R is an open source programming language for statistical computing and graphics, it is widely used by statisticians and data miners.Embaucher des R Programmers
Tengo un par de indicadores en TRADINGVIEW quiero crear las alarmas para que avise cuando salgan las etiquetas de comprar o vender ( una solo alarma para ambos movimientos ) . Requiero un programador de PINE de TRADINGVIEW
create a new data set that has the columns ID, DATE, START, STOP, DUR_s, DMI_kg. When the time between entries (bunk visits) is less than 1 minute, they should be collapsed in to a single entry, containing the new stop time and summing the DUR and DMI_kg columns to account for the increased length of visit, as we as the associated date and ID. This will be done by ID by day for all entries in the data set. What I DON'T want is just a summary of duration and DMI_kg by day, I only want to summarize if the time between visits in less than 1 minute. New data described above.
I need someone help me carry out statistical analysis similar to the ones presented in this paper: Particularly: - there will be two groups, patients vs controls, propensity score matched - there will be a date for patients when the event occurred - there will be a longitudinal biomarker (weekle average of steps) for each patient and control - I want to see how the biomarker changes in the two groups with respect with the occurrence of the event as they did, with the wonderful plot they presented (also with the one with the standardized difference) You can carry out the analysis in R, SAS or Python I don't care, but in any case i need to have the code to check how you have done it
I have a dataset of 3,000 lines and 4,000 columns and the rows are grouped into 5 classes and I need to find out which columns, and combination of columns, are more discriminative for each combination of classes (re classifying into binary one versus the other or one combination versus all etc). The best would be to first find the combination of features most relevant for a class (the identity of the features is what is of interest here), and then building a model to find and test the accuracy for predicting a class from those features. I am looking for a machine learning expert in R so I can understand and replicate each step.
I have MatLab code for a Naïve Bayes model which incorporates Markov transition probability into it. I have all the data the forecasted results and all the matlab code. However I don’t have MatLab and would like to see the model in R. All the information for this model can be found at The link for files is under the header “Additional Files”.
Interactive R Shiny dashboard for Genomics Data using 2 Bioconductor packages
Looking for a statistical analysis to identify abnormal behaviour in data. With dynamic dimensions. For example A data has following dimensions 1. Date (1-DEC , 2-DEC … 25-DEC) 2. hour (0, 1, 2 …. 23) 3. type (website, app etc.) With following measures No. Of hits We want a solution that can pick each dimension figure and try to identify anomalies For example Pick each day and compare with other days (against each dimension) , hours and types Pick each hour and compare with other hour, days and types Pick each type and compare with other types, days and hour Identify if the traffic trends and look for drops and increase systematically This is just an example .. the number of dimensions/measures should be flexible/dynamic ,
Description of the dataset: I have an experiment with 2 treatments and 10 cultivars with three replicates each (2x10x3). I did an unequal amount of measurements (>8000 observations) in each of them over several days. I would like to make a proper statistical analysis of it in R and compare my results among cultivars and treatments and, if possible, also over the days. Other variables influencing our cultivars and treatments are temperature, humidity, sunlight and location. The idea is to set up a mixed model (I need help to define the model) and run in R (I need the R code).
Attached is a fake data set of 100,000 customer records with some demographic data. In the last column is information about their favourite ice cream. However, only about 10% of customers have told us their favourite ice cream, the rest have “No Answer”. For the customers that have “No Answer” can you please create a quick predictive model which tells us what type of ice cream is likely their favourite based on the information we have about customers where we know the answer.
The project should be a detailed analysis completely build on r. it should be completed by 14th may 2022 .