Logistic regression, quadratic data?

General
1

Hi. I have some data x where the reds plot as an upside down V and the blues plot as a V. Red and blue is a factor variable y. I want to do a logistic regression and my first thought was y ~ I(x^2), but that doesn't make sense. What general form of regression makes sense here?

library(ggplot2)

df <- data.frame(x = c(20,30,30,40,40,40,50,50,60, 20,20,20,30,30,40,50,50,60,60,60),
y = c(0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1))

ggplot(data=df, aes(x = x, fill=factor(y))) +
geom_histogram(position = "dodge", binwidth=10, alpha=.5) +
scale_fill_manual(values=c("red","blue"))

model <- glm(y ~ I(x^2), data=df, family=binomial)
summary(model)

image
image798×752 14.9 KB

2

Your data looks like

image
image750×531 2.89 KB

so there doesn't appears to be a relation between x and y. Can you say more about what you're trying to accomplish?

3

If x is continuous it's hard to see where any glm model will go

# Create the data frame
d <- data.frame(x = c(20,30,30,40,40,40,50,50,60, 20,20,20,30,30,40,50,50,60,60,60),
                y = c(0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1))

# Define the formulas and family arguments
formulas <- list(y ~ x, y ~ I(x^2), y ~ I(sin(x)), y ~ I(sin(x^2)))
families <- list(binomial(link = "logit"),
                 gaussian(link = "identity"),
                 poisson(link = "log"),
                 quasi(link = "identity", variance = "constant"),
                 quasibinomial(link = "logit"),
                 quasipoisson(link = "log"))

# Fit the models and store them in a list
models <- list()
for (f in formulas) {
  for (family in families) {
    model <- glm(f, data = d, family = family)
    models <- append(models, list(model))
  }
}


