Polytomous and ordinal
Let us load the packages:
In the code chunk below, we create a function called invalid.exclude. This function can be used to exclude invalid responses from the dataset, where don’t know, refusal, and not stated are considered invalid responses.
# Function to exclude invalid responses
invalid.exclude <- function(Data, Var){
subset.data <- subset(Data, eval(parse(text = Var)) != "DON'T KNOW" &
eval(parse(text = Var)) != "REFUSAL" &
eval(parse(text = Var)) != "NOT STATED")
x1 <- dim(Data)[1]
x2 <- dim(subset.data)[1]
cat( format(x1-x2, big.mark = ","),
"subjects deleted, and current N =" , format(x2, big.mark = ",") , "\n")
return(subset.data)
}Data
# Data
analytic <- readRDS("Data/nonbinary/cmh.Rds")
str(analytic)
#> 'data.frame': 2628 obs. of 9 variables:
#> $ MHcondition : Factor w/ 3 levels "Good","Poor or Fair",..: 2 2 3 3 3 2 3 1 3 3 ...
#> $ CommunityBelonging: Factor w/ 4 levels "SOMEWHAT STRONG",..: 1 1 3 3 3 2 1 1 3 1 ...
#> $ Age : Factor w/ 6 levels "15 TO 24 YEARS",..: 4 1 1 4 3 5 2 5 3 1 ...
#> $ Sex : Factor w/ 2 levels "FEMALE","MALE": 1 1 2 2 2 2 2 1 1 2 ...
#> $ RaceEthnicity : Factor w/ 2 levels "NON-WHITE","WHITE": 2 1 2 2 1 2 2 2 2 1 ...
#> $ MainIncome : Factor w/ 5 levels "EI/WORKER'S COMP",..: 2 2 2 2 2 5 2 2 2 2 ...
#> $ ReceivedHelp : Factor w/ 4 levels "DON'T KNOW","NO",..: 4 4 4 2 2 4 2 2 4 2 ...
#> $ Weight : num 678 1298 196 917 2384 ...
#> $ Disorder : Factor w/ 1 level "YES": 1 1 1 1 1 1 1 1 1 1 ...Let us drop invalid responses
Multinomial logistic
Unweighted Tables
Let us see the summary statistic of the variables, stratified by mental health condition:
require("tableone")
tab1 <- CreateTableOne(vars = c("CommunityBelonging", "Age", "Sex", "RaceEthnicity", "MainIncome"),
strata = "MHcondition", data = analytic, test = F)
print(tab1, showAllLevels = TRUE, smd = T)
#> Stratified by MHcondition
#> level Good Poor or Fair
#> n 885 1002
#> CommunityBelonging (%) SOMEWHAT STRONG 362 (40.9) 288 (28.7)
#> SOMEWHAT WEAK 309 (34.9) 358 (35.7)
#> VERY STRONG 96 (10.8) 74 ( 7.4)
#> VERY WEAK 118 (13.3) 282 (28.1)
#> Age (%) 15 TO 24 YEARS 264 (29.8) 191 (19.1)
#> 25 TO 34 YEARS 167 (18.9) 141 (14.1)
#> 35 TO 44 YEARS 119 (13.4) 185 (18.5)
#> 35 TO 54 YEARS 139 (15.7) 220 (22.0)
#> 55 TO 64 YEARS 113 (12.8) 198 (19.8)
#> 65 years or older 83 ( 9.4) 67 ( 6.7)
#> Sex (%) FEMALE 487 (55.0) 616 (61.5)
#> MALE 398 (45.0) 386 (38.5)
#> RaceEthnicity (%) NON-WHITE 140 (15.8) 184 (18.4)
#> WHITE 745 (84.2) 818 (81.6)
#> MainIncome (%) EI/WORKER'S COMP 78 ( 8.8) 195 (19.5)
#> EMPLOYMENT INC. 641 (72.4) 560 (55.9)
#> NOT STATED 23 ( 2.6) 25 ( 2.5)
#> OTHER 36 ( 4.1) 66 ( 6.6)
#> SENIOR BENEFITS 107 (12.1) 156 (15.6)
#> Stratified by MHcondition
#> Very good/excellent SMD
#> n 741
#> CommunityBelonging (%) 355 (47.9) 0.425
#> 190 (25.6)
#> 116 (15.7)
#> 80 (10.8)
#> Age (%) 285 (38.5) 0.420
#> 167 (22.5)
#> 89 (12.0)
#> 79 (10.7)
#> 68 ( 9.2)
#> 53 ( 7.2)
#> Sex (%) 304 (41.0) 0.277
#> 437 (59.0)
#> RaceEthnicity (%) 134 (18.1) 0.045
#> 607 (81.9)
#> MainIncome (%) 36 ( 4.9) 0.400
#> 598 (80.7)
#> 9 ( 1.2)
#> 22 ( 3.0)
#> 76 (10.3)Multinomial Model fitting
Before fitting the multinomial regression, let us redefine the reference categories of the variables.
