I have run a hidden markov model with five variables on very different scales. Because of this I normalized the input data beforehand using Carets preprocessing:
# Using preProcess to scale the data
data_mapping <- preProcess(data[, column to normalize], method = c('range')) # preProcess creates a mapping for the chosen variables
pdata_norm <- predict(data_mapping, data) # we transform the data using predict() and the mapping
I understand that my data has now been scaled to the interval between zero and one. I am now struggling to understand if I am interpreting the outputs correctly. I have looked at similar posts What is the interpretation of scaled regression coefficients when only the predictors are scaled? But I am not mathematically minded! Could someone please confirm in simple terms how to interpret results that have been normalized. Should I be thinking of this data in standard deviations?
A sample of my output is:
Re1.(Intercept) Re1.CV1 Re1.CV2 Re1.sd Re2.(Intercept) Re2.CV1 Re2.CV2 Re2.sd
St1 0.076 0.060 -0.064 0.079 0.135 0.123 0.378 0.158
St2 0.052 0.017 -0.051 0.036 0.177 0.081 0.247 0.183
St3 0.017 0.004 0.001 0.020 0.245 -0.029 0.131 0.174
(St= state, re= response, CV= covariate).
Re1 was in seconds prior to normalisation and Re2 was in meters.
So, for example, am I correct in interpreting this as response 2 in state 3 is 0.11 standard deviations less than when in state 1 (0.245-0.135=0.11). Or is this incorrect? How can I convert the new coefficients to units of the original data (seconds, meters)? Something like this is what I am after but for the outputs of a HMM e.g.0.076 Normalized regression coefficients - interpretation
Thank you so much!!
