similar to: Impute Values for Forest Inventory

Dear All, I am not able to run the random forest with my dataset.. X<- 280 records with satellite data(28 columns) - B1min, b1max, b1std etc.. y<- 280 records with 3 columns - TotBasal Area, Stem density and Volume yref <- y[1:230,] #Keeping 1st 230 records as reference records want to set 0 to y values for records 231 to 280.. yimp <- y[231:280,] #records for which we want

Dear fellow R-users I am trying to do some imputation using K-NN with the yaImpute library. All seems to be going well until I try to use AsciiGridImpute. All my data are correctly formatted and I am able to run and view the results of yai. Below is my code: **************************************************************************************************** library("yaImpute") data

Dear R users, I am looking for some advice on the proper construction of a mixed model in R, using the difference in means as the response and treating within-means residuals as a random effect. I have a dataframe (my own, a snippet of which is given below) that is composed of observations of pollen viability in flowers along tree branches. Flowers (1 to 3 per position) were collected from

I'm using randomForest in yaImpute to create a yai-type object which associates "L" with landscape features. Then I use the sp() package to impute L to a landscape consisting of four ascii files) I keep getting the message "NA's generated due to illegal level(s)" when I do the imputation. It's probably because one of the landscape features ("as", for

I have a data frame with two columns, one of which is a factor (Species) and the other is numeric (BA, which stands for basal area). Here's a sample: Species BA ACSA 55.7632696 FRAM 122.9933524 ACSA 67.54424205 ACSA 89.22123136 ACSA 82.46680716 ACSA 22.46238747 ACSA 19.94911335 ACSA 20.42035225 ACSA 19.00663555 ACSA 21.67698931 ACSA 57.80530483 ACSA 30.31636911 Dead 43.98229715 Dead

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Dear Michael, One key is adjustment of nls optimizer tolerance. I notice it has to be higher than usual, but, I recovered your noisy "known" parameter values with an error of K1 (-7%) and k1 (-6%): #### Miller problem with Dalgaard modifications ## Linares 1/22/2004 ## Solution 1 nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=K1, k2=k2))[,2], data=C1.lsoda,

Dear R-Users, I would like to know whether it is possible to draw several stacked barplots (i.e. side by side on the same sheet)... my data look like : Cond1 Cond1' Cond2 Cond2' Compartment 1 11,81 2,05 12,49 0,70 Compartment 2 10,51 1,98 13,56 0,85 Compartment 3 1,95 0,63 2,81 0,22 Compartment 4 2,08 0,17

Dear Colleagues, Our group is also working on implementing the use of R for pharmacokinetic compartmental analysis. Perhaps I have missed something, but > fit <- nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=0.5, k2=0.5)), + data=C1.lsoda, + start=list(K1=0.3, k2=0.7), + trace=T + ) Error in eval(as.name(varName), data) : Object

> >For example (and this is only an example), a private namespace may be >assigned for each user at login time (at the level of the login shell). >Thus, the user''s "ls" commands see files in whatever directory the >private namespace is rooted, and for all intents and purposes it appears >to be an ordinary filesystem. Yet no other users can see this. User runs

I've been trying to figure out how to accomplish the following... I've got a list (returned from a function) and I would like to "cbind()" the lists together to create a "cross tab" report or simply bind them together somehow the function returns a list that looks like the following: > all$BM $species [1] "BM" $vbar.nobs [1] 3 $vbar.sum [1] 54.05435

Hi everyone, I have been working some days in a nested loop in R but I can't find the solution. I have a data.frame with an unique ID for individuals and unique ID for different stands, for each indiviadual I have a dbh record and a SBA (stand basal area) field. Pma<-rep (1:40) P<-seq(1,4, 1) Plot<-rep(P,10) dbh2<-rnorm(40, mean=200, sd=5) SBA2<-rnorm(40, mean=10, sd=1) As

Dear all, I have a Pharmokinetics data set where single dose is used for several time points on 8 subjects. I wanted to fit a two compartment model on the data set to see whether it is reasonable. This is the first time to analyze a PK data set and am not familar with various concepts in the compartment models. It seems that PK.fit function in the PK.fit package can be used to fit

This might be OT, but it is CentOS related.? I've been running Ansible on C7 for a handful of months now, and updated to 2.4 as soon as it was available. I've been building inventories by hand in that time (mostly due to the fact we had no actual documentation on the managed external customer servers). However, as we have a multiple VMware clusters, thought it might be time to tinker

Hey everyone, I am new to R, and I'm making a scatter plot graph where i have a bunch of plots/points that fall into 9 unique categories. I want each category to have a unique colour, however, with the coding I have (below), the colour black is repeated for two of my plot types. Does anyone know a quick way to get 9 unique colours?? Coding: plotba = plot (predictedba ~ actualba,

On 11/08/2017 12:37 PM, Mark Haney wrote: > This might be OT, but it is CentOS related.? I've been running Ansible > on C7 for a handful of months now, and updated to 2.4 as soon as it was > available. I've been building inventories by hand in that time (mostly > due to the fact we had no actual documentation on the managed external > customer servers). However, as we have a

Yeah, it's the Extras repo Ansible package. So, my next (probably stupid) question, is there a way to get the vmware_inventory plugin setup on my system? <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> Virus-free. www.avast.com

Hi there LARTC, We are running a set of three systems for semiconductor technology, and would like to finalize our work in getting them to interoperate properly, but have run into some issues that touch on the very fabric of TCP/IP expertise. Iptables has already been used to solve "part of this", can we somehow use Diffserv capabilities to acheive the aims of modfiying packets such

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

Dear List, I'm an [R] novice starting analysis of an ecological dataset containing the basal areas of different tree species in a number of research plots. Example data follow: > Trees<-data.frame(SppID=as.factor(c(rep('QUEELL',2), rep('QUEALB',3), 'CORAME', 'ACENEG', 'TILAME')), BA=c(907.9, 1104.4, 113.0, 143.1, 452.3, 638.7, 791.7, 804.3),