微信公眾號：醫(yī)學統(tǒng)計與R語言如果你覺得對你有幫助，歡迎轉發(fā)

輸入1：

?install.packages("metafor")
?library(metafor)
?dat.bcg

結果1：

???trial???????????????author?year?tpos??tneg?cpos??cneg?ablat??????alloc
1??????1??????????????Aronson?1948????4???119???11???128????44?????random
2??????2?????Ferguson?&?Simes?1949????6???300???29???274????55?????random
3??????3??????Rosenthal?et?al?1960????3???228???11???209????42?????random
4??????4????Hart?&?Sutherland?1977???62?13536??248?12619????52?????random
5??????5?Frimodt-Moller?et?al?1973???33??5036???47??5761????13??alternate
6??????6??????Stein?&?Aronson?1953??180??1361??372??1079????44??alternate
7??????7?????Vandiviere?et?al?1973????8??2537???10???619????19?????random
8??????8???????????TPT?Madras?1980??505?87886??499?87892????13?????random
9??????9?????Coetzee?&?Berjak?1968???29??7470???45??7232????27?????random
10????10??????Rosenthal?et?al?1961???17??1699???65??1600????42?systematic
11????11???????Comstock?et?al?1974??186?50448??141?27197????18?systematic
12????12???Comstock?&?Webster?1969????5??2493????3??2338????33?systematic
13????13???????Comstock?et?al?1976???27?16886???29?17825????33?systematic

輸入2：

dat?<-?escalc(measure="RR",?ai=tpos,?bi=tneg,?ci=cpos,?di=cneg,?data=dat.bcg)sdat?<-?summary(dat,?transf=exp)sdat?

Measures for Dichotomous Variables:

outcome1outcome1

group1	ai	bi
group2	ci	di

Measures for Quantitative Variables:

meansdgroup size

group1	m1i	sd1i	n1i
group2	m2i	sd2i	n2i

結果2：

???trial???????????????author?year?tpos??tneg?cpos??cneg?ablat??????alloc?????yi?????vi????sei?
1??????1??????????????Aronson?1948????4???119???11???128????44?????random?0.4109?0.3256?0.5706?
2??????2?????Ferguson?&?Simes?1949????6???300???29???274????55?????random?0.2049?0.1946?0.4411?
3??????3??????Rosenthal?et?al?1960????3???228???11???209????42?????random?0.2597?0.4154?0.6445?
4??????4????Hart?&?Sutherland?1977???62?13536??248?12619????52?????random?0.2366?0.0200?0.1415?
5??????5?Frimodt-Moller?et?al?1973???33??5036???47??5761????13??alternate?0.8045?0.0512?0.2263?
6??????6??????Stein?&?Aronson?1953??180??1361??372??1079????44??alternate?0.4556?0.0069?0.0831?
7??????7?????Vandiviere?et?al?1973????8??2537???10???619????19?????random?0.1977?0.2230?0.4722?
8??????8???????????TPT?Madras?1980??505?87886??499?87892????13?????random?1.0120?0.0040?0.0629?
9??????9?????Coetzee?&?Berjak?1968???29??7470???45??7232????27?????random?0.6254?0.0564?0.2376?
10????10??????Rosenthal?et?al?1961???17??1699???65??1600????42?systematic?0.2538?0.0730?0.2702?
11????11???????Comstock?et?al?1974??186?50448??141?27197????18?systematic?0.7122?0.0124?0.1114?
12????12???Comstock?&?Webster?1969????5??2493????3??2338????33?systematic?1.5619?0.5325?0.7297?
13????13???????Comstock?et?al?1976???27?16886???29?17825????33?systematic?0.9828?0.0714?0.2672?
?????????zi??ci.lb??ci.ub?
1???-1.5586?0.1343?1.2574?
2???-3.5941?0.0863?0.4864?
3???-2.0917?0.0734?0.9186?
4??-10.1908?0.1793?0.3121?
5???-0.9613?0.5163?1.2536?
6???-9.4599?0.3871?0.5362?
7???-3.4323?0.0784?0.4989?
8????0.1899?0.8946?1.1449?
9???-1.9760?0.3926?0.9962?
10??-5.0747?0.1494?0.4310?
11??-3.0460?0.5725?0.8860?
12???0.6111?0.3737?6.5284?
13??-0.0648?0.5821?1.6593?

