我已经花了整整一天的时间来首先对我的数据 进行格式化(在通过BondedDust的表(TM)建议找到错误后更新)适当地用于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)
我失败的地方是尝试建模我的数据.
model <- mlogit(selected ~ food + plate | sex + age +hand, data = TM)
solve.default(H,g [!fixed])出错:系统计算奇异:倒数条件数= 6.26659e-18
我真的会在这个主题上提供一些帮助.害怕我要带一点香蕉.
数据本身来自一个实验,我们让1000人在食物板块之间做出决定(我们改变食物的外观 - 无论是角形还是圆形 - 并且改变板的形状 - 是角形还是圆形).
带着最美好的祝福,安迪.
PS害怕我是StackOverflow上统计Qs的新手.
该模型无法将您的dishId解释为替代索引(alt.var),因为您有不同的密钥对用于不同的选择.例如,您有"TS"和"RS"作为.csv文件中第一个选择的备用索引键,但您有"RR"和"RS"作为选择键3634.此外,您还没有指定名称替代品(alt.levels).由于alt.levels没有填写的事实,mlogit.data将自动尝试基于备选索引检测备选方案,这是无法正确解释的.这基本上是一切都出错的地方:"食物"和"板块"变量不会被解释为替代品,但它们被视为个别特定变量,最终导致奇点问题.
您有两种方法可以解决此问题.您可以mlogit.data通过alt.levels参数将实际替代项作为输入:
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,以便您可以通过输入将它们作为输入alt.var.mlogit.data现在可以正确猜出您的替代品是什么:
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:
> 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的结果相比.请注意,替代方案已正确识别,但名称未明确添加到模型中:
> 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)