similar to: Newbie - Summarize function

Dear List, Im new to R - making a transition from SAS. I have a space delimited file with the following structure. Each line in the datafile is identified by the first letter. A = Inventory (Inventory) X = Stratum (Stratum_no Total Ye=year established) P = Plot (Plot_no age slope= species) T = Tree (tree_no frequency) L = Leader (leader diameter height) F = Feature (start_height finish_height

Hi All, I'm using the Hmisc summarize function and used list instead of llist to provide the by variables. It generated an error message. Is this a bug, or do I misunderstand how Hmisc works with lists? The program below demonstrates the error message. Thanks, Bob x<-1:8 group <- c(1,1,1,1,2,2,2,2) gender<- c(1,2,1,2,1,2,1,2) mydata<-data.frame(x,group,gender)

Hi all, I'd like to use the Hmisc::summarize function, but it uses a function (FUN) of a single vector argument to create the statistical summaries. Consider an easy case: I'd like to compute the correlation between two variables in my dataframe, grouped according to other variables in the same dataframe. For exemple, consider the following dataframe D: V1 V2 V3 A 1 -1 A 1

Hi, I'm working on a data set that contains a couple of factors and a number of dependent variables. From all of these dependent variables I would like to calculate mean, standard deviation and quantiles. With the function FUN I get all the means and stdev that I want but quantiles are only calculated for the first of the dependent variables (column 8 in the summarize command). What do I

I was using summarize() in a data set in which one of the levels of the by variable was "". The summary statistic was consistently off by one level and the "" level was not in the output data frame. I tried to report it as a bug, but I could not log into the Hmisc bug reporting website to do so. I searched for this in the email archives. If it's there, I failed to find

Dear colleagues, I have a question regarding controlling the jitter when plotting predictions in the rms package. Below I've simulated some data that reflect what I'm working with. The model predicts a continuous variable with an ordinal score, a two-level group, and a continuous covariate. Of primary interest is a plot of the group by score interaction, where the score is the ordinal

Following up on this attempt of implementing the tail-recursion optimisation ? now that I?ve finally had the chance to look at it again ? I find that non-local return implemented with callCC doesn?t actually incur much overhead once I do it more sensibly. I haven?t found a good way to handle parallel assignments that isn?t vastly slower than simply introducing extra variables, so I am going with

Dear WizaRds, having met some of you in person in Vienna, I think even more fondly of this community and hope to continue on this route. It was great talking with you and learning from you. Thank you. I am trying to work through an artificial example in post stratification. This is my dataset: library(survey) age <- data.frame(id=1:8, stratum=rep(

Interestingly, the <<- operator is also a lot faster than using a namespace explicitly, and only slightly slower than using <- with local variables, see below. But, surely, both must at some point insert values in a given environment ? either the local one, for <-, or an enclosing one, for <<- ? so I guess I am asking if there is a more low-level assignment operation I can get my

I am basically trying to append a value(vector) to one dataframe using a relational value from another dataframe. Obviously, I can use a loop to accomplish this. However, is there a way to vectorize it? Example: > data <- data.frame(c(1,1,1,2,2,2,3,3,3),rep(2,9)); names(data) <- > c("Sample","Score") > meta <-

No clue, but see ?assign perhaps if you have not done so already. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 27, 2018 at 6:51 AM, Thomas Mailund <thomas.mailund at gmail.com> wrote: > Interestingly, the

Dear WizaRds, Working through sampling theory, I tried to comprehend the concept of stratification and apply it with Survey to a small example. My question is more of theoretic nature, so I apologize if this does not fully fit this board's intention, but I have come to a complete stop in my efforts and need an expert to help me along. Please help: age<-matrix(c(rep(1,5), rep(2,3),

Dear list, I got some surprising results when using the svytotal routine from the survey package with data containing missing values. Some example code demonstrating the behaviour is included below. I have a stratified sampling design where I want to estimate the total income. In some strata some of the incomes are missing. I want to ignore these missing incomes. I would have expected that

Hi Jason, Overall patches look good. Just one doubt I have is below: > > We use spinlock to synchronize the work list now which may cause > unnecessary contentions. So this patch switch to use llist to remove > this contention. Pktgen tests shows about 5% improvement: > > Before: > ~1300000 pps > After: > ~1370000 pps > > Signed-off-by: Jason Wang <jasowang

Hi Jason, Overall patches look good. Just one doubt I have is below: > > We use spinlock to synchronize the work list now which may cause > unnecessary contentions. So this patch switch to use llist to remove > this contention. Pktgen tests shows about 5% improvement: > > Before: > ~1300000 pps > After: > ~1370000 pps > > Signed-off-by: Jason Wang <jasowang

Can someone please enlighten me as to why the following happens? > -2.7^8.6 [1] -5125.407 > p<- -2.7 > q<- 8.6 > p^q [1] NaN R seems perfectly able to calculate -2.7^8.6, but fails when the exact same values are assigned to variables and then the computation is repeated. Thanks in advance for any suggetsions. Kris.

Dear R Users, I am trying to write a wrapper around summarize and xYplot from Hmisc and am having trouble understanding how to pass arguments from the function I am writing to the nested functions. There must be a way, but I have not been able to figure it out. An example is below. Any advice would be greatly appreciated. Thanks, Dan # some example data df=expand.grid(rep=1:4,

Dear all, We are analysing some survey data and we are not sure if we are using the correct syntax for our design. The population of interest is a set of 4416 polygons with different sizes ranging from 0.003 to 45.6 ha, 7460 ha in total. Each polygon has a binary attribute (presence/absence) and we want to estimate the probability of presence in the population. We used sampling with replacement

Hi, I am playing with clogit(case~spontaneous+induced+strata(stratum),data=infert) from clogit help file. This line works. 1. But, why strata(stratum) doesn't have a coefficient like spontaneous and induced? 2. When I remove strata(stratum) from the command, this function seems to keep running forever. Why? 3. I think the equation for clogit looks like P=1/(1+

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram: