如何从 R 中的 mixedCor {psych} 相关性中提取 p 值
How to extract the p values from mixedCor {psych} correlation in R
您好，我正在使用包 psych 中的 mixedCor 来分析混合数据。
有没有人有使用这个包的经验？是否可以设置显着性水平以及如何绘制结果？
library(psych)
# I have data set consist of continues variables:
> class(scd)
[1] "data.frame"
> dim(scd)
[1] 1000 7
# and data set consist of dummy variables:
> class(bian)
[1] "data.frame"
> dim(bian)
[1] 1000 4
n<-mixed.cor(x = scd, p = bian)
> n
Call: mixed.cor(x = scd, p = bian)
Age Rlgsn Intip IoNPO Incom Dntnf Dntna Gendr Chldr Pet Vlntr
Age 1.00
Religiousness -0.07 1.00
Interest in politics -0.19 0.04 1.00
Impact of NPO's 0.01 0.10 0.09 1.00
Income 0.37 -0.02 -0.14 0.01 1.00
Donation frequency 0.08 -0.15 -0.06 -0.08 0.00 1.00
Donation amount 0.06 -0.10 -0.05 0.04 0.20 0.04 1.00
Gender -0.06 0.15 -0.20 0.16 0.08 -0.05 -0.04 1.00
Children 1.00 -0.18 -0.23 0.05 0.42 0.11 0.18 -0.15 1.00
Pet 0.06 0.18 0.06 0.03 0.02 0.02 -0.07 -0.17 0.08 1.00
Volunteer 0.05 -0.24 -0.23 -0.21 0.03 0.09 0.07 -0.02 0.10 0.04 1.00
问题是我无法将此输出 table 复制到 data.frame，而且我找不到过滤重要值的方法。
> n$rho
Call:
Error: $ operator is invalid for atomic vectors
这是我在尝试访问相关性时遇到的错误 table
您正在寻找这样的东西吗？
n <- cor.ci(bfi[,c(1:5,26,28)],poly=TRUE, plot=F)
CorrTable <- n["rho"]
> CorrTable
$rho
A1 A2 A3 A4 A5 gender age
A1 1.0000000 -0.4084638 -0.32251744 -0.1754424 -0.2278395 -0.22858169 -0.17421138
A2 -0.4084638 1.0000000 0.55545103 0.3897144 0.4488210 0.25291876 0.12489782
A3 -0.3225174 0.5554510 1.00000000 0.4080689 0.5728565 0.20226959 0.07472984
A4 -0.1754424 0.3897144 0.40806891 1.0000000 0.3552859 0.19972576 0.16000917
A5 -0.2278395 0.4488210 0.57285647 0.3552859 1.0000000 0.13536493 0.13808548
gender -0.2285817 0.2529188 0.20226959 0.1997258 0.1353649 1.00000000 0.06197663
age -0.1742114 0.1248978 0.07472984 0.1600092 0.1380855 0.06197663 1.00000000
#Get table of confidence intervals and only keep those with p<.05
require(dplyr)
Significant <- data.frame(n["ci"]) %>% mutate(Correlation=row.names(.)) %>% filter(ci.p<.05)
> Significant
ci.lower ci.low.e ci.upper ci.up.e ci.p Correlation
1 -0.452232197 -0.453877196 -0.3619042 -0.3604374 0.000000e+00 A1-A2
2 -0.361218278 -0.356008043 -0.2747760 -0.2771704 0.000000e+00 A1-A3
3 -0.218952806 -0.219826548 -0.1276200 -0.1333996 5.060397e-13 A1-A4
4 -0.264688484 -0.259502965 -0.1872032 -0.1842328 0.000000e+00 A1-A5
5 -0.284548998 -0.281365660 -0.1692228 -0.1760545 2.300382e-13 A1-gendr
6 -0.212549644 -0.208996817 -0.1330437 -0.1246278 2.220446e-16 A1-age
7 0.521920083 0.521444647 0.5913877 0.5843988 0.000000e+00 A2-A3
8 0.352456028 0.354520611 0.4295087 0.4306129 0.000000e+00 A2-A4
9 0.410979114 0.413714402 0.4873637 0.4893516 0.000000e+00 A2-A5
10 0.203734982 0.209240328 0.2988768 0.2938920 0.000000e+00 A2-gendr
11 0.082020319 0.085666776 0.1642994 0.1645434 7.251809e-09 A2-age
12 0.368357960 0.372292822 0.4385225 0.4361084 0.000000e+00 A3-A4
13 0.535389947 0.537376171 0.6081013 0.6029797 0.000000e+00 A3-A5
14 0.151224464 0.156345001 0.2473505 0.2419796 5.551115e-15 A3-gendr
15 0.027669312 0.022842270 0.1163394 0.1134321 1.520197e-03 A3-age
16 0.312018205 0.310561702 0.3944081 0.3923113 0.000000e+00 A4-A5
17 0.141115630 0.143278359 0.2585775 0.2595806 1.389824e-10 A4-gendr
18 0.120141021 0.122818756 0.1954795 0.1906974 1.110223e-15 A4-age
19 0.080374786 0.085360533 0.1917875 0.1914606 2.492361e-06 A5-gendr
20 0.091283846 0.088516320 0.1776239 0.1734189 1.957320e-09 A5-age
