This is a follow-up question to this (see data and previous commands).
Starting with a list of models in mods, i am now able to find the model with the least AIC (corresponds to the best model):
mods <- lapply(methods, function(m)
update(amod.null, correlation = getFunction(m)(1, form = ~ x + y), method="ML"))
names(mods) <- methods
list.AIC <- lapply(mods, function(x) AIC(x))
best.mod <- names(which.min(list.AIC))
Now, i need to do some testing on the model, e.g. Tukey between dates. The syntax is very simple, e.g. for amod.null
library(multcomp)
res <- glht(amod.null, mcp(Date = "Tukey"))
The tricky part is, how can i tell glht to use the model which was put into best.mod (note: this is all happening within a loop). I tried
res <- glht(paste("mods$", as.factor(best.mod),sep = "") , mcp(Date = "Tukey"))
but to no avail, as glht needs to find a model-object in the first argument.
/edit:
Possibly useful:
names(mods)
[1] "corExp" "corGaus" "corLin" "corRatio" "corSpher"
Since the models are stored in the list mods, you can access the "best model" by using the index of which.min(list.AIC):
list.AIC <- sapply(mods, AIC)
best.mod <- mods[which.min(list.AIC)]
best.mod[[1]]
Related
there has been a similar question to mine 6 years+ ago and it hasn't been solve (R -- Can I apply the train function in caret to a list of data frames?)
This is why I am bringing up this topic again.
I'm writing my own functions for my big R project at the moment and I'm wondering if there is an opportunity to sum up the model training function train() of the pakage caret for different dataframes with different predictors.
My function should look like this:
lda_ex <- function(data, predictor){
model <- train(predictor ~., data,
method = "lda",
trControl = trainControl(method = "none"),
preProc = c("center","scale"))
return(model)
}
Using it afterwards should work like this:
data_iris <- iris
predictor_iris <- "Species"
iris_res <- lda_ex(data = data_iris, predictor = predictor_iris)
Unfortunately the R formula is not able to deal with a variable as input as far as I tried.
Is there something I am missing?
Thank you in advance for helping me out!
Solving this would help me A LOT to keep my function sheet clean and safe work for sure.
By writing predictor_iris <- "Species", you are basically saving a string object in predictor_iris. Thus, when you run lda_ex, I guess you incur in some error concerning the formula object in train(), since you are trying to predict a string using vectors of covariates.
Indeed, I tried the following toy example:
X = rnorm(1000)
Y = runif(1000)
predictor = "Y"
lm(predictor ~ X)
which gives an error about differences in the lengths of variables.
Let me modify your function:
lda_ex <- function(data, formula){
model <- train(formula, data,
method = "lda",
trControl = trainControl(method = "none"),
preProc = c("center","scale"))
return(model)
}
The key difference is that now we must pass in the whole formula, instead of the predictor only. In that way, we avoid the string-related problem.
library(caret) # Recall to specify the packages needed to reproduce your examples!
data_iris <- iris
formula_iris = Species ~ . # Key difference!
iris_res <- lda_ex(data = data_iris, formula = formula_iris)
I am modelling a time series as a GARCH(1,1)-process:
And the z_t are t-distributed.
In R, I do this in the fGarch-package via
model <- garchFit(formula = ~garch(1,1), cond.dist = "std", data=r)
Is this correct?
Now, I would like to understand the output of this to check my formula.
Obviously, model#fit$coefs gives me the coefficients and model#fitted gives me the fitted r_t.
But how do I get the fitted sigma_t and z_t?
I believe that the best way is to define extractor functions when generics are not available and methods when generics already exist.
The first two functions extract the values of interest from the fitted objects.
get_sigma_t <- function(x, ...){
x#sigma.t
}
get_z_t <- function(x, ...){
x#fit$series$z
}
Here a logLik method for objects of class "fGARCH" is defined.
logLik.fGARCH <- function(x, ...){
x#fit$value
}
Now use the functions, including the method. The data comes from the first example in help("garchFit").
N <- 200
r <- as.vector(garchSim(garchSpec(rseed = 1985), n = N)[,1])
model <- garchFit(~ garch(1, 1), data = r, trace = FALSE)
get_sigma_t(model) # output not shown
get_z_t(model) # output not shown
logLik(model)
#LogLikelihood
# -861.9494
Note also that methods coef and fitted exist, there is no need for model#fitted or model#fit$coefs, like is written in the question.
fitted(model) # much simpler
coef(model)
# mu omega alpha1 beta1
#3.541769e-05 1.081941e-06 8.885493e-02 8.120038e-01
It is a list structure. Can find the structure with
str(model)
From the structure, it is easier to extract with $ or #
model#fit$series$z
model#sigma.t
I have a loop that needs to be executed; within which are 6 models. The objects that those models are stored in then need to get passed into a function that executes an AIC analysis. However, sometimes one of the models does not work, which then breaks the code for the AIC function because it does not recognize whatever model that failed because it was not stored as an object.
So, I need a way to pull those models that worked into the AIC function.
Here is an example, but keep in mind it is important that this can all be executed within a loop. Here are three hypothetical models:
hn.1 <- ds(data)
hn.1.obs <- ds(data,formula = ~OBSCODE)
hn.1.obs.mas <- ds(dataformula = ~OBSCODE+MAS)
And this would be my AIC function that compares the models:
summarize_ds_models(hn.1, hn.1.obs, hn.1.obs.mas)
But I get an error if say, the hn.1.obs.mas model failed.
I tried to use "get" and "ls" and I successfully pull the models that exist when I call:
get(ls(pattern='hn.15*'))
But that just returns a character vector, so that when I call:
summarize_ds_models(get(ls(pattern='hn.15*')))
it only conducts the AIC analysis on the first model in the above character vector.
