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Hi to all of you, I'm fitting an full factorial probit model from an experiment, and I've the independent variables as factors. The model is as follows: fit16<-glm(Sube ~ as.factor(CE)*as.factor(CEBO)*as.factor(Luz), family=binomial(link="probit"), data=experimento) but, when I took a look to the results I've obtained the following: glm(formula = Sube ~ CE * CEBO *

I have a series of nertCDF files containing global data for a particular variable, e.g. tmin/tmax/precipiation/windspeed/relative humuidity/radiation etc. I get the following information when using *nc_open* function in R: datafile: https://www.dropbox.com/s/xpo7zklcmtm3g5r/gfdl_preci.nc?dl=0 File gfdl_preci.nc (NC_FORMAT_NETCDF4_CLASSIC): 1 variables (excluding dimension variables):

Two questions: 1. Is the order of the dimensions shown what is shown if you look at str(ncin) - I mean shown at the end where it describes the variable and its dimensions? 2. Is you problem how to subset the netcdf file, how to write to the .csv file, or both? -Roy > On Aug 28, 2017, at 2:21 PM, Eeusha Nafi <eshad002 at gmail.com> wrote: > > I have a series of nertCDF files

Good-day Sir, I am R.Language users but am try to? estimate parameter of beta distribution particular dataset but give this error, which is not clear to me: (Initial value in "vmmin" is not finite) beta.fit <- fitdistr(data,densfun=dbeta,shape1=value , shape2=value) kindly assist. expecting your reply:

Dear all, This question is partly statistics and partly R and I apologise in advance for my (usual) verbosity! My data is a little more complicated that this suggests, but essentially I have proportion data from different studies (id), each from a specific country and region of the World. I would like to examine the variables that affect the proportion, but these factors are hierarchical. In case

Hi! I am trying to estimate Engel curves using a big sample (>42,000) using lm and taking heteroskedasticity into account by using the summaryHCCM posted here by John Fox (Mon Dec 25 16:01:59 CET 2006). Having used the SIC (with MASS stepAIC) to determine how many powers to use I estimate the model: > # ========================================= > summary.lm(fit.lm.5) Call: lm(formula

Dear Adrian and Ian (and r-helpers), I encountered a curious result in developing an example using the bicreg function in the BMA package: I noticed that pairs of models with equal R^2 and equal numbers of predictors had nevertheless different BIC values. Looking at the bicreg function, the definition of BIC appears to be the usual one, or close to it [bic <- n * log(1 - r2/100) + (size - 1) *

Hi R, I have a vector with all positive values and have fit the GEV distribution (shape parameter=0) to the data and I get the negative value of the location parameter. Is this possible? library(evd) r=read.table("clipboard",header=F)[,1] > length(r) [1] 2087 > sum(r>=0) #All data points are positive [1] 2087 > sum(r>=1000000) #All data points are positive

I am using a nested model in R and the lm output shows 47 not defined because of singularities and I have no idea why. Any help on why this is happening or how to fix this problem would be very much appreciated. Below is the output I received from R. Thanks and happy holidays! Call: lm(formula = Dist ~ Treatment/SiteL/Territory) Residuals: Min 1Q Median 3Q Max -6.646 -1.443

I was wondering if somebody could explain why I get different results here: >treats[,2]<-as.factor(treats[,2]) >treats[,5]<-as.factor(treats[,5]) >treats[,4]<-as.factor(treats[,4]) #there are 'c' on more days than I have 'h2o2', where treats[,4] is the day. I only want 'c' that correspond to the same days that I have a 'h2o2' also.

Hi there, I'm using R1.0.1 Windows 98. This file contains some inputs and an aov function code. Can someone check it for me? Somehow I got completely different answer when typing them in R and in Splus. Splus gives me this: > summary( Turnip.aov ) Error: Blocks Df Sum of Sq Mean Sq F Value Pr(F) Residuals 3 163.7367 54.57891 Error: Plots %in% Blocks