As a note beforehand, I think I should mention that I am working with highly sensitive medical data that is protected by HIPAA. I cannot share real data with dput- it would be illegal to do so. That is why I made a fake dataset and explained my processes to help reproduce the error.
I have been trying to estimate an elastic net model in r using glmnet. However, I keep getting an error. I am not sure what is causing it. The error happens when I go to train the data. It sounds like it has something to do with the data type and matrix.
I have provided a sample dataset. Then I set the outcomes and certain predictors to be factors. After setting certain variables to be factors, I label them. Next, I create an object with the column names of the predictors I want to use. That object is pred.names.min. Then I partition the data into the training and test data frames. 65% in the training, 35% in the test. With the train control function, I specify a few things I want to have happen with the model- random paraments for lambda and alpha, as well as the leave one out method. I also specify that it is a classification model (categorical outcome). In the last step, I specify the training model. I write my code to tell it to use all of the predictor variables in the pred.names.min object for the trainingset data frame.
library(dplyr)
library(tidyverse)
library(glmnet),0,1,0
library(caret)
#creating sample dataset
df<-data.frame("BMIfactor"=c(1,2,3,2,3,1,2,1,3,2,1,3,1,1,3,2,3,2,1,2,1,3),
"age"=c(0,4,8,1,2,7,4,9,9,2,2,1,8,6,1,2,9,2,2,9,2,1),
"L_TartaricacidArea"=c(0,1,1,0,1,1,1,0,0,1,0,1,1,0,1,0,0,1,1,0,1,1),
"Hydroxymethyl_5_furancarboxylicacidArea_2"=
c(1,1,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,0,1,0,1),
"Anhydro_1.5_D_glucitolArea"=
c(8,5,8,6,2,9,2,8,9,4,2,0,4,8,1,2,7,4,9,9,2,2),
"LevoglucosanArea"=
c(6,2,9,2,8,6,1,8,2,1,2,8,5,8,6,2,9,2,8,9,4,2),
"HexadecanolArea_1"=
c(4,9,2,1,2,9,2,1,6,1,2,6,2,9,2,8,6,1,8,2,1,2),
"EthanolamineArea"=
c(6,4,9,2,1,2,4,6,1,8,2,4,9,2,1,2,9,2,1,6,1,2),
"OxoglutaricacidArea_2"=
c(4,7,8,2,5,2,7,6,9,2,4,6,4,9,2,1,2,4,6,1,8,2),
"AminopentanedioicacidArea_3"=
c(2,5,5,5,2,9,7,5,9,4,4,4,7,8,2,5,2,7,6,9,2,4),
"XylitolArea"=
c(6,8,3,5,1,9,9,6,6,3,7,2,5,5,5,2,9,7,5,9,4,4),
"DL_XyloseArea"=
c(6,9,5,7,2,7,0,1,6,6,3,6,8,3,5,1,9,9,6,6,3,7),
"ErythritolArea"=
c(6,7,4,7,9,2,5,5,8,9,1,6,9,5,7,2,7,0,1,6,6,3),
"hpresponse1"=
c(1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,1,0,1,0,0,1),
"hpresponse2"=
c(1,0,1,0,0,1,1,1,0,1,0,1,0,1,1,0,1,0,1,0,0,1))
#setting variables as factors
df$hpresponse1<-as.factor(df$hpresponse1)
df$hpresponse2<-as.factor(df$hpresponse2)
df$BMIfactor<-as.factor(df$BMIfactor)
df$L_TartaricacidArea<- as.factor(df$L_TartaricacidArea)
df$Hydroxymethyl_5_furancarboxylicacidArea_2<-
as.factor(df$Hydroxymethyl_5_furancarboxylicacidArea_2)
#labeling factor levels
df$hpresponse1 <- factor(df$hpresponse1, labels = c("group1.2", "group3.4"))
df$hpresponse2 <- factor(df$hpresponse2, labels = c("group1.2.3", "group4"))
df$L_TartaricacidArea <- factor(df$L_TartaricacidArea, labels =c ("No",
"Yes"))
df$Hydroxymethyl_5_furancarboxylicacidArea_2 <-
factor(df$Hydroxymethyl_5_furancarboxylicacidArea_2, labels =c ("No",
"Yes"))
df$BMIfactor <- factor(df$BMIfactor, labels = c("<40", ">=40and<50",
">=50"))
#creating list of predictor names
pred.start.min <- which(colnames(df) == "BMIfactor"); pred.start.min
pred.stop.min <- which(colnames(df) == "ErythritolArea"); pred.stop.min
pred.names.min <- colnames(df)[pred.start.min:pred.stop.min]
#partition data into training and test (65%/35%)
set.seed(2)
n=floor(nrow(df)*0.65)
train_ind=sample(seq_len(nrow(df)), size = n)
