R Notebook
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## 1
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## Call:
## lm(formula = pe_strp ~ case_famcontrol, data = df.pe.a.temp1)
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## Residuals:
## Min 1Q Median 3Q Max
## -1.48597 -0.43290 0.05673 0.45314 1.52417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4834 0.1355 3.567 0.00083 ***
## case_famcontrolMutationPos -0.1528 0.1779 -0.859 0.39480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.621 on 48 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.01513, Adjusted R-squared: -0.00539
## F-statistic: 0.7373 on 1 and 48 DF, p-value: 0.3948##
## Call:
## lm(formula = pe_humi ~ case_famcontrol, data = df.pe.a.temp1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2966 -0.2837 -0.0116 0.3045 1.4825
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3252 0.1214 2.679 0.00997 **
## case_famcontrolMutationPos -0.1768 0.1572 -1.124 0.26619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 0.5562 on 50 degrees of freedom
## (109 observations deleted due to missingness)
## Multiple R-squared: 0.02466, Adjusted R-squared: 0.005158
## F-statistic: 1.264 on 1 and 50 DF, p-value: 0.2662##
## Call:
## lm(formula = pe_nback ~ case_famcontrol, data = df.pe.a.temp1)
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## Residuals:
## Min 1Q Median 3Q Max
## -3.5139 -0.4725 -0.1589 0.4095 3.1468
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2176 0.2492 0.873 0.387
## case_famcontrolMutationPos 0.1692 0.3272 0.517 0.607
##
## Residual standard error: 1.142 on 48 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.005543, Adjusted R-squared: -0.01517
## F-statistic: 0.2675 on 1 and 48 DF, p-value: 0.6074##
## Call:
## lm(formula = pe_flk ~ case_famcontrol, data = df.pe.a.temp1)
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## Residuals:
## Min 1Q Median 3Q Max
## -0.59019 -0.12926 -0.02336 0.12686 0.78070
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21982 0.06178 3.558 0.000829 ***
## case_famcontrolMutationPos -0.07368 0.08002 -0.921 0.361558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 0.2831 on 50 degrees of freedom
## (109 observations deleted due to missingness)
## Multiple R-squared: 0.01668, Adjusted R-squared: -0.002991
## F-statistic: 0.8479 on 1 and 50 DF, p-value: 0.36161a: PE and CDR+NACC-FTLD sum of boxes at baseline, full sample
a. Full Sample
Regressions in full sample. FTLD SOB ~ PE for each task. Seperate models
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## [1] "pe_strp"
## [1] 139
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -1.6924 -1.0548 -0.8772 -0.1082 11.2526
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2093 0.2331 5.187 8.12e-07 ***
## target -0.4181 0.2949 -1.418 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.153 on 128 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.01546, Adjusted R-squared: 0.00777
## F-statistic: 2.01 on 1 and 128 DF, p-value: 0.1587## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_flk"
## [1] 198
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -3.089 -2.164 -1.659 1.603 14.180
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9351 0.2820 6.861 1.05e-10 ***
## target 1.5153 0.6593 2.298 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.275 on 181 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02836, Adjusted R-squared: 0.02299
## F-statistic: 5.282 on 1 and 181 DF, p-value: 0.02269## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 15 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_gonogo"
## [1] 157
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.116 -2.031 -1.651 1.024 14.619
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.92236 0.28050 6.853 1.92e-10 ***
## target 0.02709 0.01309 2.069 0.0404 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.207 on 145 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.02867, Adjusted R-squared: 0.02197
## F-statistic: 4.279 on 1 and 145 DF, p-value: 0.04035## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 10 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_nback"
## [1] 137
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.4853 -1.1230 -0.7836 0.3150 11.2764
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1230 0.1934 5.807 4.87e-08 ***
## target -0.4356 0.1704 -2.556 0.0118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.132 on 126 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.0493, Adjusted R-squared: 0.04175
## F-statistic: 6.534 on 1 and 126 DF, p-value: 0.01177## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_humi"
## [1] 198
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.995 -2.240 -1.619 1.450 13.976
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4076 0.2672 9.009 2.9e-16 ***
## target -0.5969 0.4609 -1.295 0.197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.307 on 181 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.009179, Adjusted R-squared: 0.003704
## F-statistic: 1.677 on 1 and 181 DF, p-value: 0.197## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 15 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "##############################"b. PE and CDR+NACC-FTLD sum of boxes at baseline with covari [age, education]
FTLD SOB ~ PE +age + education
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## [1] "pe_strp"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.4092 -1.1211 -0.6194 0.1067 11.0707
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.416372 1.620854 -0.874 0.3839
## target -0.386601 0.293360 -1.318 0.1899
## baseline_age 0.041748 0.019214 2.173 0.0317 *
## educ.demo 0.008656 0.075083 0.115 0.9084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.129 on 126 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.05163, Adjusted R-squared: 0.02905
## F-statistic: 2.287 on 3 and 126 DF, p-value: 0.08188
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## [1] "pe_flk"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.1379 -2.1919 -0.8157 1.4402 13.8510
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.80335 2.02353 -1.385 0.167661
## target 1.29176 0.65119 1.984 0.048820 *
## baseline_age 0.09196 0.02395 3.839 0.000171 ***
## educ.demo -0.04976 0.09668 -0.515 0.607388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.165 on 179 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1023, Adjusted R-squared: 0.08724
## F-statistic: 6.798 on 3 and 179 DF, p-value: 0.0002295
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## [1] "pe_gonogo"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.4227 -1.9465 -0.8388 1.1969 14.6534
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.42595 2.12492 -1.142 0.255500
