I was trying to estimate a causal effect using inverse probability weighting from the causalweightspackage. However, I keep running into the following error message:
Error in model.frame.default(formula = d ~ x, drop.unused.levels = TRUE) :
variable lengths differ (found for 'x')
I want to estimate the causal effect taking into consideration a matrix including multiple control variables. When using a single control from the data-set, R manages to generate an estimate, but when I try to use the matrix including all my control variables, I receive the above-mentioned error message.
My code is as follows and appears to generate estimates when using a single control instead of my predefined matrix of multiple controls as observable in the following code:
attach(data_clean2)
controls <- cbind(marits_1, nationality1, mother_tongue1, educ1,
lastj_fct1, child_subsidies, contr_2y,
unempl_r, gdp_gr, insured_earn)
ipw_atet <- treatweight(y = duration_ue2, # take initial data
d = treatment,
x = controls,
ATET = TRUE, # if = FALSE, estimates ATE (default)
trim = (1-pscore_max0),
boot = 2)
Has anyone encountered similar problems and found a solution?
Thanks in advance
Related
I'm trying to test the variable importance before running the actual regression. But, when I attempt to do so, I get this error:
Error in varImp(regressor, scale = FALSE) :
trying to get slot "responses" from an object (class "randomForest.formula") that is not an S4 object
I've tried looking up the error, but there wasn't much information available online. What can I do to fix this?
all = read.csv('https://raw.githubusercontent.com/bandcar/massShootings/main/all.csv')
# Check Variable importance with randomForest
regressor <- randomForest::randomForest(total_victims ~ . , data = all, importance=TRUE) # fit the random forest with default parameter
caret::varImp(regressor, scale = FALSE) # get variable importance, based on mean decrease in accuracy
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
When trying to graph the conditional fixed effects of a glmmTMB model with two random intercepts in GGally I get the error:
There was an error calling "tidy_fun()". Most likely, this is because the
function supplied in "tidy_fun=" was misspelled, does not exist, is not
compatible with your object, or was missing necessary arguments (e.g. "conf.level=" or "conf.int="). See error message below.
Error: Error in "stop_vctrs()":
! Can't recycle "..1" (size 3) to match "..2" (size 2).`
I have tinkered with figuring out the issue and it seems to be related to the two random intercepts included in the model. I have also tried extracting the coefficient and standard error information separately through broom.mixed::tidy and then feeding the data frame into GGally:ggcoef() with no avail. Any suggestions?
# Example with built-in randu data set
data(randu)
randu$A <- factor(rep(c(1,2), 200))
randu$B <- factor(rep(c(1,2,3,4), 100))
# Model
test <- glmmTMB(y ~ x + z + (0 +x|A) + (1|B), family="gaussian", data=randu)
# A few of my attempts at graphing--works fine when only one random effects term is in model
ggcoef_model(test)
ggcoef_model(test, tidy_fun = broom.mixed::tidy)
ggcoef_model(test, tidy_fun = broom.mixed::tidy, conf.int = T, intercept=F)
ggcoef_model(test, tidy_fun = broom.mixed::tidy(test, effects="fixed", component = "cond", conf.int = TRUE))
There are some (old!) bugs that have recently been fixed (here, here) that would make confidence interval reporting on RE parameters break for any model with multiple random terms (I think). I believe that if you are able to install updated versions of both glmmTMB and broom.mixed:
remotes::install_github("glmmTMB/glmmTMB/glmmTMB#ci_tweaks")
remotes::install_github("bbolker/broom.mixed")
then ggcoef_model(test) will work.
I am building a predictive model with caret/R and I am running into the following problems:
When trying to execute the training/tuning, I get this error:
Error in if (tmps < .Machine$double.eps^0.5) 0 else tmpm/tmps :
missing value where TRUE/FALSE needed
After some research it appears that this error occurs when there missing values in the data, which is not the case in this example (I confirmed that the data set has no NAs). However, I also read somewhere that the missing values may be introduced during the re-sampling routine in caret, which I suspect is what's happening.
In an attempt to solve problem 1, I tried "pre-processing" the data during the re-sampling in caret by removing zero-variance and near-zero-variance predictors, and automatically inputting missing values using a carets knn automatic imputing method preProcess(c('zv','nzv','knnImpute')), , but now I get the following error:
Error: Matrices or data frames are required for preprocessing
Needless to say I checked and confirmed that the input data set are indeed matrices, so I dont understand why I get this second error.
The code follows:
x.train <- predict(dummyVars(class ~ ., data = train.transformed),train.transformed)
y.train <- as.matrix(select(train.transformed,class))
vbmp.grid <- expand.grid(estimateTheta = c(TRUE,FALSE))
adaptive_trctrl <- trainControl(method = 'adaptive_cv',
number = 10,
repeats = 3,
search = 'random',
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
allowParallel = TRUE)
fit.vbmp.01 <- train(
x = (x.train),
y = (y.train),
method = 'vbmpRadial',
trControl = adaptive_trctrl,
preProcess(c('zv','nzv','knnImpute')),
tuneGrid = vbmp.grid)
The only difference between the code for problem (1) and (2) is that in (1), the pre-processing line in the train statement is commented out.
In summary,
-There are no missing values in the data
-Both x.train and y.train are definitely matrices
-I tried using a standard 'repeatedcv' method in instead of 'adaptive_cv' in trainControl with the same exact outcome
-Forgot to mention that the outcome class has 3 levels
Anyone has any suggestions as to what may be going wrong?
As always, thanks in advance
reyemarr
I had the same problem with my data, after some digging i found that I had some Inf (infinite) values in one of the columns.
After taking them out (df <- df %>% filter(!is.infinite(variable))) the computation ran without error.
I am trying to build a CART model via cross validation using the train function of "caret" package.
My data is 4500 x 110 data frame, where all the predictor variables (except the first two, UserId and YOB (Year of Birth) which I am not using for model building) are factors with 2 levels except the dependent variable which is of type integer (although has only two values 1 and 0). Gender is one of the independent variables.
When I ran rpart command to get CART model (using the package "rpart"), i didn't have any problem with the predict function. However, I wanted to improve the model via cross validation, and so used the train function from the package "caret" with the following command:
tr = train(y ~ ., data = subImpTrain, method = "rpart", trControl = tr.control, tuneGrid = cp.grid)
This build the model with the following warning
Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures.
But it did give me a final model (best.tree). However, when I am trying to run the predict function using the following command:
best.tree.pred = predict(best.tree, newdata = subImpTest)
on the test data, it is giving me the following error:
Error in eval(expr, envir, enclos) : object 'GenderMale' not found
The Gender variable has two values: Female, Male
Can anybody help me understand the error
As #lorelai suggested, caret dummy-codes your variables if you supply it a formula. An alternative is to provide it the variables themselves, like so:
tr = train(y = subImpTrain$y, x = subImpTrain[, -subImpTrain$y],
method = "rpart", trControl = tr.control, tuneGrid = cp.grid)
More importantly, however, you shouldn't use predict.rpart and instead use predict.train, like so:
predict(tr, subImpTest)
In which case it would work just fine with the formula interface.
I have had a similar problem in the past, although concerning another algorithm.
Basically, some algorithms transform the factor variables into dummy variables and rename them accordingly.
My solution was to create my own dummies and leave them in numerical format.
I read that decision trees manage to work properly even so.