小编Adr*_*ian的帖子

我在 Windows 中使用 Rstudio。没有红色八角形供我点击。我试过在控制台中按 ESC 和 Ctrl + C 和 Ctrl + Z 但这些都没有用。

library(ggplot2)
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("D0.5", "D1", "D2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))
head(df2)
ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity")

我有一个简单的堆叠条形图，我想手动更改颜色。更具体地说，我想翻转用于的颜色fill = supp（即 OJ 的青色）。我已经尝试向其中添加一个color = ...参数，geom_bar但这只是勾勒出条形图而不是将它们着色。

我想将轴标签移近我的情节.我怎样才能做到这一点？

set.seed(3)
plot(rnorm(10), xlab = "Age", ylab = "Weight", cex.lab = 1.5)

library(nlme)
fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
            data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
> summary(fm1)
Nonlinear mixed-effects model fit by maximum likelihood
  Model: height ~ SSasymp(age, Asym, R0, lrc) 
 Data: Loblolly 
       AIC      BIC    logLik
  239.4856 251.6397 -114.7428

Random effects:
 Formula: Asym ~ 1 | Seed
            Asym  Residual
StdDev: 3.650642 0.7188625

Fixed effects: Asym + R0 + …

dat <- data.frame(ID = c(1, 2, 2, 2), Gender = c("Both", "Both", "Male", "Female"))
> dat
  ID Gender
1  1   Both
2  2   Both
3  2   Male
4  2 Female

对于每个 ID，如果 Gender 是Both, Male, and Female，我想删除带有Both. 也就是说，我想要的数据是这样的：

  ID Gender
1  1   Both
2  2   Male
3  2 Female

我尝试使用下面的代码来做到这一点：

library(dplyr)
> dat %>% 
  group_by(ID) %>% 
  mutate(A = ifelse(length(unique(Gender)) >= 3 & Gender == 'Both', F, T)) %>% 
  filter(A) %>% 
  select(-A)

# A tibble: …

在 R 中，我有以下数字向量：

numbers <- c(0.0193738397702257, 0.0206218006695066, 0.021931558829559, 
             0.023301378178208, 0.024728095594751, 0.0262069239112787, 0.0277310799996657, 
             0.0292913948762414, 0.0308758879014822, 0.0324693108459748, 0.0340526658271053, 
             0.03560271425176, 0.0370915716288017, 0.0384863653635563, 0.0397490272396821, 
             0.0408363289939899, 0.0417002577578561, 0.0422890917131629, 0.0425479537267193, 
             0.0424213884467212, 0.0418571402964338, 0.0408094991140723, 0.039243951482081, 
             0.0371450856007627, 0.0345208537496488, 0.0314091884865658, 0.0278854381969885, 
             0.0240607638577763, 0.0200808932436969, 0.0161193801903312, 0.0123615428382314, 
             0.00920410652651576, 0.00628125319205829, 0.0038816517651031, 
             0.00214210795679701, 0.00103919307280354, 0.000435532895812429, 
             0.000154730641092234, 4.56593150728962e-05, 1.09540661898799e-05, 
             2.08952167815574e-06, 3.10045314287095e-07, 3.51923218134997e-08, 
             3.02121734299694e-09, 1.95269500257237e-10, 9.54697530552714e-12, 
             3.5914029230041e-13, 1.07379981978647e-14, 2.68543048763588e-16, 
             6.03891613157815e-18, 1.33875697089866e-19, 3.73885699170518e-21, 
             1.30142752487978e-22, 5.58607581840324e-24, 2.92551478380617e-25, 
             1.85002124085815e-26, 1.39826890505611e-27, 1.25058972437096e-28, 
             1.31082961467944e-29, 1.59522437605631e-30, 2.23371981458205e-31, 
             3.5678974253211e-32, 6.44735482309705e-33, 1.30771083084868e-33, 
             2.95492180915218e-34, 7.3857554006177e-35, 2.02831084124162e-35, 
             6.08139499028838e-36, 1.97878175996974e-36, 6.94814886769478e-37, 
             2.61888070029751e-37, 1.05433608968287e-37, 4.51270543356897e-38, 
             2.04454840598946e-38, 9.76544451781597e-39, …

library(meta)
event <- c(81, 15, 0, 1)
n <- c(263, 148, 20, 29)
#
m1 <- metaprop(event, n, sm="PLOGIT", method.ci="SA")

