Models0522 with Cancer

h1: close proximity

population density
public transit

h2: greater travel

tourism employment
% working outside county

h3: socioeconomic status

college
% without health insurance
poverty rate

h4: race

black
hispanic
asian
other
neighborhood diversity

other controls

age
gender
All cancer per 1000 residents

case6 <- read.csv("~/Dropbox (ASU)/Papers/Neighborhood COVID/Data/FullData.csv")
case6$CancerPC <- case6$CaseCount/(case6$Pop*1000)
   
## reading in shapefile
tractLA <- shapefile('~/Dropbox (ASU)/Papers/Neighborhood COVID/Data/tl_2019_22_tract/tl_2019_22_tract.shp')


tractLA2 <- merge(tractLA, case6, by.x="GEOID",  by.y="Tract.ID")

f1 <- cpcln~ log(popdens)+pubtransit+ outsidecounty+tourismemp+collegepct+povpct+noinsurance+blackpct+hisppct+asianpct+otherracepct+DiversityIndex+over70pct+male+log(CancerPC+1)

summary(ols <-lm(f1, data=tractLA2@data))

## 
## Call:
## lm(formula = f1, data = tractLA2@data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44265 -0.35932 -0.00162  0.31551  1.69273 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -7.291e-01  2.811e-01  -2.594 0.009660 ** 
## log(popdens)       9.359e-02  1.345e-02   6.957 6.85e-12 ***
## pubtransit         4.219e+00  7.538e-01   5.596 2.93e-08 ***
## outsidecounty      1.268e+00  1.064e-01  11.916  < 2e-16 ***
## tourismemp         6.845e-01  3.748e-01   1.826 0.068133 .  
## collegepct         5.987e-02  1.633e-01   0.367 0.714000    
## povpct            -1.085e+00  2.187e-01  -4.962 8.38e-07 ***
## noinsurance       -1.219e+00  4.667e-01  -2.612 0.009150 ** 
## blackpct           1.411e+00  1.002e-01  14.086  < 2e-16 ***
## hisppct            3.699e+00  4.054e-01   9.124  < 2e-16 ***
## asianpct           2.028e+00  5.887e-01   3.445 0.000599 ***
## otherracepct       1.851e+00  7.265e-01   2.548 0.011009 *  
## DiversityIndex    -1.852e-01  1.310e-01  -1.413 0.158010    
## over70pct          1.461e+00  6.683e-01   2.187 0.029036 *  
## male               3.670e-01  4.365e-01   0.841 0.400740    
## log(CancerPC + 1)  9.361e+04  2.192e+04   4.270 2.17e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5086 on 871 degrees of freedom
##   (261 observations deleted due to missingness)
## Multiple R-squared:  0.5113, Adjusted R-squared:  0.5028 
## F-statistic: 60.74 on 15 and 871 DF,  p-value: < 2.2e-16

covid_nbq <- poly2nb(tractLA)  #Queen’s neighborhood
covid_nbq_w <- nb2listw(covid_nbq, style="W")

## Spatial Lag Model

summary(covid_LAG <- lagsarlm(f1, data = tractLA2@data, covid_nbq_w, zero.policy = T))

## Warning: Function lagsarlm moved to the spatialreg package

## Warning in spatialreg::lagsarlm(formula = formula, data = data, listw = listw, : inversion of asymptotic covariance matrix failed for tol.solve = 1e-10 
##   reciprocal condition number = 7.78663e-15 - using numerical Hessian.

