Integral for random vibration analysis?

I'm trying to analyze temporal trends in a community of amphibians and looking to see what variables may be important in understanding declines.

My response is a single vector of ln transformed abundance (it was averaged across multiple "traps" at a plot and so is not integer based).

My variables of interest are

plot = the level of sampling (fixed effect nested within site; coded as chr)

site = multiple plots per site (fixed effect nested within region; coded as chr)

region = multiple sites per region (random effect; coded as chr)

wateryear = just the year the sample was taken (fixed effect, coded as a factor in the data frame for repeated measures )

depth.30 = mean water depth 30 days prior to sample (fixed effect)

Here is my current model =

model= lmer(LN_total_1 ~ WATERYEAR*(1|REGION)*depth30.mean +

(1|REGION:SITE) +

(WATERYEAR|PLOT2) + ## this is the issue term data=ANOVA_basefile2)

I'm trying to use the (WATERYEAR|PLOT2) to denote that the plot is what is being measured repeatedly, but I'm not sure if this is the correct way of specifying that. Is this the correct way to code for repeated measures in this syntax?

I also tried doing WATERYEAR:PLOT2 -1 which would check to see if there was a sig difference across plots for each wateryear (I think...?) but that returns, "Error in is.nloptr(ret) : objective in x0 returns NA"

Also for the (1|REGION:SITE) that's specifying the fixed effect of site within the random effect of region, correct?

Thanks for the help!

I have a factorial design with two factors (water treatment x variety) and for each water treatment I have 3 replicates (3 for one treatment and three for the other). 

I am doing a variance component analysys with fixed factors (water treatment and variety) and a random factor (plot) but I am not sure if it is correct. By the way is interesting for me to evaluate differences between water treatments for each variety

Thank you very much in advance

There is a popular question, called "Best statistics question ever".

If you choose an answer to this question at random, what is the chance you will be correct?

A) 25% B) 50% C) 60% D) 25%

This task is not very difficult, the correct answer is 0%. But if we modify it like this:

If you choose an answer to this question at random, what is the chance you will be correct?

A) 50% B) 25% C) 60% D) 50%

What will be the correct answer? Do we have two correct answers: 25% and 50%, or there is no correct answer, as with these two correct answers the chance of choosing the correct answer is in fact 75% (but we do not have 75% written on the desk)?

By the way. Does the answer 0% remains the correct answer, the third correct answer in this case?

I am doing a repeated measures ANCOVA.

My independent variable is visual clutter measured as a between-subjects 2 levels categorical variable (Higher visual clutter versus lower visual clutter). For either the Higher visual clutter or lower visual clutter conditions, the participants in those respective groups either see the SPORT TYPE soccer first or basketball first (Note that everything is randomized so there are no order effects).

The dependent variable is consumer satisfaction and I have measured this separately for basketball and soccer. so two continuous dependent variables satisfaction (SATS) soccer and SATS basketball.

In the covariates box, I added the moderator: the continuous variable escapism and I added the interaction effect of visual clutter and escapism gender and age

My supervisor who is now unavailable told me in her feedback that if the difference in the means of sport type is significant I should include it as a covariate in the ANCOVA model.

But either I completely misunderstood her feedback or I am correct that it is impossible to create a dummy variable for a within-subjects continuous variable (sport type satisfaction).

And even so, in the repeated measures ANCOVA, the within-subjects test shows that there is no significant difference in means between basketball and football. But a simple paired t-test shows that for the higher visual clutter condition, there is a significant difference in means for the satisfaction levels between basketball and soccer while in the lower visual clutter condition there is not.

Can I drop sports-type based on the results of the repeated measures ANCOVA, the within-subjects effect being insignificant and conduct a regular ANCOVA afterwards?

My thesis is due in five days and I would deeply appreciate the help.

Sebastian Brom

for randomness testing using NIST suit.

if I am generating 1000, 160 bit key sequences using Hash function, if they are 160 bit binary, what whould be the input type I should select in the last step? Ascii or Binary ?

In what case i should select the Ascii option?

can any one share me the sample input files?

I am taking binary output of 160 bits from Hash function instead of hex. My NIST suit is not working on selecting binary option, gives overflow error, and works well with ASCII option. Is it the correct way I am going?

I have data showing which month a disease started for 119 subjects. The months are represented by 1-12 (Jan - December). We hypothesize the disease more often onsets during winter months. What test should I use?

This is the data we have.

Month Number of people with disease onset in this month

1 11

2 9

3 10

4 7

5 7

6 5

7 9

8 9

9 16

10 11

11 14

12 11

So far, I've attempted to use a Poisson test in SPSS and my interpretation of the results is that there's no significant difference from a random distribution (Omnibus test 0.155 significance).

However, I was hoping I could confirm with someone that this is the correct test and that I'm interpreting this accurately? Just eye-balling the data suggests there does seem to be less counts in the summer months, so I was a bit surprised there was no significance.

If there's a different test I should do in SPSS, please let me know!

Thank you.

I have a tabular dataset made of floats which follow a simple rule like:

"if features A and B are in a certain range then target class is 1, otherwise target class is 0."

Since I want to get some interpretability from my neural network model, I opted for using the integrated gradients method implemented by alibi.

Unfortunately, most of individual samples don't show A and B as the leading features as expected. Even more weird is the fact that, when I average the attributions of all the individual samples, A and B get the highest score. In other words, local explanations fail but, on average, the global explanation is correct.

Can anyone help me out to understand why this happens? Isn't integrated gradients method suitable for tabular datasets?

By the way, my baseline is based on a uniform distribution of random floats ranging from 0 to the maximum of each column. Should it be fine or, perhaps, is there a better choice for tabular data?

There are two signals- consider them two vectors over equal time .

The length of the two signals are same.

Both the signals are continuous and random .

They have the same frequency and wavelength but different amplitude.

Visually they look similar.

How do I mathematically detect the change of periodicity.

Something like - both the signals are now pointing North.Both the signals are now pointing South.