So I'm trying to fit a cubic, natural, and smoothing spline to the Auto dataset from the ISLR package. I'm having some trouble and am getting some warning/error messages which makes me think there is something wrong with my data or a matrix that I created.
What is really confusing is how this basic command throws an error.
natural.splines.fit <- lm(horsepower ~ ns(mpg, knots = c(25, 50, 75)), data = Auto)
Error in qr.default(t(const)) : NA/NaN/Inf in foreign function call
(arg 1)
There are additional errors/warnings in my code but the thing is: I had essentially copied the code from somewhere and I also ran it, which it worked for the Carseats dataset and modified it to change the variables to match the Auto dataset. This is why it is confusing me. I'm not understanding why I get errors for the Auto dataset but not the Carseats dataset. Does anyone have some insight?
The problem that you have is that you are defining the knots outside the range of the predictor variable. Here is a basic code that will work (I just defined knots that are within the range of the variable mpg).
x <- ISLR::Auto
natural.splines.fit <- lm(horsepower ~ ns(mpg, knots = c(10,20,30,40)), data = x)
summary(natural.splines.fit)
I believe that you are trying to place the knots for the 25th, 50th, and 75th percentile, so I recommend first getting the values corresponding to those locations and then fitting the model.
Here is how I did it
target_quantiles <- unname(quantile(x$mpg, probs = c(0.25,0.5,0.75)))
natural.splines.fit2 <- lm(horsepower ~ ns(mpg, knots = target_quantiles), data = x)
summary(natural.splines.fit2)
Related
I am using the package lqmm, to run a linear quantile mixed model on an imputed object of class mira from the package mice. I tried to make a reproducible example:
library(lqmm)
library(mice)
summary(airquality)
imputed<-mice(airquality,m=5)
summary(imputed)
fit1<-lqmm(Ozone~Solar.R+Wind+Temp+Day,random=~1,
tau=0.5, group= Month, data=airquality,na.action=na.omit)
fit1
summary(fit1)
fit2<-with(imputed, lqmm(Ozone~Solar.R+Wind+Temp+Day,random=~1,
tau=0.5, group= Month, na.action=na.omit))
"Error in lqmm(Ozone ~ Solar.R + Wind + Temp + Day, random = ~1, tau = 0.5, :
`data' must be a data frame"
Yes, it is possible to get lqmm() to work in mice. Viewing the code for lqmm(), it turns out that it's a picky function. It requires that the data argument is supplied, and although it appears to check if the data exists in another environment, it doesn't seem to work in this context. Fortunately, all we have to do to get this to work is capture the data supplied from mice and give it to lqmm().
fit2 <- with(imputed,
lqmm(Ozone ~ Solar.R + Wind + Temp + Day,
data = data.frame(mget(ls())),
random = ~1, tau = 0.5, group = Month, na.action = na.omit))
The explanation is that ls() gets the names of the variables available, mget() gets those variables as a list, and data.frame() converts them into a data frame.
The next problem you're going to find is that mice::pool() requires there to be tidy() and glance() methods to properly pool the multiple imputations. It looks like neither broom nor broom.mixed have those defined for lqmm. I threw together a very quick and dirty implementation, which you could use if you can't find anything else.
To get pool(fit2) to run you'll need to create the function tidy.lqmm() as below. Then pool() will assume the sample size is infinite and perform the calculations accordingly. You can also create the glance.lqmm() function before running pool(fit2), which will tell pool() the residual degrees of freedom. Afterwards you can use summary(pooled) to find the p-values.
tidy.lqmm <- function(x, conf.int = FALSE, conf.level = 0.95, ...) {
broom:::as_tidy_tibble(data.frame(
estimate = coef(x),
std.error = sqrt(
diag(summary(x, covariance = TRUE,
R = 50)$Cov[names(coef(x)),
names(coef(x))]))))
}
glance.lqmm <- function(x, ...) {
broom:::as_glance_tibble(
logLik = as.numeric(stats::logLik(x)),
df.residual = summary(x, R = 2)$rdf,
nobs = stats::nobs(x),
na_types = "rii")
}
Note: lqmm uses bootstrapping to estimate the standard error. By default it uses R = 50 bootstrapping replicates, which I've copied in the tidy.lqmm() function. You can change that line to increase the number of replicates if you like.
WARNING: Use these functions and the results with caution. I know just enough to be dangerous. To me it looks like these functions work to give sensible results, but there are probably intricacies that I'm not aware of. If you can find a more authoritative source for similar functions that work, or someone who is familiar with lqmm or pooling mixed models, I'd trust them more than me.
Apologies for a very basic question. I'm struggling to get R to recognise the y values for a ROC
I'm trying to do a basic ROC but can't seem to set the vector for y.
fullmodel= glm(culture_positive ~ No_symptoms + sex + art_status_v1 +current_cd4 +
bmi_v1 +nurse_tb_diagnosis_crp_v1 + temperature_v1,
family="binomial", data= Data1)
roc(y , fullmodel$fitted.values, plot=TRUE)
Error in roc(y, fullmodel$fitted.values, plot = TRUE) :
object 'y' not found
So 'y' is a column in my dataset Data1 labelled 'culture_positive' as per the glm but whatever I try I keep getting this message that 'y' is not found.
Once again, apologies for a basic question but it is really holding me up.
