Greeting to everyone.
I sucessfully computed pls-r model in R using the code below
pls_modB_Kexch_2 <- plsr(Av.K_exc~., data = trainKexch.sar.veg, scale=TRUE,method= "s",validation='CV')
The regression coeffiecents for ncomps =11 were
(
Intercept)= -4.692966e+05,
Easting = 6.068582e+03, Northings= 7.929767e+02,
sigma_vv = 8.024741e+05, sigma_vh = -6.375260e+05,
gamma_vv = -7.120684e+05, gamma_vh = 4.330279e+05,
beta_vv = -8.949598e+04, beta_vh = 2.045924e+05,
c11_db = 2.305016e+01, c22_db = -4.706773e+01,
c12_real = -1.877267e+00.)
It predicts well new data sets when applied with in R enviroment.
My challenge is presenting this model in form of y=sum(AX)+Bo equation where A are coeffiecents of respective variablesX
Or any other mathmetical form, that can be presented academically.
I tried a direct way by multiplying the coeff.to each variable and suming them up, aquick manual trial for predictions gave me strange results. Am missing something here, please help.
Related
I am working in the functional time series using the multivariate time series data(hourly time series data). I am using FAR model more than one order for which no statistical package is available in R, so for this I convert my data into functional form and obtained the functional principle component and from those FPCA I extract their corresponding** FPCscores**. Know I use the VAR model on those FPCscores for the forecasting of each 24 hours through the VAR model, but the VAR give me the forecasted value for all 23hours when I put phat=23, but whenever I put phat=24 for example want to predict each 24 hours its give the results in the form of NA. the code is given below
library(vars)
library(fda)
fdata<- function(mat){
nb = 27 # number of basis functions for the data
fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data
args=seq(0,1,length=24)
fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y
return(fdata1)
}
prediction.ffpe = function(fdata1){
n = ncol(fdata1$coef)
D = nrow(fdata1$coef)
#center the data
#mu = mean.fd(fdata1)
data = center.fd(fdata1)
#ffpe = fFPE(fdata1, Pmax=10)
#p.hat = ffpe[2] #order of the model
d.hat=23
p.hat=6
#fPCA
fpca = pca.fd(data,nharm=D, centerfns=TRUE)
scores = fpca$scores[,0:d.hat]
# to avoid warnings from vars predict function below
colnames(scores) <- as.character(seq(1:d.hat))
VAR.pre= predict(VAR(scores, p.hat), n.ahead=1, type="const")$fcst
}
kindly guide me that how can I solve out my problem or what error I doing. THANKS
I am trying to run a parametric RD regression using the rddtools R package. However, the package documentation is not very clear to me.
First: the function to define an RD object is:
rdd_data(y, x, covar, cutpoint, z, labels, data)
where covar, in the help file, means only "Exogeneous variables" . But what type? A data frame? A list?
Second: The function rdd_reg_lm again demands informing covariates in this way:
rdd_reg_lm(rdd_object, covariates = NULL, order = 1, bw = NULL,
slope = c("separate", "same"), covar.opt = list(strategy = c("include",
"residual"), slope = c("same", "separate"), bw = NULL),
covar.strat = c("include", "residual"), weights)
Where, according to the help file, the covariates argument means simply "Formula to include covariates". Again, it is not clear to me what is exactly the correct way of applying these covariates.
Moreover, is it possible to include multiple covariates in this function rdd_data() and rdd_reg_lm()?
I appreciate some help here. I have already read the help and vignette files again and again, searched in many blogs and still nothing.
I have already checked this topic below
How to include a linear trend in a regression discontinuity design using rddtools
which showed me the following example:
rd.medic <- rdd_data(y = er ,x = ageyrs, covar = ageyrs, cutpoint=65, data = medicare)
rd.reg <- rdd_reg_lm(rdd_object=rd.medic, covariates = 'ageyrs', slope =
("same"), covar.opt = list("include"))
Even so, the syntax is still not clear to me, as I am trying to add multiple covariates without success
Thanks!