yi is a vector with the effect size estimates (log relative risks in the first example, standardized mean differences in the second example), vi is a vector with the corresponding sampling variances, and xi is a vector with the covariate/moderator values

輸入3：

?res1?<-?rma(yi,?vi,?data=dat,digits=3)summary(res1)

method="RE"

結果3：

Random-Effects?Model?(k?=?13;?tau^2?estimator:?REML)

??logLik??deviance???????AIC???????BIC??????AICc?
?-12.202????24.405????28.405????29.375????29.738???

tau^2?(estimated?amount?of?total?heterogeneity):?0.313?(SE?=?0.166)
tau?(square?root?of?estimated?tau^2?value):??????0.560
I^2?(total?heterogeneity?/?total?variability):???92.22%
H^2?(total?variability?/?sampling?variability):??12.86

Test?for?Heterogeneity:
Q(df?=?12)?=?152.233,?p-val?.001

Model?Results:

estimate?????se????zval???pval???ci.lb???ci.ub?
??-0.715??0.180??-3.974??<.001??-1.067??-0.362??***?

---
Signif.?codes:??0?‘***’?0.001?‘**’?0.01?‘*’?0.05?‘.’?0.1?‘?’?1

輸入4：

?confint(res1)

結果4：

?estimate??ci.lb??ci.ub?
tau^2?????0.313??0.120??1.111?
tau???????0.560??0.346??1.054?
I^2(%)???92.221?81.918?97.678?
H^2??????12.856??5.530?43.068?

輸入5：

forest(res1,xlim=c(-15,?5),at=log(c(0.05,?0.25,?1,?8)),atransf=exp,slab?=?paste(dat$author,?as.character(dat$year),?sep?=?",?"),
????????ilab=cbind(dat.bcg$tpos,?dat.bcg$tneg,?dat.bcg$cpos,?dat.bcg$cneg),
????????ilab.xpos=c(-9,-7.5,-5.5,-4),?cex=0.8,???
????????xlab="Risk?Ratio",?mlab="",ylim=c(-1,?16))##?order=order(dat.bcg$year)
??text(c(-9,-7.5,-5.5,-4),?15?,c("TB+",?"TB-",?"TB+",?"TB-"))
??text(c(-8,?-5),?16,?c("Vaccinated",?"Control"))
??text(-15,?15,?"Author(s)?and?Year",?pos?=4)
??text(5,?15,?"Relative?Risk?[95%?CI]",?pos?=2)
??text(-15,?-1,?pos=4,?cex=0.75,?bquote(paste("RE?Model?for?All?Studies?(Q?=?",
??????????????????????????????????????????????.(formatC(res1$QE,?digits=2,?format="f")),?",?df?=?",?.(res1$k?-?res1$p),
??????????????????????????????????????????????",?p?=?",?.(formatC(res1$QEp,?digits=2,?format="f")),?";?",?I^2,?"?=?",
??????????????????????????????????????????????.(formatC(res1$I2,?digits=1,?format="f")),?"%)")))

http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups

結果5：

輸入6：

par(mfrow=c(2,2))
funnel(res1,?main="Standard?Error")
funnel(res1,?yaxis="vi",?main="Sampling?Variance")
funnel(res1,?yaxis="seinv",?main="Inverse?Standard?Error")
funnel(res1,?yaxis="vinv",?main="Inverse?Sampling?Variance")

