search for: fm4

...uMIn) data(Cement) # option 1, create model.selection object using dredge fm0 <- lm(y ~ ., data = Cement) print(dd <- dredge(fm0)) fm1 <- lm(formula = y ~ X1 + X2, data = Cement) fm2 <- lm(formula = y ~ X1 + X2 + X4, data = Cement) fm3 <- lm(formula = y ~ X1 + X2 + X3, data = Cement) fm4 <- lm(formula = y ~ X1 + X4, data = Cement) fm5 <- lm(formula = y ~ X1 + X3 + X4, data = Cement) # ranked with AICc by default # obviously this works model.avg(get.models(dd, delta < 4)) # option 2: the aim is to produce a model selection object comparable to that from get.models(dd, del...

This is an example from chapter 11 of the 6th edition of Devore's engineering statistics text. It happens to be a balanced data set in two factors but the calculations will also work for unbalanced data. I create a factor called 'cell' from the text representation of the Variety level and the Density level using '/' as the separator character. The coefficients for the linear

...MASS) options(contrasts=c("contr.sum", "contr.poly")) X <- as.integer(gl(4, 4, 16)) - 1 Y <- as.integer(gl(4, 1, 16)) - 1 data.2 <- data.frame(Freq, X = factor(X), Y = factor(Y)) summary(fm3 <- glm(Freq ~ X + Y, data = data.2, family = poisson())) dummy.coef(fm3) fm4 <- loglm(Freq ~ X + Y, data = data.2, param = T, fit = T) fm4; fm4$param My question is this: can glm() or some other function be used in the manner Agresti employed for ordinal count data? Thank you, ANDREW Andrew Criswell Professor of Finance Graduate School Bangkok University -.-.-.-.-....

Hi there, I am trying to fit the following model with a sum of exponentials - y ~ Ae^(-md) + B e^(-nd) + c the model has 5 parameters A, b, m, n, c I am using nls to fit the data and I am using DEoptim package to pick the most optimal start values - fm4 <- function(x) x[1] + x[2]*exp(x[3] * -dist) + x[4]*exp(x[5] * -dist) fm5 <- function(x) sum((wcorr-fm4(x))^2) fm6 <- DEoptim(fm5, lower=c(0,0.1,1,0.1,1), upper=c(10e7, 100, 300,100, 300 ), control=list(trace=FALSE)) par2 <- fm6$optim$bestmem names(par2) <- c("c", &quot...

Hi R Users, When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3),

...status by getsockopt(2) after selecting for write. But the poll branch does not. To make things wrong, the poll implementaion takes precedense. The bug makes icecast thinking the connection succeded even it's not true. You can reproduce the bug by requesting mount point pointing to <http://fm4.nobody.at:8080/fm4-mq.ogg>. Given domain name has both AAAA and A records, but the AAAA is unreachable. Current code failes on writing request to the server and it does not try another IP address to connect to. I copied the code from select implementation into poll branch and it fixed the probl...

Hello, I wanted to fit a linear mixed model to a data that is similar in terms of design to the 'Machines' data in 'nlme' package except that each worker (with triplicates) only operates one machine. I created a subset of observations from 'Machines' data such that it looks the same as the data I wanted to fit the model with (see code below). I fitted a model in

...eated measures on students and those students are nested in schools. We can fit a model with random intercepts and slopes for students and students within schools. The first model is fit using lme and the second is fit using lmer. fm3 <- lme(math ~ year, random=~year|schoolid/childid, egsingle) fm4 <- lmer(math ~ year +(year|schoolid:childid) +(year|schoolid), egsingle, control=list(gradient = FALSE, niterEM = 0)) Both result in parameter estimates that are exactly the same. The newest release of lme4 allows for an even easier transition between lme and lmer. Model syntax in lmer can be s...

...X: 0 1 2 3 -0.07101181 0.26753870 0.06069753 -0.25722441 Y: 0 1 2 3 -1.0217353 -0.4667389 0.6163210 0.8721532 > > fm4 <- loglm(Freq ~ X + Y, data = data.2, param = T, fit = T) > fm4; fm4$param Call: loglm(formula = Freq ~ X + Y, data = data.2, param = T, fit = T) Statistics: X^2 df P(> X^2) Likelihood Ratio 12.03686 9 0.2112391 Pearson 11.98857 9 0.2139542 $"(Inte...

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

Hello everyone, I'm trying to test the accurracy of R on the Eckerle4 dataset from NIST and I don't understand how the control option of the nls function works. I tought nls(...) was equivalent to nls(...control=nls.control()) i.e nls.control() was the default value of control, but here is the error I get : > n2=nls(V1~(b1/b2) *

Hi there. http://www.radiofrance.fr/services/aide/difflive.php#ogg Looks like radio france has started its ogg stream ! Thanks to them. France Inter : http://ogg.tv-radio.fr:1441/encoderfinter.ogg France Info : http://ogg.tv-radio.fr:1441/encoderfinfo.ogg France Culture : http://ogg.tv-radio.fr:1441/encoderfculture.ogg France Musiques : http://ogg.tv-radio.fr:1441/encoderfmusiques.ogg

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

...rI>36s2EazZCcsxnw;9hZfR6!HfCL~3NCCD2ssS~C zTEI5ICxGpM9e|yHT}E-YE5iqPXg4=M#rJN&9>Dv6y?}jy{RZ)~n}_!QE1dm2w1=Aq z4ElG_C}QRzqfMP;er9xRZXPx|wlI$v9j`Et8Xd1Rj~N|Xnx7jTo0-Rrj#rr{jE+~E zUl?uTl6lhT*vkCU=y;9!mC^B9^OVuC(EQryc%6Bg-p<h5H%7<n&9g?w*5)~*W0Co- z(Xmap7U4Z&qm1V2l>2O*a-YyC_i=pFm4q4z7FtN!K=N#Z8mgjal^UvbPc+AB)liB8 zQ)*}jJ$I<~)b_IG%Bb}uqXud;m}y~ey_$%vr-pvT!$=XvAP*x2j1*xE_Aqu3#;YF2 z4q)scj3JsT*_#WuY0)#B{+{JIm_<O-Ja0U+*=SF1Df?}^&|5;=RcnK4UNEAYjc%;a zzr~Pa+lkm~nheo(Q+ at qmgG%~CqqP2zbQOos(~84Tjl_~|7DJ2me{4wRm%QoKG;29G zs|mB1?uw=T(y)o8@@mjdfCr?1T~oNKVf-b at 2K!oF=Gb6gtHK...