在来自独立源的数据的随机集合中，通常观察到数据的分布是正常的。 这意味着，在绘制水平轴上的变量值和垂直轴上的值的计数的图形时，我们得到钟形曲线。 曲线的中心表示数据集的平均值。 在图中，50％的值位于平均值的左侧，另外50％位于图表的右侧。 这在统计学中被称为正态分布。
R语言有四个内置函数来产生正态分布。 它们描述如下。
<pre class="result notranslate" style="margin: 15px 0px; padding: 10px 5px; position: relative; width: auto; max-width: 700px; box-sizing: border-box; display: block; line-height: 1.7; background: rgb(239, 239, 239); border-radius: 3px; font-size: 14px; font-family: Consolas, "Courier New", Courier, monospace; overflow-x: auto; border: 1px solid rgb(221, 221, 221); word-wrap: break-word !important; white-space: pre-wrap !important; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">dnorm(x, mean, sd)
pnorm(x, mean, sd)
qnorm(p, mean, sd)
rnorm(n, mean, sd)
</pre>
以下是在上述功能中使用的参数的描述 -
x是数字的向量。
p是概率的向量。
n是观察的数量（样本大小）。
mean是样本数据的平均值。 它的默认值为零。
sd是标准偏差。 它的默认值为1。
dnorm（）
该函数给出给定平均值和标准偏差在每个点的概率分布的高度。
<pre class="prettyprint notranslate tryit" style="margin: 15px 0px; padding: 10px 5px; position: relative; width: auto; max-width: 700px; box-sizing: border-box; display: block; line-height: 1.7; background: rgb(239, 239, 239); border-radius: 3px; font-size: 14px; font-family: Consolas, "Courier New", Courier, monospace; overflow-x: auto; border: 1px solid rgb(221, 221, 221); word-wrap: break-word !important; white-space: pre-wrap !important; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># Create a sequence of numbers between -10 and 10 incrementing by 0.1.
x <- seq(-10, 10, by = .1)
Choose the mean as 2.5 and standard deviation as 0.5.
y <- dnorm(x, mean = 2.5, sd = 0.5)
Give the chart file a name.
png(file = "dnorm.png")
plot(x,y)
Save the file.
dev.off()
</pre>
当我们执行上面的代码，它产生以下结果 -
pnorm（）
该函数给出正态分布随机数的概率小于给定数的值。 它也被称为“累积分布函数”。
<pre class="prettyprint notranslate tryit" style="margin: 15px 0px; padding: 10px 5px; position: relative; width: auto; max-width: 700px; box-sizing: border-box; display: block; line-height: 1.7; background: rgb(239, 239, 239); border-radius: 3px; font-size: 14px; font-family: Consolas, "Courier New", Courier, monospace; overflow-x: auto; border: 1px solid rgb(221, 221, 221); word-wrap: break-word !important; white-space: pre-wrap !important; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># Create a sequence of numbers between -10 and 10 incrementing by 0.2.
x <- seq(-10,10,by = .2)
Choose the mean as 2.5 and standard deviation as 2.
y <- pnorm(x, mean = 2.5, sd = 2)
Give the chart file a name.
png(file = "pnorm.png")
Plot the graph.
plot(x,y)
Save the file.
dev.off()
</pre>
当我们执行上面的代码，它产生以下结果 -
qnorm（）
该函数采用概率值，并给出累积值与概率值匹配的数字。
<pre class="prettyprint notranslate tryit" style="margin: 15px 0px; padding: 10px 5px; position: relative; width: auto; max-width: 700px; box-sizing: border-box; display: block; line-height: 1.7; background: rgb(239, 239, 239); border-radius: 3px; font-size: 14px; font-family: Consolas, "Courier New", Courier, monospace; overflow-x: auto; border: 1px solid rgb(221, 221, 221); word-wrap: break-word !important; white-space: pre-wrap !important; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># Create a sequence of probability values incrementing by 0.02.
x <- seq(0, 1, by = 0.02)
Choose the mean as 2 and standard deviation as 3.
y <- qnorm(x, mean = 2, sd = 1)
Give the chart file a name.
png(file = "qnorm.png")
Plot the graph.
plot(x,y)
Save the file.
dev.off()
</pre>
当我们执行上面的代码，它产生以下结果 -
RNORM（）
此函数用于生成分布正常的随机数。 它将样本大小作为输入，并生成许多随机数。 我们绘制一个直方图来显示生成的数字的分布。
<pre class="prettyprint notranslate tryit" style="margin: 15px 0px; padding: 10px 5px; position: relative; width: auto; max-width: 700px; box-sizing: border-box; display: block; line-height: 1.7; background: rgb(239, 239, 239); border-radius: 3px; font-size: 14px; font-family: Consolas, "Courier New", Courier, monospace; overflow-x: auto; border: 1px solid rgb(221, 221, 221); word-wrap: break-word !important; white-space: pre-wrap !important; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"># Create a sample of 50 numbers which are normally distributed.
y <- rnorm(50)
Give the chart file a name.
png(file = "rnorm.png")
Plot the histogram for this sample.
hist(y, main = "Normal DIstribution")
Save the file.
dev.off()
</pre>
当我们执行上面的代码，它产生以下结果 -