## target 0.02294 0.01282 1.789 0.075726 .
## baseline_age 0.08780 0.02539 3.459 0.000716 ***
## educ.demo -0.05930 0.10678 -0.555 0.579560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.102 on 143 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1037, Adjusted R-squared: 0.08486
## F-statistic: 5.513 on 3 and 143 DF, p-value: 0.001304
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## [1] "pe_nback"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.1953 -1.1237 -0.6106 0.4160 11.0972
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.94486 1.63680 -0.577 0.5648
## target -0.40027 0.17055 -2.347 0.0205 *
## baseline_age 0.03852 0.01922 2.004 0.0472 *
## educ.demo -0.01312 0.07572 -0.173 0.8627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.115 on 124 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.07914, Adjusted R-squared: 0.05686
## F-statistic: 3.552 on 3 and 124 DF, p-value: 0.01645
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## [1] "pe_humi"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.3698 -1.9593 -0.9768 1.2988 14.1849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.37468 2.01010 -1.181 0.239
## target -0.70834 0.44594 -1.588 0.114
## baseline_age 0.09884 0.02400 4.118 5.83e-05 ***
## educ.demo -0.07414 0.09554 -0.776 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 3.178 on 179 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0953, Adjusted R-squared: 0.08014
## F-statistic: 6.285 on 3 and 179 DF, p-value: 0.0004448
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## [1] "##############################"c. PE and CDR+NACC-FTLD sum of boxes at baseline with covari [age, education, avg cog task]
FTLD SOB ~ PE +age + education + visit_average_task (e.g., avg of flanker at chapter 1,2,3)
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## [1] "pe_strp"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.7847 -0.8861 -0.3365 0.3987 6.4589
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.97367 1.36274 0.714 0.476
## target -0.37166 0.24050 -1.545 0.125
## baseline_age -0.02864 0.01809 -1.583 0.116
## educ.demo 0.08965 0.06240 1.437 0.153
## visit_avg_strp -1.36018 0.17208 -7.905 1.21e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 1.746 on 125 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.3677, Adjusted R-squared: 0.3475
## F-statistic: 18.17 on 4 and 125 DF, p-value: 8.665e-12
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## [1] "pe_flk"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -5.7467 -1.5575 -0.6598 0.8868 11.0334
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50982 1.59546 0.320 0.750
## target 0.80581 0.50611 1.592 0.113
## baseline_age 0.01102 0.01996 0.552 0.582
## educ.demo 0.04911 0.07539 0.651 0.516
## visit_avg_flk -2.23930 0.20390 -10.983 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.451 on 178 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.4649, Adjusted R-squared: 0.4529
## F-statistic: 38.66 on 4 and 178 DF, p-value: < 2.2e-16
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## [1] "pe_gonogo"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -4.616 -1.848 -0.540 1.017 12.769
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.63661 2.06473 -0.793 0.429303
## target 0.02293 0.01238 1.852 0.066082 .
## baseline_age 0.06611 0.02533 2.609 0.010043 *
## educ.demo -0.02573 0.10357 -0.248 0.804161
## visit_avg_gonogo -0.82807 0.24502 -3.380 0.000937 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.995 on 142 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1704, Adjusted R-squared: 0.147
## F-statistic: 7.291 on 4 and 142 DF, p-value: 2.276e-05
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## [1] "pe_nback"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -2.4474 -1.1741 -0.6269 0.4688 10.5732
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.35811 1.60096 -0.224 0.82338
## target -0.31432 0.16808 -1.870 0.06384 .
## baseline_age 0.01979 0.01971 1.004 0.31725
## educ.demo 0.01053 0.07392 0.142 0.88697
## visit_avg_nback -0.64271 0.21844 -2.942 0.00389 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.052 on 123 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.1397, Adjusted R-squared: 0.1117
## F-statistic: 4.993 on 4 and 123 DF, p-value: 0.0009186
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## [1] "pe_humi"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -5.137 -1.493 -0.507 1.158 10.326
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.871500 1.687518 1.702 0.0906 .
