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Merge pull request #10 from yufongpeng/release-0.2.4
Release 0.2.4
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@@ -18,7 +18,7 @@ jobs: | |
| fail-fast: false | ||
| matrix: | ||
| version: | ||
| - '1.6' | ||
| - '1.8' | ||
| - '1' | ||
| - 'nightly' | ||
| os: | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,85 @@ | ||
| #' implementation of "between-within" df-guessing algorithm | ||
| #' based on Pinheiro and Bates 2000 description, but tries | ||
| #' to do better with random-slopes models | ||
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| #' @examples | ||
| #' calcDenDF(~age,"Subject",nlme::Orthodont) | ||
| #' calcDenDF(~age,data=nlme::Orthodont,random=~1|Subject) | ||
| #' calcDenDF(~age,data=nlme::Orthodont,random=~age|Subject) | ||
| #' ## off by 1 | ||
| #' calcDenDF(~poly(age,2),data=nlme::Orthodont, | ||
| #' random=~poly(age,2)|Subject) | ||
| #' library(nlme) | ||
| #' lmeDF <- function(formula=distance~age,random=~1|Subject) { | ||
| #' mod <- lme(formula,random,data=Orthodont) | ||
| #' aa <- anova(mod) | ||
| #' return(setNames(aa[,"denDF"],rownames(aa))) | ||
| #' } | ||
| #' lmeDF() | ||
| #' lmeDF(random=~age|Subject) ## wrong! | ||
| #' @param fixed fixed-effect formula | ||
| #' @param grps (character) vector of grouping vectors | ||
| #' @param random random-effect formula (can be specified instead of grps) | ||
| #' @param n0 ?? number of parameters fitted at the population level | ||
| calcDenDF <- function(fixed,grps,data,random=NULL,n0=1) { | ||
| ## n0 is a hack (still not matching some values properly) | ||
| ## try to construct random effects hierarchy | ||
| ## see whether effects vary within groups | ||
| testlevel1 <- function(grp,dmat) { | ||
| vv <- apply(dmat,2, | ||
| function(x) | ||
| tapply(x,list(grp),function(x) length(unique(x)))) | ||
| apply(vv>1,2,any) | ||
| } | ||
| ## | ||
| vlist <- list() | ||
| if (!is.null(random)) { | ||
| grps <- character(0) | ||
| if (!is.list(random)) random <- list(random) | ||
| lapply(random, | ||
| function(x) { | ||
| v <- deparse(x[[2]][[2]]) ## left side of bar | ||
| g <- deparse(x[[2]][[3]]) ## grouping factor (NB may be wrong for complex groupings) | ||
| v2 <- setdiff(colnames(model.matrix(reformulate(v),data=data)),"(Intercept)") | ||
| vlist[[g]] <<- c(vlist[[g]],v2) | ||
| grps <<- union(grps,g) ## NB may not be correctly ordered for multiple groups | ||
| }) | ||
| ## hack to combine | ||
| vnames <- unlist(Map(rep,as.list(names(vlist)),sapply(vlist,length))) | ||
| vlist <- setNames(unlist(vlist),vnames) | ||
| } | ||
| ## get grouping factors even if they are expressed as interactions | ||
| fdata <- lapply(data,factor) | ||
| gfacs <- data.frame(setNames(lapply(grps, | ||
| function(x) eval(parse(text=x),list2env(fdata))),grps), | ||
| check.names=FALSE) | ||
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| X <- model.matrix(fixed,data=data) | ||
| np <- ncol(X) | ||
| has.int <- "(Intercept)" %in% colnames(X) ## FIXME: not tested | ||
| ## apply level-variation function to each grouping factor | ||
| lvary <- t(sapply(gfacs,testlevel1,dmat=X)) | ||
| ## add intercept and observation levels | ||
| lvary <- rbind(pop=c(FALSE,rep(TRUE,np-1)), | ||
| lvary, | ||
| obs=rep(FALSE,np)) | ||
| ## | ||
| for (i in seq_along(vlist)) { | ||
| w <- which(names(vlist[i])==rownames(lvary)) | ||
| lvary[w,vlist[i]] <- FALSE | ||
| } | ||
| ## find index of first FALSE value | ||
| lev <- apply(lvary,2,function(x) which(!x)[1]) | ||
| p <- table(factor(lev,levels=seq(nrow(lvary)))) ## number of parameters estimated at each level | ||
| N <- nrow(X) | ||
| n <- c(pop=n0,sapply(gfacs,function(x) length(unique(x))),obs=N) ## number of obs at each level | ||
| ndiff <- n[-1] - n[-length(n)] | ||
| dfs <- c(NA,setNames(ndiff-p[-1],names(p[-1]))) | ||
| r <- setNames(dfs[as.character(lev)],names(lev)) | ||
| if (has.int) { | ||
| r["(Intercept)"] <- dfs[length(dfs)] | ||
| } else { | ||
| r <- r[-1] | ||
| } | ||
| r | ||
| } |
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@JuliaRegistrator register
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Registration pull request created: JuliaRegistries/General/101930
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