Influence operationalizations

---

We will show how the influence statistics in RSiena behave. We assume that ego is connected to all alters.

Functions

First, we define some functions:

Centering behavior variables

fcentering <- function(actors) {
    centered <- actors - mean(actors)
    return(centered)
}

Calculating similarity scores

\[ sim^z_{ij} = 1 - \frac{|z_i - z_j|}{r_V} \]

fsimij <- function(actors, min, max) {
    # rv <- max(actors) - min(actors)
    rv <- max - min
    mat1 <- matrix(actors, nrow = length(actors), ncol = length(actors), byrow = TRUE)
    mat2 <- t(mat1)
    simij <- 1 - (abs(mat1 - mat2)/rv)
    return(simij)
}

linear shape

flinear <- function(ego, alters, ...) {
    actors <- c(ego, alters)  #define the network
    beh_centered <- fcentring(actors)  #center behavior scores

    statistic <- beh_centered[1]  #the actual statistic

    return(statistic)
}

quadratic shape

fquad <- function(ego, alters, ...) {
    actors <- c(ego, alters)  #define the network
    beh_centered <- fcentring(actors)  #center behavior scores

    statistic <- (beh_centered[1])^2  #the actual statistic

    return(statistic)
}

The average similarity effect (avSim)

\[ s^{beh}_{i5}(x,z) = x^{-1}_{i+}\sum_j x_{ij}(sim^z_{ij} - \widehat{sim^z}) \]

favSim <- function(ego, alters, min, max) {
    actors <- c(ego, alters)  #define the network
    beh_centered <- fcentering(actors)  #center behavior scores
    simij <- fsimij(beh_centered, min, max)  #calculate the similarity scores
    diag(simij) <- NA
    msimij <- mean(simij, na.rm = TRUE)  #calculate the mean similarity score. only calculate mean on non-diagonal cells??!!
    simij_c <- simij - msimij  #center the similarity scores

    statistic <- sum(simij_c[1, ], na.rm = TRUE)/length(alters)  #the actual statistic

    return(statistic)
}

favSim2 <- function(ego, alters, min, max) {
    actors <- c(ego, alters)  #define the network
    beh_centered <- fcentring(actors)  #center behavior scores
    simij <- fsimij(beh_centered, min, max)  #calculate the similarity scores
    diag(simij) <- NA
    # msimij <- mean(simij, na.rm=TRUE) #calculate the mean similarity score. only calculate mean
    # on non-diagonal cells??!!
    simij_c <- simij  # - msimij #center the similarity scores

    statistic <- sum(simij_c[1, ], na.rm = TRUE)/length(alters)  #the actual statistic

    return(statistic)
}

The average attraction towards higher effect (avAttHigher)

\[ s^{beh}_{i23}(x,z) = x^{-1}_{i+}\sum_j x_{ij} (\, I \{ (z_j)>z_i \} sim^z_{ij} + I \{ (z_j ) \le z_i \} )\,\]

favAttHigher <- function(ego, alters, min, max) {
    actors <- c(ego, alters)
    beh_centered <- fcentering(actors)
    simij <- fsimij(beh_centered, min, max)
    diag(simij) <- NA

    simijH <- simij[1, ]
    simijH[beh_centered <= beh_centered[1]] <- 1
    simijH[1] <- NA
    statistic <- sum(simijH, na.rm = TRUE)/length(alters)

    return(statistic)
}

The average attraction towards higher alters effect (avAttLower)

\[ s^{beh}_{i24}(x,z) = x^{-1}_{i+}\sum_j x_{ij} (\, I \{ (z_j)<z_i \} sim^z_{ij} + I \{ (z_j ) \ge z_i \} )\,\]

favAttLower <- function(ego, alters, min, max) {
    actors <- c(ego, alters)
    beh_centered <- fcentering(actors)
    simij <- fsimij(beh_centered, min, max)
    diag(simij) <- NA

