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## 26 361a: PE at baseline
a. PE at baseline full sample no covari
Stroop (-), flanker (+), gonogo(+), nbck(-) humi (x)
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## [1] "pe_strp"
## [1] 139
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6924 -1.0548 -0.8772 -0.1082 11.2526
##
## 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
##
## 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
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.089 -2.164 -1.659 1.603 14.180
##
## 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
##
## 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
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.116 -2.031 -1.651 1.024 14.619
##
## 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
##
## 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
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4853 -1.1230 -0.7836 0.3150 11.2764
##
## 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
##
## 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
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.995 -2.240 -1.619 1.450 13.976
##
## 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
##
## 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] "##############################"a. PE at baseline full sample with covari [age, edu]
Stroop (x), flanker (+), gonogo(x), nbck(-) humi (x)
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4092 -1.1211 -0.6194 0.1067 11.0707
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1379 -2.1919 -0.8157 1.4402 13.8510
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4227 -1.9465 -0.8388 1.1969 14.6534
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1953 -1.1237 -0.6106 0.4160 11.0972
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## 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
##
## 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## `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] "##############################"a. PE at baseline full sample with covari [age, edu, AND test]
Stroop (t-), flanker (t+), gonogo(t+), nbck(x-) humi (x)
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7847 -0.8861 -0.3365 0.3987 6.4589
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7467 -1.5575 -0.6598 0.8868 11.0334
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.616 -1.848 -0.540 1.017 12.769
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4474 -1.1741 -0.6269 0.4688 10.5732
##
## 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
##
## 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## `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"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.137 -1.493 -0.507 1.158 10.326
##
## 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
##
## 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## `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] "##############################"1b: PE at baseline cdr less 1
a. PE at baseline cdr less 1 sample no covari
Stroop (-), flanker (+), gonogo(+), nbck(-) humi (x)
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5130 -0.4135 -0.3842 -0.2164 3.1277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43325 0.10020 4.324 3.3e-05 ***
## target -0.06899 0.13078 -0.528 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8528 on 114 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.002435, Adjusted R-squared: -0.006315
## F-statistic: 0.2783 on 1 and 114 DF, p-value: 0.5988## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82579 -0.58011 -0.52898 -0.01456 3.02501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4967 0.1012 4.907 2.72e-06 ***
## target 0.4321 0.2800 1.543 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.003 on 130 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01799, Adjusted R-squared: 0.01043
## F-statistic: 2.381 on 1 and 130 DF, p-value: 0.1252## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6672 -0.5365 -0.5245 -0.0193 3.0168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.527896 0.098915 5.337 5.34e-07 ***
## target 0.001719 0.006090 0.282 0.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9801 on 107 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.0007442, Adjusted R-squared: -0.008595
## F-statistic: 0.07969 on 1 and 107 DF, p-value: 0.7783## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
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## [1] "##############################"
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## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9769 -0.4431 -0.3325 0.1024 2.9845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43834 0.08088 5.42 3.47e-07 ***
## target -0.17222 0.07145 -2.41 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8318 on 112 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.04932, Adjusted R-squared: 0.04083
## F-statistic: 5.81 on 1 and 112 DF, p-value: 0.01756## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -0.65868 -0.57980 -0.55362 -0.05214 2.95603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.56066 0.09901 5.663 9.11e-08 ***
## target 0.05422 0.16236 0.334 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 130 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.0008572, Adjusted R-squared: -0.006829
## F-statistic: 0.1115 on 1 and 130 DF, p-value: 0.7389## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline cdr less 1 sample with covari [age, edu]
Stroop (x), flanker (+), gonogo(x), nbck(-) humi (x)
## [1] "##############################"
## [1] "##############################"
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## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6559 -0.4200 -0.3675 -0.1871 3.1194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.215528 0.672095 0.321 0.749
## target -0.065518 0.131744 -0.497 0.620
## baseline_age 0.005819 0.007998 0.727 0.468
## educ.demo -0.007588 0.031000 -0.245 0.807
##
## Residual standard error: 0.8582 on 112 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.007453, Adjusted R-squared: -0.01913
## F-statistic: 0.2803 on 3 and 112 DF, p-value: 0.8395## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
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## [1] "##############################"
## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9830 -0.6307 -0.3912 0.0182 3.1716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.341016 0.719378 0.474 0.636
## target 0.310600 0.287522 1.080 0.282
## baseline_age 0.013396 0.008535 1.569 0.119
## educ.demo -0.037785 0.034793 -1.086 0.280
##
## Residual standard error: 0.9984 on 128 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.04241, Adjusted R-squared: 0.01996
## F-statistic: 1.89 on 3 and 128 DF, p-value: 0.1346## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
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## [1] "##############################"
## [1] "##############################"
## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.00429 -0.58427 -0.37267 0.01224 2.94564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.127019 0.743942 0.171 0.8648
