我还处于R的开始阶段，但我已经完成了一些功能，现在我正在寻找我的最终“项目”。
我创建了一个函数，它接收我的四个数据源（不同的群体）中的每一个并创建直方图，执行kolmogorov-smirnov测试，然后绘制给定行的任何重要结果。我想要做的是把它变成一个应用函数。但是，问题是我的函数需要四个变量，而且我不知道如何使应用程序获取四个数据源。
hist_fx <- function(w,x,y,z) {
hist(w,prob=TRUE,col="green",xlim=c(-1,1),ylim=c(0,3))
lines(density(w),col="red")
abline(v=c(mean(w)),col="red")
hist(x,prob=TRUE,col="blue",xlim=c(-1,1),ylim=c(0,3))
lines(density(x),col="red")
abline(v=c(mean(x)),col="red")
hist(y,prob=TRUE,col="yellow",xlim=c(-1,1),ylim=c(0,3))
lines(density(y),col="red")
abline(v=c(mean(y)),col="red")
hist(z,prob=TRUE,col="purple",xlim=c(-1,1),ylim=c(0,3))
lines(density(z),col="red")
abline(v=c(mean(z)),col="red")
all <- c(w,x,y,z)
hist(all,prob=TRUE,xlim=c(-1,0.5),ylim=c(0,3))
lines(density(w),col="purple")
lines(density(x),col="red")
lines(density(y),col="blue")
lines(density(z),col="green")
plot(ecdf(w),col="green")
plot(ecdf(x),col="blue",add=TRUE)
plot(ecdf(y),col="red",add=TRUE)
plot(ecdf(z),col="purple",add=TRUE)
t1 <- ks.test(w,x)
print(t1)
t2 <- ks.test(w,y)
print(t2)
t3 <- ks.test(w,z)
print(t3)
if(t1$p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(x),col="blue",add=TRUE)
}
if(t2p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(y),col="red",add=TRUE)
}
if(t3$p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(z),col="purple",add=TRUE)
}
}
我能够使用此功能同时申请一个群体（即将hist_fx转换为一个变量的函数）。但是，我找不到一种方法可以同时为所有四个人群服务。我已经搞砸了一些for循环，虽然它们还没有成功。
可能有用的最后一件事：我的数据被安排成独立变量是行，而因变量是列。因此，我需要每行运行这些（因此我想到了for循环）。
编辑：
以下是其中一个人群的输入数据：
dput（K2） 结构（c（-0.15,0.13,0.23，-0.23,0.06，-0.11,0.107,0.06， -0.17,0.12,0.06，-0.25，-0.32,0.13,0.06，-0.2，-0.08,0.06， 0.12,0.02,0.11，-0.11，-0.15,0.097,0.347，-0.307,0.097， -0.047,0.09,0.01，-0.217,0.117,0.03，-0.3，-0.33,0.13,0.19， -0.24，-0.08，-0.01,0.15,0.61,0.18，-0.15，-0.103,0.135， 0.31，-0.25,0.157，-0.105，-0.08,0.01，-0.165,0.17,0.1，-0.23， -0.28,0.15,0.13，-0.14，-0.06,0.01,0.07，-0.02,0.11，-0.06， -0.123,0.13,3.55，-0.27,0.165，-0.065,0.135,0.13，-0.17， 0.135,0.08，-0.21，-0.25,0.2,0.16，-0.18，NA，-0.04,0.05， -0.02,0.13，-0.14，-0.13,0.098,0.27，-0.193,0.062，-0.08， 0.057,0.028，-0.199,0.1,0.04，-0.24，-0.32,0.13,0.13，-0.15， -0.05,0.01,0.08，-0.04,0.1，-0.1，-0.14,0.154,0.261，-0.194， 0.1，-0.129,0.063,0.142，-0.136,0.136,0.08，-0.23，-0.24， 0.12,0.1，-0.16，-0.06,0.04,0.09，-0.01,0.04，-0.08，-0.127， 0.133,0.337，-0.06,0.11，-0.107,0.16,0.167，-0.183,0.103， 0.05，-0.2，-0.3,0.22，-0.01，-0.17，-0.14,0.02,0.07,0.01， 0.11，-0.11，-0.155,0.221,0.22，-0.172,0.09，-0.15,0.12， 0.03，-0.153,0.146,0.11，-0.2，-0.24,0.16,0.07，-0.19，-0.1， 0.03,0.17,0.02,0.09，-0.16，-0.062,0.19,0.269，-0.265,0.118， -0.11,0.126,0.094，-0.186,0.151,0.08，-0.26，-0.31,0.13， 0.09，-0.23，-0.12,0.05,0.13,0.01,0.11，-0.14，-0.095,0.14， 0.24，-0.46,0.09，-0.17,0.08,0.01，-0.24,0.16,0.04，-0.38， -0.39,0.11,0.06，-0.31，-0.25,0.03,0.21，-0.14,0，-0.22， -0.07,0.148,0.311，-0.27,0.11，-0.055,0.16,0.04，-0.197， 0.064,0.09，-0.24，-0.34,0.17,0.07，-0.15，-0.18,0.03,0.13， 0.07,0.13，-0.08，-0.136,0.142,0.27，-0.257,0.1，-0.13,0.103， 0.064，-0.197,0.118,0.06，-0.29，-0.35,0.13,0.1，-0.19，-0.13， 0.01,0.1，-0.01,0.13，-0.15）,. Dim = c（22L，12L））
