Problems with pooling Multiply Imuputed datasets, of multilevel logistic models, using R (MICE) - missing coefficent

0
votes

I am having problems with the MICE package in R, particularity with pooling the imputed data sets.

I am running a multilevel binomial logistic regression, with Level1 - topic (participant response to 10 questions on different topics, e.g. Darkness, Day) nested within Level2 - individuals.

The model is created using R2MLwiN, the formula is > fit1 <-runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)

Unfortunately, there is missing data in all of the Level1 (topic) responses. I have been using the mice package ([CRAN][1]) to multiply impute the missing values.

I can fit the model to the imputed datasets, using the formula > fitMI <- (with(MI.Data, runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)))

However, when I come to pool the analyses with the call code > pool(fitMI) it fails, with the Error:

Error in pool(with(tempData, runMLwiN(c(probit(T_Darkness, cons), probit(T_Day, : Object has no coef() method.

I am not sure why it is saying there is no coefficient, as the analyses of the individual MI datasets provide both fixed parts (coefficients) and random parts (covariances)

Any help with what is going wrong would be much appreciated.

I should warn you that this is my first foray into using R and multilevel modelling. Also I know there is a MlwiN package ([REALCOM][2]) that can do this but I don't have the background to use the MLwiN software.

thanks johnny

Update - R reproducible example

Libraries used

library(R2MLwiN)

library(mice)

Subset of data `

T_Darkness <- c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, NA, 1, 0, NA,NA, 1, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, NA, 1, 0)

T_Day <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, NA, NA, 0)

T_Light <- c(0, 0, NA, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0)

T_Night <- c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,NA, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0)

T_Rain <- c(1, 0, 0, 1, 1, 0, 0, NA, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, NA, 1, NA, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 1, NA, 0, 0)

T_Rainbows <- c(1, 1, 1, 1, 0, 1, 0, 1, 0, 1, NA, 1, 1, 0, 0, 1, 0, NA, 0, 1, 0, NA, 0, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, NA, 1, 0, 1, NA, 0, 0, 1, 0, 1, 1, 1, 0, 1)

T_Snow <- c(0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, NA, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, NA, 1, 0, 1, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0)

T_Storms <- c(0, 0, 0, 1, 1, 1, 0, 1, 0, 1, NA, 0, 0, 0, 0, 1, 0, NA, 0, 0, 1, 0, 0, NA, 1, 1, NA, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, NA, 1, 0, NA, 0, 0, 0, 1, 1, 0, 1, NA, NA, 1)

T_Waterfalls <- c(0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, NA, 0)

T_Waves <- c(0, 1, 0, 1, 1, 0, 1, NA, 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, 0, 0, 1, 0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, NA, 1, 0, 0, 0, 1, 0, 0, NA, 0, 1, 0, 0, 0, 0, 0, 1, 1, NA, 1, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0)

data <- data.frame (T_Darkness, T_Day, T_Light, T_Night, T_Rain, T_Rainbows, T_Snow, T_Storms, T_Waterfalls, T_Waves)

data$cons <- 1

`

Data imputed using mice with

MI.Data <- mice(data,m=5,maxit=50,meth='pmm',seed=500)

1
rlogistic-regressionpoolingr-mice
I'm voting to close this question as off-topic because it is about understanding an R error message without a reproducible example. – gung - Reinstate Monica
I hope the above is a reproducible example – j.halls
Thanks, we should be able to migrate your question to Stack Overflow now. – gung - Reinstate Monica

1 Answers

0
votes

This appears to be due to some of the model extraction methods in R2MLwiN not being correctly found, which should have been fixed in the recently released 0.8-2 version of the package. Running your example with this gives me the following results:

> pool(fitMI)
Call: pool(object = fitMI)

