Graph of the Conditional Densities of a Linear Regression

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I have this date frame with two columns: Y and X. 

data=structure(list(Y = c(NA, -1.793, -0.642, 1.189, -0.823, -1.715, 
    1.623, 0.964, 0.395, -3.736, -0.47, 2.366, 0.634, -0.701, -1.692, 
    0.155, 2.502, -2.292, 1.967, -2.326, -1.476, 1.464, 1.45, -0.797, 
    1.27, 2.515, -0.765, 0.261, 0.423, 1.698, -2.734, 0.743, -2.39, 
    0.365, 2.981, -1.185, -0.57, 2.638, -1.046, 1.931, 4.583, -1.276, 
    1.075, 2.893, -1.602, 1.801, 2.405, -5.236, 2.214, 1.295, 1.438, 
    -0.638, 0.716, 1.004, -1.328, -1.759, -1.315, 1.053, 1.958, -2.034, 
    2.936, -0.078, -0.676, -2.312, -0.404, -4.091, -2.456, 0.984, 
    -1.648, 0.517, 0.545, -3.406, -2.077, 4.263, -0.352, -1.107, 
    -2.478, -0.718, 2.622, 1.611, -4.913, -2.117, -1.34, -4.006, 
    -1.668, -1.934, 0.972, 3.572, -3.332, 1.094, -0.273, 1.078, -0.587, 
    -1.25, -4.231, -0.439, 1.776, -2.077, 1.892, -1.069, 4.682, 1.665, 
    1.793, -2.133, 1.651, -0.065, 2.277, 0.792, -3.469, 1.48, 0.958, 
    -4.68, -2.909, 1.169, -0.941, -1.863, 1.814, -2.082, -3.087, 
    0.505, -0.013, -0.12, -0.082, -1.944, 1.094, -1.418, -1.273, 
    0.741, -1.001, -1.945, 1.026, 3.24, 0.131, -0.061, 0.086, 0.35, 
    0.22, -0.704, 0.466, 8.255, 2.302, 9.819, 5.162, 6.51, -0.275, 
    1.141, -0.56, -3.324, -8.456, -2.105, -0.666, 1.707, 1.886, -3.018, 
    0.441, 1.612, 0.774, 5.122, 0.362, -0.903, 5.21, -2.927, -4.572, 
    1.882, -2.5, -1.449, 2.627, -0.532, -2.279, -1.534, 1.459, -3.975, 
    1.328, 2.491, -2.221, 0.811, 4.423, -3.55, 2.592, 1.196, -1.529, 
    -1.222, -0.019, -1.62, 5.356, -1.885, 0.105, -1.366, -1.652, 
    0.233, 0.523, -1.416, 2.495, 4.35, -0.033, -2.468, 2.623, -0.039, 
    0.043, -2.015, -4.58, 0.793, -1.938, -1.105, 0.776, -1.953, 0.521, 
    -1.276, 0.666, -1.919, 1.268, 1.646, 2.413, 1.323, 2.135, 0.435, 
    3.747, -2.855, 4.021, -3.459, 0.705, -3.018, 0.779, 1.452, 1.523, 
    -1.938, 2.564, 2.108, 3.832, 1.77, -3.087, -1.902, 0.644, 8.507
    ), X = c(0.056, 0.053, 0.033, 0.053, 0.062, 0.09, 0.11, 0.124, 
    0.129, 0.129, 0.133, 0.155, 0.143, 0.155, 0.166, 0.151, 0.144, 
    0.168, 0.171, 0.162, 0.168, 0.169, 0.117, 0.105, 0.075, 0.057, 
    0.031, 0.038, 0.034, -0.016, -0.001, -0.031, -0.001, -0.004, 
    -0.056, -0.016, 0.007, 0.015, -0.016, -0.016, -0.053, -0.059, 
    -0.054, -0.048, -0.051, -0.052, -0.072, -0.063, 0.02, 0.034, 
    0.043, 0.084, 0.092, 0.111, 0.131, 0.102, 0.167, 0.162, 0.167, 
    0.187, 0.165, 0.179, 0.177, 0.192, 0.191, 0.183, 0.179, 0.176, 
    0.19, 0.188, 0.215, 0.221, 0.203, 0.2, 0.191, 0.188, 0.19, 0.228, 
    0.195, 0.204, 0.221, 0.218, 0.224, 0.233, 0.23, 0.258, 0.268, 
    0.291, 0.275, 0.27, 0.276, 0.276, 0.248, 0.228, 0.223, 0.218, 
    0.169, 0.188, 0.159, 0.156, 0.15, 0.117, 0.088, 0.068, 0.057, 
    0.035, 0.021, 0.014, -0.005, -0.014, -0.029, -0.043, -0.046, 
    -0.068, -0.073, -0.042, -0.04, -0.027, -0.018, -0.021, 0.002, 
    0.002, 0.006, 0.015, 0.022, 0.039, 0.044, 0.055, 0.064, 0.096, 
    0.093, 0.089, 0.173, 0.203, 0.216, 0.208, 0.225, 0.245, 0.23, 
    0.218, -0.267, 0.193, -0.013, 0.087, 0.04, 0.012, -0.008, 0.004, 
    0.01, 0.002, 0.008, 0.006, 0.013, 0.018, 0.019, 0.018, 0.021, 
    0.024, 0.017, 0.015, -0.005, 0.002, 0.014, 0.021, 0.022, 0.022, 
    0.02, 0.025, 0.021, 0.027, 0.034, 0.041, 0.04, 0.038, 0.033, 
    0.034, 0.031, 0.029, 0.029, 0.029, 0.022, 0.021, 0.019, 0.021, 
    0.016, 0.007, 0.002, 0.011, 0.01, 0.01, 0.003, 0.009, 0.015, 
    0.018, 0.017, 0.021, 0.021, 0.021, 0.022, 0.023, 0.025, 0.022, 
    0.022, 0.019, 0.02, 0.023, 0.022, 0.024, 0.022, 0.025, 0.025, 
    0.022, 0.027, 0.024, 0.016, 0.024, 0.018, 0.024, 0.021, 0.021, 
    0.021, 0.021, 0.022, 0.016, 0.015, 0.017, -0.017, -0.009, -0.003, 
    -0.012, -0.009, -0.008, -0.024, -0.023)), .Names = c("Y", "X"
    ), row.names = c(NA, -234L), class = "data.frame")

