I'm trying to use the dredge() function to evaluate models by completing every combination of variables (up to five variables per model) and comparing models using AIC corrected for small sample size (AICc).
However, I'm presented with one error and two warning messages as follows:
Fixed term is "(Intercept)"
Warning messages: 1: In dredge(MaxN.model,
m.min = 2, m.max = 5) : comparing models fitted by REML 2: In
dredge(MaxN.model, m.min = 2, m.max = 5) : arguments 'm.min' and
'm.max' are deprecated, use 'm.lim' instead
I've tried changing to 'm.lim' as specified but it comes up with the error:
Error in dredge(MaxN.model, m.lim = 5) : invalid 'm.lim' value In
addition: Warning message: In dredge(MaxN.model, m.lim = 5) :
comparing models fitted by REML
The code I'm using is:
MaxN.model<-lme(T_MaxN~Seagrass.cover+composition.pca1+composition.pca2+Sg.Richness+traits.pca1+
land.use.pc1+land.use.pc2+seascape.pc2+D.landing.site+T_Depth,
random=~1|site, data = sgdf, na.action = na.fail, method = "REML")
Dd_MaxN<-dredge(MaxN.model, m.min = 2 , m.max = 5)
What am I doing wrong?
You didn't tell us what you tried to specify for m.lim. ?dredge says:
m.lim ...optionally, the limits ‘c(lower, upper)’ for number of terms in a single model
so you should specify a two-element numeric (integer) vector.
You should definitely be using method="ML" rather than method="REML". The warning/error about REML is very serious; comparing models with different fixed effects that are fitted via REML will lead to nonsense.
So you should try:
MaxN.model <- lme(..., method = "ML") ## where ... is the rest of your fit
Dd_MaxN <- dredge(MaxN.model, m.lim=c(2,5))
Related
Sorry this is crossposting from https://stats.stackexchange.com/questions/593717/nlme-regression-with-weights-syntax-in-r, but I thought it might be more appropriate to post it here.
I am trying to fit a power curve to model some observations in an nlme. However, I know some observations to be less reliable than others (reliability of each OBSID reflected in the WEIV in the dummy data), relatively independent of variance, and I quantified this beforehand and wish to include it as weights in my model. Moreover, I know a part of my variance is correlated with my independent variable so I cannot use directly the variance as weights.
This is my model:
coeffs_start = lm(log(DEPV)~log(INDV), filter(testdummy10,DEPV!=0))$coefficients
nlme_fit <- nlme(DEPV ~ a*INDV^b,
data = testdummy10,
fixed=a+b~ 1,
random = a~ 1,
groups = ~ PARTID,
start = c(a=exp(coeffs_start[1]), b=coeffs_start[2]),
verbose = F,
method="REML",
weights=varFixed(~WEIV))
This is some sample dummy data (I know it is not a great fit but it's fake data anyway) : https://github.com/FlorianLeprevost/dummydata/blob/main/testdummy10.csv
This runs well without the "weights" argument, but when I add it I get this error and I am not sure why because I believe it is the correct syntax:
Error in recalc.varFunc(object[[i]], conLin) :
dims [product 52] do not match the length of object [220]
In addition: Warning message:
In conLin$Xy * varWeights(object) :
longer object length is not a multiple of shorter object length
Thanks in advance!
This looks like a very long-standing bug in nlme. I have a patched version on Github, which you can install via remotes::install_github() as below ...
remotes::install_github("bbolker/nlme")
testdummy10 <- read.csv("testdummy10.csv") |> subset(DEPV>0 & INDV>0)
coeffs_start <- coef(lm(log(DEPV)~log(INDV), testdummy10))
library(nlme)
nlme_fit <- nlme(DEPV ~ a*INDV^b,
data = testdummy10,
fixed=a+b~ 1,
random = a~ 1,
groups = ~ PARTID,
start = c(a=exp(coeffs_start[1]),
b=coeffs_start[2]),
verbose = FALSE,
method="REML",
weights=varFixed(~WEIV))
packageVersion("nlme") ## 3.1.160.9000
I am looking for a way to compute average marginal effects with clustered standard errors which i seem to be having a few problems with. My model is as follows:
cseLogit <- miceadds::glm.cluster(data = data_long,
formula = follow ~ f1_distance + f2_distance + PolFol + MediaFol,
cluster = "id",
family = binomial(link = "logit"))
Where the dependent variable is binary (0/1) and all explanatory variables are numeric. I've tried to different ways of getting average marginal effects. The first one is:
marginaleffects <- margins(cseLogit, vcov = your_matrix)
Which gives me the following error:
Error in find_data.default(model, parent.frame()) :
'find_data()' requires a formula call
I've also tried this:
marginaleffects <- with(cseLogit, margins(glm_res, vcov=vcov))
which gives me this error:
Error in eval(predvars, data, env) :
object 'f1_distance' was not found
In addition: warnings:
1: In dydx.default(X[[i]], ...) :
Class of variable, f1_distance, is unrecognized. Returning NA.
2: In dydx.default(X[[i]], ...) :
Class of variable, f2_distance, is unrecognized. Returning NA.
Can you tell me what i'm doing wrong? If i haven't provided enough information, please let me know. Thanks in advance.
