troubleshooting(secr)
troubleshooting()所属R语言包：secr
Problems in Fitting SECR Models
在拟合SECR模型的问题
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Although secr.fit is quite robust, it does not always work. Inadequate data or an overambitious model occasionally cause numerical problems in the algorithms used for fitting the model, or problems of identifiability, as described for capture–recapture models in general by Gimenez et al. (2004). Here are some tips that may help you.
虽然secr.fit是非常强大的，它并不总是工作。的数据不足或过于雄心勃勃模型的算法，用于拟合模型，或所描述的捕获 - 再捕获模型的可辨识性的问题，一般由希门尼斯等人偶尔会引起数值问题。 （2004）。这里有一些提示，可以帮助你。
secr.fit饰面，但某些或所有的方差缺少----------secr.fit finishes, but some or all of the variances are missing----------
This usually means the model did not fit and the estimates should not be used. Extremely large variances or standard errors also indicate problems.
这通常意味着该模型不适合，不应该使用的估计。非常大的方差或标准误差也表示出现问题。
Try another maximization method (method = 'Nelder-Mead' is more robust than the default). A message may suggest using method = 'BFGS', but this is not so often useful as originally thought. The same maximum likelihood should be found regardless of method, so AIC values are comparable across methods.
尝试另一个最大化的方法（method = 'Nelder-Mead'是更强大的比默认值）。一条消息可能会建议使用method = 'BFGS'，但这是不是经常有用的，原本以为。应该找到相同的最大似然方法，使AIC值是在方法相媲美。
Repeat the maximization with different starting values. You can use secr.fit(..., start = last.model) where last.model is a previously fitted secr object.
重复的最大化，不同的初始值。您可以使用secr.fit(..., start = last.model)last.model是一个预先安装的秘书服务对象。
Try a finer mask (e.g., vary argument nx in make.mask). Check that the extent of the mask matches your data.
尝试更好的屏蔽（例如，不同的参数nxmake.mask）。检查的程度，面具相匹配的数据。
The maximization algorithms work poorly when the beta coefficients are of wildly different magnitude. This may happen when using covariates: ensure beta coefficients are similar (within a factor of 5–10 seems adequate, but this is not based on hard evidence) by scaling any covariates you provide. This can be achieved by setting the typsize argument of nlm or the parscale control argument of optim.
最大化算法不佳时的贝塔系数是幅度很大的不同。这可能发生在使用协变量：确保缩放任何你所提供的协变量，β系数是相似的（5-10内的一个因素似乎是足够的，但是这是没有根据确凿的证据）。要做到这一点通过设置typsize参数nlm或parscale控制参数的optim。
Examine the model. Boundary values (e.g., g0 near 1.0) may give problems. In the case of more complicated models you may gain insight by fixing the value of a difficult-to-estimate parameter (argument fixed).
检查模型。边界值（例如，，G0近1.0）可能会出现问题。在更复杂的模型的情况下，你可能了解通过固定一个难以估算的参数值（参数fixed）。
See also the section "Potential problems" in ../doc/secr-densitysurfaces.pdf.
请参阅节“潜在的问题”在.. / DOC / SECR的，densitysurfaces.pdf的。
secr.fit完成与警告NLM代码1或3----------secr.fit finishes with warning nlm code 1 or 3----------
These conditions do not invariably indicate a failure of model fitting. Proceed with caution, checking as suggested in the preceding section.
这些条件并非一成不变表明一个故障模型拟合。请小心操作，检查在上一节的建议。
secr.fit崩溃的方式，通过最大化----------secr.fit crashes part of the way through maximization----------
A feature of the maximization algorithm used by default in nlm is that it takes a large step in the parameter space early on in the maximization. The step may be so large that it causes floating point underflow or overflow in one or more real parameters. This can be controlled by passing the "stepmax" argument of nlm in the ... argument of secr.fit (see first example). See also the previous point about scaling of covariates.
默认情况下，使用nlm的最大化算法的一个特点是，它需要一个早期步骤的参数空间的最大化。该步骤可以是如此之大，它会导致在一个或多个实际参数的浮点下溢或溢出。这是可以控制通过通过“stepmax”的说法nlm的...参数secr.fit（见示例一）。另请参见前面关于扩展点的协变量。
secr.fit近端崩溃----------secr.fit crashes near end----------
When fitting a model with verify = TRUE you see the log likelihood converge (assuming trace = TRUE), but secr.fit crashes without returning, perhaps with an obscure message referring to nls, or "Error in integrate... : the integral is probably divergent". This is most likely due to numerical problems in the optional bias check with bias.D. Simply set verify = FALSE and repeat.
当拟合模型与verify = TRUE，“你看到的对数似然收敛（假设trace = TRUE），但secr.fit崩溃，而不返回，也许一个不起眼的消息，指的是nls或 "Error in integrate... : the integral is probably divergent"。这很可能是由于数值问题的可选偏置检查与bias.D。简单的设置verify = FALSE和重复。
secr.fit需要更多的内存比----------secr.fit demands more memory than is available----------
This is a problem particularly when using individual covariates in a model fitted by maximizing the conditional likelihood. The memory required is then roughly proportional to the product of the number of individuals, the number of occasions, the number of detectors and the number of latent classes (for finite-mixture models). When maximizing the full-likelihood, substitute "number of groups" for 'number of individuals'. [The limit is reached in external C used for the likelihood calculation, which uses the R function "R_alloc".]
这是一个问题，特别是当通过最大化的条件的可能性，嵌合在一个模型中使用个别的协变量。然后，所需的内存是大致成比例的个体的数量的乘积的数目的场合，检测器的数量和潜类（有限混合模型）的数目。当最大化的可能性，“个人组”的替代品的数量。 [达到极限在外部C用于计算的可能性，它使用的R的功能R_alloc“”。
The mash function may be used to reduce the number of detectors when the design uses many identical and independent clusters. Otherwise, apply your ingenuity to simplify your model, e.g., by casting "groups" as "sessions". Memory is less often an issue on 64-bit systems (see link below).
mash函数可以使用，以减少检测器的数目，在设计时使用许多相同的和独立的聚类。否则，运用你的智慧，以简化您的模型，例如，通过“会议”铸造“组”。记忆是不经常在64位系统的问题（见下面的链接）。
参考文献----------References----------
Methods for investigating parameter redundancy. Animal Biodiversity and Conservation 27, 561–572.
参见----------See Also----------
secr.fit, Memory-limits
secr.fit，Memory-limits
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-30 00:07:22
