similar to: Construction of Dataset for time varying COXPH analysis

I've a data set with 60000 rows of data representing 6000+ distinct loans. I did a coxph() regression on it (see call below), but a subsequent survfit() call on the coxph object is almost certainly wrong. It gives n=6 when it should be more like 6000+ (I think) > survfit(resultag) Call: survfit.coxph(object = resultag) n events median 0.95LCL 0.95UCL 6 489 Inf

I get the following error when I try reading in a table. How are 1.1, 1.2, 1.3 duplicate row names? Thx. > table <- read.table('latestWithNumber.txt', header=T) Error in "row.names<-.data.frame"(`*tmp*`, value = c("1.1", "1.2", "1.3", : duplicate 'row.names' are not allowed Yongchuan

Hi - I have a data frame with a large number of observations (62,000 rows, but only 2 columns - a character ID and a result list). Sample: > my.df <- data.frame(id=c("ID1", "ID2", "ID3"), result=1:3) > my.df id result 1 ID1 1 2 ID2 2 3 ID3 3 I have a list of ID vectors. This list will have anywhere from 100 to 1000 members, and

I'm doing a cox regression with frailty: model <- coxph(Surv(Start,Stop,Terminated)~ X + frailty(id),table) I understand that model$frail returns the group level frailty terms. Does this mean this is the average of the frailty values for the respective groups? Also, if I'm fitting it to a gamma frailty, how do I extract the rate and scale parameters for the different gamma

Hi, I am working on fitting a proportional hazard model to predict the probability of default for mortgage loans. I have a question regarding survfit function. My historical data set is a pool of loans with monthly observed default status for the next 24 months. The data is left truncated (delayed entry to observation window after the loan is opened) and right censored. I would like to

Hola, Si este comportamiento se ve con cualquier data.frame que luego conviertas a lista: > > > v <- 1:10 > w <- split(v, 1:2) > w $`1` [1] 1 3 5 7 9 $`2` [1] 2 4 6 8 10 > object.size(v) 88 bytes > object.size(w) 464 bytes > object.size(w[[1]]) 72 bytes Y he encontrado una explicación que entiendo parcialmente está aquí:

Estimados ¿que pasa con opciones como bigdata o semejantes? Tendría que probar, leer, etc., pero puede ser que algún algoritmo distinto solucione esos problemas. Recuerdo una presentación que creo que la realizó alguno de la lista donde utilizaba millones de datos, eran de aeropuertos, tráfico aéreo, algo de eso había leído en una oportunidad. Javier Marcuzzi -----Original Message-----

Estimado Carlos Sobre: ¿Seré yo el único al que los dichosos factores le han hecho perder horas y horas de trabajo? Si usted supiese en mi caso las cosas fáciles que me hacen perder tiempo. ¿Hay alguna herramienta que verifique lo que realiza el código? Yo no soy programador pero vi en visualstudio o en netbeans, algunas formas donde colocando puntos en ciertas partes del código es posible

Hola, ¿qué tal? Tengo un df relativamente chiquito: > dim(loans) [1] 15172 22 > object.size(loans) 7488144 bytes Sin embargo, empresas <- split(loans, loans$number_id) length(empresas) #8748 object.size(empresas) # 50643907728 bytes Es decir, el objeto resultante de partir un df de 15000 filas en 8700 dfs ocupa 5GB. ¿Alguna idea de por qué ocurre esto? (Uso R 3.0.1 en un

Interesante... y preocupante. No propuso alguien, no hace mucho, reescribir R desde el principio por este tipo de cosas? El 13/06/13 20:04, Carlos Ortega escribió: > Hola, > > Si este comportamiento se ve con cualquier data.frame que luego conviertas > a lista: > >> >> v <- 1:10 >> w <- split(v, 1:2) >> w > $`1` > [1] 1 3 5 7 9 > > $`2`

Hola, ¿qué tal? Lo que quieres es un "debugger visual" (como el de muchos IDEs, incluyendo Netbeans y Eclipse) para R, como el de Revolution, http://www.youtube.com/watch?feature=player_embedded&v=7shLKEINM9A#! Pues sí, efectivamente, existe, pero solo con la versión de Revolutions de R. Y verás también que está basado en el "debugger" tradicional de R, que permite

Javier, R también tiene opciones de "debug" como la que estás comentando. Te adjunto un ejemplo práctico: http://www.burns-stat.com/an-r-debugging-example/?utm_source=rss&utm_medium=rss&utm_campaign=an-r-debugging-example Saludos, Carlos Ortega www.qualityexcellence.es El 13 de junio de 2013 21:45, Marcuzzi, Javier Rubén < javier.ruben.marcuzzi@gmail.com> escribió:

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Hi All I found survfit function was very slow for a large dataset and I am looking for an alternative way to quickly get the predicted survival probabilities. My historical data set is a pool of loans with monthly observed default status for 24 months. I would like to fit the proportional hazard model with time varying covariate such as unemployment rates and time constant variables at loan

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Hola, ¿qué tal? Al final he descubierto el motivo del problema. Cierto que esperaba cierto nivel de _overhead_, pero nunca tantísimo. Por mucho _overhead_ que haya, no se pasa de 7MB a 5GB. El problema era que (a pesar de mi opción stringsAsFactors = F) los niveles de los factores se copiaban íntegros en cada una de las columnas de tipo factor de cada una de las tablas resultantes. Aunque no se

I''ve a number of places in my rails application where I have redirects, but I find they are not always redirected. I think part of the problem is that my understanding of pre-requisites is insufficient. I''m not sure how to frame a search to get the answer I need. I''m using Rails 0.14.3 under cygwin with ruby 1.8.2 How do I make sure that data received in @params[] on

Hi there. First, I'm not subscribed to the list, so if anyone actually replies to this, please CC me. Thanks. I have a problem which I feel sure can be solved with cunning use of Samba, CVS, and the like. Basically, I need a shared document repository with some sort of version control or sign-off. It has to be accessible to both Windows and *NIX users. Currently we have a home-grown system.

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