[英]Generate correlation matrix with specific columns and only with significant values in corrplot
I have a data.frame database with 14 columns.我有一个包含 14 列的 data.frame 数据库。 I split these columns into two groups: [,1:6] and [,7:14]
.我将这些列分为两组: [,1:6] and [,7:14]
。
df<-read.csv("http://renatabrandt.github.io/EBC2015/data/varechem.csv", row.names=1)
df
I would like to calculate the correlation between these two groups of columns.我想计算这两组列之间的相关性。 For that I used this command and it worked very well:为此,我使用了这个命令并且效果很好:
#I want to correlate columns [1:6] with [7:14] only.
correlation_df<-cor(df[,1:6],
df[,7:14], method="spearman", use="pairwise.complete.obs")
#graph correlation especific colunms
corrplot(correlation_df,
method="color", addCoef.col = "black")
However, in addition to calculating the correlation, I would like the graph to show only the significant correlations (p-value<0.05).但是,除了计算相关性之外,我希望图表仅显示显着相关性(p 值<0.05)。 I tried the following code but it didn't work because the view was wrong.我尝试了以下代码,但由于视图错误,它不起作用。
#I can get the significance level matrix
correlation_df_sig<-cor.mtest(df, conf.level = 0.95, method = "spearman")
correlation_df_sig
#Generate correlation matrix only with significant values #仅生成具有显着值的相关矩阵
plot2<-corrplot(correlation_df,
p.mat = correlation_df_sig$p,
insig='blank',
addCoef.col = "black")
plot2
What could I do to fix this view?我能做些什么来解决这个观点?
OBS: I tried to generate a complete array without considering the [,1:6] and [,7:14]
groups, but it also went wrong. OBS:我试图在不考虑[,1:6] and [,7:14]
组的情况下生成一个完整的数组,但它也出错了。 Also, I don't want to calculate the correlation between columns in the same group.另外,我不想计算同一组中列之间的相关性。 Ex: column 1 with column 2, column 1 with column 3...例如:第 1 列与第 2 列,第 1 列与第 3 列...
plot1<-corrplot(cor(df, method = 'spearman', use = "pairwise.complete.obs"),
method = 'color',
addCoef.col = 'black',
p.mat = correlation_df_sig$p,
insig='blank',
diag = FALSE,
number.cex = 0.5,
type='upper'
)
plot1
I would use the well established Hmisc::rcorr
for the calculations.我会使用成熟的Hmisc::rcorr
进行计算。 In corrplot::corrplot
, subset both the corr=
and the p.mat=
with [1:6, 7:14]
.在corrplot::corrplot
中,使用[1:6, 7:14]
对corr=
和p.mat=
进行子集化。
c_df <- Hmisc::rcorr(cor(correlation_df), type='spearman')
library(corrplot)
corrplot(corr=c_df$r[1:6, 7:14], p.mat=c_df$P[1:6, 7:14], sig.level=0.05,
method='color', diag=FALSE, addCoef.col=1, type='upper', insig='blank',
number.cex=.8)
This appears to correspond to the p-values.这似乎对应于 p 值。
m <- c_df$P[1:6, 7:14] < .05
m[lower.tri(m, diag=TRUE)] <- ''
as.data.frame(replace(m, lower.tri(m, diag=TRUE), ''))
# Al Fe Mn Zn Mo Baresoil Humdepth pH
# N FALSE FALSE TRUE FALSE FALSE FALSE FALSE
# P TRUE TRUE FALSE FALSE FALSE FALSE
# K TRUE FALSE FALSE FALSE TRUE
# Ca FALSE TRUE TRUE FALSE
# Mg TRUE TRUE TRUE
# S FALSE FALSE
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