    simijL <- simij[1, ]
    simijL[beh_centered >= beh_centered[1]] <- 1
    simijL[1] <- NA
    statistic <- sum(simijL, na.rm = TRUE)/length(alters)

    return(statistic)
}

Average alter effect (avAlt)

\[ s^{beh}_{i27}(x,z) = z_i(\textstyle \sum_jx_{ij}z_j)/(\textstyle \sum_jx_{ij}) \]

favAlt <- function(ego, alters, ...) {
    actors <- c(ego, alters)
    beh_centered <- fcentering(actors)

    statistic <- beh_centered[1] * (sum(beh_centered[-1], na.rm = TRUE)/length(alters))

    return(statistic)
}

Average attraction mean (AttMean)

We are attracted to the mean of our alters!

(behaves approximately similar to avAlt, different shape.)

\[ s^{beh}_{i}(x,z) = 1 - \frac{\lvert(z_i - x^{-1}_{i+}\sum_j x_{ij}z_j)\rvert}{r_Z} \]

fAttMean <- function(ego, alters, min, max, ...) {
    rv <- max - min
    actors <- c(ego, alters)
    beh_centered <- fcentring(actors)

    statistic <- 1 - abs(beh_centered[1] - (sum(beh_centered[-1], na.rm = TRUE)/length(alters)))/rv  #thus we strive for a highest local similarity score!

    return(statistic)
}

Make plot of statistic values

finluenceplot <- function(alters, min, max, fun, params, results = TRUE, plot = TRUE) {
    # check correct number of parameters are given
    if (length(fun) != length(params))
        stop("Please provide one (and only one) parameter for each of the behavioral effects!")

    # calculuate value of evaluation function
    s <- NA
    for (i in min:max) {
        s[i] <- 0
        for (j in 1:length(fun)) {
            s[i] <- s[i] + params[j] * fun[[j]](i, alters, min, max)
        }
    }

    # calculate the probabilities
    p <- NA
    for (i in min:max) {
        p[i] <- exp(s[i])/sum(exp(s))
    }

    # calculate the probabilities of choice set
    p2 <- NA
    for (i in min:max) {
        if (i == min) {
            p2[i] <- exp(s[i])/sum(exp(s[i]) + exp(s[i + 1]))
        } else if (i == max) {
            p2[i] <- exp(s[i])/sum(exp(s[i]) + exp(s[i - 1]))
        } else {
            p2[i] <- exp(s[i])/sum(exp(s[i]) + exp(s[i - 1]) + exp(s[i + 1]))
        }
    }

    # calculate the probability ratio
    r <- NA
    for (i in min:max) {
        r[i] <- p[i]/p[1]
    }

    # some simple plots
    if (plot) {
        name <- deparse(substitute(fun))
        name <- stringr::str_sub(as.character(name), 6, -2)
        par(mfrow = c(2, 2))
        plot(y = s, x = min:max, xlab = "ego behavioral score", ylab = name, type = "b")
        mtext("EVALUATION", line = 1)
        mtext(paste("alters:", paste0(alters, collapse = ",")))
        plot(y = p, x = min:max, xlab = "ego behavioral score", ylab = name, ylim = c(0, 1), type = "b")
        mtext("PROBABILITIES", line = 1)
        mtext(paste("alters:", paste0(alters, collapse = ",")))
        plot(y = p2, x = min:max, xlab = "ego behavioral score", ylab = name, ylim = c(0, 1), type = "b")
        mtext("PROBABILITIES_choice_set", line = 1)
        mtext(paste("alters:", paste0(alters, collapse = ",")))
        plot(y = r, x = min:max, xlab = "ego behavioral score", ylab = name, type = "b")
        mtext("RATIOS", line = 1)
        mtext(paste("alters:", paste0(alters, collapse = ",")))
    }

    # return results for more fancy plotting
    if (results) {
        x <- min:max
        df <- data.frame(x, s, p, p2, r)
        return(df)
    }

