I'm trying out a package regarding double machine learning (https://rdrr.io/github/yixinsun1216/crossfit/) and in trying to run the main function dml(), I get the following "Error: No tidy method for objects of class dgCMatrix" using example dataframe data. When looking through the documentation (https://rdrr.io/github/yixinsun1216/crossfit/src/R/dml.R), I can't find anything wrong with how tidy() is used. Does anyone have any idea what could be going wrong here?
R version 4.2.1
I have already tried installing broom.mixed, although broomextra doesn't seem to be available for my R version, and the same problem occurs. Code used below;
install.packages("remotes")
remotes::install_github("yixinsun1216/crossfit", force = TRUE)
library("remotes")
library("crossfit")
library("broom.mixed")
library("broom")
# Effect of temperature and precipitation on corn yield in the presence of
# time and locational effects
data(corn_yield)
library(magrittr)
dml_yield <- "logcornyield ~ lower + higher + prec_lo + prec_hi | year + fips" %>%
as.formula() %>%
dml(corn_yield, "linear", n = 5, ml = "lasso", poly_degree = 3, score = "finite")
Related
I'm using tbl_svysummary to report weighted means. Additionally I would like to add another column with confidence intervals using add_ci().
If I try, I get the following error:
Error in UseMethod("add_ci") :
no applicable method for 'add_ci' applied to an object of class "c('tbl_svysummary', 'gtsummary')
The same happens if I run the example-code provided by the reference page in the web, that you can see below (https://www.danieldsjoberg.com/gtsummary/reference/add_ci.html).
Package versions:
gtsummary 1.5.0,
survey 4.1-1
Code:
# Example 3 ----------------------------------
data(api, package = "survey")
add_ci_ex3 <-
survey::svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) %>%
tbl_svysummary(
include = c(api00, hsg, stype),
statistic = hsg ~ "{mean} ({sd})"
) %>%
add_ci(
method = api00 ~ "svymedian"
)
Basically I just tried around wit the example code an my own data, but I only get the same error message again and again. Searching google and stack overflow doesn't give me any further information on this error. Any ideas what could be the reason?
I'm learning how to use the car package in R and I've run into a minor format/syntactical issue with regards to using the scatterplot() function. My personal preference when using functions in R is to use the name of the arguments when applying the function. So in this case I would write:
scatterplot(formula = prestige ~ income, data = Prestige, id = list(n = 4))
When I do this I am shot the error:
> Error in scatterplot(formula = prestige ~ income, data = Prestige, id = list(n = 4)):
argument "x" is missing, with no default
But when writing the function without the formula argument in particular:
scatterplot(prestige ~ income, data = Prestige, id = list(n = 4))
Everything goes through just fine.
I'm trying to understand why this is the case? I have read up from a previous post on the difference between using = and <- as assignment operators in What are the differences between "=" and "<-" assignment operators in R?, but I don't think that is what is happening here. Is there a way to reconcile this? I do like to keep things along the lines of best practices so I don't fall into lazy habits.
I am very new to R, and this is my first time of encountering the eval() function. So I am trying to use the med and boot.med function from the following package: mma. I am using it to conduct mediation analysis. med and boot.med take in models such as linear models, and dataframes that specify mediators and predictors and then estimate the mediation effect of each mediator.
The author of the package gives the flexible option of specifying one's own custom.function. From the source code of med, it can be seen that the custom.function is passed to the eval(). So I tried insert the gbmt function as the custom function. However, R kept giving me error message: Error during wrapup: Number of trees to be used in prediction must be provided. I have been searching online for days and tried many ways of specifying the number of trees parameter n.trees, but nothing works (I believe others have raised similar issues: post 1, post 2).
The following codes are part of the source code of the med function:
cf1 = gsub("responseY", "y[,j]", custom.function[j])
cf1 = gsub("dataset123", "x2", cf1)
cf1 = gsub("weights123", "w", cf1)
full.model[[j]] <- eval(parse(text = cf1))
One custom function example the author gives in the package documentation is as follows:
temp1<-med(data=data.bin,n=2,custom.function = 'glm(responseY~.,data=dataset123,family="quasibinomial",
weights=weights123)')
Here the glm is the custom function. This example code works and you can replicate it easily (if you have mma installed and loaded). However when I am trying to use the gbmt function on a survival object, I got errors and here is what my code looks like:
temp1 <- med(data = data.surv,n=2,type = "link",
custom.function = 'gbmt(responseY ~.,
data = dataset123,
distribution = dist,
train_params = start_stop,
cv_folds=10,
keep_gbm_data = TRUE,
)')
Anyone has any idea how the argument about number of trees n.trees can be added somewhere in the above code?