## target -0.250740 0.359134 -0.698 0.4860
## baseline_age -0.018726 0.022418 -0.835 0.4047
## educ.demo 0.008259 0.076766 0.108 0.9144
## visit_avg_humi -2.392507 0.236390 -10.121 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 2.539 on 178 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.4258, Adjusted R-squared: 0.4129
## F-statistic: 32.99 on 4 and 178 DF, p-value: < 2.2e-16
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## [1] "##############################"d. PE-age-resid. and CDR+NACC-FTLD sum of boxes at baseline with covari
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## [1] "pe_strp_resid"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -3.1525 -0.9067 -0.4021 0.4705 6.8341
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56627 1.03550 -0.547 0.585
## target -0.33828 0.24122 -1.402 0.163
## educ.demo 0.07409 0.06208 1.193 0.235
## visit_avg_strp -1.22217 0.15093 -8.098 4.11e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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## Residual standard error: 1.757 on 126 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.3542, Adjusted R-squared: 0.3388
## F-statistic: 23.03 on 3 and 126 DF, p-value: 5.92e-12
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## [1] "pe_flk_resid"
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## Call:
## lm(formula = lp.fml, data = df.pe.loop)
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## Residuals:
## Min 1Q Median 3Q Max
## -5.9651 -1.5510 -0.7073 0.8587 11.1143
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## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.14890 1.23618 0.929 0.354
## target 0.80552 0.50527 1.594 0.113
## educ.demo 0.05705 0.07423 0.769 0.443
## visit_avg_flk -2.28792 0.18875 -12.121 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.447 on 179 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.4637, Adjusted R-squared: 0.4547
## F-statistic: 51.58 on 3 and 179 DF, p-value: < 2.2e-16
##
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## [1] "pe_nback_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8168 -1.1385 -0.8286 0.3473 10.9207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.098293 1.457985 0.753 0.4530
## target -0.540870 0.210891 -2.565 0.0118 *
## educ.demo -0.008335 0.088207 -0.094 0.9249
## visit_avg_gonogo -0.186019 0.245451 -0.758 0.4502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.26 on 104 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.06035, Adjusted R-squared: 0.03324
## F-statistic: 2.226 on 3 and 104 DF, p-value: 0.0895
##
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## [1] "pe_humi_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3608 -1.1820 -0.5714 0.2018 10.9060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13883 1.24365 0.112 0.911296
## target -0.42820 0.34601 -1.238 0.218221
## educ.demo 0.04466 0.07451 0.599 0.550028
## visit_avg_nback -0.77825 0.20465 -3.803 0.000223 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.07 on 124 degrees of freedom
## (70 observations deleted due to missingness)
## Multiple R-squared: 0.1179, Adjusted R-squared: 0.0966
## F-statistic: 5.527 on 3 and 124 DF, p-value: 0.001355
##
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## [1] "pe_strp"
## [1] 31
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3961 -0.8795 0.1032 0.5697 2.0318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8054 0.2219 8.137 9.69e-09 ***
## target -0.3813 0.2483 -1.536 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9624 on 27 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.08031, Adjusted R-squared: 0.04625
## F-statistic: 2.358 on 1 and 27 DF, p-value: 0.1363## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_flk"
## [1] 46
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3795 -0.9885 0.1610 0.7411 1.7795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7287 0.1764 9.803 2.62e-12 ***
## target 0.1627 0.3520 0.462 0.646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 41 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.005182, Adjusted R-squared: -0.01908
## F-statistic: 0.2136 on 1 and 41 DF, p-value: 0.6464## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_gonogo"
## [1] 36
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4712 -0.8064 0.1936 0.7470 1.7033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.806368 0.182322 9.908 4e-11 ***
## target -0.004848 0.008737 -0.555 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9877 on 31 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009833, Adjusted R-squared: -0.02211
## F-statistic: 0.3078 on 1 and 31 DF, p-value: 0.583## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_nback"
## [1] 29
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.53019 -0.70967 0.06937 0.46975 1.58356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6780 0.1691 9.925 3.73e-10 ***
## target -0.5553 0.1756 -3.163 0.00407 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8749 on 25 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2858, Adjusted R-squared: 0.2573
## F-statistic: 10.01 on 1 and 25 DF, p-value: 0.004065## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_humi"
## [1] 46
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.57681 -0.89541 0.08984 0.70604 1.68476
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9102 0.1857 10.286 6.37e-13 ***
## target -0.3889 0.2896 -1.343 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9985 on 41 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.04211, Adjusted R-squared: 0.01875
## F-statistic: 1.802 on 1 and 41 DF, p-value: 0.1868## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51556 -0.67382 0.08445 0.58168 2.27388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.19137 2.10357 -0.566 0.5762
## target -0.47610 0.25674 -1.854 0.0755 .
## baseline_age 0.02614 0.02396 1.091 0.2858
## educ.demo 0.09277 0.07365 1.260 0.2195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9603 on 25 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.152, Adjusted R-squared: 0.05026
## F-statistic: 1.494 on 3 and 25 DF, p-value: 0.2404
##
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3773 -1.0023 0.1696 0.7481 1.7796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.924392 1.451669 1.326 0.193
## target 0.157402 0.385943 0.408 0.686
## baseline_age -0.001277 0.017409 -0.073 0.942
## educ.demo -0.007275 0.064145 -0.113 0.910
##
## Residual standard error: 1.043 on 39 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.005673, Adjusted R-squared: -0.07081
## F-statistic: 0.07417 on 3 and 39 DF, p-value: 0.9735
##
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4317 -0.8460 0.1896 0.6669 1.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.706620 1.625630 1.050 0.302
## target -0.003749 0.010005 -0.375 0.711
## baseline_age 0.009801 0.017246 0.568 0.574
## educ.demo -0.032066 0.077474 -0.414 0.682
##
## Residual standard error: 1.013 on 29 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.02632, Adjusted R-squared: -0.07441
## F-statistic: 0.2613 on 3 and 29 DF, p-value: 0.8527
##
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3812 -0.6430 -0.0493 0.5703 1.6230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.25157 1.94012 -0.645 0.5252
## target -0.59914 0.17658 -3.393 0.0025 **
## baseline_age 0.03120 0.02195 1.422 0.1685
## educ.demo 0.06674 0.06831 0.977 0.3387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8683 on 23 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.3528, Adjusted R-squared: 0.2684
## F-statistic: 4.18 on 3 and 23 DF, p-value: 0.01683
##
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55984 -0.90489 0.07113 0.69716 1.72021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8045141 1.4258187 1.266 0.213
## target -0.3964325 0.3071279 -1.291 0.204
## baseline_age 0.0001914 0.0165994 0.012 0.991
## educ.demo 0.0060817 0.0625589 0.097 0.923
##
## Residual standard error: 1.024 on 39 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.04234, Adjusted R-squared: -0.03132
## F-statistic: 0.5748 on 3 and 39 DF, p-value: 0.635
##
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## [1] "##############################"c. CDR 0 and 0.5. Covari for age, education, and task
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.40738 -0.62299 -0.03928 0.65300 2.28915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.01404 2.17815 -0.466 0.6457