trainingset=df[train_ind,]
testingset=df[-train_ind,]
#specifying that I want to use the leave one out cross-
#validation method and
use "random" as search for elasticnet
tcontrol <- trainControl(method = "LOOCV",
search="random",
classProbs = TRUE)
#training model
elastic_model1 <- train(as.matrix(trainingset[,
pred.names.min]),
trainingset$hpresponse1,
data = trainingset,
method = "glmnet",
trControl = tcontrol)
After I run the last chunk of code, I end up with this error:
Error in { :
task 1 failed - "error in evaluating the argument 'x' in selecting a
method for function 'as.matrix': object of invalid type "character" in
'matrix_as_dense()'"
In addition: There were 50 or more warnings (use warnings() to see the first
50)
I tried removing the "as.matrix" arguemtent:
elastic_model1 <- train((trainingset[, pred.names.min]),
trainingset$hpresponse1,
data = trainingset,
method = "glmnet",
trControl = tcontrol)
It still produces a similar error.
Error in { :
task 1 failed - "error in evaluating the argument 'x' in selecting a method
for function 'as.matrix': object of invalid type "character" in
'matrix_as_dense()'"
In addition: There were 50 or more warnings (use warnings() to see the first
50)
When I tried to make none of the predictors factors (but keep outcome as factor), this is the error I get:
Error: At least one of the class levels is not a valid R variable name; This
will cause errors when class probabilities are generated because the
variables names will be converted to X0, X1 . Please use factor levels that
can be used as valid R variable names (see ?make.names for help).
How can I fix this? How can I use my predictors (both the numeric and categorical ones) without producing an error?
glmnet does not handle factors well. The recommendation currently is to dummy code and re-code to numeric where possible:
Using LASSO in R with categorical variables
Bert-toolkit is a very nice package to call R functions from Excel. See: https://bert-toolkit.com/
I have used bert-toolkit to call a fitted neuralnet (avNNnet fitted with Caret) within a wrapper function in R from Excel VBA. This runs perfect. This is the code to load the model within the wrapper function in bert-toolkit:
load("D:/my_model_avNNet.rda")
neuraln <- function(x1,x2,x3){
xx <- data.frame(x1,x2,x3)
z <- predict(my_model_avNNET, xx)
z
}
Currently I tried to do this with a fitted GAM (fitted with package mgcv). Although I do not succeed. If I call the fitted GAM from Excel VBA it gives error 2015. If I call the fitted GAM from a cell it gives #VALUE! At the same time the correct outcome of the calculation is shown in the bert-console!
This is the code to load the model in the wrapperfunction in bert-toolkit:
library(mgcv)
load("D:/gam_y_model.rda")
testfunction <- function(k1,k2){
z <- predict(gam_y, data.frame(x = k1, x2 = k2))
print (z)
}
The difference between the avNNnet-model (Caret) and the GAM-model (mgcv) is that the avNNnet-model does NOT need the Caret library to be loaded to generate a prediction, while the GAM-model DOES need the mgcv library to be loaded.
It seems to be not sufficient to load the mgvc-library in the script with the GAM-model which loads the GAM-model in a wrapper function in bert-toolkit, as I did in the code above. Although the correct outcome of the model is shown in the bert-console. It does not generate the correct outcome in Excel.
I wonder how this is possible and can be solved. It seems to me that maybe there are two instances of R running in bert-toolkit.
How can I load the the mgcv-library in such a way that it can be used by the GAM-model within the function called from Excel?
