I try to estimate the above nonlinear model by Stata. Unfortunately, I am not comfortable with Stata. Can anyone help me about writing the above function in Stata?
How can we write regional dummy, time fixed effect and country fixed effect in nl command in Stata?
Is there a way to write the summation in the above equation in Stata? Alternatively, is it easier to estimate the equation for each individual region?
Stata 15 introduced a native command for fitting non-linear panel data models.
https://www.stata.com/new-in-stata/nonlinear-panel-data-models-with-random-effects/
That might help get you started, but you need Stata 15.
Related
I have a dataset which is categorical dataset. I am using WEKA software for feature selection. I have used CfsSubsetEval as attribute evaluator with Greedystepwise method. I came to know this link that CFS uses Pearson correlation to find the strong correlation between the dataset. I also found out how to calculate Pearson correlation coefficient using this link. As per the link the data values need to be numerical for evaluation. Then how can WEKA did the evaluation on my categorical dataset?
The strange result is that Among 70 attributes CFS selects only 10 attributes. Is it because of the categorical dataset? Additionally my dataset is a highly imbalanced dataset where imbalanced ration 1:9(yes:no).
A Quick question
If you go through the link you can found the statement the correlation coefficient to measure the strength and direction of the linear relationship between two numerical variables X and Y. Now I can understand the strength of the correlation coefficient which is varied in between +1 to -1 but what about the direction? How can I get that? I mean the variable is not a vector so it should not have a direction.
The method correlate in the CfsSubsetEval class is used to compute the correlation between two attributes. It calls other methods, depending on the attribute types, which I've linked here:
two numeric attributes: num_num
numeric/nominal attributes: num_nom2
two nominal attributes: nom_nom
I am new to Pvlib and just started few days ago. We have four different solar cells installed in our university. I have the specifications of the four cells including Isc, Voc, Vmpp, Impp etc. I want to add these cells into the PVlib library and then do further modeling on each of them. Can you please guide me how to proceed. I just need to know that how can I use the specifications of each solar cell mentioned below to integrate them with pvlib. In the cec and sandia database we only have silicon based solar cells. I would be grateful for your assistance.
If you only have Voc, Isc, Imp, & Vmp at STC conditions you may be able to use the pv parameter estimation functions but you will have difficulty coming up with temperature coefficients, but perhaps you have those already separately? Then use calcparams_<model> where model is the same as what you estimated parameters for, one of CEC, PVsyst, Sandia, or DeSoto. This will give you the temperature and irradiance specific parameters to use in singlediode to get max power (or any operating point) for each timestep corresponding to the temperatures and irradiances of interest.
In my case, I’ve 33 labels per samples. The input label tensors for a corresponding image are like [0,0,1,0,1,1,1,0,0,0,0,0…...33]. And the samples for some labels are quite low and some are high. I'm looking for predict the regression values. So what will be the best approach to improve the prediction? I would like to apply data balancing technique. But so far I found the balancing technique available only for multi-class. I’m grateful to you if you share your best knowledge about regarding my problem or any other idea to improve the performance. Thanks in Advance.
When using a single.model to regress multiple values, it is usually beneficial to preprocess the predictions to be in roughly the same range.
Look for example on the way detection models predict (regress) bounding box coordinates: values are scaled and the net predicts only corrections.
I am a frequent user of scikit-learn, I want some insights about the “class_ weight ” parameter with SGD.
I was able to figure out till the function call
plain_sgd(coef, intercept, est.loss_function,
penalty_type, alpha, C, est.l1_ratio,
dataset, n_iter, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), est.random_state,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/stochastic_gradient.py
After this it goes to sgd_fast and I am not very good with cpython. Can you give some celerity on these questions.
I am having a class biased in the dev set where positive class is somewhere 15k and negative class is 36k. does the class_weight will resolve this problem. Or doing undersampling will be a better idea. I am getting better numbers but it’s hard to explain.
If yes then how it actually does it. I mean is it applied on the features penalization or is it a weight to the optimization function. How I can explain this to layman ?
class_weight can indeed help increasing the ROC AUC or f1-score of a classification model trained on imbalanced data.
You can try class_weight="auto" to select weights that are inversely proportional to class frequencies. You can also try to pass your own weights has a python dictionary with class label as keys and weights as values.
Tuning the weights can be achieved via grid search with cross-validation.
Internally this is done by deriving sample_weight from the class_weight (depending on the class label of each sample). Sample weights are then used to scale the contribution of individual samples to the loss function used to trained the linear classification model with Stochastic Gradient Descent.
The feature penalization is controlled independently via the penalty and alpha hyperparameters. sample_weight / class_weight have no impact on it.
I am trying to implement a difference-in-differences estimator with a GLM model with Stata 13.0. The parameter I am interested in is the derivative of the expected value with respect to the interaction of binary treatment group indicator T and binary post-treatment period indicator S only (T#S, rather than the full derivative with respect to T). This approach is explained towards the end of this thread on Statalist. This is my code:
glm y i.T##i.S, exposure(e) cluster(user_id) link(log) family(poisson) robust
preserve
replace e = 30
margins rb0.T#rb0.S
restore
The preserve/replace/restore step is necessary because margins does not allow the at() option to be used with exposure variables.
Two questions.
How would I get a p-value for this effect?
Is it possible to get the effect in semi-elasticity form, perhaps by using margins with eydx() in some way?