predict

I am trying to predict a time series in python statsmodels ARIMA package with the inclusion of an exogenous variable, but cannot figure out the correct way to insert the exogenous variable in the predict step. See here for docs. import numpy as np from scipy import stats import pandas as pd import statsmodels.api as sm vals = np.random.rand(13) ts = pd.TimeSeries(vals) df = pd.DataFrame(ts, columns=["test"]) df.index = pd.Index(pd.date_range("2011/01/01", periods = len(vals), freq = 'Q')) fit1 = sm.tsa.ARIMA(df, (1,0,0)).fit() #this works fine: pred1 = fit1.predict(start=12, end = 16) print

I am currently using caretEnsemble package in R for combining multiple models trained in caret. I have got the list of final trained models (say model_list ) using caretList function from the same package as follows. model_list <- caretList( x = input_predictors, y = input_labels, metric = 'Accuracy', tuneList = list( randomForestModel = caretModelSpec(method='rf', tuneLength=1, preProcess=c('BoxCox', 'center', 'scale')), ldaModel = caretModelSpec(method='lda', tuneLength=1, preProcess=c('BoxCox', 'center', 'scale')), logisticRegressionModel = caretModelSpec(method='glm', tuneLength=1,

I am trying to predict values over time (Days in x axis) for a glmer model that was run on my binomial data. Total Alive and Total Dead are count data. This is my model, and the corresponding steps below. full.model.dredge<-glmer(cbind(Total.Alive,Total.Dead)~(CO2.Treatment+Lime.Treatment+Day)^3+(Day|Container)+(1|index), data=Survival.data,family="binomial") We have accounted for overdispersion as you can see in the code (1:index). We then use the dredge command to determine the best fitted models with the main effects (CO2.Treatment, Lime.Treatment, Day) and their corresponding interactions.

I have this data set: x <- c(0, 40, 80, 120, 160, 200) y <- c(6.52, 5.10, 4.43, 3.99, 3.75, 3.60) I calculated a linear model using lm() : model <- lm(y ~ x) I want know the predicted values of x if I have new y values, e.g. ynew <- c(5.5, 4.5, 3.5) , but if I use the predict() function, it calculates only new y values. How can I predict new x values if I have new y values? Since this is a typical problem in chemistry (predict values from a calibration), package chemCal provides inverse.predict . However, this function is limited to "univariate model object[s] of class lm or rlm with model

I am trying to predict fitted values over data containing NA s, and based on a model generated by plm . Here's some sample code: require(plm) test.data <- data.frame(id=c(1,1,2,2,3), time=c(1,2,1,2,1), y=c(1,3,5,10,8), x=c(1, NA, 3,4,5)) model <- plm(y ~ x, data=test.data, index=c("id", "time"), model="pooling", na.action=na.exclude) yhat <- predict(model, test.data, na.action=na.pass) test.data$yhat <- yhat When I run the last line I get an error stating that the replacement has 4 rows while data has 5 rows. I have no idea how to get predict return a vector of length 5... If instead of