}

Examples

alters1 <- rep(c(3, 3, 3, 3, 3, 3), 1)

finluenceplot(alters = alters1, min = 1, max = 6, list(flinear, fquad), params = c(0.1, -1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favSim), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favAlt), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(fAttMean), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favAttHigher), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favAttLower), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    1))

finluenceplot(alters = alters1, min = 1, max = 6, list(fAttMean, favAttHigher), params = c(1, 1))

#>   x          s           p         p2           r
#> 1 1 -3.1102041 0.021521281 0.09197159  1.00000000
#> 2 2 -0.8204082 0.212477940 0.29649713  9.87292259
#> 3 3  0.0000000 0.482628079 0.50946830 22.42562059
#> 4 4 -0.6489796 0.252211177 0.32961799 11.71915274
#> 5 5 -2.7673469 0.030322785 0.10700672  1.40896747
#> 6 6 -6.3551020 0.000838739 0.02691585  0.03897254
#>   x          s         p        p2         r
#> 1 1 -0.2857143 0.1537345 0.4643463 1.0000000
#> 2 2 -0.1428571 0.1773428 0.3310773 1.1535650
#> 3 3  0.0000000 0.2045764 0.3657971 1.3307122
#> 4 4 -0.1428571 0.1773428 0.3310773 1.1535650
#> 5 5 -0.2857143 0.1537345 0.3310773 1.0000000
#> 6 6 -0.4285714 0.1332691 0.4643463 0.8668779
#>   x          s         p        p2         r
#> 1 1 -0.4897959 0.1416047 0.4091822 1.0000000
#> 2 2 -0.1224490 0.2044628 0.3542543 1.4438988
#> 3 3  0.0000000 0.2310965 0.3610757 1.6319831
#> 4 4 -0.1224490 0.2044628 0.3542543 1.4438988
#> 5 5 -0.4897959 0.1416047 0.3348927 1.0000000
#> 6 6 -1.1020408 0.0767685 0.3515473 0.5421325
#>   x   s         p        p2         r
#> 1 1 0.6 0.1480744 0.4501660 1.0000000
#> 2 2 0.8 0.1808585 0.3289329 1.2214028
#> 3 3 1.0 0.2209011 0.3791525 1.4918247
#> 4 4 0.8 0.1808585 0.3289329 1.2214028
#> 5 5 0.6 0.1480744 0.3289329 1.0000000
#> 6 6 0.4 0.1212331 0.4501660 0.8187308
#>   x   s         p        p2        r
#> 1 1 0.6 0.1221195 0.4501660 1.000000
#> 2 2 0.8 0.1491571 0.3289329 1.221403
#> 3 3 1.0 0.1821809 0.3547696 1.491825
#> 4 4 1.0 0.1821809 0.3333333 1.491825
#> 5 5 1.0 0.1821809 0.3333333 1.491825
#> 6 6 1.0 0.1821809 0.5000000 1.491825
#>   x   s         p        p2         r
#> 1 1 1.0 0.1984969 0.5000000 1.0000000
#> 2 2 1.0 0.1984969 0.3333333 1.0000000
#> 3 3 1.0 0.1984969 0.3547696 1.0000000
#> 4 4 0.8 0.1625155 0.3289329 0.8187308
#> 5 5 0.6 0.1330564 0.3289329 0.6703200
#> 6 6 0.4 0.1089374 0.4501660 0.5488116
#>   x   s         p        p2         r
#> 1 1 1.6 0.1480744 0.4501660 1.0000000
#> 2 2 1.8 0.1808585 0.3289329 1.2214028
#> 3 3 2.0 0.2209011 0.3791525 1.4918247
#> 4 4 1.8 0.1808585 0.3289329 1.2214028
#> 5 5 1.6 0.1480744 0.3289329 1.0000000
#> 6 6 1.4 0.1212331 0.4501660 0.8187308
#>   x   s         p        p2        r
#> 1 1 1.2 0.1080764 0.4013123 1.000000
#> 2 2 1.6 0.1612311 0.3162411 1.491825
#> 3 3 2.0 0.2405285 0.4017596 2.225541
#> 4 4 1.8 0.1969281 0.3289329 1.822119
#> 5 5 1.6 0.1612311 0.3289329 1.491825
#> 6 6 1.4 0.1320048 0.4501660 1.221403

