我正在尝试分析通过RPy用lme4.lmer（）生成的一组线性模型的偏差。 This notebook here显示了一个完整的示例，其中包括我导入dep，加载文件，运行lme4.lmer（）以及使它们无法运行方差分析的情况。
为了您的方便，这里还是粘贴了一条失败的行，我希望看到它。
compare = stats.anova(res[0], res[1], res[2])
Error in Ops.data.frame(data, data[[1]]) :
list of length 3 not meaningful
In addition: Warning message:
In anova.merMod(<S4 object of class "lmerMod">, <S4 object of class "lmerMod">, :
failed to find unique model names, assigning generic names
---------------------------------------------------------------------------
RRuntimeError Traceback (most recent call last)
<ipython-input-47-fe0ffa3b55de> in <module>()
----> 1 compare = stats.anova(res[0], res[1], res[2])
/usr/lib64/python2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, args, **kwargs)
84 v = kwargs.pop(k)
85 kwargs[r_k] = v
---> 86 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
/usr/lib64/python2.7/site-packages/rpy2/robjects/functions.pyc in __call__(self, *args, **kwargs)
33 for k, v in kwargs.iteritems():
34 new_kwargs[k] = conversion.py2ri(v)
---> 35 res = super(Function, self).__call__(*new_args, **new_kwargs)
36 res = conversion.ri2py(res)
37 return res
RRuntimeError: Error in Ops.data.frame(data, data[[1]]) :
list of length 3 not meaningful
这段代码可以在R中完美运行，如下所示：
> mydata = read.csv("http://chymera.eu/data/test/r_data.csv")
> library(lme4)
Loading required package: lattice
Loading required package: Matrix
> lme1 = lme4.lmer(formula='RT~cat2 + (1|ID)', data=mydata, REML=FALSE)
Error: could not find function "lme4.lmer"
> lme1 = lmer(formula='RT~cat1 + (1|ID)', data=mydata, REML=FALSE)
> lme2 = lmer(formula='RT~cat2 + (1|ID)', data=mydata, REML=FALSE)
> anova(lme1,lme2)
> lme3 = lmer(formula='RT~cat2*cat1 + (1|ID)', data=mydata, REML=FALSE)
> stats::anova(lme1, lme2, lme3)
Data: mydata
Models:
lme1: RT ~ cat1 + (1 | ID)
lme2: RT ~ cat2 + (1 | ID)
lme3: RT ~ cat2 * cat1 + (1 | ID)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
lme1 4 116.68 122.29 -54.342 108.68
lme2 4 149.59 155.19 -70.793 141.59 0.000 0 1
lme3 6 117.19 125.59 -52.594 105.19 36.398 2 1.248e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
您能帮我也使其在RPy中运行吗？
最佳答案
当在R中时，stats::anova()可以从函数调用中未计算的表达式中推断模型名称。在这里是lme1，'lme2和lme3。
现在考虑重写R代码而不使用变量名，因为这将更接近当前使用rpy2实现的情况，因为数据DataFrame和拟合的模型未绑定到变量名。这将给出以下内容（请注意：“更紧密”而不是“相等”-有关此的详细信息只会分散重点）：
stats::anova(lmer(formula='RT~cat1 + (1|ID)',
data=read.csv("http://chymera.eu/data/test/r_data.csv"),
REML=FALSE),
lmer(formula='RT~cat2 + (1|ID)',
data=read.csv("http://chymera.eu/data/test/r_data.csv"),
REML=FALSE),
lmer(formula='RT~cat2*cat1 + (1|ID)',
data=read.csv("http://chymera.eu/data/test/r_data.csv"),
REML=FALSE))
结果是R中的错误。
Error in names(mods) <- sub("@env$", "", mNms) :
'names' attribute [6] must be the same length as the vector [3]
In addition: Warning message:
In anova.merMod(lmer(formula = "RT~cat1 + (1|ID)", data = read.csv("http://chymera.eu/data/test/r_data.csv"), :
failed to find unique model names, assigning generic names
这表明R函数
lme4:::anova.meMod做出的假设很容易被违反，应该通知软件包的作者。它还显示了表达式将用于在结果文本输出中标识模型。
以下内容可能缺乏一点优雅，但应既是一种解决方法，又是一种使模型的标签简短的方法。
# bind the DataFrame to an R symbol
robjects.globalenv['dataf'] = dfr
# build models, letting R fetch the symbol `dataf` when it is evaluating
# the parameters in the function call
res = list()
for formula in formulae:
lme_res = lme4.lmer(formula=formula, data=base.as_symbol("dataf"), REML='false')
res.append(lme_res)
# This is enough to work around the problem
compare = stats.anova(res[0], res[1], res[2])
# if not happy with the model names displayed by `compare`,
# globalenv can be filled further
names = list()
for i, value in enumerate(res):
names.append('lme%i' % i)
robjects.globalenv[names[i]] = value
# call `anova`
compare = stats.anova(*[base.as_symbol(x) for x in names])