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hello for exampl, i have this programme # Generating data which are right truncated library(DTDA) library(splines) library(survival) n<-25 X<-runif(n,0,1) V<-runif(n,0.75,1) for (i in 1:n){ while (X[i]>V[i]){ X[i]<-runif(1,0,1) V[i]<-runif(1,0.75,1) }} res<-lynden(X=X,U=NA, V=V, boot=TRUE) attach(res) temps = time M_i = n.event L_t = res

> On May 12, 2018, at 9:42 AM, malika yassa via R-help <r-help at r-project.org> wrote: > > > hello > for exampl, i have this programme > # Generating data which are right truncated > library(DTDA) > library(splines) > library(survival) > n<-25 > X<-runif(n,0,1) > V<-runif(n,0.75,1) > for (i in 1:n){ > while (X[i]>V[i]){ >

We need some idea of the problem. http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example http://adv-r.had.co.nz/Reproducibility.html On Thursday, May 10, 2018, 11:07:30 a.m. EDT, malika yassa via R-help <r-help at r-project.org> wrote: Hello Do You help me, i have the problem in the package DTDA for ?find the probability of truncation

Hello Do You help me, i have the problem in the package DTDA for ?find the probability of truncation ?(alpha) thank you [[alternative HTML version deleted]]

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Hi, I am about to write functions for multivariate kernel densitiy estimation with mixed categorical and continuous date (accoring to Jeff Racine and Qi Li), and the leave-one-out window esitmation needs a lot of computation. I am now optimizing the code performance and therefore fhe following questions: As R uses call-by-value for functions, is it computational expensive to pass large matrices

Signed-off-by: Simon COURTOIS <scourtois at linagora.com> --- src/app/controllers/vm_controller.rb | 16 ++++++++++++++ src/app/views/vm/edit_vmpool.rhtml | 36 ++++++++++++++++++++++++++++++++ src/app/views/vm/show.rhtml | 3 ++ src/public/images/icon_vmpool_11px.png | Bin 0 -> 542 bytes 4 files changed, 55 insertions(+), 0 deletions(-) create mode 100644

Signed-off-by: Simon COURTOIS <scourtois at linagora.com> --- src/app/controllers/vm_controller.rb | 16 +++++++++++++ src/app/views/vm/edit_vmpool.rhtml | 39 ++++++++++++++++++++++++++++++++ src/app/views/vm/show.rhtml | 3 ++ src/public/images/icon_vmpool_11px.png | Bin 0 -> 542 bytes 4 files changed, 58 insertions(+), 0 deletions(-) create mode 100644

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

I tried the version you used, too. the resulting executable was still broken. I guess the reason is due to fastcall on function pointers, which Clang does not recognize. Consider the following snippet. #include <stdio.h> void __attribute__((fastcall)) f(int i) { printf("%d\n", i); } typedef void (*__attribute__((fastcall)) f_t)(int i); //typedef void

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression

Still failed. The first secret is in your email program settings, to use Plain Text format (at least for emails you send to this mailing list). The second secret tool to use is the reprex package to let you verify that your code example will do on our computers what it is doing on your computer before you send it to us. That will also involve giving us some sample data or referencing some data

Sorry for messed up text. Here it goes again: I am learning to use the gsDesign package. I have a question about Pocock and OBF boundary. As far as I can understand, these 2 boundaries require equal spacing between interim analyses (maybe this is not correct?). But looks like I can still use gsDesign to run an analysis based on unequal spacing:? >

Dear R users, I have panel data on the amount of money spent by travellers from 8 origin countries in 4 destinations. I would like to carry out analysis for destinations, origins and time. However, it seems to me that the package "plm" can only esitmate two-way panel data (indexed by a two-dimensional array). Any suggestions would be greatly appreciated. Thank you. Best regards,

can someone show me how to convert a table to a data.frame or a matrix ? I tried below and as.data.frame rearranges the columns similarly to a melt from reshape and as.matrix didn't change it. I actually would prefer to change it to a dataframe but if someone can show me how to convert it to a matrix, then as.data.frame will work on that. I was thinking about just changing the class to

Hi, I am learning to use your gsDesign package!?I have a question about Pocock and OBF boundary. As far as Iunderstand, these 2 boundaries require equal spacing between interim analyses(maybe this is not correct?). But I can still use gsDesign to run an analysisbased on unequal spacing:?gsDesign(k=2,test.type=2,timing=c(0.75,1),alpha=0.05,sfu='Pocock')Symmetrictwo-sided group sequential

Hi Everyone, I just don't know how to extract the information I want from the summary of a linear regression model fitting. For example, I fit the following simple linear regression model: results = lm(y_var ~ x_var) summary(results) gives me: Call: lm(formula = y_var ~ x_var) Residuals: Min 1Q Median 3Q Max -5.9859 -1.5849 0.4574 2.0163 4.6015 Coefficients:

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

I have a simple table below called temptable and i want to obtain the same structure that prop.table creates except get the counts rather than the proportions. margin.table seems to create one table with columns and rows whereas I am looking for the three table type structure that prop.table gives. Thanks. temptable<-structure(c(0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,