Hi,
Someone help-me on this task some weeks ago, and it is working find to me. I
use something like:
-----
findmodels <- function(modeltype = "lm", dataset, pattern) {
ls <- ls(".GlobalEnv", pattern=pattern)
mods <- ls[sapply(ls, function(x) inherits(get(x), modeltype))]
if (!missing(dataset)) {
data.name <- function(x) as.character(x$call[["data"]])
mods <- mods[sapply(mods, function(x) data.name == dataset)]
}
models <- lapply(mods, get)
class(models) <- c("ensemble", class(models))
models
}
mod_1<-glm(....)
mod_2<-glm(....)
models<-NULL
models<- findmodels(pattern="mod_")
-------------
So you can use a "for (i in 1:length(models))" to extract the statistics tha
you want for each model and save it in a file (I´m saving it easily in Access
using RODBC).
I hope this help,
Miltinho
Brazil
Indermaur Lukas <[EMAIL PROTECTED]> escreveu:
Hello
I want to batch job the calculation of many GLM-models, extract some values and
store them in a file. Almost everything in the script below works (read file,
extract values and write them to file) except I fail in indexing the GLM with
the modelstructure it should run. Running GLM's conventionally is no problem.
Conventionally a GLM is calculated as:
--------------------------------------
glm(ZlogHRS ~ ZRi+ZE+ZPROX_MN+ZED+ZAlwd+ZT2+ZW+ZN+Sex+y, family = gaussian,
data=t.data)
(just a note: dependent variable is ZlogHRS, while the others are indepent
variables)
Desired way: sequentially run GLM
---------------------------------
I want R to take the model structure to take from a vector called
"modelstructure" and paste it into the GLM like:
glm(modelstructure[i], family = gaussian, data=t.data).
It would considerably ease my workload if there is a solution to the indexing
problem within a GLM. I appreciate any hint.
Best regards
Lukas
p.s.
my R skills are rather poor
----------------------------------------START
R-CODE-----------------------------------
# Read file
t.url <-
"C://HR_calculations/2005_2006/HR_SIZE/Kandidatenmodelle_Berechnung/inputfiles/"
t.tuti <- read.table(paste(t.url, "All_animals.txt", sep=""),header=T)
collect.results <- function(x) {
#resets vectors which will be filled
i <- 0
AICA <- NA;
#put models names hierarchically in vector
modelnames <- c("1=global", "2=biotic1", "3=biotic2", "4=abiotic") #keep track
of changes in model names and number
for (i in 1:length(modelnames))
#model structure of the four models given for all models to run
#global
modelstructure <- c(
"ZlogHRS ~ ZRi+ZE+ZPROX_MN+ZED+ZAlwd+ZT2+ZW+ZN+Sex+y",
#biotic1
"ZlogHRS ~ ZRi",
#biotic2
"ZlogHRS ~ ZPROX_MN",
#abiotic
"ZlogHRS ~ ZE")
ts.model <- glm(modelstructure[i], family = gaussian, data=t.data)
# Extracts some mode results
n[i] <- length(resid(ts.model))
AICA[i] <- AIC(ts.model)
}
#Writes results to data file
x = data.frame(
modelnames, n, AICA)
write.table(x, paste(t.url, file="Results.txt"), sep="\t", quote=F)
----------------------------------------END
R-CODE-----------------------------------
°°°
Lukas Indermaur, PhD student
eawag / Swiss Federal Institute of Aquatic Science and Technology
ECO - Department of Aquatic Ecology
Überlandstrasse 133
CH-8600 Dübendorf
Switzerland
Phone: +41 (0) 71 220 38 25
Fax : +41 (0) 44 823 53 15
Email: [EMAIL PROTECTED]
www.lukasindermaur.ch
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