Nested - Science topic

If I use Generalized Linear Model (GLM) in SPSS, how should I arrange my data (2 Years) and interpret the results? Are there any reliable source for understanding this process?

Hello,

I am new to coding in R and have come up with the following code to perform a nested 2-way ANOVA (with Tukey post-hoc) to be able to account for individual animal variability within each group. I am wondering if someone can confirm this is correct or provide alternative methods? I am assessing the effects of diet and stress on certain cellular outcomes, with n=3-5 animals/group. Thank you!

# Load required packages

library(lme4)

library(emmeans)

library(ggplot2)

# Convert factors

Data_For_R$Diet <- as.factor(Data_For_R$Diet)

Data_For_R$Stress <- as.factor(Data_For_R$Stress)

Data_For_R$Animal <- as.factor(Data_For_R$Animal)

# Nested 2-way ANOVA models

model_aov_stress <- aov(SomaVolume ~ Stress / Animal, data = Data_For_R)

model_aov_diet <- aov(SomaVolume ~ Diet / Animal, data = Data_For_R)

model_aov_combine <- aov(SomaVolume ~ Diet * Stress / Animal, data = Data_For_R)

# Mixed-effects model accounting for animal variability

model_lmer <- lmer(SomaVolume ~ Diet * Stress + (1 | Animal), data = Data_For_R)

# Obtain estimated marginal means for Diet and Stress, considering random effect for Animal

emmeans_result <- emmeans(model_lmer, ~ Diet * Stress)

# Perform pairwise comparisons for the interaction between Diet and Stress, adjusting for animal variability

pairs(emmeans_result, adjust = "tukey")

# Create a new factor to represent the combination of Diet, Stress, and Animal

Data_For_R$Diet_Stress_Animal <- interaction(Data_For_R$Diet, Data_For_R$Stress, Data_For_R$Animal, drop = TRUE)

# Summaries of the models

summary(model_aov_stress)

summary(model_aov_diet)

summary(model_aov_combine)

why ants relocated coloney every few month and keep the complete old nest？

I'm analysing a dataset from a field survey designed to test how tw types of marine protected areas affect species composition of marine seagrasses, and now struggle how to properly deal with the nested nature of our data.

Our design is a mixed model nested ANOVA (following the terminology in Quinn and Keough 2002), with three factors:

1) Management - fixed factor with three levels (open, closure and park)

2) Site: random factor with a total of 12 levels, nested within 'Management'. For each level of management there are 4 unique 'site' levels.

3) Transect; fixed factor with 3 levels (shallow, mid, reef) which is crossed with 'Management'.

Along each 'Transect' there's seagrass species-level shot count data from ca 10 stations (replicates). Sampling was done 1 time in each station, so there's no repeated measures.

We're trying to test the effects of 'Management', 'Transect' and their interaction on seagrass species composition using PERMANOVA as implemented in the adonis() routine in the vegan package for R. The standard code for a design with a blocked (crossed) random factor would be:

adonis(species ~ management * transect, strata = env$site, data = d)

However, in our case the random factor is nested under the main factor - not crossed with it. As I understand it is possible to constrain the permutations using the 'permutations = how' argument, and then specify a custom permutation design. See, for example, here:

But I've never worked with customized permutation designs before and struggle to find tutorials, so would really appreciate any form of feedback.

Anyone can provide some advise?

I've also looked into the nested.npmanova() function in the BiodiversityR package. This can properly handle a design like ours with 2 factors (one main, one nested) - but we have 3 factors...

We're also open to instead using the mvabund() routine, i.e. a GLM- rather than distance-based framework, if it can help us properly deal with the nested nature of our random 'site' factor. But so far I've only found examples where it can be used to handle crossed random factors.

Preciso de saber quais são os estudos neste capítulo que demonstram os benefícios das artes na psicoterapia, especialmente na melhoria da saúde mental e emocional (Gonzalez, 2023) .e as respetivas referências.

We are monitoring Leatherback turtles in French Guiana since 25 years, on Cayenne beaches where a large rookery still nest. We are now at the end of the nesting season, and full season of hatchlings. We observed since 2 weeks something we did not observed before : hatchlings coming out from the sand, and dying after some centimeters on the beach. But extremely brutal death, as "freezed", and could involved 15-20 animals all dying simultaneously.

We first though about heat (that is higher and higher, as everywhere) but the last records were at dusk, and the Temperature was not so high.

Any hypothesis ? We could sample, make some analysis, necropsies, but looking for what ?

Thanks for your comments !

Regards,

Dear colleagues

I would like to know which Astigmata species I found in left cormorant nests.

Could somebody help me with the identification?

Dear Colleagues,

Im looking for a solution to fitting multivariate multiple regression with mixed effects in R.

In my case I have multiple dependent and independent variables with a hierarchical structure: samples are nested inside treatment groups A/B, and treatments are nested in sites.

As far as I know the lmer and glmmTMB packages works well with mixed effects, but dont accept multiple response variables.

I also interested in Bayesian modeling, but it seems that bnsp or rstanarm packages cannot do both.

Can you recommend a solution to this?