## target -0.50206 0.26793 -1.874 0.0732 .
## baseline_age 0.02110 0.02704 0.780 0.4428
## educ.demo 0.09770 0.07576 1.290 0.2095
## visit_avg_strp -0.09411 0.21893 -0.430 0.6711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9764 on 24 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1585, Adjusted R-squared: 0.01824
## F-statistic: 1.13 on 4 and 24 DF, p-value: 0.3659
##
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2568 -0.8670 -0.1166 0.6620 1.8207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.83152 1.34416 2.107 0.04182 *
## target 0.50074 0.36585 1.369 0.17913
## baseline_age -0.03410 0.01893 -1.801 0.07962 .
## educ.demo 0.05491 0.06132 0.895 0.37619
## visit_avg_flk -0.89451 0.28687 -3.118 0.00346 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9429 on 38 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2083, Adjusted R-squared: 0.1249
## F-statistic: 2.499 on 4 and 38 DF, p-value: 0.05865
##
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51734 -0.79592 0.02604 0.78116 1.87203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.462370 1.687654 0.867 0.394
## target -0.004294 0.010147 -0.423 0.675
## baseline_age 0.014648 0.019042 0.769 0.448
## educ.demo -0.035322 0.078459 -0.450 0.656
## visit_avg_gonogo 0.138162 0.218761 0.632 0.533
##
## Residual standard error: 1.023 on 28 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03999, Adjusted R-squared: -0.09715
## F-statistic: 0.2916 on 4 and 28 DF, p-value: 0.8809
##
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.45242 -0.65086 0.01927 0.57093 1.58774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.23356 1.98287 -0.622 0.54027
## target -0.60127 0.18057 -3.330 0.00304 **
## baseline_age 0.02984 0.02318 1.287 0.21133
## educ.demo 0.06930 0.07064 0.981 0.33725
## visit_avg_nback -0.04709 0.20410 -0.231 0.81965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8868 on 22 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.3544, Adjusted R-squared: 0.237
## F-statistic: 3.019 on 4 and 22 DF, p-value: 0.03979
##
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.52365 -0.79580 0.02989 0.63417 2.00021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.85317 1.35509 1.368 0.1795
## target -0.40392 0.29187 -1.384 0.1745
## baseline_age -0.01359 0.01689 -0.804 0.4263
## educ.demo 0.04399 0.06173 0.713 0.4805
## visit_avg_humi -0.52409 0.23009 -2.278 0.0285 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9728 on 38 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1574, Adjusted R-squared: 0.06869
## F-statistic: 1.774 on 4 and 38 DF, p-value: 0.1541
##
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## [1] "##############################"d. CDR 0 and 0.5. PE-age-resid. and CDR+NACC-FTLD sum of boxes at baseline with covari
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1600 -0.8942 -0.4089 0.4743 6.8162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.44286 1.03381 -0.428 0.669
## target -0.35295 0.24164 -1.461 0.147
## educ.demo 0.07485 0.06206 1.206 0.230
## visit_avg_strp -1.22614 0.15069 -8.137 3.32e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.756 on 126 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.355, Adjusted R-squared: 0.3397
## F-statistic: 23.12 on 3 and 126 DF, p-value: 5.46e-12
##
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9420 -1.5441 -0.6975 0.8618 11.1141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.05678 1.24815 0.847 0.398
## target 0.82029 0.50445 1.626 0.106
## educ.demo 0.05638 0.07409 0.761 0.448
## visit_avg_flk -2.28087 0.18911 -12.061 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.446 on 179 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.464, Adjusted R-squared: 0.455
## F-statistic: 51.65 on 3 and 179 DF, p-value: < 2.2e-16
##
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1181 -1.6966 -0.9825 0.9741 12.3762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.51610 1.70797 0.888 0.3762
## target 0.02578 0.01258 2.050 0.0422 *
## educ.demo 0.02665 0.10365 0.257 0.7975
## visit_avg_gonogo -0.99003 0.24179 -4.095 7.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.055 on 143 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.1306, Adjusted R-squared: 0.1124
## F-statistic: 7.161 on 3 and 143 DF, p-value: 0.000163
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3071 -1.1341 -0.6531 0.3004 10.5975
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.66855 1.23206 0.543 0.588357
