similar to: SamplingStrata R package

Hi all I am hoping to use the SamplingStrata R package for a dataset describing a population of businesses wherein I have information on the type of business, as well as, for designated employment number bands, number of employees and business turnover information. So in this context the employment number bands can be described as micro, small, medium and large, i.e. size of business. Hence I

Hi Bert thank you for the reply. Not a coding query as such. Just wanted some pointers towards how to handle strata using the package in my situation, i.e. business type X business size with information for count and financial turnover. many thanks On 31 October 2017 at 14:37, Bert Gunter <bgunter.4567 at gmail.com> wrote: > 1. There is no question here. > > 2. In any case, this

1. There is no question here. 2. In any case, this is not a code writing service, so a question about how to code models without any offering of your own attempts might not be replied to anyway. 3. For what sorts of queries you might expect replies to, please read and follow the posting guide below. Also, if you do post, please post in plaint text, not html, as the latter (especially code) can

Dear R users, I would like to announce that on the CRAN is now available a new package (SamplingStrata version 0.9) for the optimal stratification of sampling frames. This package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample size under the condition to satisfy precision constraints in a multivariate and

Dear R users, I would like to announce that on the CRAN is now available a new package (SamplingStrata version 0.9) for the optimal stratification of sampling frames. This package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample size under the condition to satisfy precision constraints in a multivariate and

I'm fitting a regression tree with rpart and I want to write the values for every leaf in a dataset. As an example take the variable turnover. Let's suppose my tree for turnover has 30 leaves and I want to have 30 datasets with dataset 1 containing the turnover values of the units in leaf 1, dataset 2 containing turnover values for the observations in leaf 2 and so on. How can I do

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male,

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male,

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

I want to draw boxplot where the geom_points are displayed based on "ERBB2.MUT" subset and they should be displayed in the right box (based both on the "ERBB2.2064" field and "ERBB2_Status"). However, given my command I currently only see "red" points corresponding to "MUT" subset in one straight line corresponding to only "ERBB2.2064"

Hello: I come to Samba with a strong Unix background but weak on the MS networking side. Hence I've read a lot of the Samba documentation, more than once... Something that continues to confuse me, however, is whether or not I want/need winbindd for the task at hand. Objective: replace aged NT4 domain/file server w/Samba based file server. Details: 1) approx. 25 users. stable staff

Using R version 1.8.1 for Windows, I obtain an error message using the following code. The data frame was constructed in the counting process style, where V1 is the start time, V2 is the stop time, and V3 is the censoring indicator. There are no zero-length time intervals. Variable V4 is the stratification factor (gender: F,M). S<-Surv(V1,V2,V3) fit<-survfit(S ~ V4,data=test.dat)

On 16/01/17 21:54, John R Pierce wrote: > On 1/16/2017 12:44 AM, Rob Kampen wrote: >>> >> Here's an idea - untested. >> set up a network on the single nic - say 192.168.55.xx/24 >> set up the dhcp to offer leases from a subset of this network - say >> 192.168.55.128/28 >> set up fixed leases based upon mac address from the remainder of the >>

I've got a newbie question and I got a little lost in the "table helps". I've got a data.frame I would like to summarize as a (and pardon for the lack of correct vernacular) data collection matrix. My data looks like, stand siteindex age acres pct.acres 1 232 116 45 8477.3105 0.56159458 2 234 121 25 11120.1530 0.73667441 3 235 132 25

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Hello forum, I am interested in investigating non-linear relationships between variables using something akin to Multivariate Adaptive Regression Splines. The problem is that my data have sample weights and stratification variables, and I don't think earth and similar packages support these. Any suggestions? It doesn't have to be MARS per se, just something to estimate non-linear

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with