## 
## Call:spatialreg::lagsarlm(formula = formula, data = data, listw = listw, 
##     na.action = na.action, Durbin = Durbin, type = type, method = method, 
##     quiet = quiet, zero.policy = zero.policy, interval = interval, 
##     tol.solve = tol.solve, trs = trs, control = control)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.11007 -0.23327 -0.02405  0.19757  1.82878 
## 
## Type: lag 
## Regions with no neighbours included:
##  950 974 982 
## Coefficients: (numerical Hessian approximate standard errors) 
##                      Estimate  Std. Error z value  Pr(>|z|)
## (Intercept)       -7.8381e-01  2.0390e-01 -3.8440 0.0001210
## log(popdens)       3.3905e-02  1.0030e-02  3.3803 0.0007240
## pubtransit         1.7609e+00  5.5067e-01  3.1977 0.0013854
## outsidecounty      4.9379e-01  8.1712e-02  6.0431 1.512e-09
## tourismemp         7.1261e-01  2.6690e-01  2.6700 0.0075861
## collegepct         1.8695e-02  1.3128e-01  0.1424 0.8867625
## povpct            -5.8690e-01  1.5793e-01 -3.7162 0.0002023
## noinsurance       -1.6330e-01  3.3541e-01 -0.4869 0.6263387
## blackpct           7.8603e-01  7.7077e-02 10.1979 < 2.2e-16
## hisppct            1.3470e+00  3.0127e-01  4.4710 7.784e-06
## asianpct           4.9733e-01  4.2549e-01  1.1688 0.2424683
## otherracepct       6.8115e-01  5.2632e-01  1.2942 0.1956031
## DiversityIndex     7.2064e-02  9.5217e-02  0.7568 0.4491500
## over70pct          8.3846e-01  4.7619e-01  1.7608 0.0782797
## male               4.2941e-01  3.1229e-01  1.3750 0.1691202
## log(CancerPC + 1)  6.5984e+04  1.5760e+04  4.1867 2.830e-05
## 
## Rho: 0.62856, LR test value: 497.51, p-value: < 2.22e-16
## Approximate (numerical Hessian) standard error: 0.022346
##     z-value: 28.129, p-value: < 2.22e-16
## Wald statistic: 791.23, p-value: < 2.22e-16
## 
## Log likelihood: -402.014 for lag model
## ML residual variance (sigma squared): 0.13094, (sigma: 0.36186)
## Number of observations: 887 
## Number of parameters estimated: 18 
## AIC: 840.03, (AIC for lm: 1335.5)

## Spatial Error Model


summary(covid_error <- errorsarlm(f1, data = tractLA2@data, covid_nbq_w, zero.policy = T))

## Warning: Function errorsarlm moved to the spatialreg package

## Warning in spatialreg::errorsarlm(formula = formula, data = data, listw = listw, : inversion of asymptotic covariance matrix failed for tol.solve = 1e-10 
##   reciprocal condition number = 1.36821e-13 - using numerical Hessian.

## 
## Call:
## spatialreg::errorsarlm(formula = formula, data = data, listw = listw, 
##     na.action = na.action, Durbin = Durbin, etype = etype, method = method, 
##     quiet = quiet, zero.policy = zero.policy, interval = interval, 
##     tol.solve = tol.solve, trs = trs, control = control)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -1.227515 -0.239766 -0.031236  0.195204  1.963498 
## 
## Type: error 
## Regions with no neighbours included:
##  950 974 982 
## Coefficients: (asymptotic standard errors) 
##                      Estimate  Std. Error z value  Pr(>|z|)
## (Intercept)        4.5964e-01  2.1315e-01  2.1564 0.0310504
## log(popdens)       1.6305e-02  1.2539e-02  1.3003 0.1934858
## pubtransit         5.6723e-01  5.8284e-01  0.9732 0.3304449
## outsidecounty      9.4962e-02  1.0737e-01  0.8845 0.3764521
## tourismemp         5.0327e-01  2.5525e-01  1.9717 0.0486467
## collegepct        -1.3560e-01  1.5528e-01 -0.8732 0.3825330
## povpct            -4.7149e-01  1.6455e-01 -2.8654 0.0041653
## noinsurance       -1.1281e-01  3.1603e-01 -0.3570 0.7211224
## blackpct           9.2168e-01  8.3935e-02 10.9809 < 2.2e-16
## hisppct            8.1391e-01  3.2528e-01  2.5022 0.0123440
## asianpct           4.3515e-01  5.7159e-01  0.7613 0.4464826
## otherracepct      -1.0806e-01  5.5557e-01 -0.1945 0.8457756
## DiversityIndex     4.9134e-02  1.0108e-01  0.4861 0.6269070
## over70pct          9.0840e-01  4.3184e-01  2.1036 0.0354177
## male               6.4809e-01  2.6904e-01  2.4089 0.0160020
## log(CancerPC + 1)  5.2458e+04  1.4570e+04  3.6004 0.0003177
## 
## Lambda: 0.80119, LR test value: 508.5, p-value: < 2.22e-16
## Approximate (numerical Hessian) standard error: 0.01978
##     z-value: 40.504, p-value: < 2.22e-16
## Wald statistic: 1640.6, p-value: < 2.22e-16
## 
## Log likelihood: -396.5213 for error model
## ML residual variance (sigma squared): 0.1181, (sigma: 0.34366)
## Number of observations: 887 
## Number of parameters estimated: 18 
## AIC: 829.04, (AIC for lm: 1335.5)

Models0522 with Cancer

van Holm

5/13/2020