## target 0.001212 0.006226 0.195 0.8460
## baseline_age 0.016714 0.008730 1.915 0.0583 .
## educ.demo -0.036121 0.037639 -0.960 0.3394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9703 on 105 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.0388, Adjusted R-squared: 0.01134
## F-statistic: 1.413 on 3 and 105 DF, p-value: 0.2432## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -0.95750 -0.45742 -0.33064 0.08797 2.91870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.439476 0.672448 0.654 0.5148
## target -0.172139 0.073094 -2.355 0.0203 *
## baseline_age 0.002805 0.007896 0.355 0.7231
## educ.demo -0.010033 0.031035 -0.323 0.7471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8385 on 110 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.05118, Adjusted R-squared: 0.0253
## F-statistic: 1.978 on 3 and 110 DF, p-value: 0.1215## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -1.07677 -0.63424 -0.41248 -0.00309 3.14822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.458987 0.725811 0.632 0.5283
## target 0.041266 0.164693 0.251 0.8026
## baseline_age 0.014587 0.008556 1.705 0.0906 .
## educ.demo -0.046580 0.034548 -1.348 0.1799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.003 on 128 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.03415, Adjusted R-squared: 0.01151
## F-statistic: 1.509 on 3 and 128 DF, p-value: 0.2154## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline cdr less 1 sample with covari [age, edu AND test]
Stroop (t-), flanker (t+), gonogo(t+), nbck(x-) humi (x)
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9301 -0.4243 -0.2779 -0.0136 3.2914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.792278 0.671810 1.179 0.24079
## target -0.094747 0.127082 -0.746 0.45751
## baseline_age -0.009228 0.009046 -1.020 0.30993
## educ.demo 0.008741 0.030267 0.289 0.77329
## visit_avg_strp -0.348115 0.110044 -3.163 0.00201 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8257 on 111 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.08954, Adjusted R-squared: 0.05673
## F-statistic: 2.729 on 4 and 111 DF, p-value: 0.03274## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -1.3427 -0.5587 -0.2243 0.1741 2.9560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6612301 0.6467631 2.569 0.0114 *
## target 0.4875167 0.2480803 1.965 0.0516 .
## baseline_age -0.0148739 0.0084090 -1.769 0.0793 .
## educ.demo 0.0009297 0.0303877 0.031 0.9756
## visit_avg_flk -1.2127113 0.1772246 -6.843 2.95e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8568 on 127 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.3004, Adjusted R-squared: 0.2783
## F-statistic: 13.63 on 4 and 127 DF, p-value: 2.832e-09## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -0.96359 -0.57397 -0.36380 -0.02465 2.87684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.183697 0.752958 0.244 0.808
## target 0.001496 0.006266 0.239 0.812
## baseline_age 0.015545 0.008996 1.728 0.087 .
## educ.demo -0.034785 0.037833 -0.919 0.360
## visit_avg_gonogo -0.059739 0.104925 -0.569 0.570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9735 on 104 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.04179, Adjusted R-squared: 0.004935
## F-statistic: 1.134 on 4 and 104 DF, p-value: 0.3447## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -0.7765 -0.4568 -0.3498 0.1408 2.8093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.631075 0.672198 0.939 0.3499
## target -0.157921 0.072627 -2.174 0.0318 *
## baseline_age -0.002092 0.008218 -0.255 0.7995
## educ.demo -0.005316 0.030773 -0.173 0.8632
## visit_avg_nback -0.179200 0.094270 -1.901 0.0600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8287 on 109 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.08163, Adjusted R-squared: 0.04792
## F-statistic: 2.422 on 4 and 109 DF, p-value: 0.05259## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -1.5525 -0.5539 -0.2400 0.2044 3.5930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.699960 0.683285 2.488 0.0141 *
## target 0.111213 0.147664 0.753 0.4528
## baseline_age -0.010757 0.008817 -1.220 0.2247
## educ.demo -0.026718 0.031062 -0.860 0.3913
## visit_avg_humi -0.662930 0.114872 -5.771 5.7e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.896 on 127 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.2348, Adjusted R-squared: 0.2107
## F-statistic: 9.743 on 4 and 127 DF, p-value: 6.619e-07## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"1c: PE at baseline cdr 0.5 plus
a. PE at baseline cdr 1+ sample no covari
Stroop (-), flanker (+), gonogo(+), nbck(-) humi (x)
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3063 -1.8742 -0.5746 0.9410 8.8416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5664 0.4927 7.238 7.61e-09 ***
## target -1.0085 0.5364 -1.880 0.0672 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.727 on 41 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.07938, Adjusted R-squared: 0.05693
## F-statistic: 3.535 on 1 and 41 DF, p-value: 0.06719## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -4.3273 -2.4087 -0.7209 1.1515 11.6038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2369 0.4176 10.146 <2e-16 ***
## target 0.6370 0.7789 0.818 0.416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.456 on 92 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.007218, Adjusted R-squared: -0.003573
## F-statistic: 0.6689 on 1 and 92 DF, p-value: 0.4155## `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()`).
## [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
## -4.9636 -2.2789 -0.7576 1.2889 12.0741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.22740 0.44063 9.594 2.5e-14 ***
## target 0.01508 0.01611 0.936 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.448 on 69 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.01253, Adjusted R-squared: -0.001786
## F-statistic: 0.8752 on 1 and 69 DF, p-value: 0.3528## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -3.1927 -1.7240 -0.9632 0.9978 9.1420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1665 0.4289 7.383 6.42e-09 ***
## target -0.8296 0.4151 -1.998 0.0527 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.746 on 39 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.09289, Adjusted R-squared: 0.06963
## F-statistic: 3.994 on 1 and 39 DF, p-value: 0.05268## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [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
## -4.712 -2.448 -0.932 1.444 12.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6954 0.3860 12.164 <2e-16 ***
## target -1.2049 0.6821 -1.767 0.0806 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.411 on 92 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.03281, Adjusted R-squared: 0.02229
## F-statistic: 3.121 on 1 and 92 DF, p-value: 0.08063## `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()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline cdr less 1 sample with covari [age, edu]
Stroop (x), flanker (+), gonogo(x), nbck(-) humi (x)
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6526 -1.3820 -0.5789 0.8566 8.4348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.13295 4.62909 -1.973 0.0556 .