21 0.005062216 0.002824092 0.1094059 0.1012318 3.179484e-02 gendr-age
您好，我正在使用包 psych 中的 mixedCor 来分析混合数据。
有没有人有使用这个包的经验？是否可以设置显着性水平以及如何绘制结果？
library(psych)
# I have data set consist of continues variables:
> class(scd)
[1] "data.frame"
> dim(scd)
[1] 1000 7
# and data set consist of dummy variables:
> class(bian)
[1] "data.frame"
> dim(bian)
[1] 1000 4
n<-mixed.cor(x = scd, p = bian)
> n
Call: mixed.cor(x = scd, p = bian)
Age Rlgsn Intip IoNPO Incom Dntnf Dntna Gendr Chldr Pet Vlntr
Age 1.00
Religiousness -0.07 1.00
Interest in politics -0.19 0.04 1.00
Impact of NPO's 0.01 0.10 0.09 1.00
Income 0.37 -0.02 -0.14 0.01 1.00
Donation frequency 0.08 -0.15 -0.06 -0.08 0.00 1.00
Donation amount 0.06 -0.10 -0.05 0.04 0.20 0.04 1.00
Gender -0.06 0.15 -0.20 0.16 0.08 -0.05 -0.04 1.00
Children 1.00 -0.18 -0.23 0.05 0.42 0.11 0.18 -0.15 1.00
Pet 0.06 0.18 0.06 0.03 0.02 0.02 -0.07 -0.17 0.08 1.00
Volunteer 0.05 -0.24 -0.23 -0.21 0.03 0.09 0.07 -0.02 0.10 0.04 1.00
问题是我无法将此输出 table 复制到 data.frame，而且我找不到过滤重要值的方法。
> n$rho
Call:
Error: $ operator is invalid for atomic vectors
这是我在尝试访问相关性时遇到的错误 table
您正在寻找这样的东西吗？
n <- cor.ci(bfi[,c(1:5,26,28)],poly=TRUE, plot=F)
CorrTable <- n["rho"]
> CorrTable
$rho
A1 A2 A3 A4 A5 gender age
A1 1.0000000 -0.4084638 -0.32251744 -0.1754424 -0.2278395 -0.22858169 -0.17421138
A2 -0.4084638 1.0000000 0.55545103 0.3897144 0.4488210 0.25291876 0.12489782
A3 -0.3225174 0.5554510 1.00000000 0.4080689 0.5728565 0.20226959 0.07472984
A4 -0.1754424 0.3897144 0.40806891 1.0000000 0.3552859 0.19972576 0.16000917
A5 -0.2278395 0.4488210 0.57285647 0.3552859 1.0000000 0.13536493 0.13808548
gender -0.2285817 0.2529188 0.20226959 0.1997258 0.1353649 1.00000000 0.06197663
age -0.1742114 0.1248978 0.07472984 0.1600092 0.1380855 0.06197663 1.00000000
#Get table of confidence intervals and only keep those with p<.05
require(dplyr)
Significant <- data.frame(n["ci"]) %>% mutate(Correlation=row.names(.)) %>% filter(ci.p<.05)
> Significant
ci.lower ci.low.e ci.upper ci.up.e ci.p Correlation
1 -0.452232197 -0.453877196 -0.3619042 -0.3604374 0.000000e+00 A1-A2
2 -0.361218278 -0.356008043 -0.2747760 -0.2771704 0.000000e+00 A1-A3
3 -0.218952806 -0.219826548 -0.1276200 -0.1333996 5.060397e-13 A1-A4
4 -0.264688484 -0.259502965 -0.1872032 -0.1842328 0.000000e+00 A1-A5
5 -0.284548998 -0.281365660 -0.1692228 -0.1760545 2.300382e-13 A1-gendr
6 -0.212549644 -0.208996817 -0.1330437 -0.1246278 2.220446e-16 A1-age
7 0.521920083 0.521444647 0.5913877 0.5843988 0.000000e+00 A2-A3
8 0.352456028 0.354520611 0.4295087 0.4306129 0.000000e+00 A2-A4
9 0.410979114 0.413714402 0.4873637 0.4893516 0.000000e+00 A2-A5
10 0.203734982 0.209240328 0.2988768 0.2938920 0.000000e+00 A2-gendr
11 0.082020319 0.085666776 0.1642994 0.1645434 7.251809e-09 A2-age
12 0.368357960 0.372292822 0.4385225 0.4361084 0.000000e+00 A3-A4
13 0.535389947 0.537376171 0.6081013 0.6029797 0.000000e+00 A3-A5
14 0.151224464 0.156345001 0.2473505 0.2419796 5.551115e-15 A3-gendr
15 0.027669312 0.022842270 0.1163394 0.1134321 1.520197e-03 A3-age
16 0.312018205 0.310561702 0.3944081 0.3923113 0.000000e+00 A4-A5
17 0.141115630 0.143278359 0.2585775 0.2595806 1.389824e-10 A4-gendr
18 0.120141021 0.122818756 0.1954795 0.1906974 1.110223e-15 A4-age
19 0.080374786 0.085360533 0.1917875 0.1914606 2.492361e-06 A5-gendr
20 0.091283846 0.088516320 0.1776239 0.1734189 1.957320e-09 A5-age
21 0.005062216 0.002824092 0.1094059 0.1012318 3.179484e-02 gendr-age