I am trying to make a c chart to analyse new diagnoses but I can't get past this stage. Please see code below:
Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class ‘"function"’ to a data.frame
Traceback:
qic(x = col_date, y = col_double(), data = measure1_data, chart = "c",
. freeze = ymd(20150201))
data.frame(x, y, n, notes, facets, cl, target)
as.data.frame(x[[i]], optional = TRUE)
as.data.frame.default(x[[i]], optional = TRUE)
stop(gettextf("cannot coerce class %s to a data.frame", sQuote(deparse(class(x))[1L])),
. domain = NA)
Related
# Use x in the left_join() function
combined_tb <-
x %>%
left_join(presentation.tree, by = "toElementId", copy = TRUE) %>%
left_join(labels, by = c("toElementId"="elementId"), copy = TRUE) %>%
select(roleId, fromElementId, toElementId, order, fromHref,
toHref, concept, period, unit, value, decimals, labelString)
I do get this error message:
Error in as.data.frame.default(y) :
cannot coerce class ‘"function"’ to a data.frame
I don't know what to do.
I tried to solve the issue but nothing works
Suppose I create 10 multiply-imputed datasets and use the (wonderful) MatchThem package in R to create weights for my exposure variable. The MatchThem package takes a mids object and converts it to an object of the class winmids.
My desired output is a mids object - but with weights. I hope to pass this mids object to BRMS as follows:
library(brms)
m0 <- brm_multiple(Y|weights(weights) ~ A, data = mids_data)
Open to suggestions.
EDIT: Noah's solution below will unfortunately not work.
The package's first author, Farhad Pishgar, sent me the following elegant solution. It will create a mids object from a winmidsobject. Thank you Farhad!
library(mice)
library(MatchThem)
#"weighted.dataset" is our .wimids object
#Extracting the original dataset with missing value
maindataset <- complete(weighted.datasets, action = 0)
#Some spit-and-polish
maindataset <- data.frame(.imp = 0, .id = seq_len(nrow(maindataset)), maindataset)
#Extracting imputed-weighted datasets in the long format
alldataset <- complete(weighted.datasets, action = "long")
#Binding them together
alldataset <- rbind(maindataset, alldataset)
#Converting to .mids
newmids <- as.mids(alldataset)
Additionally, for BRMS, I worked out this solution which instead creates a list of dataframes. It will work in fewer steps.
library("mice")
library("dplyr")
library("MatchThem")
library("brms") # for bayesian estimation.
# Note, I realise that my approach here is not fully Bayesian, but that is a good thing! I need to ensure balance in the exposure.
# impute missing data
data("nhanes2")
imp <- mice(nhanes2, printFlag = FALSE, seed = 0, m = 10)
# MathThem. This is just a fast method
w_imp <- weightthem(hyp ~ chl + age, data = imp,
approach = "within",
estimand = "ATE",
method = "ps")
# get individual data frames with weights
out <- complete(w_imp, action ="long", include = FALSE, mild = TRUE)
# assemble individual data frames into a list
m <- 10
listdat<- list()
for (i in 1:m) {
listdat[[i]] <- as.data.frame(out[[i]])
}
# pass the list to brms, and it runs as it should!
fit_1 <- brm_multiple(bmi|weights(weights) ~ age + hyp + chl,
data = listdat,
backend = "cmdstanr",
family = "gaussian",
set_prior('normal(0, 1)',
class = 'b'))
brm_multiple() can take in a list of data frames for its data argument. You can produce this from the wimids object using complete(). The output of complete() with action = "all" is a mild object, which is a list of data frames, but this is not recognized by brm_multiple() as such. So, you can just convert it to a list. This should look like the following:
df_list <- complete(mids_data, "all")
class(df_list) <- "list"
m0 <- brm_multiple(Y|weights(weights) ~ A, data = df_list)
Using complete() automatically adds a weights column to the resulting imputed data frames.
I'm trying to use the knn function (from the class package) on my dataset. It has 12 columns of features, and the 13th is what I want to be able to predict. I'm doing a 67/33 split.
