102219394
25: 2011-01-02 x2 -0.82681063 0.28813098 0.03680233 1.000000000 -0.670768877 -0.81895896 0.665160850
26: 2011-01-02 x3 0.37233376 0.44439836 -0.21498926 -0.670768877 1.000000000 0.96249265 -0.171852151
27: 2011-02-02 x2 0.78406829 0.87265449 0.44109646 1.000000000 -0.170824594 -0.31318417 0.419451424
28: 2011-02-02 x3 0.18246574 -0.53572698 0.34238233 -0.170824594 1.000000000 0.21508906 0.456834070
29: 2011-03-02 x2 -0.56012875 0.61468934 0.37723894 1.000000000 -0.628199774 -0.66904419 -0.051205610
30: 2011-03-02 x3 0.38559989 0.19407633 0.47882281 -0.628199774 1.000000000 0.26507006 0.457066183
31: 2011-04-02 x2 0.06213453 0.27072902 -0.56611872 1.000000000 0.756519929 -0.01807478 -0.240390425
32: 2011-04-02 x3 -0.34954142 -0.40531930 -0.44445227 0.756519929 1.000000000 0.44535133 0.364169201
33: 2011-05-02 x2 0.91869834 -0.52157721 0.09026303 1.000000000 -0.267341912 0.17065244 -0.958919185
34: 2011-05-02 x3 -0.07579900 0.12441121 0.43261423 -0.267341912 1.000000000 0.76093821 0.328896757
35: 2011-06-02 x2 0.14630187 -0.02121530 0.23351755 1.000000000 0.214190295 -0.44671768 0.593123871
36: 2011-06-02 x3 0.92090833 -0.22766946 -0.53092628 0.214190295 1.000000000 -0.01796403 -0.439337427
37: 2011-07-02 x2 0.24085187 -0.01442061 0.46151828 1.000000000 -0.634303063 0.60757379 0.581212136
38: 2011-07-02 x3 -0.65826708 -0.52006935 -0.17321499 -0.634303063 1.000000000 -0.38542234 -0.856055763
39: 2011-08-02 x2 0.13923866 -0.01168973 0.40294949 1.000000000 0.163579759 0.05517129 -0.006048453
40: 2011-08-02 x3 0.11823951 0.80057447 -0.26414984 0.163579759 1.000000000 0.71011089 -0.061702941
Time var Id norm y x2 x3 x4 x5