I am inspired by this paper (see the excert below)

REVIEW

published: 31 October 2019

doi: 10.3389/fnhum.2019.00378

Edited by:

Felix Blankenburg,

Freie Universität Berlin, Germany

Reviewed by:

Timothy Joseph Lane,

Taipei Medical University, Taiwan

Jakub Limanowski,

University College London,

United Kingdom

*Correspondence:

Tam Hunt

Specialty section:

This article was submitted to

Cognitive Neuroscience,

a section of the journal

Frontiers in Human Neuroscience

Received: 08 January 2019

Accepted: 07 October 2019

Published: 31 October 2019

Citation:

Dehaene (2014) states the problem clearly (p. 211): “[C]ould any brain image ever prove or disprove the existence of a mind?” He answers this question in the affirmative, with various discussions about the neural correlates of consciousness and “signatures of consciousness” (what he considers to be the necessary and sufficient correlates of consciousness), but also recognizes that (p. 214) “no single test will ever prove, once and for all, whether consciousness is present.” He instead recommends a battery of tests be developed to give more confidence about the presence of consciousness in various contexts, focused on human subjects.

Testing the framework presented here should focus initially on the three conjectures in our Table 1. This approach follows the Lakatosian research program (Lakatos, 1968) that focuses on testing the “hard core” principles of any given theory. Conjectures 1–3 in Table 1 are the core of General Resonance Theory. There are many ways that various MCC may be measured to test conjectures 1–3 and we are fleshing out these ideas in other work.

Here again are the three primary conjectures of General Resonance Theory:

Conjecture 1: Shared resonance is what leads to the combination of micro-conscious entities into macro-conscious entities (“the shared resonance conjecture”).

Conjecture 2: The boundaries of a macro-conscious entity depend on the velocity and frequency of the resonance chains connecting its constituents (“the boundary conjecture”).

Conjecture 3: Any biological macro-conscious entity will have various levels of subsidiary/nested micro- and macro-conscious entities (“the nested consciousness conjecture”).

I have read about the topic and I was wondering if anybody could help me to interpret the following description of repeated measurements I found in a manuscript (very detailed but still confuse in my point of view):

"Year, location, rotation type, late-season input treatment, cultivar MG nested within the location, cultivar nested within MG and location, and their interactions were considered as fixed factors in the model. To simplify field operations of cover crop planting and termination, the rotation type was randomized within two sections in the field (lots) and each lot was then split into two blocks. Lot, which was nested within year and location, and block, which was nested within lot, and their interactions with other fixed effects were considered as random factors. The effect of rotation type and late-season input treatment on the variables measured was analyzed by generating the least significant differences for the highest level interaction with these fixed effects that was significant at P < 0.05".

I would appreciate your help/thoughts on it

Thank you!

LM

While conducting SEM analysis, if in the model there exists one variable which is a moderator and that is nested, would this necessarily require multilevel analysis? Are there any strategies/possibilities to opt out of the multi level analysis (to not deal with certain statistical complications)? Any citations would be helpful. Thanks in advance!

Over the last few years, I have observed some species apparently "disoriented" by the very hot and dry autumns in central Italy. In particular golden eagles in courtship or very territorial flights and corvids species bringing twigs to used nests (or new nesting sites). Do you have any studies you can tell me about similar behaviors? The causes? Some believe it is due to "false estrus", or the hormonal response that autumn temperatures similar to spring temperatures could cause. What do you think about this?

Dear WRF-Chem users,

I am interested in acquiring knowledge regarding the use of the restart functionality inside the Weather Research and Forecasting (WRF) model. There are three domains in a nested structure, with sizes of 9, 3, and 1 km.

The whole duration of the run was allocated to a period of 19 days, with the initial 14 days designated for spin-up.

Could you please provide instructions on how to utilize the restart option? Furthermore, while checking the user guide, I discovered that it was inadequate in providing clear instructions to follow.

Could you kindly provide me with guidance in this matter?

Sincere regards,

Nowadays, and with the global spread of English, non-native English-speaking teachers (NNESTs) do outnumber native English-speaking teachers (NESTs). However, NESTs are the most preferred type of teachers by policymakers, schools, students, and parents. Please feel free to share your thoughts on this issue here. Thank you.

Nowadays, non-native English-speaking teachers (NNESTs) do outnumber native English-speaking teachers (NESTs), but the latter are still the most preferred type of teachers by the majority of policymakers, schools, students, and parents.

Are there international projects where you can research birds and share data, create joint articles? For example, it concerns phenology, bird nesting which is inhabited by artificial nests box or hollows?

What is the nesting area of small carpenter bee (Ceratina)? In wood, like large carpenter bee?

And what is the nesting material? soil?

Thanks!

I'm wondering if you should differ between presence data of highly mobile species such as Raptors and immobile species (e.g. Plants). The dispersal of plants is limited to a certain distance, so the occurance might be clustered because of that. Birds on the other hand should be able to search for suitable nesting sites. If nesting sites are close together, could that be an indicator for great suitability?

Thanks, Tim

Hey, just like Book et al. (2015) did in their article ( ) I would like to compare two models of canonical correlations, whilst one is nested in the other. Just like Book et al. I would like to see, if the addition of some variables maximize the explained variance (1-Wilks Lambda = R²).

I'll conduct my analysis in R.

Greetings

Hello everyone,

I am facing a problem that I cannot solve at the moment. I want to calculate a population size (small mammals) and my choice fell on the Jolly-Seber model. Everything is clear as far as the calculation is concerned. My problem at the moment is the data. The season is from April to October. During this time, traps are set and nest boxes are checked at undefined intervals (but the nest boxes are always checked monthly). The animals are tagged and released. My data range from 2019 to 2022.