This is some example code to fit the GAM with mgcv and save to model (after running this code the model can uploaded in bert-toolkit with the code above) :
library(mgcv)
# construct some sample data:
x <- seq(0, pi * 2, 0.1)
x2 <- seq(0, pi * 20, 1)
sin_x <- sin(x)
tan_x2 <- tan(x2)
y <- sin_x + rnorm(n = length(x), mean = 0, sd = sd(sin_x / 2))
Sample_data <- data.frame(y,x,x2)
# fit gam:
gam_y <- gam(y ~ s(x) + s(x2), method = "REML")
# Make predictions with the fitted model:
x_new <- seq(0, max(x), length.out = 100)
x2_new <- seq(0, max(x2), length.out = 100)
y_pred <- predict(gam_y, data.frame(x = x_new, x2 = x2_new))
# save model, to load it later in bert-toolkit:
setwd("D:/")
save(gam_y, file = "gam_y_model.rda")
One of R's signatures is method dispatching where users call the same named method such as predict but internally a different variant is run such as predict.lm, predict.glm, or predict.gam depending on the model object passed into it. Therefore, calling predict on an avNNet model is not the same predict on a gam model. Similarly, just as the function changes due to the input, so does the output change.
According to MSDN documents regarding the Excel #Value! error exposed as Error 2015:
#VALUE is Excel's way of saying, "There's something wrong with the way your formula is typed. Or, there's something wrong with the cells you are referencing."
Fundamentally, without seeing actual results, Excel may not be able to interpret or translate into Excel range or VBA type the result R returns from gam model especially as you describe R raises no error.
For example, per docs, the return value of the standard predict.lm is:
predict.lm produces a vector of predictions or a matrix of predictions...
However, per docs, the return value of predict.gam is a bit more nuanced:
If type=="lpmatrix" then a matrix is returned which will give a vector of linear predictor values (minus any offest) at the supplied covariate values, when applied to the model coefficient vector. Otherwise, if se.fit is TRUE then a 2 item list is returned with items (both arrays) fit and se.fit containing predictions and associated standard error estimates, otherwise an array of predictions is returned. The dimensions of the returned arrays depends on whether type is "terms" or not: if it is then the array is 2 dimensional with each term in the linear predictor separate, otherwise the array is 1 dimensional and contains the linear predictor/predicted values (or corresponding s.e.s). The linear predictor returned termwise will not include the offset or the intercept.
Altogether, consider adjusting parameters of your predict call to render a numeric vector for easy Excel interpretation and not a matrix/array or some other higher dimension R type that Excel cannot render:
testfunction <- function(k1,k2){
z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="response")
return(z)
}
testfunction <- function(k1,k2){
z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="lpmatrix")
return(z)
}
testfunction <- function(k1,k2){
z <- mgcv::predict.gam(gam_y, data.frame(x = k1, x2 = k2), type=="linked")
return(z$fit) # NOTICE fit ELEMENT USED
}
...
Further diagnostics:
Check returned object of predict.glm with str(obj) and class(obj)/ typeof(obj) to see dimensions and underlying elements and compare with predict in caret;
Check if high precision of decimal numbers is the case such as Excel's limits of 15 decimal points;
Check amount of data returned (exceeds Excel's sheet row limit of 220 or cell limit of 32,767 characters?).
I am running a simple generalized linear model, calling JAGS from R. The model is negatively binomially distributed. The model is being fitted to data on counts of fish, with the majority of individual counts ('C' in the data set below) being zeros.
I initially ran the model with one covariate, temperature ('Temp'). About half of the time the model ran and the other half of the time the model gave me the error, 'Error in node C[###] Invalid parent values.' The value for C[###] in the error message changes with each successive attempt to run the model.
Since my success at running the model was inconsistent, I tried adding another covariate, salinity ('Salt'). Then the model would not run at all, with the same error message as above.
Any ideas or suggestions on the source of the error are greatly appreciated.
I am suspecting that the initial values for the dispersion parameter, r, may be the issue. Ideally I add several more covariates into model fitting if this error can be addressed.
The data set and code are immediately below. For sake of getting the data to load properly on this website, I have omitted 662 of the 672 total values; even with the reduced data set (n = 10 instead of n = 672) the problem remains.
