I am using WarbleR in R to do some acoustic analyses. As freq_range couldn't detect all the bottom frequencies very well, I have created a data frame manually with all the right bottom frequencies, loaded this into R and turned it into a selection table. Traq_freq_contour and compare.methods and freq_DTW all work fine (although freq_DTW does give a warning message:
Warning message: In (0:(n - 1)) * f : NAs produced by integer overflow
However. If I try to do the function cross_correlation, I get the following error:
Error in if (ncol(spc1$amp) > ncol(spc2$amp)) { :
argument is of length zero
I do not get this error with a selection table with the bottom and top frequency added with the freq_range function in R instead of manually. What could be the issue here? The selection tables both look similar:
This is the selection table partly made by R through freq_range:
And this is the one with the bottom frequencies added manually (which has more sound files than the one before):
This is part of the code I use:
#Comparing methods for quantitative analysis of signal structure
compare.methods(X = stnew, flim = c(0.6,2.5), bp = c(0.6,2.5), methods = c("XCORR", "dfDTW"))
#Measure acoustic parameters with spectro_analysis
paramsnew <- spectro_analysis(stnew, bp = c(0.6,2), threshold = 20)
write.csv(paramsnew, "new_acoustic_parameters.csv", row.names = FALSE)
#Remove parameters derived from fundamental frequency
paramsnew <- paramsnew[, grep("fun|peakf", colnames(paramsnew), invert = TRUE)]
#Dynamic time warping
dm <- freq_DTW(stnew, length.out = 30, flim = c(0.6,2), bp = c(0.6,2), wl = 300, img = TRUE)
str(dm)
#Spectrographic cross-correlation
xcnew <- cross_correlation(stnew, wl = 300, na.rm = FALSE)
str(xc)
Any idea what I'm doing wrong?
I am analyzing the ratio of (biomass of one part of a plant community) vs. (total plant community biomass) across different treatments in time (i.e. repeated measures) in R. Hence, it seems natural to use beta regression with a mixed component (available with the glmmTMB package) in order to account for repeated measures.
My problem is about computing post hoc comparisons across my treatments with the function lsmeans from the lsmeans package. glmmTMB objects are not handled by the lsmeans function so Ben Bolker on recommended to add the following code before loading the packages {glmmTMB} and {lsmeans}:
recover.data.glmmTMB <- function(object, ...) {
fcall <- getCall(object)
recover.data(fcall,delete.response(terms(object)),
attr(model.frame(object),"na.action"), ...)}
lsm.basis.glmmTMB <- function (object, trms, xlev, grid, vcov.,
mode = "asymptotic", component="cond", ...) {
if (mode != "asymptotic") stop("only asymptotic mode is available")
if (component != "cond") stop("only tested for conditional component")
if (missing(vcov.))
V <- as.matrix(vcov(object)[[component]])
else V <- as.matrix(.my.vcov(object, vcov.))
dfargs = misc = list()
if (mode == "asymptotic") {
dffun = function(k, dfargs) NA
}
## use this? misc = .std.link.labels(family(object), misc)
contrasts = attr(model.matrix(object), "contrasts")
m = model.frame(trms, grid, na.action = na.pass, xlev = xlev)
X = model.matrix(trms, m, contrasts.arg = contrasts)
bhat = fixef(object)[[component]]
if (length(bhat) < ncol(X)) {
kept = match(names(bhat), dimnames(X)[[2]])
bhat = NA * X[1, ]
bhat[kept] = fixef(object)[[component]]
modmat = model.matrix(trms, model.frame(object), contrasts.arg = contrasts)
nbasis = estimability::nonest.basis(modmat)
}
else nbasis = estimability::all.estble
list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun,
dfargs = dfargs, misc = misc)
}
Here is my code and data:
trt=c(rep("T5",13),rep("T4",13),
rep("T3",13),rep("T1",13),rep("T2",13),rep("T1",13),
rep("T2",13),rep("T3",13),rep("T5",13),rep("T4",13))
year=rep(2005:2017,10)