The problem at the moment is the data base. Do I calculate the population size from survey to survey, e.g. April 2019 to May 2019, then from May to June, from June to July and so on, or can I calculate from year to year? Then I could look at how many individuals were captured and tagged in 2019, how many of those individuals were recaptured in 2020, how many were added in 2020 and so on until 2022.

Then I could theoretically calculate the population size from year to year or based on 2019 (which individuals from 2019 were also captured in 2022?).

My question is whether this calculation is possible from year to year or whether I have to calculate from session to session within a season? Between the seasons is winter, during which the animals hibernate. An individual caught in 2019 could be caught again in 2022!

Calculating from year to year seems to make more sense to me and is much more bearable, especially when preparing the data - although this plays a subordinate role.

Thanks in advance.

Cheers

Dear RG community members,

I hope you are well and helthy and ready for small discussion. My question is, can we efficiently increase the population of wetfowls in wetland areas by constracting and using artificial nests suitable for specific taxa? If you also have any reference on that issue, I would be grateful.

Thank you.

Zlatko

Hi everyone,

What is the best method for converting nested data stored in a .mat file (created in MATLAB) to a format that can be read by R? (e.g., csv)

Thanks :)

Hello,

For a paper we need to resubmit soon we are asked to perform nested cv instead of cross-validation as we already have. The analyses for continuous outcomes were done in caret with PLS and bagged-CART notably, which to my knowledge are not available with mlr. I would need a small sample of a script that performs nested cv with caret, or the link to resources explaining how to do this. Thanks in advance for finding the time to answer this !

Cheers,

Eric

Hi all,

How would you recommend to analyse a longitudinal experiment? In my case, I have a between-participants factor (participants either receive an intervention or not) and the outcomes are measured immediately, a month later, and three months later. The data is nested, so measures are nested within participants, and participants are nested within institutions. I also have a few mediators and moderators I'd need to take into account. I would think a multi-level structural equation model could analyse the data or am I forgetting something?

Best wishes,

Lukas

I'm making a nested, high resolution simulation (~300m*300m) of WRF in the polar region. The existing static sea ice data is of low resolution and I want to update this static data with a satellite based high resolution data. I need to update the static field only for the inner domain.

How can I do it?

What are the best tools for the same?

Anything in particular I should be careful about?

Thank you in advance.

Hi all,

I am doing an CFA analysis of categorical data with the WRMR estimation method in Mplus 7. I want to compare nested models and I was wondering whether there is an online tool which is doing a chi2 difference test for me based on this data/estimation method. As far as I can see most tools just provide this on the basis of maximum likelihood estimations. Any suggestions?

Thank in advance,

Gert

I have 3 different PCR methods to compare,

all are different compositions and different volumes.

is it okay to unify the concentrations or amount of primers and template for the comparison?

2 PCR methods are nested and the other is qPCR trying to find the sensitivity between them.

I am needing to do a nested two way ANOVA with three fixed factors (Time (5 levels) and Site (4 levels ) nested within Condition( 2 levels), followed by a pairwise comparison post hoc test to identify interactions. I need to use SPSS as this is the software I have used throughout my thesis. So far I have been able to gain outputs for the nested ANOVA but I can’t figure out how to gain a post hoc test output. I use syntax to code but when I add code for post hoc it gives a warning saying it can’t recognise my fixed factors. Attached images below of codes I have tried. Any help would be appreciated!

Good morning Dear all

Please, can you provide me some reference where I can find a relationship between great apes' age (juveniles, adults mature,) or body size with their nest diameter. I have collected nests diameter of great apes in my study area and I would like to sort them in classes of diameter according to the age of the animal that constructed the nest.

Thank you in advance and good day

Does anyone know of papers or data discussing the historical connection between the Syr Darya and the Tarim rivers? Loaches found in these two rivers' basins are nested on the same clade of the phylogenetic tree when studied molecularly. We speculate whether these two basins were previously connected.

Many thanks in advance!

Dear experts,

I am working on a very large scale data (20k). the research is on data collected from different schools from different district and different states in India. How to give nested data command in AMOS. I want to CFA in AMOS and want to include effect of nested data for the model fit. please guide me. I am not able to find any videos or lectures that provide hands-on training on this topic.

I had a set of data with factor A (a), factor B nested in A (b), and replication (r) in each unit. Recommendations have explicitly told the details of how to deal with data with equal variance in R, from nested ANOVA test to corrected multiple comparison (e.g. lme/lmer, glht/lsmeans). But these processes require the homoskedastic data, otherwise the violated assumption about variance may brought about poor inference efficiency.

However, similar recommendations on heteroskedastic data are limited. One of them is the Permutation ANOVA, which calculate the similarity of centroids and dispersion of groups. But directly using welch's anova for data of different groups (e.g., x1=data$y[data$y=='A1'], x2=data$y[data$y=='A2'], t.test(x1,x2,var.equal=TURE) -> p, following a bonferroni corrected p<p^{H_0}/m calculation). Is the plausible process correct?

Another method is the log transformation, which could used to compare means directedly, but only for some data. The inverse standard deviation of the error term for weighted least square was also introduced, but could this weighted data applicable for direct t.test (bonferron or tukey corrected?).

I investigated the extent and prevalence of physical activity during the school day and sedentary behavior during inactive screen time in youth and their effect on overweight and obesity. Participants were stratified by age (children - adolescents) and gender. Data were compared using t-test and ANOVA and the effect on overweight and obesity was studied using MLR. Are my data considered nested? If so, how do I calculate the ICC requested by the reviewer?

