如何从R中的mixedCor{psych}关联中提取p值
您好，我正在使用软件包如何从R中的mixedCor{psych}关联中提取p值,r,correlation,p-value,mixed,R,Correlation,P Value,Mixed,您好，我正在使用软件包psych中的mixedCor分析混合数据。 是否有使用此软件包的经验？是否可以设置显著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(bi
psychmixedCorlibrary(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
这是我在尝试访问相关表时遇到的一个错误您正在查找类似的内容
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
n如果你能描述一下你已经尝试过的东西和一个最小的可重复的例子，那就太好了。这是一个很好的指导原则，可以帮助编辑您的问题感谢您的回复，它在使用mixedCor
时有效，但在使用mixed.cor
时无效。另一个问题是，是否有可能找到哪一次是重要的。我编辑了我的答案。让我知道，如果你正在寻找是的，它的工作完美，谢谢你。这是一个与主题无关的问题，您能否指导我将对同一类数据进行回归分析的软件包或解决该问题的论文打包？很难确切地说出您使用的数据类型，但我假设是分类数据。因此，您可以查看多水平逻辑回归。
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