Site and gender effect
Contents
Site and gender effect¶
Deformation Fields¶
Component #1¶
path_csv = file.path(getwd(), "..", "..", "..", "resources", "compressed_images.csv")
data = read.csv(path_csv)
lm_df_comp1 = lm(df_comp_1~age + I(age^2) + site + gender + site*gender, data=data)
summary(lm_df_comp1)
Call:
lm(formula = df_comp_1 ~ age + I(age^2) + site + gender + site *
gender, data = data)
Residuals:
Min 1Q Median 3Q Max
-1.6190 -0.3985 -0.0967 0.2983 4.1348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0766027 0.2534632 0.302 0.7626
age -0.0520514 0.0109098 -4.771 2.35e-06 ***
I(age^2) 0.0009953 0.0001099 9.054 < 2e-16 ***
siteHH -0.1566780 0.0873679 -1.793 0.0735 .
siteIOP -0.0265730 0.1416690 -0.188 0.8513
gender -0.3361001 0.0727215 -4.622 4.74e-06 ***
siteHH:gender 0.1816633 0.1196839 1.518 0.1296
siteIOP:gender 0.3807770 0.1778851 2.141 0.0327 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6362 on 552 degrees of freedom
Multiple R-squared: 0.6082, Adjusted R-squared: 0.6032
F-statistic: 122.4 on 7 and 552 DF, p-value: < 2.2e-16
Component #2¶
lm_df_comp2 = lm(df_comp_1~age + site + gender + site*gender, data=data)
summary(lm_df_comp2)
Call:
lm(formula = df_comp_1 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-1.5013 -0.4496 -0.0745 0.3316 4.4014
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.037535 0.105553 -19.303 < 2e-16 ***
age 0.045561 0.001787 25.490 < 2e-16 ***
siteHH -0.164925 0.093541 -1.763 0.07843 .
siteIOP -0.068357 0.151606 -0.451 0.65225
gender -0.377366 0.077711 -4.856 1.56e-06 ***
siteHH:gender 0.234504 0.127995 1.832 0.06747 .
siteIOP:gender 0.541277 0.189516 2.856 0.00445 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6812 on 553 degrees of freedom
Multiple R-squared: 0.55, Adjusted R-squared: 0.5451
F-statistic: 112.7 on 6 and 553 DF, p-value: < 2.2e-16
Grey Matter¶
Component #1¶
lm_gm_comp1 = lm(gm_comp_1~age + site + gender + site*gender, data=data)
summary(lm_gm_comp1)
Call:
lm(formula = gm_comp_1 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-1.61115 -0.46160 -0.02664 0.43518 2.16073
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.390200 0.099120 24.114 < 2e-16 ***
age -0.038385 0.001678 -22.869 < 2e-16 ***
siteHH -0.246503 0.087840 -2.806 0.00519 **
siteIOP 0.092864 0.142366 0.652 0.51449
gender -0.757023 0.072974 -10.374 < 2e-16 ***
siteHH:gender 0.059455 0.120194 0.495 0.62104
siteIOP:gender -0.245289 0.177965 -1.378 0.16867
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6397 on 553 degrees of freedom
Multiple R-squared: 0.5945, Adjusted R-squared: 0.5901
F-statistic: 135.1 on 6 and 553 DF, p-value: < 2.2e-16
Component #2¶
lm_gm_comp2 = lm(gm_comp_2~age + site + gender + site*gender, data=data)
summary(lm_df_comp2)
Call:
lm(formula = df_comp_1 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-1.5013 -0.4496 -0.0745 0.3316 4.4014
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.037535 0.105553 -19.303 < 2e-16 ***
age 0.045561 0.001787 25.490 < 2e-16 ***
siteHH -0.164925 0.093541 -1.763 0.07843 .
siteIOP -0.068357 0.151606 -0.451 0.65225
gender -0.377366 0.077711 -4.856 1.56e-06 ***
siteHH:gender 0.234504 0.127995 1.832 0.06747 .
