imputation

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 have a dataframe similar to below id A B C D E 1 2 3 4 5 5 1 NaN 4 NaN 6 7 2 3 4 5 6 6 2 NaN NaN 5 4 1 I want to do a null value imputation for columns A , B , C in a forward filling but for each group. That means, I want the forward filling be applied on each id . How can I do that? 回答1: Use GroupBy.ffill for forward filling per groups for all columns, but if first values per groups are NaN s there is no replace, so is possible use fillna and last casting to integers: print (df) id A B C D

问题 My model has a LogNormal RV, C, of shape (W,D). Each row in W and each column in D has a parameter that is being fit. I have tried to specify my observations as a (W,D) matrix, however, that is leading to a theano compile error raise Exception('Compilation failed (return status=%s): %s' % Exception: ('The following error happened while compiling the node', Alloc(Elemwise{switch,no_inplace}.0, TensorConstant{10}, TensorConstant{10}), '\n', 'Compilation failed (return status=3): ', '[Alloc(

问题 My model has a LogNormal RV, C, of shape (W,D). Each row in W and each column in D has a parameter that is being fit. I have tried to specify my observations as a (W,D) matrix, however, that is leading to a theano compile error raise Exception('Compilation failed (return status=%s): %s' % Exception: ('The following error happened while compiling the node', Alloc(Elemwise{switch,no_inplace}.0, TensorConstant{10}, TensorConstant{10}), '\n', 'Compilation failed (return status=3): ', '[Alloc(

问题 (EDIT: totally refined question) using package mitools & survey and followiing Anthony Damico's code, I am working with Survey of Consumer Finance dataset for several days. original list of datasets is "scf_imp", and the imputation imposed list of datasets is "scf_design". The problem is the following: 5 multiple imputation data frames have different columns and therefore if I make a subset of samples with that column variable ("houses" in my case), data frames with missing value in that

问题 Trying to impute values based on the present values over time- for a given series, within a given country. i.e. Do not want the imputed values of the series for Argentina, to be swayed by that same series's values for USA, or China, etc... Also, do not want the imputed values to be swayed based on other series, such as GDP, GNI, etc. Goal is to iterate over each series, for each country, and impute any missing values based on the present data for that feature, under that country. Currently

问题 So I am using the mice package to impute missing data. I'm new to imputation so I've got to a point but have run into a steep learning curve. To give a toy example: library(mice) # Using nhanes dataset as example df1 <- mice(nhanes, m=10) So as you can see I imputed df1 10 times using mostly default settings - and I am comfortable using this result in regression models, pooling results etc. However in my real life data, I have survey data from different countries. And so levels of missings

问题 I'd like to do a simple weighted hot deck imputation in Stata. In SAS the equivalent command would be the following (and note that this is a newer SAS feature, beginning with SAS/STAT 14.1 in 2015 or so): proc surveyimpute method=hotdeck(selection=weighted); For clarity then, the basic requirements are: Imputations most be row-based or simultaneous. If row 1 donates x to row 3, then it must also donate y . Must account for weights. A donor with weight=2 should be twice as likely to be

问题 I have the following question: I have a pandas dataframe, in which missing values are marked by the string na . I want to run an Imputer on it to replace the missing values with the mean in the column. According to the sklearn documentation, the parameter missing_values should help me with this: missing_values : integer or “NaN”, optional (default=”NaN”) The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the