## target -1.19140 0.52405 -2.273 0.0286 *
## baseline_age 0.12907 0.05288 2.441 0.0193 *
## educ.demo 0.30179 0.17312 1.743 0.0892 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.555 on 39 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.2316, Adjusted R-squared: 0.1725
## F-statistic: 3.919 on 3 and 39 DF, p-value: 0.01544## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0956 -2.3040 -0.5842 1.4391 11.9833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.12263 3.48888 -0.322 0.7484
## target 0.65568 0.79456 0.825 0.4114
## baseline_age 0.08034 0.04307 1.866 0.0654 .
## educ.demo 0.01470 0.15021 0.098 0.9222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.426 on 90 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.04531, Adjusted R-squared: 0.01349
## F-statistic: 1.424 on 3 and 90 DF, p-value: 0.241## `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_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1177 -2.1730 -0.6053 1.5491 12.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.45754 3.92532 -0.117 0.9076
## target 0.01476 0.01618 0.912 0.3648
## baseline_age 0.08414 0.04743 1.774 0.0806 .
## educ.demo -0.04251 0.17848 -0.238 0.8125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.42 on 67 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.05683, Adjusted R-squared: 0.0146
## F-statistic: 1.346 on 3 and 67 DF, p-value: 0.267## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8042 -1.4783 -0.1843 0.9906 8.6822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.65681 4.77979 -2.230 0.03193 *
## target -1.10004 0.39119 -2.812 0.00783 **
## baseline_age 0.16093 0.05315 3.028 0.00447 **
## educ.demo 0.24509 0.17529 1.398 0.17038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.508 on 37 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.2824, Adjusted R-squared: 0.2242
## F-statistic: 4.854 on 3 and 37 DF, p-value: 0.006021## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.755 -2.268 -0.735 1.636 12.387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.150320 3.387390 -0.044 0.9647
## target -1.101743 0.682667 -1.614 0.1101
## baseline_age 0.074736 0.042796 1.746 0.0842 .
## educ.demo 0.003856 0.144905 0.027 0.9788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.391 on 90 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.06514, Adjusted R-squared: 0.03398
## F-statistic: 2.09 on 3 and 90 DF, p-value: 0.107## `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()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline cdr less 1 sample with covari [age, edu AND test]
Stroop (t-), flanker (t+), gonogo(t+), nbck(x-) humi (x)
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## [1] "##############################"
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.422 -1.510 -0.199 1.135 5.154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.33997 4.04339 -1.568 0.12518
## target -1.19829 0.45032 -2.661 0.01135 *
## baseline_age 0.04425 0.05050 0.876 0.38639
## educ.demo 0.37659 0.15003 2.510 0.01645 *
## visit_avg_strp -1.18175 0.30700 -3.849 0.00044 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.195 on 38 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.4472, Adjusted R-squared: 0.389
## F-statistic: 7.685 on 4 and 38 DF, p-value: 0.0001221## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
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## [1] "##############################"
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7199 -2.1299 -0.4642 1.4618 10.2572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.911984 2.928661 0.311 0.756
## target 0.504972 0.663394 0.761 0.449
## baseline_age 0.005776 0.037806 0.153 0.879
## educ.demo 0.134380 0.126743 1.060 0.292
## visit_avg_flk -1.708855 0.269273 -6.346 9.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.859 on 89 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3427, Adjusted R-squared: 0.3132
## F-statistic: 11.6 on 4 and 89 DF, p-value: 1.26e-07## `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()`).
## [1] "##############################"
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1536 -2.2865 -0.5532 1.4622 10.7132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.484424 3.846772 0.126 0.9002
## target 0.014032 0.015758 0.890 0.3765
## baseline_age 0.053440 0.048315 1.106 0.2727
## educ.demo 0.008919 0.175405 0.051 0.9596
## visit_avg_gonogo -0.805430 0.372655 -2.161 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.33 on 66 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1192, Adjusted R-squared: 0.06579
## F-statistic: 2.232 on 4 and 66 DF, p-value: 0.07489## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4813 -1.6343 -0.3701 1.1388 8.4878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.76652 4.79588 -2.245 0.0310 *
## target -1.05038 0.39640 -2.650 0.0119 *
## baseline_age 0.14903 0.05499 2.710 0.0102 *
## educ.demo 0.27915 0.18002 1.551 0.1297
## visit_avg_nback -0.43992 0.49911 -0.881 0.3839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.515 on 36 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.2976, Adjusted R-squared: 0.2195
## F-statistic: 3.813 on 4 and 36 DF, p-value: 0.01101## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3500 -2.0185 -0.5225 1.4843 9.7325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.592872 2.910680 0.547 0.586
## target -0.816814 0.585541 -1.395 0.166
## baseline_age -0.009476 0.039313 -0.241 0.810
## educ.demo 0.116878 0.125357 0.932 0.354
## visit_avg_humi -2.363817 0.404282 -5.847 8.14e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.898 on 89 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3246, Adjusted R-squared: 0.2942
