r-mice

来源： https://stackoverflow.com/questions/58831710/mice-glm-fit-algorithm-did-not-converge

问题 I have a matrix that is half-sparse. Half of all cells are blank (na) so when I try to run the 'mice' it tries to work on all of them. I'm only interested in a subset. Question: In the following code, how do I make "mice" only operate on the first two columns? Is there a clean way to do this using row-lag or row-lead, so that the content of the previous row can help patch holes in the current row? set.seed(1) #domain x <- seq(from=0,to=10,length.out=1000) #ranges y <- sin(x) +sin(x/2) + rnorm

问题 I want to perform multigroup SEM on imputed data using the R packages mice and semTools , specifically the runMI function that calls Lavaan . I am able to do so when imputing the entire dataset at once, but whilst trawling through stackoverflow/stackexchange I have come across the recommendation to impute data separately for each level of a grouping variable (e.g. men, women), so that the features of each group are preserved (e.g. https://stats.stackexchange.com/questions/149053/questions-on

问题 I want to perform multigroup SEM on imputed data using the R packages mice and semTools , specifically the runMI function that calls Lavaan . I am able to do so when imputing the entire dataset at once, but whilst trawling through stackoverflow/stackexchange I have come across the recommendation to impute data separately for each level of a grouping variable (e.g. men, women), so that the features of each group are preserved (e.g. https://stats.stackexchange.com/questions/149053/questions-on

问题 I'm tring to run a R function in Pyton via Jupyter Notebook. the problem is, that my function name (from mice lib) - containing dot. the name of the function is md.pattern , and this is the code that I'm tring to run: from rpy2.robjects.packages import importr mice = importr('mice') mice.md.pattern(train) and this is the error that I get: AttributeError: module 'mice' has no attribute 'md' I also tried to run: from rpy2.robjects.packages import importr mice = importr('mice') pattern =

问题 I'm tring to run a R function in Pyton via Jupyter Notebook. the problem is, that my function name (from mice lib) - containing dot. the name of the function is md.pattern , and this is the code that I'm tring to run: from rpy2.robjects.packages import importr mice = importr('mice') mice.md.pattern(train) and this is the error that I get: AttributeError: module 'mice' has no attribute 'md' I also tried to run: from rpy2.robjects.packages import importr mice = importr('mice') pattern =

问题 I'm tring to run a R function in Pyton via Jupyter Notebook. the problem is, that my function name (from mice lib) - containing dot. the name of the function is md.pattern , and this is the code that I'm tring to run: from rpy2.robjects.packages import importr mice = importr('mice') mice.md.pattern(train) and this is the error that I get: AttributeError: module 'mice' has no attribute 'md' I also tried to run: from rpy2.robjects.packages import importr mice = importr('mice') pattern =

问题 The R package mice comes with following example: library("mice") imp <- mice(nhanes) fit <- with(data=imp,exp=lm(bmi~hyp+chl)) I want a flexible call of with() like: model_formula <- bmi~hyp+chl fit <- with(data=imp,exp=lm(model_formula)) But this throws Error in eval(predvars, data, env) : object 'bmi' not found . I searched for similar problems. The closet problem I found was Help understand the error in a function I defined in R. My impression is, that writing exp=lm(model_formula) the

问题 I used coxph() from the survival package in multiply imputed dataset and encountered a warning when trying to pool the results. The warning message states: "In mice.df(m, lambda, dfcom, method) : Large sample assumed. A reproducible example is below (with publically available data, without worrying to much about the appropriateness of using both mice and coxph is these data): library(mice) library(survival) #load publically available data data(pbc) #select variables for the reproducable

问题 I used coxph() from the survival package in multiply imputed dataset and encountered a warning when trying to pool the results. The warning message states: "In mice.df(m, lambda, dfcom, method) : Large sample assumed. A reproducible example is below (with publically available data, without worrying to much about the appropriateness of using both mice and coxph is these data): library(mice) library(survival) #load publically available data data(pbc) #select variables for the reproducable