I am developing an artificial model to simulate the growth of two types of biological cells under different conditions. The data I obtained from my model takes the form of two data-sets representing the number of cells in the culture across time in what we will call condition A and conditon B. Ie: A = … Read more
Usually, the validation error is higher than training error, but are there any cases when they are equal? Best Answer Reason 1: the model is underfitted, i.e. it has a high bias: Reason 2: the model is near perfect. Reason 3: the training set is very similar to the validation set, e.g. if some data … Read more
Usually, the validation error is higher than training error, but are there any cases when they are equal? Best Answer Reason 1: the model is underfitted, i.e. it has a high bias: Reason 2: the model is near perfect. Reason 3: the training set is very similar to the validation set, e.g. if some data … Read more
I have a data set of x- and y-values, that I want make a linear fit of. Using polyfit(x,y,1) I get the coefficients a and b for a linear fit ax = b for this data, but I would also like to find the uncertainty or standard deviation for these coefficients. Does anyone know an … Read more
Suppose we have toy daily temperate data and we want to fit a model. A reasonable thing to do is fitting a periodic model with Fourier basis $$ f(x)=beta_0+beta_1 cos(2pi x/24)+beta_2 sin(2pi x/24) $$ So the Fourier basis expansion of data matrix $mathbf X$ is $$ begin{bmatrix} 1&cos 0 & sin 0 \ 1&cos frac … Read more
Context: Latent Growth Curve Modeling for continuous variable with 15 time points. One intercept, two slopes (for first and second half of all time points). N=146, >90% data coverage, MLR estimator in MPlus, no covariates. Problem: High SRMR=0.20 despite good fit based on other indices: chi-square goodness of fit p=.08, RMSEA=0.04, CFI/TLI=0.97. My beginner's attempt: … Read more
I'm trying to fit raw data to curves, which works well on an individual basis. However, I'd like to "share" parameters (sometimes referred as nested parameters) across more than one data series. Is there a way to do this in R? Best Answer If the error variance is also common across data series, the usual … Read more
I am looking for the name of a fitting method that will work even if points from multiple dataseries are meshed together. As far as I understand there are two major methods, least squares and least absolute error; the method I am looking for would be the next step in the direction from LS to … Read more
I am looking for the name of a fitting method that will work even if points from multiple dataseries are meshed together. As far as I understand there are two major methods, least squares and least absolute error; the method I am looking for would be the next step in the direction from LS to … Read more
There are a lot of references in the statistic literature to "functional data" (i.e. data that are curves), and in parallel, to "high dimensional data" (i.e. when data are high dimensional vectors). My question is about the difference between the two type of data. When talking about applied statistic methodologies that apply in case 1 … Read more