## F-statistic: 10.69 on 4 and 89 DF, p-value: 4.024e-07## `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()`).
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"1d: PE at baseline mutation + cdr less 1
a. PE at baseline no covari
## [1] "##############################"
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## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2413 -0.5631 0.3429 0.4721 0.8278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5719 0.3111 5.053 0.00233 **
## target -0.5355 0.4872 -1.099 0.31384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8091 on 6 degrees of freedom
## Multiple R-squared: 0.1676, Adjusted R-squared: 0.02887
## F-statistic: 1.208 on 1 and 6 DF, p-value: 0.3138## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1680 -0.5315 0.1330 0.4616 1.0767
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5698 0.2753 5.702 0.000453 ***
## target -0.8569 0.6337 -1.352 0.213285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7748 on 8 degrees of freedom
## Multiple R-squared: 0.186, Adjusted R-squared: 0.0843
## F-statistic: 1.829 on 1 and 8 DF, p-value: 0.2133## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.26155 -0.36836 0.02541 0.50693 1.16801
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4752 0.2460 5.998 0.000324 ***
## target 0.0179 0.0121 1.479 0.177328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.761 on 8 degrees of freedom
## Multiple R-squared: 0.2148, Adjusted R-squared: 0.1166
## F-statistic: 2.188 on 1 and 8 DF, p-value: 0.1773## `geom_smooth()` using formula = 'y ~ x'
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## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.27462 -0.03043 0.24110 0.30086 0.53773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7589 0.2639 6.665 0.000552 ***
## target -1.2104 0.5135 -2.357 0.056517 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.639 on 6 degrees of freedom
## Multiple R-squared: 0.4808, Adjusted R-squared: 0.3942
## F-statistic: 5.555 on 1 and 6 DF, p-value: 0.05652## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9761 -0.8436 0.2916 0.6388 1.0885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3483 0.3682 3.662 0.00639 **
## target 0.1820 0.8772 0.207 0.84085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8565 on 8 degrees of freedom
## Multiple R-squared: 0.00535, Adjusted R-squared: -0.119
## F-statistic: 0.04303 on 1 and 8 DF, p-value: 0.8408## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline with covari [age, edu]
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.608754 0.314436 -0.428989 0.315998 0.560082 0.206408 -0.003339 -0.355843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.56099 2.98793 -1.526 0.2016
## target -0.66884 0.34895 -1.917 0.1278
## baseline_age 0.02622 0.03470 0.756 0.4919
## educ.demo 0.29147 0.10139 2.875 0.0453 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5559 on 4 degrees of freedom
## Multiple R-squared: 0.738, Adjusted R-squared: 0.5415
## F-statistic: 3.756 on 3 and 4 DF, p-value: 0.1168## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6446 -0.2692 -0.1451 0.1109 1.0437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.37522 2.83597 -1.543 0.1738
## target -0.47577 0.53435 -0.890 0.4075
## baseline_age 0.03005 0.03132 0.959 0.3745
## educ.demo 0.25988 0.10252 2.535 0.0444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6216 on 6 degrees of freedom
## Multiple R-squared: 0.6071, Adjusted R-squared: 0.4106
## F-statistic: 3.09 on 3 and 6 DF, p-value: 0.1113## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8952 -0.2015 -0.0722 0.0277 0.8840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.489210 3.613504 -1.519 0.1795
## target -0.004294 0.014650 -0.293 0.7793
## baseline_age 0.031977 0.033916 0.943 0.3822
## educ.demo 0.314660 0.144505 2.178 0.0723 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6567 on 6 degrees of freedom
## Multiple R-squared: 0.5614, Adjusted R-squared: 0.3422
## F-statistic: 2.56 on 3 and 6 DF, p-value: 0.1509## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.05722 0.01650 -0.20815 -0.11357 0.43071 0.17353 -0.50285 0.26106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.50261 2.07191 -2.173 0.0955 .
## target -1.08123 0.31335 -3.451 0.0260 *
## baseline_age 0.04080 0.02357 1.731 0.1584
## educ.demo 0.24943 0.07074 3.526 0.0243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3861 on 4 degrees of freedom
## Multiple R-squared: 0.8736, Adjusted R-squared: 0.7788
## F-statistic: 9.216 on 3 and 4 DF, p-value: 0.02866## `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
## -0.96251 -0.15464 -0.09189 0.06077 0.90007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.89849 2.98252 -1.642 0.1516
## target -0.04656 0.69092 -0.067 0.9485
## baseline_age 0.03023 0.03392 0.891 0.4072
## educ.demo 0.28626 0.10577 2.706 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6611 on 6 degrees of freedom
## Multiple R-squared: 0.5555, Adjusted R-squared: 0.3333
## F-statistic: 2.499 on 3 and 6 DF, p-value: 0.1565## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. PE at baseline with covari [age, edu, test]
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.447708 0.221111 -0.299996 0.001729 0.687063 0.258110 0.008499 -0.428808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.26582 4.01592 -1.560 0.2166