alters1 <- rep(c(1, 1, 1, 5, 5, 5), 1)
finluenceplot(alters = alters1, min = 1, max = 6, list(favSim), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favAlt), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(fAttMean), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    0))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(0,
    1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    3))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    -1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(-1,
    1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(flinear, favAttLower, favAttHigher), params = c(1,
    -1, 1))
finluenceplot(alters = alters1, min = 1, max = 6, fun = list(flinear, favAttLower, favAttHigher), params = c(1,
    -1, 1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(flinear), params = c(1))

#>   x           s         p        p2         r
#> 1 1  0.05714286 0.1704484 0.5000000 1.0000000
#> 2 2  0.05714286 0.1704484 0.3333333 1.0000000
#> 3 3  0.05714286 0.1704484 0.3333333 1.0000000
#> 4 4  0.05714286 0.1704484 0.3333333 1.0000000
#> 5 5  0.05714286 0.1704484 0.3488115 1.0000000
#> 6 6 -0.08571429 0.1477580 0.4643463 0.8668779
#>   x          s         p        p2         r
#> 1 1 -0.4897959 0.1416047 0.4091822 1.0000000
#> 2 2 -0.1224490 0.2044628 0.3542543 1.4438988
#> 3 3  0.0000000 0.2310965 0.3610757 1.6319831
#> 4 4 -0.1224490 0.2044628 0.3542543 1.4438988
#> 5 5 -0.4897959 0.1416047 0.3348927 1.0000000
#> 6 6 -1.1020408 0.0767685 0.3515473 0.5421325
#>   x   s         p        p2         r
#> 1 1 0.6 0.1480744 0.4501660 1.0000000
#> 2 2 0.8 0.1808585 0.3289329 1.2214028
#> 3 3 1.0 0.2209011 0.3791525 1.4918247
#> 4 4 0.8 0.1808585 0.3289329 1.2214028
#> 5 5 0.6 0.1480744 0.3289329 1.0000000
#> 6 6 0.4 0.1212331 0.4501660 0.8187308
#>   x   s         p        p2         r
#> 1 1 1.0 0.2135147 0.5249792 1.0000000
#> 2 2 0.9 0.1931961 0.3322250 0.9048374
#> 3 3 0.8 0.1748111 0.3322250 0.8187308
#> 4 4 0.7 0.1581756 0.3322250 0.7408182
#> 5 5 0.6 0.1431232 0.3420088 0.6703200
#> 6 6 0.4 0.1171794 0.4501660 0.5488116
#>   x   s         p        p2        r
