compareCountDist(tweeDEseq)
compareCountDist()所属R语言包:tweeDEseq
Compare count data distributions
比较计算数据分布
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Compares the empirical and estimated distributions for different count data models
比较不同的计数数据模型的实证研究和估计的分布
用法----------Usage----------
compareCountDist(x, plot=TRUE, ...)
参数----------Arguments----------
参数:x
numeric vector containing the read counts.
读取计数的数字向量。
参数:plot
If TRUE (the default) then the plot with the ECDF function for the counts and the three different Poisson-Tweedie distributions is produced, otherwise no graphical output is given and this only makes sense if one is interested in the returned value (see value section below).
如果TRUE(默认),然后与厄立特里亚社区发展基金的数量和三种不同的泊松特威迪分布函数的图产生,否则没有图形输出,并给出这仅是有意义的,如果一个人在返回的值感兴趣(见下文值)。
参数:...
Further arguments to be passed to the plot function.
进一步的参数被传递到绘图功能。
Details
详情----------Details----------
This function serves the purpose of comparing a empirical distribution of counts with three Poisson-Tweedie distributions arising from estimating mean, dispersion and setting a=1 for comparing against a Poisson, a=0 for comparing against a negative binomial and estimating the shape parameter a from data too. The legend shows the values of the a parameter and the P-value of the likelihood ratio test on whether the expression profile follows a negative binomial distribution (H_0:a=0).
此功能提供了与3个泊松特威迪分布估计平均,分散和设置a=1对泊松比较,a=0对一个负二项分布比较和估计产生的经验分布计数的目的从数据形状参数。传说a参数和表达谱是否遵循负二项分布(H_0:a=0)的似然比检验的P值显示值。
值----------Value----------
List with the following components:
名单由以下部分组成:
参数:a
shape parameter estimated from the input data x.
从输入数据x形状参数的估计。
参数:p.value
P-value for the test that the data follows a negative binomial distribution, i.e., H_0:a=0.
P值的测试数据如下负二项分布,即H_0:a=0。
参考文献----------References----------
A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. Submitted.
参见----------See Also----------
qqchisq testShapePT
qqchisqtestShapePT
举例----------Examples----------
# Generate 500 random counts following a Poisson Inverse Gaussian[产生500泊松逆高斯随机数]
# distribution with mean = 20 and dispersion = 5[分布均值为= 20和分散= 5]
randomCounts <- rPT(n = 500, mu = 20, D = 5, a = 0.5)
xx <- compareCountDist(randomCounts, plot=FALSE)
xx
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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