So, rotate my basic linear regression: lm(data[,1]~data[,2])

Now, conditioned on the values of X = quantile(data[,2],c(0.10,0.5,.70)) , I need to construct the respective conditional densities.

However, I would like to plot these density plots in the scatter chart below: 

plot(data[,2],data[,1])
abline(h=0,col="red")
abline(lm(data[,1]~data[,2]))

Is it too complicated to do this? Any help?

    
asked by anonymous 15.11.2016 / 23:08

1 answer

2

Here is a hint of code to make this chart: 

# fazer a regressao com esta sintaxe evita 
# erros e warnings com a funcao predict
ajuste <- lm(Y ~ X, data=data) 

# medias de cada Y previsto

X <- quantile(data[,2], c(0.10,0.5,.70))
X <- data.frame(X=X)

previsao <- predict(ajuste, newdata=X)

# variancia dos Y previstos 
# (mesmo valor para todas, pois variancia constante
# eh uma das hipoteses da regressao linear)

var.erro <- anova(ajuste)$"Mean Sq"[2]

# grafico da regressao

plot(Y ~ X, data=data)
abline(h=0, col="red")
abline(ajuste)

# graficos das distribuicoes Y|X

# criar o dominio da funcao no intervalo
# (media-3*desvpad, media+3*desvpad)
# e calcular a densidade correspondente

densidadeRotada <- function(valor.predito, var.erro){
    dominio <- seq(valor.predito-3*sqrt(var.erro), 
        valor.predito+3*sqrt(var.erro), length.out=100)
    imagem  <- dnorm(dominio, mean=valor.predito, sd=sqrt(var.erro))
    return(data.frame(Dominio=dominio, Imagem=imagem))
}

# o truque eh plotar o grafico com dominio e imagem 
# trocados de posicao

curva.previsao <- densidadeRotada(previsao[1], var.erro)
lines(curva.previsao$Imagem, curva.previsao$Dominio)

# este primeiro resultado nao parece bom
# seria interessante brincar com a escala 
# do plot

lines(0.5*curva.previsao$Imagem, curva.previsao$Dominio, col="blue")

I left the other curves, in the 50th and 70th percentiles, for the reader to practice and understand what is being done.

Note that the scale of the curve is completely arbitrary. In some cases it will be higher than the real, in some cases lower. Having constructed it within ± 3 standard deviations was also arbitrary. Maybe building between ± 2 gives a better effect.

In short, they are aesthetic decisions. Just work from this example and adapt to your needs.

    
16.11.2016 / 17:18
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