Pooled coefficients:
  FP_Intercept_T_Darkness        FP_Intercept_T_Day      FP_Intercept_T_Light      FP_Intercept_T_Night       FP_Intercept_T_Rain 
            -0.9687210917             -1.0720602274             -0.9584792256             -1.1816471815             -0.7082406878 
  FP_Intercept_T_Rainbows       FP_Intercept_T_Snow     FP_Intercept_T_Storms FP_Intercept_T_Waterfalls      FP_Intercept_T_Waves 
            -0.0455361903             -0.7537600398             -0.3883027434             -1.2365225554             -0.6423609257 
          RP1_var_bcons_1   RP1_cov_bcons_1_bcons_2           RP1_var_bcons_2   RP1_cov_bcons_1_bcons_3   RP1_cov_bcons_2_bcons_3 
             1.0000000000              0.0508168936              1.0000000000              0.2744663656              0.1625871509 
          RP1_var_bcons_3   RP1_cov_bcons_1_bcons_4   RP1_cov_bcons_2_bcons_4   RP1_cov_bcons_3_bcons_4           RP1_var_bcons_4 
             1.0000000000              0.0013987361              0.0576194786              0.0201622359              1.0000000000 
  RP1_cov_bcons_1_bcons_5   RP1_cov_bcons_2_bcons_5   RP1_cov_bcons_3_bcons_5   RP1_cov_bcons_4_bcons_5           RP1_var_bcons_5 
            -0.0220604800              0.1620389074              0.0956511647             -0.0242812764              1.0000000000 
  RP1_cov_bcons_1_bcons_6   RP1_cov_bcons_2_bcons_6   RP1_cov_bcons_3_bcons_6   RP1_cov_bcons_4_bcons_6   RP1_cov_bcons_5_bcons_6 
             0.2644620836              0.0555731133              0.1911445856              0.2584619522              0.1523280591 
          RP1_var_bcons_6   RP1_cov_bcons_1_bcons_7   RP1_cov_bcons_2_bcons_7   RP1_cov_bcons_3_bcons_7   RP1_cov_bcons_4_bcons_7 
             1.0000000000              0.1877118051              0.0872156173              0.2800109982              0.1433261335 
  RP1_cov_bcons_5_bcons_7   RP1_cov_bcons_6_bcons_7           RP1_var_bcons_7   RP1_cov_bcons_1_bcons_8   RP1_cov_bcons_2_bcons_8 
            -0.0006230903              0.1582182944              1.0000000000             -0.0749104023              0.1435756236 
  RP1_cov_bcons_3_bcons_8   RP1_cov_bcons_4_bcons_8   RP1_cov_bcons_5_bcons_8   RP1_cov_bcons_6_bcons_8   RP1_cov_bcons_7_bcons_8 
             0.0537744537              0.2291038185              0.2553031743              0.2716509402              0.1914017051 
          RP1_var_bcons_8   RP1_cov_bcons_1_bcons_9   RP1_cov_bcons_2_bcons_9   RP1_cov_bcons_3_bcons_9   RP1_cov_bcons_4_bcons_9 
             1.0000000000              0.1936145425              0.2835071683              0.0144172618              0.3326070011 
  RP1_cov_bcons_5_bcons_9   RP1_cov_bcons_6_bcons_9   RP1_cov_bcons_7_bcons_9   RP1_cov_bcons_8_bcons_9           RP1_var_bcons_9 
             0.1372590512              0.2854030728              0.0750594735              0.2545967996              1.0000000000 
 RP1_cov_bcons_1_bcons_10  RP1_cov_bcons_2_bcons_10  RP1_cov_bcons_3_bcons_10  RP1_cov_bcons_4_bcons_10  RP1_cov_bcons_5_bcons_10 
             0.3137466609              0.3498021364              0.2846792042              0.1126367375              0.2416045219 
 RP1_cov_bcons_6_bcons_10  RP1_cov_bcons_7_bcons_10  RP1_cov_bcons_8_bcons_10  RP1_cov_bcons_9_bcons_10          RP1_var_bcons_10 
             0.2137401104              0.1849118918              0.2134640366              0.6101759672              1.0000000000 

Fraction of information about the coefficients missing due to nonresponse: 
  FP_Intercept_T_Darkness        FP_Intercept_T_Day      FP_Intercept_T_Light      FP_Intercept_T_Night       FP_Intercept_T_Rain 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  FP_Intercept_T_Rainbows       FP_Intercept_T_Snow     FP_Intercept_T_Storms FP_Intercept_T_Waterfalls      FP_Intercept_T_Waves 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
          RP1_var_bcons_1   RP1_cov_bcons_1_bcons_2           RP1_var_bcons_2   RP1_cov_bcons_1_bcons_3   RP1_cov_bcons_2_bcons_3 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
          RP1_var_bcons_3   RP1_cov_bcons_1_bcons_4   RP1_cov_bcons_2_bcons_4   RP1_cov_bcons_3_bcons_4           RP1_var_bcons_4 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  RP1_cov_bcons_1_bcons_5   RP1_cov_bcons_2_bcons_5   RP1_cov_bcons_3_bcons_5   RP1_cov_bcons_4_bcons_5           RP1_var_bcons_5 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  RP1_cov_bcons_1_bcons_6   RP1_cov_bcons_2_bcons_6   RP1_cov_bcons_3_bcons_6   RP1_cov_bcons_4_bcons_6   RP1_cov_bcons_5_bcons_6 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
          RP1_var_bcons_6   RP1_cov_bcons_1_bcons_7   RP1_cov_bcons_2_bcons_7   RP1_cov_bcons_3_bcons_7   RP1_cov_bcons_4_bcons_7 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  RP1_cov_bcons_5_bcons_7   RP1_cov_bcons_6_bcons_7           RP1_var_bcons_7   RP1_cov_bcons_1_bcons_8   RP1_cov_bcons_2_bcons_8 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  RP1_cov_bcons_3_bcons_8   RP1_cov_bcons_4_bcons_8   RP1_cov_bcons_5_bcons_8   RP1_cov_bcons_6_bcons_8   RP1_cov_bcons_7_bcons_8 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
          RP1_var_bcons_8   RP1_cov_bcons_1_bcons_9   RP1_cov_bcons_2_bcons_9   RP1_cov_bcons_3_bcons_9   RP1_cov_bcons_4_bcons_9 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
  RP1_cov_bcons_5_bcons_9   RP1_cov_bcons_6_bcons_9   RP1_cov_bcons_7_bcons_9   RP1_cov_bcons_8_bcons_9           RP1_var_bcons_9 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
 RP1_cov_bcons_1_bcons_10  RP1_cov_bcons_2_bcons_10  RP1_cov_bcons_3_bcons_10  RP1_cov_bcons_4_bcons_10  RP1_cov_bcons_5_bcons_10 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367 
 RP1_cov_bcons_6_bcons_10  RP1_cov_bcons_7_bcons_10  RP1_cov_bcons_8_bcons_10  RP1_cov_bcons_9_bcons_10          RP1_var_bcons_10 
                0.5714367                 0.5714367                 0.5714367                 0.5714367                 0.5714367