search for: modelc

...Hopkins U, Baltimore MD, USA for (i in 16:nmax){ line<-scan(file=paste(file),nlines=1,skip=(i-1),what="integer",sep=",") d<-as.numeric(line[-1]) name<-line[1] modela <- lm(s1~a+a2+b+s+M+W) modelb <- lm(s2~a+a2+b+s+M+W+d) modelc <- lm(s3~a+2+b+s+M+W+d+d*s) p_main <- anova(modela,modelb)$P[2] p_main_i <- anova(modela,modelc)$P[2] p_i <- anova(modelb,modelc)$P[2] cat(c(name,p_main,p_main_i,p_i),file=paste("out",".txt",sep=""),append=T) cat("\n&qu...

Hello all, I have two comboboxen, comboA is popultaed when :controller/list is retrieved first time. When comboA is selected, I want to auto-populate comboB (modelB belongs_to modelA). The population of tableC (modelC belongs_to modelB and belongs_to modelA) should not populate until a "GO" button is clicked (link_to with submit). Help? I need an example of how to filter the post @params and also how to preserve teh selections when "GO" is clicked. In my current code, "GO" returns...

...Here follows part of the code do give better understanding of what I am doing: modela <- lm(RSSYS10 ~ RS_AGE + RS_AGESQ + SEX + RS_BMI) frssys <- function(SNP_A1,data=sys_pheno, model=modela){ modelb <- lm(RSSYS10 ~ RS_AGE + RS_AGESQ + SEX + RS_BMI + SNP_A1,data=data) modelc <- lm(RSSYS10 ~ RS_AGESQ + SEX + RS_BMI + SNP_A1 * RS_AGE,data=data) p1 <- anova(model,modelb)$P[2] p2 <- anova(model,modelc)$P[2] p3 <- anova(modelb,modelc)$P[2] } result_matrix <- apply(mldose_asmatrix[,2:ncol(mldose_asmatrix)],2,frssys) Data frame sys_pheno h...

Hello, I''m using find_with_ferret to search multiple models and it works great. The trouble is I need to filter the results using :include and :conditions. I get two errors depending on the syntax I use in the search. Reading the source, I see the retrieve_records method seems to filter the :include and :conditions so that they only apply to the relevant model when searching

...over time, using lme package. Results are at the end of this message. The outcome is score and the covariate is age. My question is: is possible (and how) to estimate both 95% confidence and prediction intervals for the mixed model as in linear regression models? I've tried this using predict(modelc,interval="confidence",level=0.95) and predict(modelc,interval="prediction",level=0.95) but the result is always the same given by fitted(model4). Thanks in advance, Jorge Velez # Results for the lme model Linear mixed-effects model fit by maximum likelihood Data: NPC.n...

...g(forage_time)),data=data,family="nbinom") modelnb<-glmm.admb(data$total_bites_rounded~age_class_back, random=~food.dif.id, group="subject", data=data, offset=offset,family="nbinom") I am not sure what I am doing wrong. My model in lmer that seemed to work was: modelc<lmer(data$total_bites_rounded~age_class_back+(1|data$focal_individual)+(1|food.dif.id)+offset(log(forage_time)),family=poisson) Where age class is my one fixed variable and focal individual (=subject) and food id are my two random variables. I have tried a number of different things in glmm....

So, I have 2 models A and B that share 5 common attributes and all other attributes are different. So I wanted to extract these 5 out into one common table and use has_one, belongs_to to knit it back together. So now there are 3 tables with 1 having the shared properties, we''ll call this table C. Table A id dollar_amount Table B id quantity_on_hand Table C id version My question is,