analytic$MHcondition2 <- relevel(analytic$MHcondition, ref = "Poor or Fair")
analytic$CommunityBelonging2 <- relevel(analytic$CommunityBelonging, ref = "VERY WEAK")
analytic$Age2 <- relevel(analytic$Age, ref = "65 years or older")
analytic$Sex2 <- relevel(analytic$Sex, ref = "FEMALE")
analytic$RaceEthnicity2 <- relevel(analytic$RaceEthnicity, ref = "NON-WHITE")Now we will fit the multinomial logistic regression model using the multinom function from the nnet package:
require(nnet)
fit4 <- multinom(MHcondition2 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 + MainIncome,
data = analytic)
#> # weights: 48 (30 variable)
#> initial value 2887.153095
#> iter 10 value 2640.386436
#> iter 20 value 2625.127331
#> iter 30 value 2622.465409
#> final value 2622.328038
#> converged
kable(round(exp(cbind(coef(fit4), confint(fit4))),2))
#> Warning in cbind(coef(fit4), confint(fit4)): number of rows of result is not a
#> multiple of vector length (arg 2)| (Intercept) | CommunityBelonging2SOMEWHAT STRONG | CommunityBelonging2SOMEWHAT WEAK | CommunityBelonging2VERY STRONG | Sex2MALE | Age215 TO 24 YEARS | Age225 TO 34 YEARS | Age235 TO 44 YEARS | Age235 TO 54 YEARS | Age255 TO 64 YEARS | RaceEthnicity2WHITE | MainIncomeEMPLOYMENT INC. | MainIncomeNOT STATED | MainIncomeOTHER | MainIncomeSENIOR BENEFITS | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Good | 0.38 | 2.72 | 1.84 | 3.08 | 1.29 | 0.70 | 0.62 | 0.32 | 0.37 | 0.36 | 1.25 | 2.29 | 1.57 | 1.14 | 1.11 | 0.21 |
| Very good/excellent | 0.09 | 3.92 | 1.64 | 5.65 | 2.19 | 1.27 | 1.08 | 0.39 | 0.37 | 0.37 | 1.15 | 3.89 | 1.25 | 1.34 | 2.11 | 2.07 |
Multinomial logistic for complex survey
Survey-weighted Tables
Now, we will set up the survey design with the survey weights and then see design-adjusted summary statistics.
require(survey)
# Design
svy.analytic <- svydesign(ids = ~ 1, weights = ~ Weight, data = analytic)
# Table 1
tab1a <- svyCreateTableOne(vars = c("CommunityBelonging", "Age", "Sex", "RaceEthnicity", "MainIncome"),
data = svy.analytic)
print(tab1a, showAllLevels = TRUE)
#>
#> level Overall
#> n 2734564.0
#> CommunityBelonging (%) SOMEWHAT STRONG 1015381.0 (37.1)
#> SOMEWHAT WEAK 948838.7 (34.7)
#> VERY STRONG 297141.6 (10.9)
#> VERY WEAK 473202.7 (17.3)
#> Age (%) 15 TO 24 YEARS 795568.6 (29.1)
#> 25 TO 34 YEARS 564720.6 (20.7)
#> 35 TO 44 YEARS 457821.1 (16.7)
