Help with distribution fitting and AIC in R

HI,

I?m having several problems trying to fit distributions to data that I have
sorted into a data frame, so the each ID has its own step length and turn
angle.



I can fit a Weibull distribution to step lengths with following code:



###create data frame###

ID1 <- na.omit(ID)

weib.test = data.frame(ID1, step1)

set.seed(144)



weib.dist<-rweibull(10000,shape=3,scale=8)

names(weib.test)<-c('ID','steplength')



g <- function(step1) {

? ? require(MASS)

? ? est <- fitdistr(step1, 'weibull')$estimate

? ? ? ? ? ? ? ?AIC(est)

? ? list(shape = est[1], scale = est[2])



?}

library(data.table)

weib.test.dt <- data.table(weib.test, key = 'ID')

weib.test.dt[, g(steplength), by = ID1]



This gives me the following output:



?ID1 ? ? shape ? ?scale

[1,] ? 1 0.8089580 131.1514

[2,] ? 2 0.8393755 106.2641

[3,] ? 3 0.8421661 196.7409

[4,] ? 4 0.9096443 192.8869

[5,] ? 5 0.8170656 172.3526

[6,] ? 6 0.9815219 136.5133

[7,] ? 7 0.9485611 133.9226

[8,] ? 8 0.9279664 158.5760

[9,] ? 9 0.7980447 250.8815

[10,] ?10 0.7882116 132.3324

[11,] ?11 0.6684602 176.5392

[12,] ?12 0.7636310 105.1567

[13,] ?13 0.7179191 126.0132

[14,] ?14 0.6833156 183.6390

[15,] ?15 0.9356103 171.3479

[16,] ?16 0.7865950 129.0388

[17,] ?17 0.8408600 157.9560

[18,] ?18 0.8023891 127.5567



Which is exactly what I need ? separate shape and scale parameters for each
ID. However I also want to get AIC values for each ID, and I have tried
several things such as

AIC(weib.test.dt, by = ID1)

or

extractAIC(fit, scale, k = 2, ...)AIC(weib.test.dt)

stopifnot(all.equal(AIC(weib.test.dt),

? ? ? ? ? ? ? ? ? ?AIC(logLik(weib.test.dt))))





But I keep getting an eror: ?Error in UseMethod("logLik") :

?no applicable method for 'logLik' applied to an object of class
"c('data.table', 'data.frame')"



I?m assuming this had to do with the multiple ID?s? Should I just fit the
distributions separately for each ID instead and then run AIC?

Furthermore, I would like to do the same thing as above with turn angles ?
but by fitting a wrapped Cauchy distribution to the several IDs. I have
tried following code:



###wrapped cauchy fitting, simple mean and rho###

mu0<- circ.mean(turn1)

rho0 <- est.rho(turn1)



wcauchy.test = data.frame(ID2, turn1)

set.seed(144)



wcauchy.dist<-rweibull(10000,mu0,rho0)

names(wcauchy.test)<-c('ID','turnangle')



z <- function(turn1) {

? ? require(MASS)

? ? est <-wrpcauchy.ml(turn1, mu0, rho0, acc=1e-015)$estimate

? ? list(mu0 = est[1], rho0 = est[2])

?}



library(data.table)

wcauchy.test.dt <- data.table(wcauchy.test, key = 'ID')



wcauchy.test.dt[, z(turnangle), by = ID2]



But I keep getting the error: Error in `[.data.table`(wcauchy.test.dt, ,
z(turnangle), by = ID2) :

?Type 0 in j column



And of course I would like to calculate the AIC as well for distribution
fit.



Can anyone help me out with this?

Thanks,

Lene Jung Kjaer