Am I on the right track or is there a better way to do this?
UPDATE with a reproducible example.
Here is a simplified version of my problem:
create and fill two data frames that will be put into a list:
data.frame <- data.frame(x = integer(4),
y = integer(4),
z = integer(4),
i = integer(4))
data.frame$x <- c(1,2,3,4)
data.frame$y <- c(1,4,9,16)
data.frame$z <- c(1,3,8,10)
data.frame$i <- c(1,5,10,15)
data.frame.2 <- data.frame[1:4,1:3]
my.list <- list(data.frame,data.frame.2)
create df to fill with best models from AIC analyses
bestmodels <- data.frame(modelname = character(2))
Here is the function that will run the loop:
myfun <- function(list) {
for (i in 1:length(my.list)){
mod.1 = lm(y ~ x, data = my.list[[i]])
mod.2 = lm(y ~ x + z, data = my.list[[i]])
mod.3 = lm(y ~ i, data = my.list[[i]])
bestmodels[i,1] <- rownames(AIC(mod.1,mod.2,mod.3))[1]#bestmodel is 1st row
}
print(bestmodels)
}
However, on the second iteration of the loop, the AIC function will fail because mod.3 will fail. So, is there a generic way to make it so the AIC function will only execute for those models that worked? The outcome I would want here would be:
> bestmodels
modelname
1 mod.1
2 mod.1
since mod.1 would be chosen for both AIC analyses.
Gregor's comment:
Use a list instead of individual named objects. Then do.call(summarize_ds_models, my_list_of_models). If it isn't done already, you can Filter the list first to make sure only working models are in the list.
solved my problem. Thanks
I want to be able to view the p-values for the lmekin object produced by the coxme package.
eg.
model= lmekin(formula = height ~ score + sex + age + (1 | IID), data = phenotype_df, varlist = kinship_matrix)
I tried:
summary(model)
coef(summary(model))
summary(model$coefficient$fixed)
fixef(model)/ sqrt(diag(vcov(model)) #(Calculates Z-scores but not p-values)
But these did not work. So how do I view the p-values for this linear mixed model?
It took me ages of searching to figure this out, but I noticed a lot of other similar questions without proper answers, so I wanted to answer this.
You use:
library(coxme)
print(model)
Note it is important to load the coxme package beforehand or it will not work.
I've also noticed a lot of posts about how to extract the p-value from lmekin objects, or how to extract the p-value from coxme objects in general. I wrote this function, which is based on the coxme:::print.coxme function code (to view code type coxme:::print.coxme directly into R). print calculates p-values on the fly - this function allows the extraction of p-values and saves them to an object.
Note that mod is your model, eg. mod <- lmekin(y~x+a+b)
Use print(mod) to double check that the tables match.
extract_coxme_table <- function (mod){
beta <- mod$coefficients$fixed
nvar <- length(beta)
nfrail <- nrow(mod$var) - nvar
se <- sqrt(diag(mod$var)[nfrail + 1:nvar])
z<- round(beta/se, 2)
p<- signif(1 - pchisq((beta/se)^2, 1), 2)
table=data.frame(cbind(beta,se,z,p))
return(table)
}
I arrived at this topic because I was looking for the same thing for just the coxme object. The function of IcedCoffee works with a micro adjustment:
extract_coxme_table <- function (mod){
beta <- mod$coefficients #$fixed is not needed
nvar <- length(beta)
nfrail <- nrow(mod$var) - nvar
se <- sqrt(diag(mod$var)[nfrail + 1:nvar])
z<- round(beta/se, 2)
p<- signif(1 - pchisq((beta/se)^2, 1), 2)
table=data.frame(cbind(beta,se,z,p))
return(table)
}
I have a list of lm models objects with possible repeated, so I'd like to find a way of checking if some of these lm objects are equal, if so them delete it. In words, I want to "deduplicate" my list.
I'd appreciate very much any help.
An example of the problem:
## Creates outcome and predictors
outcome <- c(names(mtcars)[1:3])
predictors <- c(names(mtcars)[4:11])
dataset <- mtcars
## Creates model list
model_list <- lapply(seq_along((predictors)), function(n) {
left_hand_side <- outcome[1]
right_hand_side <- apply(X = combn(predictors, n), MARGIN = 2, paste, collapse = " + ")
paste(left_hand_side, right_hand_side, sep = " ~ ")
})
## Convert model list into a verctor
model_vector <- unlist(model_list)
## Fit linear models to all itens from the vector of models
list_of_fit <- lapply(model_vector, function(x) {
formula <- as.formula(x)
fit <- step(lm(formula, data = dataset))
fit
})
# Exclude possible missing
list_of_fit <- Filter(Negate(function(x) is.null(unlist(x))), list_of_fit)
# These models are the same in my list
lm253 <- list_of_fit[[253]];lm253
lm254 <- list_of_fit[[254]];lm254
lm255 <- list_of_fit[[255]];lm255
I want to exclude duplicated entries in list_of_fit.
It seems wasteful to fit so many models and then throw away most of them. Your object names make your code hard to read for me, but it seems your models can be distinguished based on their formula. Maybe this helps:
lista_de_ajustes[!duplicated(vapply(lista_de_ajustes,
function(m) deparse(m$call),
FUN.VALUE = "a"))]
I made a simple correction in you code Roland, so it worked for me.
I changed from deparse(m$call) to deparse(formula(m)), due this I'm able to compare the complete formulas.
lista_de_ajustes[!duplicated(vapply(lista_de_ajustes, function(m) deparse(formula(m)), FUN.VALUE = "a"))]
Thank you very much!