imputation

问题 I follow the method in this post to replace missing values with the group mode, but encounter the "IndexError: index out of bounds". df['SIC'] = df.groupby('CIK').SIC.apply(lambda x: x.fillna(x.mode()[0])) I guess this is probably because some groups have all missing values and do not have a mode. Is there a way to get around this? Thank you! 回答1: mode is quite difficult, given that there really isn't any agreed upon way to deal with ties. Plus it's typically very slow. Here's one way that

问题 Consider data which contains some nan below: Column-1 Column-2 Column-3 Column-4 Column-5 0 NaN 15.0 63.0 8.0 40.0 1 60.0 51.0 NaN 54.0 31.0 2 15.0 17.0 55.0 80.0 NaN 3 54.0 43.0 70.0 16.0 73.0 4 94.0 31.0 94.0 29.0 53.0 5 99.0 52.0 77.0 91.0 58.0 6 84.0 19.0 36.0 NaN 97.0 7 41.0 91.0 62.0 67.0 68.0 8 44.0 38.0 27.0 53.0 37.0 9 58.0 NaN 63.0 57.0 28.0 10 66.0 68.0 89.0 36.0 47.0 11 7.0 81.0 5.0 99.0 16.0 12 43.0 55.0 64.0 88.0 NaN 13 8.0 90.0 91.0 44.0 4.0 14 29.0 52.0 94.0 71.0 47.0 15 22.0

问题 I am new in R programming language. I just wanted to know is there any way to impute null values of just one column in our dataset. Because all of imputation commands and libraries that I have seen, impute null values of the whole dataset. 回答1: Here is an example using the Hmisc package and impute library(Hmisc) DF <- data.frame(age = c(10, 20, NA, 40), sex = c('male','female')) # impute with mean value DF$imputed_age <- with(DF, impute(age, mean)) # impute with random value DF$imputed_age2 <

问题 I have a dataset will some missing data that looks like this: id category value 1 A NaN 2 B NaN 3 A 10.5 4 C NaN 5 A 2.0 6 B 1.0 I need to fill in the nulls to use the data in a model. Every time a category occurs for the first time it is NULL. The way I want to do is for cases like category A and B that have more than one value replace the nulls with the average of that category. And for category C with only single occurrence just fill in the average of the rest of the data. I know that I

问题 I have a data that are observations over time. Unfortunately, some large gaps of time points are missing on a treatment. They are not coded as NA and if I make a plot out of them it becomes apparent. My data frame looks like this. The number of samples per time points are irregular. (edit: sorry for not making the example reproducible)s structure(list(A = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 3 years ago . I want to perform multiple imputation for a set of variables using the MICE package in R. # Example data data <- data.frame( gcs = c(3, 10, NA, NA, NA, 15, 14, 15, 15, 14, 15, NA, 13, 15, 15), hf = c(50, 66, 78, 99, NA, NA, 56, 55, NA, 76, 98, 105, NA, NA, 65), ... ) The minimum for gcs is 3 and the maximum is 15 , and it may not be a fractional number, how can I set these

问题 I have a dataset containing missing values. I have imputed this dataset, as follows: library(mice) id <- c(1,2,3,4,5,6,7,8,9,10) group <- c(0,1,1,0,1,1,0,1,0,1) measure_1 <- c(60,80,90,54,60,61,77,67,88,90) measure_2 <- c(55,NA,88,55,70,62,78,66,65,92) measure_3 <- c(58,88,85,56,68,62,89,62,70,99) measure_4 <- c(64,80,78,92,NA,NA,87,65,67,96) measure_5 <- c(64,85,80,65,74,69,90,65,70,99) measure_6 <- c(70,NA,80,55,73,64,91,65,91,89) dat <- data.frame(id, group, measure_1, measure_2, measure_3

问题 I´m using the package MICE in R to do multiple imputations. I´ve done several imputations with only numerical variables, the imputation method is predictive mean matching, and when I use the command stripplot(name of imputed dataset) I get to see the observed and imputed values of all the variables. The problem occurs when I try to do imputation on a combination of categorical and numerical variables. The imputation method then is PMM for the numerical variables, and logistical regression for

问题 I am running a logistic regression with a binary outcome variable on data that has been multiply imputed using MICE. It seems straightforward to pool the coefficients of the glm model: imp=mice(nhanes2, print=F) imp$meth fit0=with(data=imp, glm(hyp~age, family = binomial)) fit1=with(data=imp, glm(hyp~age+chl, family = binomial)) summary(pool(fit1)) However, I can't figure out a way to pool other output generated by the glm. For instance, the glm function produces AIC, Null deviance and

问题 I have a table with missing values and I'm trying to write a function that will replace the missing values with a calculation based on the nearest two non-zero values. Example: X Tom 1 4.3 2 5.1 3 NA 4 NA 5 7.4 For X = 3 , Tom = 5.1 + (7.4-5.1)/2 . For X = 4 , Tom = (5.1 + (7.4-5.1)/2) + (7.4-5.1)/2 Does this function already exist? If not, any advice would be greatly appreciated. 回答1: A more usual way to do this (but not equivalent to the question) is to use linear interpolation: library(zoo