Thank you.
setwd("C:/Users/John/Desktop")
library('coda')
library('rjags')
library('R2jags')
set.seed(1000000000)
#data
n=10
C=c(0,0,0,0,0,1,0,0,0,1)
Temp=c(0,29.3,25.3,28.7,28.7,24.4,25.1,25.1,24.2,23.3)
Salt=c(6,6,0,6,6,0,12,12,6,12)
sink("My Model.txt")
cat("
model {
r~dunif(0,10)
beta0~dunif (-20,20)
beta1~dunif (-20,20)
beta2~dunif (-20,20)
for (i in 1:n) {
C[i] ~ dnegbin(p[i], r)
p[i] <- r/(r+lambda[i])
log(lambda[i]) <- mu[i]
mu[i] <- beta0 + beta1*Temp[i] + beta2*Salt[i]
}
}
", fill=TRUE)
sink()
n=n
C=C
Temp=Temp
Salt=Salt
#bundle data
bugs.data = list(
"n",
"C",
"Temp",
"Salt")
#parameters to monitor
params<-c(
"r",
"beta0",
"beta1",
"beta2")
#initial values
inits <- function(){list(
r=floor(runif(1,0,5)),
beta0=runif(1,-5,5),
beta1=runif(1,-5,5),
beta2=runif(1,-5,5))}
model.file <- 'My Model.txt'
jagsfit <- jags(data=bugs.data, inits=inits, params, n.iter=1000, n.thin=10, n.burnin=100, model.file)
print(jagsfit, digits=5)
This works fine for me most of the time, but it would fail with the error you describe if the inits function samples a value of r of 0 - which you have made more likely by using floor() in the inits function (not sure why you did that - r is not restricted to integers but is strictly positive). Also, every time you run the model you will get different initial values (unless setting a random seed in R) which is making your life more complicated that it needs to be. I generally recommend picking fixed (and probably over dispersed) initial values, such as r=0.01 and r=10 for the two chains in your example.
However, JAGS picks usable initial values for this model as you can see by not providing your own inits e.g.:
library('runjags')
listdata <- lapply(bugs.data, get)
names(listdata) <- unlist(bugs.data)
run.jags(model.file, params, listdata)
I would also have a think about the prior you are using for r - it could well be that this will have a bigger effect on your posterior than intended. Another (not necessarily better) option is something like a gamma prior.
Matt
I'm new to R. I found very useful code, which I've tried to use for my purposes. however, I get an error:
Error in func(time, state, parms, ...) : object 'k4' not found and Error in func(time, state, parms, ...) : object 'E' not found
I don't know where the problem is as I can see all parameters and data.frame is correct as well.
Thank you everyone for taking time to look at this. I've tried to reduce the number of parameters to3 (k10, k11,k12), and using estimated values for the remaining (embeded values in the code). However, I still get an error message, the E value from data.frame is not passed into rxnrate function and as result ode can't use it. I've tried to use events and forcing functions but it doesn't seem to work. Thank you for spotting P4, it was a typo, should be P, I've corrected already.
Editors note: This was crossposted to Rhelp and that message included the source of this code as a stackoverflow question "r-parameter and initial conditions fitting ODE models with nls.lm."
#set working directory
setwd("~/R/wkspace")
#load libraries
library(ggplot2)
library(reshape2)
library(deSolve)
library(minpack.lm)
time=c(22,23,24,46,47,48)
cE=c(15.92,24.01,25.29,15.92,24.01,25.29)
cP=c(0.3,0.14,0.29,0.3,0.14,0.29)
cL=c(6.13,3.91,38.4,6.13,3.91,38.4)
df<-data.frame(time,cE,cP,cL)
df
names(df)=c("time","cE","cP","cL")
#rate function
rxnrate=function(t,c,parms){
#rate constant passed through a list called
k1=parms$k1
k2=parms$k2
k3=parms$k3
k4=parms$k4
k5=parms$k5
k6=parms$k6
k7=parms$k7
k8=parms$k8
k9=parms$k9
k10=parms$k10
#c is the concentration of species
#derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=(k1+(k2*E^k10)/(k3^k10+E^k10))/(1+P/k6)-k4* ((1+k5*P)/(1+k7*E))*c["pLH"]; #dRP_LH/dt
r[2]=(1/k8)*k4*((1+k5*P)/(1+k7*E))*c["p"]-k9*c["L"] #dL/dt
return(list(r))
}
ssq=function(myparms){
#initial concentration
cinit=c(pLH=unname(myparms[11]),LH=unname(myparms[12]))
print(cinit)
#time points for which conc is reported
#include the points where data is available
t=c(seq(0,46,2),df$time)
t=sort(unique(t))
#parameters from the parameters estimation
k1=myparms[1]
k2=myparms[2]