我有兴趣在荟萃分析中结合比例.在上面的例子中,我有4个研究,每个研究报告一个比例.似乎metaprop计算了4项研究中每项研究的CI.但是,由于4项研究已经报告了CI(在原始论文中),是否有办法将实际的,报告的CI纳入荟萃分析计算,而不是单独metaprop计算它们？

如果他们允许我指定CI,我也愿意探索其他包.

xgb.train下面是包中函数的源代码xgboost。

library(xgboost)
> xgb.train
function (params = list(), data, nrounds, watchlist = list(), 
    obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L, 
    early_stopping_rounds = NULL, maximize = NULL, save_period = NULL, 
    save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), 
    ...) 
{
    check.deprecation(...)
    params <- check.booster.params(params, ...)
    check.custom.obj()
    check.custom.eval()
    dtrain <- data
    if (!inherits(dtrain, "xgb.DMatrix")) 
        stop("second argument dtrain must be xgb.DMatrix")
    if (length(watchlist) > 0) {
        if (typeof(watchlist) != "list" || !all(vapply(watchlist, 
            inherits, logical(1), …

下面是一个可重现的示例。首先是我的数据：

myvec <- c(-0.531346314931298, 0.298659434001583, -0.691720475359108)
mymat <- new("dgeMatrix", x = c(16.3574344575224, -15.8734447380907, -2.5840252937711, 
                                3.06801501320276, 1.02979983522784, -0.54581011579616, -18.9562379944301, 
                                -13.26737180927, -15.3314450728231, -16.892164730877, -16.232802331708, 
                                -15.9908074719921, 8.05590245092502, -8.05590245092502, -1.51374510440886, 
                                1.51374510440887, 0.12099742985792, -0.120997429857927, -9.56964755533389, 
                                -6.54215734651616, -8.05590245092502, -8.05590245092502, -7.9349050210671, 
                                -8.17689988078295, 3.78595636574668, -4.26994608517835, -1.09039597003911, 
                                0.606406250607442, -0.632174774229305, 0.14818505479763, 1.33542428436906, 
                                -0.303932077216888, 4.33320538312598, -3.30171317597381, 25.7164288780753, 
                                -24.6849366709232, 2.96628530035713, -4.59014893387888, 24.3728836566943, 
                                -25.9967472902161, -1.06980486854892, -0.554058764972834, 25.5514562424382, 
                                24.7395244256774, 28.9907656065072, 21.3002150616084, 24.8998122739105, 
                                25.3911683942051, 2.74913208137828, -1.12526844785653, 13.3846769837898, 
                                -11.760813350268, 1.33987291197775, 0.283990721544011, -0.921712011956532, 
                                -0.109780195195655, 1.3543960057099, -2.38588821286208, 12.0963242028502, 
                                -13.1278164100024, -12.8382805804706, -12.4583324395663, -14.9147553487727, 
                                -10.3818576712642, -13.0374407869434, -12.2591722330935, -1.30406270512594, 
                                1.30406270512594, 1.49880108324396e-15, …

library(ggplot2)
mydat <- data.frame(mean = c(23, 24, 15, 27, 18, 19, 23, 20, 32),
           lower = c(20, 19, 13, 15, 14, 18, 20, 17, 20),
           upper = c(25, 29, 17, 39, 22, 20, 26, 23, 40),
           class = c("A", "B", "C", "A", "B", "C", "A", "B", "C"),
           domain = c("North", "North", "North", "West", "West", "West", "South", "South", "South"))
mydat$class <- as.factor(mydat$class)
mydat$domain <- as.factor(mydat$domain)

mydat %>% ggplot(aes(x = mean, y = class, color = domain, Group = domain, xmin = …