I would like to do a regression in R
The formula is y_t = alpha +beta* x_t-1 & x_t = theta + rho * x_t-1.
Since I would like to estimate the covariance matrix of the error. I do not know how to run regression for both equation together. Thank you.
I tried
lm(c(y[2:756],x[2:756])~c(x[1:755],x[1:755]),data=data1)
756 is the length of vector, it does not work.
Your example looks like you are trying to fit an autoregressive model with lm. Try autoregressive models instead. For multivariate autoregressive models I suggest using the MTS package. Something like the following should work:
require("MTS")
VAR(data.frame(x=x, y=y))
For more detail, check out ?VAR. You may also want to have a look at the time series task view on CRAN.
Related
I fit a given data using Cox model via glmnet R package and my
little R example is:
library(fastcox);data(FHT);attach(FHT) #
library(glmnet)
library(survival)
fit = glmnet(x,Surv(y,status),family="cox",alpha=1)
From the help document, we know glmnet fits penalized models like
-loglik/nobs + λ*penalty
i.e., objective function = loss function + penalty function.
I want to fetch -loglik/nobs (loss function value,
the negative partial log-likelihood of the fitted model
or two term
Taylor series expansions of the log likelihoods) from the fit object.
Any idea? Tks
BTW, we also tried
fit0 = glmnet(x,Surv(y,status),family="cox",alpha=1,lambda=0)
according to -loglik/nobs + λ*penalty, but it shows errors.
I'm sure this is something that can be done, just not sure how!
I have a dataset that is around 500 rows(csv) and it shows footballers match stas(e,g passes, shots on target)etc.I have some of their salaries(around 10) and I'n trying to predict their salaries using a linear regression equation.
In the below, if Y is salaries, is there a way on R to essentially autopopulate? what the rest of the salaries might be based on the ten salaries I do have?
lm(y ~ x1 + x2 +x3)
Any help would be much appreciated.
This is what the predict function does.
Note that you don't need to call predict.lm explicitly. Because the result of a call to lm is an object with class "lm", R "knows" to use predict.lm when you call predict on it.
Eg:
lm1 <- lm(y ~ x1 + x2 +x3)
y.fitted <- predict(lm1)
You should also be able to test the predictive accuracy of your model using cross validation with the function cv.lm in the DAAG library. With this function you create test data to test the model which is generated using training data.
I'm a R novice but I'm looking for a way to determine the three parameters A, B and C related by the following function in R:
y = A * (x1^B) * (x2^C)
Can someone give me some hints about R method(s) that would help me to achieve such a fitting?
One option is the nls function as #SvenHohenstein suggested. Another option is to convert your nonlinear regression into a linear regression. In the case of this equation just take the log of both sides of the equation and do a little algebra and you will have a linear equation. You can run the regression using something like:
fit <- lm( log(y) ~ log(x1) + log(x2), data=mydata)
The intercept will be log(A) so use exp to get the value, the B and C parameters will be the 2 slopes.
The big difference here is that nls will fit the model with normal errors added to the original equation and the lm fit with logs assumes that the errors in the original model are from a lognormal distribution and are multiplied instead of added to the model. Many datasets will give similar results for the 2 methods.
You can fit a nonlinear least-squares model with the function nls.
nls(y ~ A * (x1^B) * (x2^C))
Why don´t you use SVM (Suppor Vector Machines) Regression? there´s a package in CRAN named e1071 that can handle regression with SVM.
You can check this tutorial: http://www.svm-tutorial.com/2014/10/support-vector-regression-r/
I hope it can help you
I am trying to use R to rerun someone else's project, so we need to use some macros in R.
Here comes a very basic question:
m1.nlme = lme(log.bp.dia ~ M25.9to9.ma5iqr + temp.c.9to9.ma4iqr + o3.ma5iqr + sea_spring + sea_summer + sea_fall + BMI + male + age_ini, data=barbara.1.clean, random = ~ 1|study_id)
Since the model is using AR(1) [autocorrelation 1 covariance model] in SAS for within person variance, I am not sure how to do this in R.
And where I can see the index for different models, like unstructured?
Thanks
I don't know what you mean by "index" for different models, but to specify an AR(1) covariance structure for the residuals, you can add corr=corAR1() to your lme call.
The correlation at lag $1$ is say $r$, where $-1< r <1$ for a stationary $AR(1)$ model. The correlation at lag $k \geq 1$ is $r^k$. This gives you the autocovariance matrix by just multiplying by the variance of $X_t$.
I have a stationary time series to which I want to fit a linear model with an autoregressive term to correct for serial correlation, i.e. using the formula At = c1*Bt + c2*Ct + ut, where ut = r*ut-1 + et
(ut is an AR(1) term to correct for serial correlation in the error terms)
Does anyone know what to use in R to model this?
Thanks
Karl
The GLMMarp package will fit these models. If you just want a linear model with Gaussian errors, you can do it with the arima() function where the covariates are specified via the xreg argument.
There are several ways to do this in R. Here are two examples using the "Seatbelts" time series dataset in the datasets package that comes with R.
The arima() function comes in package:stats that is included with R. The function takes an argument of the form order=c(p, d, q) where you you can specify the order of the auto-regressive, integrated, and the moving average component. In your question, you suggest that you want to create a AR(1) model to correct for first-order autocorrelation in the errors and that's it. We can do that with the following command:
arima(Seatbelts[,"drivers"], order=c(1,0,0),
xreg=Seatbelts[,c("kms", "PetrolPrice", "law")])
The value for order specifies that we want an AR(1) model. The xreg compontent should be a series of other Xs we want to add as part of a regression. The output looks a little bit like the output of summary.lm() turned on its side.
Another alternative process might be more familiar to the way you've fit regression models is to use gls() in the nlme package. The following code turns the Seatbelt time series object into a dataframe and then extracts and adds a new column (t) that is just a counter in the sorted time series object:
Seatbelts.df <- data.frame(Seatbelts)
Seatbelts.df$t <- 1:(dim(Seatbelts.df)[1])
The two lines above are only getting the data in shape. Since the arima() function is designed for time series, it can read time series objects more easily. To fit the model with nlme you would then run:
library(nlme)
m <- gls(drivers ~ kms + PetrolPrice + law,
data=Seatbelts.df,
correlation=corARMA(p=1, q=0, form=~t))
summary(m)
The line that begins with "correlation" is the way you pass in the ARMA correlation structure to GLS. The results won't be exactly the same because arima() uses maximum likelihood to estimate models and gls() uses restricted maximum likelihood by default. If you add method="ML" to the call to gls() you will get identical estimates you got with the ARIMA function above.
What is your link function?
The way you describe it sounds like a basic linear regression with autocorrelated errors. In that case, one option is to use lm to get a consistent estimate of your coefficients and use Newey-West HAC standard errors.
I'm not sure the best answer for GLM more generally.