I used this formula to get woe with
library("woe")
woe.object <- woe(data, Dependent="target", FALSE,
Independent="shop_id", C_Bin=20, Bad=0, Good=1)
Then I want to predict woe for the test data
test.woe <- predict(woe.object, newdata = test, replace = TRUE)
And it gives me an error
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "data.frame"
Any suggestions please?
For prediction, you cannot do it with the package woe. You need to use the package. Take note of the masking of the function woe, see below:
#let's say we woe and then klaR was loaded
library(klaR)
data = data.frame(target=sample(0:1,100,replace=TRUE),
shop_id = sample(1:3,100,replace=TRUE),
another_var = sample(letters[1:3],100,replace=TRUE))
#make sure both dependent and independent are factors
data$target=factor(data$target)
data$shop_id = factor(data$shop_id)
data$another_var = factor(data$another_var)
You need two or more dependent variables:
woemodel <- klaR::woe(target~ shop_id+another_var,
data = data)
If you only provide one, you have an error:
woemodel <- klaR::woe(target~ shop_id,
data = data)
Error in woe.default(x, grouping, weights = weights, ...) : All
factors with unique levels. No woes calculated! In addition: Warning
message: In woe.default(x, grouping, weights = weights, ...) : Only
one single input variable. Variable name in resulting object$woe is
only conserved in formula call.
If you want to predict the dependent variable with only one independent, something like logistic regression will work:
mdl = glm(target ~ shop_id,data=data,family="binomial")
prob = predict(mdl,data,type="response")
predicted_label = ifelse(prob>0.5,levels(data$target)[1],levels(data$target)[0])
I am building a decision tree using the tree library in R. I have attempted to fit my model as follows:
model <- tree(Outcome ~ Age + Sex + Income, data = train, type = "class")
Running the above line gives me an error as follows:
Error in tree.control(nobs, ...) : unused argument (type = "class")
I down sampled so that each class is equal and so did not specify any weights. If I remove the argument, type = "class", the model runs but when I predict using the model, it seems that it is building a regression model which I do not want.
Can someone help?
If you look at the help page ?tree there is no argument called type. If you are getting a regression, that is because Outcome is a numeric argument. I expect that you can fix this by adding
train$Outcome = factor(train$Outcome)
I have a problem with specifying the learning rate using the caret package with the method "mlpWeightDecay" from RSNNS package.
The tuning parameters of "mlpWeightDecay" are size and decay.
An example leaving size constant at 4 and tuning decay over c(0,0.0001, 0.001, 0.002):
data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]
fit1 <- train(TrainData, TrainClasses,
method = "mlpWeightDecay",
preProcess = c("center", "scale"),
tuneGrid=expand.grid(.size = 4, .decay = c(0,0.0001, 0.001, 0.002)),
trControl = trainControl(method = "cv")
)
But I also want to manipulate the learning rate of the model and not just taking the default learning rate of 0.2.
I know that I can use further arguments of the mlpWeightDecay method from RSNNS via the "..." parameter.
"learnFuncParams" would be the RSNNS parameter I would need to insert. It takes 4 parameters (learning rate, weight decay, dmin, dmax).
Going on with the example it looks like this:
fit1 <- train(TrainData, TrainClasses,
method = "mlpWeightDecay",
preProcess = c("center", "scale"),
tuneGrid=expand.grid(.size = 4, .decay = c(0,0.0001, 0.001, 0.002)),
trControl = trainControl(method = "cv"),
learnFuncParams=c(0.4,0,0,0)
)
BUT the documentation of the caret train function tells me for the "..." parameter:
arguments passed to the classification or regression routine (such as randomForest). Errors will occur if values for tuning parameters are passed here.
The problem is that one of the 4 "learningFuncParams" parameters (weight decay) IS a tuning parameter.
Consequently I get an error and warnings:
Error in train.default(TrainData, TrainClasses, method = "mlpWeightDecay", :
final tuning parameters could not be determined
In addition: There were 50 or more warnings (use warnings() to see the first 50)
Warning messages:
1: In method$fit(x = if (!is.data.frame(x)) as.data.frame(x) else x, ... :
Over-riding weight decay value in the 'learnFuncParams' argument you passed in. Other values are retained
2: In eval(expr, envir, enclos) :
model fit failed for Fold01: size=4, decay=0e+00 Error in mlp.default(x = structure(list(Sepal.Length = c(-0.891390168709482, :
formal argument "learnFuncParams" matched by multiple actual arguments
How can I set the learning rate without coming in conflicts with the tuning parameter "decay" if both is set in the same parameter "learningFuncParams"?
Thanks!
It looks like you can specify your own learnFuncParams in "...". caret checks if you've provided your own set of parameters and will only override learnFuncParams[3] (which is the decay). It will take the learnFuncParams[1,2,4] that you have provided.
A very convenient way to find out what caret does is to type getModelInfo("mlpWeightDecay") and then scroll up to the $mlpWeightDecay$fit part. It shows how caret will call the real training function:
$mlpWeightDecay$fit
if (any(names(theDots) == "learnFuncParams")) {
prms <- theDots$learnFuncParams
prms[3] <- param$decay
warning("Over-riding weight decay value in the 'learnFuncParams' argument you passed in. Other values are retained")
}
It checks if you've provided your own learnFuncParams. If you did, it uses it, but inserts its own decay. You can ignore the warning.
I think the error you've got ("final tuning parameters could not be determined") has another reason. Have you tried a lower learning rate?