結果6：

輸入7：

?par(mfrow=c(1,1))
?res?3)
?preds?0:60),?transf=exp)
?size?1?/?sqrt(dat$vi)
?size??plot(NA,?NA,?xlim=c(10,60),?ylim=c(0.05,8),
??????xlab="Absolute?Latitude",?ylab="Risk?Ratio",
??????las=1,??bty="l",log="y")
?symbols(dat$ablat,?exp(dat$yi),?squares=size,?inches=FALSE,?add=TRUE,?bg="blue")
?install.packages("gplots")
?library(gplots)
?plotCI(dat$ablat?,exp(dat$yi),?ui=sdat$ci.ub,li=sdat$ci.lb?,err="y",??
?pch=19,?lty=1,?lwd=1,col="blue",barcol="black",gap=0.1,sfrac=sdat$sei/60,add=TRUE)
?lines(0:60,?preds$pred,lty="solid",col="red")
?lines(0:60,?preds$ci.lb,?lty="dashed")
?lines(0:60,?preds$ci.ub,?lty="dashed")
?abline(h=1,?lty="dotted")
?pos?1,3,3,3,2,3,2,3,4,1,3,3)
?text(dat$ablat,?exp(dat$yi),dat$trial,?pos=pos,cex=1.1)

require(plotrix)
pos:a position specifier for the text. If specified this overrides any adj value given.
Values of 1, 2, 3 and 4, respectively indicate positions below,to the left of, above and to the right of the specified (x,y) coordinates.

結果7：

前文鏈接：

醫(yī)學統(tǒng)計與R語言：多列分組正態(tài)性檢驗

醫(yī)學統(tǒng)計與R語言：aggregate.plot了解一下

醫(yī)學統(tǒng)計與R語言：有序Probit回歸(Ordered Probit Model)

醫(yī)學統(tǒng)計與R語言：Probit回歸模型及邊際效應(Marginal effects)

醫(yī)學統(tǒng)計與R語言：Lord’s Paradox

醫(yī)學統(tǒng)計與R語言：協(xié)方差分析(ANCOVA)+plus

醫(yī)學統(tǒng)計與R語言：Kendall是誰？樣本量是自變量的10倍？

醫(yī)學統(tǒng)計與R語言：方差分析中計劃好的多重比較(Planned Comparisons and Post Hoc Tests)

醫(yī)學統(tǒng)計與R語言：圓形樹狀圖(circular dendrogram)

醫(yī)學統(tǒng)計與R語言：畫一個姑娘陪著我，再畫個花邊的被窩

醫(yī)學統(tǒng)計與R語言：雙因素重復測量方差分析(Two-way repeated measures ANOVA)

醫(yī)學統(tǒng)計R語言：分面畫boxplot

醫(yī)學統(tǒng)計與R語言：調節(jié)效應分析(Moderation Analysis)

醫(yī)學統(tǒng)計與R語言：結構方程模型(structural equation model)

醫(yī)學統(tǒng)計與R語言：中介效應分析(mediation effect analysis)

醫(yī)學統(tǒng)計與R語言：生存曲線(survival curves)with ?risk.table

醫(yī)學統(tǒng)計與R語言：如何比較兩種診斷試驗的靈敏度和特異度？

醫(yī)學統(tǒng)計與R語言：你知道nomogram的points和total points怎么算嗎？

醫(yī)學統(tǒng)計與R語言：Cleveland dot plot

醫(yī)學統(tǒng)計與R語言：交互作用模型中分組效應及標準誤的計算

醫(yī)學統(tǒng)計與R語言：多條ROC曲線的AUC多重比較

醫(yī)學統(tǒng)計與R語言：來，今天學個散點圖！

醫(yī)學統(tǒng)計與R語言：一份簡單的數(shù)據(jù)整理分析

醫(yī)學統(tǒng)計與R語言：利用金字塔圖比較多個指標

醫(yī)學統(tǒng)計與R語言：點圖(dotplot)