plot=rep(LETTERS[1:10],each=13)
ratio=c(0.0046856237844411,0.00100861922394448,0.032516291436091,0.0136507743972955,0.0940240065096705,0.0141337428305094,0.00746709315018945,0.437009092691189,0.0708021091805216,0.0327952505849285,0.0192685194751524,0.0914696394299481,0.00281889216102303,0.0111928453399615,0.00188119596836005,NA,0.000874623692966351,0.0181192859074754,0.0176635391424644,0.00922358069727823,0.0525280029990213,0.0975006760149882,0.124726170684951,0.0187132600944396,0.00672592365451266,0.106399234215126,0.0401776844073239,0.00015382736648373,0.000293356424756535,0.000923659501144292,0.000897412901472504,0.00315930225856196,0.0636501228611642,0.0129422445492391,0.0143526630252398,0.0136775931834926,0.00159292971508751,0.0000322313783211749,0.00125352390811532,0.0000288862579879126,0.00590690336494395,0.000417043974238875,0.0000695808216192379,0.001301299696752,0.000209355138230326,0.000153151660178623,0.0000646279598274632,0.000596704590065324,9.52943306579156E-06,0.000113476446629278,0.00825405312309618,0.0001025984082064,0.000887617767039489,0.00273668802742924,0.00469409165130462,0.00312377000134233,0.0015579322817235,0.0582615988387306,0.00146933878743163,0.0405139497779372,0.259097955479886,0.00783997376383192,0.110638003652979,0.00454029511918275,0.00728290246595241,0.00104674197030363,0.00550563937846687,0.000121380392484705,0.000831904606687671,0.00475778829159394,0.000402799910756391,0.00259524300745195,0.000210249875492504,0.00550104485802363,0.000272849546913495,0.0025389089622392,0.00129370075116459,0.00132810234020792,0.00523285954007915,0.00506230599388357,0.00774104695265855,0.00098348404576587,0.174079173227248,0.0153486840317039,0.351820365452281,0.00347674458928481,0.147309225196026,0.0418825705903947,0.00591271021100856,0.0207139520537443,0.0563647804012055,0.000560012457272534,0.00191564842393647,0.01493480083524,0.00353400674061077,0.00771828473058641,0.000202009136938048,0.112695841130448,0.00761492172670762,0.038797330459115,0.217367765362878,0.0680958660605668,0.0100870294641921,0.00493875324236991,0.00136539944656238,0.00264262100866192,0.0847732305020654,0.00460985241335143,0.235802638543116,0.16336020383325,0.225776236687456,0.0204568107372349,0.0455390585228863,0.130969863489582,0.00679523322812889,0.0172325334280024,0.00299970176999806,0.00179347656925317,0.00721658257996989,0.00822443690003783,0.00913096724026346,0.0105920192618379,0.0158013204589482,0.00388803567197835,0.00366268607026078,0.0545418725650633,0.00761485067129418,0.00867583194858734,0.0188232707241144,0.018652666214789)
dat=data.frame(trt,year,plot,ratio)
require(glmmTMB)
require(lsmeans)
mod=glmmTMB(ratio~trt*scale(year)+(1|plot),family=list(family="beta",link="logit"),data=dat)
summary(mod)
ls=lsmeans(mod,pairwise~trt)`
Finally, I get the following error message that I've never encountered and on which I could find no information:
In model.matrix.default(trms, m, contrasts.arg = contrasts) :
variable 'plot' is absent, its contrast will be ignored
Could anyone shine their light? Thanks!
This is not an error message, it's a (harmless) warning message. It occurs because the hacked-up method I wrote doesn't exclude factor variables that are only used in the random effects. You should worry more about this output:
NOTE: Results may be misleading due to involvement in interactions
which is warning you that you are evaluating main effects in a model that contains interactions; you have to think about this carefully to make sure you're doing it right.
I'm trying to run this code, and I'm using mhadaptive package, but the problem is that when I run these code without writing metropolis_hastings (that is one part of mhadaptive package) error does not occur, but when I add mhadaptive package the error occur. What should I do?
