I am trying to get lapply to run models (more specifically path models from the piecewiseSEM package). These path models use separate models (from the nlme package) and then are combine to build the final path model. I've had to utilize some custom made functions from this post to get the models to work. However, now when I try and run the path model using lapply-created objects the models do not run. However they run perfectly fine when not using lapply. I want to use lapply because I want to also utilize Parlapply later on. Here is a reproducible example:
This is the code that I used for custom functions for the package nlme:
library(nlme)
library(piecewiseSEM)
#### corHaversine - spatial correlation with haversine distance
# Calculates the geodesic distance between two points specified by radian latitude/longitude using Haversine formula.
# output in km
haversine <- function(x0, x1, y0, y1) {
a <- sin( (y1 - y0)/2 )^2 + cos(y0) * cos(y1) * sin( (x1 - x0)/2 )^2
v <- 2 * asin( min(1, sqrt(a) ) )
6371 * v
}
# function to compute geodesic haversine distance given two-column matrix of longitude/latitude
# input is assumed in form decimal degrees if radians = F
# note fields::rdist.earth is more efficient
haversineDist <- function(xy, radians = F) {
if (ncol(xy) > 2) stop("Input must have two columns (longitude and latitude)")
if (radians == F) xy <- xy * pi/180
hMat <- matrix(NA, ncol = nrow(xy), nrow = nrow(xy))
for (i in 1:nrow(xy) ) {
for (j in i:nrow(xy) ) {
hMat[j,i] <- haversine(xy[i,1], xy[j,1], xy[i,2], xy[j,2])
}
}
as.dist(hMat)
}
## for most methods, machinery from corSpatial will work without modification
Initialize.corHaversine <- nlme:::Initialize.corSpatial
recalc.corHaversine <- nlme:::recalc.corSpatial
Variogram.corHaversine <- nlme:::Variogram.corSpatial
corFactor.corHaversine <- nlme:::corFactor.corSpatial
corMatrix.corHaversine <- nlme:::corMatrix.corSpatial
coef.corHaversine <- nlme:::coef.corSpatial
"coef<-.corHaversine" <- nlme:::"coef<-.corSpatial"
## Constructor for the corHaversine class
corHaversine <- function(value = numeric(0), form = ~ 1, mimic = "corSpher", nugget = FALSE, fixed = FALSE) {
spClass <- "corHaversine"
attr(value, "formula") <- form
attr(value, "nugget") <- nugget
attr(value, "fixed") <- fixed
attr(value, "function") <- mimic
class(value) <- c(spClass, "corStruct")
value
} # end corHaversine class
environment(corHaversine) <- asNamespace("nlme")
Dim.corHaversine <- function(object, groups, ...) {
if (missing(groups)) return(attr(object, "Dim"))
val <- Dim.corStruct(object, groups)
val[["start"]] <- c(0, cumsum(val[["len"]] * (val[["len"]] - 1)/2)[-val[["M"]]])
## will use third component of Dim list for spClass
names(val)[3] <- "spClass"
val[[3]] <- match(attr(object, "function"), c("corSpher", "corExp", "corGaus", "corLin", "corRatio"), 0)
val
}
environment(Dim.corHaversine) <- asNamespace("nlme")
## getCovariate method for corHaversine class
getCovariate.corHaversine <- function(object, form = formula(object), data) {
if (is.null(covar <- attr(object, "covariate"))) { # if object lacks covariate attribute
if (missing(data)) { # if object lacks data
stop("need data to calculate covariate")
}
covForm <- getCovariateFormula(form)
if (length(all.vars(covForm)) > 0) { # if covariate present
if (attr(terms(covForm), "intercept") == 1) { # if formula includes intercept
covForm <- eval(parse(text = paste("~", deparse(covForm[[2]]),"-1",sep=""))) # remove intercept
}
# can only take covariates with correct names
if (length(all.vars(covForm)) > 2) stop("corHaversine can only take two covariates, 'lon' and 'lat'")
if ( !all(all.vars(covForm) %in% c("lon", "lat")) ) stop("covariates must be named 'lon' and 'lat'")
covar <- as.data.frame(unclass(model.matrix(covForm, model.frame(covForm, data, drop.unused.levels = TRUE) ) ) )
covar <- covar[,order(colnames(covar), decreasing = T)] # order as lon ... lat
}
else {
covar <- NULL
}
if (!is.null(getGroupsFormula(form))) { # if groups in formula extract covar by groups
grps <- getGroups(object, data = data)
if (is.null(covar)) {
covar <- lapply(split(grps, grps), function(x) as.vector(dist(1:length(x) ) ) ) # filler?
}
else {
giveDist <- function(el) {
el <- as.matrix(el)
if (nrow(el) > 1) as.vector(haversineDist(el))
else numeric(0)
}
covar <- lapply(split(covar, grps), giveDist )
}
covar <- covar[sapply(covar, length) > 0] # no 1-obs groups
}
else { # if no groups in formula extract distance
if (is.null(covar)) {
covar <- as.vector(dist(1:nrow(data) ) )
}
else {
covar <- as.vector(haversineDist(as.matrix(covar) ) )
}
}
if (any(unlist(covar) == 0)) { # check that no distances are zero
stop("cannot have zero distances in \"corHaversine\"")
}
}
covar
} # end method getCovariate
environment(getCovariate.corHaversine) <- asNamespace("nlme")
Here is the reproducible example/problem with the mtcars dataset:
set.seed(42) ## for sake of reproducibility
mtcars <- within(mtcars, {
lon <- runif(nrow(mtcars))
lat <- runif(nrow(mtcars))
})
#this makes a list of dataframes
empty_list<-replicate(n = 10,
expr = mtcars,
simplify = F)
#doing it the lapply method
model1<-lapply(empty_list, FUN = function(i)
nlme::gls(disp ~ wt,
correlation = corHaversine(form=~lon+lat,mimic="corSpher"),
data = i)
)
model2<-lapply(empty_list, FUN = function(i)
nlme::gls(wt ~ hp,
correlation = corHaversine(form=~lon+lat,mimic="corSpher"),
data = i)
)
model1.2<-psem(model1[[1]],model2[[1]], data = empty_list[[1]])
summary(model1.2, .progressBar = F, standardize = "scale")
This results in this error:
Error in max(sapply(nm[dfdetect], nrow)) :
invalid 'type' (list) of argument
But when I do this without lapply, it works out fine:
model3<-nlme::gls(disp ~ wt,
correlation = corHaversine(form=~lon+lat,mimic="corSpher"),
data = empty_list[[1]])
model4<-nlme::gls(wt ~ hp,
correlation = corHaversine(form=~lon+lat,mimic="corSpher"),
data = empty_list[[1]])
model3.4<-psem(model3, model4)
summary(model3.4, .progressBar = F, standardize = "scale")