#> 1 1 0.6 0.1305469 0.4750208 1.000000
#> 2 2 0.7 0.1442766 0.3322250 1.105171
#> 3 3 0.8 0.1594504 0.3322250 1.221403
#> 4 4 0.9 0.1762199 0.3322250 1.349859
#> 5 5 1.0 0.1947531 0.3442533 1.491825
#> 6 6 1.0 0.1947531 0.5000000 1.491825
#>   x   s         p        p2        r
#> 1 1 2.8 0.1043514 0.4501660 1.000000
#> 2 2 3.0 0.1274551 0.3289329 1.221403
#> 3 3 3.2 0.1556740 0.3289329 1.491825
#> 4 4 3.4 0.1901406 0.3289329 1.822119
#> 5 5 3.6 0.2322383 0.3791525 2.225541
#> 6 6 3.4 0.1901406 0.4501660 1.822119
#>   x    s          p        p2         r
#> 1 1  0.4 0.25939861 0.5498340 1.0000000
#> 2 2  0.2 0.21237762 0.3289329 0.8187308
#> 3 3  0.0 0.17388009 0.3289329 0.6703200
#> 4 4 -0.2 0.14236097 0.3289329 0.5488116
#> 5 5 -0.4 0.11655531 0.3289329 0.4493290
#> 6 6 -0.6 0.09542741 0.4501660 0.3678794
#>   x    s          p        p2        r
#> 1 1 -0.4 0.09542741 0.4501660 1.000000
#> 2 2 -0.2 0.11655531 0.3289329 1.221403
#> 3 3  0.0 0.14236097 0.3289329 1.491825
#> 4 4  0.2 0.17388009 0.3289329 1.822119
#> 5 5  0.4 0.21237762 0.3289329 2.225541
#> 6 6  0.6 0.25939861 0.5498340 2.718282
#>   x         s           p        p2          r
#> 1 1 -2.114286 0.003309969 0.2578558   1.000000
#> 2 2 -1.057143 0.009526540 0.2366537   2.878136
#> 3 3  0.000000 0.027418678 0.2366537   8.283667
#> 4 4  1.057143 0.078914683 0.2366537  23.841519
#> 5 5  2.114286 0.227127190 0.2366537  68.619135
#> 6 6  3.171429 0.653702940 0.7421442 197.495201
#>   x         s           p        p2          r
#> 1 1 -2.114286 0.003309969 0.2578558   1.000000
#> 2 2 -1.057143 0.009526540 0.2366537   2.878136
#> 3 3  0.000000 0.027418678 0.2366537   8.283667
#> 4 4  1.057143 0.078914683 0.2366537  23.841519
#> 5 5  2.114286 0.227127190 0.2366537  68.619135
#> 6 6  3.171429 0.653702940 0.7421442 197.495201
#>   x          s           p        p2         r
#> 1 1 -1.7142857 0.007969358 0.2979366  1.000000
#> 2 2 -0.8571429 0.018779143 0.2644949  2.356418
#> 3 3  0.0000000 0.044251518 0.2644949  5.552708
#> 4 4  0.8571429 0.104275094 0.2644949 13.084503
#> 5 5  1.7142857 0.245715754 0.2644949 30.832565
#> 6 6  2.5714286 0.579009134 0.7020634 72.654424