Many thanks in advance!
Update: in order to replicate the example code, please install mma and try the following:
library("mma")
data("weight_behavior") ##binary x #binary y
x=weight_behavior[,c(2,4:14)]
pred=weight_behavior[,3]
y=weight_behavior[,15]
data.bin<-data.org(x,y,pred=pred,contmed=c(7:9,11:12),binmed=c(6,10), binref=c(1,1),catmed=5,catref=1,predref="M",alpha=0.4,alpha2=0.4)
temp1<-med(data=data.bin,n=2) #or use self-defined final function
temp1<-med(data=data.bin,n=2, custom.function = 'glm(responseY~.,data=dataset123,family="quasibinomial",
weights=weights123)')
I changed the custom.function to gbmt and used a survival object as responseY and the error occurs. When I use the gbmt function on my data outside the med function, there is no error.
I obtain an error when using stargazer in conjunction with polr from the MASS package in R. Here is an example:
library(MASS)
library(stargazer)
# Fake data
set.seed(1234)
fake_data <- data.frame(y = as.factor(sample.int(4, 20, replace = TRUE)),
x1 = rnorm(20, mean = 1, sd = 1),
x2 = rnorm(20, mean = -1, sd = 1))
# Ordered logistic regression
o_log <- MASS::polr(y ~ x1 + x2,
data = fake_data,
Hess = TRUE, method = "logistic")
summary(o_log)
# Create regression table
stargazer(o_log)
I receive the following error message:
% Error: Unrecognized object type.
Does anyone know how to solve this? Thanks in advance.
P.S.: I'm on OS X 10.13, using R 3.4.3, MASS 7.3.47, and stargazer 5.2.
EDIT: According to stargazer's vignette, objects from polr should be supported.
I don't know the reason but when I change MASS::polr into plor, the error is removed and it works fine. It seems that it is a bug of package stargazer.
I encountered the same problem. For some strange reason, this only happens when you call the function using :: (in your case: MASS::polr). It doesn't happen when you first load the package via library(MASS) and then call the specific function.
See: Why do I get different results when using library(MASS) vs. MASS::?
I guess it was because you didn't load the MASS library and instead called the function using ::. MASS library doing some updates on how summary works for polr, which is being used by stargazer to generate the table. By not loading the library, the update was not happened, hence bringing you some trouble with stargazer.
Problem
I am using R 3.3.3 on Windows 10 (x64 bit). I get the following prediction output from the glmmPQL prediction function as follows:
library(MASS)
library(nlme)
library(dplyr)
model<-glmmPQL(a ~ b + c + d, data = trainingDataSet, family = binomial, random = list( ~ 1 | e), correlation = corAR1())
The prediction values are given as follows:
p <- predict(model, newdata=testingDataSet, type="response",level=0) (1.0)
The output it gives is as follows:
I then try to measure the performance of this output using the following code:
pr <- prediction(p, testingDataSet$a)(1.1)
It gives us the following error as follows:
Error in prediction(p, testingDataSet$a) :
Format of predictions is invalid. (1.2)
I have successfully been able to use the prediction method in R using other functions (glm,svm,nn) when the data looks something like as follows:
model<-glm(a ~ b + c + e, family = binomial(link = 'logit'), data = trainingDataSet)
p <- predict(model, newdata=testingDataSet, type="response") (1.3)
Attempts
I believe the fix to the above problem is to get it into the format shown below (1.3). I have tried the following things using R and have been failing.
I have tried casting p in 1.0 using as.numeric() and as.list() and other things. I want to get look like the p R object in 1.3. In other words, I believe the format is reason why things not working for me?
No matter what mutate or casting I try, I can't seem to get it into the form in 1.3 and image shown as desired. Especially with the index as columns features.
I'm coming up empty handed on stackoverflow and the R help files. When I use the command class(p) both tell me they are numeric.
Question
Give the above, can someone tell me how I can use R to get the output from glmmPQL in a format that the prediction function can use as shown above please?
In other words, how can I make sure the output in 1.0 can made to match the output in 1.3 in R? My attempts have failed and I would deeply appreciate someone more skilled in R to point out where I am failing?
If you use as.numeric(p) then you'll get the values you want - then the only difference is that the GLM output has names. You can add these in with something like:
p <- as.numeric(p)
names(p) <- 1:length(p)
If this doesn't work, you can use str(p) to examine the structure of the object in more depth.