lapply(models,summary)
#> [[1]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 2.007e-01  1.348e+00   0.149    0.882
#> x           1.600e-18  3.178e-02   0.000    1.000
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.526  on 18  degrees of freedom
#> AIC: 31.526
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[2]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 5.500e-01  3.518e-01   1.563    0.135
#> x           3.806e-19  8.292e-03   0.000    1.000
#> 
#> (Dispersion parameter for gaussian family taken to be 0.275)
#> 
#>     Null deviance: 4.95  on 19  degrees of freedom
#> Residual deviance: 4.95  on 18  degrees of freedom
#> AIC: 34.831
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[3]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -5.978e-01  9.045e-01  -0.661    0.509
#> x            6.096e-18  2.132e-02   0.000    1.000
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.152  on 18  degrees of freedom
#> AIC: 39.152
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[4]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 5.500e-01  3.518e-01   1.563    0.135
#> x           3.806e-19  8.292e-03   0.000    1.000
#> 
#> (Dispersion parameter for quasi family taken to be 0.275)
#> 
#>     Null deviance: 4.95  on 19  degrees of freedom
#> Residual deviance: 4.95  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[5]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.007e-01  1.421e+00   0.141    0.889
#> x           1.600e-18  3.350e-02   0.000    1.000
#> 
#> (Dispersion parameter for quasibinomial family taken to be 1.111123)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.526  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[6]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -5.978e-01  6.396e-01  -0.935    0.362
#> x            6.096e-18  1.508e-02   0.000    1.000
#> 
#> (Dispersion parameter for quasipoisson family taken to be 0.5000009)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.152  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[7]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.0338639  0.8366883   0.040    0.968
#> I(x^2)      0.0000930  0.0003948   0.236    0.814
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.470  on 18  degrees of freedom
#> AIC: 31.47
#> 
#> Number of Fisher Scoring iterations: 4
#> 
#> 
#> [[8]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) 5.087e-01  2.183e-01   2.330   0.0317 *
#> I(x^2)      2.294e-05  1.024e-04   0.224   0.8253  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.2742355)
#> 
#>     Null deviance: 4.9500  on 19  degrees of freedom
#> Residual deviance: 4.9362  on 18  degrees of freedom
#> AIC: 34.775
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[9]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -6.734e-01  5.712e-01  -1.179    0.238
#> I(x^2)       4.137e-05  2.616e-04   0.158    0.874
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.128  on 18  degrees of freedom
#> AIC: 39.128
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[10]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) 5.087e-01  2.183e-01   2.330   0.0317 *
#> I(x^2)      2.294e-05  1.024e-04   0.224   0.8253  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasi family taken to be 0.2742355)
#> 
#>     Null deviance: 4.9500  on 19  degrees of freedom
#> Residual deviance: 4.9362  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[11]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.033864   0.881800   0.038    0.970
#> I(x^2)      0.000093   0.000416   0.224    0.826
#> 
#> (Dispersion parameter for quasibinomial family taken to be 1.110741)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.470  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 4
#> 
#> 
#> [[12]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -6.734e-01  4.040e-01  -1.667    0.113
#> I(x^2)       4.137e-05  1.850e-04   0.224    0.826
#> 
#> (Dispersion parameter for quasipoisson family taken to be 0.5003108)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.128  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[13]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept)  0.20207    0.44983   0.449    0.653
#> I(sin(x))   -0.06305    0.63278  -0.100    0.921
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.516  on 18  degrees of freedom
#> AIC: 31.516
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[14]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   0.5503     0.1173   4.692 0.000181 ***
#> I(sin(x))    -0.0156     0.1650  -0.095 0.925705    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.2748634)
#> 
#>     Null deviance: 4.9500  on 19  degrees of freedom
#> Residual deviance: 4.9475  on 18  degrees of freedom
#> AIC: 34.821
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[15]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)  
#> (Intercept) -0.59746    0.30152  -1.981   0.0475 *
#> I(sin(x))   -0.02837    0.42441  -0.067   0.9467  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.148  on 18  degrees of freedom
#> AIC: 39.148
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[16]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   0.5503     0.1173   4.692 0.000181 ***
#> I(sin(x))    -0.0156     0.1650  -0.095 0.925705    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasi family taken to be 0.2748634)
#> 
#>     Null deviance: 4.9500  on 19  degrees of freedom
#> Residual deviance: 4.9475  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[17]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  0.20207    0.47416   0.426    0.675
#> I(sin(x))   -0.06305    0.66702  -0.095    0.926
#> 
#> (Dispersion parameter for quasibinomial family taken to be 1.111133)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.516  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[18]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) -0.59746    0.21321  -2.802   0.0118 *
#> I(sin(x))   -0.02837    0.30010  -0.095   0.9257  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasipoisson family taken to be 0.4999959)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.148  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[19]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.2008760  0.4939680   0.407    0.684
#> I(sin(x^2)) 0.0006551  0.6539372   0.001    0.999
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.526  on 18  degrees of freedom
#> AIC: 31.526
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[20]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 0.5500508  0.1288682   4.268 0.000462 ***
#> I(sin(x^2)) 0.0001621  0.1706006   0.001 0.999252    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.275)
#> 
#>     Null deviance: 4.95  on 19  degrees of freedom
#> Residual deviance: 4.95  on 18  degrees of freedom
#> AIC: 34.831
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[21]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)  
#> (Intercept) -0.5977447  0.3313258  -1.804   0.0712 .
#> I(sin(x^2))  0.0002948  0.4386113   0.001   0.9995  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.152  on 18  degrees of freedom
#> AIC: 39.152
#> 
#> Number of Fisher Scoring iterations: 5
#> 
#> 
#> [[22]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 0.5500508  0.1288682   4.268 0.000462 ***
#> I(sin(x^2)) 0.0001621  0.1706006   0.001 0.999252    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasi family taken to be 0.275)
#> 
#>     Null deviance: 4.95  on 19  degrees of freedom
#> Residual deviance: 4.95  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 2
#> 
#> 
#> [[23]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.2008760  0.5206907   0.386    0.704
#> I(sin(x^2)) 0.0006551  0.6893139   0.001    0.999
#> 
#> (Dispersion parameter for quasibinomial family taken to be 1.111123)
#> 
#>     Null deviance: 27.526  on 19  degrees of freedom
#> Residual deviance: 27.526  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 3
#> 
#> 
#> [[24]]
#> 
#> Call:
#> glm(formula = f, family = family, data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) -0.5977447  0.2342830  -2.551    0.020 *
#> I(sin(x^2))  0.0002948  0.3101453   0.001    0.999  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for quasipoisson family taken to be 0.5000009)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.152  on 18  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 5