## target -0.89521 0.49349 -1.814 0.1673
## baseline_age 0.04525 0.04598 0.984 0.3976
## educ.demo 0.33459 0.12471 2.683 0.0749 .
## visit_avg_strp 0.63377 0.90315 0.702 0.5334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.595 on 3 degrees of freedom
## Multiple R-squared: 0.7749, Adjusted R-squared: 0.4749
## F-statistic: 2.583 on 4 and 3 DF, p-value: 0.2309
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## 0.165507 0.502905 -0.421661 -0.390476 0.358038 -0.009779 0.476402 -0.541796
## 9 10
## -0.246202 0.107063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.68482 3.52883 0.194 0.8538
## target 0.08515 0.53075 0.160 0.8788
## baseline_age -0.04747 0.04793 -0.990 0.3675
## educ.demo 0.22524 0.08699 2.589 0.0489 *
## visit_avg_flk -1.37176 0.71260 -1.925 0.1122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.516 on 5 degrees of freedom
## Multiple R-squared: 0.7743, Adjusted R-squared: 0.5938
## F-statistic: 4.289 on 4 and 5 DF, p-value: 0.07102
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9 10
## 0.1418 0.1013 -0.1656 -0.3500 0.1398 0.1833 0.5570 -0.3340 -0.4412 0.1676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.808116 3.519769 0.798 0.4612
## target 0.006459 0.009963 0.648 0.5454
## baseline_age -0.060288 0.036645 -1.645 0.1608
## educ.demo 0.147131 0.106704 1.379 0.2264
## visit_avg_gonogo -1.054799 0.338065 -3.120 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.419 on 5 degrees of freedom
## Multiple R-squared: 0.8512, Adjusted R-squared: 0.7321
## F-statistic: 7.15 on 4 and 5 DF, p-value: 0.02672
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.23491 -0.03279 -0.09770 0.22934 0.26137 0.04982 -0.37341 0.19828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.15755 1.89605 -2.193 0.1160
## target -0.57467 0.46746 -1.229 0.3066
## baseline_age 0.01325 0.02940 0.451 0.6827
## educ.demo 0.29967 0.07397 4.051 0.0271 *
## visit_avg_nback -0.43348 0.31760 -1.365 0.2656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3502 on 3 degrees of freedom
## Multiple R-squared: 0.922, Adjusted R-squared: 0.8181
## F-statistic: 8.869 on 4 and 3 DF, p-value: 0.05189
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9 10
## -0.4077 0.6911 -0.1300 -0.5230 0.3552 -0.6085 0.7883 -0.1321 -0.2270 0.1938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.995176 3.089676 -1.293 0.2525
## target 0.281469 0.756105 0.372 0.7250
## baseline_age -0.003982 0.047179 -0.084 0.9360
## educ.demo 0.331684 0.113891 2.912 0.0333 *
## visit_avg_humi -0.592370 0.571514 -1.036 0.3475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6571 on 5 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.3414
## F-statistic: 2.166 on 4 and 5 DF, p-value: 0.2093
##
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"2a: PE change in FTLD SOB
a. Full sample change in FTLD SOB
No covari: No sig findings
df.baseline.analsyes.a2<-df.pe.a.baselinemeged %>%
filter(visit==1,
!is.na(max_dif_ftld_global)) #%>%
#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.mut%>%
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
## -0.3997 -0.3032 -0.2153 0.2597 0.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2763 0.1865 1.482 0.189
## target 0.1444 0.2920 0.494 0.639
##
## Residual standard error: 0.485 on 6 degrees of freedom
## Multiple R-squared: 0.03914, Adjusted R-squared: -0.121
## F-statistic: 0.2444 on 1 and 6 DF, p-value: 0.6386## `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
## -0.58600 -0.15248 -0.09702 0.24622 0.55040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1303 0.1305 0.999 0.3473
## target 0.6040 0.3005 2.010 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3674 on 8 degrees of freedom
## Multiple R-squared: 0.3356, Adjusted R-squared: 0.2525
## F-statistic: 4.04 on 1 and 8 DF, p-value: 0.07927## `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
## -0.33052 -0.21549 -0.14155 -0.00482 0.91597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.215492 0.133160 1.618 0.144
## target -0.008216 0.006551 -1.254 0.245
##
## Residual standard error: 0.412 on 8 degrees of freedom
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.05986
## F-statistic: 1.573 on 1 and 8 DF, p-value: 0.2452## `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
## -0.45248 -0.16511 -0.03077 0.19851 0.40899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1209 0.1372 0.881 0.4122
## target 0.7215 0.2671 2.702 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3323 on 6 degrees of freedom
## Multiple R-squared: 0.5489, Adjusted R-squared: 0.4737
## F-statistic: 7.3 on 1 and 6 DF, p-value: 0.03549## `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
## -0.3079 -0.2466 -0.2184 0.1128 0.8351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3079 0.1914 1.609 0.146
## target -0.2036 0.4560 -0.446 0.667
##
## Residual standard error: 0.4452 on 8 degrees of freedom
## Multiple R-squared: 0.02431, Adjusted R-squared: -0.09765
## F-statistic: 0.1993 on 1 and 8 DF, p-value: 0.6671## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. sample change in FTLD SOB full sample with covari [age, edu]
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2737 -0.1942 -0.1583 -0.1133 10.8241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.035127 0.770877 -0.046 0.964