## target -0.32123 0.16794 -1.913 0.058088 .
## educ.demo 0.01855 0.07349 0.252 0.801203
## visit_avg_nback -0.71357 0.20673 -3.452 0.000763 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.052 on 124 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.1326, Adjusted R-squared: 0.1117
## F-statistic: 6.321 on 3 and 124 DF, p-value: 0.0005036
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0735 -1.5614 -0.5075 1.1514 10.5223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.924030 1.248369 1.541 0.125
## target -0.287418 0.356139 -0.807 0.421
## educ.demo -0.001822 0.075747 -0.024 0.981
## visit_avg_humi -2.290200 0.202014 -11.337 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.537 on 179 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.4235, Adjusted R-squared: 0.4138
## F-statistic: 43.83 on 3 and 179 DF, p-value: < 2.2e-16
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"2a: PE change in FTLD SOB CDR 0 and 0.5
a. Change in SOB for those with CDR 0 or 0.5 global at baseline
df.baseline.analsyes.a2<-df.pe.a.baselinemeged %>%
filter(visit==1,
!is.na(max_dif_ftld_global)) %>%
filter(baseline_ftldcdr_glob<=0.5)#%>%
#group_by(unique_id) %>%
# slice(1)
lp.fml<-"max_dif_ftldbox~target"
for (i.raw in lst.pe) {
# lp.fml<-paste("ftldcdr_box.computed~target+baseline_age+educ.demo+",
# lst.pe.covari[grep(i.raw,lst.pe)])
df.pe.loop<-df.baseline.analsyes.a2%>%
mutate(target=UQ(sym(i.raw)))%>%
filter(!is.na(target)) %>%
group_by(unique_id, visit)%>%
mutate(rslice=1:n())%>%
filter(rslice==1) %>%
ungroup()
lm.pe.loop<-NA
lm.pe.loop= lm(formula = lp.fml,
data = df.pe.loop)
sum<-summary(lm.pe.loop)
print("##############################")
print("##############################")
print("##############################")
print("##############################")
print(i.raw)
print(sum)
print(ggplot(df.pe.loop, aes(x=target, y=max_dif_ftldbox))+
geom_point()+
geom_smooth(method="lm")+
ggtitle(label = i.raw)
)
print("##############################")
print("##############################")
print("##############################")
}## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16722 -0.07184 -0.04924 -0.02501 1.41563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02655 0.02449 1.084 0.2803
## target 0.05701 0.03165 1.801 0.0741 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.218 on 121 degrees of freedom
## Multiple R-squared: 0.02612, Adjusted R-squared: 0.01807
## F-statistic: 3.245 on 1 and 121 DF, p-value: 0.07413## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4658 -0.1353 -0.1044 -0.0612 3.3281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07047 0.04513 1.562 0.1207
## target 0.26979 0.12644 2.134 0.0346 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4649 on 138 degrees of freedom
## Multiple R-squared: 0.03194, Adjusted R-squared: 0.02492
## F-statistic: 4.553 on 1 and 138 DF, p-value: 0.03463## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2988 -0.1263 -0.1121 -0.0909 3.3879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.107405 0.042286 2.54 0.0124 *
## target 0.002363 0.002339 1.01 0.3146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4409 on 113 degrees of freedom
## Multiple R-squared: 0.00895, Adjusted R-squared: 0.0001797
## F-statistic: 1.02 on 1 and 113 DF, p-value: 0.3146## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09169 -0.05784 -0.05166 -0.04534 1.43837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04991 0.02111 2.364 0.0197 *
## target 0.01159 0.01857 0.624 0.5338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2223 on 119 degrees of freedom
## Multiple R-squared: 0.003262, Adjusted R-squared: -0.005114
## F-statistic: 0.3894 on 1 and 119 DF, p-value: 0.5338## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1594 -0.1251 -0.1146 -0.1038 3.3811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11054 0.04469 2.473 0.0146 *
## target 0.02705 0.07430 0.364 0.7164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4722 on 138 degrees of freedom
## Multiple R-squared: 0.0009595, Adjusted R-squared: -0.00628
## F-statistic: 0.1325 on 1 and 138 DF, p-value: 0.7164## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"b. Covari for age and education
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18808 -0.08182 -0.04306 -0.00001 1.33659
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.379720 0.164206 2.312 0.0225 *
## target 0.059022 0.031146 1.895 0.0605 .
## baseline_age -0.001027 0.001948 -0.527 0.5991
## educ.demo -0.017966 0.007607 -2.362 0.0198 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2145 on 119 degrees of freedom
## Multiple R-squared: 0.07343, Adjusted R-squared: 0.05007
## F-statistic: 3.143 on 3 and 119 DF, p-value: 0.02783## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5112 -0.1490 -0.0887 -0.0283 3.2827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.219913 0.327808 0.671 0.5034
## target 0.226366 0.130566 1.734 0.0852 .
## baseline_age 0.002895 0.003872 0.748 0.4560
## educ.demo -0.019290 0.015930 -1.211 0.2280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4652 on 136 degrees of freedom
## Multiple R-squared: 0.04468, Adjusted R-squared: 0.02361
## F-statistic: 2.12 on 3 and 136 DF, p-value: 0.1005## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3270 -0.1526 -0.1005 -0.0355 3.3151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.281243 0.328734 0.856 0.394
## target 0.002809 0.002395 1.173 0.243
## baseline_age 0.004227 0.003882 1.089 0.279
## educ.demo -0.026301 0.016595 -1.585 0.116
##
## Residual standard error: 0.4385 on 111 degrees of freedom
## Multiple R-squared: 0.03726, Adjusted R-squared: 0.01124
## F-statistic: 1.432 on 3 and 111 DF, p-value: 0.2373## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15306 -0.07580 -0.04968 -0.00544 1.36283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4056912 0.1722218 2.356 0.0202 *
## target 0.0045987 0.0185967 0.247 0.8051
## baseline_age -0.0009093 0.0020098 -0.452 0.6518
## educ.demo -0.0182935 0.0079620 -2.298 0.0234 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.219 on 117 degrees of freedom
## Multiple R-squared: 0.04921, Adjusted R-squared: 0.02483
## F-statistic: 2.018 on 3 and 117 DF, p-value: 0.1151## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3106 -0.1534 -0.1010 -0.0435 3.3085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.324235 0.329874 0.983 0.3274
## target 0.033386 0.075255 0.444 0.6580
## baseline_age 0.003569 0.003911 0.913 0.3631
## educ.demo -0.026275 0.015796 -1.663 0.0985 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4699 on 136 degrees of freedom
## Multiple R-squared: 0.02498, Adjusted R-squared: 0.003468
## F-statistic: 1.161 on 3 and 136 DF, p-value: 0.327## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"c. Covari for age and education and task
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.19291 -0.07872 -0.04253 -0.00174 1.28888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.439720 0.171758 2.560 0.0117 *