#> 35 TO 54 YEARS 447324.8 (16.4)
#> 55 TO 64 YEARS 319374.3 (11.7)
#> 65 years or older 149754.6 ( 5.5)
#> Sex (%) FEMALE 1312605.0 (48.0)
#> MALE 1421958.9 (52.0)
#> RaceEthnicity (%) NON-WHITE 565784.5 (20.7)
#> WHITE 2168779.4 (79.3)
#> MainIncome (%) EI/WORKER'S COMP 245758.5 ( 9.0)
#> EMPLOYMENT INC. 2059689.1 (75.3)
#> NOT STATED 60101.5 ( 2.2)
#> OTHER 116260.6 ( 4.3)
#> SENIOR BENEFITS 252754.4 ( 9.2)
# table 1 stratified by MHcondition
tab1b <- svyCreateTableOne(vars = c("CommunityBelonging", "Age", "Sex", "RaceEthnicity", "MainIncome"),
strata = "MHcondition", data = svy.analytic)
print(tab1b, showAllLevels = TRUE)
#> Stratified by MHcondition
#> level Good Poor or Fair
#> n 949208.1 973112.1
#> CommunityBelonging (%) SOMEWHAT STRONG 346216.2 (36.5) 273163.9 (28.1)
#> SOMEWHAT WEAK 378431.4 (39.9) 365731.0 (37.6)
#> VERY STRONG 97528.8 (10.3) 69438.0 ( 7.1)
#> VERY WEAK 127031.7 (13.4) 264779.1 (27.2)
#> Age (%) 15 TO 24 YEARS 286103.0 (30.1) 187610.9 (19.3)
#> 25 TO 34 YEARS 189084.8 (19.9) 184528.6 (19.0)
#> 35 TO 44 YEARS 140796.4 (14.8) 199172.7 (20.5)
#> 35 TO 54 YEARS 171968.1 (18.1) 194689.1 (20.0)
#> 55 TO 64 YEARS 109114.5 (11.5) 148432.2 (15.3)
#> 65 years or older 52141.3 ( 5.5) 58678.6 ( 6.0)
#> Sex (%) FEMALE 436830.4 (46.0) 582311.1 (59.8)
#> MALE 512377.7 (54.0) 390801.0 (40.2)
#> RaceEthnicity (%) NON-WHITE 167887.9 (17.7) 220525.5 (22.7)
#> WHITE 781320.2 (82.3) 752586.6 (77.3)
#> MainIncome (%) EI/WORKER'S COMP 66003.9 ( 7.0) 151095.4 (15.5)
#> EMPLOYMENT INC. 752208.0 (79.2) 608703.4 (62.6)
#> NOT STATED 23589.0 ( 2.5) 28371.0 ( 2.9)
#> OTHER 32247.9 ( 3.4) 68453.1 ( 7.0)
#> SENIOR BENEFITS 75159.3 ( 7.9) 116489.2 (12.0)
#> Stratified by MHcondition
#> Very good/excellent p test
#> n 812243.7
#> CommunityBelonging (%) 396000.8 (48.8) <0.001
#> 204676.3 (25.2)
#> 130174.7 (16.0)
#> 81391.9 (10.0)
#> Age (%) 321854.6 (39.6) <0.001
#> 191107.2 (23.5)
#> 117852.1 (14.5)
#> 80667.6 ( 9.9)
#> 61827.6 ( 7.6)
#> 38934.6 ( 4.8)
#> Sex (%) 293463.5 (36.1) <0.001
#> 518780.2 (63.9)
#> RaceEthnicity (%) 177371.1 (21.8) 0.219
#> 634872.7 (78.2)
#> MainIncome (%) 28659.2 ( 3.5) <0.001
#> 698777.7 (86.0)
#> 8141.5 ( 1.0)
#> 15559.6 ( 1.9)
#> 61105.8 ( 7.5)Setting up the design
Let us redefine the reference categories within the survey design and convert the design to use replicate weights.
