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Hello to everyone. I´m writing you because I´m feeling a bit frustrated with my work. My work consists in finding the relation between the amount of fires and the weather, so, my response variable is the amount of fires in a fire season and the explanatory variables are the temperature, the amount of precipitation and the some others…. my problem is this; I keep getting the wrong sign in the

Hello to everyone. I'm trying to use the %in% to match to vectors in ff format. a<-as.ff(data[,1]) %in% fire$fecha > aff (open) logical length=3653 (3653) [1] [2] [3] [4] [5] [6] [7] [8] [3646] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE : FALSE [3647] [3648] [3649] [3650] [3651] [3652] [3653] FALSE FALSE FALSE FALSE FALSE FALSE

Hello to everyone. I need your help. I´m trying to fit the same *glm.nb* to a different data set and i am getting these errors in some of the data. Sometimes, one data set has two of these errors when fitting the model. 1.- Error en while ((it <- it + 1) < limit && abs(del) > eps) { : valor ausente donde TRUE/FALSE es necesario 2.- Mensajes de aviso perdidos 1: In sqrt(1/i)

Hello to everyone. In MATLAB you can find a function that executes a text string. I have not find it in R. Does anyone knows a similiar function in R, i think a valuable tool. Thank you. Lucas. [[alternative HTML version deleted]]

Dear R people. I´m facing a big problem. I need to create a matrix with 10.000 columns and 750.000 rows. matrix<- as.data.frame(matrix(data=0L, nrow=750000, ncol=10000) as you can see, the data frame has huge dimesions. I was able to find out about thr "L" in data, this way I´m telling that my data is "integer" class. By doing this object.size(matrix) tells me that this is

Hello to everyone. I'm fitting a glm.nb model to a count data. I'm using about 8 predictive variables. Once a run the script I do get a result but it tells me that the iteration limit has been reached. So, can i trust the results given by the model? Could it be a multicollinearity problem? Thank you for taking the time to help me. Greetings Lucas. -- View this message in context:

Dear R community. I´m working with a generalized linear model which the response variable is a categorical one and the predictive variables are weather conditions. I have 250 different places where I need to fit the model. In some of these places I have strong correlations between some of the variables so I need to deal with this problem. I found a work similar than mine where they use tha

Hello to everyone. I´m using this function to download some information from a website. This is the URL: http://164.77.222.61/climatologia/php/vientoMaximo8.php?IdEstacion=330007&FechaIni=01-1-1980 If you go to that website you´ll find a table with meteorological information. One column is called "Intesidad Máxima Diaria", and that is the one i need. I´ve been traying to extract that

Dear R friends. I have a question about running a glm( family= 'binomial', *offset=T*), (I know offset is a vector of values) My doubt is about predicting the values on a new data. Does the predict() function considers the offset? o should I especified something? Here is the model I´m using: *model<-stepAIC(glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/offset))

Dear R friends. I´m trying to fit a Logistic Regression using glm( family='binomial'). Here is the model: *model<-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/off)), data=mydata, family='binomial')* mydata has 76820 observations. The response variable f_ocur) is a 0-1. This data is a SAMPLE of a bigger dataset, so the idea of setting the offset is to

Good evening to all. I find myself trying to create some Thiessen Polygons, in order to finish a meteorology research. This is the script I found to create the Polygons: * * *voronoipolygons <- function(x) {* * require(deldir)* * if (.hasSlot(x, 'coords')) {* * crds <- x@coords * * } else crds <- x* * z <- deldir(crds[,1], crds[,2])* * w <- tile.list(z)* * polys

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and

Dear R friends. After having some troubles learning how to create a ffdf object, now I find myself having problems saving it. this is the data i´d like to save: str(DATA) List of 3 $ virtual: 'data.frame': 6 obs. of 7 variables: .. $ VirtualVmode : chr "double" "short" "integer" "integer" ... .. $ AsIs : logi FALSE FALSE FALSE