Does anyone have a nice clean way to get predict behavior for felm models?
library(lfe)
model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species)
predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
# Works
model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species)
predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
# Does not work
UPDATE (2020-04-02): The answer from Grant below using the new package fixest provides a more parsimonious solution.
As a workaround, you could combine felm, getfe, and demeanlist as follows:
library(lfe)
lm.model <- lm(data=demeanlist(iris[, 1:2], list(iris$Species)), Sepal.Length ~ Sepal.Width)
fe <- getfe(felm(data = iris, Sepal.Length ~ Sepal.Width | Species))
predict(lm.model, newdata = data.frame(Sepal.Width = 3)) + fe$effect[fe$idx=="virginica"]
The idea is that you use demeanlist to center the variables, then lm to estimate the coefficient on Sepal.Width using the centered variables, giving you an lm object over which you can run predict. Then run felm+getfe to get the conditional mean for the fixed effect, and add that to the output of predict.
predict line, wouldn't you want to use a demeaned value for Sepal.Width since the original input for the model is demeaned, and you're adding the FE at the end? –
Netta lm equivalent: lm2 <- lm(data = iris, Sepal.Length ~ Sepal.Width + factor(Species)) predict(lm2, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) –
Olivier Late to the party, but the new fixest package (link) has a predict method. It supports high-dimensional fixed effects (and clustering, etc.) using a very similar syntax to lfe. Somewhat remarkably, it is also considerably faster than lfe for the benchmark cases that I've tested.
library(fixest)
model_feols <- feols(data = iris, Sepal.Length ~ Sepal.Width | Species)
predict(model_feols, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
# Works
This might not be the answer that you are looking for, but it seems that the author did not add any functionality to the lfe package in order to make predictions on external data by using the fitted felm model. The primary focus seems to be on the analysis of the group fixed effects. However, it's interesting to note that in the documentation of the package the following is mentioned:
The object has some resemblance to an 'lm' object, and some postprocessing methods designed for lm may happen to work. It may however be necessary to coerce the object to succeed with this.
Hence, it might be possible to coerce the felm object to an lm object in order to obtain some additional lm functionality (if all the required info is present in the object to perform the necessary computations).
The lfe package is intended to be run on very large datasets and effort was made to conserve memory: As a direct result of this, the felm object does not use/contain a qr decomposition, as opposed to the lm object. Unfortunately, the lm predict procedure relies on this information in order to compute the predictions. Hence, coercing the felm object and executing the predict method will fail:
> model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species)
> class(model2) <- c("lm","felm") # coerce to lm object
> predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
Error in qr.lm(object) : lm object does not have a proper 'qr' component.
Rank zero or should not have used lm(.., qr=FALSE).
If you really must use this package to perform the predictions then you could maybe write your own simplified version of this functionality by using the information that you have available in the felm object. For example, the OLS regression coëfficients are available via model2$coefficients.
This should work for cases where you wish to ignore the group effects in the prediction, are predicting for new X's, and only want confidence intervals. It first looks for a clustervcv attribute, then robustvcv, then vcv.
predict.felm <- function(object, newdata, se.fit = FALSE,
interval = "none",
level = 0.95){
if(missing(newdata)){
stop("predict.felm requires newdata and predicts for all group effects = 0.")
}
tt <- terms(object)
Terms <- delete.response(tt)
attr(Terms, "intercept") <- 0
m.mat <- model.matrix(Terms, data = newdata)
m.coef <- as.numeric(object$coef)
fit <- as.vector(m.mat %*% object$coef)
fit <- data.frame(fit = fit)
if(se.fit | interval != "none"){
if(!is.null(object$clustervcv)){
vcov_mat <- object$clustervcv
} else if (!is.null(object$robustvcv)) {
vcov_mat <- object$robustvcv
} else if (!is.null(object$vcv)){
vcov_mat <- object$vcv
} else {
stop("No vcv attached to felm object.")
}
se.fit_mat <- sqrt(diag(m.mat %*% vcov_mat %*% t(m.mat)))
}
if(interval == "confidence"){
t_val <- qt((1 - level) / 2 + level, df = object$df.residual)
fit$lwr <- fit$fit - t_val * se.fit_mat
fit$upr <- fit$fit + t_val * se.fit_mat
} else if (interval == "prediction"){
stop("interval = \"prediction\" not yet implemented")
}
if(se.fit){
return(list(fit=fit, se.fit=se.fit_mat))
} else {
return(fit)
}
}
To extend the answer from pbaylis, I created a slightly longwinded function that extends nicely to allow for more than one fixed effect. Note that you have to manually enter the original dataset used in the felm model. The function returns a list with two items: the vector of predictions, and a dataframe based on the new_data that includes the predictions and fixed effects as columns.
predict_felm <- function(model, data, new_data) {
require(dplyr)
# Get the names of all the variables
y <- model$lhs
x <- rownames(model$beta)
fe <- names(model$fe)
# Demean according to fixed effects
data_demeaned <- demeanlist(data[c(y, x)],
as.list(data[fe]),
na.rm = T)
# Create formula for LM and run prediction
lm_formula <- as.formula(
paste(y, "~", paste(x, collapse = "+"))
)
lm_model <- lm(lm_formula, data = data_demeaned)
lm_predict <- predict(lm_model,
newdata = new_data)
# Collect coefficients for fe
fe_coeffs <- getfe(model) %>%
select(fixed_effect = effect, fe_type = fe, idx)
# For each fixed effect, merge estimated fixed effect back into new_data
new_data_merge <- new_data
for (i in fe) {
fe_i <- fe_coeffs %>% filter(fe_type == i)
by_cols <- c("idx")
names(by_cols) <- i
new_data_merge <- left_join(new_data_merge, fe_i, by = by_cols) %>%
select(-matches("^idx"))
}
if (length(lm_predict) != nrow(new_data_merge)) stop("unmatching number of rows")
# Sum all the fixed effects
all_fixed_effects <- base::rowSums(select(new_data_merge, matches("^fixed_effect")))
# Create dataframe with predictions
new_data_predict <- new_data_merge %>%
mutate(lm_predict = lm_predict,
felm_predict = all_fixed_effects + lm_predict)
return(list(predict = new_data_predict$felm_predict,
data = new_data_predict))
}
model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species)
predict_felm(model = model2, data = iris, new_data = data.frame(Sepal.Width = 3, Species = "virginica"))
# Returns prediction and data frame
I think what you're looking for might be the lme4 package. I was able to get a predict to work using this:
library(lme4)
data(iris)
model2 <- lmer(data = iris, Sepal.Length ~ (Sepal.Width | Species))
predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica"))
1
6.610102
You may have to play around a little to specify the particular effects you're looking for, but the package is well-documented so it shouldn't be a problem.
Error: sum(nb) == q is not TRUE –
Stila lmer. –
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data(iris), iris data is lazyloaded already. – Ursasfelm()function (and the functions it calls) would be necessary as the current implementation does not store the fixed effect coefficients, or even apparently the intercept -- see this answer on a question that is at least a near duplicate of this one. – Unknowable