0
votes
0answers
34 views

Linear regression vs additive modelling, any meaning when linear modelling has higher accuracy?

I have a simple question. Is there any important meaning when non-linear modelling (general additive modelling such as gamm in R) has lower accuracy than linear modelling? some useful plots are ...
0
votes
0answers
20 views

Need to forecast a small data set. Suggest best method to go about

Hi I have sales data for previous 3 years(6 half years). I need to predict / forecast the sales for next 1-2 years. Tell me which method / model I should use. As always sales dependent on country ...
0
votes
1answer
40 views

Can someone look at my method for fitting a GEE to my data?

I’ve been doing some statistical analyses in R on some data. It’s for use in a manuscript I’m hoping to get published in a biological journal. Unfortunately, the tests I ended up having to run are ...
1
vote
1answer
27 views

Zero-inflated negative binomial model for true zeros

The zeroinfl function in the pscl package in R assumes that zeros include both false zeros and true zeros. I have a zero ...
0
votes
2answers
46 views

Difference in predicted value using two different methods

Take these two vectors: ...
2
votes
2answers
69 views

How to predict the amount of data needed for modeling?

Is there a way to estimate the amount of data (or the number of records) required to build a statistical model? I read few blogs and I feel that most of the responses concur that there is no way or ...
1
vote
0answers
33 views

R: Is it possible to estimate the poisson noise?

I have a dataset of many discrete counts (RNAseq read counts per base), which contain both real signals and background noise. The noise is random, and should be poisson distributed. What I would ...
3
votes
1answer
90 views

What resolution should I be using for residuals vs fitted values plot from a linear regression?

I made this linear regression that shows how well estimated animal locations (longitude) predict actual animal locations. ...
0
votes
0answers
71 views

Arima function doesn't consider seasonal components

Currently trying to fit several models to some data sets in order to find an accurate enough one, I ran into some difficulties with the Arima function of the ...
1
vote
1answer
26 views

Model specification with Deflators: methodological question on forecast model

I am trying to build a model to predict one year ahead Earnings per share $(t+1)$ based on variables in year $t$. I’ve seen a lot of models in practice that use the following methodology: ...
0
votes
0answers
37 views

VARMAX model in R

Is there a function in R that estimates the VARMAX model? There is one for a VARX (MTS package), but I didn't find one that works with the MA part also...
4
votes
1answer
100 views

Model with non-linear transformation

I don't understand this concept well and need help. I was choosing whether to use a linear model or apply a non-linear transformation in my model formula. To do a diagnostic, I quickly plotted my ...
0
votes
1answer
52 views

Specifying lag in `dlnm` when passing arguments to `crossbasis`

I am using the dlnm package to build a finite distribute lag linear model. I intend on testing the model-fit based on various lag levels to assess which lag is ...
1
vote
1answer
76 views

How to capture & present lm model output from R

After running iterations of lm() in R, I am now stuck with which components of the model's output to present and how to present them. I know that the $R^{2}$ value, ...
0
votes
1answer
97 views

modelling data that contains only ones and zeros

I'm new to modelling this type of data, and I got punished for asking this on stackoverflow... I have a dataset where the predictive variables contain only ones and zeros, and the response variable ...
3
votes
1answer
39 views

Fit nonlinear parameter

I'm attempting to fit this model: $P = C_0 + C_1*U^r$ Given known vectors of observations $P$ and $U$, I want to fit values for $C_0$, $C_1$ and $r$. How do I make this fit in R? or preferably GSL ...
2
votes
1answer
64 views

Interaction effects in big data sets

I'm looking for a method to identify a shortlist of potentially good 2-way interaction terms rather than trying all possible interactions. This question is similarly asked before here but in a more ...
1
vote
0answers
85 views

ARIMA - SARIMAX modelling with R

I am really new to R and to time series. My field of studies is in the field of Networks and Telecommunication, but my summer internship is about trying to find a statistical model for some sets of ...
0
votes
0answers
42 views

Nested logit program in R

My question is specific to transportation modelling using a nested logit (NL) model. I wonder how to make a program including a t-parameter, t-value, and likelihood in R. I did estimation in a ...
2
votes
0answers
20 views

Polynomial model with unpaired data

I'm trying to model data as a 2nd degree polynomial, but the data is unpaired and each data point of average values has a standard error for each axis. My data: A time series in minutes (time ...
0
votes
1answer
113 views

If you convert factors into indicator variables, do you treat them as continuous predictors?

Let's say I have a data matrix X where one feature is a factor with 8 levels. If I change this to be 7 indicator variables of 1's and 0's, do I need to make these columns factors as well? Or if I am ...
0
votes
0answers
28 views

Logistic Regression Performance on training data set V/s AIC

I am fitting a logistic Regression on data set having 700 variables (after Chisquare test) and 15000 rows. For that I did best subset analysis using glmulti package in R on first 70 variables and got ...
5
votes
5answers
817 views

Logistic Regression on Big Data

I have a data set of around 5000 features. For that data I first used Chi Square test for feature selection; after that, I got around 1500 variables which showed significance relationship with the ...
0
votes
1answer
66 views

Alternative Specific Variables in R

I am building a discrete choice model (rail and auto). I have the cost of a trip for each mode (rail versus auto). The utility equations I am building are denoted below: \begin{align*} V_\text{auto} ...
1
vote
1answer
42 views

The differences between models via their resampling distributions.