## target 0.049443 0.136885 0.361 0.719
## baseline_age 0.004036 0.009142 0.442 0.660
## educ.demo -0.003803 0.036042 -0.106 0.916
##
## Residual standard error: 1.048 on 135 degrees of freedom
## Multiple R-squared: 0.002316, Adjusted R-squared: -0.01986
## F-statistic: 0.1044 on 3 and 135 DF, p-value: 0.9574## `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
## -1.2692 -0.2851 -0.1637 -0.0122 10.0036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.519961 0.588992 -0.883 0.378444
## target 0.707532 0.192258 3.680 0.000302 ***
## baseline_age 0.002675 0.007069 0.378 0.705526
## educ.demo 0.025399 0.028050 0.905 0.366330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9577 on 193 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06788, Adjusted R-squared: 0.05339
## F-statistic: 4.685 on 3 and 193 DF, p-value: 0.003502## `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
## -0.9626 -0.2680 -0.2032 -0.1310 10.4954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.142662 0.718534 -0.199 0.843
## target 0.006597 0.004117 1.603 0.111
## baseline_age 0.004345 0.008636 0.503 0.616
## educ.demo 0.004638 0.036071 0.129 0.898
##
## Residual standard error: 1.078 on 152 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02047, Adjusted R-squared: 0.001133
## F-statistic: 1.059 on 3 and 152 DF, p-value: 0.3686## `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
## -0.7601 -0.2471 -0.1605 -0.0487 10.3978
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.278183 0.778879 0.357 0.722
## target -0.172457 0.081842 -2.107 0.037 *
## baseline_age 0.001971 0.009135 0.216 0.829
## educ.demo -0.010924 0.036341 -0.301 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.039 on 133 degrees of freedom
## Multiple R-squared: 0.03376, Adjusted R-squared: 0.01197
## F-statistic: 1.549 on 3 and 133 DF, p-value: 0.2048## `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
## -0.3264 -0.2332 -0.1921 -0.1521 10.8037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.175433 0.602075 -0.291 0.771
## target -0.027237 0.135630 -0.201 0.841
## baseline_age 0.004647 0.007310 0.636 0.526
## educ.demo 0.006320 0.028566 0.221 0.825
##
## Residual standard error: 0.9907 on 193 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002676, Adjusted R-squared: -0.01283
## F-statistic: 0.1726 on 3 and 193 DF, p-value: 0.9148## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a. sample change in FTLD SOB full sample with covari +test [age, edu, AND test]
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"
## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4110 -0.2507 -0.0994 0.0967 9.7483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.71655 0.75987 0.943 0.347383
## target 0.05582 0.13040 0.428 0.669265
## baseline_age -0.01584 0.01013 -1.564 0.120118
## educ.demo 0.01473 0.03467 0.425 0.671579
## visit_avg_strp -0.37000 0.09623 -3.845 0.000185 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9985 on 134 degrees of freedom
## Multiple R-squared: 0.1015, Adjusted R-squared: 0.07464
## F-statistic: 3.783 on 4 and 134 DF, p-value: 0.006012## `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
## -1.2382 -0.2909 -0.1633 -0.0019 10.0109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.572097 0.599187 -0.955 0.34089
## target 0.715196 0.193234 3.701 0.00028 ***
## baseline_age 0.004051 0.007594 0.533 0.59433
## educ.demo 0.023460 0.028369 0.827 0.40928
## visit_avg_flk 0.039396 0.078400 0.503 0.61589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9596 on 192 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0691, Adjusted R-squared: 0.04971
## F-statistic: 3.563 on 4 and 192 DF, p-value: 0.007895## `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
## -0.9612 -0.3424 -0.1938 -0.0093 10.4581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2877830 0.7201480 -0.400 0.690
## target 0.0066157 0.0040945 1.616 0.108
## baseline_age 0.0075993 0.0088180 0.862 0.390
## educ.demo 0.0009099 0.0359475 0.025 0.980
## visit_avg_gonogo 0.1391305 0.0852596 1.632 0.105
##
## Residual standard error: 1.072 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03744, Adjusted R-squared: 0.01194
## F-statistic: 1.468 on 4 and 151 DF, p-value: 0.2146## `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
## -0.5596 -0.2692 -0.1661 -0.0265 10.3310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.407942 0.783948 0.520 0.6037