w.design <- svydesign(id=~1, weights=~Weight, data=analytic)
w.design <- update(w.design , MHcondition2 = relevel(MHcondition, ref = "Poor or Fair"),
CommunityBelonging2 = relevel(CommunityBelonging, ref = "VERY WEAK"),
Age2 = relevel(Age, ref = "65 years or older"),
Sex2 = relevel(Sex, ref = "FEMALE"),
RaceEthnicity2 = relevel(RaceEthnicity, ref = "NON-WHITE"))
# Convert a survey design to use replicate weights
w.design2 <- as.svrepdesign(w.design, type = "bootstrap" , replicates = 50)Multinomial Model fitting
Now, we will fit the design-adjusted multinomial logistic regression:
| 2.5 % | 97.5 % | ||
|---|---|---|---|
| (Intercept):1 | 16.57 | 6.63 | 41.44 |
| (Intercept):2 | 3.24 | 0.93 | 11.30 |
| CommunityBelonging2SOMEWHAT STRONG:1 | 0.22 | 0.15 | 0.34 |
| CommunityBelonging2SOMEWHAT STRONG:2 | 0.56 | 0.35 | 0.89 |
| CommunityBelonging2SOMEWHAT WEAK:1 | 0.58 | 0.36 | 0.92 |
| CommunityBelonging2SOMEWHAT WEAK:2 | 1.18 | 0.74 | 1.87 |
| CommunityBelonging2VERY STRONG:1 | 0.15 | 0.08 | 0.28 |
| CommunityBelonging2VERY STRONG:2 | 0.46 | 0.26 | 0.80 |
| Sex2MALE:1 | 0.39 | 0.27 | 0.55 |
| Sex2MALE:2 | 0.68 | 0.50 | 0.92 |
| Age215 TO 24 YEARS:1 | 0.56 | 0.30 | 1.02 |
| Age215 TO 24 YEARS:2 | 0.65 | 0.32 | 1.31 |
| Age225 TO 34 YEARS:1 | 0.83 | 0.42 | 1.64 |
| Age225 TO 34 YEARS:2 | 0.68 | 0.32 | 1.48 |
| Age235 TO 44 YEARS:1 | 1.74 | 0.89 | 3.37 |
| Age235 TO 44 YEARS:2 | 0.91 | 0.42 | 1.95 |
| Age235 TO 54 YEARS:1 | 2.02 | 1.13 | 3.60 |
| Age235 TO 54 YEARS:2 | 1.48 | 0.65 | 3.37 |
| Age255 TO 64 YEARS:1 | 1.91 | 1.08 | 3.38 |
| Age255 TO 64 YEARS:2 | 1.33 | 0.68 | 2.57 |
| RaceEthnicity2WHITE:1 | 0.91 | 0.57 | 1.45 |
| RaceEthnicity2WHITE:2 | 1.22 | 0.78 | 1.93 |
| MainIncomeEMPLOYMENT INC.:1 | 0.25 | 0.13 | 0.45 |
| MainIncomeEMPLOYMENT INC.:2 | 0.58 | 0.27 | 1.26 |
| MainIncomeNOT STATED:1 | 0.91 | 0.23 | 3.58 |
| MainIncomeNOT STATED:2 | 1.37 | 0.28 | 6.75 |
| MainIncomeOTHER:1 | 1.43 | 0.43 | 4.81 |
| MainIncomeOTHER:2 | 1.30 | 0.44 | 3.82 |
| MainIncomeSENIOR BENEFITS:1 | 0.39 | 0.19 | 0.80 |
| MainIncomeSENIOR BENEFITS:2 | 0.49 | 0.19 | 1.26 |
Ordinal Regression
Ordering outcome
Let’s define MHcondition as an ordinal outcome. We can do it by using ordered = TRUE in the factor function.
Ordinal logistic
Let’s fit the ordinal logistic using the polr function:
require(MASS)
fit5o1 <- polr(MHcondition3 ~CommunityBelonging2 +
Sex2 + Age2 + RaceEthnicity2 + MainIncome,
data=analytic)
kable(round(exp(cbind(coef(fit5o1), confint(fit5o1))),2))
#> Waiting for profiling to be done...
#>
#> Re-fitting to get Hessian| 2.5 % | 97.5 % | ||
|---|---|---|---|
| CommunityBelonging2SOMEWHAT STRONG | 2.69 | 2.05 | 3.55 |
| CommunityBelonging2SOMEWHAT WEAK | 1.83 | 1.40 | 2.41 |
| CommunityBelonging2VERY STRONG | 3.15 | 2.15 | 4.66 |
| Sex2MALE | 1.30 | 1.07 | 1.58 |
| Age215 TO 24 YEARS | 0.69 | 0.42 | 1.11 |
| Age225 TO 34 YEARS | 0.60 | 0.36 | 0.98 |
| Age235 TO 44 YEARS | 0.32 | 0.19 | 0.53 |
| Age235 TO 54 YEARS | 0.37 | 0.23 | 0.59 |
| Age255 TO 64 YEARS | 0.36 | 0.22 | 0.56 |
| RaceEthnicity2WHITE | 1.26 | 0.97 | 1.63 |
| MainIncomeEMPLOYMENT INC. | 2.26 | 1.68 | 3.06 |
| MainIncomeNOT STATED | 1.53 | 0.79 | 2.94 |
| MainIncomeOTHER | 1.15 | 0.70 | 1.90 |
| MainIncomeSENIOR BENEFITS | 1.10 | 0.70 | 1.70 |
Ordinal logistic for complex survey
The same as before, we can set up the design, relevel variables within the design, define MHcondition as an ordinal variable, and then fit the design-adjusted ordinal logistic regression.