I have a multi-stage stochastic programming model. I have 3 groups of variables: the first group takes values ​​at the beginning of the planning horizon before the first realization and does not change until the end of the planning horizon and has no t index (they are binary and continuous), the second option is “here and now” variables that before each realization Are taken value and are continuous, the third group are “wait and see” variables that take value after each realization (binary and continuous). The model is SMINLP. I converted it to SMILP through linearization and solved it by CPLEX solver with generating a small number of scenarios ... I want to consider a continuous distribution for the stochastic parameter and generate a large number of scenarios by sampling and run an algorithm for it. nested benders decomposition or progressive hedging algorithms are more efficient for this model?

If anyone has experience, thank you in advance for your help.

Dear colleagues,

we conducted an experiment with a dependent sample and two conditions (treatment A and treatment B), with a crossover design to control for sequential effects. Participants (level 1; n = 34) are nested within different therapists (level 2; n = 8). The therapists conducted both treatment A and treatment B. I was wandering, if multilevel analysis is a suitable alternative to a paired t-test in this case? Unfortunately, the design is unbalanced as each therapist conducted a different amount of treatments. Thank you!

Ants in the genus Aphaenogaster is referred to as the "funnel ants" due to their conical-shaped nest structure. Some species serve as model organisms for studying of foraging pattern and tool-using in the world of insects. But the under-ground behaviors of these ants with impact on soil processes appeared to have seldom been investigated. Does anyone has knowledge about any aspects of nest-construction behaviors by Aphaenogaster ants please?

Thank you for checking this post out!

I'll use the following example to discuss the challenge I'm facing:

My logistic-exposure model asks whether a study species' nest success (1/0) can be explained by the density of the overstory (a proportion) around each nest, and the distance (meters, continuous) from the nest to the edge of the habitat patch, i.e.:

NestSuccess ~ OverstoryDen + DisToEdge

The independent variables are scaled - mean subtracted, divided by standard deviation, using R function scale().

The model output gives me:

OverstoryDen estimate = 2.91

DisToEdge estimate = 0.87

I am interested in interpreting the output in real, useful terms, but I'm not sure I'm getting this right. This is what I've done:

Let's look at OverstoryDen first: Odds Ratio = e^2.91 = 18.36

So, this implies that the probability of a nest succeeding is 18.36 greater in areas where overstory density if one standard deviation greater, correct?

Now, here's the part that stumps me: The standard deviation of OverstoryDen is 0.146, and, recall, this parameter is a proportion, i.e. 0 - 1. So, can I say anything more general/relevant about the relationship between nest success and OverstoryDen? i.e. would it be prudent to divide 18.36 by 0.146 and say that for every 1% increase in density the probability of success increases by a factor of 2.26? Or is there a linearity issue here?

Similarly, for DisToEdge, the odds ratio = 2.39, sd of the variable = 14 meters, so would it be prudent to divide per meter and say that success increases by 17.1% with each added meter of distance?

Thanks so much for any help you can offer!

I have been getting a band in the second (nested) amplification of the negative from the initial PCR, but not in my negative control testing for contamination. Any ideas?

Information:

I am using a nested PCR for different Ehrlichia species in whole blood.

The initial PCR is an Ehrlichia screen using one set of primers. For 20 cycles.

The nested PCR's have specific primers based on the Ehrlichia species. For 30 cycles.

The initial negative controls have RNases free water, Taq polymerase, and the Ehrlichia screen primers.

When I run the nested, I take 1 uL from the initial negative (which is clean on the gel).

I've recently changed out my reagents, and the band is around 500 bp so not primer dimers.

Could be something specific in the initial primers but I only see it in the samples that should be negative and it's a pretty big piece to be related to the primers.

Thank you

Researchers in the social sciences have to report some measure of reliability. Standard statistics packages provide functions to calculate (Cronbach's) Alpha or procedures to estimate (MacDonalds) Omega in straightforward way. However, things become a bit more complicated when your data have a nested structure. For instance, in experience sampling research (ESM) researchers usually have self-reports or observations nested in persons. In this case, Geldhof et al. (2014) suggest that reliability be estimated for each level of analysis separately. Albeit this is easy to do with commerical packages like MPlus, R users face some challenges. To the best of my knowledge most multilevel packages in R do not provide a function to estimate reliability at the within vs. the between person level of analysis (e.g., misty or multilevel).

So far, I have been using a tool created by Francis Huang (2016) which works fine for Alpha. However, more and more researchers prefer (MacDonalds) Omega instead (e.g., Hayes & Coutts, 2020).

After working with workarounds for years I accidentially found that the R package semTools provides a function to estimate multilevel Alpha, different variants of Omega, and average variance extracted for multilevel data. I would like to use this post to share this with anyone struggling with estimation of multilevel reliability in R.

I find this post helpful, feel free to let me know.

Oliver

Bliese, P. (o. J.). multilevel: Multilevel Functions. Comprehensive R Archive Network (CRAN). [Computer software]. https://CRAN.R-project.org/package=multilevel

Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91. https://doi.org/10.1037/a0032138

Huang, F. L. (2016). Conducting multilevel confirmatory factor analysis using R. http://faculty.missouri.edu/huangf/data/mcfa/MCFAinRHUANG.pdf

Hayes, A. F., & Coutts, J. J. (2020). Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…. Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629

Yanagida, T. (2020). misty: Miscellaneous Functions „T. Yanagida“ (0.3.2) [Computer software]. https://CRAN.R-project.org/package=misty

Dear colleagues,

I'm experiencing difficulties with running a mixed model in SPSS (version 26) so I'm reaching out to the ResearchGate community.

