I am trying to fit a linear growth model (LGM) in R, and I understand that the primary steps would be to fit a Null model with time as a predictor of my independent variable Y (allowing for random effects) and a Null model not allowing for random effects, then compare the two and see whether the random effect is strong enough to justify the usage of the model with random intercept.
I managed to fit the model with random intercept with the lmer function of the lme4 package, but I can't find a function in that package that allows me to fit a model without random intercept.
I have tried to fit models both with random intercept (lme function) and without (gls function) with the nlme package, but neither of them have been working for me.
My original code was:
library(nlme)
LMModel <- lme(Y~Time, random=~Time| ID, data=dataset,
method="ML")
and running that, I got an error saying "missing values in object" (apparently referring to my Time variable). I thus added a transformation of my dataset into a matrix with "matr <- as.matrix(dataset)" and added the missing data management part to my code, which ended up being:
LMModel <- lme(Y~Time, random=~Time| ID, data=dataset,
method="ML", na.action = na.exclude(matr))
Running this, I get the error: ' could not find function "1" '
I further tried to fit a model with no random effect with the gls function of nlme and got the exact same error.
I feel quite lost as I can't seem to figure out what that function 1 means. Any ideas of what might be happening here?
Thanks a lot in advance for the help!
Federico
Related
I would like to estimate a random effects (RE) multinomial logit model.
I have been applying mblogit from the mclogit package. However, once I introduce RE into my model, it fails to converge.
Is there a workaround this?
For instance, I tried to adjust the fitting process of mblogit and increase the maximal number of iterations (maxit), but did not succeed to correctly write the syntax for the control function. Would this be the right approach? And if so, could you advise me how to implement it into my model which so far looks as follows:
meta.mblogit <- mblogit(Migration ~ ClimateHazard4 , weights = logNsquare,
data = meta.df, subset= Panel==1, random = ~1|StudyID,
)
Here, both variables (Migration and ClimateHazard4) are factor variables.
Or is there an alternative approach you could recommend me for an estimation of RE multinomial logit?
Thank you very much!
I am adjusting a mixed effects model which, due to the observed heteroscedasticity, it was necessary to include an effect to accommodate it. Therefore, using the lme function of the nlme package, this was easy to be solved, see the code below:
library(nlme)
library(lme4)
Model1 <- lme(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5),
random = ~1|Var6, Data1, method="REML",
weights = varIdent(form=~1|Var7))
#Var6: It is a factor with several levels.
#Var7: It is a Dummy variable.
However, I need to readjust the model described above using the lme4 package, that is, using the lmer function. It is known and many are the materials that inform some limitations existing in the lme4, such as, for example, modeling heteroscedasticity. What motivated me to readjust this model is the fact that I have an interest in using a specific package that in cases of mixed models it only accepts if they are adjusted through the lmer function. How could I resolve this situation? Below is a good part of the model adjusted using the lmer function, however, this model is not considering the effect to model the observed heteroscedasticity.
Model2 <- lmer(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5)+(1|Var6),
Data1, REML=T)
Regarding the choice of the random effect (Var6) and the inclusion of the effect to consider the heterogeneity by levels of the variable (Var7), these were carefully analyzed, however, I will not put here the whole procedure so as not to be an extensive post and to be more objective .
This is hackable. You need to add an observation-level random effect that is only applied to the group with the larger residual variance (you need to know this in advance!), via (0+dummy(Var7,"1")|obs); this has the effect of multiplying each observation-level random effect value by 1 if the observation is in group "1" of Var7, 0 otherwise. You also need to use lmerControl() to override a few checks that lmer does to try to make sure you are not adding redundant random effects.
Data1$obs <- factor(seq(nrow(Data1)))
Model2 <- lmer(log(Var1)~log(Var2)+log(Var3)+
(Var4)+(Var5) + (1|Var6) +
(0+dummy(Var7,"1")|obs),
Data1, REML=TRUE,
control=lmerControl(check.nobs.vs.nlev="ignore",
check.nobs.vs.nRE="ignore"))
all.equal(REMLcrit(Model2), c(-2*logLik(Model1))) ## TRUE
all.equal(fixef(Model1), fixef(Model2), tolerance=1e-7)
If you want to use this model with hnp you need to work around the fact that hnp doesn't pass the lmerControl option properly.
library(hnp)
d <- function(obj) resid(obj, type="pearson")
s <- function(n, obj) simulate(obj)[[1]]
f <- function(y.) refit(Model2, y.)
hnp(Model2, newclass=TRUE, diagfun=d, simfun=s, fitfun=f)
You might also be interested in the DHARMa package, which does similar simulation-based diagnostics.
