position = "bottom") 还可以画成这样连线的图,是不是感觉经过经过评分匹配后,协变量的平衡好很多,threshold = c(.1)表示设置0.1为阈值,虚线表示,mean.diffs是平均差异的意思。 love.plot(m.out,binary = "std",stats = c("mean.diffs"), threshold = c(.1), var.order = "unadjusted", line = TRU...
library(tableone) covs <- colnames(lindner)[c(1,4:10)] tab <- CreateTableOne(vars = covs, strata = "abcix", data = lindner ) print(tab,showAllLevels = T,smd = T) ## Stratified by abcix ## level 0 1 p test SMD ## n 298 698 ## lifepres (mean (SD)) 11.02 (2.54) 11...
One Sample t-test data: intake t = -2.8203, df = 10, p-value = 0.01815 alternative hypothesis: true mean is not equal to 7725 95 percent confidence interval: 5986.394 7521.061 sample estimates: mean of x 6753.727 结果解释:One Sample t-test 这里是对所做的检验类型的描述,告诉我们是单样本的...
均值已知,方差未知的单样本t检验 One Sample t-test data:k t=-2.7951,df=4,p-value=0.04906alternative hypothesis:true mean is not equal to24095percent confidence interval:239.0033239.9967sample estimates:mean of x239.5k<-c(239.7,239.6,239,240,239.2)t.test(k,alternative="two.sided",mu=240) 双...
使用t.test 函数比较体重均值差异， 使用boxplot画出对比两个班体重分布的箱线图 举例：# 输入体重数据...
Welch Two Sample t-test data: NOTE[PART == "volontaire"] and NOTE[PART != "volontaire"] t = 29.511, df = 50.73, p-value < 2.2e-16 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: ...
在R软件中可用ks.test()函数计算未加权的KS值,利用twang包进行倾向性评分分析时,可输出加权后的KS值并利用KS值进行均衡性检验来选择最优模型。 1.2.3 有效样本量(effective sample sizes,ESS) ESS的计算公式为: 即该组研究对象总权重的平方与各研究对象权重的平方之和的比值[17-18]。ESS可理解为加权后该组...
wilcox_test(): perform one-sample, two-sample and pairwise Wilcoxon tests sign_test(): perform sign test to determine whether there is a median difference between paired or matched observations. anova_test(): an easy-to-use wrapper aroundcar::Anova()to perform different types of ANOVA tests...
Welch Two Sample t-test data: NOTE[PART == "volontaire"] and NOTE[PART != "volontaire"] t = 29.511, df = 50.73, p-value < 2.2e-16 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: ...
plot(density(Diff),xlab="mean(x1)-mean(x2)",ylab="Density",main="均值差的抽样分布(等方差)",cex.main=0.7,cex.lab=0.7) points(mean(Diff),sd(Diff),pch=1,col=1) S1<-mean(Sdx1) S2<-mean(Sdx2) Sp<-((100-1)*S1^2+(120-1)*S2^2)/(100+120-2) ...