My experiment is a 2x2 factorial design in which we provide light and/or larvae to chickens. I have individual (continuous) measurements from behavior tests as outcome parameters. But since the chickens are housed in groups (pens), and the light and larvae are provided on pen level, these individual measurements within a pen are not independent. Therefore I need to run a linear mixed model with light, larvae and their interaction as fixed effects and pen as random effect.

However, SPSS gives me the following warning:

"The final Hessian matrix is not positive definite although all convergence criteria are satisfied. the MIXED procedure continues despite this warning. Validity of subsequent results cannot be ascertained."

It probably says so because the pens are linked to the light/larvae treatment. But it should be possible to run this analysis in some way, right? Can anyone help me find out what I'm doing wrong?

Thank you so much!

Does vegetation structure (e.g. tree raminification pattern) affect bird nesting activity and nest abundance? Could you please recommend studies related to this topic? Thanks

Hello everyone,

I have a question regarding a statistic test.

I will give you the background first.

I am testing different characteristics of microglial cells. I am testing the difference between WT and KO mice. I have a sample of n=6. In each row, each genotype represents a mouse. Each value represents 1 cell.

If it is important - parameters from each mouse were taken from 4 fields (both hemispheres were imaged from 2 slices = 4 fields).

In the added table, I used nested t-test, as a representative from Prism suggested.

Do you think nested t-test is the best test for my DATA?

Is it legitimate to use this test in this experiment?

Ela.

Hello all,

I'd greatly appreciate any help to clear my confusion about two-way crossed and nested design!

I am counting the abundance of each common genera of microalgae from the mangroves and tidal flats of 2 different sites, 1 sandy and oligotrophic, and the other muddy and eutrophic. The data that I have collected look like this:

1. muddy site - mangrove (n=6)

2. muddy site - tidal flat (n=8)

3. sandy site - mangrove (n=6)

4. sandy site - tidal flat (n=8)

And each set of data is a genus-abundance matrix.

I have always thought that my design is two-way crossed, but was just made aware that it could be a nested design, since the data obtained from say, muddy-mangrove is dependent on it being in the muddy site...

Is this a nested design, afterall? I read that sample sizes must be equal for a nested ANOVA, is that also a requirement for ANOSIM?

Thanks a lot in advance!

In gsem of STATA we can test random-intercept and random-slope models (multilevel) (see example38g in the manual). STATA MULTILEVEL MIXED-EFFECTS "me" deals with multilevel mixed-models, in particular MIXED for continuous outcomes.

I asked myself: Do I get the same results if I use gsem or MIXED? For the moment my answer is yes and no.....

In MIXED we have several options: we can use ML or REML estimation method; we can define different residual variance structures,....

I ran a gsem 2-level random-intercept model (id defines level 2 and session_coded defines level 1 nested within level 2) using own data

gsem (rd <- mpa_level i.session_coded i.order M1[id])

I found out that I get exactly the same results with the following mixed model

mixed rd c.mpa_level i.session_coded i.order ||id:, ml cformat(%9.4f)

However, using reml is prefarable; furthermore, an heterogenous residual variance better fits the specific data rather than the default. So the "best" MIXED model I would use is

mixed rd i.session_coded c.mpa_level i.order ||id:, reml residuals(ind, by(session_coded)) cformat(%9.4f)

With this model, the results are quite different.

Why am I asking this question? Because in a second stage I would like to run multilevel-mediation analyses using gsem but ideally I would like to keep the level of "sophistication" that I have with MIXED (reml, residual variance, etc.).

Best regards,

Can someone share their syntax for how to set the nested fixed effect in SPSS for the repeated measure MANOVA?

Hello,

I would like to measure provisioning rates in Eurasian Blue tits. My plan is to color ring them, so that I can distinguish between male and female.

In my system they breed in nest boxes, however I do not have the possibility to place a camera within a nest box. PIT tags are also not an option for me. Visual surveys can have problems because If the bird does not land on the nest box hole, then it would be impossible to know if it was a male or female.

I need a camera that could film at least 6h (with batteries). I plan to film at least 30 nests and to move the cameras around, so I may need about 12 cameras.

Of course I have a quality vs quantity vs cost trade off.

Somebody suggested using a security camera, but the problem is that there is no screen to see what you are filming and no WIFI for me to connect the camera to my phone in the field.

Others have suggested camcorders, but their models are no longer being sold and they can get pretty expensive.

Any technical suggestions will be super helpful.

Sincerely,

Chase

After the estimation of nested logit using mlogit in R, I need to estimate the marginal effect or elasticity for each alternative levels of the covariates against the reference. Pls I would appreciate if anyone can help with the code.

Hi researchers,

Is it possible to perform nested GLM models or nested ANOVA of GLM models with >2 categorical variables in R, or does it make sense?

Thank you

Hey!

I am looking at the finding out if there is a significant difference in the number of predation events by dogs, foxes, and crab for two different turtle species (G+L), across three locations (W,A,N) in R.

the question I am trying to answer is what are the main predators for each turtle species , whether it varies across location, and whether greens or loggers are predated more (My prediction is that species L are predated more because they lay shallower nests, and that location W Suffers the most predation, and that foxes are the most prominent)

Im struggling to find what statistical test to use, and how to set up the data. Do I do individual tests for each and compare those results, or is there one statistical that can do all?

many thanks!

