小编nad*_*zan的帖子

我想知道如何scale_size() {ggplot2}在大小和颜色来自相同数据的图中着色size_scale .

例:

 library(ggplot2)
    df<-as.data.frame(cbind(rep(1:10,10),
                            rep(1:10,each=10),
                            rnorm(100)))

    ggplot(df,aes(V1,V2))+
      geom_point(aes(colour=V3,size=V3))+
      scale_colour_gradient(low="grey", high="black")+
      scale_size(range=c(1,10))

如您所见V3,数据点的颜色和大小相同.如何将颜色渐变合并到大小比例(除了在Illustrator等程序中手动执行此操作...)？谢谢!

我创造了一个例子 data.table

library(data.table)
set.seed(1)
siz <- 10
my <- data.table(
  AA=c(rep(NA,siz-1),"11/11/2001"),
  BB=sample(c("wrong", "11/11/2001"),siz, prob=c(1000000,1), replace=T),
  CC=sample(siz),
  DD=rep("11/11/2001",siz),
  EE=rep("HELLO", siz)
)

my[2,AA:=1]


         NA wrong  3 11/11/2001 HELLO
          1 wrong  2 11/11/2001 HELLO
         NA wrong  6 11/11/2001 HELLO
         NA wrong 10 11/11/2001 HELLO
         NA wrong  5 11/11/2001 HELLO
         NA wrong  7 11/11/2001 HELLO
         NA wrong  8 11/11/2001 HELLO
         NA wrong  4 11/11/2001 HELLO
         NA wrong  1 11/11/2001 HELLO
 11/11/2001 wrong  9 11/11/2001 HELLO

如果我运行此代码

patt <-  "^\\d\\d?/\\d\\d?/\\d{4}$" 
sapply(my, function(x)   (grepl(patt,x )))

TRUE …

此问题与R中的应用成本函数有关

我想知道如何保存为每次迭代生成的系数optim.trace=TRUE使我能够获得打印的每次迭代的系数,但是如何保存它们？

示例代码:

set.seed(1)
X <- matrix(rnorm(1000), ncol=10) # some random data
Y <- sample(0:1, 100, replace=TRUE)

# Implement Sigmoid function
sigmoid <- function(z) {
  g <- 1/(1+exp(-z))
  return(g)
}

cost.glm <- function(theta,X) {
  m <- nrow(X)
  g <- sigmoid(X%*%theta)
  (1/m)*sum((-Y*log(g)) - ((1-Y)*log(1-g)))
}

X1 <- cbind(1, X)

df <- optim(par=rep(0,ncol(X1)), fn = cost.glm, method='CG',
            X=X1, control=list(trace=TRUE))

哪个输出:

 Conjugate gradients function minimizer
Method: Fletcher Reeves
tolerance used in gradient test=2.00089e-11
0 1 …

Run Code Online (Sandbox Code Playgroud)