search for: extime

Hi, What is cast telling me when it says the following? Aggregation requires fun.aggregate: length used as default What is 'length'? I've taken a small subset of data and wondered what EnTime vs ExTime might look like. cast is kind enough to give me a table but I don't understand the values in the table. They seem to sum up ro be the same as the total dimension of the data so I'm guessing (but cannot prove) that it's something like whether each experiment has values for both EnTime an...

...nsible and change the first line in the code to point at it. The R code I'm having trouble with is this: Y = read.table("C:\\test.txt") names(Y)[7] = "value" Y ReShape.Y = reshape(Y, varying=list(names(Y)[7]), direction='long') ReShape.Y cast(ReShape.Y, EnTime ~ ExTime) When run the last two steps output the following: > ReShape.Y Trade PosType EnDate EnTime ExDate ExTime time value id 1 1 -1 1080103 800 1080103 1310 1 520 1 2 2 -1 1080104 755 1080104 1310 1 530 2 3 3 1 1080107 945 1080107 13...

Good morning, (sorry for my english) i have some problems to put off by extimation ARIMA coefficients the ones not significatives. Exist a method to extimate only that significatives? i use the command: arima(). thanks to all Stefano [[alternative HTML version deleted]]

I have a data.frame that was built from a number of smaller data.frames with rbind. Each ssmaller data.frame bound together runs over the same date ranges. The format of the whole thing looks like this: Trade PosType EnDate EnTime ExDate ExTime PL_Pos 1 1 1 1040107 915 1040107 1300 164 2 2 1 1040108 909 1040108 1300 184 3 3 1 1040115 921 1040115 1300 64 4 4 1 1040120 1134 1040120 1300 124 5 5 1 1040121 923 1040121 1300 84 6 6...

...maybe as.numeric or something else first to ensure R sees the number the way I'm thinking about the number? Cheers, Mark > dim(X) [1] 25 425 > > ReShapeX <- melt(X, id = c("Trade", "PosType", "EnDate", "EnTime", "ExDate", "ExTime", "PL_Pos", "Costs", "Save2")) > > dim(ReShapeX) [1] 10400 11 > > MyResults <- subset(ReShapeX, value > 0) > > dim(MyResults) [1] 1105 11 > > MyResults.GroupA <- subset(MyResults, PosType = 1) > > dim(MyResults.GroupA) [...

I'm having a very hard time making R CMD check produce a clean check on examples because of the timings inserted into examples by R CMD check. I am getting a difference on every example output caused by timing information being inserted by 'R CMD check'. The current 'Writing R Extensions' manual[1] states on p. 14: If directory tests has a subdirectory Examples

This SO post: https://stackoverflow.com/q/76583828 describes a strange R CMD check error. Depending on the contents of a comment in one of the examples sections of a help page, an error like this could be triggered: > base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv") >

Dear all, I need to use R for one estimation, and i have readily available stata command, but i need also the R version of the same command. the estimation in stata is as following: 1. Compute mean values of relevant variables . sum inno lnE lnM Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------

...t; "2020-01-06" "2020-01-07" "2020-01-08" "2020-01-09" "2020-01-10" "2020-01-13" "2020-01-14" "2020-01-15" "2020-01-16" [12] "2020-01-17" > > > MyDate2 Trade PosType EnDate EnTime ExDate ExTime 1 1 -1 1040127 919 1040127 932 2 2 1 1040127 1155 1040127 1208 3 3 -1 1040127 1300 1040127 1313 4 4 -1 1040128 958 1040128 1313 5 5 -1 1040129 1024 1040129 1129 6 6 1 1040129 1234 1040129 1313 7 7 1 1040202...

Am 02.07.23 um 18:01 schrieb Duncan Murdoch: > This SO post: https://stackoverflow.com/q/76583828 describes a strange R > CMD check error. Depending on the contents of a comment in one of the > examples sections of a help page, an error like this could be triggered: > > > base::assign(".dptime", (proc.time() - get(".ptime", pos = >

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

From: saint-filth@hotmail.com To: saint-filth@hotmail.com Subject: RE: Date: Wed, 9 Jun 2010 09:53:20 +0000 OK! sorry thats my fault, here the translations of the stata commands 1st step is to get the mean values of the variables, well that doesnt need explanation i guess, 2nd step is to estimate the model on panel data estimation method which is:

Full_Name: Menelaos Stavrinides Version: 1.3. 1 OS: Windows 98 Submission from: (NULL) (193.129.76.90) When model simplification is used in glm (binomial errors) and anova is used two compare two competitive models one can use either an "F" or a "Chi" test. R always performs an F test (Although when test="Chi" the test is labeled as Chi, there isn't any

...alid for factors. I can certainly find ways to munge the data set, changing EnTime values but that seems like stuff I shouldn'y be doing. that said it's pretty easy to do I suspect. (Modulo 15, etc.) Is there a way to use cast() to grab time increments. Instead of cast(Results, EnTime ~ ExTime, ...) returning by the exact value, can cast collect values together in groups? Any ideas appreciated before I start banging away at doing my less interesting methods. Thanks, Mark

Thanx for your response, yeah, i know i didnst specified the indexes when i wrote the 2nd mail, in fact in the 1st mail i wrote already that i dont have problem with the estimation of the model... thats the reason why i didnt write in fact since the issue is not to estimate the model but to get the marginal effect, anyway, i figured out that predict(), doesnt work for panel data... and well, my

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...c2ca1ye595821a75b9ddc8@mail.gmail..com> Content-Type: text/plain; charset=UTF-8 Hi,    What is cast telling me when it says the following? Aggregation requires fun.aggregate: length used as default What is 'length'?    I've taken a small subset of data and wondered what EnTime vs ExTime might look like. cast is kind enough to give me a table but I don't understand the values in the table. They seem to sum up ro be the same as the total dimension of the data so I'm guessing (but cannot prove) that it's something like whether each experiment has values for both EnTime an...