When we are working with two-level control say the primary and secondary level of the grid, I have four PID controllers, two at the primary section and two at the secondary section. Now, for tuning purposes what should be the order, and how we can tune each controller co-officiants?

I am having difficulty understanding random stratified sampling when there are nested categories within them. Take for instance a size of 1000, and you are interested in sampling gender and smoking. Lets assum there are 500 females, so does that mean you create a first gender stratum with 500 females and 500 males. Subseqeuntly you randomly select participants from each gender category, and then create a second strata within each gender categories for smoker vs. and non-smoker. Then you select a random sample from each of these cateogory. Is that how stratified random sampling works? Because that sounds like multi-stage sampling to me? Please help I'm trying to visualise how it all works.

Can I do Multilevel nested model analysis on SPSS software when I don't have continuous variables?

Mammals ravage the nests of artificial nesting birds. There are probably modern methods that display data from burglary attempts, etc.

Hi,

I am trying to run WRF with three nested domains of resolutions 18km, 6km and 2km.

I need the extent of the outermost domain to be 80 degrees longitudinally, ie from east to west, and 55 degrees latitudinally.

Are there any constraints to be kept in mind while deciding the extents of the inner domains. For eg, should the extent be some ratio of the extent of the parent domain? For my case, since the domain ratio is 3, should my second domain have a longitudinal extent of at least 27 degrees?

Is there any constrain like that, or can we choose the sizes, ie, extents of our domains as per the location of our area of interest that we plan to study?

Are there any other things/ constraints/ rules we need to keep in mind while deciding the domain configuration?

Kindly help me out with these queries.

Thankyou!

Many software that handles nested logit regression such as R(mlogit) , stata (nlogit), python (pylogit,biogeme) with the exception of Gauss does not have the option of marginal effect as a post estimation test. However, the marginal effect for similar model such as multinominal logit, ordered logit etc can be executed using margin in R and stata and statsmodel in python. Does this really implies that marginal effect is not a relevant post estimation test for nested logit and if this is, I would love to have suggestion on the preferred pre - estimation and post estimation test for nested logit with references and preferred software of estimation.

Dear scholars, can anyone recommend a python package that estimates nested logit together with the marginal effects of each covariates. I have been trying pylogit but I don't seem to see the function for nested logit marginal effects. Any another free and easy software is also welcomed.

I have three patients and three controls and I need to calculate the number of biological experiments I must perform. Thank you.

Hi everyone! I have a statistical problem that is puzzling me. I have a very nested paradigm and I don't know exactly what analysis to employ to test my hypothesis. Here's the situation.

I have three experiments differing in one slight change (Exp 1, Exp 2, and Exp 3). Each subject could only participate in one experiment. Each experiment involves 3 lists of within-subjects trials (List A, B, and C), namely, the participants assigned to Exp 1 were presented with all the three lists. Subsequently, each list presented three subsets of within-subjects trials (let's call these subsets LEVEL, being I, II, and III).

The dependent variable is the response time (RT) and, strangely enough, is normally distributed (Kolmogorov–Smirnov test's p = .26).

My hypothesis is that no matter the experiment and the list, the effect of this last within-subjects variable (i.e., LEVEL) is significant. In the terms of the attached image, the effect of the LEVEL (I-II-III) is significant net of the effect of the Experiment and Lists.

Crucial info:

- the trials are made of the exact same stimuli with just a subtle variation among the LEVELS I, II, and III; therefore, they are comparable in terms of length, quality, and every other aspect.

- the lists are made to avoid that the same subject could be presented with the same trial in two different forms.

The main problem is that it is not clear to me how to conceptualize the LIST variable, in that it is on the one hand a between-subjects variable (different subjects are presented with different lists), but on the other hand, it is a within-subject variable, in that subjects from different experiments are presented with the same list.

For the moment, here's the solutions I've tried:

1 - Generalized Linear Mixed Model (GLMM). EXP, LIST, and LEVEL as fixed effect; and participants as a random effect. In this case, the problem is that the estimated covariance matrix of the random effects (G matrix) is not positive definite. I hypothesize that this happens because the GLMM model expects every subject to go through all the experiments and lists to be effective. Unfortunately, this is not the case, due to the nested design.

2 – Generalized Linear Model (GLM). Same family of model, but without the random effect of the participants’ variability. In this case, the analysis runs smoothly, but I have some doubts on the interpretation of the p values of the fixed effects, which appear to be massively skewed: EXP p = 1, LIST p = 1, LEVEL p < .0001. I’m a newbie in these models, so I don’t know whether this could be a normal circumstance. Is that the case?

3 – Three-way mixed ANOVA with EXP and LIST as between-subjects factors, and LEVEL as the within-subjects variable with three levels (I, II, and III). Also in this case, the analysis runs smoothly. Nevertheless, together with a good effect of the LEVEL variable (F= 15.07, p < .001, η² = .04), I also found an effect of the LIST (F= 3.87, p = .022, η² = .02) and no interaction LEVEL x LIST (p = .17).

The result seems satisfying to me, but is this analysis solid enough to claim that the effect of the LEVEL is by no means affected by the effect of the LIST?

Ideally, I would have preferred a covariation perspective (such as ANCOVA or MANCOVA), in which the test allows an assessment of the main effect of the between-subjects variables net of the effects of the covariates. Nevertheless, in my case the classic (M)ANCOVA variables pattern is reversed: “my covariates” are categorical and between-subjects (i.e., EXP and LIST), so I cannot use them as covariates; and my factor is in fact a within-subject one.