## target 0.055792 0.031219 1.787 0.0765 .
## baseline_age -0.002519 0.002324 -1.084 0.2808
## educ.demo -0.016504 0.007697 -2.144 0.0341 *
## visit_avg_strp -0.032898 0.028072 -1.172 0.2436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2141 on 118 degrees of freedom
## Multiple R-squared: 0.08409, Adjusted R-squared: 0.05304
## F-statistic: 2.708 on 4 and 118 DF, p-value: 0.03348## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7296 -0.1338 -0.0819 -0.0147 3.3746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.452058 0.341936 1.322 0.1884
## target 0.259682 0.129906 1.999 0.0476 *
## baseline_age -0.001876 0.004443 -0.422 0.6736
## educ.demo -0.013527 0.015968 -0.847 0.3984
## visit_avg_flk -0.198520 0.094140 -2.109 0.0368 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4594 on 135 degrees of freedom
## Multiple R-squared: 0.07514, Adjusted R-squared: 0.04774
## F-statistic: 2.742 on 4 and 135 DF, p-value: 0.03114## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3367 -0.1638 -0.0921 -0.0222 3.2819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.216518 0.332401 0.651 0.516
## target 0.002552 0.002400 1.063 0.290
## baseline_age 0.005258 0.003967 1.326 0.188
## educ.demo -0.026668 0.016563 -1.610 0.110
## visit_avg_gonogo 0.055400 0.045854 1.208 0.230
##
## Residual standard error: 0.4376 on 110 degrees of freedom
## Multiple R-squared: 0.04987, Adjusted R-squared: 0.01532
## F-statistic: 1.443 on 4 and 110 DF, p-value: 0.2246## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.21605 -0.09536 -0.03636 0.00249 1.28807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.462589 0.170736 2.709 0.00776 **
## target 0.011175 0.018463 0.605 0.54620
## baseline_age -0.002458 0.002080 -1.182 0.23975
## educ.demo -0.016597 0.007847 -2.115 0.03655 *
## visit_avg_nback -0.053764 0.022928 -2.345 0.02073 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2149 on 116 degrees of freedom
## Multiple R-squared: 0.09224, Adjusted R-squared: 0.06094
## F-statistic: 2.947 on 4 and 116 DF, p-value: 0.02318## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5478 -0.1711 -0.0825 0.0276 3.1839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.661856 0.340460 1.944 0.0540 .
## target 0.062042 0.073827 0.840 0.4022
## baseline_age -0.003059 0.004412 -0.693 0.4894
## educ.demo -0.022282 0.015422 -1.445 0.1508
## visit_avg_humi -0.167358 0.056483 -2.963 0.0036 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4571 on 135 degrees of freedom
## Multiple R-squared: 0.08451, Adjusted R-squared: 0.05739
## F-statistic: 3.116 on 4 and 135 DF, p-value: 0.0173## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"c. Covari for and education and task RESID PE
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20618 -0.08080 -0.04290 -0.00461 1.30832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.334786 0.126248 2.652 0.0091 **
## target 0.058786 0.031084 1.891 0.0610 .
## educ.demo -0.017689 0.007609 -2.325 0.0218 *
## visit_avg_strp -0.017232 0.023459 -0.735 0.4641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2141 on 119 degrees of freedom
## Multiple R-squared: 0.07608, Adjusted R-squared: 0.05278
## F-statistic: 3.266 on 3 and 119 DF, p-value: 0.02381## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_flk_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7195 -0.1387 -0.0815 -0.0259 3.3637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.38363 0.25793 1.487 0.1392
## target 0.25505 0.12858 1.984 0.0493 *
## educ.demo -0.01444 0.01563 -0.924 0.3571
## visit_avg_flk -0.18406 0.08091 -2.275 0.0245 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4579 on 136 degrees of freedom
## Multiple R-squared: 0.07451, Adjusted R-squared: 0.0541
## F-statistic: 3.65 on 3 and 136 DF, p-value: 0.0143## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20446 -0.09710 -0.05039 -0.00877 1.31745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.402306 0.155384 2.589 0.0111 *
## target 0.007132 0.023467 0.304 0.7618
## educ.demo -0.021294 0.009440 -2.256 0.0263 *
## visit_avg_gonogo 0.038261 0.026600 1.438 0.1535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2363 on 97 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.07146, Adjusted R-squared: 0.04275
## F-statistic: 2.489 on 3 and 97 DF, p-value: 0.06495## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi_resid"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22092 -0.08657 -0.03919 0.00244 1.29341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.371772 0.129748 2.865 0.00494 **
## target 0.038979 0.036658 1.063 0.28983
## educ.demo -0.019601 0.007818 -2.507 0.01354 *
## visit_avg_nback -0.043306 0.021373 -2.026 0.04502 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2146 on 117 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.08655, Adjusted R-squared: 0.06313
## F-statistic: 3.695 on 3 and 117 DF, p-value: 0.01386## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"3. LMEs.
a. Full sample. FTLD_SOB predicted by age-residualized baseline PEs.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0145209 (tol = 0.002, component 1)## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_strp_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 1773.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5718 -0.0834 -0.0314 0.0737 7.2680
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 3.113e+00 1.764480
## days_from_login.computed 4.842e-05 0.006958 0.45
## Residual 2.923e-01 0.540646
## Number of obs: 653, groups: unique_id, 139
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.623e-03 8.411e-01 2.141e+02 0.008
