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When data contains both factor and numeric variables, how to get quartiles for all numeric variables? n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <- factor(1*(x5=='a' | x5=='c')) data1 <- cbind(x1,x2,x3,x4,x5,x6) data

Hi List, I am new to R, this may be simple. I want to store directory path as parameter which in turn to be used while reading and writing data from csv files. How I can use dir defined in the below mentioned example while reading the csv file. Example: dir <- "C:/Users/Desktop" #location of file temp_data <- read.csv("dir/bs_dev_segment_file.csv") If I run this

Dear List, Java Exception error while reading large data in R from DB using RJDBC. I am trying to read large data from DB table(Vectorwise), using RJDBC connection. I have tested the connection with small size data and was able to fetch DB tables using same connection(conn as in my code). Please suggest where am i going wrong or alternate option to solve such issues while reading large DB

Hi All, I am working on the dataset in which some of the variables have more than one observations with outliers . I am using below mentioned sample script library(outliers) x1 <- c(10, 10, 11, 12, 13, 14, 14, 10, 11, 13, 12, 13, 10, 19, 18, 17, 10099, 10099, 10098) outlier_tf1 = outlier(x1,logical=TRUE) find_outlier1 = which(outlier_tf1==TRUE, arr.ind=TRUE) beh_input_ro1 =

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

Dear All, I am new to R, I have one question which might be easy. I have a large data with more than 250 variable, i am reducing number of variables by redun function as in the example below, n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <-

Hi All, This might be simple question, I need to retrive data for modelling from the databases. Eveytime date values changes so I countnot fix date value in the code, it is required to pass as parameter. When I pass the date as parameter, it throws error. (ERROR: column "start_dt" does not exist Position: 285) My script is as below, please guide me where am I going wrong? All parameters

Dear All, I have got the limits for removing extreme values for each variables using following function . f=function(x){quantile(x, c(0.25, 0.75),na.rm = TRUE) - matrix(IQR(x,na.rm = TRUE) * c(1.5), nrow = 1) %*% c(-1, 1)} #Example: n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <-

Hi, I am new to R for solving optimization problems, I have set of communication channels with limited capacity with two types of costs, fixed and variable cost. Each channel has expected gain for a single communication. I want to determine optimal number of communications for each channel maximizing ROI)return on investment) with overall budget as constraint.60000 is the budget allocated.

Dear List, Couple of issues while using functions from ?BCA? library: 1. I am trying to use ?lift.chart? function from ?BCA? library, but facing issues while using model where model formula is passed as formula object in glm. When model formula is written as text, then it works fine. In my case input variables and target variables are going to change dynamically, so have to used formula as

Dear R-users, I would like to determine the probability of event at specific time using cox model fit. On the development sample data I am able to get the probability of a event at time point(t). I need probability score of a event at specific time, using scoring scoring dataset which will have only covariates and not the response variables. Here is the sample code: n = 1000 beta1 = 2; beta2 =

Hi List, Being new to R, I am trying to apply boot.stepAIC() for Model selection by bootstrapping the stepAIC() procedure. I had gone through the discussion in various thread on the variable selection methods. Understood the pros and cons of various method, also going through the regression modelling strategies in rms. I want to read Final model or Formula or list of variables automatically

Hi List, I am reading table from postgres database into R session using RJDBC, table contains 150 columns and 200000 rows. Sample code is as below, which works fine with smaller tables. ######################################################################## db_driver <- mydir$db_driver db_jar_file <- mydir$db_jar_file db_server <- mydir$db_server db_server_lgn <- mydir$db_server_lgn