This is my code so far:
nrow(Company_bankruptcy_papernorm)
random <- sample(nrow(Company_bankruptcy_papernorm),
size = 0.33*nrow(Company_bankruptcy_papernorm),replace = FALSE)
Company_bankruptcy_test <- Company_bankruptcy_papernorm[random,]
Company_bankruptcy_train <- Company_bankruptcy_papernorm[-random,]
able(Company_bankruptcy_paper$`Bankrupt?`)
table(Company_bankruptcy_paper$`Bankrupt?`[random]) *-> length 2250*
table(Company_bankruptcy_paper$`Bankrupt?`[-random]) *-> length 4569*
Bankruptcy_train_labels <- Company_bankruptcy_paper[-random,13]
Bankruptcy_test_labels <- Company_bankruptcy_paper[random,13]
length(Bankruptcy_train_labels) -> Answer: NULL
For KNN I tried
KNN_pred1 <- knn(train = Company_bankruptcy_train,
test = Company_bankruptcy_test,
cl = Bankruptcy_train_labels, k=83)
KNN_pred1 <- knn(train = Company_bankruptcy_train,
test = Company_bankruptcy_test,
cl = Bankruptcy_train_labels$`Bankrupt?`, k=83)
But both don't work.
What can I do?
Thank you in advance!
I got the data from: https://www.kaggle.com/datasets/fedesoriano/company-bankruptcy-prediction
I am trying to upload a table to SQL from R using dbWriteTable but I am having issues as some of my columns that have all NA's in them. I've learned that if the class is logical than it works but if it is anything else, it throws me an error.
Anybody have a solution? The columns which will have all NA's will be random so I can't just set a column to.logical() and I can't figure out a way to do it using lapply. I also do not want to get rid of these columns either.
Works
test <- data.frame(Name = c("Fred","Wilma","George"), Villians = c(2,4,3), Information = c(NA,NA,NA), stringsAsFactors = FALSE)
if (dbExistsTable(con, "test")) {dbRemoveTable(con, "test")}
dbWriteTable(con, name = "test", value = test, row.names = FALSE)
> sapply(test,class)
Name Villians Information
"character" "numeric" "logical"
Throws an error
test <- data.frame(Name = c("Fred","Wilma","George"), Villians = c(2,4,3), Information = c(NA,NA,NA), stringsAsFactors = FALSE)
test$Information <- as.character(test$Information)
if (dbExistsTable(con, "test")) {dbRemoveTable(con, "test")}
dbWriteTable(con, name = "test", value = test, row.names = FALSE)
Warning message:
In max(nchar(as.character(x)), na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
> sapply(test,class)
Name Villians Information
"character" "numeric" "character"
I am using a company server so if there is any configuration that needs to be made on that end, I may or may not be able to get it done.
I got an error when try to use structural.em in "bnlearn" package
This is the code:
cut.learn<- structural.em(cut.df, maximize = "hc",
+ maximize.args = "restart",
+ fit="mle", fit.args = list(),
+ impute = "parents", impute.args = list(), return.all = FALSE,
+ max.iter = 5, debug = FALSE)
Error in check.data(x, allow.levels = TRUE, allow.missing = TRUE,
warn.if.no.missing = TRUE, : at least one variable has no observed
values.
Did anyone have the same problems, please tell me how to fix it.
Thank you.
I got structural.em working. I am currently working on a python interface to bnlearn that I call pybnl. I also ran into the problem you desecribe above.
Here is a jupyter notebook that shows how to use StructuralEM from python marks.
The gist of it is described in slides-bnshort.pdf on page 135, "The MARKS Example, Revisited".
You have to create an inital fit with an inital imputed dataframe by hand and then provide the arguments to structural.em like so (ldmarks is the latent-discrete-marks dataframe where the LAT column only contains missing/NA values):
library(bnlearn)
data('marks')
dmarks = discretize(marks, breaks = 2, method = "interval")
ldmarks = data.frame(dmarks, LAT = factor(rep(NA, nrow(dmarks)), levels = c("A", "B")))
imputed = ldmarks
# Randomly set values of the unobserved variable in the imputed data.frame
imputed$LAT = sample(factor(c("A", "B")), nrow(dmarks2), replace = TRUE)
# Fit the parameters over an empty graph
dag = empty.graph(nodes = names(ldmarks))
fitted = bn.fit(dag, imputed)
# Although we've set imputed values randomly, nonetheless override them with a uniform distribution
fitted$LAT = array(c(0.5, 0.5), dim = 2, dimnames = list(c("A", "B")))
# Use whitelist to enforce arcs from the latent node to all others
r = structural.em(ldmarks, fit = "bayes", impute="bayes-lw", start=fitted, maximize.args=list(whitelist = data.frame(from = "LAT", to = names(dmarks))), return.all = TRUE)
You have to use bnlearn 4.4-20180620 or later, because it fixes a bug in the underlying impute function.