## target -0.150110 0.083563 -1.796 0.0747 .
## baseline_age -0.002097 0.009670 -0.217 0.8286
## educ.demo -0.005890 0.036480 -0.161 0.8720
## visit_avg_nback -0.131696 0.104478 -1.261 0.2097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 132 degrees of freedom
## Multiple R-squared: 0.04525, Adjusted R-squared: 0.01632
## F-statistic: 1.564 on 4 and 132 DF, p-value: 0.1876## `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
## -0.5983 -0.2938 -0.1679 -0.0644 10.6420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.163739 0.623743 0.263 0.7932
## target 0.012967 0.136339 0.095 0.9243
## baseline_age -0.003525 0.008425 -0.418 0.6761
## educ.demo 0.013425 0.028614 0.469 0.6395
## visit_avg_humi -0.167903 0.087830 -1.912 0.0574 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9839 on 192 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0213, Adjusted R-squared: 0.0009149
## F-statistic: 1.045 on 4 and 192 DF, p-value: 0.3853## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"2b: PE change in FTLD SOB CDR 0 or 0.5
a. Change in SOB for those with CDR 0 or 0.5
No covari: No sig findings
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.mut%>%
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
## -0.3997 -0.3032 -0.2153 0.2597 0.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2763 0.1865 1.482 0.189
## target 0.1444 0.2920 0.494 0.639
##
## Residual standard error: 0.485 on 6 degrees of freedom
## Multiple R-squared: 0.03914, Adjusted R-squared: -0.121
## F-statistic: 0.2444 on 1 and 6 DF, p-value: 0.6386## `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
## -0.58600 -0.15248 -0.09702 0.24622 0.55040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1303 0.1305 0.999 0.3473
## target 0.6040 0.3005 2.010 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3674 on 8 degrees of freedom
## Multiple R-squared: 0.3356, Adjusted R-squared: 0.2525
## F-statistic: 4.04 on 1 and 8 DF, p-value: 0.07927## `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
## -0.33052 -0.21549 -0.14155 -0.00482 0.91597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.215492 0.133160 1.618 0.144
## target -0.008216 0.006551 -1.254 0.245
##
## Residual standard error: 0.412 on 8 degrees of freedom
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.05986
## F-statistic: 1.573 on 1 and 8 DF, p-value: 0.2452## `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
## -0.45248 -0.16511 -0.03077 0.19851 0.40899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1209 0.1372 0.881 0.4122
## target 0.7215 0.2671 2.702 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3323 on 6 degrees of freedom
## Multiple R-squared: 0.5489, Adjusted R-squared: 0.4737
## F-statistic: 7.3 on 1 and 6 DF, p-value: 0.03549## `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
## -0.3079 -0.2466 -0.2184 0.1128 0.8351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3079 0.1914 1.609 0.146
## target -0.2036 0.4560 -0.446 0.667
##
## Residual standard error: 0.4452 on 8 degrees of freedom
## Multiple R-squared: 0.02431, Adjusted R-squared: -0.09765
## F-statistic: 0.1993 on 1 and 8 DF, p-value: 0.6671## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
## [1] "##############################"a, sample change in FTLD SOB full sample with covari [age, edu]
## [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] "##############################"a. sample change in FTLD SOB full sample with covari +test [age, edu, AND test]
## [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] "##############################"2c: PE change in FTLD SOB CDR 0.5 or more
a. Change in SOB for those with 0.5 or 1
No covari: No sig findings
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.mut%>%
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
## -0.3997 -0.3032 -0.2153 0.2597 0.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2763 0.1865 1.482 0.189
## target 0.1444 0.2920 0.494 0.639
##
## Residual standard error: 0.485 on 6 degrees of freedom
## Multiple R-squared: 0.03914, Adjusted R-squared: -0.121
## F-statistic: 0.2444 on 1 and 6 DF, p-value: 0.6386## `geom_smooth()` using formula = 'y ~ x'
## [1] "##############################"
## [1] "##############################"
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58600 -0.15248 -0.09702 0.24622 0.55040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1303 0.1305 0.999 0.3473
## target 0.6040 0.3005 2.010 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3674 on 8 degrees of freedom
## Multiple R-squared: 0.3356, Adjusted R-squared: 0.2525
## F-statistic: 4.04 on 1 and 8 DF, p-value: 0.07927## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33052 -0.21549 -0.14155 -0.00482 0.91597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.215492 0.133160 1.618 0.144
## target -0.008216 0.006551 -1.254 0.245
##
## Residual standard error: 0.412 on 8 degrees of freedom
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.05986