醫(yī)學統(tǒng)計與R語言：幕后高手出馬！

醫(yī)學統(tǒng)計與R語言：Calibration plot with 置信區(qū)間

醫(yī)學統(tǒng)計與R語言：還說自己不會畫Calibration plot！

醫(yī)學統(tǒng)計與R語言：KS曲線，KS plot，lift plot

醫(yī)學統(tǒng)計與R語言：身體酸痛，醒來學個卡方檢驗

醫(yī)學統(tǒng)計與R語言：利用午睡幾分鐘，學習下Population Pyramid

醫(yī)學統(tǒng)計與R語言：有序Logistic回歸平行線檢驗(Test proportional odds assumption )

醫(yī)學統(tǒng)計與R語言:你的基金標書里還少這幅圖！

醫(yī)學統(tǒng)計與R語言：這里的坑你踩過幾回，有序多分類Logistic回歸(Ordinal Logistic Regression)

醫(yī)學統(tǒng)計與R語言：logsitc回歸校準曲線 Calibration curve

醫(yī)學統(tǒng)計與R語言：多分格相關系數(shù)(polychoric)多序列相關系數(shù)(polyserial)Coefficient Omega

醫(yī)學統(tǒng)計與R語言：Tobit回歸中的Marginal effect

醫(yī)學統(tǒng)計與R語言：定量變量的無監(jiān)督離散化( unsupervised discretization)

醫(yī)學統(tǒng)計與R語言：Welch's ANOVA and Games-Howell post-hoc test

醫(yī)學統(tǒng)計與R語言：配對均值檢驗可視化加label

醫(yī)學統(tǒng)計與R語言：線性固定效應模型(Linear fix effect model )

醫(yī)學統(tǒng)計與R語言：Tobit回歸模型

函數(shù)醫(yī)學統(tǒng)計與R語言：隨機森林與Logistic預測(randomForest vs Logistic regression)

醫(yī)學統(tǒng)計與R語言：多重比較P值的可視化

醫(yī)學統(tǒng)計與R語言：腫瘤研究中的waterfall plot(瀑布圖)

醫(yī)學統(tǒng)計與R語言：多元方差分析與非參數(shù)多元方差分析

醫(yī)學統(tǒng)計與R語言：使用R語言實現(xiàn)Johnson-Neyman分析

醫(yī)學統(tǒng)計與R語言：多層線性模型圖示

醫(yī)學統(tǒng)計與R語言：多層線性模型(混合線性模型

醫(yī)學統(tǒng)計與R語言：best subset of inputs for the glm famil

醫(yī)學統(tǒng)計與R語言：多重線回歸自變量篩選的幾種方法

醫(yī)學統(tǒng)計與R語言：關聯(lián)規(guī)則Apriori算

醫(yī)學統(tǒng)計與R語言：列聯(lián)表可視化的4種方

醫(yī)學統(tǒng)計與R語言：盤它！什么格式文件都可

醫(yī)學統(tǒng)計與R語言：離群值分析(Outlier Detection

醫(yī)學統(tǒng)計與R語言：決策樹CHAI

醫(yī)學統(tǒng)計與R語言：主成分分析(PCA)及可視

醫(yī)學統(tǒng)計與R語言：可能是最全R語言操作手冊(cheatsheets)

醫(yī)學統(tǒng)計與R語言：聽說你還在手動畫三線表！

醫(yī)學統(tǒng)計與R語言：合并多個Excel文

醫(yī)學統(tǒng)計與R語言：政策效果評價之合成控制

醫(yī)學統(tǒng)計與R語言：線性回歸模型假設條件驗證與診斷

醫(yī)學統(tǒng)計與R語言：想發(fā)表sci就畫這種Bland-Altman Plot

總結

以上是生活随笔為你收集整理的语言nomogram校准曲线图_医学统计与R语言：Meta 回归作图（Meta regression Plot）的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內容還不錯，歡迎將生活随笔推薦給好友。