To sum up, my final questions are:

- Is the three-way mixed ANOVA good enough to claim what I need to claim?

- Is there a way to use categorical between-subjects variables as “covariates”? Perhaps moderation analysis with a not-significant role of the moderator(s)?

- do you propose any other better ways to analyze this paradigm?

I hope I have been clear enough, but I remain at your total disposal for any clarification.

Best,

Alessandro

P.S.: I've run a nested repeated measures ANOVA, wherein LIST is nested within EXP and LEVEL remain as the within-subjects variable. The results are similar, but the between-subjects nested effect LIST within EXP is significant (p = .007 η² = .06). Yet, the question on whether I can claim what I need to claim remains.

We are working with white-winged snowfinches, alpine bird species breeding in rock crevices, roofs and skilift pylons and struggle to access nests in some of the deeper cavities (50 cm to 1 m). We are using an endoscope, but it is often difficult to access nests in deep cavities when they are really contorted (as we dont know the internal structure of the cavities).

We are mainly interested in counting the number of chicks, but also to place ibuttons for temperature measurements if someone has an idea how to place (and retreat!) them.

I am interested to know which methods people working on similar cavity-breeding birds (preferably rock crevices, as we are facing unique problems ina ccessability and cavity structure as f.i. woodpeckers will not have) use to gather nest information (if at all?).

Thank you very much in advance, Christian

Hi,

I have problem to choose i_parent_start and j_parent_start values in order to get the most inner domain to surround a given area/zone with wanted dimension (e.g: 10km*10km).

I want to compare collected data and that simulated using WRF.

Anderson, de Palma & Thisse have demonstrated the equivalence between a demand function derived from a CES direct utility function and a discrete-continuous logit model. I am pretty sure I have once seen that this can be extended to the nested extensions of CES utility functions and logit models. But I have lost track of the reference and I somehow do not find it on the web. Any hints?

It has long been recognised that bumble bees most often nest in abandoned mammal (usually rodent) nests, and I believe various researchers have tested the effects of mammal scent in attracting queens to field hives, but I'm not aware of any positive results. However I have not been keeping up with literature for a while and may have missed it.

Do you know of results confirming the effect of mammalian odours?

To ask my questions, I need to set up a hypothetical experimental design.

Let's say that I am interested in participant evaluations of art made by two different people (famous artist A and unknown artist B)...each artist has painted 10 paintings in the same 10 different colors/styles (e.g. one blue sad painting, one red angry painting, one yellow happy painting, etc). Thus, I now have 10 pairs of incredibly similar paintings, half from each artist.

Under the assumption that participants can't tell which paintings came from which artist (pretested), I am now curious as to whether or not using the name of each artist as a label will influence participant evaluations of skill on a 1-7 Likert scale. Thus, half my participants see the true labels and half see false (totally reversed) labels.

Here are my questions:

What kind of multi-level model (if any) would be most appropriate in this setting? Nesting within individual? Nesting within pairing (e.g. red pair, blue pair, green pair)?

Please correct me if I am wrong as I am very new to multi-level analysis. So far, it seems like the answer is to try both types of nesting using a random intercept and randoms slope?

Hello,

(I hope i made sense and thank you so much for your help. I am an undergrad student and a beginner and it would really help with clarity.)

I am working on a correlational meta-analysis paper. One of the variables "Empathy" is organised in three subgroups and the other variables has outcomes that are more than one i.e. effect sizes from the subscale elements correlation with the subgroup of empathy.

So, if empathy has three subgroups - x , y and z and the second variable, say measures professional competency using a scale that has dimensions like, working speed, resilience and if i have effect sizes from all of these groups like, x with working speed, x with resilience and so on. Would that make a multi-level study?

Hi, I have been trying to run the WRF model for 24 hours over a month (September 2018). I am using three two way nested domains (9km, 3km and 1km) with 63 explicitly defined vertical levels over a part of peninsular India. My timestep is 30. I am getting cfl errors only on some days, while for others the model run is completed without any errors. I have used the same domains and other runtime options for the month of January, 2019 as well and had no issues there. What might be the issue here and how can I attempt to resolve it? Please help me out.

Not exactly sure what happened but this error appears every time I try to do a GAM with random effects (bs="re", with mgcv package). This is strange since appears not only to new models but even to models that previously worked (multiple times). I made sure the data has no NA's, scientific data, or random formulas. Also, I am not using the date format to avoid errors has previously worked as it is.

I also tried to transform the data into a data frame via as.data.frame(x) but the same error occurred.

I have been playing a bit with the formula and it appears that every time the random effects bs="re" are present, either the 2 of them (Site, State) or only one of them (Site), it is when the error occurs. If I take them completely out of the formula it works perfectly.

```

```

Total_Items = Number of items of debris found per event;

DayI0 = Number of days since first clean up (numeric);

Site = Site of sampling (Sites are within States);

State = State of sampling;

EffortDayC = Effort(Length of the beach, number of volunteers, duration of sampling)*DayC(interval of sampling);

See the str (data) below.

Hi,

I am trying to set up WRF model and I would like to specify explicit eta levels. My study will primarily be focused on storms and the boundary layer, so ideally, I would like to have very high resolution upto 3 km altitude. I am using three domains of 9km, 3km and 1km resolutions, with two way nesting. I am unaware of any thumb rules we need to follow or factors we need to keep in mind while deciding the vertical levels. Can you please educate me about them and help me out?

Also, I was told that, in case we don't want to specify eta levels, the number of vertical levels needs to be

Height of model top/ (0.1*Resolution of innermost domain)

Can you please help me understand why this is the case?