## F-statistic: 1.573 on 1 and 8 DF, p-value: 0.2452## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45248 -0.16511 -0.03077 0.19851 0.40899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1209 0.1372 0.881 0.4122
## target 0.7215 0.2671 2.702 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3323 on 6 degrees of freedom
## Multiple R-squared: 0.5489, Adjusted R-squared: 0.4737
## F-statistic: 7.3 on 1 and 6 DF, p-value: 0.03549## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3079 -0.2466 -0.2184 0.1128 0.8351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3079 0.1914 1.609 0.146
## target -0.2036 0.4560 -0.446 0.667
##
## Residual standard error: 0.4452 on 8 degrees of freedom
## Multiple R-squared: 0.02431, Adjusted R-squared: -0.09765
## F-statistic: 0.1993 on 1 and 8 DF, p-value: 0.6671## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6815 -0.5114 -0.3891 -0.2557 10.6028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.894198 2.884887 -0.310 0.758
## target -0.013577 0.350329 -0.039 0.969
## baseline_age 0.008654 0.033985 0.255 0.800
## educ.demo 0.049377 0.114154 0.433 0.668
##
## Residual standard error: 1.801 on 43 degrees of freedom
## Multiple R-squared: 0.005594, Adjusted R-squared: -0.06378
## F-statistic: 0.08064 on 3 and 43 DF, p-value: 0.9702## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5583 -0.5077 -0.2791 0.0310 9.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.87750 1.23646 -0.710 0.4796
## target 0.79714 0.29348 2.716 0.0078 **
## baseline_age -0.00207 0.01588 -0.130 0.8965
## educ.demo 0.07235 0.05322 1.360 0.1771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 99 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0751, Adjusted R-squared: 0.04707
## F-statistic: 2.679 on 3 and 99 DF, p-value: 0.05108## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2544 -0.5189 -0.3755 -0.2255 10.2999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.151072 1.645649 -0.092 0.927
## target 0.006480 0.006952 0.932 0.354
## baseline_age -0.002265 0.020349 -0.111 0.912
## educ.demo 0.042109 0.075428 0.558 0.578
##
## Residual standard error: 1.512 on 73 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01882, Adjusted R-squared: -0.0215
## F-statistic: 0.4668 on 3 and 73 DF, p-value: 0.7063## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4328 -0.6062 -0.3539 0.1117 9.2492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.09019 2.89212 -0.723 0.4740
## target -0.61216 0.26590 -2.302 0.0265 *
## baseline_age 0.02098 0.03320 0.632 0.5309
## educ.demo 0.07785 0.11234 0.693 0.4922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.735 on 41 degrees of freedom
## Multiple R-squared: 0.1182, Adjusted R-squared: 0.05373
## F-statistic: 1.833 on 3 and 41 DF, p-value: 0.1563## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5630 -0.4306 -0.3475 -0.2077 10.6247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.207654 1.258261 -0.165 0.869
## target -0.051899 0.263675 -0.197 0.844
## baseline_age -0.001684 0.016490 -0.102 0.919
## educ.demo 0.043065 0.054101 0.796 0.428
##
## Residual standard error: 1.348 on 99 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.006561, Adjusted R-squared: -0.02354
## F-statistic: 0.218 on 3 and 99 DF, p-value: 0.8837## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_strp"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8087 -0.5528 -0.2558 0.1388 9.4371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.362071 2.848533 0.127 0.899
## target -0.005796 0.337890 -0.017 0.986
## baseline_age -0.024542 0.036535 -0.672 0.505
## educ.demo 0.069236 0.110516 0.626 0.534
## visit_avg_strp -0.478014 0.232413 -2.057 0.046 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.737 on 42 degrees of freedom
## Multiple R-squared: 0.09659, Adjusted R-squared: 0.01055
## F-statistic: 1.123 on 4 and 42 DF, p-value: 0.3588## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_flk"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4800 -0.5271 -0.2816 0.0007 9.7717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.975141 1.240925 -0.786 0.43387
## target 0.808456 0.293803 2.752 0.00706 **
## baseline_age 0.002472 0.016557 0.149 0.88163
## educ.demo 0.063463 0.054015 1.175 0.24287
## visit_avg_flk 0.115601 0.119225 0.970 0.33463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.301 on 98 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.08388, Adjusted R-squared: 0.04649
## F-statistic: 2.243 on 4 and 98 DF, p-value: 0.06983## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_gonogo"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3387 -0.6343 -0.2671 -0.0192 10.1664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.488819 1.629693 -0.300 0.7651
## target 0.007114 0.006850 1.039 0.3025
## baseline_age 0.007164 0.020668 0.347 0.7299
## educ.demo 0.028454 0.074592 0.381 0.7040
## visit_avg_gonogo 0.291211 0.158133 1.842 0.0697 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.488 on 72 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06296, Adjusted R-squared: 0.0109
## F-statistic: 1.209 on 4 and 72 DF, p-value: 0.3143## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_nback"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0779 -0.6800 -0.3645 0.1338 9.2079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.08307 2.91948 -0.714 0.4797
## target -0.59225 0.27152 -2.181 0.0351 *
## baseline_age 0.01599 0.03505 0.456 0.6507
## educ.demo 0.08949 0.11590 0.772 0.4446
## visit_avg_nback -0.16793 0.34543 -0.486 0.6295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.751 on 40 degrees of freedom
## Multiple R-squared: 0.1234, Adjusted R-squared: 0.03577
## F-statistic: 1.408 on 4 and 40 DF, p-value: 0.2488## `geom_smooth()` using formula = 'y ~ x'
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## [1] "pe_humi"
##
## Call:
## lm(formula = lp.fml, data = df.pe.loop)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7518 -0.4446 -0.3483 -0.1721 10.5505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15887 1.26211 -0.126 0.900
## target -0.03113 0.26546 -0.117 0.907
## baseline_age -0.00630 0.01751 -0.360 0.720
## educ.demo 0.05175 0.05529 0.936 0.352
## visit_avg_humi -0.14101 0.17749 -0.794 0.429
##
## Residual standard error: 1.35 on 98 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01292, Adjusted R-squared: -0.02737
## F-statistic: 0.3207 on 4 and 98 DF, p-value: 0.8635## `geom_smooth()` using formula = 'y ~ x'
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