I replaced missing data by using MICE package.
I realized the linear equation modelling by using : summary(pool(with(imputed_base_finale,lm(....)))
I tried to obtain standardized coefficients by using the function lm.beta, however it doesn't work.
lm.beta (with(imputed_base_finale,lm(...)))
Error in lm.beta(with(imputed_base_finale, lm(...)))
object has to be of class lm
How can I obtain these standardized coefficients ?
Thank you for you help!!!
lm.scale works on lm objects and adds standardized coefficients. This however was not build to work on mira objects.
Have you considered using scale on the data before you build a model, effectively getting standardized coefficients?
Instead of standardizing the data before imputation, you could also apply it with post processing during imputation.
I am not sure which of these would be the most robust option.
require(mice)
# non-standardized
imp <- mice(nhanes2)
pool(with(imp,lm(chl ~ bmi)))
# standardized
imp_scale <- mice(scale(nhanes2[,c('bmi','chl')]))
pool(with(imp_scale,lm(chl ~ bmi)))
I am a newbie in R and I am trying to do my best to create my first model. I am working in a 2- classes random forest project and so far I have programmed the model as follows:
library(randomForest)
set.seed(2015)
randomforest <- randomForest(as.factor(goodkit) ~ ., data=training1, importance=TRUE,ntree=2000)
varImpPlot(randomforest)
prediction <- predict(randomforest, test,type='prob')
print(prediction)
I am not sure why I don't get the overall prediction for my model.I must be missing something in my code. I get the OOB and the prediction per case in the test set but not the overall prediction of the model.
library(pROC)
auc <-roc(test$goodkit,prediction)
print(auc)
This doesn't work at all.
I have been through the pROC manual but I cannot get to understand everything. It would be very helpful if anyone can help with the code or post a link to a good practical sample.
Using the ROCR package, the following code should work for calculating the AUC:
library(ROCR)
predictedROC <- prediction(prediction[,2], as.factor(test$goodkit))
as.numeric(performance(predictedROC, "auc")#y.values))
Your problem is that predict on a randomForest object with type='prob' returns two predictions: each column contains the probability to belong to each class (for binary prediction).
You have to decide which of these predictions to use to build the ROC curve. Fortunately for binary classification they are identical (just reversed):
auc1 <-roc(test$goodkit, prediction[,1])
print(auc1)
auc2 <-roc(test$goodkit, prediction[,2])
print(auc2)
I have fit my discrete count data using a variety of functions for comparison. I fit a GEE model using geepack, a linear mixed effect model on the log(count) using lme (nlme), a GLMM using glmer (lme4), and a GAMM using gamm4 (gamm4) in R.
I am interested in comparing these models and would like to plot the expected (predicted) values for a new set of data (predictor variables). My goal is to compare the predicted effects for each model under particular conditions (x variables). Of particular interest is the comparison between marginal (GEE) and conditional estimates.
I think my main problem might be getting the new data in the correct form with the correct labels and attributes and such. I am still very much an R novice and struggle with this stuff (no course on this at my university unfortunately).
I currently have fitted models
gee1 lme1 lmer1 gamm1
and can extract their fixed effect coefficients and standard errors without a problem. I also don't have a problem converting them from the log scale or estimating confidence intervals accounting for the random effects.
I also have my new dataframe newdat which has 365 observations of 23 variables (average environmental data for each day of the year).
I am stuck on how to predict new count estimates from this. I played around with the model.matrix function but couldn't get it to work. For example, I tried:
mm = model.matrix(terms(glmm1), newdat) # Error in model.frame.default(object,
# data, xlev = xlev) : object is not a matrix
newdat$pcount = mm %*% fixef(glmm1)
Any suggestions or good references would be greatly appreciated. Can anyone help with the error above?
Getting predictions for lme() and lmer() is documented on http://glmm.wikidot.com/faq