My model top will be around 20km (50 hPa), so if I go by this rule, I will end up using 200 vertical levels. Please help me understand why it is necessary to have these many levels.

Thankyou.

I am exploring passive monitoring methods to determine nest box use. The design will combine regular physical visits to each nest box, a subset of boxes with camera surveillance, but the question is what to do with the others.

I have considered using a simple temperature logger (e.g., ibutton) to record changes in temperatures within boxes. However, this is complicated by the likelihood each box will have its own ambient temperature profile. This would have to be determined for each box and over seasons to allow calibration and be able to differentiate the presence/absence of a homeotherms using the box (the focus of the study). This is further complicated due to some of the species may enter torpor while in the box (resulting in a false negative).

I would be grateful for any suggestions. Any device/method will need to be simple, cheap, and low maintenance.

Hello all-

I am trying to only detect (meaning no relative expression yet) a certain marker via taqman probe in DNA from old FFPE samples. I have tried to "increase my signal" by performing a nested PCR by running the profile without the probe and then purifying the PCR product (column based-Qiagen). I then went on to the secondary PCR using the Taqman probe. My detection was clean but low (high Ct values). The nested workflow resulted in pretty much the same Ct values as a single run (non-nested). I fear I am losing anything I am gaining during the purification process.

Is it absolutely required to purify between primary and secondary PCR's when doing a nested process?

Thanks!

Andrew

Fellow Researchers,

I am currently running an experiment where primary cell lines derived from 4 patients are being cultured in 2 different conditions. I am examining expression levels of several genes in 3 technical replicates in both conditions and trying to find out whether change in culturing method has a signifcant impact on them considering all cell lines.

I am using Nested t-test for my statistical analyses, but having doubts whether or not it is the right choice since data from the same cell line in diffrent conditions is not paired. Would you suggest me to change the test or keep using it? What alternative would you suggest?

I've conducted an RCT in which I'm testing the effect of a group mindfulness intervention on depressive symptoms. Only one group was running at a time so there were four study waves, with each wave of participants being randomized to intervention or control. Outcomes were measured bi-weekly for 6 months. I'm testing the effect of intervention using PROC MIXED in SAS with bi-weekly assessments nested within participant identified in the repeated statement.

A reviewer has suggested that I include treatment wave as a random factor in the model. However, the interaction between treatment and study wave (as fixed effects) is not even close to significant (p = .99), suggesting that the effect of treatment is the same across waves. Is this sufficient justification to keep my analyses as they are and not include treatment wave as a random factor? Thanks!

Hi,

Is it possible in GENLINMIXED (SPSS version 25) to specify a model with two crossed random effects (not nested) for a binomial outcome variable? (It is possible with MIXED).

I can do this in STATA https://www.stata.com/manuals13/memelogit.pdf

but I would prefer using SPSS

PAtrick

I am trying to analyze a model on SPSS with:

- one within-subject factor with 2 levels.

- two between-subject factors: one categorical (2 levels) and another continuous.

- also, we are hypothesizing an interaction term between the two (the categorical and the continuous variables) between-subject factors.

I am having a hard time running the model on SPSS. I have found recommendations with similar problems asking to run a nested model (not sure how to run that on SPSS). However, I am not sure what model to run and how to run it on SPSS.

Hello!

I have a dataset of n=3000 nested within 8 countries with approximately 200 or 400 responses in each country. I originally planned to perform multilevel modelling with 4 dependent variables (DV) as fixed effects in SPSS.

The DV variables are responses in a scale of 1-100 and this kind of variables is treated as metric in psychology.

However, all my DV and the error terms are clearly skewed or clearly curtotic. My questions are:

1. I have read that in some cases the size of the dataset or the number of nesting groups allow to use the general linear model. Does it make sense, however, to do so if the dataset clearly shows extreme tendencies? It looks to me like clearly different distributions, but I am not sure how to define them. Should I regard them as continuous distributions?

2. Am I right to think that data transformation is not a good option if there is a different form of distribution?

3. What would be the advantages and disadvantages of bootstrapping or simulation?

4. What would be good reasons for using a generalized linear or a mixed model?

5. Would it be appropriate to perform a factor analysis of the four DV. If not, are there alternatives?

I would appreciate if someone can answer any of these questions or suggest some not very technical references !

HI all,

I have been working with daily diaries data ( 6 emotions of which 3 positive and 3 negative) were assessed for 30 days. These observations (1level data) are then nested within subjects (2nd level data) who are further nested in three groups (approx.50 in each group, 3rd level data).

What am I struggling with is finding out how to manage the time covariate in 1level data because the daily observations were further nested in odd and even days and then three groups of people toward who the participants were asked to indicate the feelings for.

In short,

on odd days a person was asked to indicate 6 feelings ( disgust, empathy, anger, sympathy, regret, respect) toward x1, s1, s2, s3, s4, s5, a1

on even days indicate the same six feelings toward x2, s6, s7, s8, s9, x3

As you can see, x represents one type of group of representatives, s second type and a1 stands alone.

My role is to find out if there is any difference in intraindividual variability of emotions between the 3 groups.

I will appreciate any tip how to manage all this complexity. Thanks so much!

I am using the NLME package to run regressions using lme(DV~IV).

I am including random intercept and random slope for two IVs, all of these by subject.

I have it currently written as lme(data = data, fixed = DV ~ X1 + X2, random = reStruct (~1+X1+X2 | subject).