part<-cutreevar(tree,3,matsim=T) #print(part) summary(part) part$var part$sim head(part$scores) test<-cbind(orgData[,3:4],part$scores) cor(test) #选择的最优变量:与自己组cluster越相关越好,与别的组cluster越不想关越好 cnt_wei_1_R2<-(1-0.98*0.98)/(1-0.51*0.51)#所以选择cnt_wei...
pca=rda(spe,scale=T) # scale=T,表示单位方差标准化,先中心化再均值化,物种数据均值为0,方差为1. pca ## 绘图数据提取 s.pca=pca$CA$u # 提取样本特征值 s.pca e.pca=pca$CA$v # 提取物种特征值 e.pca # 可执行选取排序轴绘制散点图 eig=pca$CA$eig # 提取特征根,计算PC1和PC2的解释度,横...
Well, if I think about the parts distribution strategy, our goal is to make sure that we service our customers as effectively and efficiently as possible, which is getting parts of them same day next day with expertise that being a critical part of it and then make sure that we're suppor...
Response of the Pseudomonas host chromosomal transcriptome to carriage of the IncP-7 plasmid pCAR1.doi:10.1111/j.1462-2920.2009.02110.xSummaryPlasmid carriage requires appropriate expression of the genes on the plasmid or host chromosome through cooperative transcriptional regulation. To clarify the ...
1 选用做DCA的数据,只保留geneID即可。 library(vegan) x<-read.csv(file.choose(),row.names=1) x[is.na(x)]<-0 dim(x) X<-t(x) dim(X) X.dca<-decorana(X) summary(X.dca) X.cca = cca(X) total.eigen =X.cca$tot.chi dca1.percent = round(X.dca$evals[1]/total.eigen,digits...
1.到此差不多大功告成,不得再次吐槽一下简书的Markdown,不能改字体,数据显示的太别扭了。 2.PCA数据优雅可视化的话需要用ggplot,放到以后再说吧。 3. 根据prcomp( )自编可计算Loading Score的函数 0611 16:11 pca<-function(matr,scla=TRUE){respca<-summary(prcomp(matr,scale=scl))n<-nrow(matr)if(sc...
summary(res) Two graphs are given by default, one for the individuals, one for the quantitative variables. But is is interesting to consider the qualitative variable to better understand the differences between wines. Wines are colored according to their label. ## Drawing wines according to the ...
summary(pc) # 1 component has > 99% variance loadings(pc) # Can see all variance is in the range in miles Importance of components: Comp.1 Comp.2 Comp.3 Comp.4Standard deviation 2259.2372556 6.907940e+01 2.871764e+01 2.259929e+01Proportion of Variance 0.9987016 9.337038e-04 1.613651e-04 ...
Summary Performance Analysis AdviceFundamentals Technicals Indicators Dividends Trends Premiums Profitability Ownership CompetitionPCAR Stock USD 110.88 1.63 1.45% PACCAR Inc fundamentals help investors to digest information that contributes to PACCAR's financial success or failures. It also enables traders to...
R - SE: model_summary - use algorithm from model_id if present, if not… 4年前 h2o-samples/src/main/java/droplets Refactor K-Means output: rename rows -> size. 10年前 h2o-security [SW-7318] Make generateSSLPair public to avoid duplication on Sparklin… 5年前 h2o-test-accurac...