## target -3.277e-01 2.340e-01 1.228e+02 -1.400
## days_from_login.computed 1.555e-03 8.602e-04 4.447e+01 1.807
## educ.demo 4.773e-02 5.030e-02 2.141e+02 0.949
## visit_avg_strp -7.500e-01 1.227e-01 2.450e+02 -6.113
## target:days_from_login.computed -4.787e-04 1.365e-03 4.721e+01 -0.351
## Pr(>|t|)
## (Intercept) 0.9937
## target 0.1639
## days_from_login.computed 0.0775 .
## educ.demo 0.3438
## visit_avg_strp 3.83e-09 ***
## target:days_from_login.computed 0.7273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target 0.031
## dys_frm_lg. 0.058 -0.031
## educ.demo -0.982 -0.055 -0.007
## vst_vg_strp 0.085 -0.050 0.039 -0.037
## trgt:dys__. 0.005 0.255 -0.062 -0.009 0.022
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0145209 (tol = 0.002, component 1)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 19 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 19 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 20.7644 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_flk_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 2716.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8765 -0.2040 -0.0661 0.1650 5.6149
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 2.4835742 1.575936
## days_from_login.computed 0.0000262 0.005118 0.24
## Residual 0.5431790 0.737007
## Number of obs: 882, groups: unique_id, 197
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.445e+00 7.437e-01 6.047e+02 1.943
## target 8.936e-01 3.298e-01 5.192e+02 2.710
## days_from_login.computed -8.312e-04 6.554e-04 5.298e+01 -1.268
## educ.demo 4.294e-02 4.469e-02 6.063e+02 0.961
## visit_avg_flk -1.800e+00 1.128e-01 6.845e+02 -15.955
## target:days_from_login.computed 5.582e-03 1.778e-03 6.276e+01 3.140
## Pr(>|t|)
## (Intercept) 0.05250 .
## target 0.00696 **
## days_from_login.computed 0.21025
## educ.demo 0.33698
## visit_avg_flk < 2e-16 ***
## target:days_from_login.computed 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target -0.203
## dys_frm_lg. 0.018 0.008
## educ.demo -0.987 0.168 -0.008
## vist_vg_flk 0.060 0.074 0.159 -0.058
## trgt:dys__. -0.006 0.052 -0.320 0.005 -0.016
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 20.7644 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.110229 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_nback_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 1781
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5236 -0.0649 -0.0261 0.0393 7.3250
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 4.220e+00 2.054220
## days_from_login.computed 5.696e-05 0.007547 0.72
## Residual 2.899e-01 0.538454
## Number of obs: 648, groups: unique_id, 137
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.574e-01 8.731e-01 1.493e+02 0.524
## target -3.235e-01 1.638e-01 1.342e+02 -1.975
## days_from_login.computed 1.746e-03 8.674e-04 3.976e+01 2.012
## educ.demo 2.730e-02 5.201e-02 1.441e+02 0.525
## visit_avg_nback -1.363e-01 8.973e-02 2.659e+02 -1.519
## target:days_from_login.computed -1.943e-03 7.651e-04 4.009e+01 -2.540
## Pr(>|t|)
## (Intercept) 0.6012
## target 0.0504 .
## days_from_login.computed 0.0510 .
## educ.demo 0.6005
## visit_avg_nback 0.1300
## target:days_from_login.computed 0.0151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target -0.024
## dys_frm_lg. 0.126 0.116
## educ.demo -0.978 0.067 -0.020
## vst_vg_nbck 0.046 -0.101 -0.083 -0.033
## trgt:dys__. 0.034 0.527 0.192 -0.013 -0.097
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.110229 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 21 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 21 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0603325 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_humi_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 2518.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.0612 -0.0809 -0.0146 0.0742 7.4963
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 7.802e+00 2.793229
## days_from_login.computed 3.534e-05 0.005944 0.19
## Residual 2.698e-01 0.519394
## Number of obs: 881, groups: unique_id, 197
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.320e+00 9.969e-01 4.719e+02 2.327
## target -3.682e-01 3.856e-01 1.723e+02 -0.955
## days_from_login.computed 1.571e-03 6.690e-04 6.643e+01 2.349
## educ.demo -1.381e-02 5.979e-02 4.914e+02 -0.231
## visit_avg_humi -8.828e-01 1.305e-01 7.589e+02 -6.766
## target:days_from_login.computed -7.595e-04 1.370e-03 6.535e+01 -0.554
## Pr(>|t|)
## (Intercept) 0.0204 *
## target 0.3410
## days_from_login.computed 0.0218 *
## educ.demo 0.8174
## visit_avg_humi 2.65e-11 ***
## target:days_from_login.computed 0.5812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target 0.065
## dys_frm_lg. 0.014 0.010
## educ.demo -0.979 -0.055 0.003
## visit_vg_hm 0.042 -0.075 -0.072 -0.016
## trgt:dys__. -0.007 0.090 0.135 0.006 -0.064
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0603325 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 31 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. Full sample. FTLD_SOB predicted by age-residualized baseline PEs.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 13.7024 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_strp_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 592.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8470 -0.2130 -0.0815 0.0245 3.7289
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 1.480e-01 0.3847017
## days_from_login.computed 3.609e-07 0.0006008 0.26
## Residual 9.298e-02 0.3049288
## Number of obs: 590, groups: unique_id, 123
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.382e-01 2.416e-01 5.239e+02 1.814
