R mlogit模型，计算上的奇异 [英] R mlogit model, computationally singular
问题描述
我整整一天都在努力格式化数据 (在通过BondedDust的table(TM)建议发现错误后更新)适用于mLogit:
I've spent the whole of today first battling with formatting my data (updated after finding a bug via BondedDust's table(TM) suggestion) appropriately for mLogit:
raw <-read.csv("C:\\Users\\Andy\\Desktop\\research\\Oxford\\Prefs\\rData.csv", header=T, row.names = NULL,id="id")
raw <-na.omit(raw)
library(mlogit)
TM <- mlogit.data(raw, choice = "selected", shape = "long", alt.var = "dishId", chid.var = "individuals", drop.index = TRUE)
当我尝试对数据建模时，我失败了.
Where I fail is when trying to model my data.
model <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)
solve.default(H，g [！fixed])中的错误:系统正在计算 单数:条件编号= 6.26659e-18
Error in solve.default(H, g[!fixed]) : system is computationally singular: reciprocal condition number = 6.26659e-18
我真的很感谢您提供有关该主题的帮助.害怕我要带一点香蕉.
I would really appreicate some help on the topic. Afraid I'm going a little bananas with it.
数据本身来自一个实验，在实验中，我们让成千上万的人在成对的食物盘子之间做出决定(我们改变食物的外观(角度或圆形)，并改变盘子的形状(角度或圆形) ).
The data itself is from an experiment where we get 1000s of people to decide between pairs of plates of food (we vary how the food looks - either Angular or Circular - and vary how the plate is shaped - is either Angular or Circular).
衷心祝愿， 安迪.
害怕PS我是StackOverflow上具有统计Qs的新手.
PS Afraid I'm a newbie with statistic Qs on StackOverflow.
推荐答案
该模型无法将您的dishId解释为替代索引(alt.var)，因为对于不同的选择您具有不同的密钥对.例如，对于.csv文件中的第一选择，您具有"TS"和"RS"作为备用索引键，但是对于选择3634，您具有"RR"和"RS"作为键.此外，您也未指定名称替代项(alt.levels).由于未填写alt.levels的事实，mlogit.data会根据替代索引无法正确解释的情况自动尝试检测替代.基本上，这就是所有错误的地方:食物"和盘子"变量并未解释为替代方案，而是被视为个别特定变量，最终最终导致了奇点问题.
The model is unable to interpret your dishId as the alternative index (alt.var) because you have different keypairs for different choices. For example, you have "TS" and "RS" as alternative index keys for the first choice in your .csv file but you have "RR" and "RS" as keys for choice 3634. Additionally, you did also not specify the names of the alternatives (alt.levels). As a result of the fact that alt.levels is not filled in, mlogit.data will automatically try to detect the alternatives based upon the alternative index, which it cannot correctly interpret. This is basically where everything goes wrong: The 'food' and 'plate' variables are not interpreted as alternatives but they are considered as individual specific variables that eventually end up causing singularity issues.
您有两种方法可以解决此问题.您可以通过alt.levels参数将实际的替代项作为mlogit.data的输入:
You have two options to fix the issue. You can give the actual alternatives as input to mlogit.data through the alt.levels parameter:
TM <- mlogit.data(raw, choice = "selected", shape = "long", alt.levels = c("food","plate"),chid.var = "individuals",drop.index=TRUE)
model1 <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)
或者，您可以选择使索引键保持一致，以便您可以通过alt.var将其作为输入. mlogit.data现在将能够正确猜测您的替代方案:
Alternatively, you could opt to make your index keys consistent so that you can give them as input via alt.var. mlogit.data will now be able to correctly guess what your alternatives are:
raw[,3] <- rep(1:2,nrow(raw)/2) # use 1 and 2 as unique alternative keys for all choices
TM <- mlogit.data(raw, choice = "selected", shape = "long", alt.var="dishId", chid.var = "individuals")
model2 <- model <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)
我们验证两个模型确实相同.模型1的结果:
We verify that both models are indeed identical. The results of model 1:
> summary(model1)
Call:
mlogit(formula = selected ~ food + plate | sex + age + hand,
data = TM, method = "nr", print.level = 0)
Frequencies of alternatives:
food plate
0.42847 0.57153
nr method
4 iterations, 0h:0m:0s
g'(-H)^-1g = 0.00423
successive function values within tolerance limits
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
plate:(intercept) -0.0969627 0.0764117 -1.2689 0.2044589
foodCirc 1.0374881 0.0339559 30.5540 < 2.2e-16 ***
plateCirc -0.0064866 0.0524547 -0.1237 0.9015835
plate:sexmale -0.0811157 0.0416113 -1.9494 0.0512512 .
plate:age16-34 0.1622542 0.0469167 3.4583 0.0005435 ***
plate:age35-54 0.0312484 0.0555634 0.5624 0.5738492
plate:age55-74 0.0556696 0.0836248 0.6657 0.5055987
plate:age75+ 0.1057646 0.2453797 0.4310 0.6664508
plate:handright -0.0177260 0.0539510 -0.3286 0.7424902
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-Likelihood: -8284.6
McFadden R^2: 0.097398
Likelihood ratio test : chisq = 1787.9 (p.value = < 2.22e-16)
与模型2的结果进行比较.请注意，正确识别了替代方案，但是名称未明确添加到模型中:
Versus the results of model 2. Note that the alternatives are correctly identified, but the names are not explicitly added to the model:
> summary(model2)
Call:
mlogit(formula = selected ~ food + plate | sex + age + hand,
data = TM, method = "nr", print.level = 0)
Frequencies of alternatives:
1 2
0.42847 0.57153
nr method
4 iterations, 0h:0m:0s
g'(-H)^-1g = 0.00423
successive function values within tolerance limits
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
2:(intercept) -0.0969627 0.0764117 -1.2689 0.2044589
foodCirc 1.0374881 0.0339559 30.5540 < 2.2e-16 ***
plateCirc -0.0064866 0.0524547 -0.1237 0.9015835
2:sexmale -0.0811157 0.0416113 -1.9494 0.0512512 .
2:age16-34 0.1622542 0.0469167 3.4583 0.0005435 ***
2:age35-54 0.0312484 0.0555634 0.5624 0.5738492
2:age55-74 0.0556696 0.0836248 0.6657 0.5055987
2:age75+ 0.1057646 0.2453797 0.4310 0.6664508
2:handright -0.0177260 0.0539510 -0.3286 0.7424902
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-Likelihood: -8284.6
McFadden R^2: 0.097398
Likelihood ratio test : chisq = 1787.9 (p.value = < 2.22e-16)
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