进一步澄清，这是实际数据框的格式：
c1 c2 c3 c4
r2 x x x
r3 x x x
r4 x x x
每列代表行上变量的星号值。因此，我想为每个数据集的每一行创建一个直方图。
对于函数的值，我只是为了简单起见而使用了这些变量。 w =人口1，x =人口2，y =人口3，z =人口4。
举个例子：
> hist_fx(k2[1,],n2[1,],j2[1,],g2[1,])
Two-sample Kolmogorov-Smirnov test
data: w and x
D = 1, p-value = 1.229e-05
alternative hypothesis: two-sided
Two-sample Kolmogorov-Smirnov test
data: w and y
D = 1, p-value = 1.229e-05
alternative hypothesis: two-sided
Two-sample Kolmogorov-Smirnov test
data: w and z
D = 1, p-value = 1.229e-05
alternative hypothesis: two-sided
我的问题是，目前我只能一次运行一行功能。我希望能够为所有行做到这一点。我正在考虑使用apply，因为我在一个非常相似的上下文中使用它，除了只有一个数据源。
答案 0 :(得分：0)
不太确定您的需求，但考虑转置，t()以列方式为行数据运行绘图。并考虑使用mapply()，应用系列的多变量类型，它同时为等长对象运行一个操作元素。即使拆分操作，因为一起运行它们可能只打印/绘制最后一次迭代到屏幕。
转置（使用的数据是发布的输出矩阵的轻微变化）
pop1 <- data.frame(t(data))
pop2 <- data.frame(t(data))
pop3 <- data.frame(t(data))
pop4 <- data.frame(t(data))
直方图
hist_fx <- function(w,x,y,z) {
whist <- hist(w,prob=TRUE,col="green",xlim=c(-1,1),ylim=c(0,3))
lines(density(w),col="red")
abline(v=c(mean(w)),col="red")
xhist <- hist(x,prob=TRUE,col="blue",xlim=c(-1,1),ylim=c(0,3))
lines(density(x),col="red")
abline(v=c(mean(x)),col="red")
yhist <- hist(y,prob=TRUE,col="yellow",xlim=c(-1,1),ylim=c(0,3))
lines(density(y),col="red")
abline(v=c(mean(y)),col="red")
zhist <- hist(z,prob=TRUE,col="purple",xlim=c(-1,1),ylim=c(0,3))
lines(density(z),col="red")
abline(v=c(mean(z)),col="red")
}
# HISTOGRAM PLOTS FOR EACH DF COLUMN
output <- mapply(hist_fx, w=pop1, x=pop2, y=pop3, z=pop4)
Kolmogorov-Smirnov测试（使用输入数据的微小变化）
hist_fx <- function(w,x,y,z) {
t1 <- ks.test(w,x)
t2 <- ks.test(w,y)
t3 <- ks.test(w,z)
if(t1$p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(x),col="blue",add=TRUE)
}
if(t2$p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(y),col="red",add=TRUE)
}
if(t3$p.value < 0.05) {
plot(ecdf(w),col="green")
plot(ecdf(z),col="purple",add=TRUE)
}
return(c(t1, t2, t3))
}
output <- mapply(hist_fx, w=pop1, x=pop2, y=pop3, z=pop4)
output
# X1
# statistic 0.1666667
# p.value 0.9962552
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and x"
# statistic 0.25
# p.value 0.8474885
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and y"
# statistic 0.08333333
# p.value 1
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and z"
# X2
# statistic 0.25
# p.value 0.8474885
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and x"
# statistic 0.08333333
# p.value 1
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and y"
# statistic 0.1666667
# p.value 0.9962552
# alternative "two-sided"
# method "Two-sample Kolmogorov-Smirnov test"
# data.name "w and z"
# ...