## target -9.925e-02 6.200e-02 4.870e+02 -1.601
## days_from_login.computed 2.010e-04 1.424e-04 7.207e+01 1.412
## educ.demo -4.484e-03 1.457e-02 5.244e+02 -0.308
## visit_avg_strp -1.943e-01 4.094e-02 5.736e+02 -4.745
## target:days_from_login.computed 2.459e-04 2.351e-04 7.060e+01 1.046
## Pr(>|t|)
## (Intercept) 0.0703 .
## target 0.1101
## days_from_login.computed 0.1622
## educ.demo 0.7583
## visit_avg_strp 2.63e-06 ***
## target:days_from_login.computed 0.2991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target 0.014
## dys_frm_lg. -0.028 0.024
## educ.demo -0.987 -0.036 0.012
## vst_vg_strp 0.089 0.034 -0.083 -0.066
## trgt:dys__. -0.026 -0.078 -0.103 0.030 -0.033
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 13.7024 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.743428 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_flk_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 767.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9279 -0.0754 -0.0375 0.0269 5.6665
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 7.975e-01 0.893052
## days_from_login.computed 4.615e-06 0.002148 0.35
## Residual 6.166e-02 0.248313
## Number of obs: 673, groups: unique_id, 140
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.909e-01 4.133e-01 3.215e+02 1.672
## target 3.988e-01 2.517e-01 1.613e+02 1.585
## days_from_login.computed 3.759e-04 2.903e-04 7.306e+01 1.295
## educ.demo -6.183e-03 2.476e-02 3.310e+02 -0.250
## visit_avg_flk -1.263e-01 9.046e-02 5.964e+02 -1.396
## target:days_from_login.computed 1.179e-05 7.782e-04 7.840e+01 0.015
## Pr(>|t|)
## (Intercept) 0.0955 .
## target 0.1150
## days_from_login.computed 0.1995
## educ.demo 0.8030
## visit_avg_flk 0.1634
## target:days_from_login.computed 0.9879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target -0.200
## dys_frm_lg. 0.026 -0.033
## educ.demo -0.981 0.172 -0.005
## vist_vg_flk -0.040 -0.033 0.251 -0.015
## trgt:dys__. -0.019 0.213 -0.250 0.014 -0.009
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.743428 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 20 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 4.50689 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "baseline_pe_nback_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 233.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0296 -0.0745 -0.0260 0.0350 5.8262
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 3.493e-01 0.5909746
## days_from_login.computed 4.322e-07 0.0006574 -0.11
## Residual 3.398e-02 0.1843488
## Number of obs: 585, groups: unique_id, 121
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.202e-01 2.974e-01 4.228e+02 2.085
## target -1.642e-01 5.087e-02 2.541e+02 -3.227
## days_from_login.computed 1.975e-04 1.188e-04 7.860e+01 1.663
## educ.demo -1.735e-02 1.786e-02 4.288e+02 -0.971
## visit_avg_nback -4.143e-02 2.691e-02 3.267e+02 -1.539
## target:days_from_login.computed 2.602e-05 1.163e-04 8.097e+01 0.224
## Pr(>|t|)
## (Intercept) 0.03765 *
## target 0.00142 **
## days_from_login.computed 0.10023
## educ.demo 0.33192
## visit_avg_nback 0.12470
## target:days_from_login.computed 0.82352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target -0.078
## dys_frm_lg. -0.046 0.006
## educ.demo -0.982 0.111 0.023
## vst_vg_nbck 0.015 -0.067 -0.272 -0.011
## trgt:dys__. -0.015 -0.096 0.101 0.012 -0.217
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 4.50689 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 19 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 19 rows containing missing values or values outside the scale range
## (`geom_point()`).## [1] "##############################"
## [1] "##############################"
## [1] "##############################"## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.05301 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
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## [1] "baseline_pe_humi_resid"
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: lp.fml
## Data: df.pe.loop
##
## REML criterion at convergence: 743.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9077 -0.1133 -0.0331 0.0878 5.6910
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## unique_id (Intercept) 6.973e-01 0.835035
## days_from_login.computed 4.933e-06 0.002221 0.27
## Residual 5.991e-02 0.244765
## Number of obs: 672, groups: unique_id, 140
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.706e-01 3.890e-01 3.234e+02 2.238
## target 8.807e-02 1.353e-01 1.753e+02 0.651
## days_from_login.computed 6.437e-04 2.867e-04 6.248e+01 2.245
## educ.demo -1.577e-02 2.349e-02 3.306e+02 -0.671
## visit_avg_humi -3.706e-01 6.119e-02 5.216e+02 -6.057
## target:days_from_login.computed 1.215e-05 5.744e-04 6.194e+01 0.021
## Pr(>|t|)
## (Intercept) 0.0259 *
## target 0.5159
## days_from_login.computed 0.0283 *
## educ.demo 0.5025
## visit_avg_humi 2.66e-09 ***
## target:days_from_login.computed 0.9832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) target dys__. edc.dm vst_v_
## target 0.101
## dys_frm_lg. 0.025 0.000
## educ.demo -0.983 -0.105 0.002
## visit_vg_hm -0.007 -0.050 -0.114 -0.007
## trgt:dys__. -0.009 0.125 0.123 0.010 -0.075
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 3.05301 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 22 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 22 rows containing missing values or values outside the scale range
## (`geom_point()`).
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