You can create a data frame with your covariates and then include it in rdd_data.
covariates<-data.frame(z1=ageyrs, z2=ageyrs2)
rd.medic <- rdd_data(y = er ,x = ageyrs, covar = covariates, cutpoint=65, data = medicare)
rd.reg <- rdd_reg_lm(rdd_object=rd.medic, covariates =TRUE, slope =("same"))
I have R code for importing text data into R, remove stop words, stem words and then create a matrix. Below is the code for
Using the create container function to split the matrix into training and test data sets.
Use train_models function to create a model based on SVM.
Execute the model on test.
Then I save the model.
library("RTextTools")
container = create_container(matrix, as.numeric(as.factor(data[, 2])),
trainSize = 1:2800,testSize = 2801:3162, virgin = FALSE)
models = train_models(container,"SVM", kernel = "linear",cost =1)
results = classify_models(container, models)
save(models, file = "my_model1.rda")
I am not able to use the saved model for prediction on new data(matrix_new) using predict function.
p <- predict(models,matrix_new)
#Error in predict.svm(X[[1L]], ...) : test data does not match model !
My question is: Is it feasible to use saved models on new data to predict sentiment ? From the error it looks like there is mismatch between the words that were used while creating the model and the new data. Please clarify if my understanding is correct.
It seems like the expected method of grid tuning the xgboost model is using the caret package, as clearly displayed here: https://stats.stackexchange.com/questions/171043/how-to-tune-hyperparameters-of-xgboost-trees
However, I struggle to make sense of the case with missing data. When creating the model without using caret, I set the missing to NA.
dtrain = xgb.DMatrix(data = data.matrix(train$data),label = train$label,missing = NA)
That allows me to create the model like so:
bst = xgboost(data = dtrain,depth = 4,eta =.3,nthread = 2,
nround = 43, print.every.n = 5,
objective = "binary:logistic",eval_metric = "auc",verbose = TRUE
)
This works very nicely, however, caret does not take this kind of object.
This is what I'm trying:
xgbtrain = train(x = train$data,y = as.factor(make.names(train$label)),
trControl = trControl, tuneGrid = my_grid,method = "xgbTree")
But for every iteration it is telling me this:
Error in xgb.DMatrix(as.matrix(x), label = y) : can not open file "NA"
That's the same error message I was getting before in regular xgb.boost when I didn't set my missing to NA. The xgb.DMatrix is not a subsettable object I could take the data from, and it is also not possible to convert it to a data frame. How do I get around this?
EDIT
Figured it out. In the end, it had nothing to do with missing data, but with having factors in the dataset. Instead of using xgboost's function to convert to a sparse matrix, I used regular model.matrix() and was able to successfully plug in the new matrix into caret's train function.
I am new to Julia programming language, however, I am fitting a Linear Mixed Effects Model and I find it difficult to save the fixed and random effects estimates in .csv files.
An example code can be found:
using MixedModels
#time modelOutput = fit(lmm(Y~ A + B + (0 + A | group), data))
There is available reference about how to obtain the fixed (fixef(modelOutput)) and random (ranef(modelOutput)) effects however using a DataFrame I am facing errors.
Any advice is appreciated.
Okay, I actually took the time to do this for you. A CoefTable is a type defined in statmodels here. Given this information, we can extract the relevant information from the CoefTable instance as follows:
df = DataFrame(variable = ct.rownms,
Estimate = ct.mat[:,1],
StdError = ct.mat[:,2],
z_val = ct.mat[:,3])
This will give an nvar-by-4 DataFrame which you can then write to csv as described earlier using writetable("output.csv",df)
I had a number of problems getting the accepted answer to work; Julia has evolved a lot since then. I rewrote it based primarily on code from the jglmm R package, with some adaptation/cobbling-together from other sources ...
"""
outfun(m, outfn="output.csv")
output the coefficient table of a fitted model to a file
"""
outfun = function(m, outfn="output.csv")
ct = coeftable(m)
coef_df = DataFrame(ct.cols);
rename!(coef_df, ct.colnms, makeunique = true)
coef_df[!, :term] = ct.rownms;
CSV.write(outfn, coef_df);
end