Here we use a few functions in MuMIn and some in base R functions. I'll leave that to you. "need a 'global.model' with a call component. If you read on the R help page for as.Date by typing ?as.Date you will see there is a default format assumed if you do not specify. x1, x2, and x1+x2, however the random effect structure remains the same as in the full model, such that even when fixed effect is only x1, the random effect will include (0+x2|Year) + (0+x2|Country). How to split a text into two meaningful words in R. n=length(y) model_a1 <- auto.arima(y) plot(x=1:n,y,xaxt="n",xlab="") axis(1,at=seq(1,n,length.out=20),labels=index(y)[seq(1,n,length.out=20)], las=2,cex.axis=.5) lines(fitted(model_a1), col = 2) The result depending on your data will be something similar: ... r,function,optimization,mathematical-optimization. #Note `diag<-` does not work for x[1x1] matrix: # diag(gloFactorTable[offsetNames, offsetNames, drop = FALSE]) <- TRUE, #@@@ TODO has subsetExpr <- exprapply0(subsetExpr, "has", .subst.term), "unrecognized names in 'subset' expression: ", # subset as expression using 'varying' variables, #} else if(capabilities("tcltk") && ("package:tcltk" %in% search())) {, #tkProgressBar(max = ncomb, title = "'dredge' in progress"), #if(iComb %% 100L == 0L) setProgressBar(progressBar, value = iComb, title = sprintf("dredge: %d/%d total", k, iComb)), ## --- Variants ---------------------------, #cvi <- variants[(iComb - 1L) %% nvariants + 1L, ]. They are still referenced by... sapply iterates through the supplied vector or list and supplies each member in turn to the function. \n Consider using 'updateable' on the modelling function", # object from 'run.mark.model' has $call of 'make.mark.model' - fixing, "'global.model' uses \"data\" that is a function value: use a variable instead", "argument \"marg.ex\" is defunct and has been ignored", "argument \"na.action\" is inappropriate here", "argument %s is not a name of formal argument of %s", "arguments %s are not names of formal arguments of %s". Given a list of English words you can do this pretty simply by looking up every possible split of the word in the list. Dredge definition is - to dig, gather, or pull out with or as if with a dredge —often used with up. how to read a string as a complex number? AIC values show that these model are not very informative. [on hold], Convert strings of data to “Data” objects in R [duplicate], Highlighting specific ranges on a Graph in R, R — frequencies within a variable for repeating values, Twitter: Get followers from multiple users at once, Subsetting rows by passing an argument to a function, Remove quotes to use result as dataset name. How (in a vectorized manner) to retrieve single value quantities from dataframe cells containing numeric arrays? You can do this with something like: get_scalar <- function(name, FUN=max) { sapply(mydata[,name], function(x) if(all(x == -999)) NA else FUN(as.numeric(x[x != -999]))) } Note that I've changed your function... As per ?zoo: Subscripting by a zoo object whose data contains logical values is undefined. Sleep Shiny WebApp to let it refresh… Any alternative? # a cumbersome way of evaluating a non-exported function in a parent frame: #extra <- eval(call(".get.extras", substitute(extra), r2nullfit = TRUE), parent.frame()), "function in 'extra' returned non-numeric result", "number of non-fixed predictors [%d] exceeds the allowed maximum of %.0f (with %d variants)". it's better to generate all the column data at once and then throw it into a data.frame. Using data.table library(data.table) setDT(df1)[, list(pages=paste(page, collapse="_")), list(user_id, date=as.Date(date, '%m/%d/%Y'))] Or using dplyr library(dplyr) df1 %>% group_by(user_id, date=as.Date(date, '%m/%d/%Y')) %>% summarise(pages=paste(page, collapse='_')) ... You can do it with rJava package. It's easier to think of it in terms of the two exposures that aren't used, rather than the five that are. You can alternatively look at the 'Large memory and out-of-memory data' section of the High Perfomance Computing task view in R. Packages designed for out-of-memory processes such as ff may help you. That's not correct. dredge returns a list with every possible combination of variables, if a variable doesn't have a value, it means it was not included in the model. If you only have 4 GBs of RAM you cannot put 5 GBs of data 'into R'. Given your criteria -- that 322 is represented as 3 and 2045 is 20 -- how about dividing by 100 and then rounding towards 0 with trunc(). It's generally not a good idea to try to add rows one-at-a-time to a data.frame. Something among these lines l <- mget(ls(patter = "m\\d+.m")) lapply(l, function(x)... Use [[ or [ if you want to subset by string names, not $. You are using it to copy a list. # change na. For example, model 3 only has 1NN, besides intercept obviously. For some reason the top and bottom margins need to be negative to line up perfectly. re(x,g)), so that dredge thinks these are fixed effects. For example: For example: # (following Ben Bolker's example above)) # Fit a null model with RE (use a non-exported function .nullFitRE or specify it by hand: nullmodel <- MuMIn:::.nullFitRE(m1) # the above step is not necessary, but avoids repeated re-fitting … #"For objects without a 'call' component the call to the fitting function \n", #" must be used directly as an argument to 'dredge'. "), # if 'update' method does not expand dots, we have a problem with, # expressions like ..1, ..2 in the call. While doing metal detecting in a swampy area, they dredged up a little item that helps to keep their quest alive. Otherwise... You could loop through the rows of your data, returning the column names where the data is set with an appropriate number of NA values padded at the end: `colnames<-`(t(apply(dat == 1, 1, function(x) c(colnames(dat)[x], rep(NA, 4-sum(x))))), paste("Impair", 1:4)) # Impair1 Impair2 Impair3 Impair4 # 1 "A" NA NA NA... A better approach would be to read the files into a list of data.frames, instead of one data.frame object per file. You cannot do that out-of-box as dredge currently omits all (x|g) expressions, but you can make a "wrapper" around (g)lmer that replaces the "|" terms in the formula with something else (e.g. Let's limit the number of times an exposure can be excluded: draw_exc <- function(exposures,nexp,ng,max_excluded = 10){ nexc <- length(exposures)-nexp exp_rem <- exposures exc <- matrix(,ng,nexc) for (i in 1:ng){... Do not use the dates in your plot, use a numeric sequence as x axis. How to use dredge in a sentence. I would create a list of all your matrices using mget and ls (and some regex expression according to the names of your matrices) and then modify them all at once using lapply and colnames<- and rownames<- replacement functions. Consider using ". You can use the dates as labels. #eval(formals(sys.function())[["beta"]])[betaMode + 1L]. Your intuition is correct. I think you want to minimize the square of a-fptotal ... ff <- function(x) myfun(x)^2 > optimize(ff,lower=0,upper=30000) $minimum [1] 28356.39 $objective [1] 1.323489e-23 Or find the root (i.e. It looks like you're trying to grab summary functions from each entry in a list, ignoring the elements set to -999. I think this code should produce the plot you want. Example: manipulates both fixed and random effects. Using IRanges, you should use findOverlaps or mergeByOverlaps instead of countOverlaps. However, without your exact dataset, I had to generate simulated data. Try.. zz <- lapply(z,copy) zz[[1]][ , newColumn := 1 ] Using your original code, you will see that applying copy() to the list does not make a copy of the original data.table. (allTerms %in% interceptLabel)], "unnamed 'subset' matrix does not have both dimensions", " equal to number of terms in 'global.model': %d", "at least some dimnames of 'subset' matrix do not ", "non-missing values exist outside the lower triangle of 'subset'". should printed term names be abbreviated? Principal == input$selectPrincipal | input$selectPrincipal == "All" ... You can create a similar plot in ggplot, but you will need to do some reshaping of the data first. These examples are from the Cambridge English Corpus and from sources on the web. Combining the example by @Robert and code from the answer featured here: How to get a reversed, log10 scale in ggplot2? Is this interpretation correct or am I missing something? The character strings "R^2" and "adjR^2" are treated in a special way, and will add a likelihood-ratio based R

__ and modified-R____ respectively to the result (this is more efficient than using r.squaredLR directly). R/dredge.R defines the following functions: `dredgeAll` dredge. Any scripts or data that you put into this service are public.
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