k3=myparms[3]
k4=myparms[4]
k5=myparms[5]
k6=myparms[6]
k7=myparms[7]
k8=myparms[8]
k9=myparms[9]
k10=myparms[10]
#solve ODE for a given set of parameters
out=ode(y=cinit,times=t,func=rxnrate,parms=list(k1=k1,k2=k2,k3=k3,k4=k4,k5=k5,k6=k6,k7=k7,k8=k8,k9=k9,k10=k10,E=cE,P=cP))
#Filter data that contains time points
outdf=data.frame(out)
outdf=outdf[outdf$time%in% df$time,]
#Evaluate predicted vs experimental residual
preddf=melt(outdf,id.var="time",variable.name="species",value.name="conc")
expdf=melt(df,id.var="time",variable.name="species",value.name="conc")
ssqres=preddf$conc-expdf$conc
return(ssqres)
}
# parameter fitting using levenberg marquart
#initial guess for parameters
myparms=c(k1=500, k2=4500, k3=200,k4=2.42, k5=0.26,k6=12.2,k7=0.004,k8=55,k9=24,k10=8,pLH=14.5,LH=3.55)
#fitting
fitval=nls.lm(par=myparms,fn=ssq)
#summary of fit
summary(fitval)
#estimated parameter
parest=as.list(coef(fitval))
I'm relatively new in R and I would appreciated if you could take a look at the following code. I'm trying to estimate the shape parameter of the Frechet distribution (or inverse weibull) using mmedist (I tried also the fitdist that calls for mmedist) but it seems that I get the following error :
Error in mmedist(data, distname, start = start, fix.arg = fix.arg, ...) :
the empirical moment function must be defined.
The code that I use is the below:
require(actuar)
library(fitdistrplus)
library(MASS)
#values
n=100
scale = 1
shape=3
# simulate a sample
data_fre = rinvweibull(n, shape, scale)
memp=minvweibull(c(1,2), shape=3, rate=1, scale=1)
# estimating the parameters
para_lm = mmedist(data_fre,"invweibull",start=c(shape=3,scale=1),order=c(1,2),memp = "memp")
Please note that I tried many times en-changing the code in order to see if my mistake was in syntax but I always get the same error.
I'm aware of the paradigm in the documentation. I've tried that as well but with no luck. Please note that in order for the method to work the order of the moment must be smaller than the shape parameter (i.e. shape).
The example is the following:
require(actuar)
#simulate a sample
x4 <- rpareto(1000, 6, 2)
#empirical raw moment
memp <- function(x, order)
ifelse(order == 1, mean(x), sum(x^order)/length(x))
#fit
mmedist(x4, "pareto", order=c(1, 2), memp="memp",
start=c(shape=10, scale=10), lower=1, upper=Inf)
Thank you in advance for any help.
You will need to make non-trivial changes to the source of mmedist -- I recommend that you copy out the code, and make your own function foo_mmedist.
The first change you need to make is on line 94 of mmedist:
if (!exists("memp", mode = "function"))
That line checks whether "memp" is a function that exists, as opposed to whether the argument that you have actually passed exists as a function.
if (!exists(as.character(expression(memp)), mode = "function"))
The second, as I have already noted, relates to the fact that the optim routine actually calls funobj which calls DIFF2, which calls (see line 112) the user-supplied memp function, minvweibull in your case with two arguments -- obs, which resolves to data and order, but since minvweibull does not take data as the first argument, this fails.
This is expected, as the help page tells you:
memp A function implementing empirical moments, raw or centered but
has to be consistent with distr argument. This function must have
two arguments : as a first one the numeric vector of the data and as a
second the order of the moment returned by the function.
How can you fix this? Pass the function moment from the moments package. Here is complete code (assuming that you have made the change above, and created a new function called foo_mmedist):
# values
n = 100
scale = 1
shape = 3
# simulate a sample
data_fre = rinvweibull(n, shape, scale)
# estimating the parameters
para_lm = foo_mmedist(data_fre, "invweibull",
start= c(shape=5,scale=2), order=c(1, 2), memp = moment)
You can check that optimization has occurred as expected:
> para_lm$estimate
shape scale
2.490816 1.004128
Note however, that this actually reduces to a crude way of doing overdetermined method of moments, and am not sure that this is theoretically appropriate.