在R中控制打印输出中的小数位数 [英] Controlling number of decimal digits in print output in R
问题描述
R中有一个选项可以控制数字显示.例如:
There is an option in R to get control over digit display. For example:
options(digits=10)
应该以10位数字给出计算结果，直到R会话结束.在R的帮助文件中，digits参数的定义如下:
is supposed to give the calculation results in 10 digits till the end of R session. In the help file of R, the definition for digits parameter is as follows:
位数:控制位数 打印数值时打印. 这只是一个建议.有效值 是 1 ... 22 ，默认为 7
digits: controls the number of digits to print when printing numeric values. It is a suggestion only. Valid values are 1...22 with default 7
因此，它只是一个建议.如果我希望始终显示10位数字，而不是多多少少怎么办?
So, it says this is a suggestion only. What if I like to always display 10 digits, not more or less?
我的第二个问题是，如果我想显示22位以上的数字，即进行100位以上的更精确的计算，该怎么办? R基座是否可以使用，还是为此需要附加的包装/功能?
My second question is, what if I like to display more than 22 digits, i.e. for more precise calculations like 100 digits? Is it possible with base R, or do I need an additional package/function for that?
由于jmoy的建议，我尝试了sprintf("%.100f",pi)，它给出了
Thanks to jmoy's suggestion, I tried sprintf("%.100f",pi) and it gave
[1] "3.1415926535897931159979634685441851615905761718750000000000000000000000000000000000000000000000000000"
，其中有48位小数.这是R可以处理的最大限制吗?
which has 48 decimals. Is this the maximum limit R can handle?
推荐答案
仅是一个建议，原因是您可以很容易地编写一个忽略options值的打印函数.内置的打印和格式化功能确实将options值用作默认值.
The reason it is only a suggestion is that you could quite easily write a print function that ignored the options value. The built-in printing and formatting functions do use the options value as a default.
对于第二个问题，由于R使用有限精度算术，因此答案的精确度不能超过15位或16位小数，因此通常不需要更多. gmp 和
As to the second question, since R uses finite precision arithmetic, your answers aren't accurate beyond 15 or 16 decimal places, so in general, more aren't required. The gmp and rcdd packages deal with multiple precision arithmetic (via an interace to the gmp library), but this is mostly related to big integers rather than more decimal places for your doubles.
Mathematica or Maple will allow you to give as many decimal places as your heart desires.
考虑小数位和有效数字之间的差异可能会很有用.如果您要进行统计检验时所依赖的差异超出第15位有效数字，那么您的分析几乎肯定是垃圾.
It might be useful to think about the difference between decimal places and significant figures. If you are doing statistical tests that rely on differences beyond the 15th significant figure, then your analysis is almost certainly junk.
另一方面，如果您只处理很小的数字，那么问题就不大了，因为R可以处理的数字小至.Machine$double.xmin(通常为2e-308).
On the other hand, if you are just dealing with very small numbers, that is less of a problem, since R can handle number as small as .Machine$double.xmin (usually 2e-308).
比较这两种分析.
x1 <- rnorm(50, 1, 1e-15)
y1 <- rnorm(50, 1 + 1e-15, 1e-15)
t.test(x1, y1) #Should throw an error
x2 <- rnorm(50, 0, 1e-15)
y2 <- rnorm(50, 1e-15, 1e-15)
t.test(x2, y2) #ok
在第一种情况下，数字之间的差异仅在许多有效数字之后出现，因此数据几乎恒定".在第二种情况下，尽管数字之间差异的大小相同，但与数字本身的大小相比，它们却很大.
In the first case, differences between numbers only occur after many significant figures, so the data are "nearly constant". In the second case, Although the size of the differences between numbers are the same, compared to the magnitude of the numbers themselves they are large.
如e3bo所述，您可以使用Rmpfr包使用多精度浮点数.
As mentioned by e3bo, you can use multiple-precision floating point numbers using the Rmpfr package.
mpfr("3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825")
与常规(双精度)numeric向量相比，它们使用起来更慢且占用更多内存，但如果条件条件不佳或算法不稳定，则很有用.
These are slower and more memory intensive to use than regular (double precision) numeric vectors, but can be useful if you have a poorly conditioned problem or unstable algorithm.
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