similar to: Failure building any package

Hello, I was just assigned to perform a cost effectiveness study in healthcare. We are studying the cost effectiveness of a proposed diagnostic vs. current screening procedures. One of the team members suggest a commercial software package called "TreeAge Pro". Looking at the description, it appears to be a nice GUI to some very simple models that could be easily constructed in R.

I have a bunch of data sets that were created for the libsvm tool. They are in "colon separated sparse format". i.e. 1 5:1 27:3 345:10 Is a row with the label of "1" and only has values in columns 5, 27, and 345. I want to read these into a data.frame in R. Is there a simple way to do this? -- Noah Silverman, M.S. UCLA Department of Statistics 8117 Math Sciences

Hello, I need to design a fairly simple front-end for someone to use an R script system that I've built. My thought was to just use the text based menus available in the base R package, perhaps in some kind of loop. How have other people done this? Any "best practices" that you can recommend? Thanks! -- Noah Silverman UCLA Department of Statistics 8117 Math Sciences Building

Hello, I need to calculate a moving average and an exponentially weighted moving average over a fairly large data set (500K rows). Doing this in a for loop works nicely, but is slow. ewma <- data$col[1] N <- dim(data)[1] for(i in 2:N){ data$ewma <- alpha * data$ewma[i-1] + (1-alpha) * data$value[i] } Since the moving average "accumulates" as we move through the data,

Hello, I have a time series object (xts) that I iterate over in a loop. Works fine. My challenge is that I want to be able to reference other entries in the series by math. i.e. For today's observation, what were the last 5 observations? If indexed numerically, it is trivial, but I can figure out how to do this with dates. This is slightly more difficult as there may not be an

Unfortunately, some data I need to work with was delivered in a rather messy Excel file. I want to import into R and clean up some things so that I can do my analysis. Pulling in a CSV from Excel is the easy part. My current challenge is dealing with some text mixed in the values. i.e. 118 5.7 <2.0 3.7 Since this column in Excel has a "<2.0" value, then R reads the

Hi, Looking at a large data set with many factors. I would like to expand each factor variable into multiple new variables for each level. (0,1) coding. My first though was just to code a big nasty loop, to take each level and cbind a column onto my data set. But, that seems painful. There must be a better way. Is there an "easy" way to do this in R? (Note, I don't want to

Hi, I have a very large data set stored as an xts object. xts is very nice about showing row labels as "human readable" dates and times. I want the actual epoch values that are stored internally. The only way I can find to access them is one-at-a-time using the internal function: xcoredata() Calling this in an entire column, the "R" way doesn't work. It will only

Hi, I have several data structures (xts structures). I then have a list of the names of those objects. I'd like to access the object by name. For example: foo1 <- as.xts(....) foo2 <- as.xts(...) foo3 <- as.xts(...) structs <- c("foo1", "foo2", "foo3") for (thisOne in structs){ print(thisOne$colA) } The above fails. Clearly I'm missing a

Hi, When using the glmnet() function of the package glmnet, A series of coefficients is returned for a list of descending lambda values. I am unable to locate anything in the documentation that explains HOW this choice of lambda series is made. (There is documentation about how to choose my own, but I want to understand how the authors are doing it) Any ideas? -- Noah Silverman UCLA

Hi, I want to keep a running file of some statistics generated by a running process. Occasionally the process will be restarted. On restart, I'd like to read the last line of the log file to set some variables to their last state, and then continue to append values to that same file. I can't seem to get the appending part to work. I can read values form the file, but then upon the

Hi, I have some data, that when plotted looks very close to a log-normal distribution. My goal is to build a regression model to test how this variable responds to several independent variables. To do this, I want to use the fitdistr tool from the MASS package to see how well my data fits the actual distribution, and also build a generalized linear model using the glm command. The summary

Hello, I have a fairly complex hierarchical model that I using rjags to fit. Short test runs verify that it works and everything appears to be setup correctly. Now that I want to collect a larger sample from the posterior (5,000 or more). This looks like it will take several days to run on my hardware (Intel core i7, 16GB RAM) I read in the rjags documentation that there is an option for

Hello, I noticed something odd when working with data frames and xts objects. If I read in a CSV file, R creates a nice data.frame. This works well. If I then convert to an XTS object, I see that all the values in the data are now quoted. My data is a mix of numeric and character. This is usually seen when converting a data.frame to a matrix, as R will treat all the data as the same class.

Hi, I'm interested in doing some sound analysis with R. Does anyone have any experience/methods for reading in a wav file? -- Noah Silverman UCLA Department of Statistics 8208 Math Sciences Building Los Angeles, CA 90095 [[alternative HTML version deleted]]

Hi, i have some data in a matrix. It has zero values scattered throughout, at random. I'd like to create a line plot, with a line for each row, that *excludes* the zero or NA values. The data looks like this (toy example) 10 12 21 0 23 0 43 0 NA 41 0 0 0 34 35 0 35 0 44 0 NA NA NA 3 2 5 0 3 2 etc... Suggestions on an easy way to do this? Thanks! -- Noah Silverman UCLA Department of

Hi, I have a rather large data frame (500 x 5000) that I want to convert to a proper xts object. I am able to properly generate an xts object with the correct time index. However, all of my numerical values are now strings. b <- as.xts(a[,2:dim(a)[2]], order.by=as.POSIXct(strptime(paste(a$Date), '%m/%d/%Y'))) My guess is that somewhere in the large data frame there are a few

Hi, I have a very large data set (aprox. 100,000 rows.) The data comes from around 10,000 "groups" with about 10 entered per group. The values are in one column, the group ID is an integer in the second column. I want to normalize the values by group: for(g in unique(groups){ x[group==g] / sum(x[group==g]) } This works find in a loop, but is slow. Is there a faster way to do

Hi, I'm having difficulty coming up with a good way to subest some data to generate statistics. My data frame has multiple observations by group. Here is an overly-simplified toy example of the data ========================== code v1 v2 G1 1.2 2.3 G1 0 2.4 G1 1.4 3.4 G2 2.9 2.3 G2 4.3 4.4 etc.. ========================= I want to normalize the data *by group* for certain variable.

Hi, I have a CSV file that is formatted well, except that the last line is a "summary" not is CSV format. Toy example: label_1, label_2, label_3 1,2,3 3,2,4 2,3,4 Total Rows: 3 When I try to import this into R with: d <- read.table("foo.csv", header=T, sep=",") It fails to import properly because of the last line. Currently, I have a shell script that strips