एलएमई () त्रुटि - पुनरावृत्ति सीमा तक पहुंच गई

Question

एक पार मिश्रित प्रभाव मॉडल को निर्दिष्ट करने में, मैं इंटरैक्शन को शामिल करने की कोशिश कर रहा हूं। हालाँकि, मुझे निम्न त्रुटि संदेश मिलता है:

Error in lme.formula(rate ~ nozzle, random = ~nozzle | operator, data = Flow) : 
nlminb problem, convergence error code = 1
message = iteration limit reached without convergence (10)

मॉडल में निम्नलिखित हैं: 1. 3 नोजल प्रकार (निश्चित प्रभाव) 2. 5 ऑपरेटर, प्रत्येक में 3 नोजल प्रकारों से ईंधन प्रवाह पर 3 दोहराए गए उपाय हैं।

मुझे मॉडल में नोजल प्रकार और ऑपरेटर के बीच बातचीत को शामिल करने के लिए कहा गया था। यह मॉडल के लिए मेरा कोड है:

flow.lme <- lme(rate ~ nozzle, error= nozzle|operator, data=Flow)

मुझे यह त्रुटि संदेश क्यों मिलेगा ??

Comments

क्या आप operator|nozzleयादृच्छिक नहीं चाहते हैं ?

— ओलिविया ग्रिग

No, operator is the random effect.

— f1r3br4nd

you can use > crtl=lmeControl(opt='optim',optimMethod = "SANN")

— AliReza Afshari Safavi

You should make @f1r3br4nd's response as answer

— JetLag

@AliRezaAfshariSafavi what are the benefits of using "SANN" versus the default BFGS ?

— gcamargo

Answer 1

मैंने errorतर्क के बारे में नहीं सुना है lmeऔर मैं इसे प्रलेखन में नहीं देखता हूं। क्या आप वाकई टाइपो नहीं हैं? लेकिन, आपके द्वारा पूछे गए प्रश्न का उत्तर देने के लिए:

प्रयत्न ?lmeControl

स्थापना maxIter, msMaxIter, niterEM, और / या msMaxEvalडिफ़ॉल्ट की तुलना में अधिक मूल्यों के लिए तर्क इसे ठीक कर सकते हैं। lmeControlकिसी ऑब्जेक्ट से आउटपुट कैप्चर करें और फिर उस ऑब्जेक्ट को controlतर्क के पास करेंlme.

या ...

नया डिफ़ॉल्ट ऑप्टिमाइज़र lmeउपयोग परतदार है। जब मैं पुराने आशावादी में वापस बदल देता हूं तो आधे समय मेरे लिए इस प्रकार की समस्याएं हल हो जाती हैं। आप की स्थापना द्वारा ऐसा करने optके लिए तर्क lmeControlके लिए'optim'.

इसलिए, इसे एक साथ रखना:

ctrl <- lmeControl(opt='optim');
flow.lme <- lme(rate ~ nozzle, error= nozzle|operator, control=ctrl, data=Flow);

Comments

In some cases it might be worth knowing, that lmeControl is a function from the nlme package

— Qaswed

Answer 2

First, this is an ANOVA model, not a mixed model.

Second, it seems to me that your model is not identified. In equation form, you have

$$\mbox{response}_{ij} = \beta_1 \mbox{nozzle type}_{1ij} + \beta_2 \mbox{nozzle type}_{2ij} + \beta_3 \mbox{nozzle type}_{3ij} + \mbox{operator}_i + \mbox{nozzle within operator}_{ij}$$ where nozzle types are fixed effects (dummy variables), operator is a random effect, and nozzle within operator is a random effect, too.

The last term has 15 separate values for 15 observations that you have. There are no degrees of freedom left to get any other terms in the model. Including interactions was a poor advice. You'd have to drop them whatsoever; even including them as crossed effects won't help, as they will then be perfectly collinear with the fixed effects, and won't be estimable. A maximum likelihood or REML model with 15 observations does not make sense; the asymptotic results of maximum likelihood theory simply won't work: this is a Ferrari you are trying to drive on a plowed field.

Comments

If there are both random and fixed effects in a model then by definition it's a mixed-effect model. Whether you call it ANOVA or regression is a separate issue and sort of a semantics question. I am a little puzzled, though, by what the OP means by an interaction. As far as I can tell, he's already doing that by using random=~nozzle|operator instead of random=~1|operator.

— f1r3br4nd

Some literatures do refer to the nested random effects as interactions between different levels of nesting; I think I've even seen this in Pinheiro & Bates. I agree that terming this properly is a matter of semantics, but I am just thinking of introducing this-does-not-have-to-be-a-mixed-model tag. On about two-thirds of the mixed-models question that I get to see, saying something to that effect is a part of my answer.

— StasK

Funny, I spend a good chunk of my time telling people they aren't using mixed models enough. I actually would like to be wrong, because it would simplify my life somewhat. What would you tell the OP the rule of thumb is for determining when a mixed model is needed?

— f1r3br4nd

Oh, so you are the villain, then. This one has a single categorical predictor, so it is an ANOVA model to me, as I said earlier. If you had information at different levels (e.g., state \ school \ students, with data on states, on school, and on students), that would sound more like a mixed model to me. Basically, if you can do this as sums of squares, that's ANOVA; if you can do this as a regression model, that's a regression model. If doing the maximum likelihood/REML is absolutely unavoidable (as it is in binary response case), that's a mixed model alright to me.

— StasK