Since y is not in your global environment you need to specify where to find y. You can either use the value you used to fit the model:
roc(culture_positive , fullmodel$fitted.values, plot=TRUE)
or the response stored in the glm object
roc(fullmodel$y , fullmodel$fitted.values, plot=TRUE)
I would recommend the second option, it's somewhat safer, because you take y and fitted.values from the same object, so they will fit together.
The question is more or less as the title indicates. I would like to use the caret::train function with beta-binomial models made with glmmTMB package (although I am not opposed to other functions capable of fitting beta-binomial models) to calculate median absolute error (MdAE) estimates through jack-knife (leave-one-out) cross-validation. The glmmTMBControl function is already capable of estimating the optimal dispersion parameter but I was hoping to retain this information somehow as well... or having caret do the calculation possibly?
The dataset I am working with looks like this:
df <- data.frame(Effect = rep(seq(from = 0.05, to = 1, by = 0.05), each = 5), Time = rep(seq(1:20), each = 5))
Ideally I would be able to pass the glmmTMB function to trainControl like so:
BB.glmm1 <- train(Time ~ Effect,
data = df, method = "glmmTMB",
method = "", metric = "MAD")
The output would be as per the examples contained in train, although possibly with estimates for the dispersion parameter.
Although I am in no way opposed to work arounds - Thank you in advance!
I am unsure how to perform the required operation with caret without creating a custom method but I trust it is fairly easy to implement it with a for (lapply) loop.
In the example I will use the sleepstudy data set since your example data throws a bunch of warnings.
library(glmmTMB)
to perform LOOCV - for every row, create a model without that row and predict on that row:
data(sleepstudy,package="lme4")
LOOCV <- lapply(1:nrow(sleepstudy), function(x){
m1 <- glmmTMB(Reaction ~ Days + (Days|Subject),
data = sleepstudy[-x,])
return(predict(m1, sleepstudy[x,], type = "response"))
})
get the median of the residuals (I think this is MdAE? if not post a comment on how its calculated):
median(abs(unlist(LOOCV) - sleepstudy$Reaction))
AIM: The aim here was to find a suitable fit, using step functions, which uses age to describe wage, in the Wage dataset in the library ISLR.
PLAN:
To find a suitable fit, I'll try multiple fits, which will have different cut points. I'll use the glm() function (of the boot library) for the fitting purpose. In order to check which fit is the best, I'll use the cv.glm() function to perform cross-validation over the fitted model.
PROBLEM:
In order to do so, I did the following:
all.cvs = rep(NA, 10)
for (i in 2:10) {
lm.fit = glm(wage~cut(Wage$age,i), data=Wage)
all.cvs[i] = cv.glm(Wage, lm.fit, K=10)$delta[2]
}
But this gives an error:
Error in model.frame.default(formula = wage ~ cut(Wage$age, i), data =
list( : variable lengths differ (found for 'cut(Wage$age, i)')
Whereas, when I run the code given below, it runs.(It can be found here)
all.cvs = rep(NA, 10)
for (i in 2:10) {
Wage$age.cut = cut(Wage$age, i)
lm.fit = glm(wage~age.cut, data=Wage)
all.cvs[i] = cv.glm(Wage, lm.fit, K=10)$delta[2]
}
Hypotheses and Results:
Well, it might be possible that cut() and glm() might not work together. But this works:
glm(wage~cut(age,4),data=Wage)
Question:
So, basically we're using the cut() function, saving it's results in a variable, then using that variable in the glm() function. But we can't put the cut function inside the glm() function. And that too, only if the code is in a loop.
So, why is the first version of the code not working?
This is confusing. Any help appreciated.
I'm fairly new to using the caret library and it's causing me some problems. Any
help/advice would be appreciated. My situations are as follows:
I'm trying to run a general linear model on some data and, when I run it
through the confusionMatrix, I get 'the data and reference factors must have
the same number of levels'. I know what this error means (I've run into it before), but I've double and triple checked my data manipulation and it all looks correct (I'm using the right variables in the right places), so I'm not sure why the two values in the confusionMatrix are disagreeing. I've run almost the exact same code for a different variable and it works fine.
I went through every variable and everything was balanced until I got to the
confusionMatrix predict. I discovered this by doing the following:
a <- table(testing2$hold1yes0no)
a[1]+a[2]
1543
b <- table(predict(modelFit,trainTR2))
dim(b)
[1] 1538
Those two values shouldn't disagree. Where are the missing 5 rows?
My code is below:
set.seed(2382)
inTrain2 <- createDataPartition(y=HOLD$hold1yes0no, p = 0.6, list = FALSE)
training2 <- HOLD[inTrain2,]
testing2 <- HOLD[-inTrain2,]
preProc2 <- preProcess(training2[-c(1,2,3,4,5,6,7,8,9)], method="BoxCox")
trainPC2 <- predict(preProc2, training2[-c(1,2,3,4,5,6,7,8,9)])
trainTR2 <- predict(preProc2, testing2[-c(1,2,3,4,5,6,7,8,9)])
modelFit <- train(training2$hold1yes0no ~ ., method ="glm", data = trainPC2)
confusionMatrix(testing2$hold1yes0no, predict(modelFit,trainTR2))
I'm not sure as I don't know your data structure, but I wonder if this is due to the way you set up your modelFit, using the formula method. In this case, you are specifying y = training2$hold1yes0no and x = everything else. Perhaps you should try:
modelFit <- train(trainPC2, training2$hold1yes0no, method="glm")
Which specifies y = training2$hold1yes0no and x = trainPC2.