Created on 2023-07-09 with reprex v2.0.2

4

If and x is discrete, even so

# Create the data frame
d <- data.frame(x = c(20,30,30,40,40,40,50,50,60, 20,20,20,30,30,40,50,50,60,60,60),
                y = c(0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1))

# Fit the model using glm() with Poisson family and log link function
model <- glm(y ~ x, data = d, family = poisson(link = "log"))

# Print the model summary
summary(model)
#> 
#> Call:
#> glm(formula = y ~ x, family = poisson(link = "log"), data = d)
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -5.978e-01  9.045e-01  -0.661    0.509
#> x            6.096e-18  2.132e-02   0.000    1.000
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 13.152  on 19  degrees of freedom
#> Residual deviance: 13.152  on 18  degrees of freedom
#> AIC: 39.152
#> 
#> Number of Fisher Scoring iterations: 5
5

My attempt:

library(tidyverse)

df1 <- data.frame(
  x = c(20, 30, 30, 40, 40, 40, 50, 50, 60, 20, 20, 20, 30, 30, 40, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
)

(smry_counts_df <- group_by(df1, x, y) |> summarise(n = n()) |> ungroup())

(smry_fractions_df <- group_by(smry_counts_df, x) |> summarise(frac = weighted.mean(
  x = y,
  w = n
)))

model <- glm(y ~ x + I(x^2), # or the more readable poly(x,2)
  data = smry_counts_df,
  weights = n,
  family = binomial()
)
summary(model)

smry_counts_df$pred <- predict(model,
  newdata = smry_counts_df,
  type = "response"
)

# in blue plot the counts
# in red the glm predictions of the ratio of y0 to y1
# in green the true values of the ratio of y0 to y1
ggplot() +
  geom_point(
    data = smry_counts_df,
    mapping = aes(
      x = x,
      y = y,
      size = n
    ), color = "blue"
  ) +
  geom_line(
    data = smry_counts_df |> distinct(x, pred),
    aes(
      x = x,
      y = pred
    ), color = "red"
  ) +
  geom_line(
    data = smry_fractions_df,
    mapping = aes(
      x = x,
      y = frac, size = 1
    ), color = "green"
  )

image

6

using library segmented you can do perfect fit with only linear segments.

library(tidyverse)
library(segmented)
df1 <- data.frame(
  x = c(20, 30, 30, 40, 40, 40, 50, 50, 60, 20, 20, 20, 30, 30, 40, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
)

(smry_counts_df <- group_by(df1, x, y) |> summarise(n = n()) |> ungroup())

(smry_fractions_df <- group_by(smry_counts_df, x) |> summarise(frac = weighted.mean(
  x = y,
  w = n
)))

model_underlying <-  lm(formula = frac~x, # dont need x^2 as its a linear either side
                        data = smry_fractions_df)
seg_model <- segmented(model_underlying)

smry_fractions_df$pred <- predict(seg_model,
  newdata = smry_fractions_df,
  type = "response"
)

# in blue plot the counts
# in red the glm predictions of the ratio of y0 to y1
# in green the true values of the ratio of y0 to y1
ggplot() +
  geom_point(
    data = smry_counts_df,
    mapping = aes(
      x = x,
      y = y,
      size = n
    ), color = "blue"
  ) +
  geom_line(
    data = smry_fractions_df,
    aes(
      x = x,
      y = pred
    ), color = "red",
    linewidth=2,
    linetype="dotdash"
  ) +
  geom_line(
    data = smry_fractions_df,
    mapping = aes(
      x = x,
      y = frac
    ), color = "green",
    linewidth=2,
    linetype="dotted",
  )

image

7

Hi. One of my frequent sins in producing a reproducible example is I make it too simple.

In reality, most but not all of my reds plot as an upside down V, and most but not all of my blues plot as a V. Think of x as ages (continuous) and y as credit default (0=non-default, 1=default).

If I added some random values to make the data a little messier, would that change anything?

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it would change some things. but maybe not enough to matter.

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