Last updated: 2021-10-26
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 48b6256 | Jenny Sjaarda | 2021-10-26 | wflow_rename("analysis/update_meeting_28_10_2021.Rmd", "analysis/update_meeting_25_10_2021.Rmd") |
Wanted to assess impact of geography on couple trait correlation. Investigated this by using both genetic PCs and birth coordinates.
Tested the following:
Next, I calculated the correlation due to counfounding as cor(X,PC)^2*cor(PC_i,PC_p)
values, and plotted them against the raw cor(X_i,X_p)
values.
The table below gives the data in the plot above:
outcome_description
corresponds to the trait.trait_couple_corr
is the correlation of outcome_description
in couples.corr_due_to_confounding_all
corresponds to the formula: cor(X,PC)^2*cor(PC_i,PC_p)
, summed across all PCs.Performed the same analysis as above but replacing PCs with North and East birth co-ordinates (data field 129 and 130, respectively).
The table below gives the data in the plot above:
outcome_description
corresponds to the trait.trait_couple_corr
is the correlation of outcome_description
in couples.corr_due_to_confounding_all
corresponds to the formula: cor(X,PC)^2*cor(PC_i,PC_p)
, summed across both coorindates.We performed two analyses to compare \(\rho\), \(\gamma\) and \(\omega\):
Version | Author | Date |
---|---|---|
529020f | jennysjaarda | 2021-09-24 |
Broad overview of results are shown in the figure below.
Going forward, we will just use the adjusted results.
In general, \(\rho\) is significantly larger that \(\gamma\), meaning that \(X_i \rightarrow Y_p\) favors paths where assortative mating is through \(X\) rather than \(Y\). This makes a lot of biological and intuitive sense. In other words, exposures are passed from index to partner, rather than outcomes.
A summary of the linear model of \(\rho\) vs \(\gamma\), forced through the intercept, is below.
Call:
lm(formula = y ~ x + 0, data = fig_data)
Residuals:
Min 1Q Median 3Q Max
-0.38531 -0.02515 0.00292 0.03829 0.54599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
x 0.60512 0.01442 41.96 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07834 on 987 degrees of freedom
Multiple R-squared: 0.6408, Adjusted R-squared: 0.6404
F-statistic: 1761 on 1 and 987 DF, p-value: < 0.00000000000000022
The corresponding figure is below. The blue line includes only BF-significant \(\rho\) and \(\gamma\), where the green line includes all points (analagous to the linear model above).
A few observations:
Call:
lm(formula = y ~ x + 0, data = fig_data)
Residuals:
Min 1Q Median 3Q Max
-0.65813 -0.03940 -0.00337 0.03210 1.99081
Coefficients:
Estimate Std. Error t value Pr(>|t|)
x 0.64639 0.01175 55.03 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1064 on 987 degrees of freedom
Multiple R-squared: 0.7542, Adjusted R-squared: 0.754
F-statistic: 3029 on 1 and 987 DF, p-value: < 0.00000000000000022
A summary of the cases where \(\omega\) is significantly larger or smaller than the sum of \(\rho\) and \(\gamma\) are shown in the two tables below (taking absolute values of each).
Question: What is the corresponding SEs on \(Y_{resid}\)?
sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
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locale:
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