li_F1<-function(pars,data) #defining first function
{
a01<-pars[1] #defining parameters
a11<-pars[2]
epsilon<<-pars[3]
b11<-pars[4]
a02<-pars[5]
a12<-pars[6]
b12<-pars[7]
h<-pars[8]
h[[i]]<-list() #I want my output is be listed in the h
h[[1]]<-0.32082184 #My first value of h is known and other values should calculate by formula
for(i in 2:nrow(F_2_))
{
h[[i]]<- ((a01+a11*(h[[i-1]])*(epsilon^2)*(h[[i-1]])*b11)+(F1[,2])*((a02+a12*(h[[i-1]])*(epsilon^2)+(h[[i-1]])*b12)))
pred<- h[[i]]
}
log_likelihood<-sum(dnorm(prod(h[i]),pred,sd = 1 ,log = TRUE))
return(h[i])
prior<- prior_reg(pars)
return(log_likelihood + prior)
options(digits = 22)
}
prior_reg<-function(pars) #defining another function
{
epsilon<<-pars[3] #error
prior_epsilon<-pt(0.95,5,lower.tail = TRUE,log.p = FALSE)
return(prior_epsilon)
}
F1<-as.matrix(F_2_) #defining my importing data and simulatunig data with them
x<-F1[,1]
y<-F1[,2]
d<-cbind(x,y)
#using mhadaptive package
mcmc_r<-Metro_Hastings(li_func = li_F1,pars=c(10,15,10,10,10,15),par_names=c('a01','a02','a11','a12','b11','b12'),data=d)
By running this code this error occur.
Error in h[[i]] <- list() : replacement has length zero
I'll so much appreciate who help me.
I'm attempting to create a genetic algorithm (not picky about library, ga and genalg produce same errors) to identify potential columns for use in a linear regression model, by minimizing -adj. r^2. Using mtcars as a play-set, trying to regress on mpg.
I have the following fitness function:
mtcarsnompg <- mtcars[,2:ncol(mtcars)]
evalFunc <- function(string) {
costfunc <- summary(lm(mtcars$mpg ~ ., data = mtcarsnompg[, which(string == 1)]))$adj.r.squared
return(-costfunc)
}
ga("binary",fitness = evalFunc, nBits = ncol(mtcarsnompg), popSize = 100, maxiter = 100, seed = 1, monitor = FALSE)
this causes:
Error in terms.formula(formula, data = data) :
'.' in formula and no 'data' argument
Researching this error, I decided I could work around it this way:
evalFunc = function(string) {
child <- mtcarsnompg[, which(string == 1)]
costfunc <- summary(lm(as.formula(paste("mtcars$mpg ~", paste(child, collapse = "+"))), data = mtcars))$adj.r.squared
return(-costfunc)
}
ga("binary",fitness = evalFunc, nBits = ncol(mtcarsnompg), popSize = 100, maxiter = 100, seed = 1, monitor = FALSE)
but this results in:
Error in terms.formula(formula, data = data) :
invalid model formula in ExtractVars
I know it should work, because I can evaluate the function by hand written either way, while not using ga:
solution <- c("1","1","1","0","1","0","1","1","1","0")
evalFunc(solution)
[1] -0.8172511
I also found in "A quick tour of GA" (https://cran.r-project.org/web/packages/GA/vignettes/GA.html) that using "string" in which(string == 1) is something the GA ought to be able to handle, so I have no idea what GA's issue with my function is.
Any thoughts on a way to write this to get ga or genalg to accept the function?
Turns out I didn't consider that a solution string of 0s (or indeed, a string of 0s with one 1) would cause the internal paste to read "mpg ~ " which is not a possible linear regression.
I'm trying to use cor.ci to obtain polychoric correlations with significance tests, but it keeps giving me an error message. Here is the code:
install.packages("Hmisc")
library(Hmisc)
mydata <- spss.get("S-IAT for R.sav", use.value.labels=TRUE)
install.packages('psych')
library(psych)
poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
poly.example
print(corr.test(poly.example$rho), short=FALSE)
Here is the error message it gives:
> library(psych)
> poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
Error in cor.ci(mydata(nvar = 10, n = 100)$items, n.iter = 10, poly = TRUE) :
could not find function "mydata"
> poly.example
Error: object 'poly.example' not found
> print(corr.test(poly.example$rho), short=FALSE)
Error in is.data.frame(x) : object 'poly.example' not found
How can I make it recognize mydata and/or select certain variables from this dataset for the analysis? I got the above code from here:
Polychoric correlation matrix with significance in R
Thanks!
You have several problems.
1) As previously commented upon, you are treating mydata as a function, but you need to treat it as a data.frame. Thus the call should be
poly.example <- cor.ci(mydata,n.iter = 10,poly = TRUE)
If you are trying to just get the first 100 cases and the first 10 variables, then
poly.example <- cor.ci(mydata[1:10,1:100],n.iter = 10,poly = TRUE)
2) Then, you do not want to run corr.test on the resulting correlation matrix. corr.test should be run on the data.
print(corr.test(mydata[1:10,1:100],short=FALSE)
Note that corr.test is testing the Pearson correlation.