The caret package offers the ability to make statistical statements about the performance of different models used for classification. According to the description, ...
0
votes
0answers
36 views

how to compute odds ratio

Given a generic classification model $y=f(x_1,x_2,..,x_p)$ where $y\in \left\lbrace 0,1 \right\rbrace$ is it possible to compute the odds ratio for each variable? A theoretical explanation and ...
3
votes
2answers
92 views

logistic regression for modelling

I have these data plotted above. The explanatory variable represents intensity levels of ground shaking at different locations in an earthquake, and the response variable represents amounts of ...
3
votes
4answers
402 views

Fitting probability distribution to data

I am trying to fit a model for the values plotted above. The explanatory variable represents amounts of compensation claim in an earthquake, and the response variable represents amounts of ...
1
vote
1answer
59 views

Cost Benefit Analysis of Pre-screening Widgets for Faults before they Fail

I want to build a model that determines whether to pre-screen my widgets for defects. If I do pre-screen, it costs a fixed amount per check and I resolve the problem 100% of the time. If I don't ...
0
votes
0answers
17 views

Asymptotic Property of the Likelihood surface

I have a questions which I am not quite sure how to frame, so I apologies if it does not make sense, but I will try my best to make it interpretable. I have been running some network models in R, ...
2
votes
2answers
626 views

Categorize continuous data effectively (taking into account a response variable)

I wonder what are the better approaches to categorize continuous data (e.g. age) than dividing them with the use of quantiles and cut function (in ...
1
vote
0answers
405 views

Time series modeling with R on weekly data

I am trying to do time series modeling and forecasting using R based on weekly data like below - ...
0
votes
0answers
22 views

How to model two variables with a cumulative exponential relationship?

Suppose I have a response variable(y), which is normally distributed, and y is generally changed with ...
1
vote
0answers
45 views

R code for predicting part fitments from the following dataset

I'm a manufacturing engineer trying to resolve an issue regarding non fitment of parts. There are a couple of components which have dimensions x1 to x6 (data below). Upon assembly, they form ...
1
vote
0answers
215 views

Radial profile and 2d log-normal distribution?

I have a case study where a person should be located. We do not know where this person is, but we have some information. The total story is basically about the person which is to be searched. The ...
3
votes
0answers
71 views

Which technique to build a model returning a vector of values (in R)

In my current project I need to build a model returning a vector of actions for each observation. I need a suggestion which statistical technique is used in general in such cases. In a project, I ...
4
votes
0answers
79 views

How do I model the probability of two empirical distributions?

I have two distributions: A, and B. Each distribution is filled with the numbers 1.0-10.0. These distributions are NOT simple functions, like the gaussian, but are merely empirical counts. ...
1
vote
1answer
42 views

How to take mobility into account: multiple regression

I have a panel including two years and I want to compare urban and rural wages: $\log \text{wage}_{it} =\beta_0 +\beta_1\text{urban}_{it} +\beta_2\text{educ}_{it}+\beta_3\text{exper}_{it}+ ...
1
vote
0answers
164 views

Visualising a linear model using effects package in R

I have ran this model in R: model <- lm(mpg ~ hp + drat + disp, data=mtcars) And I have visualised this model using ...
6
votes
2answers
370 views

Modelling for soccer scores

In Dixon, Coles (1997), they have used the maximum likelihood estimation for the two modified independent Poisson models in (4.3) to model the scores in soccer. I am trying to use R in order to ...
1
vote
0answers
283 views

Methods to combine ( e1071 svm ) models in R to generate a more complete, accurate model

I am using the function svm from the package e1071 in R to generate a support vector ...
0
votes
0answers
38 views

Warning message with loglin/dmod function, package gRim

after executing following code: library(gRim) TA=table(ALL) upp.model<- dmod(~.^3, margin=colnames(ALL),data=TA) I get this warning message: ...
0
votes
2answers
266 views

Which model should I use to fit my data ? ordinal and non-ordinal, not normal and not homoscedastic

Here is the kind of data I have: I have two predictor variables: 1) discrete non-ordinal --> c('a','b','c') 2) discrete ordinal --> c(10,100,200,500) Response variable: Proportion of TRUE over a ...
4
votes
1answer
73 views

Different powers of coefficient - solvable within GLM?

I have model where coefficient is of different powers: $$\mbox{log} ( \mu_{i} ) = \alpha + \beta x_1 + \beta^2x_2 + \beta^3x_3 + ... + \beta^nx_n \\ \\ N_{i} \sim \mbox{Poiss} ( \mu_{i} ) $$ $N_i$ ...
1
vote
1answer
808 views

Providing starting values for a Generalized Linear Mixed Model with glmmPQL

I am trying to run a Generalized Linear Mixed Model on some data. What I am trying to do is use distances from habitat features to predict a distance between 2 animal locations. I ran a PCA on the ...
2
votes
4answers
188 views

How to model distributions which are not normally distributed

I would like to model the performance of a rainwater tank, which has a stochastic input (rainfall). The data are the empty volume in the tank at the end of each day. The values are skewed towards the ...
1
vote
0answers
183 views

Fit Gaussian random field for spatial data

I'm dealing with spatial data where the response variable is the gas concentration. In addition, I've the x,y-coordinate values, and another covariates. I'm thinking to fit a Gaussian random field ...
0
votes
1answer
101 views

How to fit a specific model to some data

I have two dependent variables, DV1 and DV2, and one independent variable IV. I want to ascertain if the fitted model between DV1 and IV explains the relationship between DV2 and IV well or not. ...
1
vote
1answer
138 views

Making various mixed effects models

I've tried to create three models (using R): an intercept only linear regression, a simple mixed effects regression and a by-subject effects mixed effects regression. An intercept only regression ...
7
votes
1answer
869 views

Coefficients paths – comparison of ridge, lasso and elastic net regression

I would like to compare models selected with ridge, lasso and elastic net. Fig. below shows coefficients paths using all 3 methods: ridge (Fig A, alpha=0), lasso (Fig B; alpha=1) and elastic net (Fig ...