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‎R/cge_analysis_script.R

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@@ -1745,6 +1745,24 @@ legend("topleft", legend = c(expression(bold("WMC Group")), "low WMC", "high WMC
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col = c(NA, 'red', 'red'), lty = c(NA, 1, 2), lwd = c(NA, 2, 2),
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bty = "o", inset = c(0.025, 0.025))
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# for SANS
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par(mfrow = c(1,2))
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par(mar = c(5, 6, 4, 4))
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# ~ low WMC
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plot(x = xval_plot, y = predict_output_m3_best_L[1:10], cex.axis = 1.3, las = 1,
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type = 'l', lwd = 5, col = 'blue',ylim = c(1.3, 1.8),
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Reaction Time (seconds)")), cex.lab = 1.5,)
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lines(x = xval_plot, y = predict_output_m3_best_L[11:20],
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lwd = 5, col = 'red', lty = 1)
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# ~ high WMC
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plot(x = xval_plot, y = predict_output_m3_best_H[1:10], cex.axis = 1.3, las = 1,
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type = 'l', lwd = 5, col = 'blue',ylim = c(1.3, 1.8),
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Reaction Time (seconds)")), cex.lab = 1.5,)
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lines(x = xval_plot, y = predict_output_m3_best_H[11:20],
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lwd = 5, col = 'red', lty = 1)
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@@ -5937,6 +5955,96 @@ wind2_m11_sepdifficulties_3ways_rfx = lmer(wind2_effort_isi_mean ~ 1 +
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(1 | subjectnumber), data = clean_data_dm, REML = F)
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summary(wind2_m11_sepdifficulties_3ways_rfx)
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# xval_plot = seq(from = 0, to = 1, length.out = 10)
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# predict_data_m3_best_H = clean_data_dm[0,];
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# predict_data_m3_best_H[1:20,] = NA;
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# predict_data_m3_best_H$all_diff_cont[1:10] = xval_plot
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# predict_data_m3_best_H$all_diff_cont[11:20] = xval_plot
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# predict_data_m3_best_H$prev_all_diff_cont[1:10] = 0;
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# predict_data_m3_best_H$prev_all_diff_cont[11:20] = 1;
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# predict_data_m3_best_H$capacity_HighP1_lowN1_best = 1;
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#
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# predict_data_m3_best_L = predict_data_m3_best_H;
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# predict_data_m3_best_L$capacity_HighP1_lowN1_best = -1;
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#
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# predict_output_m3_best_H = predict(m3_best, newdata = predict_data_m3_best_H, type = 'response', re.form = NA)^2
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# predict_output_m3_best_L = predict(m3_best, newdata = predict_data_m3_best_L, type = 'response', re.form = NA)^2
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par(mfrow = c(1,2))
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par(mar = c(5, 6, 4, 4))
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xval_plot = seq(from = 0, to = 1, by = .1)
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# cd = c(0,1) # this is redundant with above
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pd = c(0,1)
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choice = c(0,1)
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wmc = c(-1, 1)
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coef_vals = fixef(wind2_m11_sepdifficulties_3ways_rfx)
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# layout(matrix(c(1, 2, 3), nrow = 1, ncol = 3), widths = c(2.5, 2.5, 2))
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# low WMC pupil
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# CD x Choice
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# ~ previous easy, risky choice
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plot(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[1] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[1] * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[1] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[1] * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'blue', lty = 1, cex.axis = 1.3, las = 1,
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Pupil Dilation (mm)")), ylim = c(4,4.25), cex.lab = 1.5)
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# ~ previous difficult, risky choice
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lines(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[2] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[2] * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[2] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[2] * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'red', lty = 1)
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# high WMC pupil
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# CD x Choice
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# ~ previous easy, risky choice
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plot(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[1] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[1] * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[1] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[1] * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'blue', lty = 1, cex.axis = 1.3, las = 1,
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Pupil Dilation (mm)")), ylim = c(4,4.25), cex.lab = 1.5)
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# ~ previous difficult, risky choice
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lines(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[2] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[2] * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[2] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[2] * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'red', lty = 1)
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# plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(0, 1), axes = FALSE)
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# legend("left", legend = c(expression(bold("Previous Easy")), "Safe Choice", "Risky Choice", NA,
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# expression(bold("Previous Difficult")), "Safe Choice", "Risky Choice"), # Labels
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# col = c(NA, "darkblue", "darkblue", NA, NA, "orange", "orange"), # Blue for Easy, Red for Difficult
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# lty = c(NA, 1, 2, NA, NA, 1, 2), lwd = 2)
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wind2_m11_2ways_rfx = lmer(wind2_effort_isi_mean ~ 1 +
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trialnumberRS * capacity_HighP1_lowN1_best +
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trialnumberRS * choice +
@@ -7428,6 +7536,79 @@ summary(wind4_m11_2ways_rfx)
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# capacity_HighP1_lowN1_best:prev_all_diff_cont 2.288e-03 6.583e-03 1.349e+04 0.348 0.72815
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# choice:prev_all_diff_cont -8.493e-04 1.232e-02 1.349e+04 -0.069 0.94503
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par(mfrow = c(1,2))
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par(mar = c(5, 6, 4, 4))
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xval_plot = seq(from = 0, to = 1, by = .1)
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# cd = c(0,1) # this is redundant with above
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pd = c(0,1)
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choice = c(0,1)
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wmc = c(-1, 1)
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coef_vals = fixef(wind4_m11_sepdifficulties_3ways_rfx)
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# layout(matrix(c(1, 2, 3), nrow = 1, ncol = 3), widths = c(2.5, 2.5, 2))
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# low WMC pupil
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# CD x Choice
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# ~ previous easy, risky choice
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plot(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[1] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[1] * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[1] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[1] * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'blue', lty = 1, cex.axis = 1.3, las = 1,
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Pupil Dilation (mm)")), ylim = c(4,4.25), cex.lab = 1.5)
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# ~ previous difficult, risky choice
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lines(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[2] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[2] * wmc[1] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[2] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[2] * wmc[1] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'red', lty = 1)
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# high WMC pupil
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# CD x Choice
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# ~ previous easy, risky choice
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plot(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[1] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[1] * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[1] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[1] * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'blue', lty = 1, cex.axis = 1.3, las = 1,
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xlab = expression(bold("Current Difficulty")), ylab = expression(bold("Pupil Dilation (mm)")), ylim = c(4,4.25), cex.lab = 1.5)
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# ~ previous difficult, risky choice
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lines(x = xval_plot, y = coef_vals["(Intercept)"] +
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xval_plot * coef_vals["all_diff_cont"] +
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pd[2] * coef_vals["prev_all_diff_cont"] +
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choice[2] * coef_vals["choice"] +
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xval_plot * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:all_diff_cont"] +
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pd[2] * wmc[2] * coef_vals["capacity_HighP1_lowN1_best:prev_all_diff_cont"] +
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xval_plot * choice[2] * coef_vals["choice:all_diff_cont"] +
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pd[2] * choice[2] * coef_vals["choice:prev_all_diff_cont"] +
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xval_plot * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:all_diff_cont"] +
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pd[2] * wmc[2] * choice[2] * coef_vals["capacity_HighP1_lowN1_best:choice:prev_all_diff_cont"],
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type = 'l', lwd = 5, col = 'red', lty = 1)
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anova(wind4_m11_sepdifficulties_3ways_rfx,wind4_m11_2ways_rfx)
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74317612

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### LOOP: Regression Loop and Plotting a Predictor across all Pupillometry Windows #####
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