siteIOP:gender 0.541277 0.189516 2.856 0.00445 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6812 on 553 degrees of freedom
Multiple R-squared: 0.55, Adjusted R-squared: 0.5451
F-statistic: 112.7 on 6 and 553 DF, p-value: < 2.2e-16
White Matter¶
Component #1¶
lm_wm_comp1 = lm(wm_comp_1~age + site + gender + site*gender, data=data)
summary(lm_wm_comp1)
Call:
lm(formula = wm_comp_1 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-1.9136 -0.5502 -0.0414 0.4930 2.8121
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.507270 0.124800 12.077 <2e-16 ***
age -0.018605 0.002113 -8.804 <2e-16 ***
siteHH -0.142862 0.110597 -1.292 0.197
siteIOP -0.273448 0.179251 -1.526 0.128
gender -0.977843 0.091881 -10.643 <2e-16 ***
siteHH:gender -0.023437 0.151333 -0.155 0.877
siteIOP:gender -0.326990 0.224072 -1.459 0.145
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8054 on 553 degrees of freedom
Multiple R-squared: 0.3862, Adjusted R-squared: 0.3796
F-statistic: 58 on 6 and 553 DF, p-value: < 2.2e-16
Component #2¶
lm_wm_comp2 = lm(wm_comp_2~age + site + gender + site*gender, data=data)
summary(lm_wm_comp2)
Call:
lm(formula = wm_comp_2 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-2.0740 -0.4249 -0.0123 0.3878 2.1331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.162433 0.103781 -11.201 < 2e-16 ***
age 0.033508 0.001757 19.067 < 2e-16 ***
siteHH -0.921654 0.091970 -10.021 < 2e-16 ***
siteIOP -0.885226 0.149060 -5.939 5.08e-09 ***
gender -0.345222 0.076406 -4.518 7.63e-06 ***
siteHH:gender 0.137278 0.125845 1.091 0.2758
siteIOP:gender 0.350391 0.186333 1.880 0.0606 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6698 on 553 degrees of freedom
Multiple R-squared: 0.5427, Adjusted R-squared: 0.5377
F-statistic: 109.4 on 6 and 553 DF, p-value: < 2.2e-16
CSF¶
Component #1¶
lm_csf_comp1 = lm(csf_comp_1~age + site + gender + site*gender, data=data)
summary(lm_csf_comp1)
Call:
lm(formula = csf_comp_1 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-1.72847 -0.45810 -0.03332 0.37638 2.73362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.444718 0.100292 -14.405 < 2e-16 ***
age 0.041228 0.001698 24.276 < 2e-16 ***
siteHH -0.341589 0.088878 -3.843 0.000135 ***
siteIOP -0.594116 0.144049 -4.124 4.29e-05 ***
gender -0.984119 0.073837 -13.328 < 2e-16 ***
siteHH:gender 0.311623 0.121615 2.562 0.010659 *
siteIOP:gender 0.735561 0.180069 4.085 5.06e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6473 on 553 degrees of freedom
Multiple R-squared: 0.5926, Adjusted R-squared: 0.5882
F-statistic: 134 on 6 and 553 DF, p-value: < 2.2e-16
Component #2¶
lm_csf_comp2 = lm(wm_comp_2~age + site + gender + site*gender, data=data)
summary(lm_csf_comp2)
Call:
lm(formula = wm_comp_2 ~ age + site + gender + site * gender,
data = data)
Residuals:
Min 1Q Median 3Q Max
-2.0740 -0.4249 -0.0123 0.3878 2.1331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.162433 0.103781 -11.201 < 2e-16 ***
age 0.033508 0.001757 19.067 < 2e-16 ***
siteHH -0.921654 0.091970 -10.021 < 2e-16 ***
siteIOP -0.885226 0.149060 -5.939 5.08e-09 ***
gender -0.345222 0.076406 -4.518 7.63e-06 ***
siteHH:gender 0.137278 0.125845 1.091 0.2758
siteIOP:gender 0.350391 0.186333 1.880 0.0606 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6698 on 553 degrees of freedom
Multiple R-squared: 0.5427, Adjusted R-squared: 0.5377
F-statistic: 109.4 on 6 and 553 DF, p-value: < 2.2e-16