w.design <- svydesign(id=~1, weights=~Weight, data=analytic)
w.design<-update(w.design ,
CommunityBelonging2=relevel(CommunityBelonging, ref="VERY WEAK"),
Age2=relevel(Age, ref="65 years or older"),
Sex2=relevel(Sex, ref="FEMALE"),
RaceEthnicity2=relevel(RaceEthnicity, ref="NON-WHITE"),
MHcondition3 = factor(MHcondition, levels=c("Poor or Fair", "Good",
"Very good or excellent"),
ordered=TRUE))
# Design-adjusted Ordinal logistic
fit5o <- svyolr(MHcondition3 ~CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 + MainIncome,
design=w.design)
kable(round(exp(cbind(coef(fit5o), confint(fit5o))),2))| 2.5 % | 97.5 % | ||
|---|---|---|---|
| CommunityBelonging2SOMEWHAT STRONG | 2.49 | 1.65 | 3.75 |
| CommunityBelonging2SOMEWHAT WEAK | 2.04 | 1.37 | 3.05 |
| CommunityBelonging2VERY STRONG | 3.02 | 1.74 | 5.26 |
| Sex2MALE | 1.76 | 1.30 | 2.38 |
| Age215 TO 24 YEARS | 1.13 | 0.52 | 2.44 |
| Age225 TO 34 YEARS | 0.77 | 0.34 | 1.74 |
| Age235 TO 44 YEARS | 0.52 | 0.24 | 1.13 |
| Age235 TO 54 YEARS | 0.70 | 0.32 | 1.52 |
| Age255 TO 64 YEARS | 0.69 | 0.32 | 1.46 |
| RaceEthnicity2WHITE | 1.31 | 0.90 | 1.91 |
| MainIncomeEMPLOYMENT INC. | 2.37 | 1.53 | 3.67 |
| MainIncomeNOT STATED | 1.42 | 0.52 | 3.86 |
| MainIncomeOTHER | 0.88 | 0.39 | 2.00 |
| MainIncomeSENIOR BENEFITS | 1.26 | 0.66 | 2.41 |
| Poor or Fair|Good | 4.79 | 1.92 | 11.91 |
| Good|Very good or excellent | 2636603.90 | 996905.04 | 6973262.13 |
Assessing model fit
We can use the regTermTest function from the survey package to do the Wald test of a regression coefficient.
regTermTest(fit5o, ~CommunityBelonging2 , df = Inf)
#> Wald test for CommunityBelonging2
#> in svyolr(MHcondition3 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 +
#> MainIncome, design = w.design)
#> Chisq = 24.53941 on 3 df: p= 1.9272e-05
regTermTest(fit5o, ~Age2 , df = Inf)
#> Wald test for Age2
#> in svyolr(MHcondition3 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 +
#> MainIncome, design = w.design)
#> Chisq = 14.00491 on 5 df: p= 0.015578
regTermTest(fit5o, ~Sex2 , df = Inf)
#> Wald test for Sex2
#> in svyolr(MHcondition3 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 +
#> MainIncome, design = w.design)
#> Chisq = 13.46032 on 1 df: p= 0.00024366
regTermTest(fit5o, ~RaceEthnicity2 , df = Inf)
#> Wald test for RaceEthnicity2
#> in svyolr(MHcondition3 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 +
#> MainIncome, design = w.design)
#> Chisq = 2.002794 on 1 df: p= 0.15701
regTermTest(fit5o, ~MainIncome , df = Inf)
#> Wald test for MainIncome
#> in svyolr(MHcondition3 ~ CommunityBelonging2 + Sex2 + Age2 + RaceEthnicity2 +
#> MainIncome, design = w.design)
#> Chisq = 22.13656 on 4 df: p= 0.00018826Video content (optional)
Tip
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