missing-data

Does anyone know how gbm in R handles missing values? I can't seem to find any explanation using google. To explain what gbm does with missing predictors, let's first visualize a single tree of a gbm object. Suppose you have a gbm object mygbm . Using pretty.gbm.tree(mygbm, i.tree=1) you can visualize the first tree on mygbm, e.g.: SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction 0 46 1.629728e+01 1 5 9 26.462908 1585 -4.396393e-06 1 45 1.850000e+01 2 3 4 11.363868 939 -4.370936e-04 2 -1 2.602236e-04 -1 -1 -1 0.000000 271 2.602236e-04 3 -1 -7.199873e-04 -1

I have several files of data that look like this: X code year month day pp 1 4515 1953 6 1 0 2 4515 1953 6 2 0 3 4515 1953 6 3 0 4 4515 1953 6 4 0 5 4515 1953 6 5 3.5 Sometimes there is data missing, but I don't have NAs, the rows simply don't exist. I need to create NAs when the data is missing. I though I could start by identifying when that occurs by converting it to a zoo object and check for strict regularity (I never used zoo before), I used the following code: z.date<-paste(CET$year, CET$month, CET$day, sep="/") z <- read.zoo(CET, order.by= z.date ) reg<-is.regular(z, strict = TRUE) But

This question already has an answer here: dplyr count non-NA value in group by [duplicate] 3 answers In R language - I have lets say I have a DF with two columns Fam and Prop both categorical, now Fam has repeated names like Algea, Fungi, etc and column Prop has categorical numbers and NA's. How can I get a table/output that for each value of A it tells me how many values are not. NA example: Fam Prop ------------- Algea one Fungi two Algea NA Algea three Fungi one Fungi NA Output : Algea 2 Fungi 2 I know using the count function should be a direction for the solution but can't seem to solve

Python newbie here, this is my first question. I tried to find a solution on similar SO questions, like this one , this one , and also this one , but I think my problem is different. Here's my situation: I have a quite large dataset with two columns: Date (datetime object), and session_id (integer). The timestamps refer to the moment where a certain action occurred during an online session. My problem is that I have all the dates, but I am missing some of the corresponding session_id values. What I would like to do is to fill these missing values using the date column: If the action occurred

I have a large tab-separated data table with thousands of rows and dozens of columns and it has missing data marked as "na". For example, na 0.93 na 0 na 0.51 1 1 na 1 na 1 1 1 na 0.97 na 1 0.92 1 na 1 0.01 0.34 I would like to calculate the mean of each column, but making sure that the missing data are ignored in the calculation. For example, the mean of column 1 should be 0.97. I believe I could use awk but I am not sure how to construct the command to do this for all columns and account for missing data. All I know how to do is to calculate the mean of a single column but it treats the