alters1 <- rep(c(1, 2, 2), 100)
finluenceplot(alters = alters1, min = 1, max = 6, list(favSim), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(favAlt), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, list(fAttMean), params = c(1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    0))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(0,
    1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(1,
    -1))

finluenceplot(alters = alters1, min = 1, max = 6, fun = list(favAttLower, favAttHigher), params = c(-1,
    1))

#>   x           s         p        p2         r
#> 1 1 -0.04385382 0.2112416 0.4834501 1.0000000
#> 2 2  0.02236988 0.2257045 0.3628788 1.0684657
#> 3 3 -0.17630122 0.1850369 0.3289906 0.8759490
#> 4 4 -0.37497231 0.1516969 0.3289906 0.7181201
#> 5 5 -0.57364341 0.1243640 0.3289906 0.5887288
#> 6 6 -0.77231451 0.1019561 0.4504950 0.4826514
#>   x             s         p        p2         r
#> 1 1 -0.0014716541 0.1698751 0.4997241 1.0000000
#> 2 2 -0.0003679135 0.1700628 0.3340690 1.0011043
#> 3 3 -0.0058866164 0.1691268 0.3340609 0.9955948
#> 4 4 -0.0180277628 0.1670858 0.3340430 0.9835802
#> 5 5 -0.0367913526 0.1639799 0.3340154 0.9652968
#> 6 6 -0.0621773858 0.1598695 0.4936538 0.9411001
#>   x         s         p        p2         r
#> 1 1 0.8666667 0.2115240 0.4833395 1.0000000
#> 2 2 0.9333333 0.2261063 0.3630769 1.0689391
#> 3 3 0.7333333 0.1851202 0.3289329 0.8751733
#> 4 4 0.5333333 0.1515636 0.3289329 0.7165313
#> 5 5 0.3333333 0.1240898 0.3289329 0.5866462
#> 6 6 0.1333333 0.1015961 0.4501660 0.4803053
#>   x         s          p        p2         r
#> 1 1 1.0000000 0.23461556 0.5166605 1.0000000
#> 2 2 0.9333333 0.21948449 0.3463000 0.9355070
#> 3 3 0.7333333 0.17969871 0.3289329 0.7659283
#> 4 4 0.5333333 0.14712486 0.3289329 0.6270891
#> 5 5 0.3333333 0.12045564 0.3289329 0.5134171
#> 6 6 0.1333333 0.09862074 0.4501660 0.4203504
#>   x         s         p        p2        r
#> 1 1 0.8666667 0.1489613 0.4667160 1.000000
#> 2 2 1.0000000 0.1702077 0.3478051 1.142631
#> 3 3 1.0000000 0.1702077 0.3333333 1.142631
#> 4 4 1.0000000 0.1702077 0.3333333 1.142631
#> 5 5 1.0000000 0.1702077 0.3333333 1.142631
#> 6 6 1.0000000 0.1702077 0.5000000 1.142631
#>   x        s         p        p2         r
#> 1 1 1.866667 0.2115240 0.4833395 1.0000000
#> 2 2 1.933333 0.2261063 0.3630769 1.0689391
#> 3 3 1.733333 0.1851202 0.3289329 0.8751733
#> 4 4 1.533333 0.1515636 0.3289329 0.7165313
#> 5 5 1.333333 0.1240898 0.3289329 0.5866462
#> 6 6 1.133333 0.1015961 0.4501660 0.4803053
#>   x           s          p        p2         r
#> 1 1  0.13333333 0.25939861 0.5498340 1.0000000
#> 2 2 -0.06666667 0.21237762 0.3289329 0.8187308
#> 3 3 -0.26666667 0.17388009 0.3289329 0.6703200
#> 4 4 -0.46666667 0.14236097 0.3289329 0.5488116
#> 5 5 -0.66666667 0.11655531 0.3289329 0.4493290
#> 6 6 -0.86666667 0.09542741 0.4501660 0.3678794
#>   x           s          p        p2        r
#> 1 1 -0.13333333 0.09542741 0.4501660 1.000000
#> 2 2  0.06666667 0.11655531 0.3289329 1.221403
#> 3 3  0.26666667 0.14236097 0.3289329 1.491825
#> 4 4  0.46666667 0.17388009 0.3289329 1.822119
#> 5 5  0.66666667 0.21237762 0.3289329 2.225541
#> 6 6  0.86666667 0.25939861 0.5498340 2.718282

Results model strava

alters1 <- rep(c(2, 2, 2, 5, 5, 5))
alters2 <- rep(c(3.5, 3.5, 3.5, 3.5, 3.5, 3.5))

# club1
finluenceplot(alters = alters1, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.15, -0.002, 2.0027, 6.0905))

finluenceplot(alters = alters2, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.15, -0.002, 2.0027, 6.0905))

# club2
finluenceplot(alters = alters1, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.24, 0.0075, 3.3213, 5.0047))

finluenceplot(alters = alters2, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.24, 0.0075, 3.3213, 5.0047))

# club3
finluenceplot(alters = alters1, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.2085, 0.021, 3.7703, 2.1283))

finluenceplot(alters = alters2, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-0.2085, 0.021, 3.7703, 2.1283))

# club4
finluenceplot(alters = alters1, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-1.3313, 0.0192, 2.5493, 4.4952))

finluenceplot(alters = alters2, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(-1.3313, 0.0192, 2.5493, 4.4952))

# club5
finluenceplot(alters = alters1, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(0.1021, 0.0743, 1.1736, 8.5486))

finluenceplot(alters = alters2, min = 1, max = 6, results = FALSE, fun = list(flinear, fquad, favAttHigher,
    favAttLower), params = c(0.1021, 0.0743, 1.1736, 8.5486))

---

Copyright © 2021 Rob Franken